Temporally sequenced recommendations and associated explanations in subscription-based systems   

Abstract: A computer-implemented method and system for temporally sequenced recommendations and associated explanations in subscription-based systems delivers to users of a subscription-based system multiple recommended objects that are arranged in a temporal sequence. The delivered recommended objects are in accordance with user subscriptions and inferences of preferences that are based, at least in part, on usage behaviors. Variations of the system and method include delivering recommended objects in accordance with the contents of the objects and user direct feedback with regard to the objects. Information as to why objects were delivered to users is provided to the users.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a divisional of U.S. patent application Ser. No. 13/270,049, filed on Oct. 10, 2011, which is a continuation of U.S. patent application Ser. No. 11/559,145, filed on Nov. 13, 2006, which is a continuation of International Patent Application No. PCT/US2005/011951, filed on Apr. 8, 2005, which claimed benefit under 35 U.S.C. §119(e) to U.S. Provisional Patent Application No. 60/572,565, filed May 20, 2004.

FIELD OF THE INVENTION

This invention relates to extending the business process paradigm so as to make processes more explicitly adaptive over time. More specifically, adaptive recombinant processes relates to processes that automatically structure and re-structure themselves so as to deliver increasing value to the participants in the processes over time.

BACKGROUND OF THE INVENTION

The business process paradigm was first introduced in a rigorous form by Rummler and Brache in the late 1980\'s, and was increasingly popularized by authors such as Michael Hammer, and a wide range of business consultants, during the 1990\'s. The terms “process redesign” or “process reengineering” have been typically used to denote the explicit establishment of processes that are optimized for specific business requirements. It should be understood that although the modifier “business” may be applied to the term “process” herein, processes are relevant to, and may apply to, non-business organizations or institutions, as well as individuals.

Business processes can be broadly defined as a set of activities that collectively perform a business function. The activities within a process are typically performed in a specific sequence, with the sequence of activities subsequent to any specified activity being potentially dependent on conditions and decisions taken at the previous activity step.

The prior art associated with process design constitutes developing processes that are optimized for current business conditions, while attempting to build in enough flexibility in the design of the process for the process to remain effective if business conditions change within a limited range over time. Training of individuals performing tasks within processes is often a mixture of formalized training, classroom and/or on-line training, as well as on-the-job experience. In general, however, the current process paradigm is not one of adaptive processes; that is, processes that can effectively change as business conditions change without significant, explicit human redesign efforts, and processes that adapt to the on-going learning needs, and more generally, the preferences or interests, of individual participants in the processes. Specifically, the current process paradigm does not have a built-in learning mechanism, resulting in a significant penalty in efficiency and effectiveness.

SUMMARY

In accordance with the embodiments described herein, a method and system for adaptive recombinant processes is disclosed.

The present invention, “adaptive recombinant processes,” is a method and system for embedding adaptation and learning within any type of process. Adaptive recombinant processes enable design and implementation of processes that automatically capture process participant behaviors associated with the use of, interaction with, or, most generally, participation in, the associated process. These process participant behaviors include both individual and community usage behaviors. The resulting adaptive process can thereby effectively reconfigure itself on a continuous and potentially real-time basis, based, at least in part, on inferences of preferences or interests derived from process interactions by participants in the process. Such inferences may be conducted on an automatic or semi-automatic basis; in either case, application of the inferences can potentially dramatically reduce explicit, manual process design and redesign efforts. Adaptive recombinant processes can also dramatically reduce traditional training costs, and effectively integrates the domains of e-learning and knowledge management directly within business processes.

Furthermore, adaptive recombinant processes can enable the syndication of processes or elements of processes among organizations, which can then be automatically or semi-automatically integrated with existing processes or process elements. This recombinant process approach can significantly increase process adaptiveness and increase efficiency through the maximizing of reuse. Furthermore, an evolutionary approach may be used to create a diversity of processes that can be evaluated automatically or semi-automatically, and then preferentially combined based on evaluation results.

Adaptive recombinant processes enables both increasing the adaptiveness of existing classes of processes and the enablement of entirely new types of processes that were not feasible with prior methods. An example of increasing the adaptiveness of existing processes is building in “real-time learning” within any instance of existing classes of processes, to create an adaptive “cockpit” that facilitates process learning, use and execution. Examples of new types of processes enabled by adaptive recombinant processes include processes that are underpinned by syndication and/or recombination of processes and sub-processes across a series or network of organizations. Such capabilities may be applied to facilitate, for example, marketing and business development, product or service/solution development and delivery, innovation, coordinated operations, and/or collaborative learning. Specific examples of new types of processes enabled by adaptive recombinant processes are adaptive online asset management, adaptive viral marketing processes, adaptive sales and marketing processes, adaptive commercial processes such as adaptive product and service bundling and pricing, processes enabled by location-aware and collectively adaptive systems, and adaptive publishing processes.

Adaptive recombinant processes can apply the fuzzy content network approach as defined in U.S. Pat. No. 6,795,826, entitled “Fuzzy Content Network Management and Access,” and adaptive recombinant systems approaches as defined in PCT Patent Application No. PCT/US04/37176, entitled “Adaptive Recombinant Systems,” filed on Nov. 4, 2004, both of which are incorporated by reference herein, as if set forth in their entirety.

Other features and embodiments will become apparent from the following description, from the drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B are block diagrams of process and organization topologies, according to the prior art;

FIGS. 2A and 2B are block diagrams of sub-processes and activities, according to the prior art;

FIG. 3 is a block diagram describing the relationship between a process and associated supporting content and computer applications, according to the prior art;

FIG. 4A is a block diagram of an adaptive process, according to some embodiments;

FIG. 4B is a detailed block diagram of the adaptive process of FIG. 4A, according to some embodiments;

FIG. 4C is a block diagram of an adaptive recombinant process, according to some embodiments;

FIG. 5 is a diagram of the process participant usage framework, according to some embodiments;

FIG. 6 is a diagram of process participant communities and associated relationships, according to some embodiments;

FIG. 7 is a block diagram of an adaptive system, according to some embodiments;

FIG. 8 is a block diagram contrasting the adaptive system of FIG. 7 with a non-adaptive system, according to some embodiments;

FIG. 9A is a block diagram of the structural aspect of the adaptive system of FIG. 7, according to some embodiments;

FIG. 9B is a block diagram of the content aspect of the adaptive system of FIG. 7, according to some embodiments;

FIG. 9C is a block diagram of the usage aspect of the adaptive system of FIG. 7, according to some embodiments;

FIG. 10 is a block diagram of the adaptive recommendations function used by the adaptive system of FIG. 7, according to some embodiments;

FIG. 11 is a block diagram showing structural subsets generated by the adaptive recommendations function of FIG. 7, according to some embodiments;

FIG. 12 is a flow chart showing how recommendations of the adaptive system of FIG. 7 are generated, whether to support system navigation and use or to update structural or content aspects of the adaptive system, according to some embodiments;

FIG. 13 is a block diagram of a fuzzy network selection operation, according to some embodiments;

FIG. 14 is a block diagram of the adaptive system of FIG. 7 in which the structural aspect is a fuzzy network, according to some embodiments;

FIG. 15 is a block diagram of a structural aspect including multiple network-based structures, according to some embodiments;

FIG. 16 is a block diagram of an adaptive recombinant system, according to some embodiments;

FIG. 17 is a block diagram of the adaptive recombinant system of FIG. 16 in which the structural aspect is a fuzzy network, according to some embodiments;

FIG. 18 is a block diagram of the fuzzy network operators used by the adaptive recombinant system of FIG. 16, according to some embodiments;

FIGS. 19A and 19B are block diagrams of alternative topologies between fuzzy networks and adaptive processes, according to some embodiments;

FIGS. 20A and 20B are block diagrams of a process topic object and a process content object, respectively, according to some embodiments;

FIGS. 21A and 21B are block diagrams of alternative structures of process activity objects, according to some embodiments;

FIGS. 22A and 22B are block diagrams of process activity networks, according to some embodiments;

FIGS. 23A and 23B are block diagrams of a process network, according to some embodiments;

FIG. 24 is a flow diagram describing structural modification of the process network of FIGS. 23A and 23B, according to some embodiments;

FIG. 25 is a block diagram of a process network selection operation, according to some embodiments;

FIG. 26 is a block diagram of a process network syndication operation, according to some embodiments;

FIG. 27 is a block diagram of a process network resulting from a combination of process networks, according to some embodiments;

FIG. 28 is a block diagram of the adaptive system of FIG. 7 in which the structural aspect is a process network, according to some embodiments;

FIG. 29 is a block diagram of the adaptive recombinant system of FIG. 16 in which the structural aspect is a process network, according to some embodiments;

FIGS. 30A and 30B are block diagrams illustrating syndication and recombination of process networks and process network subsets, according to some embodiments;

FIGS. 31A and 31B are block diagrams illustrating syndication and recursive recombination of process networks and process network subsets, according to some embodiments;

FIG. 32 is a block diagram of the process network topologies, according to some embodiments;

FIG. 33 is a block diagram of extensions to the process network topologies of FIG. 32, according to some embodiments;

FIG. 34 is a diagram of a process lifecycle framework, according to some embodiments;

FIG. 35 is a diagram of process functionality layers, according to some embodiments;

FIG. 36 is a diagram of a process lifecycle management framework, according to some embodiments;

FIG. 37 is a block diagram of an adaptive asset management system and process, according to some embodiments;

FIG. 38 is a block diagram of a real-time learning system interface, according to some embodiments;

FIG. 39 is a block diagram of an adaptive system to support an innovation process, according to some embodiments;

FIG. 40 is a block diagram of a system and process for adaptive publishing, according to some embodiments;

FIG. 41 is a block diagram of a system and process for adaptive commerce, according to some embodiments;

FIG. 42 is a block diagram of a system and process for adaptive price discovery, according to some embodiments;

FIG. 43 is a block diagram of a system and process for adaptive commercial solutions, according to some embodiments;

FIG. 44 is a block diagram of location aware collectively adaptive systems, according to some embodiments;

FIG. 45 is a block diagram of a possible configuration of the location aware collectively adaptive systems of FIG. 44, according to some embodiments;

FIG. 46 is a block diagram of an alternative configuration of the location aware collectively adaptive systems of FIG. 45, according to some embodiments;

FIG. 47 is a block diagram of syndication and combination of content networks within the structural aspect of the adaptive recombinant system of FIG. 16, according to some embodiments;

FIG. 48 is a block diagram of syndication and combination of elements of the structural aspects and usage aspects across multiple instances of adaptive systems of FIG. 7 within the adaptive recombinant system of FIG. 16, according to some embodiments;

FIGS. 49A and 49B are block diagrams of recursive syndication and combination of networks of the structural aspects of the adaptive recombinant systems of FIG. 47 or 48 across organizations, according to some embodiments;

FIG. 50 is a block diagram of an evolvable adaptive recombinant system and process, according to some embodiments; and

FIG. 51 is a diagram of alternative computing topologies of adaptive recombinant processes, according to some embodiments.

DETAILED DESCRIPTION

In the following description, numerous details are set forth to provide an understanding of the present invention. However, it will be understood by those skilled in the art that the present invention may be practiced without these details and that numerous variations or modifications from the described embodiments may be possible.

In accordance with the embodiments described herein, a method and a system for development, management and application of adaptive processes is disclosed.

Processes are ubiquitous throughout the business world, and apply as well to non-business institutions such as government and non-profit organizations and institutions. In the following descriptions of processes and the application of adaptive recombinant processes, business examples will typically be used, but it should be understood that the descriptions of processes and related features, and the application of adaptive recombinant processes, extends to non-business institutions and organizations.

Processes can be defined as categorizations of activities, along with associated inputs and outputs. A process may apply to, but is not limited to, the following general application areas: marketing, sales, price determination, innovation, research and development (R&D), product development, service and solutions development, business development, tangible or intangible asset management, manufacturing, supply chain management, logistics and transportation, procurement, finance and accounting, investment and portfolio management, human resources, education, entertainment, information technology, security, military, legal, administrative processes and business strategy.

FIGS. 1A, 1B, 2A, 2B and 3 describe prior art and definitions associated with processes.

FIG. 1A depicts a business enterprise 110 including a plurality of processes, a specific example being “process 3” 105. A business may include one or more processes. It is a typical practice to determine a number of processes that can be effectively remembered and managed by people in the associated business—for example, seven processes (plus or minus two) is a commonly selected number of processes for an organization. Although not explicitly shown in FIG. 1A, each process may have one or more linkages to another process. The linkages may denote a workflow between the processes, or the linkage may denote an information flow, or a linkage may denote both workflow and information flow.

As depicted in FIG. 1B, processes may extend across businesses or enterprises, or most broadly, organizations. For example, in FIG. 1B, “Process 8” 120 is shown extending across “Enterprise A” 110A and “Enterprise B” 110B. It should be understood that, in general, multiple processes may extend across multiple enterprises or organizations.

FIG. 2A illustrates that each process 125 may include one or more sub-processes. As in the case of processes, sub-processes may have one or more directed linkages 132 to other sub-processes within the process, or to processes outside the process within which the sub-process exists. These external links may constitute inbound links 132a or outbound links 132d. There may exist a plurality of links between any two sub-processes, and the plurality of links may include inbound 132b or outbound links 132c. Although not explicitly shown in FIG. 2A, each sub-process may contain one or more other sub-processes, and this recursive decomposition of sub-processes can continue without limit. It should be noted, as defined herein, that the only essential distinguishing feature of a sub-process with regard to a process is that a sub-process is understood to be a subset of a process. Where the term sub-process is used herein, it is understood that the term process could be used without loss of generality.

FIG. 2B depicts a sub-process. A sub-process 135 is comprised of other sub-processes (not shown), and/or a series of activities, for example, “Activity 1” 140. These activities are conducted by process participants 200. In a business setting, each activity typically represents a unit of work to be conducted in a prescribed manner by one or more participants 200 in the process, and possibly according to a prescribed workflow. However, as defined herein, an activity may also simply constitute a process participant 200 action or behavior. For example, a process participant 200 for a sales process might be a prospective customer, and a behavior of the prospective customer may constitute an activity. In such cases a process participant, for example, a customer or prospective customer, may not be aware that their behaviors or interactions with a process constitute conducting a formally defined activity, although from the perspective of another process participant or the process owner, the activity may constitute a formally defined activity.

Participants in a process 200, or “process participants,” are defined as individuals that perform some activity within a process, or otherwise interact with a process, or provide input to, or use the output from, a process or sub-process. For example, a process participant in a sales process may include sales people that perform selling activities, but may also include customers or prospective customers that interact with the sales process, including the review and consideration of, and/or the purchasing of goods or services. Further, managers who rely on input from, and/or provide guidance to, the sales process may be considered process participants in the sales process. Further, specific actions or behaviors of the customer or prospective customer may be defined as activities corresponding to the process or sub-process.

Although more than one activity is depicted in FIG. 2B, it should be understood that a process or sub-process may include only a single activity.

Any two activities may be linked, which implies a temporal sequencing or workflow, as for example the linkage 155 between “Activity 1” 140 and “Activity 2” 150. An activity may be cross-linked, back linked, or forward linked to more than one other activity. An activity may contain conditional decisions that determine which forward links to other activities, such as depicted by links 155a and 155b, are selected during execution of the antecedent activity 150. Parallel activities may exist as represented by “Activity 3” 161 and “Activity 4” 160. Inbound links 145 to activities of the sub-process 135 from other processes, sub-processes or activities may exist, as well as outbound links 165 from activities of the sub-process 135 to other processes, sub-processes, or activities.

FIG. 3 illustrates a general approach to information and computing infrastructure support for processes. The workflow of activities within a process or sub-process 168 may be managed by a computer-based workflow application 169 that enables the appropriate sequencing of workflow. Each activity, as for example “Activity 2” 170, may be supported by on-line content or computer applications 175. On-line content or computer applications 175 include pure content 180, a computer application 181, and a computer application that includes content 182. Information or content may be accessed by the sub-process 168 from each of these sources, shown as content access 180a, information access 181a, and information access 182a.

For example, content 180 may be accessed 180a (a content access 180a) as an activity 170 is executed. Although multiple activities are depicted in FIG. 3, a process or sub-process may include only one activity. The term “content” is defined broadly herein, to include text, graphics, video, audio, multi-media, computer programs or any other means of conveying relevant information. During execution of the activity 170, an interactive computer application 181 may be accessed. During execution of the activity 170, information 181a may be delivered to, as well as received from the computer application 181. A computer application 182, accessible by process participants 200 during execution of the activity 170, and providing and receiving information 182a during execution of the activity 170, may also contain and manage content such that content and computer applications and functions that support an activity 170 may be combined within a computer application 182. An unlimited number of content and computer applications may support a given activity, sub-process or process. A computer application 182 may directly contain the functionality to manage workflow 169 for the sub-process 168, or the workflow functionality may be provided by a separate computer-based application.

FIGS. 4A and 4B depict the application of adaptive recommendations to support a process or sub-process, according to some embodiments. In FIG. 4A, an adaptive process 900 is depicted, which includes one or more process participants 200, an adaptive instance of a process or sub-process 930 (hereinafter, adaptive process instance 930 or process instance 930), and an adaptive computer-based application 925. In FIG. 4B, the adaptive process 900 may include many of the features of the prior art process in FIG. 3. Thus, the adaptive process instance 930 features the workflow application 169, if applicable, with multiple activities 170, one or more of which may be linked. Further, the adaptive computer-based application 925 is depicted as part of supporting content and computer applications 175. FIG. 4A provides a broad overview of the adaptive process 900 while FIG. 4B includes many more details.

One or more participants 200 in the adaptive process instance 930 generate behaviors associated with their participation in the process instance 930. The participation in the process instance 930 may include interactions with computer-based systems 181 and content 180, such as content access 180a and information access 181a, but may also include behaviors not directly associated with interactions with computer-based systems or content.

Process participants 200 may be identified by the adaptive computer-based application 925 through any means of computer-based identification, including, but not limited to, sign-in protocols or bio-metric-based means of identification; or through indirect means based on identification inferences derived from selective process usage behaviors 920.

The adaptive process 900 includes an adaptive computer-based application 925, which includes one or more system elements or objects, each element or object being executable software and/or content that is meant for direct human access. The adaptive computer-based application 925 tracks and stores selective process participant behaviors 920 associated with a process instance 930. It should be understood that the tracking and storing of selective behaviors by the adaptive computer-based application 925 may also be associated with one or more other processes, sub-processes, and activities other than the process instance 930, though this is not explicitly depicted in FIGS. 4A and 4B. In addition to the direct tracking and storing of selective process usage behaviors, the adaptive computer-based application 925 may also indirectly acquire selective behaviors associated with process usage through one or more other computer-based applications that track and store selective process participant behaviors.

FIGS. 4A and 4B also depict adaptive recommendations 910 being generated and delivered by the adaptive computer-based application 925 to process participants 200. The adaptive recommendations 910 are shown being delivered to one or more process participants 200 engaged in “Activity 2” 170 of the adaptive process instance 930 in FIG. 4B. It should be understood that the adaptive recommendations 910 may be delivered to process participants 200 during any activity or any other point during participation in a process or sub-process.

The adaptive recommendations 910 delivered by the adaptive computer-based application 925 are informational or computing elements or subsets of the adaptive computer-based application 925, and may take the form of text, graphics, Web sites, audio, video, interactive content, other computer applications, or embody any other type or item of information. These recommendations are generated to facilitate participation in, or use of, an associated process, sub-process, or activity. The recommendations are derived by combining the context of what the process participant is currently doing and the inferred preferences or interests of the process participant based, at least in part, on the behaviors of one or more process participants, to generate recommendations. As the process, sub-process or activity is executed more often by the one or more process participants, the recommendations adapt to become increasingly effective. Hence, the adaptive process 900 itself can adapt over time to become increasingly effective.

Furthermore, the adaptive recommendations 910 may be applied to automatically or semi-automatically self-modify 905 the structure, elements, objects, content, information, or software of a subset 1632 of the adaptive computer-based application 925, including representations of process workflow. (The terms “semi-automatic” or “semi-automatically,” as used herein, are defined to mean that the described activity is conducted through a combination of one or more automatic computer-based operations and one or more direct human interventions.) For example, the elements, objects, or items of content of the adaptive computer-based application 925, or the relationships among elements, objects, or items of content associated with the adaptive computer-based application 925 may be modified 905 based on inferred preferences or interests of one or more process participants. These modifications may be based solely on inferred preferences or interests of the one or more process participants 200 derived from process usage behaviors, or the modifications may be based on inferences of preferences or interests of process participants 200 from process usage behaviors integrated with inferences based on the intrinsic characteristics of elements, objects or items of content of the adaptive computer-based application 925. These intrinsic characteristics may include patterns of text, images, audio, or any other information-based patterns.

For example, inferences of subject matter based on the statistical patterns of words or phrases in a text-based item of content associated with the adaptive computer-based application 925 may be integrated with inferences derived from the process usage behaviors of one or more process participants to generate adaptive recommendations 910 that may be applied to deliver to participants in the process, or may be applied to modify 905 the structure of the adaptive computer-based application 925, including the elements, objects, or items of content of the adaptive computer-based application 925, or the relationships among elements, objects, or items of content associated with the adaptive computer-based application 925.

Structural modifications 905 applied to the adaptive computer-based application 925 enables the structure to adapt to process participant preferences, interests, or requirements over time by embedding inferences on these preferences, interests or requirements directly within the structure of the adaptive computer-based application 925 on a persistent basis.

Adaptive recommendations generated by the adaptive computer-based application 925 may be applied to modify the structure, including objects and items of content, of other computer-based systems 175, including the computer-based workflow application 169, supporting, or accessible by, participants in the process instance 930. For example, a system that manages workflow 169 may be modified through application of adaptive recommendations generated by the adaptive computer-based application 925, potentially altering activity sequencing or other workflow aspects for one or more process participants associated with the adaptive process instance 930.

In addition to adaptive recommendations 910 being delivered to process participants 200, process participants 200 may also access or interact 915 with adaptive computer-based application 925 in other ways. The access of, or interaction with, 915 the adaptive computer-based application 925 by process participants 200 is analogous to the interactions 182a with computer application 182 of FIG. 3. However, a distinguishing feature of adaptive process 900 is that the access or interaction 915 of the adaptive computer-based application 925 by process participants 200 may include elements 1632 of the adaptive computer-based application 925 that have been adaptively self-modified 905 by the adaptive computer-based application 925.

FIG. 4C depicts an extension of the adaptive process 900 of FIG. 4A in which the adaptive recombinant function 850 is combined with the adaptive computer-based application 925 to form an adaptive recombinant computer-based application 925R. The adaptive recombinant computer-based application 925R enables the management of multiple computer-based representations of adaptive process or sub-process instances 930, where each process or sub-process representation may be in whole or in part. Further, the adaptive recombinant computer-based application 925R enables the management of multiple information structures associated with a specific process instance 930. The management of the representations of process or sub-process instances 930 and/or multiple information structures thereof, may include the distribution and combination of the representations of process or sub-process instances 930 and/or other information structures, within or across computing systems and/or organizations. These capabilities enable the adaptive recombinant process 901.

For some process applications described herein, adaptive process 900 is sufficient to implement the application. Other process applications described herein utilize the additional adaptive recombinant capabilities 850 provided by the adaptive recombinant process 901 for full implementation. Notwithstanding that the term “adaptive recombinant processes” is the general term used herein to describe the present invention, it should be understood that in some process application areas, the additional adaptive recombinant capabilities 850 of the adaptive recombinant process 901 (that are extensions to the adaptive process capabilities of the adaptive process 900) are not necessary for implementation.

In Table 1, several different process participant behaviors 920, which may also be described as process “usage” behaviors without loss of generality, are identified by the adaptive computer-based application 925 and categorized. The usage behaviors 920 may be associated with the entire community of process participants, one or more sub-communities, or with individual process participants or users associated with the sub-process instance 930.

TABLE 1 Usage behavior categories and usage behaviors usage behavior category usage behavior examples navigation and access activity, content and computer application accesses, including buying/selling paths of accesses or click streams subscription and personal of community subscriptions to self-profiling process topical areas interest and preference self-profiling affiliation self-profiling (e.g., job function) collaborative referral to others discussion forum activity direct communications (voice call, messaging) content contributions or structural alterations reference personal or community storage and tagging personal or community organizing of stored or tagged information direct feedback user ratings of activities, content, computer applications and automatic recommendations user comments attention direction of gaze brain patterns physical location current location location over time relative location to users/object references

A first category of process usage behaviors 920 is known as system navigation and access behaviors. System navigation and access behaviors include usage behaviors 920 such as accesses to, and interactions with online computer applications and content such as documents, Web pages, images, videos, audio, multi-media, interactive content, interactive computer applications, e-commerce applications, or any other type of information item or system “object.” These process usage behaviors may be conducted through use of a keyboard, a mouse, oral commands, or using any other input device. Usage behaviors 920 in the system navigation and access behaviors category may include, but are not limited to, the viewing or reading of displayed information, typing written information, interacting with online objects orally, or combinations of these forms of interactions with computer-based applications.

System navigation and access behaviors may also include executing transactions, including commercial transactions, such as the buying or selling of merchandise, services, or financial instruments. System navigation and access behaviors may include not only individual accesses and interactions, but the capture and categorization of sequences of information or system object accesses and interactions over time.

A second category of usage behaviors 920 is known as subscription and self-profiling behaviors. Subscriptions may be associated with specific topical areas or other elements of the adaptive computer-based application 925, or may be associated with any other subset of the adaptive computer-based application 925. Subscriptions may thus indicate the intensity of interest with regard to elements of the adaptive computer-based application 925. The delivery of information to fulfill subscriptions may occur online, such as through electronic mail (email), on-line newsletters, XML feeds, etc., or through physical delivery of media.

Self-profiling refers to other direct, persistent (unless explicitly changed by the user) indications explicitly designated by the one or more process participants regarding their preferences and interests, or other meaningful attributes. A process participant 200 may explicitly identify interests or affiliations, such as job function, profession, or organization, and preferences, such as representative skill level (e.g., novice, business user, advanced). Self-profiling enables the adaptive computer-based application 925 to infer explicit preferences of the process participant. For example, a self-profile may contain information on skill levels or relative proficiency in a subject area, organizational affiliation, or a position held in an organization. A process participant 200 that is in the role, or potential role, of a supplier or customer may provide relevant context for effective adaptive e-commerce applications through self-profiling. For example, a potential supplier may include information on products or services offered in his or her profile. Self-profiling information may be used to infer preferences and interests with regard to system use and associated topical areas, and with regard to degree of affinity with other process participant community subsets. A process participant may identify preferred methods of information receipt or learning style, such as visual or audio, as well as relative interest levels in other communities.

A third category of usage behaviors 920 is known as collaborative behaviors. Collaborative behaviors are interactions among the one or more process participants. Collaborative behaviors may thus provide information on areas of interest and intensity of interest. Interactions including online referrals of elements or subsets of the adaptive computer-based application 925, such as through email, whether to other process participants or to non-process participants, are types of collaborative behaviors obtained by the adaptive computer-based application 925.

Other examples of collaborative behaviors include, but are not limited to, online discussion forum activity, contributions of content or other types of objects to the adaptive computer-based application 925, or any other alterations of the elements, objects or relationships among the elements and objects of adaptive computer-based application 925. Collaborative behaviors may also include general user-to-user communications, whether synchronous or asynchronous, such as email, instant messaging, interactive audio communications, and discussion forums, as well as other user-to-user communications that can be tracked by the adaptive computer-based application 925.

A fourth category of process usage behaviors 920 is known as reference behaviors. Reference behaviors refer to the saving or tagging of specific elements or objects of the adaptive computer-based application 925 for recollection or retrieval at a subsequent time. The saved or tagged elements or objects may be organized in a manner customizable by process participants. The referenced elements or objects, as well as the manner in which they are organized by the one or more process participants, may provide information on inferred interests of the one or more process participants and the associated intensity of the interests.

A fifth category of process usage behaviors 920 is known as direct feedback behaviors. Direct feedback behaviors include ratings or other indications of perceived quality by individuals of specific elements or objects of the adaptive computer-based application 925, or the attributes associated with the corresponding elements or objects. The direct feedback behaviors may therefore reveal the explicit preferences of the process participant. In the adaptive computer-based application 925, the adaptive recommendations 910 may be rated by process participants 200. This enables a direct, adaptive feedback loop, based on explicit preferences specified by the process participant. Direct feedback also includes user-written comments and narratives associated with elements or objects of the computer-based system 925.

A sixth category of process usage behaviors is known as attention behaviors. These behaviors are associated with the focus of attention of process participants and/or the intensity of the intention. For example, the direction of the visual gaze of one or more process participants may be determined. This behavior can inform inferences associated with preferences or interests even when no physical interaction with the adaptive computer-based application 925 is occurring. Even more direct assessment of the level of attention may be conducted through access to the brain patterns or signals associated with the one or more process participants. Such patterns of brain functions during participation in a process can inform inferences on the preferences or interests of process participants, and the intensity of the preferences or interests. The brain patterns assessed may include MRI images, brain wave patterns, relative oxygen use, or relative blood flow by one or more regions of the brain.

Attention behaviors may include any other type of physiological response of a process participant 200 that may be relevant for making preference or interest inferences, independently, or collectively with the other usage behavior categories. Other physiological responses may include, but are not limited to, utterances, gestures, movements, or body position. Attention behaviors may also include other physiological responses such as breathing rate, blood pressure, or galvanic response.

A seventh category of process usage behaviors is known as physical location behaviors. Physical location behaviors identify physical location and mobility behaviors of process participants. The location of a process participant may be inferred from, for example, information associated with a Global Positioning System or any other positionally or locationally aware system or device. The physical location of physical objects referenced by elements or objects of adaptive computer-based application 925 may be stored for future reference. Proximity of a process participant to a second process participant, or to physical objects referenced by elements or objects of the computer-based application, may be inferred. The length of time, or duration, at which one or more process participants reside in a particular location may be used to infer intensity of interests associated with the particular location, or associated with objects that have a relationship to the physical location. Derivative mobility inferences may be made from location and time data, such as the direction of the process participant, the speed between locations or the current speed, the likely mode of transportation used, and the like. These derivative mobility inferences may be made in conjunction with geographic contextual information or systems, such as through interaction with digital maps or map-based computer systems.