Abstract:
The aggregation of facts from various sources about an individual may produce an individual profile that may inform personalized services. However, a compilation of facts may be supplemented by monitoring activities of the individual and formulating inferences regarding the individual&#39;s individual details, and the confidence of such inferences. Accordingly, a device may compare the detected activities with a behavioral rule set indicating correlations between activities and inferred individual details (e.g., frequently spent weekday evenings and morning departures from a residence imply that the residence is the individual&#39;s home; frequent bicycling to work, chosen over other available modes of transportation, implies that the individual is a bicycling enthusiast) to add inferred individual details to the individual profile. Continued monitoring may enable updating based on changes to the individual details. Multiple profiles may be synchronized while respecting the individual&#39;s privacy, obtaining the individual&#39;s consent to share information, and automatically resolving information conflicts.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    The present application is a continuation of and claims priority to U.S. patent application Ser. No. 13/924,052, filed Jun. 21, 2013 and titled “ACTIVITY-BASED PERSONAL PROFILE INFERENCE,” the entirety of which is incorporated by reference as if fully rewritten herein. 
     
    
     BACKGROUND 
       [0002]    Within the field of computing, many scenarios involve generation of an individual profile compiled from facts assembled from a set of data signals and sources. For example, an individual may input facts about the individual&#39;s life and preferences to a database. As a second example, a history of purchases through a commerce site may be examined to identify items that the individual owns. As a third example, a social profile of the individual provided by a social network may be examined to extract facts about the individual. The facts of the individual profile maybe inform various services, such as the recommendation of products that may be of interest to the individual. 
       SUMMARY 
       [0003]    This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key factors or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. 
         [0004]    While the assembly of an individual profile from a collected set of facts may provide useful information, additional information may be derived from inferences about activities of the individual. As a first such example, a device having a global positioning system (GPS) receiver that is configured to monitor the location of the individual over time may provide factual information about the individual&#39;s travel history, but may also enable inferences of individual details of the individual, the significance of the visited locations to the individual. For example, a location to which the individual often travels on weekday evenings and departs on weekday mornings may be inferred as the individual&#39;s residence, and a location where the individual regularly visits during consistent hours on weekdays may be inferred as the individual&#39;s workplace. As a second such example, a device having a physiological sensor may track the physical activities of the individual (e.g., detecting when the individual engages in bicycling), and depending on the contextual details of the bicycling activity, the device may infer various individual details of the individual, such as whether the individual is a bicycle enthusiast (e.g., choosing to bicycle long distances when other modes of transportation are available), a bicycle commuter (e.g., choosing to bicycle from home to a workplace and back), or utility bicyclist (e.g., bicycling for transportation only when other modes of transportation are unavailable, such as when the individual&#39;s automobile is unavailable). 
         [0005]    In accordance with these observations, the present techniques enable the generation of an individual profile of an individual based on inferences derived from the activities of the individual. These techniques involve the generation of a behavioral rule set indicating, for one or more individual details, a set of activities that are correlated with the individual details. A device that is capable of monitoring the activities of the individual may compare such activities with the behavioral rule set, and may therefore infer individual details about the individual to be assembled into the individual profile of the individual. Additionally, the inferences may be identified according to a particular confidence (e.g., a certainty of the inference based on the number, frequency, and/or strength of correlation between the activities and the inference). The device may continue evaluating the confidence of the respective inferences over time in order to verify, maintain, update, and/or correct the individual profile of the individual over time (e.g., as new information becomes available, or as the individual&#39;s individual details change). The individual profile derived from such inferences may be used in many ways, such as recommending products to the individual based on the inferred individual details; initiates social connections between the individual and individuals having similar individual details; and updating a social profile of the individual in a social network. 
         [0006]    To the accomplishment of the foregoing and related ends, the following description and annexed drawings set forth certain illustrative aspects and implementations. These are indicative of but a few of the various ways in which one or more aspects may be employed. Other aspects, advantages, and novel features of the disclosure will become apparent from the following detailed description when considered in conjunction with the annexed drawings. 
     
    
     
       DESCRIPTION OF THE DRAWINGS 
         [0007]      FIG. 1  is an illustration of an exemplary scenario featuring an assembly of an individual profile of an individual based on an extraction of facts from a variety of sources. 
           [0008]      FIG. 2  is an illustration of an exemplary scenario featuring a set of inferences about an individual derived from the individual&#39;s activities, resulting in a set of inferred individual details and a confidence that the individual detail currently describes the individual, in accordance with the techniques presented herein. 
           [0009]      FIG. 3  is a component block diagram of an exemplary system configured to generate an individual profile of an individual through inferences based on the activities of the individual in accordance with the techniques presented herein. 
           [0010]      FIG. 4  is a flow diagram of an exemplary method of generating an individual profile of an individual through inferences based on the activities of the individual in accordance with the techniques presented herein. 
           [0011]      FIG. 5  is an illustration of an exemplary memory device storing instructions that embody one or more of the techniques presented herein. 
           [0012]      FIG. 6  is an illustration of an exemplary behavioral rule set that may be utilized to infer individual details based on the activities of an individual in accordance with the techniques presented herein. 
           [0013]      FIG. 7  is an illustration of an exemplary scenario featuring a merging of an inferred individual profile of an individual and a social profile of the individual in accordance with the techniques presented herein. 
           [0014]      FIG. 8  illustrates an exemplary computing environment wherein one or more of the provisions set forth herein may be implemented. 
       
    
    
     DETAILED DESCRIPTION 
       [0015]    The claimed subject matter is now described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the claimed subject matter. It may be evident, however, that the claimed subject matter may be practiced without these specific details. In other instances, structures and devices are shown in block diagram form in order to facilitate describing the claimed subject matter. 
       A. INTRODUCTION 
       [0016]      FIG. 1  presents an illustration of an exemplary scenario  100  featuring the generation of a n individual profile  106  of an individual  102  of a device  104  based on an extraction of facts from various factual sources. The device  104  may comprise, e.g., a personal computer of the individual  102 , such as a workstation, laptop, or game console; a portable device of the individual  102 , such as a mobile phone, tablet, personal media player, or global positioning system (GPS) navigation device; or a server providing a service utilized by the individual  102 . In this exemplary scenario  100 , the device  104  assembles the individual profile  106  from individual details  110  provided by the individual  102  as user input (e.g., the individual&#39;s indication of his or her name, age, and home address). As a second example, the device  104  accesses a commerce site  112  to retrieve a history of products  114  purchased by the individual  102 . As a third example, the individual profile  106  may include information extracted from a social profile  118  specified by the individual  102  within a social network  116 , which may include, e.g., factual statements about the individual  102  made by the individual  102  and his or her social contacts. The device  104  may utilize the individual profile  106  assembled from the publicly available facts about the individual  102  to inform various tasks, such as recommending additional products  114  to the individual  102  (e.g., if the purchase history of the individual  102  indicates a recent purchase of a bicycle, the device  104  may recommend to the individual  102  the purchase of a bicycle helmet). 
       B. PRESENTED TECHNIQUES 
       [0017]    While the assembly of individual profiles  106  of individuals  102  based on a collection of facts may inform various services and processes, the evaluation of the activities of the individual  102  may often enable the inference of individual details about the individual  102  that may significantly extend the individual profile  106 . 
         [0018]      FIG. 2  presents an illustration of an exemplary scenario  200  featuring an inference  220  of individual details  222  about an individual  102  based on an evaluation of the travel of the individual  102 . In this exemplary scenario, the individual  102  may often carry a location-aware device, such as a global positioning system (GPS) receiver, which may track the travel of the individual  102  over time. During an evening  202  of a first day, the individual  102  may be located at a location that the device may determine (e.g., by comparing the coordinates of the individual  102  with a location database) to be a residence  204  in a residential neighborhood. During a first day  210 , the individual  102  may be detected to travel from the residence  204  to a second location comprising an office  206  in a commercial district; and during the evening  212  of the first day  210 , the individual  102  may be detected to travel from the office  206  to the residence  204 . During a second day  214 , the individual  102  may be detected to travel from the residence  204  to a third location comprising a bowling alley  208 , and, again, during the evening  216  of the third day  214 , the individual  102  may be detected to return to the residence  204  from the bowling alley  208 . During a fourth day  218 , the individual  102  may again be detected to travel from the residence  204  to the location of the office  206 . 
         [0019]    In addition to tracking the travel history of the individual  102  through these four days, the device may also be able to perform a set of inferences  220  from the travel activities of the individual  102  to the individual details  222  of the individual  102 . As a first example, the high frequency with which the individual  102  returns to the residence  204  each evening, and with which the individual  102  may support an inference  220  that the individual  102  lives in the residence  204 . As a second example, the frequency with which the individual  102  visits the office  206  during the daytime (particularly if such days are coordinated with typical working hours, such as 9:00 am to 5:00 pm on weekdays) may enable an inference  220  that the individual  102  is employed, and that the office  206  is the workplace of the individual  102 . As a third example, the visit of the individual  102  to the bowling alley  208  may enable an inference  220  that the individual  102  enjoys bowling. However, each inference  220  may be reached with a particular confidence  224 ; e.g., the confidence  224  in the inference  220  that the individual  102  lives in the residence  204  may be high, due to the regularity with which the individual  102  visits the residence  204 , while the inference  220  that the individual  102  works in the office  206  may have a medium confidence  224  (due to the availability of other explanations, e.g., the individual  102  is visiting the office as a client of a professional service, and the divergence on the third day  214  to visit the bowling alley  208 ), and the inference  220  of the interest of the individual  102  in bowling may have a low confidence  224 . In this manner, the use of the location data collected by the global positioning system (GPS) receiver may be used to infer about the individual  102  a significant number of individual details  222 , and an estimation of the confidence  224  of such inferences. 
         [0020]    In view of these observations, a device may be configured to formulate inferences about the individual  102  according to the activities of the individual  102 . In particular, the inferences  220  may be reached by comparing the activities of the individual  102  with a behavioral rule set indicating the correlation of activities with particular individual details  222 . For example, the behavioral rule set may indicate that regular visits to the residence  204 , particularly over weekday evenings, may indicate that the individual  102  resides in the residence. By storing a behavioral rule set specifying the correlation of activities and individual details  222 , as well as the confidence  224  of such correlations, a device may utilize the behavioral rule set to achieve the inferences  220  of the individual details  222  of the individual  120  in accordance with the techniques presented herein. 
       C. EXEMPLARY EMBODIMENTS 
       [0021]      FIG. 3  presents a first exemplary embodiment of the techniques presented herein, illustrated as an exemplary scenario  300  featuring an exemplary system  306  configured to cause a device  302  to generate an individual profile  106  of an individual  102  of the device  302 . The exemplary system  306  may be implemented, e.g., as a set of components respectively comprising a set of instructions stored in a memory component of a device  302  having a processor  304 , where the instructions of respective components, when executed on the processor  304 , cause the device  302  to perform respective portions of the techniques presented herein, such that the interoperation of the components causes the device  302  to operate according to the techniques presented herein. The exemplary system  306  includes at least one sensor  308  that detects at least one activity  314  of the individual  102  in accordance with the techniques presented herein. For example, the sensor  308  may comprise a global positioning system (GPS) receiver configured to detect geographic coordinates indicating the travel activities  314  of the individual  102 . The exemplary system  306  also includes an individual detail inferrer  310  that infers at least one individual detail  222  of the individual  102  according to the at least one activity  314  and a behavioral rule set  316  indicating correlations  318  of activities  314  and individual details  222 . The exemplary system  306  also comprises an individual profile generator  312  that generates the individual profile  106  of the individual  102  comprising at least one individual detail  222  inferred from the activities  314  of the individual  102  according to the behavioral rule set  316 . The individual profile  106  may then be used, e.g., to provide a variety of personalized services to the individual  102  based on the inference of individual details  222 , such as product recommendations of products  114 , social networking, and the updating of a social profile  118 . In this manner, the device  302  achieves the generation of the individual profile  106  including at least one inference  220  of an individual detail  222  in accordance with the techniques presented herein. 
         [0022]      FIG. 4  presents a second exemplary embodiment of the techniques presented herein, illustrated as an exemplary method  400  of generating an individual profile  106  of an individual  102 . The exemplary method  400  may be implemented, e.g., as a set of instructions stored in a memory component of a device  302 , such as a memory circuit, a platter of a hard disk drive, a solid-state storage device, or a magnetic or optical disc, and organized such that, when executed on a processor  304  of the device  302 , cause the device  302  to operate according to the techniques presented herein. The exemplary method  400  begins at  402  and involves executing  404  the instructions on a processor  304  of the device  302 . Specifically, the instructions are configured to detect  406  at least one activity  314  of the individual  102  (e.g., by receiving a notification from a sensor  308  that is monitoring the activities  314  of the individual  102 ). The instructions are also configured to compare  408  the at least one activity  314  of the individual  102  to a behavioral rule set  316  to infer at least one individual detail  222  of the individual  102 , and a confidence  224  of the inference  220  of the individual detail  222 . The instructions are also configured to generate  410  the individual profile  106  of the individual  102  comprising the respective individual details  222  and the confidence  224  of the inferred individual detail  222 . In this manner, the exemplary method  300  achieves the generation of the individual profile  106  of the individual  102  according to inferences  220  of individual details  222  in accordance with the techniques presented herein, and so ends at  412 . 
         [0023]      FIG. 5  presents an illustration of an exemplary scenario  500  featuring an exemplary memory device  502  (e.g., a CD-R, DVD-R, or a platter of a hard disk drive), on which is encoded computer-readable data  504 . The computer-readable data  504  in turn comprises a set of computer instructions  506  configured to operate according to the principles set forth herein. In one such embodiment, the processor-executable instructions  506  may be configured to implement a system for generating an individual profile  106  of an individual  102 , such as the exemplary system  306  of  FIG. 3 . In another such embodiment, the processor-executable instructions  506  may be configured to perform a method  508  of generating an individual profile  106  of an individual  102 , such as the exemplary method  400  of  FIG. 4 . In a third such embodiment, the processor-executable instructions  506  may be configured to cause a device  302  to generate an individual profile  106  of the individual  102  by monitor the individual  102  to detect at least one activity  314 ; inferring an individual detail set of the individual  102 , comprising at least one individual detail  222  matching the at least one activity  314  of the individual  102  according to the behavioral rule set  316 ; identify a confidence  224  of the at least one individual detail  222  of the individual  102  inferred from the activity  314 ; and generate the individual profile  106  of the individual  102  from the individual details  222  having a confidence  224  above a confidence threshold. Many such memory devices may be devised by those of ordinary skill in the art that are configured to operate in accordance with the techniques presented herein. 
       D. VARIATIONS 
       [0024]    The techniques discussed herein may be devised with variations in many aspects, and some variations may present additional advantages and/or reduce disadvantages with respect to other variations of these and other techniques. Moreover, some variations may be implemented in combination, and some combinations may feature additional advantages and/or reduced disadvantages through synergistic cooperation. The variations may be incorporated in various embodiments (e.g., the exemplary system  306  of  FIG. 3 ; the exemplary method  400  of  FIG. 4 ; and/or the exemplary memory device  502  of  FIG. 2 ) to confer individual and/or synergistic advantages upon such embodiments. 
         [0025]    D1. Scenarios 
         [0026]    A first aspect that may vary among embodiments of these techniques relates to the scenarios wherein such techniques may be utilized. 
         [0027]    As a first variation of this first aspect, the techniques presented herein may be utilized to achieve the configuration of a variety of devices  104 , such as workstations, servers, laptops, tablets, mobile phones, game consoles, portable gaming devices, portable or non-portable media players, media display devices such as televisions, appliances, home automation devices, and supervisory control and data acquisition (SCADA) devices. Part or all of the techniques may be implemented, e.g., on a personal device of the individual  102 , and/or on a server, such as a cloud server providing data services to one or more individual  102 . A collection of devices may also interoperate to achieve the completion of the techniques presented herein. 
         [0028]    As a second variation of this first aspect, many techniques may be utilized to detect the activities  314  of the individual  102 . As a first such example, a global positioning system (GPS) receiver may be configured to track the location of the individual  102  over time, which may enable inferences  220  based on the travel history of the individual  102 . As a second such example, a physiological monitor may detect various physiological signals from the individual  102 , such as heart rate, respiration, and body position and orientation, in order to identify physical activities  314  such as sitting, standing, walking, running, swimming, bicycling, and driving an automobile or boat. As a third such example, a portable device may comprise a set of sensors  308  measuring various properties of the environment while the individual  102  performs various activities  314  (such as accelerometers and gyroscopes measuring the orientation of the device  302 ; light sensors measuring the ambient light; temperature sensors detecting the ambient temperature; and microphones detecting an ambient noise level and possibly identifying the noise, e.g., as an automobile engine), and may therefore infer the activities  314  of the individual  102 . As a fourth such example, a device  302  comprising a camera may utilize a variety of still and/or motion image processing techniques to infer the activities  314  from captured images of the individual  102 . As a fifth such example, the device  302  may receive input from the individual  102  or a service (e.g., a calendar managed by the individual  102 ) indicating one or more activities  314  performed by the individual  102 . 
         [0029]    As a third variation of this first aspect, the individual profile  106  of the individual  102  may be generated in a general manner, e.g., a comprehensive description of the individual  102 . Alternatively, the inferences  220  and selected individual details  222  may be oriented toward a particular type of individual profile  106 , such as a demographic individual profile; an academic individual profile; a professional individual profile; a commercial individual profile; or a personality type individual profile. Those of ordinary skill in the art may devise a variety of such scenarios wherein the techniques presented herein may be utilized. 
         [0030]    D2. Behavioral Rule Sets and Inferences 
         [0031]    A second aspect that may vary among embodiments of the techniques presented herein involves the details of the behavioral rule set  316  and the inferences  220  derived therefrom. 
         [0032]    As a first variation of this second aspect, the behavior rule set  316  may be specified as an administrator as a set of logical conditions representing each inference  220 , such as an algorithm provided to determine whether a set of location coordinates detected by a location-aware device are likely to support an inference  220  of the residence of the individual  102 . Alternatively, the rules of the behavioral rule set  316  may be specified by an administrator as natural-language expression, and one or more natural-language parsing techniques may be applied to derive from the natural-language expression one or more logical conditions enabling the determination of an inference  220  from a set of inputs. For example, a user such as an administrator may provide a natural-language statement specifying an association, such as “if the individual frequently spends weekday evenings at a location, then the location is likely the home of the individual,” and the device  302  may translate this association into logical constraints encoding the inference  220  expressed by the user. Alternatively or additionally, the behavioral rule set  316  may be automatically generated, e.g., by a behavioral rule set evaluator that evaluates a set of individual profiles  106  to identify correlations  318  of individual details  222  with activities  314 . Various machine-learning techniques may be utilized for this automated generation of the behavioral rule set  316 , including Bayesian classifiers, artificial neural networks, and/or genetic algorithms that are configured to identify statistically consistent patterns in data sets, including the automated generation of inferences  220  of correlations  317  between the activities  314  and individual details  222 . 
         [0033]    As a second variation of this second aspect, respective rules of the behavioral rule set  316  may specify additional information additional to an activity  314  upon which an inference  220  of an individual detail  222  may be based. Accordingly, a sensor  308  of a device  302  may detect at least one contextual descriptor of a context in which the individual  102  performed at least one activity  314 , and compare the activities  314  of the individual  102  with the behavioral rule set  316  particularly in the context associated with the at least one contextual descriptor. As a first such example, if an individual  102  is detected as bicycling to work for several days in a row, some inferences  220  may be applied to determine that the individual  102  has an individual detail  222  involving a bicycling enthusiast. However, detected contextual factors may alter this inference  220 , such as a detection of car repairs performed upon an automobile of the individual  102 ; a closure of a driving road between the home of the individual  102  and the workplace of the individual  102  while a bicycle path remains available; and/or the suspension or termination of a public transportation service utilized by the individual  102 , such as the cancellation of a bus or train route. These contextual descriptors may alter the selection of an inference  220 , and/or may enable the selection of substitute inferences  220 . As a second such example, the determination that the individual  102  is visiting particular locations may be informed with contextual descriptors involving details of the locations retrieved from a location database. For example, if the individual  102  is determined to frequent a particular restaurant, a location data set may provide contextual descriptors indicating a type of cuisine served by the restaurant, thus enabling an inference  220  of the dietary tastes of the individual  102 . 
         [0034]    As a third variation of this second aspect, the behavioral rule set  316  may specify one or more levels of confidence  224  in respective inferences  220 . As a first such example, the confidence  224  may be related to the frequency of the performance of the activity  314 ; e.g., an activity  314  that is performed occasionally by an individual  102  may be less indicative of an individual detail  222  than an activity  314  that is frequently performed by the individual  102 . As a second such example, the confidence  224  may be related to the strength of the correlation  318  between the activity  314  and the individual detail  222 . For example, frequently spent weekend evenings in a residence may provide a lower-confidence inference  220  that the residence is the home of the individual  102  than an inference  220  based on frequently spent weekday evenings in a residence (e.g., the residence may be the home of a family member or friend whom the individual  102  frequently visits on weekends). Additionally, some behavioral rule sets  316  may specify several different confidences  224  based on the performance of an activity  314  and the correlation  318  with an individual detail  222 . As a first such example, a behavioral rule set  316  may specify, for an activity  314 , a first confidence threshold comprising an individual detail likelihood (e.g., an indication that an individual  102  performing the activity  314  at a certain level, such as a first instance count or a first instance frequency, is likely to exhibit the individual detail  222 ), and also a second confidence threshold comprising an individual detail assurance (e.g., an indication that an individual  102  performing the activity  314  at a higher level, such as a second instance count that is higher than the first instance count or a second instance frequency that is higher than the first instance frequency, is assured to exhibit the individual detail  222 ). 
         [0035]      FIG. 6  presents an illustration of an exemplary behavioral rule set  600  specifying a variety of inferences  220  of individual details  222  based on various activities  314 . In this exemplary behavioral rule set  600 , various activities  314  are specified as a set of conditions that may be detected by a device  302  having one or more sensors  308 , optionally with reference to other data sources that may provide contextual descriptors (e.g., the residence of the individual  102  is in an area where, according to a location data set, many of the residents are pet owners). Additionally, some inferences  220  of the exemplary behavioral rule set  600  may be based on individual details  222  identified by other inferences  220 ; e.g., a first inference  220  that an individual  102  is a pet owner (e.g., because the individual  102  regularly visits a pet supply store) may inform a second inference  220  that the individual  102  is a dog owner (e.g., because the individual  102  exhibits behaviors that are typical of pet owners whose pets are dogs, such as frequently taking a short walk upon returning from work). The exemplary behavioral rule set  600  also specifies two levels of confidence  224  for the respective inferences  220 . For example, an individual  102  who travels from home to work on 50% of occasions may be identified as a likely bicycle commuter, while an individual  102  who travels from home to work via bicycle on 75% of occasions may be identified as an assured bicycle commuter. In this manner, the exemplary behavioral rule set  600  may facilitate many types and levels of inferences  220  of the individual details  222  of individuals  102 . 
         [0036]    As a fourth variation of this second aspect, the individual profile  106  of the individual  102  may include additional sources of information that may supplement the inferences  220  of individual details  222 . As a first such example, the individual profile  106  may be supplemented with user input  108 ; e.g., the device  302  may request the individual  102  to verify an individual detail  222  selected as a result of an inference  220 , and may generate the individual profile  106  of the individual  102  including only the individual details  222  that have been verified by the individual  102 . As a second such example, the individual profile  106  may include information retrieved from a social profile  118  of the individual  102  provided by a social network  116 . 
         [0037]    As a fifth variation of this second aspect, the individual profile  106  of the individual  102  may be conditionally updated with some individual details  222  selected by inference  220  from the activities  314  of the individual  102 . For example, some inferences  220  may seem likely but not assured. Additional information may later raise the confidence in the inference  220  to a certainty, and the inference  220  may then be added as an individual detail  222  to the individual profile  106 . 
         [0038]    As a sixth variation of this second aspect, a device  302  may be configured to, after selecting an inference  2220  of an individual detail  222  for an individual  102 , continue monitoring the activities  314  of the individual  102  to verify and update the individual profile  106  in view of additional and changing information. As a first such example, an individual  102  may discover a new individual detail  222 , such as a new pastime, and the device  302  may detect the new individual detail  222  based on the detection of new activities  314  and new inferences  220  related thereto. As a second such example, an individual detail  222  of an individual  102  may lapse due to changing interests or circumstances, and the device  302  may detect a lapsing by the individual  102  of an activity  314  associated with an individual detail  222  and accordingly reduce the confidence  224  of the individual detail  222  in the individual profile  106  of the individual  102  (e.g., if the individual  102  stops commuting to a workplace via bicycle, the confidence  224  in the inference  220  of the individual  102  as a bicycle commuter may steadily diminish until falling below a threshold, and may then be removed from the individual details  222  of the individual profile  106  of the individual  102  and/or marked as a past individual detail  222 ). Those of ordinary skill in the art may devise many such inferences  220  and behavioral rule sets  316  in accordance with the techniques presented herein. 
         [0039]    D3. Uses of Inferences and Individual Profiles 
         [0040]    A third aspect that may vary among embodiments of the techniques presented herein involves various uses of the individual profiles  106  generated through inferences  220  of individual details  222 . 
         [0041]    As a first variation of this third aspect, an exemplary system  306  may include a product recommendation module that recommends at least one product to the individual  102 , where such products are associated with the at least one individual detail  222 . For example, the inference  220  that the individual  102  is a bicycle enthusiast may inform the recommendation of products such as bicycling clothing and equipment. 
         [0042]    As a second variation of this third aspect, an exemplary system  306  may be utilized in the context of social relationships. For example, a social network may infer that the individual  102  has some hobbies that are similar to those of a second individual, such as inferences that the individuals have an overlapping set of close friends, and may initiate an introduction between the individual  102  and the second individual as members of the same social circle. 
         [0043]      FIG. 7  presents an illustration of an exemplary scenario  700  featuring a third variation of this third aspect, involving the merging of an individual profile  106  and a social profile  118 . An exemplary system  306  may include a social network interface that merges at least a portion of the individual profile  106  of the individual  102  with elements  702  of a social profile  118  created by the individual  102  in a social network  116 . In this exemplary scenario  700 , the device  302  may seek to merge the social profile  118  of the individual  102  with the profile  106  generated by the device  302  through inferences  220 . However, respecting the interests of the individual  102  in the privacy of the individual profile  106 , it may not be desirable to automatically share the inferences  220  with the social network  116  until receiving the consent of the individual  102 ; e.g., the user may consent to sharing his or her residential address in a social profile  118 , but may wish to keep information about the individual&#39;s enjoyment of bowling within the individual profile  106 . Accordingly, in furtherance of the privacy of the individual  102 , the device  302  may present a set of options  704  permitting the individual  102  to select the individual details  222  to be merged with the elements  702  of the social profile  118  (e.g., only merging  708  the portions of the individual profile  106  that have been authorized for merging by the individual  102 , such as by detecting a selection of an option  704  with a pointing device  706 ). Additionally, while completing the merging  708 , the device  302  may detect a conflict  712  between an element  702  of the social profile  118  and an individual detail  222  of the individual profile  106  (e.g., conflicting information about the location of the residence of the individual  102 ). At a second time  710 , the device  302  may present to the individual  102  a notification  714  of the discovered conflict  712 , and options  704  for completing the merging  708 . Upon detecting a selection of an option  704  with a pointing device  706  indicating a clarification of the conflict  712 , the device  302  may complete the merging  708  according to the selected option  704 , and, at a third time point  716 , may therefore achieve the synchronization  718  of the individual profile  106  and the social profile  118  in accordance with the techniques presented herein. 
       E. COMPUTING ENVIRONMENT 
       [0044]      FIG. 8  and the following discussion provide a brief, general description of a suitable computing environment to implement embodiments of one or more of the provisions set forth herein. The operating environment of  FIG. 8  is only one example of a suitable operating environment and is not intended to suggest any limitation as to the scope of use or functionality of the operating environment. Example computing devices include, but are not limited to, personal computers, server computers, hand-held or laptop devices, mobile devices (such as mobile phones, Personal Digital Assistants (PDAs), media players, and the like), multiprocessor systems, consumer electronics, mini computers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. 
         [0045]    Although not required, embodiments are described in the general context of “computer-readable instructions” being executed by one or more computing devices. Computer-readable instructions may be distributed via computer-readable media (discussed below). Computer-readable instructions may be implemented as program modules, such as functions, objects, Application Programming Interfaces (APIs), data structures, and the like, that perform particular tasks or implement particular abstract data types. Typically, the functionality of the computer-readable instructions may be combined or distributed as desired in various environments. 
         [0046]      FIG. 8  illustrates an example of a system  800  comprising a computing device  802  configured to implement one or more embodiments provided herein. In one configuration, computing device  802  includes at least one processing unit  806  and memory  808 . Depending on the exact configuration and type of computing device, memory  808  may be volatile (such as RAM, for example), non-volatile (such as ROM, flash memory, etc., for example) or some combination of the two. This configuration is illustrated in  FIG. 8  by dashed line  804 . 
         [0047]    In other embodiments, device  802  may include additional features and/or functionality. For example, device  802  may also include additional storage (e.g., removable and/or non-removable) including, but not limited to, magnetic storage, optical storage, and the like. Such additional storage is illustrated in  FIG. 8  by storage  810 . In one embodiment, computer-readable instructions to implement one or more embodiments provided herein may be in storage  810 . Storage  810  may also store other computer-readable instructions to implement an operating system, an application program, and the like. Computer-readable instructions may be loaded in memory  808  for execution by processing unit  806 , for example. 
         [0048]    The term “computer-readable media” as used herein includes memory devices that, as a class of technology, categorically excludes electromagnetic signals and non-statutory embodiments. Such memory devices may be volatile and/or nonvolatile, removable and/or non-removable, and may involve various types of physical devices storing computer-readable instructions or other data. Examples of such memory devices include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVDs) or other optical storage, magnetic cassettes, magnetic tape, and magnetic disk storage or other magnetic storage devices. 
         [0049]    Device  802  may also include communication connection(s)  816  that allows device  802  to communicate with other devices. Communication connection(s)  816  may include, but is not limited to, a modem, a Network Interface Card (NIC), an integrated network interface, a radio frequency transmitter/receiver, an infrared port, a USB connection, or other interfaces for connecting computing device  802  to other computing devices. Communication connection(s)  816  may include a wired connection or a wireless connection. Communication connection(s)  816  may transmit and/or receive communication media. 
         [0050]    The term “computer-readable media” also includes communication media, as a distinct and mutually exclusive category of computer-readable media than memory devices. Communication media typically embodies computer-readable instructions or other data in a “modulated data signal” such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” may include an electromagnetic signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. 
         [0051]    Device  802  may include input device(s)  814  such as keyboard, mouse, pen, voice input device, touch input device, infrared cameras, video input devices, and/or any other input device. Output device(s)  812  such as one or more displays, speakers, printers, and/or any other output device may also be included in device  802 . Input device(s)  814  and output device(s)  812  may be connected to device  802  via a wired connection, wireless connection, or any combination thereof. In one embodiment, an input device or an output device from another computing device may be used as input device(s)  814  or output device(s)  812  for computing device  802 . 
         [0052]    Components of computing device  802  may be connected by various interconnects, such as a bus. Such interconnects may include a Peripheral Component Interconnect (PCI), such as PCI Express, a Universal Serial Bus (USB), Firewire (IEEE 1394), an optical bus structure, and the like. In another embodiment, components of computing device  802  may be interconnected by a network. For example, memory  808  may be comprised of multiple physical memory units located in different physical locations interconnected by a network. 
         [0053]    Those skilled in the art will realize that storage devices utilized to store computer-readable instructions may be distributed across a network. For example, a computing device  820  accessible via network  818  may store computer-readable instructions to implement one or more embodiments provided herein. Computing device  802  may access computing device  820  and download a part or all of the computer-readable instructions for execution. Alternatively, computing device  802  may download pieces of the computer-readable instructions, as needed, or some instructions may be executed at computing device  802  and some at computing device  820 . 
       F. USAGE OF TERMS 
       [0054]    Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims. 
         [0055]    As used in this application, the terms “component,” “module,” “system”, “interface”, and the like are generally intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a controller and the controller can be a component. One or more components may reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers. 
         [0056]    Furthermore, the claimed subject matter may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer to implement the disclosed subject matter. The term “article of manufacture” as used herein is intended to encompass a computer program accessible from any computer-readable device, carrier, or media. Of course, those skilled in the art will recognize many modifications may be made to this configuration without departing from the scope or spirit of the claimed subject matter. 
         [0057]    Various operations of embodiments are provided herein. In one embodiment, one or more of the operations described may constitute computer-readable instructions stored on one or more memory devices, where the execution of such instructions by a computing device causes the computing device to perform the operations described. The order in which some or all of the operations are described should not be construed as to imply that these operations are necessarily order dependent. Alternative ordering will be appreciated by one skilled in the art having the benefit of this description. Further, it will be understood that not all operations are necessarily present in each embodiment provided herein. 
         [0058]    Moreover, the word “exemplary” is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “exemplary” is not necessarily to be construed as advantageous over other aspects or designs. Rather, use of the word exemplary is intended to present concepts in a concrete fashion. As used in this application, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or”. That is, unless specified otherwise, or clear from context, “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then “X employs A or B” is satisfied under any of the foregoing instances. In addition, the articles “a” and “an” as used in this application and the appended claims may generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form. 
         [0059]    Also, although the disclosure has been shown and described with respect to one or more implementations, equivalent alterations and modifications will occur to others skilled in the art based upon a reading and understanding of this specification and the annexed drawings. The disclosure includes all such modifications and alterations and is limited only by the scope of the following claims. In particular regard to the various functions performed by the above described components (e.g., elements, resources, etc.), the terms used to describe such components are intended to correspond, unless otherwise indicated, to any component which performs the specified function of the described component (e.g., that is functionally equivalent), even though not structurally equivalent to the disclosed structure which performs the function in the herein illustrated exemplary implementations of the disclosure. In addition, while a particular feature of the disclosure may have been disclosed with respect to only one of several implementations, such feature may be combined with one or more other features of the other implementations as may be desired and advantageous for any given or particular application. Furthermore, to the extent that the terms “includes”, “having”, “has”, “with”, or variants thereof are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising.”