Adaptive commerce systems and methods

Abstract

A system and method for adaptive commerce is disclosed. adaptive commerce enables recommendations of products or services based on usage behaviors and commercial contextual information. commercial contextual information may include the business environment of the recommendation recipient, purchase histories, and product or service attributes. Bundles of products and/or services, or specific product or service configurations may be recommended. Corresponding prices may be determined in accordance with behavioral inferences and commercial contextual information.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application
where X, Y, and Z are network subsets and W is the resolution function 834. The resolution function 834 designates how duplicative relationships among nodes common to fuzzy network subsets X and Y are resolved.

Specifically, the fuzzy network intersection operator 826 first determines the common nodes of network subsets X and Y, applying the object evaluation function 830 to determine the degree to which nodes are identical, to form a set of nodes, network subset Z. The fuzzy network intersection operator 826 then determines the relationships and associated relationship value and indicators uniquely deriving from X among the nodes in Z (that is, relationships that do not also exist in Y), and adds them into Z (attaching them to the associated nodes in Z). The operator then determines the relationships and relationship indicators and associated values uniquely deriving from Y (that is, relationships that do not also exist in X) and applies them to Z (attaching them to the associated nodes in Z).

For relationships that are common to X and Y, the resolution function 834 is applied. The resolution function 834 may be any mathematical function or algorithm that takes the relationship values of X and Y as arguments, and determines a new relationship value and associated relationship indicator.

The resolution function 834, Resolution_Function, may be a linear combination of the corresponding relationship value of X and the corresponding relationship value of Y, scaled accordingly. For example:
Resolution_Function(X_RV,Y_RV)=(c₁*X_RV+c₂*Y_RV)/(c₁+c₂)
where X_RV and Y_RVare relationship values of X and Y, respectively, and c₁ and c₂ are coefficients. If c₁=1, and c₂=0, then X_RV completely overrides Y_RV. If c₁=0 and c₂=1, then Y_RV completely overrides X_RV. If c₁=1 and c₂=1, then the derived relationship is a simple average of X_RV and Y_RV. Other values of c₁ and c₂ may be selected to create weighted averages of X_RV and Y_RV. Nonlinear combinations of the associated relationships values, scaled appropriately, may also be employed.

The Fuzzy_Network_Union operator 824 may be derived from the Fuzzy_Network_Intersection operator 826, as follows:
Z=Fuzzy_Network_Union(X,Y,W)
where X, Y, and Z are network subsets and W is the resolution function 834. Accordingly,
Z=Fuzzy_Network_Intersection(X,Y,W)+(X−Y)+(Y−X)
That is, fuzzy network unions of two network subsets may be defined as the sum of the differences of the two network subsets (the nodes and relationships that are uniquely in X and Y, respectively) and the fuzzy network intersection of the two network subsets. The resulting network subset of the difference operator contains any unique relationships between nodes uniquely in an originating network subset and the fuzzy network intersection of the two subsets. These relationships are then added to the fuzzy network intersection along with all the unique nodes of each originating network subset, and all the relationships among the unique nodes, to complete the resulting fuzzy network subset.

For the adaptive recombinant system 800, the resolution function 834 that applies to operations that combine multiple networks may incorporate usage behavioral inferences related to one or all of the networks. The resolution function 834 may be instantiated directly by the adaptive recommendations function 240 (FIG. 16), or the resolution function 834 may be a separate function that invokes the adaptive recommendations function. The resulting relationships in the combined network will therefore be those that are inferred by the system to reflect the collective usage histories and preference inferences of the predecessor networks.

For example, where one of the predecessor networks was used by larger numbers of individuals, or by individuals that members of communities or affinity groups that are inferred to be best informed on the subject of the associated content, then the resolution function 834 may choose to preferentially weight the relationships of that predecessor network higher versus the other predecessor networks. The resolution function 834 may use any or all of the usage behaviors 270, along with associated user segmentations and affinities obtained during usage behavior pre-processing 204 (see FIG. 9C), as illustrated in FIG. 6 and Table 1, and combinations thereof, to determine the appropriate resolution of common relationships and relationship values among two or more networks that are combined into a new network.

The object evaluation function 830 may applied when the adaptive recombinant system 800 of FIG. 16 is used to combine networks. Combining networks requires a determination of which objects 212 in two or more networks are identical, or near enough to being identical to be considered identical, for the purposes of combining the networks. In some embodiments, the object evaluation function 830 may enable a global identification management process in which each object 212 has a unique system designator, which enables direct determination of identity of the objects. This approach may be augmented by the tracking versions or generations of objects 212, such that the adaptive recombinant system 800 has options for using more recent versions of an object 212 when networks are combined. In other embodiments, the object evaluation function 830 may compare the intrinsic information associated with two objects 212 to determine whether they are identical or nearly identical enough to be considered identical for the purposes of combining the networks. For example, for text-based objects 212, associated meta-information 234 or information 232 may be compared between two objects using text-based pattern matching or statistical algorithms. For audio or video-based objects 212, other appropriate pattern matching algorithms may be applied by the object evaluation function 830 to the associated meta-information 234 or information 232

Fuzzy Process Networks

In some embodiments, implementation of a fuzzy network-based process may be through connecting an existing or new process with a fuzzy network 500A, as is shown in FIG. 19A. For example, an activity 45 within a process or sub-process 136 may precede another activity 50 in the sub-process, with an explicit workflow 55 between the activities. It should be understood that there may be a greater number of activities in the process or sub-process 136 than the minimal number illustrated in FIG. 19A. The fuzzy content network 500A, managed by the adaptive computer-based application 925, which is “external” to the activities 45, 50 in the sub-process 136, may be accessible 56, 57 by one or more of the activities 45, 50.

In other embodiments, implementation of a fuzzy network-based process may be through including an existing or new process within a fuzzy network 500B managed by the adaptive computer-based application 925, as is shown in FIG. 19B. For example, an activity 65 within a process or sub-process 137 may precede another activity 70 in the sub-process, with an explicit workflow 75 between the activities 75. These activities and their relationships are represented directly within the fuzzy network 500B in this case. It should be understood that there may be a greater number of activities in the process/sub-process 137 than the minimal number illustrated in FIG. 19B.

In some embodiments, adaptive recombinant processes may employ structures based on fuzzy content networks, as defined in U.S. Pat. No. 6,795,826, entitled “Fuzzy Content Network Management and Access.” These structures may include the use or adaptation of fuzzy content networks and associated topic objects and content objects, as defined therein.

For “inclusive” fuzzy network embodiments, such as the fuzzy content network 500B of FIG. 19B, according to some embodiments, FIG. 20A depicts the structure of a process topic object 445t, which consists of meta-information 450t only, and is analogous to a fuzzy content network topic object. Likewise, FIG. 20B depicts a process content object 445c, which consist of embedded information, or references (for example, pointers or URLs) to information 455c, and the associated meta-information 450c. Fuzzy process content objects 455c are analogous to fuzzy content network content objects. According to some embodiments, process activities may be included within the fuzzy content network, and as shown in FIG. 21A, and a process activity object 445a contains meta-information 450a, analogous to the process topic object 455t of FIG. 20A. In other embodiments, as shown in FIG. 21B, process activities may be included within the fuzzy content network, and a process activity object 446a will contain meta-information 451a, as well as information or a pointer to information 456a, analogous to the process content object 445c of FIG. 20B. For all of these fuzzy network object structures, relationships and associated relationship indicators may be established between any two process objects in the process network, and there may be plurality of types of relationships and associated relationship indicators between any two process objects. In some embodiments, at least one relationship type denotes process sequence or workflow, and is typically applied among process activity objects, but may apply among other process objects as well.

As reviewed previously, FIGS. 20A, 20B, 21A and 21B depict in some embodiments how fuzzy network objects may be converted to process network objects, and how special process objects, process activity objects 445a and 446a may be defined.

FIG. 22A illustrates a process activity “network A” 460, including four activities (465a, 465b, 465c, and 465d) and work flow relationships among the activities (470a, 470b, 470c, and 470d), as well as relationships to activities external to process activity “network A” 470e. Each relationship has an associated relationship indicator 471. In some embodiments, the relationship indicator is represented in the form:
Sequence(Relationship type,First Activity,Second Activity)
The relationship indicator “S(1,1,2)” 470 of relationship 470a thus implies a relationship of type 1 between activity 1 and activity 2, in that sequence.

FIG. 22B illustrates a process activity network 475, which may have multiple relationship types 476a and 476b outbound from an activity (activity 1 474a), and may also have multiple relationship types inbound 476b and 476c to an activity (activity 4 474b). Furthermore, multiple relations of different relationship types may be outbound from one or more activities in the process activity network to destinations outside the process activity network. For example, in FIG. 22B, relationship 476d of relationship type 2 (S(2,4,M)) is outbound from activity 4 474b; likewise, relationship 476e having relationship type 1 (S(1,4,N)) is also outbound from activity 4 474b.

According to some embodiments, FIGS. 23A and 23B depict process networks 480A and 480B (collectively, process network 480). The process networks 480A and 480B are depicted for a particular relationship and associated relationship indicators, at particular times (t₀ and t₂), in some embodiments. The process networks 480A and 480B are process activity networks (see FIGS. 22A and 22B). The process networks 480A and 480B are integrated with process content objects, for example, “content object 1” 485a and process topic objects, for example, “topic object 1” 485b. Relationships and associated relationship indicators may exist between process activity objects and process content or topic objects, for example, 490.

FIG. 24 is a flow diagram illustrating how process usage information associated with the process networks 480A and 480B are processed, according to some embodiments, over a period of time. During time t₁, usage behavior information 920 is tracked and processed (block 4495). The adaptive recommendations function 240 of the adaptive system 100 is invoked (block 4500), and the process structure of the process network 480A is automatically or semi-automatically updated (block 4505), resulting in process network 480B at time t₂. Thus, process network 480A at time t₀ (FIG. 23A) automatically or semi-automatically becomes process network 480B at time t₂ (FIG. 23B), using the procedure in FIG. 24. Structures that may be updated within the process network 480 include relationship indicators; for example, relationship indicators 515 between content object 1 485a and activity 1 520 had values of 0.4 and 0.6 at time t₀ (FIG. 23A); at time t₂, the relationship indicators 515 have values of 0.8 and 0.6 (FIG. 23B). Relationships may be deleted, as for example between process activity 1 520, and process activity 4 525 (formerly S(2,1,2) in FIG. 23A). Relationships and associated relationship indicators may be added, as for example 530 between activity 4 525 and content object 4 540. And process objects, and associated relationships may be deleted. For example the former content object 5 of FIG. 23A and its associated relationships and relationship indicators, is not part of process network 480B.

FIG. 25 depicts process network 480B (FIG. 24B) at time t₂. Process activity objects (shaded) are selected, along with the associated relationships between these process activity objects, as well as other selected process objects that have a relationship to the selected process activity objects, and the associated relationships. In some embodiments, the selection of the process network subset may be through application of network neighborhood metrics, such as degrees of separation metrics, or fuzzy degrees of separation network neighborhood metrics. In other embodiments, other selection methods may be used, including individually specifying process objects and associated relationships. In this example, the result of the selection/sub-setting 555 of process network 480B is process network 560.

Adaptive Recombinant Processes

FIGS. 26 and 27 illustrate the syndication and combination of process networks by the adaptive recombinant system 800C. (The process network activity objects are shaded, to distinguish from the content and topic objects.) In FIG. 26, process network subset B 560 (FIG. 25) is syndicated to an existing process network C 580 that may exist on the same computer system or a different computer system. It should be noted that a process network need not represent a “complete” or “functional” process. For example, process network C 580 contains two process activity objects 581, 582 that do not have a direct relationship to one another. In addition, associated relationships 581r and 582r have no corresponding forward sequence process activity object within the process network 580. In general, a process network may be fragmentary, without completeness of process objects and relationships.

FIG. 27 illustrates the results of the combination of process network B 560 and process network C 580 by the adaptive recombinant system 800C, and the application of the fuzzy network operators function 820, the adaptive recommendations function 240 and the object evaluation function 830 (FIG. 17). The result is process network D 590. Note that all distinct process activity objects from 560 and 580 reside in 590, and the associated relationships among the process activity objects are resolved and established. Note also that these relationships may be reflexive, as in the case of 591 and 592. In the process network subset C 580 (FIG. 26), a relationship indicator “S(2,M,4)” is indicated, although no “activity 4” is present in the sub-network 580. Once syndication with process network subset B 560, which includes “Activity 4,” occurs, the adaptive recombinant system 800C automatically relates the two activities 4 and M, as shown in FIG. 27. Other process objects and corresponding relationships may be resolved as previously described.

FIG. 28 illustrates that the process network 560 may be encompassed by the structural aspect 210C of adaptive system 100C (FIG. 7). The process network 560 may be the sole content network within structural aspect 210C, or may be one of multiple network or non-network structures within 210C, as is more generally depicted in FIG. 15, above.

Likewise, FIG. 29 illustrates that the process network 560 may be encompassed by the structural aspect 210C of the adaptive system 100C, which may form part of the adaptive recombinant system 800C. Again, the process network 560 may be the sole content network within structural aspect 210C, or may be one of multiple networks within 210C, and may be syndicated, modified, and combined with other content or process networks, as is more generally depicted in FIGS. 47 and 48, below. The process network 560 or another process network structure within the structural aspect 210C may correspond to the adaptive process instance 930 of FIGS. 4A and 4B, and hence FIGS. 15, 29, 47 and 48 illustrate the ability to syndicate and combine representations of adaptive process instances 930, thereby enabling the adaptive recombinant process 901.

FIGS. 30A, 30B, 31A, and 31B illustrate the general approaches associated with process network syndication and combinations, as managed by the adaptive recombinant system 800C, and applied as part of a particular type of application of the adaptive recombinant process 901, designated in FIGS. 30A, 30B, 31A and 31B as process application type 901A. FIG. 30A illustrates a hypothetical starting condition, and depicts three organizations, 650, 655, 660. These may be organizations (which may be individuals) within the same business or institution, or one or more may be in businesses or institutions external to the others. A first process network, “process network 1” 665, is used solely by, or resides within, “organization 1” 650. A second process network, “process network 2” 670, is used solely by, or resides within, “organization 2” 655. “Organization 3” 660 does not have a process network initially, in this example.

FIG. 30B illustrates that a subset of “process network 1” 665 is selected to form “process network 1A” 680. “Process network 1A” 680 is then syndicated as “process network 1A” 685 to “organization 2.” “Organization 2” 655 then syndicates “process network 1A” 685 to “organization 3” 660 as “process network 1A” 690. Thus, FIG. 30B illustrates how process networks, or subsets of process networks, can be syndicated among organizations without limit.

FIG. 31A depicts a subset of “process network 1” 665 and “process network 1A” 695 residing in “organization 1,” in which “process network 1a” 695 is syndicated to “organization 2” 655 as “process network 1A” 700. “Process network 1A” 700 and the existing “process network 2” 670 in “organization 2” are combined 710 to form “process network 2a” 715 in organization 2 655. “Process network 2a” 715 is then syndicated to “organization 3” 660 as process network 2A 720.

FIG. 31B represents a continuation of FIG. 31A, in which additional combination and syndication takes place. “Process network 2a” 720 in “organization 3” 660 is syndicated to “organization 1” 650 as process network 2A 730. Process network 2A 730 is then combined with the original “process network 1” 665 in “organization 1” 650 to generate “process network 3” 740 in “organization 1” 650.

FIGS. 30A, 30B, 31A, and 31B demonstrate that, in some embodiments, adaptive recombinant processes may indefinitely enable sub-setting of process networks, syndicating the subsets to one or more destinations, and enabling the syndicated process networks to be combined with one or more process networks at the destinations. At each combination step, the relationship resolution function 834 (of the fuzzy network operators 820—see FIG. 18) and the adaptive recommendations function 240 may be invoked to create and update process structure (and content) as appropriate.

According to some embodiments, FIG. 32 depicts possible deployments of process networks within and across organizations or business enterprises. In FIG. 32, two enterprises 1810, 1815 are depicted, but it should be understood the following described process and process network topologies can apply to any plurality of organizations, individuals, or business enterprises. One topology is represented by “Process 1” 1811 containing one process network, 1812, within one enterprise, 1810. In another topology, a process 1816 contains a plurality of process networks 1817, 1818 within one business enterprise, 1815. In another topology, a process 1820 may extend across more than one enterprise 1810 and 1815, and may contain a plurality of process networks 1821, 1822, and 1823. A process network 1823 may extend across business enterprises 1810 and 1815. Process networks may have common subsets, as exemplified by 1822 and 1823. Processes and process networks may extend across an unlimited number of organizations or business enterprises as depicted by process 1830 and process network 1832.

According to some embodiments, FIG. 33 depicts a process network topology in which a process network 1840 includes multiple processes, each process contained partially or as a whole within the process network 1840, and include a multiplicity of other process networks, each process contained partially or as a whole, where each contained process or process network may span a plurality of organizations or business enterprises.

Process Lifecycle Framework

In some embodiments, as shown in FIG. 34, a process lifecycle framework 3000 may be used as an implementation framework for migrating to adaptive processes, based on the implementation of adaptive recombinant processes, or other methods and technologies.

The process lifecycle framework 3000 has two primary dimensions. The horizontal dimension denotes how the organizing topology 3010 of a process is managed—either in a centralized 3011 or decentralized 3012 manner. The vertical dimension relates to the local differentiation 3020 of a process—how differentiated 3021 or customized 3022 the process is for local applications or implementations. The process may be standardized across all local applications 3021, or customizable to local applications 3022. The intersections of these dimensions denote fundamental process lifecycle positions. For example, a centralized organizing topology, coupled with standardization of processes across local applications, may be called a “cost and control” quadrant 3030. The focus in this quadrant is typically to ensure low cost processes that enforce broad standards across organization and application areas. This is the typical architecture of prior art processes supported by Enterprise Resource Planning (ERP) software that are implemented on a truly enterprise basis.

A decentralized organizing topology, coupled with standardization of processes across local applications, may be called the “ad hoc” quadrant 3040. The focus in this quadrant is to enforce broad standards across organization and application areas, but through a decentralized process management and infrastructure approach. This quadrant often represents an inconsistency of objectives, and may be the result of organizational combinations, such as through a merger or acquisition. It is often desirable to not remain in this quadrant in the long-term, as ad hoc implementation typically generates more costs to deliver the same results as the “cost and control” quadrant 3030.

A decentralized organizing topology, coupled with customization of processes across local applications, may be called the “Niche Advantages” quadrant 3050. The emphasis of this quadrant is to maximize the value of the process in specific application areas through a decentralized process management and infrastructure approach that enables maximum flexibility and tailoring to local needs. This quadrant represents a potentially high value, but also high cost approach. It is often consistent with the development of new processes that provide competitive advantages, where the generation of value from the processes overrides inefficiencies stemming from decentralized process management and heterogeneous enabling infrastructure. Over time, however, as competitive advantages potentially dissipate, the cost penalty associated with this quadrant may be too high compared to the derived benefits.

A centralized organizing topology, coupled with customization of processes across local applications, may be called the “Adaptive Processes” quadrant 3060. The emphasis of this quadrant is to maximize the value of the process in specific application areas, but through an efficient, centralized process management and infrastructure approach that enables maximum flexibility and tailoring to local needs. This quadrant represents a potentially high value and low cost approach, and provides advantages versus the other three quadrants. An adaptive process approach has been very difficult to achieve with prior art process and supporting process infrastructure and systems. The adaptive processes quadrant 3060 is the quadrant, in particular, that adaptive recombinant processes advantageously addresses.

According to some embodiments, FIG. 35 is a framework 3100 that describes how processes typically include multiple functionality layers 3110. For example, these layers may comprise information technology layers, with the highest level corresponding to process work flow and business logic, and lower layers corresponding to more generalized information technology, such as content management, database management systems, and communications networks.

In a process implementation, then, different layers may have different process lifecycle quadrants. For example, the top-most layer may be a niche advantage quadrant 3120, the directly supporting layer may be an adaptive processes quadrant 3130, and the directly supporting layer of that layer may be a cost and control quadrant 3140. In general, it is good practice that the lower process layers should be at least as standardized as the layers above.

According to some embodiments, FIG. 36 represents a process lifecycle management framework 3200 that may be advantageously used by businesses and institutions to ensure the highest possible value from their processes over time. The framework 3200 may be understood to represent one specific process lifecycle functionality layer.

Business innovations 3210 may be the source of processes (or process functionality layers) in the Niche Advantages quadrant. Business combinations 3230 may be the source of processes in the Ad Hoc Implementation quadrants. It is usually advantageous to migrate from the Ad Hoc Implementation quadrant to the Cost and Control quadrant through more effective leverage of scale 3240. It may be advantageous to migrate from the Niche Advantages quadrant to the Adaptive Processes quadrant through leverage of mass customization techniques 3220. It may also be advantageous to migrate from the Cost and Control quadrant to the Adaptive Processes quadrant through leverage of mass customization techniques 3250. Alternatively, it may also be advantageous to externalize the process 3260 from the Cost and Control quadrant, where external sources can provide process advantages, typically either through cost effectiveness, or through more effective customization or adaptation to local applications and the same cost.

Adaptive Process Application Areas

Recall from FIGS. 3, 4A, 4B, and 4C that adaptive recombinant processes may be applied to improve the functionality of any process 168 by integrating adaptive recommendations functions into the process 168 and applying the adaptive recommendations to facilitate the more effective use of the process instance 930. The application of the adaptive recommendations may be through delivery of adaptive recommendations 910 to process participants 200 or by applying the adaptive recommendations to modify the structure 905 and/or content 935 of computer-based applications 175 supporting the process, or both.

The following pages include descriptions of several adaptive processes 900 and adaptive recombinant processes 901. Table 3 lists embodiments of the adaptive process 900, including an associated figure and claim.

TABLE 3
Adaptive Process Embodiments
Embodiment	Figure	Claim
Adaptive process 900	FIG. 4A	Claim 1
Adaptive asset management process 900A	FIG. 37	Claim 8
Adaptive real-time learning process 900B	FIG. 38	Claim 25
Innovation network process 900C	FIG. 39	Claim 34
Adaptive publishing process 900D	FIG. 40	Claim 35
Adaptive commerce process 900E	FIG. 41	Claim 27
Adaptive price discovery process 900F	FIG. 42	Claim 28
Adaptive commercial solutions process 900G	FIG. 43	Claim 29
Location-aware collectively adaptive process 900H	FIG. 44	Claim 37

Likewise, Table 4 lists embodiments of the adaptive recombinant process, including an associated figure and claim.

TABLE 4
Adaptive Recombinant Process Embodiments
Embodiment	Figure	Claim
Adaptive recombinant process 901	FIG. 4C	Claim 22
Recombinant process network process 901A	FIGS. 30A-B	Claim 23
Adaptive viral marketing process 901B	FIGS. 49A-B	Claim 31
Evolvable process 901E	FIG. 50	Claim 24

Tables 3 and 4 are provided for convenience in understanding the following passages, and are not meant as an exhaustive presentation of the possible applications of the adaptive process 900 or the adaptive recombinant process 901. Further, the cited figures and claims are not exhaustive, but are meant as a guide to assist in understanding the following exemplary embodiments.

FIGS. 37-43 depict specific applications of the adaptive process 900 (processes 900A-900H) or adaptive recombinant process 901 (processes 901A, 901B, 901E). In some of these applications, the adaptive process 900 will include an adaptive system 100 (FIG. 7), in which the adaptive system may include some non-adaptive elements (FIG. 8), a fuzzy network structure (FIG. 14), a combination of network and non-network-based structure (FIG. 15), or a process network structure (FIG. 28). Further, the adaptive recombinant process 901 in some of these applications may include an adaptive recombinant system 800 (FIG. 16), which may include a fuzzy network structure (FIG. 17), or a process network structure (FIG. 29).

The following illustrations are specific process application areas for which the adaptive process 900 or adaptive recombinant process 901 may be advantageously applied, although it should be understood that these application areas do not constitute all the possible applications of the adaptive process 900 or adaptive recombinant process 901.

Adaptive Asset Management

According to some embodiments, the adaptive process 900 may be used to establish online asset management systems and processes. An on-line asset is defined as any item of software or content, or any tangible or intangible asset that the software or on-line content represents. In other words, the asset to be managed may also be derivative from the representations of the software or content of adaptive process 900.

Recall from FIGS. 4A and 4B that the adaptive computer-based application 925 may integrate with existing and/or new online computer applications 175 to enable capture and analysis of usage behavior information 920. This information may then be used to determine the value of the online computer and software assets. This determination of value of online assets can then be applied beneficially to facilitate asset management processes associated with the on-line assets, optionally including applying a function to automatically or semi-automatically modify the one or more computer applications 175 in alignment with the inferred value of the online assets of computer applications 175 to process participants 200.

FIG. 37 depicts an adaptive process 900A, including an adaptive asset management system 1500. The asset management system 1500 includes the adaptive computer-based application 925 and an asset management function 1510. Although in FIG. 37, the asset management function 1510 is shown to be external to the adaptive computer-based application 925, it should be understood that the asset management function 1510 may be configured to be internal to the adaptive computer-based application 925. Further, although not shown in FIG. 37, the adaptive computer-based application 925 may contain the adaptive system 100.

The asset management function 1510 receives information 1520 associated with data regarding the usage behaviors 920 of process participants 200, or inferences of the preferences and interests of online assets associated with the process participant usage behaviors 920. The asset management function 1510 uses the information 1520 to derive the value of online assets. The derived value may be of different magnitudes for different individuals or communities of process participants 200. The asset valuation information determined by the asset management function 1510 may be applied to decide near-term or long-term online asset changes and directions. For example, a high-value on-line asset might be made more prominently available for process participants 200, while less valuable assets might be made less prominent, or eliminated from the content and computer applications 175. New development projects to deliver on-line assets that are expected to be of high value based on the valuations of the asset management function 1510 may be conducted. Further, in addition to on-line assets, features associated with the assets may be evaluated by the asset management function 1510, and appropriate asset modifications or development projects initiated. For some modifications, the asset management function 1510 may be used to support making the appropriate changes.

The asset management function 1510 may automatically or semi-automatically modify 1505 the adaptive computer-based application 925. For alternative embodiments in which the asset management function 1510 is internal to the adaptive computer-based application 925, the adaptive self-modification operation 1505 is analogous to the structural modifications 905 of the adaptive system 100, the adaptive recombinant system 800, and the generalized adaptive computer-based application 925, described above. Likewise, the asset management function 1510 may automatically or semi-automatically modify 1515 content within adaptive computer-based application 925. For embodiments in which the asset management function 1510 is internal to the adaptive computer-based application 925, the adaptive self-modification of content 1515 is analogous to the content-based modifications 935, 905 of the aforementioned systems 100, 800, 925 (represented in parentheses). Further, other computer applications and content 175 may be automatically or semi-automatically modified 1525 by the asset management function 1510 in accordance with valuations derived by asset management function 1510. In such cases, even if direct usage behavioral information 920 are not available for non-adaptive computer application 181 and content 180, the asset management function 1510 may make inferences based on analogy from interactions of the process participants 200 with the adaptive computer-based application 925 to generate appropriate valuations.

Note that adaptive recommendations 910 delivered to process participants 200 is not an essential feature for enabling process application 900A.

Adaptive Real-Time Learning

The adaptive process 900 may be used to establish an adaptive process environment 930 (FIGS. 4A and 4B) to promote enhanced learning by process participants 200, including real-time learning, for existing or new processes through the implementation of adaptive recommendations 910 that are delivered directly to the process participant or user 200, or indirectly through adaptive modification of the process network structure 905 or content 935. In some embodiments, the resulting environment may be metaphorically termed an adaptive online “cockpit” of process knowledge and activities that effectively “surrounds” the process user. This approach facilitates the real-time learning of process participants 200, rather than relying solely or primarily on classroom or other episodic forms of education or training.

FIG. 38 illustrates an adaptive process 900B, or adaptive real-time learning process, including an exemplary process participant interface 1600 associated with a computing device 964 that is interacted with by process participants 200. It should be understood that although FIG. 38 illustrates a visual, display-oriented process participant interface, the interface could be audio-based, tactile or kinesthetically-based, or the interface could be comprised of combinations of visual, audio, or kinesthetic elements. The process participant interface 1600 of the adaptive process 900B may include one or more instances of displayed adaptive recommendations 910 associated with the adaptive computer-based application 925, in which the adaptive recommendations 910 are formatted for viewing in a specified manner. In FIG. 38, a first formatted instance 1610 and a second formatted instance 1620 of adaptive recommendations 910 are shown. The process participant interface 1600 may contain other information 915 derived from the adaptive computer-based application 925, formatted as appropriate for display. A formatted instance 1630 of information 915 from the adaptive computer-based application 925 is shown. A formatted instance 1630 may contain one or more instances of adaptive information 1632 and/or non-adaptive information 1634. Recall from FIG. 4A that adaptive information 1632 is content, structural elements, objects, information, or computer software that has been adaptively self-modified 905, 935 by the adaptive computer-based application 925 based, at least in part, on usage behaviors 920 of process participants 200. Non-adaptive information 1634 denotes any other information, content, objects, or computer software encompassed by the adaptive computer-based application 925 that has not been adaptively self-modified 905, 935.

The process participant interface 1600 may also contain formatted instances 1640 of other information such as information derived from other content 180a and other computer applications 181a that are relevant to process participants 200.

Formatted instances 1610, 1620 of adaptive recommendations 910 and formatted instances of adaptive computer application information 915 may contain explicit educational or training information or content, or relevant references or “help” information, in addition to more general information or content relevant to the associated process. In some embodiments, the adaptive computer-based application 925 may include or interact with a learning management system that may provide guidance on the appropriate educational or training information to include in the adaptive recommendations 910.

Innovation Networks

According to some embodiments, the adaptive process 900 may be used to create adaptive “innovation networks” that may be applied to facilitate collaborative research and development processes. These processes may be applied within an organization, or span an unlimited number of organizations or individuals. In some embodiments, adaptive recombinant processes may utilize the systems and methods of PCT Patent Application No. PCT/US05/001348, entitled “Generative Investment Process,” filed on Jan. 18, 2005, which is hereby incorporated by reference as if set forth in its entirety, to enable innovation networks and processes.

FIG. 39 illustrates an adaptive process 900C, or innovation network process, including the adaptive computer-based application 925, which includes the adaptive system 100. The structural aspect 210 of the adaptive system 100 encompasses an innovation map 1700, which associates opportunities 1710 to capability components 1730, shown in FIG. 39 organized within capability component categories or types 1720. Opportunities, capability component types, and capability components may be collectively termed the “elements” of innovation map 1700. It should be understood that although the innovation map 1700 is depicted in FIG. 39 in a table format, the innovation map 1700 may be organized in network structure, including a fuzzy network structure. Further, the innovation map 1700 may be incorporated within a process network, such as in FIG. 25 (not explicitly shown in FIG. 39) within the structural aspect 210.

“Opportunities,” as defined herein are ideas that can potentially generate value and that involve investments of time, resources, or financial commitments. These opportunities may be within defined processes, such as business development and growth processes, commercial venture capital, corporate venturing processes, business incubation processes, marketing processes, research and development processes, and innovation processes, or the investment processes and associated activities may be more ad hoc in nature.

Typically, opportunities 1710 consist of a bundle of two or more capability components, such as “cc 5” and “cc 7” 1730. For example, even if a business idea (opportunity) is based on a technological break-through, the overall business venture idea is likely to also include other differentiating components, such as processes (e.g., marketing processes). It is the uniqueness of the bundle of components that typically provides the economic value-creating potential of the idea.

Capability components 1730 may include both tangible and intangible aspects of an opportunity 1710. The capability components 1730 may constitute a mutually exclusive, collectively exhaustive set for each opportunity 1710. (The term collectively exhaustive, as used herein, means that the elements of a set comprise the totality of the set.) Or, the capability components 1730 may represent just a subset of the opportunity 1710 defined and may simultaneously be represented in multiple opportunities 1710. A myriad of possibilities exist for representing opportunities 1710 using capability components 1730.

The capability components 1730 of the innovation map 1700 are individual instances of capability component categories or types 1720. Capability types 1720 may include, but are not limited to, products (including prototypes), technologies, services, skills, relationships, brands, mindshare, methods, processes, financial capital and assets, intellectual capital, intellectual property, physical assets, compositions of matter, life forms, physical locations, and individual or collections of people.

The objective of any innovation process is to maximize the volume of high value opportunities 1710 generated at the lowest possible cost. Meeting this objective is a function of multiple variables. One variable is the volume, breadth and quality of the capability components 1730. Another variable is the ability to combine capability components in a large variety and novel ways. A third variable is the degree to which the greatest diversity of human attention to be applied, and applied in the right places. The adaptive process 900C can be used to enable processes that beneficially affect these key variables of innovation process success.

The adaptive computer-based application 925, together with the innovation map 1700, enables more effective innovation-based processes in several ways. First, elements of the innovation map 1700 may include adaptive recommendations 250 that are delivered to process participants 200. This approach can help make process participants 200 aware of particularly relevant elements of the innovation map 170. Second, the adaptive recommendations function 240 may be applied to modify 905 the innovation map 1700 based on, at least in part, inferences on process participant 200 preferences or interests. This can facilitate the efficient development and maintenance of a collective innovation map that can most beneficially serve the interests of the process participants 200, including maximizing the number of high value opportunities generated within innovation map 1700. Third, elements of the innovation map 1700 may be syndicated, modified, and recombined among process participants 200 through the application of the adaptive recombinant system 800, enabling multiple, distributed innovation map instances. This structure can facilitate both shared and private innovation maps, effectively balancing the advantages of economies of scale and local interests. The adaptive recombinant system approaches of FIGS. 47, 48, 49A, and 49B may be applied to the syndication, modification, and recombination of elements of innovation map 1700.

The adaptive computer-based application 925 may contain, or interact with, auxiliary functions (not shown in FIG. 39) that may additionally facilitate innovation processes. For example, the adaptive computer-based application 925 may contain functions to enable automatic or semi-automatic evaluation of opportunities 1710, to automatically or semi-automatically generate additional opportunities 1710 through combinatorial operations on capability components 1730, and/or to facilitate effective information gathering or experimental design associated with uncertainties with regard to capability components 1730 or other elements of innovation map 1700. These additional functions may all be managed within an adaptive process network, such as the adaptive process network of FIG. 25 within the structural aspect 210 of the adaptive system 100.

Adaptive Publishing

The adaptive process 900 may be applied to enable adaptive publishing systems and processes. The adaptive process 900, when applied to enable adaptive publishing systems and processes, may generate adaptive analogs to non-adaptive “broadcasted” media such as print publications, radio programs, music albums or soundtracks, television programs, films, or interactive games; as well as generating adaptive media that may not have specific broadcast analogs. In some embodiments, the methods and systems defined by U.S. patent application Ser. No. 10/715,174, entitled “A Method and System for Customized Print Publication and Management,” may be integrated with adaptive recombinant processes to enable an adaptive publishing process.

FIG. 40 depicts an adaptive process 900D, or adaptive publishing process, according to some embodiments. An adaptive publishing function 2000 that is included within the adaptive computer-based application 925 (although in other embodiments, the adaptive publishing function 2000 may be external to the adaptive computer-based application 925) receives input from the adaptive system 100. The input may be in the form of adaptive recommendations 940 suitable for the adaptive publishing purposes, generated from adaptive recommendations 250, or the input may be in the form of informational content 2031 contained in the content aspect 230 of the adaptive system 100. The content originating from the content aspect 230 may have been modified 935 by the adaptive recommendations function 240. In either case, the adaptive publishing function 2000 uses the inputs from the adaptive system 100 to generate media that is appropriately customized for the recipients of the media 200, 260. This customization of an adaptive publication, or media instance, may include the specific elements of content that will be contained in a media instance, and also the arrangement of the elements of content in the media instance. Thus, a media instance, as used herein, is a distinct set of objects or information in combination with a unique arrangement of the objects or information. The customization of media into specific media instances is performed on the basis of inferred media recipient 200, 260 preferences and interests, which are in turn based on recipient interactions with the adaptive system 100, or through inferred affinities between the media instance recipient and other individuals that have interacted with adaptive system 100.

As shown in FIG. 40, the adaptive publishing function 2000 generates one or more instances of media 2030, adapted appropriately to the preferences or interests of the media recipients 200, 260. Each media instance contains one or more elements of content, some or all of which may be objects 212 or information 232 (FIG. 9A) contained in the adaptive system 100. Although not shown explicitly in FIG. 40, a media instance may also explicitly or implicitly include relationships among objects 214 associated with the structural aspect 210 of the adaptive system 100.

As shown in the example of FIG. 40, media instance 2010 contains multiple objects in a particular configuration, including “Object A” 2012 and “Object D” 2014. Recall that the objects 212 of the adaptive system 100 may contain any form of digital information, including text, graphics, audio, video, and executable software. These objects may be transformed to alternative media forms by the adaptive publishing function 2000. An individual media instance can therefore be defined as a set of information objects 212 or information items 232 and a particular arrangement of the objects of information items. So, as one example, on-line textual objects 212 may be transformed into printed media by the adaptive publishing function 2000. In the case of printed media, a specific media instance is determined by not only the objects to be included in a media instance, but also the arrangement or print layout of the objects 212 and any other content included within the media instance. The information objects 212 within a media instance may be substantive in nature, or non-substantive (e.g., promotional or advertising information).

In accordance with inferred preferences and interests of the intended recipients, media instance 2020 contains a different set of objects and a different arrangement of objects than media instance 2010. For example, “Object A” 2012 exists in both media instance 2010 and 2020, but for example, “Object D” 2014 is unique to media instance 2010 and “Object E” 2024 is unique to media instance 2020.

Although the adaptive media instances 2030 of FIG. 40 depict differing arrangements of objects and other items of content in accordance with a spatial orientation, consistent with, for example, physical spatially-oriented media such as printed media, including newsletters, newspapers, magazines, and books, it should be understood that the customized object selection and arrangement of the adaptive publishing function 2000 may apply to other media types as well. In such cases, the arrangement of elements of the media instance may be other than spatial in nature; for example, the arrangement may be temporal-based for media containing information than is typically “consumed” sequentially. For example, for audio objects 212 or information 232 such as songs, the specific songs selected, and arrangement of the songs in a sound track may be different across media instances. For video or multi-media objects 212 or information 232, customized media instances may include applying the adaptive publishing function 2000 to choose different musical sound tracks for corresponding elements of video, or even generating different media instances containing different elements of, or a different sequence of, the plot or story line of the video. For interactive media, such as computer-based games, the game instance may be customized by the adaptive publishing function 2000 through the selection of different software modules of the game, or by arranging the software modules of the game in different ways in different media instances.

For audio and/or video-based objects 212 or information 232, the adaptive publishing function 2000 may generate media instances that constitute “programs,” which are adaptive analogues of radio programs, television programs or other broadcasted forms.

Media instances may be delivered or otherwise made available 2002 to process participants 200, or made available 2004 to non-process participants 260. Media instances may take a purely digital form that can be embodied in a variety of physical forms. They may be available to recipients in the purely digital form, or they may be available to process participants 200, or to non-process participants 260 through other physical embodiments. A media instance may be printed, for example. A media instance may be stored on portable storage media such as CD-ROMs or DVD's.

The adaptive publishing function 2000 of the adaptive process 900D may apply additional logic or information in generating adaptive media instances 2030 that may not be available from the usage aspect 220 of the adaptive system 100. For example, a record of what objects 212 or information 232 have been contained in media instances received by particular recipients may be used to ensure that a recipient does not receive another media instance that contains information the recipient is likely to have already seen or heard. (This rule might be relaxed or adjusted, for example, for non-substantive content that is included for advertising or promotional purposes.) The adaptive publishing function 2000 may also include special capabilities for managing advertising or promotional information within each media instance. These capabilities seek to optimize or to control advertising or promotional content such that the content will be of the most value to consumers or producers of the media instances 2030, while aligning the frequency and prominence of the advertising or promotional information with the terms and conditions agreed to by suppliers of the advertising or promotional content. The advertising or promotional content may exist within the adaptive system 100, or may be managed within the adaptive publishing function 2000.

The adaptive publishing function 2000 may apply other global considerations or rules in generating adaptive media instances. For example, limits on the amount of information within a media instance may influence the composition of the media instances. The informational limits may be measured, for example, in terms of the number of words or number of pages for text-based media, or, for example, by duration for audio or video-based media. Furthermore, there may be limits on the number of unique media instances generated, and in this case the adaptive publishing function 2000 may apply optimization algorithms to determine media instance composition and arrangement so as to collectively benefit media recipients 200, 260 while conforming to the limits on the number of unique media instances.

The adaptive publishing function 2000 may also apply specific formatting features to media instances. For example, for text-based media instances, specific fonts, font-size, colors, line spacing, and other format variations may be applied in accordance with inferred preferences of media recipients 200, 260. The adaptive publishing function 2000 may also apply alternative languages to media instances in accordance with inferred preferences of media recipients 200, 260.

Although not explicitly shown in FIG. 40, information regarding media instances and the corresponding recipients within the adaptive publishing function 2000 may be made available to the adaptive system 100, and constitute another behavioral aspect incorporated by the usage aspect 220, that can be used by the adaptive recommendations function 240 in generating subsequent recommendations.

Adaptive Commerce

Adaptive processes may be employed to recommend products or services 910 based not only on customer 200 buying behaviors and patterns, but also within the context of auxiliary information or rules that may be specific to the customer or potential customer 200, the customer's organization, and/or the products and services purchased.

According to some embodiments, FIG. 41 depicts an adaptive process 900E, or adaptive commerce process, which includes the functions of an adaptive commerce application. A commerce contextualization function 2100 within the adaptive computer-based application 925 provides additional contextualization to the adaptive system 100 for use by the adaptive recommendations function 240. The commerce contextualization function 2100 may deliver information to the adaptive system 100 directly 2141 to the adaptive recommendations function 240, or through information transfer 2140 to the usage aspect 220. It should be understood that the commerce contextualization function 2100 may be external to the adaptive computer-based application 925, in some embodiments, and transfer information to the adaptive computer-based application 925; which may, in turn, transfer the information to the adaptive system 100. It should also be understood that although the commerce contextualization function 2100 is shown in FIG. 41 to be external to the adaptive system 100, some or all of the functions of commerce contextualization function 2100 could alternatively be internal to the adaptive system 100. For example, some or all of the information associated with the commerce contextualization function 2100 could be directly derived from process participant behaviors 920 and stored and processed in usage aspect 220.

The commerce contextualization function 2100 of the adaptive process 900E includes one or more functional elements, each of which may include relevant information and procedures or algorithms. As shown in FIG. 41, the commerce contextualization function 2100 may include a customer context function 2110, a purchase history function 2120, and a product/service attributes function 2130. The customer context function 2110 includes contextualization information associated with the commercial process participants 200, or customers, that are not available through inferences from customer behaviors 920. For example, for business customers, the customer context function 2110 may include information regarding office site and layout or other business environment-related information. Such information may prove useful in providing recommendations 910 that are most relevant given the business environment of the customer. As another example, pertaining to recommendations to consumers, the customer contextualization function 2110 may contain information on family members of a particular customer, including gender, age, etc., thereby enabling tuning of recommendations 910 (as one example, in the case of gift recommendations) appropriately.

The commerce contextualization function 2100 may also, or alternatively, include a purchase history function 2120. This function includes a mapping of customers to purchases of products or services over time. This information can be used by the adaptive recommendations function 240 to deliver more effective recommendations 910. For example, purchase patterns that are embedded in the information associated with the purchase history function 2120, combined with usage behaviors 920, may enable the recommendation function 240 to generate improved recommendations 910 through incorporation of insights associated with purchase timing patterns. For example, it may be determined by application of the purchase history function 2120 that a certain business customer buys certain products only twice a year, and always in conjunction with another product type. The recommendations 910 may then be appropriately aligned with this pattern.

The commerce contextualization function 2100 may also, or alternatively, include a product or service attributes function 2130. This function includes additional information or context for product or services. As an example, for durable products or goods, a schedule of depreciation may be included in the product/service attributes function 2130. Such information may enable the adaptive recommendations function 240 to tune recommendations to be consistent with the expected lifespan of previously purchased products.

The customer context function 2110, the purchase history function 2120, and the product/service attributes function 2130 may be applied independently, or collectively, in providing additional information to adaptive system 100 to be used by the adaptive recommendations function 240.

Adaptive commerce applications may be applied to adaptive price discovery processes, so as to more advantageously determine prices for products or services. Thus, an adaptive process 900F, or adaptive price discovery process, is depicted in FIG. 42, according to some embodiments. In addition to the commerce contextualization function 2100, the adaptive computer-based application 925 may include, or have access to, a price discovery function 2150 that provides input to the adaptive recommendations function 240 of the adaptive system 100.

Process participant behaviors 920 may be used to infer conscious or unconscious intensity of desire for a product or service, or a collection of products or services. Based on these inferences, as well as information or rules 2151 from the price discovery function 2150, and optionally, information from the commerce contextualization function 2100, the adaptive recommendations function 240 generates adaptive recommendations 910 that include prices for products or services that, in some embodiments, are optimized to yield the highest price that is expected to achieve a sale of the associated product or service to the process participant 200. In other words, the price may be set at a level that is expected to maximize the seller's capture of the buyer's economic rent. The process participant behaviors and associated inferences may be transferred 2152 from the adaptive recommendations function 240 to the price discovery function 2150. Other contextual information may be applied by the combination of the price discovery function 2150 and the adaptive recommendations function 240 to price appropriately. For example, the price optimization may be adjusted as appropriate based on whether the sales transaction is expected to constitute a one-time relationship, or whether future transactions may take place. The results from the recommended prices 910 may be used to determine inferred price sensitivities and elasticities 2155 for one or more process participants 200. Thus, the price discovery function 2150 may supply useful information 2151 to the adaptive recommendations function 240, enabling optimal product pricing; likewise, the adaptive recommendations function 240 may supply useful information 2152 to the price discovery function 2150 for determining prices, price elasticities, or other pricing functions.

The price discovery function 2150 may include a price discovery experimental design function that is applied to optimize the testing of prices through the adaptive system 100. Hence, the combination of the price discovery function 2150 and the adaptive system 100 can constitute a “closed” loop adaptive pricing function that applies insights on process participant 200 behaviors 920 to adjust pricing. In some embodiments, the price discovery function 2150 may apply the methods and systems described in U.S. Provisional Patent Application Ser. No. 60/652,578, entitled “Adaptive Decision Process.”

The adaptive price discovery function 2150 may employ price discovery and pricing methods other than setting a fixed price for a product or service. For example, the function 2150 may apply a bidding processes in which multiple process participants 200 bid on the product or service, or other collective price formation that utilize direct or indirect interactions among process participants 200.

The adaptive price discovery function 2150 may utilize other supplier contextual information to establish prices. This information may be accessed directly from the commerce contextualization function (not shown), or from 2152 the adaptive recommendations function 240. This information may include the associated inventory level, production cost, production plan, and/or other supply chain considerations that may be relevant in establishing price levels for a product or service.

This adaptive pricing approach of the adaptive process 900F may be particularly applicable in setting prices for collections, combinations or “bundles” of products and services that may be specific or even unique to a given customer or set of customers 200. The uniqueness of the bundle enables the provision of a maximum value-add to the customer by fine-tuning a recommended “solution” to a perceived customer need that is comprised of multiple products or services. Such a customized solution can increase the value, or economic rent, to the customer. But, the uniqueness of the bundle also decreases the ability of the customer to “comparison shop,” and this reduced transparency enables the supplier to potentially capture a greater portion of the value-add of the customer. Hence, there is an opportunity for the supplier to create more value for customers and to prominently share in the increased value.

FIG. 43 depicts an adaptive process 900G, or adaptive commercial solutions process. In addition to featuring the adaptive system 100, commerce contextualization function 2100, and price discovery function 2150, the adaptive process 900G includes a product and/or service bundling function 2160 within the adaptive computer-based application 925. (A specific product/service bundle or combination may be termed a “solution.”) The product/service bundling function 2160 provides information 2161a to the adaptive recommendations function 240 to enable adaptive recommendations 910 to include product/service bundles or solutions to process participants 200 that are expected to be relevant or compelling to the process participants 200. Likewise, the adaptive recommendations function 240 provides information 2161b associated with inferences on the preferences or interests of process participants or customers 200. The product/service bundling function 2160 may be applied in concert, or interact with 2162, the price discovery function 2150; together comprising a solution development and pricing process. The adaptive recommendations function 240 may combine inputs from the product/service bundling function 2160, the price discovery function 2150, and the commerce contextualization function 2100, along with information from the usage aspect 220 in generating recommendations that may include solutions and associated pricing.

The product/service bundling function 2160 may provide suggested product or service configurations 2161a, in addition to, or instead of, product and service bundle suggestions or options 2161a. The term “configurations” as used herein in conjunction with the product/service bundling function 2160 denotes a set of product or service features. For example, various product components or features may be combined on a customized basis for a specific customer or customers 200. One example is the customization of the configuration of a personal computer—a specific CPU, with specific storage devices, peripherals, monitor type, etc., may be suggested by the product/service bundling function 2160 based on information 2161b on inferred preferences from the adaptive recommendations function 240.

Continuing the example, the suggested customized personal computer may then be bundled by the product/service bundling function 2160 with a digital camera and a special warranty that encompasses both the personal computer and the camera. This bundle of products and services may then be specially priced by the price discovery function 2150, with the entire bundle of products and services, the configurations of the products and services, and bundle pricing being informed by the inferred preferences and interests of process participants (customers) 200.

The product/service bundling function 2160 and adaptive price discovery function 2150 may be applied together to create a bidding process for product/service bundles. The product/service bundling function 2160 may generate bundles or solutions applicable to multiple process participants 200, and the adaptive price discovery function 2150 is used to organize and manage the bids. The adaptive computer-based application 925 may use the adaptive system 100 and the product/service bundling function 2160 to determine the best mix of bundles and process participants to maximize the value of the auction.

The product/service bundling function 2160 and adaptive price discovery function 2150 may utilize other supplier contextual information to establish solutions and associated prices. This information may be accessed directly from the commerce contextualization function (not explicitly shown in FIG. 43), or indirectly from 2152, 2161b the adaptive recommendations function 240. This supplier contextual information may include the associated inventory level, production cost, production plan, and/or other supply chain considerations that may be relevant in establishing price levels for one or more products or services, and/or configurations thereof.

Location-Aware Adaptive Sales and Marketing

Recall from Table 1 that process participant behaviors 920 may include behaviors associated with physical location, and the movement among physical locations, of process participants 200. According to some embodiments, the adaptive process 900 may be applied to enable sales or procurement-related processes in which the sales processes of a potential supplier monitor physical locations of potential customers 200 and deliver adaptive recommendations 910 that are appropriately contextualized for the customer's location, or location history. Further, the customers or potential customers 200 may themselves employ systems that interact at varying levels of interaction and cooperation with the supplier's sales processes. Where both the supplier and the potential customers employ adaptive recombinant processes and the potential customer and/or the potential supplier is mobile, a location-aware collectively adaptive system and associated location-aware collectively adaptive commercial process 900H is enabled

FIG. 44 depicts a location-aware collectively adaptive process 900H, including a location-aware collectively adaptive system 2200. Four separate instances of adaptive computer applications within system 2200 are shown; each instance corresponds to an instance of the adaptive computer-based application 925 of FIGS. 4A and 4B. Two of the instances are mobile adaptive computer applications; a first mobile adaptive computer-based application 925m1, and a second mobile adaptive computer-based application 925m2. Two of the instances are stationary adaptive computer applications, a first stationary adaptive computer-based application 925s1, and a second stationary adaptive computer-based application 925s2. Each of the adaptive computer-based application instances may interact with any of the other instances, as depicted by the flow of information 2210 between the first stationary adaptive computer-based application instance 925s1 and the first mobile adaptive computer-based application instance 925m1.

The information flow 2210 between any two adaptive computer-based application instances of the location-aware collectively adaptive system 2200 may include the following:

• • 1) Polling and detection of a second adaptive computer-based application instance by a first adaptive computer-based application instance.
  • 2) Identifying the detected second adaptive computer-based application instance by the first adaptive computer-based application instance.
  • 3) Determining a mutual contextual basis for further interaction—that is, either a) from the potentially supplier-side adaptive computer-based application instance, determining whether the potential receiving or customer-side adaptive computer-based application instance encompasses a customer context or set of inferred preferences or interests that would enable one or more relevant recommendations 910 to be generated for the process participants 200 of the customer-side adaptive computer-based application instance; or b) from the potentially receiving or customer-side adaptive computer-based application instance, determining whether the supplier-side adaptive computer-based application instance encompasses a supplier context and product or service attributes that would enable an expected one or more relevant recommendations 910 to be generated for the process participants 200 of the customer-side adaptive computer-based application instance. This determination of a mutual contextual basis for further interaction may be made by one or the other, or both instances.
  • 4) Receiving from, or supplying to, the second adaptive computer-based application instance contextualized information that enables either a) the adaptive recommendations 910 of the first adaptive computer-based application instance to selectively utilize the contextualized information of the second adaptive computer-based application instance; or b) enables the adaptive recommendations 910 of the second adaptive computer-based application instance to selectively utilize the contextualized information of the first adaptive computer-based application instance.
    It should be noted that the interactions 2210 may occur between any two adaptive computer-based applications 925. For example, the interactions 2210 may be between two stationary adaptive computer-based application instances, such as the information flow 2250 between instance 925s1 and instance 925s2. Or the information flow 2230 may be between two mobile adaptive computer application instances, such as instance 925m1 and instance 925m2. Finally, the interactions 2220 may be between a stationary adaptive computer-based application instance 925s1 and a mobile adaptive computer-based application instance 925m2.

According to some embodiments, FIGS. 45 and 46 depict two examples of location-aware collectively adaptive systems 2200. FIG. 45 (2200A) provides additional details regarding the constituent adaptive computer application instances, and the interactions among the instances, of the location-aware collectively adaptive system 2200 of FIG. 44. A stationary adaptive computer application instance 925s includes an adaptive system 100 and a supplier commerce contextualization function 2300 (see FIG. 43). The supplier commerce contextualization function 2300 is comprised of one or more of 1) a supplier context function 2310, 2) a purchase history function 2120, and 3) a product and service attribute function 2130. Although not shown in FIG. 45, the supplier commerce contextualization function 2300 may also include a customer context function 2110. The supplier context function 2310 includes contextual information about the potential supplier that is utilizing or applying the adaptive computer-based application instance 925s, that is not contained in product and service attributes function 2130. For example, supplier context function 2310 may include the physical location of the supplier, the hours of business, the history of the business, and any other information that may be relevant to a customer or prospective customer. The adaptive system 100 of the adaptive computer-based application 925s interacts 2305 with the supplier commerce contextualization function 2300, as desired, to deliver effective adaptive recommendations 910s to process participants 200s.

The stationary adaptive computer-based application instance 925s interacts 2415 with the mobile adaptive computer-based application instance 925m. The mobile adaptive computer-based application instance 925m includes an adaptive system 100 and a mobile customer commerce contextualization function 2400. The mobile customer commerce contextualization function 2400 includes one or both of a 1) customer context function 2110 and 2) a preferences and interests function 2420. The preferences and interests function 2420 contains inferred preferences and interests of process participants 200m based on their interactions with adaptive system 100.

The stationary adaptive computer-based application instance 925s initially interacts 2415 with the mobile adaptive computer-based application instance 925m through an initial detection by one or the other of the instances, or through mutual detection. Next, an interaction 2425 is invoked that seeks to establish a basis for commercial interaction between the two instances. Information from mobile customer commerce contextualization function 2400 is compared to information in the supplier commerce contextualization function 2300. So for example, a service station employing instance 925s detecting a mobile process participant 200m that is a child riding a bicycle is unlikely to have a basis for initiating a commercial interaction, and therefore interactions would cease, whereas if the mobile process participant 200m was a truck driver driving a truck that was due for service, then a basis for commercial interaction may exist.

The adaptive computer-based application instances 925s, 925m may apply location information, or inferences derived from location and time, in establishing a context for commercial interaction or for generation of adaptive recommendations within the location-aware collectively adaptive system 2200. The adaptive computer-based application instances 925s, 925m may utilize geographic-related context or information such as through access to digitized maps in making inferences from location and time information associated with process participants 200.

For example, the respective physical locations of two or more instances may be a determinant of a basis for commercial interaction or for generating adaptive recommendations. The prospective customer or prospective supplier may have thresholds of distance that may be applied to determine a basis for commercial interaction. This threshold distance may be in absolute terms, or in terms of expected transit time between a mobile adaptive computer-based instance and a stationary instance or another mobile instance. Inferred direction and speed of a mobile instance may be calculated and used as input to establishing context for commercial interaction or for generating adaptive recommendations. Further, the inferred mode of transportation of the mobile process participant 200 may be a determinant for commercial interaction or generation of recommendations, as such information may affect the expected transit time or ease of access to the supplier.

Assuming that a basis for commercial interaction is established, a next level of interaction 2435 may be established between the two instances 925m, 925s. The preferences and interests 2420 of the mobile adaptive computer-based instance 925m are accessed by the stationary adaptive computer-based instance 925s to determine if there is a basis for providing suggested products or services to the mobile adaptive computer instance 925m. If the supplier commerce contextualization function 2300 determines that there is a basis for suggesting or recommending products, then these are transmitted 2445 to mobile adaptive computer application instance 925m.

The suggested products or services 2445 are incorporated by the adaptive recommendations function 240 of the adaptive system 100 of mobile adaptive computer-based application 925m in generating recommendations 910m to process participants 200m.

FIG. 46 (2200B) illustrates that the mobile adaptive computer-based application instance 925m, along with the associated process participants 200m, may be considered the process participants 200sm of the stationary adaptive computer-based application instance 925s. The interactions described in FIG. 45 are conducted through the process participant behaviors 920 transmission to the instance 925s, and through the adaptive recommendations 910s generated by instance 925s and received by process participants 200sm. Although in FIG. 46, the respective adaptive application instances 925s, 925m are stationary and mobile, respectively, it should be understood that the example may be reversed, or two stationary or two mobile instances may utilize the same topology for interactions, as depicted in FIG. 46.

The location-aware collectively adaptive system 2200 and process 900H (FIG. 44) may be applied to a variety of sales and procurement process areas. For example, restaurants can apply such processes by providing prospective diners that are in the vicinity of relevant recommended options, tuned to the prospective diner's particular preferences and tastes.

The location-aware collectively adaptive system 2200 and process 900H may further apply the adaptive price discovery systems and processes of FIG. 42 or the adaptive commercial solutions systems and process of FIG. 43.

A mobile adaptive computer application instance 82bm1 may be embodied within a portable computing device, such as a mobile phone or personal digital assistant (PDA). A mobile adaptive computer application instance 82bm1 may be contained in mobile apparatus, such as vehicles or other transportation devices. In some embodiments, a mobile adaptive computer application instance 82bm1 may reside within a self-propelled device or appliance.

Adaptive Viral Marketing

In the prior art, viral marketing techniques are known that promote the initial recipients of a sales or marketing-related message to re-send the message to others. For example, viral marketing through e-mail messages is a familiar technique. However, prior art viral marketing techniques exhibit two significant limitations: 1) there is little ability for a recipient to easily modify the received message for the benefit of others he or she will re-send the message to, and 2) the structure of the message is typically a single item of information embodied in a single computer file (such as a e-mail message, or a text document).

According to some embodiments, an application of adaptive recombinant process 901, adaptive recombinant process 901B, may be used to advantageously transform customer relationships, promote sales, facilitate business development, enhance public relations or generally increase “share of mind.” In contrast to the prior art, through the application adaptive recombinant process 901B, content networks or process networks comprising multiple units of interconnected information may be syndicated to potential customers or individuals or institutions for whom influence is sought. The content or process networks may then be syndicated to the customer's customers or influence targets, and so on, potentially without limit. At each stage of syndication and receipt, one or more content or process networks may be modified or combined, optionally enabled by an adaptive recommendations function 240. The content within the syndicated content networks may be substantive or non-substantive (e.g., advertising or promotional content). This application of adaptive recombinant process 901B provides a much more powerful and comprehensive approach to viral marketing and public relations than is possible with prior art approaches.

FIG. 47 illustrates an adaptive recombinant systems construct to manage syndication and recombination of network structures for a variety of process purposes, including enabling adaptive viral marketing process 901B. Recall from FIGS. 16 and 17 that the adaptive recombinant computer-based application 925R may include the adaptive recombinant system 800C, which in turn, may encompass the adaptive system 100C (FIG. 14). In the embodiment of FIG. 47, the adaptive system 100C manages multiple networks within the structural aspect 210C. These networks may be content networks or process networks, and may be fuzzy networks. For example, some or all of “network 1” 2510 may be syndicated 2515 to “network 2” 2520 and combined, followed by some or all of the resulting network combination syndicated 2525 to “network 3” 2530 and combined with “network 3” 2520. A closed loop may be formed, as some or all of this last network combination may be syndicated 2535 back to the original “network 1” 2510 and combined with “network 1” 2510. This process may continue indefinitely. At each stage, it should be understood that a network may be syndicated to a recipient that does not possess a network. Such a recipient may nevertheless modify the network and re-syndicate. For each stage, the selection, syndication, modification, or combination is enabled by the functions of the adaptive recombinant system 800C, as described previously. Thus, the adaptive recommendations function 240 may be applied to facilitate these syndications, modifications, and combinations based, in part, on inferences of preferences and interests from the usage behaviors 920 of process participants 200.

FIG. 48 illustrates an alternative adaptive recombinant systems construct using an adaptive recombinant system 800i to manage syndication and recombination of network structures for a variety of process purposes, including enabling adaptive viral marketing process 901B. Adaptive recombinant system 800i includes multiple instances of adaptive system 100i. Although not shown in FIG. 48, each adaptive system instance, such as adaptive system 100i1, may have its own independent set of process participants 200, or the process participants 200 of each adaptive system instance may overlap.

In the embodiment of FIG. 48, each adaptive system instance 100i manages one or more networks within its structural aspect 210 (not shown). These networks may be content networks or process networks, and may be fuzzy networks. As an example, some or all of the structural aspect and/or usage aspect of the first adaptive system instance 100i1 may be syndicated 2555 to a second adaptive system instance 100i2, and the structural and/or usage aspects optionally combined. Some or all of the structural and/or usage aspects of the second adaptive system instance 100i2 may then be syndicated 2565 to a third adaptive system instance 100i3, and the structural and/or usage aspects optionally combined. A closed loop may be formed, as some or all of the structural and/or usage aspects of the third adaptive system instance 100i3 may be syndicated 2575 back to the original adaptive system instance 100i1.

Thus, the process of syndication, modification, and combination may continue indefinitely. At each stage, it should be understood that an entire adaptive system instance 100i may be syndicated to a recipient that does not have access to the adaptive system instance 800i1. And at each stage, the selection, syndication, modification, or combination is enabled by the functions of the adaptive recombinant system 800, as described previously. Thus, the adaptive recommendations function 240 of each adaptive system instance 100i may be applied to facilitate these syndications, modifications, and combinations based, in part, on inferences of preferences and interests from usage behaviors 920 of process participants 200.

The systems and methods described in FIG. 47 and FIG. 48 may be applied to enabling adaptive viral marketing process 901B, in some embodiments, as depicted in FIGS. 49A and 49B. In FIGS. 49A and 49B, the syndication and recombination of content networks across organization are described. It should be understood that the content networks described may or may not be fuzzy networks, and may or may not be process networks. It should also be understood that the networks may include usage behavioral information associated with the usage aspect 220, in addition to, or instead of content networks associated with structural aspect 210c of the adaptive system 100. Further, although the syndication is to “organizations,” it should be understood that the term as used herein may include a single person.

FIG. 49A depicts a the selection or sub-setting of content network “network 1” 2735 residing in “organization 1” 2650 to form “network 1a” 2695. “Network 1a” 2695 may contain substantive or non-substantive information (such as advertising or promotional content), and is syndicated to “organization 2” 2655 for the purposes of either direct promotion, with an option for indirect promotion through re-syndication by “organization 2” 2655; or the syndication to “organization 2” 2655 may be for the primary or sole purpose of indirect promotion through “organization 2's” 2655 expected re-syndication of the network.

In this example, “network 1a” 2700 and the existing “network 2” 2705 in “organization 2” are combined 2710 to form “network 2a” 2715 in “organization 2” 2655. This combination may be either for the direct benefit of “organization 2” 2655, or the purposes of continuing the chain of promotion through re-syndication of a network of substantive and/or non-substantive information that is expected to be increasingly valuable to each new generation of recipients.

Continuing the example, “network 2a” 2715 is then syndicated to “organization 3” 2660, wherein “organization 3” 2660 does not already possess or have access to a content network.

FIG. 49B represents a continuation of FIG. 49A to depict the potentially closed-loop aspect of the adaptive viral marketing process. “Network 2a” 2725 in “organization 3” 2660 is syndicated to “organization 1” 2655. “Network 2a” 2725 is then combined with the original “network 1” 2735 in “organization 1” 2650 to generate “network 3” 2740 in “organization 1” 2650.

FIGS. 49A and 49B demonstrate that, in some embodiments, the adaptive recombinant process 901B may, without limit, enable sub-setting of networks of substantive and/or non-substantive information, syndicating the subsets to one or more destinations, and enabling the syndicated networks to be combined with one or more process networks at the destinations. At each combination step, functions of adaptive recombinant system 800C may be applied, including the relationship resolution functions and the adaptive recommendations function, to create and update process structure (and content) as appropriate. The participants 200 in the adaptive viral marketing process may or may not be directly conscious of playing a role in marketing or promotion.

As a specific example of the economics of viral marketing, the originator of the adaptive viral marketing process 901B may supply a product or service for which there are complementary products or services; by complementary, it is meant that the supplier can sell more of its product or services to a customer if the customer has access to, or can purchase, the complementary products or services. So, for example, commentary by other process participants, particularly process participants with special expertise of relevant reputation, may be a complement to selling a tangible or intangible product, such as a video. Through the initiation of the viral marketing approach, delivery or targeted, complementary commentary may be efficiently achieved that could stimulate greater demand for the video itself.

The adaptive viral marketing process 901B of FIGS. 49A and 49B may also apply methods associated with location-aware collectively adaptive system 2200 and process 900H, and may further apply the systems and methods of the adaptive commercial solutions process (900G) depicted in FIG. 43.

Evolvable Processes

According to some embodiments, the adaptive recombinant process 901 may be used to deploy an evolvable process 901E across one or more organizations or environments. FIG. 50 depicts an embodiment of the adaptive recombinant computer-based application 925R of FIG. 4C, which includes an evolvable adaptive recombinant system 800e, which itself includes the adaptive recombinant function 850. The adaptive recombinant function 850 in turn includes a syndication function 810, a fuzzy network operators function 820, and an object evaluation function 830, all of which were described previously. The evolvable adaptive recombinant system 800e also contains one or more instances 100i of the adaptive system 100. Process participants 200 generate process usage behaviors 920 that are tracked and processed by the one or more adaptive system instances 800i. In addition, the evolvable adaptive recombinant system 800e contains a network evaluation function 860, which is used to evaluate the “fitness” of one or more content networks, which may include process networks, and works in concert 2905 with the adaptive recombinant function 850 to generate new generations of content networks from a previous generation of content networks deemed to be most fit by the network evaluation function 860.

Recall from FIG. 47 that an instance of the adaptive system 100 may contain multiple content networks. The network evaluation function 860 may evaluate 2915 one or more networks within an adaptive system instance 100i3. The adaptive recombinant function 850 may then be applied to create a new generation of recombinant content networks within the adaptive system instance 100i3, based on the individual fitness of the previous generation of content networks.

Alternatively, the network evaluation function 860 may evaluate 2935 content networks across adaptive systems instances 100i. The adaptive recombinant function 850 may then be applied to create a new generation of recombinant content networks across adaptive system instances 100i, based on the individual fitness of the previous generation of content networks across system instances 100i.

The network evaluation function 860 may apply criteria derived from inferences on preferences and interests of usage behaviors 920 of process participants 200. These criteria may be augmented by additional evaluation criteria and logic as required.

The adaptive recombinant function 850 may generate new generations of content networks based on purely the inheritance of characteristics derived from combinations of previous generations of content networks (Lamarkian approach to network evolution), and/or the adaptive recombinant function 850 may apply random changes to the content networks, so as to create network mutations, which, in turn, increases network variation (Darwinian approach to network evolution). Genetic algorithms may be applied to generate network mutations and combinations.

Evolvable adaptive recombinant system 800e can therefore enable the evolvable process 901E, which can serve as a means of accelerating the development of the most adaptive possible processes for a given organizational environment.

Computing Infrastructure

FIG. 51 depicts various hardware topologies that the adaptive process 900, the adaptive recombinant process 901, the adaptive computer-based application 925, the adaptive recombinant computer-based application 925R, the adaptive system 100, or the adaptive recombinant system 800 may embody. Further, the adaptive asset management process 900A, the adaptive real-time learning process 900B, the innovation network process 900C, the adaptive publishing process 900D, the adaptive commerce process 900E, the adaptive price discovery process 900F, the adaptive commercial solutions process 900G, the location-aware collectively adaptive process 900H, the recombinant process network process 901A, the adaptive viral marketing process 901B, the evolvable process 901E, or other applications of the adaptive process 900 or adaptive recombinant process 901 not described herein may utilize the hardware and computing topologies of FIG. 51. These various systems are referred to as the “relevant systems,” below.

Servers 950, 952, and 954 are shown, perhaps residing at different physical locations, and potentially belonging to different organizations or individuals. A standard PC workstation 956 is connected to the server in a contemporary fashion. In this instance, the relevant systems, in part or as a whole, may reside on the server 950, but may be accessed by the workstation 956. A terminal or display-only device 958 and a workstation setup 960 are also shown. The PC workstation 956 may be connected to a portable processing device (not shown), such as a mobile telephony device, which may be a mobile phone or a personal digital assistant (PDA). The mobile telephony device or PDA may, in turn, be connected to another wireless device such as a telephone or a GPS receiver.

FIG. 51 also features a network of wireless or other portable devices 962. The relevant systems may reside, in part or as a whole, on all of the devices 962, periodically or continuously communicating with the central server 952, as required. A workstation 964 connected in a peer-to-peer fashion with a plurality of other computers is also shown. In this computing topology, the relevant systems, as a whole or in part, may reside on each of the peer computers 964.

Computing system 966 represents a PC or other computing system, which connects through a gateway or other host in order to access the server 952 on which the relevant systems, in part or as a whole, reside. An appliance 968, includes software “hardwired” into a physical device, or may utilize software running on another system that does not itself host the relevant systems. The appliance 968 is able to access a computing system that hosts an instance of one of the relevant systems, such as the server 952, and is able to interact with the instance of the system.

The relevant systems may utilize database management systems, including relational database management systems, to manage to manage associated data and information, including objects and/or relationships among objects. The relevant systems may apply intelligent “swarm” peer-to-peer file sharing techniques to facilitate the syndication of large networks of content, by enabling a plurality of peer computing devices to collectively serve as file servers, thus acting to de-bottleneck the sharing of large networks of information. Further, adaptive recombinant processes may apply intelligent swarm peer-to-peer sharing to the entire network of information (objects and relationships) that is to be syndicated, rather than just individual files. The relevant systems may apply special algorithms to optimally syndicate elements of one or more networks of information across a plurality of peer computing devices to enable the collective set of peer computing devices to be utilized as servers in a manner to enable the most efficient syndication of large-scale networks of information.

While the present invention has been described with respect to a limited number of embodiments, those skilled in the art will appreciate numerous modifications and variations therefrom. It is intended that the appended claims cover all such modifications and variations as fall within the scope of this present invention.

Claims

1. An adaptive commerce system, comprising:

a usage function implemented on a processor-based computing device comprising captured usage behaviors, wherein the usage behaviors are associated with one or more users of a computer-implemented system;

a production cost; and

a computer-implemented function to generate an adaptive recommendation of one or more products based, at least in part, on a plurality of usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories and the production cost.

2. The system of claim 1, further comprising:

a commerce context function, the commerce context function being selected from a group consisting of a customer context function, a purchase history function, and a product attribute function.

3. The system of claim 1, further comprising:

a price discovery function.

4. The system of claim 1, further comprising:

a product bundling function.

5. The system of claim 1, wherein a computer-implemented function to generate an adaptive recommendation of one or more products based, at least in part, on a plurality of usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories and the production cost comprises:

a bundle-recommending function comprising at least a first product and a second product.

6. The system of claim 1, wherein a computer-implemented function to generate an adaptive recommendation of one or more products based, at least in part, on a plurality of usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories and the production cost comprises:

a product configuration-generating function.

7. The system of claim 1, wherein a computer-implemented function to generate an adaptive recommendation of one or more products based, at least in part, on a plurality of usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories and the production cost comprises:

a price-generating function wherein the price generated by the price-generating function corresponds to the one or more recommended products.

8. An adaptive commercial solutions system, comprising:

a usage function implemented on a processor-based computing device comprising captured usage behaviors, wherein the usage behaviors are associated with one or more users;

a product-bundling function;

a price discovery function; and

a computer-implemented function to generate an adaptive solutions recommendation comprising a plurality of products based, at least in part, on a plurality of usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories and a production cost.

9. The system of claim 8, wherein a price discovery function comprises:

a pricing function, the pricing function being selected from a group consisting of a price elasticity function, a pricing experimental design function, and a collective price formation process function.

10. The system of claim 8, further comprising:

supplier contextual information.

11. The system of claim 8, wherein a computer-implemented function to generate an adaptive solutions recommendation comprising a plurality of products based, at least in part, on a plurality of usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories comprises:

a recommended bundle comprising at least a first product and a second product, and a price corresponding to the bundle.

12. The system of claim 8, wherein a computer-implemented function to generate an adaptive solutions recommendation comprising a plurality of products based, at least in part, on a plurality of usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories comprises:

a recommended product configuration and a corresponding price.

13. An adaptive commerce method, comprising:

capturing usage behaviors, wherein the usage behaviors are associated with one or more users interacting with a processor-based computing device; and

generating an adaptive commercial recommendation based, at least in part, on a plurality of usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories and a production cost.

14. The method of claim 13, wherein generating an adaptive commercial recommendation based, at least in part, on a plurality of usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories and a production cost comprises:

applying supplier contextual information.

15. The method of claim 13, wherein generating an adaptive commercial recommendation based, at least in part, on a plurality of usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories and a production cost comprises:

generating a recommendation of a product.

16. The method of claim 15, wherein generating a recommended product comprises:

generating a price corresponding to the recommended product.

17. The method of claim 13, wherein generating an adaptive commercial recommendation based, at least in part, on a plurality of usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories and a production cost comprises:

generating a recommendation of a bundle comprising at least a first product and a second product.

18. The method of claim 17, further comprising:

generating a price corresponding to the recommended bundle.

19. The method of claim 13, wherein generating an adaptive commercial recommendation based, at least in part, on a plurality of usage behaviors associated with the one or more users corresponding to a plurality of usage behavior categories and a production cost comprises:

generating a recommendation of a product configuration.

20. The method of claim 19, further comprising:

generating a price corresponding to the recommended product configuration.