Patent Publication Number: US-2020286009-A1

Title: Neural Network-based Content Inferencing Method and System

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application is a continuation-in-part of U.S. patent application Ser. No. 15/947,825, filed on Apr. 8, 2018, which is a continuation of U.S. patent application Ser. No. 13/269,979, 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 priority under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application Ser. No. 60/572,565, filed May 20, 2004, all of which are hereby incorporated by reference as if set forth herein in their entirety. 
    
    
     FIELD OF THE INVENTION 
     This invention relates to applying machine learning to automatically interpret forms of content such as video and automatically generating adaptive recommendations that are based on the interpretations. 
     BACKGROUND OF THE INVENTION 
     It can be a time-wasting exercise for a user to determine the portion of a computer-implemented content that is most relevant to her, particularly for content such as video, due to its sequential nature and the relative opaqueness to the user of what lies ahead within the content. Thus, there is a technical need for a more effective way to identify, and navigate to, the portion of content that is most relevant to a specific user. 
     SUMMARY OF THE INVENTION 
     In accordance with the embodiments described herein, a method and system for applying neural networks to facilitate the navigation and consumption of video-based and/or other associated content formats is disclosed. These capabilities may be embodied within an adaptive recommendations system. 
     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 
       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”  110 A and “Enterprise B”  110 B. 
     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  132   a  or outbound links  132   d . There may exist a plurality of links between any two sub-processes, and the plurality of links may include inbound  132   b  or outbound links  132   c . 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  155   a  and  155   b , 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  180   a , information access  181   a , and information access  182   a.    
     For example, content  180  may be accessed  180   a  (a content access  180   a ) 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  181   a  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  182   a  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. 
     Adaptive Processes 
       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  180   a  and information access  181   a , 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  182   a  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  925 R. The adaptive recombinant computer-based application  925 R 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  925 R 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. 
     Process Participant Behavior Categories 
     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 or 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. 
     In addition to the usage behavior categories depicted in Table 1, usage behaviors may be categorized over time and across user behavioral categories. Temporal patterns may be associated with each of the usage behavioral categories. Temporal patterns associated with each of the categories may be tracked and stored by the adaptive computer-based application  925 . The temporal patterns may include historical patterns, including how recently an element, object or item of content associated with adaptive computer-based application  925 . For example, more recent behaviors may be inferred to indicate more intense current interest than less recent behaviors. 
     Another temporal pattern that may be tracked and contribute to preference inferences that are derived is the duration associated with the access or interaction with the elements, objects or items of content of the adaptive computer-based application  925 , or the user&#39;s physical proximity to physical objects referenced by system objects of the adaptive computer-based application  925 , or the user&#39;s physical proximity to other process participants. For example, longer durations may generally be inferred to indicate greater interest than short durations. In addition, trends over time of the behavior patterns may be captured to enable more effective inference of interests and relevancy. Since adaptive recommendations  910  may include one or more elements, objects or items of content of the adaptive computer-based application  925 , the usage pattern types and preference inferencing may also apply to interactions of the one or more process participants with the adaptive recommendations  910  themselves. 
     Process Participant Behavior and Usage Framework 
       FIG. 5  depicts a usage framework  1000  for performing preference inferencing of tracked or monitored usage behaviors  920  associated with a process or sub-process instance  930  by the adaptive computer-based application  925 . The usage framework  1000  summarizes the manner in which process usage patterns are managed within the adaptive computer-based application  925 . Usage behavioral patterns associated with an entire community, affinity group, or segment of process participants  1002  are captured by the adaptive computer-based application  925 . In another case, usage patterns specific to an individual, shown in  FIG. 5  as individual usage patterns  1004 , are captured by the adaptive computer-based application  925 . Various sub-communities of usage associated with process participants may also be defined, as for example sub-community A usage patterns  1006 , sub-community B usage patterns  1008 , and sub-community C usage patterns  1010 . 
     Memberships in the communities are not necessarily mutually exclusive, as depicted by the overlaps of the sub-community A usage patterns  1006 , sub-community B usage patterns  1008 , and sub-community C usage patterns  1010  (as well as and the individual usage patterns  1004 ) in the usage framework  1000 . Recall that a community may include a single process participant or multiple process participants. Sub-communities may likewise include one or more process participants. Thus, the individual usage patterns  1004  in  FIG. 5  may also be described as representing the process usage patterns of a community or a sub-community. For the adaptive computer-based application  925 , usage behavior patterns may be segmented among communities and individuals so as to effectively enable adaptive recommendations  910 ,  905  for each sub-community or individual. 
     The communities identified by the adaptive computer-based application  925  may be determined through self-selection, through explicit designation by other process participants or external administrators (e.g., designation of certain process participants as “experts”), or through automatic determination by the adaptive computer-based application  925 . The communities themselves may have relationships between each other, of multiple types and values. In addition, a community may be composed not of human users, or solely of human users, but instead may include one or more other computer-based systems, which may have reason to interact with the adaptive computer-based application  925 . Or, such computer-based systems may provide an input into the adaptive computer-based application  925 , such as by being the output from a search engine. The interacting computer-based system may be another instance of the adaptive computer-based application  925 . 
     The usage behaviors  920  included in Table 1 may be categorized by the adaptive computer-based application  925  according to the usage framework  1000  of  FIG. 5 . For example, categories of usage behavior may be captured and categorized according to the entire community usage patterns  1002 , sub-community usage patterns  1006 , and individual usage patterns  1004 . The corresponding usage behavior information may be used to infer preferences and interests at each of the user levels. 
     Multiple usage behavior categories shown in Table 1 may be used by the adaptive computer-based application  925  to make reliable inferences of the preferences of a process participant with regard to elements, objects, or items of content associated with the adaptive computer-based application  925 . There are likely to be different preference inferencing results for different process participants. In addition, preference inferencing may be different with regard to optimizing the delivery of adaptive recommendations  910  to process participants than the preference inferencing optimized for modifying the structure  905  of the adaptive computer-based application  925 , as modifications to the structure are likely to be persistent and affect many process participants. 
     As an example, simply using the sequences of content accesses as the sole relevant usage behavior on which to base updates to the structure will generally yield unsatisfactory results. This is because the structure itself, through navigational proximity, will create a tendency toward certain navigational access sequence biases. Using just object or content access sequence patterns as the basis for updates to the structural will therefore tend to reinforce the pre-existing structure of the adaptive computer-based application  925 , which may limit the adaptiveness of the adaptive computer-based application  925 . 
     By introducing different or additional behavioral characteristics, such as the duration of access of an item of content, on which to base updates to the structure of adaptive computer-based application  925 , a more adaptive process is enabled. For example, duration of access will generally be much less correlated with navigational proximity than access sequences will be, and therefore provide a better indicator of true user preferences. Therefore, combining access sequences and access duration will generally provide better inferences and associated system structural updates than using either usage behavior alone. Effectively utilizing additional usage behaviors as described above will generally enable increasingly effective system structural updating. In addition, the adaptive computer-based application  925  may employ user affinity groups to enable even more effective system structural updating than are available merely by applying either individual (personal) usage behaviors or entire community usage behaviors. 
     Furthermore, relying on only one or a limited set of usage behavioral cues and signals may more easily enable potential “spoofing” or “gaming” of the computer-based application  925 . “Spoofing” or “gaming” the adaptive computer-based application  925  refers to conducting consciously insincere or otherwise intentional usage behaviors  920 , so as to influence the adaptive recommendations  910  or adaptive modifications  905  to the intrinsic elements and structure of the adaptive computer-based application  925 . Utilizing broader sets of system usage behavioral cues and signals may lessen the effects of spoofing or gaming. One or more algorithms may be employed by computer-based application  925  to detect such contrived usage behaviors, and when detected, such behaviors may be compensated for by the preference and interest inferencing algorithms of computer-based application  925 . 
     In some embodiments, the computer-based application  925  may provide process participants  200  with a means to limit the tracking, storing, or application of their usage behaviors  920 . A variety of limitation variables may be selected by the process participant  200 . For example, a process participant  200  may be able to limit usage behavior tracking, storing, or application by usage behavior category described in Table 1. Alternatively, or in addition, the selected limitation may be specified to apply only to particular user communities or individual process participants  200 . For example, a process participant  200  may restrict the application of the full set of her process usage behaviors  920  to preference or interest inferences by adaptive computer-based application  925  for application to only herself, and make a subset of process behaviors  920  available for application to process participants only within her workgroup, but allow none of her process usage behaviors to be applied by computer-based application  925  in making inferences of preferences or interests for other process participants. 
     Process Participant Communities 
     As described above, a process participant associated with an adaptive process instance  930  may be a member of one or more communities of interest, or affinity groups, with a potentially varying degree of affinity associated with the respective communities. These affinities may change over time as interests of the user  200  and communities evolve over time. The affinities or relationships among process participants and communities may be categorized into specific types. An identified process participant  200  may be considered a member of a special sub-community containing only one member, the member being the identified process participant. A process participant can therefore be thought of as just a specific case of the more general notion of process participant or user segments, communities, or affinity groups. 
       FIG. 6  illustrates the affinities among user communities and how these affinities may automatically or semi-automatically be updated by the adaptive computer-based application  925  based on user preferences which are derived from process participant behaviors  920 . An entire community  1050  is depicted in  FIG. 6 . The community may extend across organizational, functional, or process boundaries. The entire community  1050  extends across process A  1060  and process B  1061 . The entire community  1050  includes sub-community A  1064 , sub-community B  1062 , sub-community C  1069 , sub-community D  1065 , and sub-community E  1070 . A process participant  1063  who is not part of the entire community  1050  is also featured in  FIG. 6 . 
     Sub-community B  1062  is a community that has many relationships or affinities to other communities. These relationships may be of different types and differing degrees of relevance or affinity. For example, a first relationship  1066  between sub-community B  1062  and sub-community D  1065  may be of one type, and a second relationship  1067  may be of a second type. (In  FIG. 6 , the first relationship  1066  is depicted using a double-pointing arrow, while the second relationship  1067  is depicted using a unidirectional arrow.) The relationships  1066  and  1067  may be directionally distinct, and may have an indicator of relationship or affinity associated with each distinct direction of affinity or relationship. For example, the first relationship  1066  has a numerical value  1068 , or relationship value, of “0.8.” The relationship value  1068  thus describes the first relationship  1066  between sub-community B  1062  and sub-community D  1065  as having a value of 0.8. 
     The relationship value may be scaled as in  FIG. 6  (e.g., between 0 and 1), or may be scaled according to another interval. The relationship values may also be bounded or unbounded, or they may be symbolically represented (e.g., high, medium, low). 
     The process participant  1063 , which could be considered a process participant community including a single member, may also have a number of relationships to other communities, where these relationships are of different types, directions and relevance. From the perspective of the process participant  1063 , these relationship types may take many different forms. Some relationships may be automatically formed by the adaptive computer-based application  925 , for example, based on interests or geographic location or similar traffic/usage patterns. Thus, for example the entire community  1050  may include process participants in a particular city. Some relationships may be context-relative. For example, a community to which the process participant  1063  has a relationship could be associated with a certain process, and another community could be related to another process. Thus, sub-community E  1070  may be the process participants associated with a product development business to which the process participant  1063  has a relationship  1071 ; sub-community B  1062  may be the members of a cross-business innovation process to which the user  1063  has a relationship  1073 ; sub-community D  1065  may be experts in a specific domain of product development to which the process participant  1063  has a relationship  1072 . The generation of new communities which include the process participant  1063  may be based on the inferred interests of the process participant  1063  or other process participants within the entire community  1050 . 
     Membership of communities may overlap, as indicated by sub-communities A  1064  and C  1069 . The overlap may result when one community is wholly a subset of another community, such as between the entire community  1050  and sub-community B  1062 . More generally, a community overlap will occur whenever two or more communities contain at least one process participant or user in common. Such community subsets may be formed automatically by the adaptive process  900 , based on preference inferencing from process participant behaviors  920 . For example, a subset of a community may be formed based on an inference of increased interest or demand of particular content or expertise of an associated community. The adaptive computer-based application  925  is also capable of inferring that a new community is appropriate. The adaptive computer-based application  925  of the adaptive process  900  will thus create the new community automatically. 
     For each process participant, whether residing within, say, sub-community A  1064 , or residing outside the community  1050 , such as the process participant  1063 , the relationships (such as arrows  1066  or  1067 ), affinities, or “relationship values” (such as numerical indicator  1068 ), and directions (of arrows) are unique. Accordingly, some relationships (and specific types of relationships) between communities may be unique to each process participant. Other relationships, affinities, values, and directions may have more general aspects or references that are shared among many process participants, or among all process participants of the adaptive process  900 . A distinct and unique mapping of relationships between process participants, such as is illustrated in  FIG. 6 , could thus be produced for each process participant by the adaptive computer-based application  925 . 
     The adaptive computer-based application  925  may automatically generate communities, or affinity groups, based on process participant behaviors  920  and associated preference inferences. In addition, communities may be identified by process participants, such as administrators of the process or sub-process instance  930 . Thus, the adaptive computer-based application  925  utilizes automatically generated and manually generated communities in generating adaptive recommendations  910 ,  905 . 
     The communities, affinity groups, or user segments aid the adaptive computer-based application  925  in matching interests optimally, developing learning groups, prototyping process designs before adaptation, and many other uses. For example, some process participants that use or interact with the adaptive computer-based application  925  may receive a preview of a new adaptation of a process for testing and fine-tuning, prior to other process participants receiving this change. 
     The process participants or communities may be explicitly represented as elements or objects within the adaptive computer-based application  925 . This feature enhances the extensibility and adaptability of the adaptive process  900 . 
     Adaptive System 
       FIG. 7  depicts a possible configuration of the adaptive computer-based application  925 , as part of the adaptive process  900  of  FIGS. 4A and 4B . The adaptive computer-based application  925  includes, at least in part, an adaptive system  100  (shaded for convenience of identification), according to some embodiments. The adaptive system  100  includes three aspects: 1) a structural aspect  210 , a usage aspect  220 , and a content aspect  230 . One or more process participants  200  (who may also be termed “users” of the adaptive process  900 ) interact with, or are monitored by, the adaptive system  100 , which tracks selected behaviors  920  of the process participants, which are in turn selectively stored and processed by the usage aspect  220 . An adaptive recommendations function  240  generates adaptive recommendations based on inputs from the usage aspect  220 , and, optionally, based on the structural aspect  210  and/or the content aspect  230 . The adaptive recommendations function  240  determines inferred interests of process participants  200 , and generates adaptive recommendations  250  that may be delivered  910  to process participants  200  or may be delivered  265  to non-process participants  260 . The adaptive recommendations function  240  may also apply adaptive recommendations to modify  905  the structural aspect  210  or to modify  935  the content aspect  230 . 
     In some embodiments, the adaptive process  900  utilizes the methods and systems of adaptive fuzzy network and process models, as defined in U.S. Pat. No. 6,795,826, entitled “Fuzzy Content Network Management and Access,” and PCT Patent Application No. PCT/US04/37176, entitled “Adaptive Recombinant Systems,” filed on Nov. 4, 2004, which are hereby incorporated by reference as if set forth in their entirety. 
       FIG. 8  contrasts the non-adaptive computer-based application  182  ( FIG. 3 ) with the adaptive computer-based application  925  ( FIGS. 4A  and  4 B). In  FIG. 8 , an adaptive computer-based application  925  includes the non-adaptive computer-based application  182  ( FIG. 3 ), plus other features of the adaptive system  100  ( FIG. 7 ). The non-adaptive computer-based application  182  includes at least a structural aspect and a content aspect, but does not include a usage aspect  220  and an adaptive recommendations function  240 , and therefore cannot generate and apply  910 ,  905 ,  935  adaptive recommendations. The structural aspect or content aspect of the non-adaptive computer-based application  182  may be integrated with a usage aspect  220  and an adaptive recommendation function  240  to create the adaptive system  100  ( FIG. 7 ), and hence, the adaptive computer-based application  925 . This integration may be through integration of the associated software functions of the structural aspect  210  and the content aspect  230  of the non-adaptive computer-based application  182  with a usage aspect  220  and an adaptive recommendation function  240 . Or, the integration may be effected through transmission of elements of the structural aspect  210  and the content aspect  230  of the non-adaptive computer-based application  182  with a second system that contains usage aspect  220  and an adaptive recommendation function  240 . 
     As used herein, one or more process participants  200  may be a single user or multiple users of the adaptive computer-based application  925 . As shown in  FIG. 8 , the one or more process participants or users  200  may receive  910  the adaptive recommendations  250 . Individuals not participating in the process  260  of the adaptive system  100  may also receive  265  adaptive recommendations  250  from the adaptive system  100 . 
     The process participant or user  200  may be a human entity, a computer system, or a second adaptive system (distinct from the adaptive system  100 ) that interacts with, or otherwise uses the adaptive computer-based application  925  and the associated adaptive system  100 . The one or more users  200  may include non-human users of the adaptive system  100 . In particular, one or more other adaptive systems may serve as virtual system “users.” These other adaptive systems may operate in accordance with the architecture of the adaptive system  100 . Thus, multiple adaptive systems may be mutual users for one another. These adaptive systems may each support the same process, or each system  100  may each support different processes. 
     It should be understood that the structural aspect  210 , the content aspect  230 , the usage aspect  220 , and the recommendations function  240  of the adaptive system  100 , and elements of each, may be contained within one computer, or distributed among multiple computers. Furthermore, one or more non-adaptive computer-based applications  182  may be modified to comprise one or more adaptive systems  100  by integrating the usage aspect  220  and the recommendations function  240  with the one or more non-adaptive computer-based applications  182 . 
     The term “computer system” or the term “system,” without further qualification, as used herein, will be understood to mean either a non-adaptive or an adaptive system. Likewise, the terms “system structure” or “system content,” as used herein, will be understood to refer to the structural aspect  210  and the content aspect  230 , respectively, whether associated with the non-adaptive system  182  or the adaptive computer-based application  925 , and associated adaptive system  100 . The term “system structural subset” or “structural subset,” as used herein, will be understood to mean a portion or subset of the structural aspect  210  of a system. 
     Structural Aspect 
     The structural aspect  210  of the adaptive system  100  is depicted in the block diagram of  FIG. 9A . The structural aspect  210  denotes a collection of system objects  212  that are part of the adaptive system  100 , as well as the relationships among the objects  214 . The relationships among objects  214  may be persistent across user sessions, or may be transient in nature. The objects  212  may include or reference items of content, such as text, graphics, audio, video, interactive content, or embody any other type or item of information. The objects  212  may also include references to content, such as pointers. Computer applications, executable code, or references to computer applications may also be stored or referenced as objects  212  in the adaptive system  100 . The content of the objects  212  is known herein as information  232 . The information  232 , though part of the object  214 , is also considered part of the content aspect  230 , as depicted in  FIG. 9B , and as described below. 
     The objects  212  may be managed in a relational database, or may be maintained in structures such as flat files, linked lists, inverted lists, hypertext networks, or object-oriented databases. The objects  212  may include meta-information  234  associated with the information  232  contained within, or referenced by the objects  212 . 
     As an example, in some embodiments, the World-wide Web may be considered a structural aspect, where web pages constitute the objects of the structural aspect and links between web pages constitute the relationships among the objects. Alternatively, or in addition, in some embodiments, the structural aspect may feature objects associated with an object-oriented programming language, and the relationships between the objects associated with the protocols and methods associated with interaction and communication among the objects in accordance with the object-oriented programming language. 
     The one or more users  200  of the adaptive system  100  may be explicitly represented as objects  212  within the system  100 , thereby becoming directly incorporated within the structural aspect  210 . The relationships among objects  214  may be arranged in a hierarchical structure, a relational structure (e.g. according to a relational database structure), or according to a network structure. 
     Content Aspect 
     The content aspect  230  of the adaptive system  100  is depicted in the block diagram of  FIG. 9B . The content aspect  230  denotes the information  232  contained in, or referenced by the objects  212  that are part of the structural aspect  210 . The content aspect  230  of the objects  212  may include text, graphics, audio, video, and interactive forms of content, such as applets, tutorials, courses, demonstrations, modules, or sections of executable code or computer programs. The one or more users  200  interact with the content aspect  230 . 
     The adaptive system  100  may enable an item of information  232  to be decomposed into other items of information  232 . For example, a text document could be decomposed into sections, each of which could become separate items of information  232 . Further, these items of information could then become an object  212 ; that is, an explicit element of the structural aspect  210 . The decomposition process may also generate appropriate relationships  214  among the decomposed objects, which also become explicit elements of the structural aspect  210 . The recursive decomposition of information  232  into other information  232  and associated objects  212  and corresponding relationships among the objects  214  may continue without limit. 
     The content aspect  230  may be updated or modified  935  ( FIG. 7 ) by the adaptive recommendations function  240  based, at least in part, on the usage aspect  220 , including usage behavior metrics. To achieve this, the adaptive system  100  may employ the usage aspect, or elements of the usage aspect, of other systems. Such systems may include, but are not limited to, other computer systems, other networks, such as the World Wide Web, multiple computers within an organization, other adaptive systems, or other adaptive recombinant systems. In this manner, the content aspect  230  benefits from usage occurring in other environments, including other process environments. 
     Usage Aspect 
     The usage aspect  220  of the adaptive system  100  is depicted in the block diagram of  FIG. 9C . Recall from  FIG. 7  that the usage aspect  220  tracks or monitor usage behaviors  920  of process participants  200 . The usage aspect  220  denotes captured usage information  202 , further identified as usage behaviors  270 , and usage behavior pre-processing  204 . The usage aspect  220  thus reflects the tracking, storing, categorization, and clustering of the use and associated usage behaviors  920  of the one or more users or process participants  200  interacting with the adaptive system  100 . 
     The captured usage information  202 , known also as system usage or system use  202 , includes any interaction by the one or more process participants or users  200  with the system, or monitored behavior by the one or more users  200 . The adaptive system  100  may track and store user key strokes and mouse clicks, for example, as well as the time period in which these interactions occurred (e.g., timestamps), as captured usage information  202 . From this captured usage information  202 , the adaptive system  100  identifies usage behaviors  270  of the one or more process participants  200  (e.g., web page access or physical location changes of the process participant). Finally, the usage aspect  220  includes usage-behavior pre-processing, in which usage behavior categories  246 , usage behavior clusters  247 , and usage behavioral patterns  248  are formulated for subsequent processing of the usage behaviors  270  by the adaptive system  100 . Some usage behaviors  270  identified by the adaptive system  100 , as well as usage behavior categories  246  designated by the adaptive system  100 , are listed in Table 1, above, and are described in more detail below. 
     The usage behavior categories  246 , usage behaviors clusters  247 , and usage behavior patterns  248  may be interpreted with respect to a single user  200 , or to multiple users  200 , in which the multiple users may be described herein as a community, an affinity group, or a user segment. These terms are used interchangeably herein. A community is a collection of one or more users, and may include what is commonly referred to as a “community of interest.” A sub-community is also a collection of one or more users, in which members of the sub-community include a portion of the users in a previously defined community. Communities, affinity groups, and user segments are described in more detail, below. 
     Usage behavior categories  246  include types of usage behaviors  270 , such as accesses, referrals to other users, collaboration with other users, and so on. These categories and more are included in Table 1, above. Usage behavior clusters  247  are groupings of one or more usage behaviors  270 , either within a particular usage behavior category  246  or across two or more usage categories. The usage behavior pre-processing  204  may also determine new “clusterings” of user behaviors  270  in previously undefined usage behavior categories  246 , across categories, or among new communities. Usage behavior patterns  248 , also known as “usage behavioral patterns” or “behavioral patterns,” are also groupings of usage behaviors  270  across usage behavior categories  246 . Usage behavior patterns  248  are generated from one or more filtered clusters of captured usage information  202 . 
     The usage behavior patterns  248  may also capture and organize captured usage information  202  to retain temporal information associated with usage behaviors  270 . Such temporal information may include the duration or timing of the usage behaviors  270 , such as those associated with reading or writing of written or graphical material, oral communications, including listening and talking, or physical location of the process participant  200 . The usage behavioral patterns  248  may include segmentations and categorizations of usage behaviors  270  corresponding to a single user of the one or more users  200  or according to multiple users  200  (e.g., communities or affinity groups). The communities or affinity groups may be previously established, or may be generated during usage behavior pre-processing  204  based on inferred usage behavior affinities or clustering. Usage behaviors  270  may also be derived from the use or explicit preferences  252  associated with other adaptive or non-adaptive systems. 
     Adaptive Recommendations Function 
     Returning to  FIG. 7 , the adaptive system  100  includes an adaptive recommendations function  240 , which interacts with the structural aspect  210 , the usage aspect  220 , and the content aspect  230 . The adaptive recommendations function  240  generates adaptive recommendations  250  based on the application of the usage aspect  220 , and, optionally, the structural aspect  210  and/or the content aspect  230 . The adaptive recommendations function  240  may also optionally apply other contextual information, rules, or algorithms through the application of other computer-based functions residing within adaptive system  100 , or through access to, or interaction with, other computer-based functions residing outside of adaptive system  100 . 
     The term “recommendations” associated with the adaptive recommendations function  240  is used broadly in the adaptive system  100 . The adaptive recommendations  250  generated by recommendations function  240  may be displayed or otherwise delivered  910 ,  265  to a recommendations recipient. As used herein, a recommendations recipient is an entity who receives the adaptive recommendations  250 . Thus, the recommendations recipient may include the one or more process participants  200  of the adaptive system  100 , as indicated by the dotted arrow  910  in  FIG. 7 , or a non-participant  260  of the associated process (see dotted arrow  265 ). However, the adaptive recommendations function  240  may also be applied internally by the adaptive system  100  to update the structural aspect  210  (see dotted arrow  905 ). In this manner, the usage behavior  270  of the one or more process participants  200  may be influenced by the system structural alterations that are automatically or semi-automatically applied. Or, the adaptive recommendations function  240  may be used by the adaptive system  100  to update the content aspect  230  (see dotted arrow  935 ). 
       FIG. 10  is a block diagram of the adaptive recommendations function  240  used by the adaptive system  100  of  FIG. 7 . The adaptive recommendations function  240  includes two algorithms, a preference inferencing algorithm  242  and a recommendations optimization algorithm  244 . These algorithms (which actually may include many more than two algorithms) are used by the adaptive system  100  to generate adaptive recommendations  250 . 
     Preferably, the adaptive system  100  identifies the preferences of the user  200  and self-adapts the adaptive system  100  in view of the preferences. Preferences describe the likes, tastes, partiality, and/or predilection of the user  200  that may be inferred during access of, interaction with, or while attention is directed to, the objects  212  of the adaptive system  100 . In general, user preferences exist consciously or sub-consciously within the mind of the user. Since the adaptive system  100  has no direct access to these preferences, they are generally inferred by the preference inferencing algorithm  242  of the adaptive recommendations function  240 . 
     The preference inferencing algorithm  242 , infers preferences based, at least in part, on information that may be obtained as the process participant  200  accesses the adaptive system  100 . Additional information may also be optionally used by the preference inferencing algorithm  242 , including meta-information  234  and intrinsic information  232  within objects  212 , and from information, rules, or algorithms accessed from other computer-based functions residing within the adaptive system  100 , or through access to, or interaction with, other computer-based functions residing outside of the adaptive system  100 . 
     The preference inferencing algorithm and associated output  242  is also described herein generally as “preference inferencing” or “preference inferences” of the adaptive system  100 . The preference inferencing algorithm  242  identifies three types of preferences: explicit preferences  252 , inferred preferences  253 , and inferred interests  254 . Unless otherwise stated, the use of the term “preferences” herein is meant to include any or all of the elements  252 ,  253 , and  254  depicted in  FIG. 10 . 
     As used herein, explicit preferences  252  describe explicit choices or designations made by the user  200  during use of the adaptive system  100 . The explicit preferences  252  may be considered to more explicitly reveal preferences than inferences associated with other types of usage behaviors. A response to a survey is one example where explicit preferences  252  may be identified by the adaptive system  100 . 
     Inferred preferences  253  describe preferences of the user  200  that are based on usage behavioral patterns  248 . Inferred preferences  253  are derived from signals and cues made by the process participant  200 , where “signals” are consciously intended communications by the process participant, and “cues” are behaviors that are not intended as explicit communications, but nevertheless provide information of a process participant with which to infer preferences and interests. 
     Inferred interests  254  describe interests of the user  200  that are based on usage behavioral patterns  248 . In general, the adaptive recommendations  250  generated by the adaptive recommendations function  240  are derived from the preference inferencing algorithm  242  and combine inferences from overall user community behaviors and preferences, inferences from sub-community or expert behaviors and preferences, and inferences from personal user behaviors and preferences. As used herein, preferences (whether explicit  252  or inferred  253 ) are distinguishable from interests ( 254 ) in that preferences imply a ranking (e.g., object A is better than object B) while interests do not necessarily imply a ranking. 
     A second algorithm  244 , designated recommendations optimization  244 , optimizes the adaptive recommendations  250  generated by the adaptive recommendations function  240  within the adaptive system  100 . The adaptive recommendations  250  may be augmented by automated inferences and interpretations about the content within individual and sets of objects  232  using statistical pattern matching of words, phrases or representations, in written or audio format, or in pictorial format, within the content. Such statistical pattern matching may include, but is not limited to, principle component analysis, semantic network techniques, Bayesian analytical techniques, neural network-based techniques, support vector machine-based techniques, or other statistical analytical techniques. 
     For image-based content, including temporally sequential images as in video content, convolutional neural networks may be applied in some embodiments to make the inferences or interpretations. The neural networks may be trained by means of a corpus of labeled images and/or sequences of images to identify physical objects, actions, and/or abstract concepts within video-based content. The identification may be directly through interpretation of patterns of pixels associated with the videos and/or through interpretations of audio-based language that is associated with the images or videos. The labels of identified objects, actions, and/or abstract concepts may be incorporated within recommendations  250  that are delivered to users  200 . 
     For text or language-based content (which may originate from audio recordings), recurrent deep learning-based systems, including long short-term memory (LSTM) neural networks, and/or associated variations of LSTM such as Gated Recurrent Units (GRUs), may be applied in some embodiments to make the inferences or interpretations within the text-based content and/or to make inferences or interpretations about other content, such as video, that is associated with the text-based content. 
     In some embodiments, combinations of deep learning-based neural networks may be applied to interpret content, such as applying convolutional neural networks to interpret image-based content, and recurrent neural networks to interpret language-based content. 
     Adaptive Recommendations 
     As shown in  FIG. 7 , the adaptive system  100  generates adaptive recommendations  250  using the adaptive recommendations function  240 . The adaptive recommendations  250 , or suggestions, enable users to more effectively use and navigate through the adaptive system  100 . 
     The adaptive recommendations  250  are presented as structural subsets of the structural aspect  210 .  FIG. 11  depicts a hypothetical structural aspect  210 , including a plurality of objects  212  and associated relationships  214 . The adaptive recommendations function  240  generates adaptive recommendations  250  based on usage of the structural aspect  210  by the one or more process participants  200 , possibly in conjunction with considerations associated with the structural aspect  210  and the content aspect  230 . 
     Three structural subsets  280 A,  280 B, and  280 C (collectively, structural subsets  280 ) are depicted. The structural subset  280 A includes three objects  212  and two associated relationships, which are reproduced by the adaptive recommendations function  240  in the same form as in the structural aspect  210  (objects are speckle shaded). The structural subset  280 B includes a single object (object is shaded), with no associated relationships (even though the object originally had a relationship to another object in the structural aspect  210 ). 
     The third structural subset  280 C includes five objects (striped shading), but the relationships between objects has been changed from their orientation in the structural aspect  210 . In the structural subset  280 C, a relationship  282  has been eliminated while a new relationship  284  has been formed by the adaptive recommendations function  240 . The structural subsets  280  depicted in  FIG. 11  represent but three of a myriad of possible structural subsets that may be derived from the original network of objects by the adaptive recommendations function  240 . 
     The illustration in  FIG. 11  shows a simplified representation of structural subsets  280  being generated from objects  212  and relationships  214  of the structural aspect  210 . Although not shown, the structural subset  280  may also include corresponding associated subsets of the usage aspect  220 , such as usage behaviors and usage behavioral patterns. As used herein, references to structural subsets  280  are meant to include the relevant subsets of the usage aspect, or usage subsets, as well. 
     The adaptive recommendations  250  may be in the context of a currently conducted activity or behavior detected by the adaptive system  100 , a currently accessed object  232 , or a communication with another process participant  200  or non-participant in the process  260 . The adaptive recommendations  250  may also be in the context of a historical path of executed system activities, accessed objects  212 , or communications during a specific user session or across user sessions. The adaptive recommendations  250  may be without context of a current activity, currently accessed object  212 , current session path, or historical session paths. Adaptive recommendations  250  may also be generated in response to direct user requests or queries. Such user requests may be in the context of a current system navigation, access or activity, or may be outside of any such context. 
     Adaptive recommendations  250  generated by the adaptive recommendations function  240  may combine inferences from community, sub-community (including expert), and personal behaviors and preferences, as discussed above, to deliver to the one or more process participants  200 , one or more system structural subsets  280 . The process participants  200  may find the structural subsets particularly relevant given the current navigational context of the user within the system, the physical location of the user, and/or a response to an explicit request of the system by the one or more users. In other words, the adaptive recommendation function  240  determines preference “signals” from the “noise” of system usage behaviors. 
     The sources of user behavioral information, which typically include the objects  212  referenced by the user  200 , may also include the actual information  232  contained therein. In generating adaptive recommendations  250 , the adaptive system  100  may thus employ search algorithms that use text matching or more general statistical pattern matching to provide inferences on the inferred themes of the information  232  embedded in, or referenced by, individual objects  212 . Furthermore, the structural aspect  210  may itself inform the specific adaptive recommendations  250  generated. For example, existing relationship structures within the structural aspect  210  at the time of the adaptive recommendations  250  may be combined with the user preference inferences based on usage behaviors, along with any inferences based on the content aspect  230  (the information  232 ). 
     Delivery of Adaptive Recommendations 
       FIG. 12  is a flow diagram showing how adaptive recommendations  250  are delivered by the adaptive system  100 . Recall from  FIG. 7  that adaptive recommendations  250  may be delivered directly to the one or more users  200  (dotted arrow  910 ), or the adaptive recommendations function  240  may be applied to automatically or semi-automatically update the structural aspect  210  (dotted arrow  905 ) or the content aspect  230  (dotted arrow  935 ), or adaptive recommendations  250  may be delivered directly to the non-user  260  of the adaptive system  100  (dotted arrow  265 ). 
     The adaptive system  100  begins by determining the relevant usage behavioral patterns  248  ( FIG. 9C ) to be analyzed (block  283 ). The adaptive system  100  thus identifies the relevant communities, affinity groups, or user segments of the one or more process participants  200 . Affinities are then inferred among objects  212 , structural subsets  280 , and among the identified affinity groups (block  284 ). This data enables the adaptive recommendations function  240  to generate adaptive recommendations  250  for multiple application purposes. The adaptive system  100  next determines whether the adaptive recommendations function  240  will generate recommendations  250  to be delivered directly to the recommendations recipients (e.g.,  910  to process participants  200  or  265  to non-participants  260 ), or are to be used to update the adaptive system  100  (e.g.,  905  to the structural aspect  210  or  935  to the content aspect  230 ) (block  285 ). Where the recommendations recipients are to directly receive the adaptive recommendations (the “no” prong of block  285 ), the adaptive recommendations  250  are generated based on mapping the context of the current system use (or “simulated” use if the current context is external to the actual use of the system) (block  286 ) to the usage behavior patterns  248  generated by the preference inferencing algorithm  242  (block  286 ). 
     Adaptive recommendations are then delivered visually and/or in other communications forms, such as audio, to the recommendations recipients (block  287 ). The recommendations recipients may be individual users or a group of users, or may be non-users  260  of the adaptive system  100 . For Internet-based applications, the adaptive recommendations  250  may be delivered through a web browser directly, or through RSS/Atom feeds and other similar protocols. 
     Where, instead, adaptive system  100  itself is to be the “recipient” of the adaptive recommendations (the “yes” prong of block  285 ), the adaptive recommendations function  240  applies the adaptive recommendations to update the structural aspect  210  ( 905 ) or the content aspect  230  ( 935 ). The adaptive recommendations  250  generated by the adaptive recommendations function  240  are determined based, at least in part, on mapping potential configurations of the structural aspect  210  or content aspect  230  to the affinities generated by the usage behavioral inferences (block  288 ). The adaptive recommendations  905  or  935  are then delivered to enable updating of the structural aspect  210  or the content aspect  230  (block  289 ), respectively. 
     The adaptive recommendations function  240  may operate completely automatically, performing in the background and updating the structural aspect  210  independent of human intervention. Or, the adaptive recommendations function  240  may be used by users or experts who rely on the adaptive recommendations  250  to provide guidance in maintaining the system structure as a whole, or maintaining specific structural subsets  280  (semi-automatic maintenance of the structural aspect  210 ). 
     The navigational context for the recommendation  250  may be at any stage of navigation of the structural aspect  210  (e.g., during the viewing of a particular object  212 ) or may be at a time when the recommendation recipient is not engaged in directly navigating the structural aspect  210 . In fact, the recommendation recipient need not have explicitly used the system associated with the recommendation  250 . 
     Some inferences will be weighted as more important than other inferences in generating the recommendation  250 . These weightings may vary over time, and across recommendation recipients, whether individual recipients or sub-community recipients. As an example, the characteristics associated with objects  212  which are explicitly stored or tagged by the user  200  in a personal structural aspect  210  would typically be a particularly strong indication of preference as storing or tagging system structural subsets requires explicit action by the user  200 . The recommendations optimization algorithms  244  may thus prioritize this type of information to be more influential in driving the adaptive recommendations  250  than, say, general community traffic patterns within the structural aspect  210 . 
     The recommendations optimization algorithm  244  will particularly try to avoid recommending objects  212  that the process participant or user  200  is already familiar with. For example, if the process participant  200  has already stored or tagged the object  212  in a personal structural subset  280 , then the object  212  may be a low ranking candidate for recommendation to the user, or, if recommended, may be delivered to the user with a designation acknowledging that the user has already saved or marked the object for future reference. Likewise, if the user  200  has recently already viewed the associated system object (regardless of whether it was saved to his personal system), then the object would typically rank low for inclusion in a set of recommended objects. 
     The preference inferencing algorithm  242  may be tuned by the individual user. The tuning may occur as adaptive recommendations  250  are provided to the user, by allowing the user to explicitly rate the adaptive recommendations. The user  200  may also set explicit recommendation tuning controls to adjust the adaptive recommendations to her particular preferences. For example, the user  200  may guide the adaptive recommendations function  240  to place more relative weight on inferences of expert preferences versus inferences of the user&#39;s own personal preferences. This may particularly be the case if the user was relatively inexperienced in the corresponding domain of knowledge associated with the content aspect  230  of the system, or a structural subset  280  of the system. As the user&#39;s experience grows, she may adjust the weighting toward inferences of the user&#39;s personal preferences versus inferences of expert preferences. 
     Adaptive recommendations, which are structural subsets of the adaptive system  100  (see  FIG. 11 ), may be displayed in variety of ways to the user. The structural subsets  280  may be displayed as a list of objects  212  (where the list may be null or a single object). The structural subset  280  may be displayed graphically. The graphical display may provide enhanced information that may include depicting relationships among objects (as in the “relationship” arrows of  FIG. 6 ). 
     In addition to the structural subset  280 , the recommendation recipient may be able to access information or logic to assist in gaining an understanding about why the particular structural subset was selected as the recommendation to be presented to the user. The reasoning may be fully presented to the recommendation recipient as desired by the recommendation recipient, or it may be presented through a series of interactive queries and associated answers, where the recommendation recipient desires more detail. The reasoning may be presented through display of the logic of the recommendations optimization algorithm  244 . A natural language (e.g., English) interface may be employed to enable the reasoning displayed to the user to be as explanatory and human-like as possible. 
     The personal preference of the user may affect the nature of the display of the information. For example some users may prefer to see the structural aspect in a visual, graphic format while other users may prefer a more interactive question and answer or textual display. 
     Users of the adaptive system  100 , and by extension, process participants  200 , may be explicitly represented as objects in the structural aspect  210  and hence embodied in structural subsets  280 . Either embodied as structural subsets or represented separately from structural subsets  280 , the adaptive recommendations  250  may include a set of users of the adaptive system  100  that are determined and displayed to recommendation recipients, providing either implicit or explicit permission is granted by the set of users to be included in the adaptive recommendations  250 . The recommendations optimization algorithm  244  may match the preferences of other users of the system with the current user. The preference matching may include applying inferences derived from the characteristics of structural subsets stored or tagged by users, their structural subset subscriptions and other self-profiling information, and their system usage patterns  248 . Information about the recommended set of users may be displayed. This information may include names, as well as other relevant information such as affiliated organization and contact information. The information may also include system usage information, such as common system objects subscribed to, etc. As in the case of structural subset adaptive recommendations, the adaptive recommendations of other users may be tuned by an individual user through interactive feedback with the adaptive system  100 . 
     The adaptive recommendations  250  may be in response to explicit requests from the user. For example, a user may be able to explicitly designate one or more objects  212  or structural subsets  280 , and prompt the adaptive system  100  for a recommendation based on the selected objects or structural subsets. The recommendations optimization algorithm  244  may put particular emphasis on the selected objects or structural subsets, in addition to applying inferences on preferences from usage behaviors, as well as optionally, content characteristics. 
     In some embodiments, the adaptive recommendations function  240  may augment the preference inferencing algorithm  242  with considerations related to enhancing the revelation of user preferences, so as to better optimize the adaptive recommendations  250  in the future. In other words, where the value of information associated with reducing uncertainty associated with user preferences is high, the adaptive recommendations function  240  may choose to recommend objects  212  or other recommended structural aspects  210  as an “experiment.” For example, the value of information will typically be highest for relatively new users, or when there appears to be a significant change in usage behavioral pattern  248  associated with the user  200 . The adaptive recommendations function  240  may employ design of experiment (DOE) algorithms so as to select the best possible “experimental” adaptive recommendations, and to optimally sequence such experimental adaptive recommendations, and to adjust such experiments as additional usage behaviors  270  are assimilated. In some embodiments, the adaptive recommendations function  240  may apply methods and systems disclosed in U.S. Provisional Patent Application Ser. No. 60/652,578, entitled “Adaptive Decision Process,” filed Feb. 14, 2005, which is incorporated by reference herein, as if set forth in its entirety. 
     The preference inferencing  242  and recommendations optimization  244  algorithms may also preferentially deliver content that is specially sponsored; for example, promotional, advertising or public relations-related content. 
     In summary, the adaptive recommendations generated by the adaptive recommendations function  240  may be delivered  910  to the users  200 , delivered  265  to the non-user  260 , or delivered  905 ,  935  back to the adaptive system  100 , for updating either the structural aspect  210  ( 905 ) or the content aspect  230  ( 935 ). The adaptive recommendations  250  generated by the adaptive recommendations function  240  will thus influence subsequent user interactions and behaviors associated with the adaptive system  100 , creating a dynamic feedback loop. 
     Automatic or Semi-Automatic System Structure Maintenance 
     The adaptive recommendations function  240 , optionally in conjunction with system structure maintenance functions that reside within, or are accessible by, the adaptive computer-based application  925  (not shown), may be used to automatically or semi-automatically update and enhance the structural aspect  210  of the adaptive system  100 . The adaptive recommendations function  240  may be employed to determine new relationships  214 , or modify existing relationships  214 , among objects  212  in the adaptive system, within structural subsets  280 , or among structural subsets associated with a specific sub-community. The automatic updating may include potentially assigning a relationship between any two objects to zero (effectively deleting the relationship between the two objects). The modified relationships may represent the workflow sequencing among objects within the structural aspect  210 , where objects represent a process, sub-process or activity. 
     In either an autonomous mode of operation, or in conjunction with human expertise, the adaptive recommendations function  240  may be used to integrate new objects  212  into the structural aspect  210 , or to delete existing objects  212  from the structural aspect. 
     The adaptive recommendations function  240  may also be extended to scan and evaluate structural subsets  280  that have special characteristics. For example, the adaptive recommendations function  240  may suggest that certain of the structural subsets that have been evaluated are candidates for special designation. This may include being a candidate for becoming a new specially designated sub-system or structural subset. The adaptive recommendations function  240  will present to human users or experts the structural subset  280  that is suggested to become a new sub-system or structural subset, along with existing sub-system or structural subsets that are deemed “closest” in relationship to the new suggested structural subset. A human user or expert may then be invited to add the object or objects  212 , and may manually create relationships  214  between the new object and existing objects. 
     As another alternative, the adaptive recommendations function  240 , optionally in conjunction with the system structure maintenance functions, may automatically generate the object or objects  212 , and may automatically generate the relationships  214  between the newly created object and other objects  212  in the structural aspect  210 . 
     This capability is extended such that the adaptive recommendations function  240 , in conjunction with system structure maintenance functions, automatically maintains the structural aspect and identified structural subsets  280 . The adaptive recommendations function  240  may identify new objects  212 , generate associated objects  212 , and generate associated relationships  214  among the new objects  212  and existing objects  212 , but also may identify objects  212  that are candidates for deletion. The adaptive recommendations function  240  may also automatically delete the object  212  and its associated relationships  214 . 
     The adaptive recommendations function  240 , in conjunction with system structure maintenance functions, may apply “global” considerations and logic when conducting modifications to the structural aspect  210  to ensure effective use and navigation of the structural aspect  210 . For example, thresholds or limits may guide the absolute number or relative number of relationships among objects. Similarly, rules may be applied to the number of elements in the structural aspect  210  as a whole, or within designated subsets of structural aspect  210 . Rules related to the duration an object  212  has been incorporated within the structural aspect  210 , or collective quality thresholds for objects  212  may also be applied. These global rules help ensure that adaptive system  100  performs at an optimum possible level of efficiency and effectiveness for process participants  200  collectively, according to some embodiments. 
     In this way the adaptive recommendations function  240 , optionally in conjunction with a system structure maintenance function, may automatically adapt the structural aspect  210  of the adaptive system  100 , whether on a periodic or continuous basis, so as to optimize the user experience. In some embodiments, each of the automatic steps listed above with regard to updating the structural aspect  210  may be employed interactively by human users and experts as desired. 
     Hence, the adaptive recommendations function  240 , driven in part by usage behaviors, automatically or semi-automatically updates the system structural aspect  210  (see dotted arrow  905  in  FIG. 7 ). The feedback loop is closed as process participant interactions with the adaptive system  100  are influenced by the structural aspect  210 , providing an adaptive, self-reinforcing feedback loop between the usage aspect  230  and the structural aspect  210 . 
     Automatic or Semi-Automatic System Content Maintenance 
     As shown in  FIG. 7 , the adaptive recommendations function  240  may provide the ability to automatically or semi-automatically update the content aspect  230  of the adaptive system  100  (see dotted arrow  935 ). Examples of on-line content or information  232  within the content aspect  230  that may be updated or modified include text, animation, audio, video, tutorials, manuals, executable code, and interactive applications. Further, meta-information  234 , such as reviews and brief descriptions of the content may also be updated or modified  935 . 
     The content aspect information items  232  may be directly modified  235  by the adaptive recommendations function  240 . Following are some illustrative examples. For text-based information  232 , words or phrases may be altered, alternative languages may be applied, and/or the formatting of information  232  may be altered  235 . Hyperlinks may be added or deleted to text-based information  232 . For image or graphical-based information  232 , images may be altered  235 , or formatting such as color may be adjusted  235 . For audio-based or video-based information  232 , alternative languages may be applied  235  and/or alternative sound tracks may be applied  235 . 
     Advertising or promotional elements may be added, deleted, or adjusted within information  232 . 
     Customized text or multi-media content suitable for online viewing or printing may be generated and stored  235  in the content aspect  230 . U.S. patent application Ser. No. 10/715,174 entitled “A Method and System for Customized Print Publication and Management” discloses relevant approaches for updating the content aspect  230  with adaptive print media instances and is incorporated by reference herein, as if set forth in its entirety. 
     The adaptive recommendations function  240  may operate automatically, performing in the background and updating the content aspect  230  independently of human intervention. Or, the adaptive recommendations function  240  may be used by users  200  or special experts who rely on the adaptive recommendations  250  to provide guidance in maintaining the content aspect  230 . 
     As in the case of the structural aspect  210 , different communities may also be used to model the maintenance of the content aspect  230 . The communities, affinity groups, and user segments are used to adapt the relevancies and to create, alter or delete relationships  214  between the objects  212 . The adaptive recommendations  250  may present the objects  212  to the user  200  in a different combination than initially may have been assembled or inputted, and may treat sections of a superordinate object  212  such as a document, book, manual, video, sound track, or interactive software as multiple subordinate objects  212  that can be recombined in a pattern that is aligned with community usage, by creating or altering relationships between sections of the superordinate object  212 . 
     In addition, as user feedback on system activities and usage behavioral patterns  248  is accumulated, the adaptive system  100  may suggest areas where additional content would be beneficial to users. For example, if the object  212  is frequently rated by users  200  as difficult to understand, or if only expert users in a community are accessing the object, the adaptive system  100  may recognize the need for generating supplemental content (e.g., in the form of documentation or online tutorials or demonstrations), and/or a need to re-structure object  212  and/or the associated meta-information  234  or information  232 . 
     The re-structuring  935  of the object  212  may include decomposing the associated meta-information  234  or information  232  into subordinate objects  212 , and/or meta-information  234  or information  232 , and applying appropriate relationships  214  to these newly created elements. 
     Hence, as shown in  FIG. 7 , the adaptive recommendations function  240 , driven in part by usage behaviors  270  (see  FIG. 9C ), automatically or semi-automatically updates  935  the content aspect  230 . The feedback loop is closed as the interactions of the user  200  with the adaptive system  100  are influenced by updates to the content aspect  230 , providing an adaptive, self-reinforcing feedback loop between the usage aspect  210  and the content aspect  230 , and, in some embodiments, between the usage aspect  210 , the structural aspect  220 , and the content aspect  230 . 
     Network-Based Embodiments 
     The structural aspect  210  of the adaptive system  100  may be based on a network structure. The structural aspect  210  thus includes two or more objects, along with associated relationships among the objects. Networks, as used herein, are distinguished from other structures, such as hierarchies, in that networks allow potential relationships between any two objects of a collection of objects. In a network, there does not necessarily exist well-defined parent objects, and associated children, grandchildren, etc., objects, nor a “root” object associated with the entire system, as there would be by definition in a hierarchy. In other words, networks may include cyclic relationships that are not permitted in strict hierarchies. As used herein, a hierarchy can be thought of as just one particular form of a network, with some additional restrictions on relationships among network objects. 
     The structural aspect  210  of the adaptive system  100  may also have a fuzzy network structure. Fuzzy networks are distinguished from other types of network structures in that the relationships between objects in fuzzy networks may be by degree. In non-fuzzy networks, the relationships between objects are binary. Thus, in non-fuzzy networks, between any two objects relationships either exist or they do not exist. 
     As used herein, a fuzzy network is defined as a network of information in which each individual item of information may be related to any other individual item of information, and the associated relationship between the two items may be by degree. A fuzzy network can be thought of abstractly as a manifestation of relationships among fuzzy sets (rather than classical sets), hence the designation “fuzzy network.” As used herein, a non-fuzzy network is a subset of a fuzzy network, in which relationships are restricted to binary values (i.e., relationship either exists or does not exist. 
     Generalizing further, both classical networks and fuzzy networks may have a-directional (also called non-directed) or directed links between nodes. Four network topologies are listed in Table 2. 
                     TABLE 2                  Network Topologies                                 network type   links between nodes   link type                       type i (classical)   binary   a-directional           type ii (classical)   binary   distinctly directional           type iii (fuzzy)   multi-valued   a-directional           type iv (fuzzy)   multi-valued   distinctly directional                        
The first two types (i and ii) are classical networks. Fuzzy networks, as used herein, are networks with topologies iii or iv.
 
     For each of the four network topologies listed in Table 2, another possible variation exists: whether the network allows only a single link or multiple links between any two nodes, where the multiple links may correspond to multiple types of links. For example, the fuzzy network types (iii and iv) of Table 2 may permit multiple directionally distinct and multi-valued links between any two nodes in the network. The adaptive system  100  encompasses any of the network topologies listed in Table 2, including those which allow multiple links and multiple link types between nodes. 
     Mathematically, for a non-fuzzy network, it can be said, without loss of generality, that a relationship translates to either a “0” or a “1”-“0,” for example if there is not a relationship, and “1” if there is a relationship. For fuzzy networks, the relationships between any two nodes, when normalized, may have values along a continuum between 0 and 1 inclusive, where 0 implies no relationship between the nodes, and 1 implies the maximum possible relationship between the nodes. 
     The structural aspect  210  of the adaptive system  100  of  FIG. 7  may support any of the network topologies described above. A-directional relationships between nodes (no arrows), directed relationships between nodes (whether single- or double-arrow), and multiple types of relationships between nodes, are supported by the adaptive system  100 . Further, relationship indicators which are binary (e.g., 0 or 1) or multi-valued (e.g., range between 0 and 1) are supported by the adaptive system. 
     It can readily be seen that a hierarchy may be described as a directed fuzzy network with the additional restrictions that the relationship values and indicators associated with each relationship must be either “1” or “0” (or the symbolic equivalent). Further, hierarchies do not support cyclic or closed relationship paths. 
       FIG. 13  illustrates a fuzzy network  500 , including a subset  502  of fuzzy network  500 . The subset  502  includes three objects  504 ,  506 , and  508 , designated as shaded for ease of identification. The subset  502  also includes associated relationships (arrows) and relationship indicators or weightings (values) among the three objects. The separated subset of the network  502  yields a fuzzy network (subset)  500   s.    
     A particular implementation of a fuzzy network structure, a fuzzy content network, which may advantageously constitute the fuzzy network  500 , is disclosed in U.S. Pat. No. 6,795,826, entitled “Fuzzy Content Network Management and Access,” and is incorporated by reference herein, as if set forth in its entirety. 
     The adaptive system  100  of  FIG. 7  may utilize fuzzy network structures, such as the fuzzy network  500  of  FIG. 13 . In  FIG. 14 , an adaptive system  100 C includes a structural aspect  210 C that is a fuzzy network  500 . Thus, adaptive recommendations  250  generated by the adaptive system  100 C are also structural subsets that are themselves fuzzy networks. Further, although not explicitly shown in  FIG. 14 , the usage aspect  220  may also be entirely, or in part, represented by a fuzzy network. 
     The structural aspect  210  of the adaptive system  100  may include multiple types of structures, comprising network-based structures, non-network-based structures, or combinations of network-based structures and non-network-based structures. In  FIG. 15 , the adaptive system  100 D includes a structural aspect  210 D, which includes multiple network-based structures and non-network-based structures. The multiple structures of  210 D may reside on the same computer system, or the structures may reside on separate computer systems. 
     Adaptive Recombinant Systems 
     In  FIG. 16 , according to some embodiments, a particular configuration of the adaptive recombinant computer-based application  925 R ( FIG. 4C ) is depicted, in which the adaptive recombinant computer-based application  925 R includes an adaptive recombinant system  800 . The adaptive recombinant system  800  includes the adaptive system  100  of  FIG. 7 , as well as the adaptive recombinant function  850 . The adaptive recombinant function  850  includes a syndication function  810 , a fuzzy network operators function  820 , and an object evaluation function  830 . Just as the adaptive system  100  may be part of the adaptive process  900 , the adaptive recombinant system  800  may be part of the adaptive recombinant process  901 . The adaptive recombinant function  850 , including the syndication function  810 , the fuzzy network operators function  820 , and the object evaluation function  830  functions may all reside within the adaptive recombinant computer-based application  925 R, as shown in  FIG. 16 , or one or all of the functions may be external to the computer-based application  925 R. 
     The adaptive recombinant system  800  is capable of syndicating and recombining structural subsets  280 . The structural subsets  280  may be derived through either direct access of the structural aspect  210  by the fuzzy network operators function  820 , or the structural subsets  280  may be generated by the adaptive recommendations function  240 . The adaptive recombinant system  800  of  FIG. 16  is capable of syndicating (sharing) and recombining the structural subsets, whether for display to the user  200  or non-user  260 , or to update the structural aspect  210  and/or the content aspect  230  of the adaptive system  100 . In addition, these functions are capable of accessing and updating multiple adaptive systems  100 , or aiding in the generation of a new adaptive system  100 . 
     The syndication function  810  may syndicate elements of the usage aspect  220  associated with syndicated structural subsets  280 , thus enabling elements of the usage clusters and patterns, along with the corresponding structural subsets, to be combined with other structural subsets and associated usage clusters and patterns. 
     As explained above, the structural aspect  210  of the adaptive system  100  may employ a network structure, and is not restricted to a particular type of network. In some embodiments, the adaptive recombinant system  800  operates in conjunction with an adaptive system in which the structural aspect  210  is a fuzzy network. The structural subsets  280  generated by the adaptive recombinant system  800  during syndication or recombination are likewise fuzzy networks in these embodiments, and are also called adaptive recombinant fuzzy networks. Recall that a structural subset is a portion or subset of the structural aspect  210  of the adaptive system  100 . The structural subset  280  may include a single object, or multiple objects, and, optionally, their associated relationships. 
     The adaptive recombinant system  800  of  FIG. 16  is able to syndicate and combine structural subsets  280  of the structural aspect  210  (where a structural subset  280  may contain the entire structural aspect  210 ). The structural subsets  280 , which are fuzzy networks, in some embodiments, may be syndicated in whole or in part to other computer networks, physical computing devices, or in a virtual manner on the same computing platform or computing network. Although the adaptive recombinant system  800  is not limited to generating structural subsets which are fuzzy networks, some of the following figures and descriptions, used to illustrate the concepts of syndication and recombination, feature fuzzy networks. Designers of ordinary skill in the art will recognize that the concepts of syndication and recombination may be generalized to other types of networks. 
     Thus, the adaptive recombinant system  800  of  FIG. 16  may utilize fuzzy network structures. In  FIG. 17 , an adaptive recombinant system  800 C includes the adaptive system  100 C of  FIG. 14 , in which the structural aspect  210 C is a fuzzy network. Thus, the adaptive recombinant system  800 C may perform syndication and recombination operations, as described above, to generate structural subsets that are fuzzy networks. 
     Fuzzy Network Subsets and Adaptive Operators 
     The adaptive recombinant system  800  of  FIG. 16  includes fuzzy network operators  820 . The fuzzy network operators  820  may manipulate one or more fuzzy or non-fuzzy networks. Some of the operators  820  may incorporate usage behavioral inferences associated with the fuzzy networks that the operators act on, and therefore these operators may be termed “adaptive fuzzy network operators.” The fuzzy network operators  820  may apply to any fuzzy network-based system structure, including fuzzy content network system structures, described further below. 
       FIG. 18  is a block diagram depicting some fuzzy network operators  820 , also called functions or algorithms, used by the adaptive recombinant system  800 . A selection operator  822 , a union operator  824 , an intersection operator  826 , a difference operator  828 , and a complement operator  832  are included, although additional logical operations may be used by the adaptive recombinant system  800 . Additionally, the fuzzy network operators  820  include a resolution function  834 , which is used in conjunction with one or more of the operators in the fuzzy network operators  820 . 
     A selection operator  822 , which selects subsets of networks, may designate the selected network subsets based on degrees of separation. For example, subsets of a fuzzy network may be selected from the neighborhood, around a given node, say Node X. The selection may take the form of selecting all nodes within the designated network neighborhood, or all the nodes and all the associated links as well within the designated network neighborhood, where the network neighborhood is defined as being within a certain degree of separation from Node X. A non-null fuzzy network subset will therefore contain at least one node, and possibly multiple nodes and relationships. 
     Two or more fuzzy network subsets may then be operated on by network operations such as union, intersection, difference, and complement, as well as any other network operators that are analogous to Boolean set operators. An example is an operation that outputs the intersection (intersection operator  826 ) of the network subset defined by the first degree or less of separation from Node X and the network subset defined by the second or less degree of separation from Node Y. The operation would result in the set of nodes and relationships common to these two network subsets, with special auxiliary rules optionally applied to resolve duplicative relationships as explained below. 
     The fuzzy network operators  820  may have special capabilities to resolve the situation in which union  824  and intersection  826  operators define common nodes, but with differing relationships or values of the relationships among the common nodes. The fuzzy network intersection operator  826 , Fuzzy_Network_Intersection, may be defined as follows: 
         Z =Fuzzy_Network_Intersection( X,Y,W ) 
     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 )=( c   1   *X   RV   +c   2   *Y   RV )/( c   1   +c   2 ) 
     where X RV  and Y RV  are relationship values of X and Y, respectively, and c 1  and c 2  are coefficients. If c 1 =1, and c 2 =0, then X RV  completely overrides Y RV . If c 1 =0 and c 2 =1, then Y RV  completely overrides X RV . If c 1 =1 and c 2 =1, then the derived relationship is a simple average of X RV  and Y RV . Other values of c 1  and c 2  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  500 A, 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  500 A, 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  500 B 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  500 B 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  500 B of  FIG. 19B , according to some embodiments,  FIG. 20A  depicts the structure of a process topic object  445   t , which consists of meta-information  450   t  only, and is analogous to a fuzzy content network topic object. Likewise,  FIG. 20B  depicts a process content object  445   c , which consist of embedded information, or references (for example, pointers or URLs) to information  455   c , and the associated meta-information  450   c . Fuzzy process content objects  455   c  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  445   a  contains meta-information  450   a , analogous to the process topic object  455   t  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  446   a  will contain meta-information  451   a , as well as information or a pointer to information  456   a , analogous to the process content object  445   c  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  445   a  and  446   a  may be defined. 
       FIG. 22A  illustrates a process activity “network A”  460 , including four activities ( 465   a ,  465   b ,  465   c , and  465   d ) and work flow relationships among the activities ( 470   a ,  470   b ,  470   c , and  470   d ), as well as relationships to activities external to process activity “network A”  470   e . 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  470   a  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  476   a  and  476   b  outbound from an activity (activity 1  474   a ), and may also have multiple relationship types inbound  476   b  and  476   c  to an activity (activity 4  474   b ). 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  476   d  of relationship type 2 (S(2,4,M)) is outbound from activity 4  474   b ; likewise, relationship  476   e  having relationship type 1 (S(1,4,N)) is also outbound from activity 4  474   b.    
     According to some embodiments,  FIGS. 23A and 23B  depict process networks  480 A and  480 B (collectively, process network  480 ). The process networks  480 A and  480 B are depicted for a particular relationship and associated relationship indicators, at particular times (t 0  and t 2 ), in some embodiments. The process networks  480 A and  480 B are process activity networks (see  FIGS. 22A and 22B ). The process networks  480 A and  480 B are integrated with process content objects, for example, “content object 1”  485   a  and process topic objects, for example, “topic object 1”  485   b . 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  480 A and  480 B are processed, according to some embodiments, over a period of time. During time t 1 , 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  480 A is automatically or semi-automatically updated (block  4505 ), resulting in process network  480 B at time t 2 . Thus, process network  480 A at time t 0  ( FIG. 23A ) automatically or semi-automatically becomes process network  480 B at time t 2  ( 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  485   a  and activity 1  520  had values of 0.4 and 0.6 at time to ( FIG. 23A ); at time t 2 , 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  480 B. 
       FIG. 25  depicts process network  480 B ( FIG. 24B ) at time t 2 . 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  480 B is process network  560 . 
     Adaptive Recombinant Processes 
       FIGS. 26 and 27  illustrate the syndication and combination of process networks by the adaptive recombinant system  800 C. (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  581   r  and  582   r  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  800 C, 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  800 C 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  210 C of adaptive system  100 C ( FIG. 7 ). The process network  560  may be the sole content network within structural aspect  210 C, or may be one of multiple network or non-network structures within  210 C, 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  210 C of the adaptive system  100 C, which may form part of the adaptive recombinant system  800 C. Again, the process network  560  may be the sole content network within structural aspect  210 C, or may be one of multiple networks within  210 C, 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  210 C 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  800 C, 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  901 A.  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. 
     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  900 A, 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  900 A. 
     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  900 B, 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  900 B 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  180   a  and other computer applications  181   a  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  900 C, 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  900 C 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  900 D, 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&#39;s. 
     The adaptive publishing function  2000  of the adaptive process  900 D 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&#39;s organization, and/or the products and services purchased. 
     According to some embodiments,  FIG. 41  depicts an adaptive process  900 E, 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  900 E 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  900 F, 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&#39;s capture of the buyer&#39;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  900 F 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  900 G, 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  900 G 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  2161   a  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  2161   b  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  2161   a , in addition to, or instead of, product and service bundle suggestions or options  2161   a . 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  2161   b  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 ,  2161   b  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&#39;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&#39;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  900 H is enabled 
       FIG. 44  depicts a location-aware collectively adaptive process  900 H, 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  925   m   1 , and a second mobile adaptive computer-based application  925   m   2 . Two of the instances are stationary adaptive computer applications, a first stationary adaptive computer-based application  925   s   1 , and a second stationary adaptive computer-based application  925   s   2 . 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  925   s   1  and the first mobile adaptive computer-based application instance  925   m   1 . 
     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  925   s   1  and instance  925   s   2 . Or the information flow  2230  may be between two mobile adaptive computer application instances, such as instance  925   m   1  and instance  925   m   2 . Finally, the interactions  2220  may be between a stationary adaptive computer-based application instance  925   s   1  and a mobile adaptive computer-based application instance  925   m   2 .
       

     According to some embodiments,  FIGS. 45 and 46  depict two examples of location-aware collectively adaptive systems  2200 .  FIG. 45  ( 2200 A) 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  925   s  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  925   s , 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  925   s  interacts  2305  with the supplier commerce contextualization function  2300 , as desired, to deliver effective adaptive recommendations  910   s  to process participants  200   s.    
     The stationary adaptive computer-based application instance  925   s  interacts  2415  with the mobile adaptive computer-based application instance  925   m . The mobile adaptive computer-based application instance  925   m  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  200   m  based on their interactions with adaptive system  100 . 
     The stationary adaptive computer-based application instance  925   s  initially interacts  2415  with the mobile adaptive computer-based application instance  925   m  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  925   s  detecting a mobile process participant  200   m  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  200   m  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  925   s ,  925   m  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  925   s ,  925   m  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  925   m ,  925   s . The preferences and interests  2420  of the mobile adaptive computer-based instance  925   m  are accessed by the stationary adaptive computer-based instance  925   s  to determine if there is a basis for providing suggested products or services to the mobile adaptive computer instance  925   m . 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  925   m.    
     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  925   m  in generating recommendations  910   m  to process participants  200   m.    
       FIG. 46  ( 2200 B) illustrates that the mobile adaptive computer-based application instance  925   m , along with the associated process participants  200   m , may be considered the process participants  200   sm  of the stationary adaptive computer-based application instance  925   s . The interactions described in  FIG. 45  are conducted through the process participant behaviors  920  transmission to the instance  925   s , and through the adaptive recommendations  910   s  generated by instance  925   s  and received by process participants  200   sm . Although in  FIG. 46 , the respective adaptive application instances  925   s ,  925   m  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  900 H ( 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&#39;s particular preferences and tastes. 
     The location-aware collectively adaptive system  2200  and process  900 H 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  82   bm   1  may be embodied within a portable computing device, such as a mobile phone or personal digital assistant (PDA). A mobile adaptive computer application instance  82   bm   1  may be contained in mobile apparatus, such as vehicles or other transportation devices. In some embodiments, a mobile adaptive computer application instance  82   bm   1  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  901 B, 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  901 B, 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&#39;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  901 B 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  901 B. Recall from  FIGS. 16 and 17  that the adaptive recombinant computer-based application  925 R may include the adaptive recombinant system  800 C, which in turn, may encompass the adaptive system  100 C ( FIG. 14 ). In the embodiment of  FIG. 47 , the adaptive system  100 C manages multiple networks within the structural aspect  210 C. 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  800 C, 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  800   i  to manage syndication and recombination of network structures for a variety of process purposes, including enabling adaptive viral marketing process  901 B. Adaptive recombinant system  800   i  includes multiple instances of adaptive system  100   i . Although not shown in  FIG. 48 , each adaptive system instance, such as adaptive system  100   i   1 , 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  100   i  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  100   i   1  may be syndicated  2555  to a second adaptive system instance  100   i   2 , 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  100   i   2  may then be syndicated  2565  to a third adaptive system instance  100   i   3 , 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  100   i   3  may be syndicated  2575  back to the original adaptive system instance  100   i   1 . 
     Thus, the process of syndication, modification, and combination may continue indefinitely. At each stage, it should be understood that an entire adaptive system instance  100   i  may be syndicated to a recipient that does not have access to the adaptive system instance  800   i   1 . 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  100   i  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  901 B, 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  210   c  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&#39;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  901 B 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  800 C 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  901 B 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  901 B of  FIGS. 49A and 49B  may also apply methods associated with location-aware collectively adaptive system  2200  and process  900 H, and may further apply the systems and methods of the adaptive commercial solutions process ( 900 G) depicted in  FIG. 43 . 
     Evolvable Processes 
     According to some embodiments, the adaptive recombinant process  901  may be used to deploy an evolvable process  901 E across one or more organizations or environments.  FIG. 50  depicts an embodiment of the adaptive recombinant computer-based application  925 R of  FIG. 4C , which includes an evolvable adaptive recombinant system  800   e , 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  800   e  also contains one or more instances  100   i  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  800   i . In addition, the evolvable adaptive recombinant system  800   e  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  100   i   3 . The adaptive recombinant function  850  may then be applied to create a new generation of recombinant content networks within the adaptive system instance  100   i   3 , 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  100   i . The adaptive recombinant function  850  may then be applied to create a new generation of recombinant content networks across adaptive system instances  100   i , based on the individual fitness of the previous generation of content networks across system instances  100   i.    
     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  800   e  can therefore enable the evolvable process  901 E, 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  925 R, the adaptive system  100 , or the adaptive recombinant system  800  may embody. Further, the adaptive asset management process  900 A, the adaptive real-time learning process  900 B, the innovation network process  900 C, the adaptive publishing process  900 D, the adaptive commerce process  900 E, the adaptive price discovery process  900 F, the adaptive commercial solutions process  900 G, the location-aware collectively adaptive process  900 H, the recombinant process network process  901 A, the adaptive viral marketing process  901 B, the evolvable process  901 E, 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.