Patent Abstract:
In many computing scenarios, individual profiles are generated from facts specified by, observed about, and/or inferred about individuals, and may be used to personalize services to such individual. Such facts may include details provoking a sensitivity of an individual, and it may be undesirable to reveal the collection and use of such facts to the individual; however, such facts may also enable accurately personalized service. These considerations may be balanced by partitioning the individual profile into a foreground profile, comprising facts that are revealed to the individual, and a background profile, comprising facts that are collected about but not revealed to the individual. Services may be covertly personalized for the individual based on the sensitive information stored in the background profile (e.g., restaurant recommendations that are overtly recommended based on the current location of the individual, and also covertly selected based on an inferred income level of the individual).

Full Description:
BACKGROUND 
       [0001]    Within the field of computing, many scenarios involve a collection of facts about an individual (e.g., directly received from the individual; observed about the individual; and/or based on an inference about the individual), where such facts are stored in an individual profile and used to personalize one or more services based on the details of the service. For example, a retail service may recommend products to an individual based on the individual&#39;s previous purchases, and a social network may present advertisements to the individual that are based on facts specified in the social profile of the individual. 
         [0002]    Many such individual profiles may be informed by inferences about the individual, based on the contents of expressions authored by the individual and/or activities performed by the individual. For example, if an individual frequently expresses an interest in a particular type of food, or frequently visits restaurants that offer a particular type of food, an inference may be made that the individual enjoys the particular type of food, even if the individual has not expressly indicated such enjoyment in the individual profile. 
       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]    Inferences about an individual may be formulated with an inference confidence that relates to the accuracy of the inference as a fact describing the individual. For example, if an individual consumes a particular type of food on a daily basis, an inference may be drawn about the individual&#39;s food preferences than if the individual only consumes the particular type of food once per month. Accordingly, the consideration of the inference confidence of the inferences while generating the individual profile through inferences (e.g., only adding inferences to the individual profile if the inference confidence in the accuracy or strength of the inference exceeds a desired threshold) may enable the formulation of a more accurate individual profile. 
         [0005]    However, the aggregation of the individual profile based on inferences may result in a large collection of facts about the individual, which may not be significantly representative of the individual&#39;s identity. Rather, such an individual profile may include a large number of facts that, even if derived from inferences having a high inference confidence and therefore accurate about the individual, are not regarded by the individual as representing his or her particular identity. As a first such example, the individual profile may include a fact that describes the individual, but also a large number of other individuals in the individual&#39;s community, and that is therefore not distinctive of the individual; e.g., a fact that the individual enjoys ice cream may also describe everyone the individual knows enjoys ice cream, the inclusion of this fact in the individual&#39;s profile may not distinguish the individual&#39;s identity. As a second such example, the individual profile may include a fact that is only incidental to the individual&#39;s identity; e.g., the individual may visit the same restaurant every day for lunch more due to convenience than personal preference, and the individual may not consider the restaurant or food type as part of his or her identity. Indeed, the individual may not even particularly enjoy the food type at the restaurant, so even though the inference confidence in the fact that the individual frequently visits the restaurant offering the associated food type is accurate, the inferred fact that the individual enjoys the associated food type As a third such example, the individual profile may include a fact that the individual considers private; e.g., the individual may enjoy a particular music group that is associated with a negative social stigma, and may not desire this inference to be added to the individual profile. 
         [0006]    In each of these scenarios, even if the inference exhibits a high inference confidence that the inference accurately describes the individual, the inferred fact may significantly reflect the identity of the individual. An individual profile populated with such inferred facts may therefore not adequately describe the individual. 
         [0007]    In view of these considerations, presented herein are techniques for generating an individual profile of an individual. Upon receiving a fact about the individual at a detection time, an embodiment may determine a significance of the fact to the identity of the individual. Upon determining that the significance of the fact to the identity of the individual exceeds a significance threshold, the embodiment may add the fact to the individual profile; and upon failing to add the fact to the individual profile within an evaluation duration of the detection time, discard the fact about the individual. By evaluating the significance of each fact to the identity of the individual, and adding to the individual profile only the facts that exceed the significance threshold, the generation of the individual profile may be achieved that is more descriptive of the individual&#39;s identity than an individual profile that is simply populated with facts having a high inference confidence. 
         [0008]    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 
         [0009]      FIG. 1  is an illustration of an example scenario featuring the generation of an individual profile using a set of inferences. 
           [0010]      FIG. 2  is an illustration of an example scenario featuring the generation of an individual profile according to the significance of the respective facts to the identity of the individual, in accordance with the techniques presented herein. 
           [0011]      FIG. 3  is a flow diagram of an example method of configuring a device to generate an individual profile in accordance with the techniques presented herein. 
           [0012]      FIG. 4  is a component block diagram of an example system provided to configure a device to generate an individual profile in accordance with the techniques presented herein. 
           [0013]      FIG. 5  is an illustration of an example computer-readable medium comprising processor-executable instructions configured to embody one or more of the provisions set forth herein. 
           [0014]      FIG. 6  is an illustration of an example scenario featuring an evaluation of the significance of the fact based on the frequencies of the individual&#39;s expressions and activities in accordance with the techniques presented herein. 
           [0015]      FIG. 7  is an illustration of an example scenario featuring an individual profile manager that evaluates the significance of the respective facts and the individual sensitivity of the individual in accordance with the techniques presented herein. 
           [0016]      FIG. 8  is an illustration of example scenario featuring a determination of the significance of respective facts to the individual profile of the individual in accordance with the techniques presented herein. 
           [0017]      FIG. 9  is an illustration of an example scenario featuring continued monitoring of the significance of a fact to the individual profile of an individual in accordance with the techniques presented herein. 
           [0018]      FIG. 10  is an illustration of an example computing environment wherein one or more of the provisions set forth herein may be implemented. 
       
    
    
     DETAILED DESCRIPTION 
       [0019]    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 
       [0020]      FIG. 1  presents an illustration of an example scenario  100  featuring a technique for generating an individual profile  110  of an individual  102 . In this example scenario  100 , at a first time  120 , various inferences about the individual  102  may be formulated in order to identify facts  112  to be added to the individual profile  110 . As a first such example, the individual  102  may be associated with a profession  104 , such as a teacher; a location  106  where the individual  102  lives, such as New York; and an interest  108  that the individual  102  holds, such as an appreciation of the sport of baseball. Such inferences may be determined with varying degrees of inference confidence, such as the duration of the individual&#39;s tenure as a teacher; the amount of time that the individual  102  spends in New York; and the number of baseball games that the individual  102  attends. The inference confidence may be used to determine whether respective inferences accurately reflect facts  112  about the individual  102 ; e.g., if the individual  102  only resides in New York a few days per year, the inference confidence may be very low, but if the individual  102  resides in New York every day, the inference confidence may be very high. An embodiment may add facts  112  to the individual profile  110  only for inferences having an inference confidence that exceeds an inference confidence threshold, indicating the reliability that the associated fact  112  is true about the individual  102 . Moreover, an embodiment may update the facts  112  of the individual profile as the inference confidence changes. At a second time  112 , if the individual  102  updates his or her mailing address from New York to Chicago, the fact  112  in the individual profile  110  indicating the individual&#39;s location  106  may be updated. Additionally, the activities  116  in which the individual  102  frequently engages may be evaluated to adjust the inference confidence; e.g., detecting that the individual  102  is engaging in activity  116  such as watching a game of soccer may enable an inference  118  that the individual  102  harbors an interest  108  in soccer, and if the inference confidence of the inference  118  is sufficiently strong, the interest  108  may be added as a fact  112  to the individual profile  110  of the individual  102 . 
         [0021]    While inference-based techniques may be used to generate an individual profile  110  of an individual  102 , such techniques based primarily upon inference confidence may result in the generation of an individual profile  110  that does not appropriately reflect the identity of the individual  102 . In a variety of ways, the addition of inferences  118  to the individual profile  110  as facts  112  about the individual  102  may not reflect the traits about the individual considers significant about his or her identity. 
         [0022]    As a first such example, an individual  102  may exhibit a profession  104  of a teacher. However, a first individual  102  consider such a profession  104  to be an integral component of his or her identity, while a second individual  102  may regard such a profession  104  only as a job or pastime. Notably, the variable attitude of such individuals  102  may be unrelated to the inference confidence that the inferences; i.e., the fact  112  that each individual  102  is a teacher may be entirely true, but may not reflect the significance of the teaching profession  104  to the identity of each individual  102 . For example, both a first individual  102  and a second individual  102  may have been continuously occupied as full-time teachers for thirty years, resulting in a high inference confidence that such facts  112  are indisputably accurate; but the first individual  102  may consider the fact  112  significant to his or her identity, while the second individual  102  may not. Indeed, the second individual  102 , having a thirty-year career as a teacher, may consider teaching to be less significant to his or her identity than a third individual  102  who has only been teaching for three months (and having a low inference confidence), or of a fourth individual  102  who aspires to be a teacher but has not yet been hired as such. 
         [0023]    As a second such example, an individual  102  may reside in a particular location  114 , but the individual  102  may not consider the location  114  to be particularly interesting or distinguishing of the individual  102 . For example, virtually all of the individual&#39;s social connections may reside in New York, may all enjoy baseball, and may all appreciate cats. Populating the individual profile  110  of the individual  102  with these facts  112  may therefore not reflect anything distinctive or interesting about the individual  102 , especially if the individual  102  anticipates sharing this individual profile  110  with the individual&#39;s social contacts who share the same traits; indeed, such facts  112  may dilute the individual profile  110  and detract from other facts  112  that the individual  102  considers representative of the individual&#39;s identity. 
         [0024]    As a third such example, a determination that the individual  102  frequently engages in a particular activity  116  may lead to an inference  118  of a fact  112  that the individual  102  has an interest  108  in the activity  116 . Even if the inference confidence is high, due to a very consistent and predictable frequency of the activity  116 , in some cases, the individual  102  may not consider the interest  112  to be representative of the individual&#39;s identity. For example, the individual  102  may regularly watch soccer as a professional sports reporter, or because the individual  102  enjoys spending time with family or friends who regularly watch soccer. However, such activities  116  may be only incidental to the identity of the individual  102 , and adding them to the individual profile  110  may not accurately reflect the individual&#39;s identity. 
         [0025]    As a fourth such example, inferences  118  about the individual  102  may be achieved that identify facts  112  that the individual  102  considers to be private. Adding such facts  112  to a publicly accessible individual profile  110 , even if such inferences  118  have a high inference confidence and accurately represent the individual  102 , may not accurately reflect the identity that the individual  102  wishes to expose to the public. 
         [0026]    In each such example, problems arise because even if the inference  118  about the individual  102  reflects a high inference confidence indicating that the fact  112  about the individual  102  is accurate, basing the individual profile  110  on such inferences may fail to account for whether such facts  112  are significant to the identity of the individual  102 . Indeed, in many cases, inference confidence and significance may be completely unrelated (e.g., an individual&#39;s self-perception that a fact  112  representing an activity  116  significantly represents the individual&#39;s interests may be unrelated to the frequency with which the individual  102  engages in the activity  116 ); and in many other cases, inference confidence and significance may be inversely related. For example, an individual  102  may frequently visit a first restaurant (resulting in a high inference confidence), and may infrequently visit a second restaurant (resulting in a low inference confidence). However, the first restaurant may be an easy, nearby, or affordable option that the individual  102  chooses out of habit or convenience, and the second restaurant may be the individual&#39;s favorite restaurant that the individual  102  chooses for special occasions. Contrary to the inference confidence of each option, the individual  102  may therefore consider the “special occasion” restaurant to be highly representative of the identity of the individual  102 , and may consider the convenient option not representative of the identity of the individual  102 . These and other disadvantages may arise from the generation of the individual profile  110  primarily relying upon the inference confidence of inferences  118  about the individual  102 . 
       B. Presented Techniques 
       [0027]    Presented herein are techniques for automatically generating an individual profile  110  of an individual  102  in view of the significance of the respective facts  112  of the individual profile  110  to the identity of the individual  102 . 
         [0028]      FIG. 2  presents an illustration of an example scenario  200  featuring the generation of an individual profile  110  in view of the significance  204  of the respective facts to the identity  218  of the individual  102 . In this example scenario  200 , at a detection time  202 , the individual  102  may be represented by an individual profile  110  that already includes a few facts  112  that the individual  102  considers significant to his or her identity  218 , such as the individual&#39;s location and profession. A determination may be made that the individual  102  has an interest  108  in soccer, and frequently engages in the activity  116  of playing the piano. However, an initial determination may be made of the significance  204  of each fact  112  to the identity  218  of the individual  102 . Because the initial determination of the significance  204  of each fact  112  is not above a significance threshold  208 , the facts  112  corresponding to the interest  108  and activity  116  are stored in storage  206  rather than in the individual profile  110 . The significance  204  of each fact  112  in storage  206  is the monitored (e.g., by comparing the respective facts  112  with additional expressions and activities  116  of the individual  102 ). At a second time  220 , when the individual  102  is further detected to engage in the activity  116  of playing soccer, the significance  204  of the interest  108  to the identity  218  of the individual  102  in the sport of soccer is determined to have exceeded the significance threshold  208 , and the fact  112  is moved from storage  206  to the individual profile  110 . Alternatively, at the second time  220 , the activity  116  of playing piano has not achieved a significance  204  to the identity  218  of the individual  102  that exceeds the significance threshold  208  within an evaluation duration  210  of the detection time  202  (e.g., the evaluation of the significance  204  of the piano-playing activity  116  has been evaluated for three days since the first instance of the activity  116 , and the individual  102  has not exhibited any further signs of interest in the activity  116 ). Rather than adding the activity  116  as a fact  112  to the individual profile  110 , a device may instead discard  216  the fact  112  corresponding to the activity  116  from storage  206 . In this manner, the individual profile  110  of the individual  102  is generated based on the evaluation of the significance  204  of respective facts  112  to the identity  218  of the individual  102  in accordance with the techniques presented herein. 
       C. Technical Effects 
       [0029]    The use of the techniques presented herein to generate an individual profile  110  may result in a variety of technical effects. 
         [0030]    As a first example of a technical effect that may be achievable by the techniques presented herein, the generation of the individual profile  110  based on the significance  204  of the facts  112  to the identity  218  of the individual  102  may enable the generation of an individual profile  110  that more accurately represents the identity  218  of the individual  102 . That is, the individual profile  110  is not diluted with facts  112  that are accurate but only incidental to the identity  218  of the individual  102 ; with facts  112  that are true of a large number of individuals  102 , and that therefore do not particularly distinguish the individual  102 ; and with facts  112  that are representative of the individual frequently performs out of habit, convenience, or obligation, but that are representative of the individual&#39;s choices. Limiting the facts  112  included in the individual profile  110  to those that have significance  204  to the identity  218  of the individual  102  may therefore provide a more concise individual profile  110  that more accurately reflects the identity  218  of the individual  102 . For example, if a first individual  102  requests to view the individual profile  110  of a second individual  102 , a device may respond by enumerating a small set of facts  112  that provide an insightful representation of the identity  218  of the second individual  102 , rather than a laundry list of facts  112  that may be accurate about the second individual  102  but may not reflect the identity  218  of the second individual  102 . 
         [0031]    As a second example of a technical effect that may be achievable by the techniques presented herein, providing an automated evaluation of respective facts  112 , and an automated determination of the significance  204  of respective facts  112  to the identity  218  of the individual  102 , may reduce the interaction of the individual  102  to generate and maintain the individual profile  110 . For example, a device may generate an individual profile  110  comprising every possible fact  112  about a particular individual  102 , and may request or allow the individual  112  to choose and arrange the facts  112  according to their significance  204  to the identity  218  of the individual  112 . However, such manually curated individual profiles  110  may be frustrating for the individual  102 , and if the individual  102  does not regularly perform such manual curation, the facts  112  of the individual profile  110  may steadily diverge from the identity  218  of the individual  102  (e.g., facts  112  may become out of date, and new facts  112  may fail to be added). By contrast, the techniques presented herein enable an automated determination of the significance  204  of the facts  112  to the identity  218  of the individual  102 , and may therefore reduce the dependency on the manual curation of the individual profile  110  by the individual  102 . 
         [0032]    As a third example of a technical effect that may be achievable by the techniques presented herein, the representation of a set of individuals  102  based on individual profiles  110  that are limited to facts  112  that have significance  204  to the identity  218  of the individual  102  may inform searches applied to the set of individuals  102 . For example, if an individual  102  searches among a social network for other individuals  102  who live in New York (e.g., for a recommendation of tourist destinations), it may not be helpful to provide a set of search results comprising every individual  102  whose individual profile  110  includes the fact  112  that the individual  102  resides or once resided in New York, as some individuals  102  may live in New York but may not be particularly interested in it; other individuals  102  may only occasionally live in New York; and still other individuals  102  may have previously lived in New York, but may no longer consider it a part of their identity  218 . Finding an individual  102  among such an individual set who is capable of and interested in presenting recommendations of New York tourist destinations may therefore be difficult. Conversely, limiting the search results to the individuals  102  for whom New York has a significance  204  to their identity  218 , including those who reside in New York and take an active interest in it, as well as those who have visited New York only occasionally but place great personal interest in such visits, may yield search results that are more suitable to the provided query. 
         [0033]    As a fourth example of a technical effect that may be achievable by the techniques presented herein, limiting the individual profiles  110  of a set of individuals  102  to facts  112  that are associated with the significance  204  of the individual  102  may promote the scalability of services and processes that depend on such individual profiles  110 . For example, maintaining an exhaustive list of every fact  112  that may accurately describe each of many thousands of individuals  102  may involve significantly greater data storage, processing, and communication capabilities than limiting each individual profile  110  to a smaller set of facts  112  that have a significance  204  to the identity  218  of each individual  102 . 
         [0034]    As a fifth example of a technical effect that may be achievable by the techniques presented herein, limiting the individual profiles  110  may enable a more thorough evaluation and monitoring of the significance  204  of such facts  112  to the identity  218  of the individual  102 . For example, attempting to monitor hundreds of facts  112  that might describe an individual  102  to maintain an updated inference confidence, e.g., every activity  116  that the individual  102  has performed at least once, and every restaurant that the individual  102  has visited at least once, may entail a significant expenditure of computational resources of the devices of the individual  102 , and may even scale to an unfeasible level of evaluation over many years of fact-gathering (e.g., the fact that the individual  102  watched a soccer game six years ago may not warrant a continued exploration of whether the sport of soccer is to be included in the individual profile  110  of the individual  102 ). Conversely, by limiting such evaluation to the facts  112  of the individual profile  110  that have previously been determined to have a significance  204  exceeding a significance threshold  208 , and to facts  112  that have recently been detected and are kept in storage  206  only briefly for the evaluation duration  210 , a device may apply a more thorough monitoring of the limited set of facts  112  that may result in a more accurate determination. Similarly, discarding  216  facts  112  that do not achieve a significance  204  above the significance threshold  208  within the evaluation duration  210  may enable a more rigorous significance evaluation of the smaller set of facts  112  that are currently kept in storage  206 . These and other technical effects may be achievable through the application of the techniques presented herein. 
       D. Example Embodiments 
       [0035]      FIG. 4  presents a first example embodiment of the techniques presented herein, illustrated as an example method  300  of representing an individual profile  110  of an individual  102 . The example method  300  may be implemented, e.g., as a set of instructions stored in a memory component of a device, 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 of the device, cause the device to operate according to the techniques presented herein. 
         [0036]    The example method  300  begins at  302  and involves executing  304  the instructions on a processor of the device. Specifically, executing  304  the instructions on the processor causes the device to, upon receiving  306  a fact  112  about the individual  102  at a detection time  202 , determine  308  a significance  204  of the fact  112  to the identity  218  of the individual  102 . Executing  304  the instructions may further cause the device to, upon determining that the significance  204  of the fact  112  to the identity  218  of the individual  102  exceeds a significance threshold  208 , add  310  the fact  112  to the individual profile  110 . Executing  304  the instructions may further cause the device to, upon failing to add the fact  112  to the individual profile  110  within an evaluation duration  210  of the detection time  202 , discard  312  the fact  112  about the individual  102 . In this manner, the instructions cause the device to represent the individual  102  with an individual profile  202  according to the techniques presented herein, and so ends at  314 . 
         [0037]      FIG. 4  presents a second example embodiment of the techniques presented herein, illustrated as an example server  402  featuring a processor  404  and a memory  406  storing an example system  408  that causes the server  402  to generate an individual profile  110  of an individual  102 . The example system  408  may be implemented, e.g., as a set of components respectively comprising a set of instructions stored in the memory  406  of the server  402 , where the instructions of respective components, when executed on the processor  404 , cause the server  402  to operate in accordance with the techniques presented herein. 
         [0038]    The example system  408  includes a significance evaluator  410  that determines a significance  204  of a fact  112  to the identity  218  of the individual  102 . The example system  408  also includes an individual profile manager  412  that, upon the significance evaluator  410  determining that the significance  204  of the fact  112  to the identity  218  of the individual  102  exceeds a significance threshold  208 , adds the fact  112  to the individual profile  110 ; and, upon failing to add the fact  112  to the individual profile  110  within an evaluation duration  210  of the detection time  202 , discards  216  the fact  112  about the individual  102 . In this manner, the example system  408  enables the server  402  to generate the individual profile  110  in accordance with the techniques presented herein. 
         [0039]    Still another embodiment involves a computer-readable medium comprising processor-executable instructions configured to apply the techniques presented herein. Such computer-readable media may include various types of communications media, such as a signal that may be propagated through various physical phenomena (e.g., an electromagnetic signal, a sound wave signal, or an optical signal) and in various wired scenarios (e.g., via an Ethernet or fiber optic cable) and/or wireless scenarios (e.g., a wireless local area network (WLAN) such as WiFi, a personal area network (PAN) such as Bluetooth, or a cellular or radio network), and which encodes a set of computer-readable instructions that, when executed by a processor of a device, cause the device to implement the techniques presented herein. Such computer-readable media may also include (as a class of technologies that excludes communications media) computer-computer-readable memory devices, such as a memory semiconductor (e.g., a semiconductor utilizing static random access memory (SRAM), dynamic random access memory (DRAM), and/or synchronous dynamic random access memory (SDRAM) technologies), a platter of a hard disk drive, a flash memory device, or a magnetic or optical disc (such as a CD-R, DVD-R, or floppy disc), encoding a set of computer-readable instructions that, when executed by a processor of a device, cause the device to implement the techniques presented herein. 
         [0040]    An example computer-readable medium that may be devised in these ways is illustrated in  FIG. 5 , wherein the implementation  500  comprises a computer-readable 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 . This 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 perform a method  608  of generating an individual profile  110  of an individual  102 , such as the example method  300  of  FIG. 3 . In another such embodiment, the processor-executable instructions  506  may be configured to implement a system for generating an individual profile  110  of an individual  102 , such as the example system  408  of  FIG. 4 . Many such computer-readable media may be devised by those of ordinary skill in the art that are configured to operate in accordance with the techniques presented herein. 
       E. Variations 
       [0041]    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 example method  300  of  FIG. 3 ; the example system  408  of  FIG. 4 ; and the example memory device  502  of  FIG. 5 ) to confer individual and/or synergistic advantages upon such embodiments. 
       E1. Scenarios 
       [0042]    A first aspect that may vary among embodiments of these techniques relates to the scenarios wherein such techniques may be utilized. 
         [0043]    As a first variation of this first aspect, the techniques presented herein may be utilized to achieve the configuration of a variety of devices, 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. 
         [0044]    As a second variation of this first aspect, the techniques presented herein may be utilized to partition and use various types of individual profiles  11 -, including social networking and social media profiles; academic and/or professional individual profiles; gaming profiles provided for a gaming service; media profiles for individuals  102  producing and/or consuming various types of media; individual behavior profiles of devices that monitor the behavior of the individual  102 ; governmental profiles of the civic details of various individuals  102 ; financial profiles of the financial status of various individuals  102 ; and commercial profiles of the savings and/or purchasing behaviors of various individuals  102 . 
         [0045]    As a third variation of this first aspect, the techniques presented herein may involve the evaluation of many types of facts  112  that may describe the individual  102 , including those specified directly by the individual  102 ; those specified by a first individual  102  about a second individual, such as a referral service; those detected about the individual  102 , such as a device that monitors one or more activities  116  of the individual  102  (e.g., a global positioning system that tracks the movement of the individual  102 ); and inferences  118  about the individual  102  (e.g., behavioral or personality traits about the individual  102  based on statistical and/or demographic factors, such as an inferred income level of an individual  102  based on the average income in a neighborhood including the personal residence of the individual  102 ). 
         [0046]    As a fourth variation of this first aspect, the individual profile  110  may be used to provide various types of services on behalf of the individual  102 , such as a commercial service; a product, media, or service recommendation service; a social network or referral service; a matchmaking service, such as a dating service or a multiplayer game matchmaking service; an employment service; an information delivery service; and an advising service, such as a financial or career advising service. Many such scenarios may provide a context for utilizing the techniques presented herein. 
       E2. Significance Determination 
       [0047]    A second aspect that may vary among embodiments of the presented techniques involves the manner of determining the significance  204  of a fact  112  to the identity  218  of the individual  102 , and may utilize various sources of information to determine the significance  204  of the fact  112  to the identity  218  of the individual  102 . 
         [0048]    As a first variation of this second aspect, an embodiment may determine the significance  204  of the fact  112  by detecting, among an expression set of expressions by the individual  102 , an expression of whether the fact  112  has significance  204  to the individual  102 . As a first such example, the individual  102  may expressly indicate that a fact  112  is significant to the identity  218  of the individual  102  (e.g., “I love golfing!”), or may indicate that a fact  112  is not significant to the identity  218  of the individual  102  (e.g., “I don&#39;t really like pizza”). As a second such example, the individual  102  may indicate the significance  204  of facts  112  while manually curating the individual profile  110 ; e.g., when the individual  102  adds, approves, and/or highlights a fact  112  in the individual profile  110 , the fact  112  may be construed as having significance  204  to the identity  218  of the individual  102 ; whereas if the individual  102  removes, disapproves, and/or downplays a fact  112  in the individual profile  110 , the fact  112  may be construed as not having significance  204  to the identity  218  of the individual  102 . In one such embodiment, the individual  102  may specify a fact order of the respective facts  112  of the individual profile  110 , wherein a first fact  112  having an earlier fact order in the individual profile is more significant than a second fact  112  having a later fact order in the individual profile; and the fact order of each fact  112  may be construed as relating to the significance  204  of the fact  112  to the identity  218  of the individual  102 . 
         [0049]    As a second variation of this second aspect, an embodiment may determine the significance  204  of the fact  112  by detecting, among an expression set of expressions by the individual, a frequency of references to the fact  112  by the individual  102 . For example, a fact  112  may be more likely to be related to the identity  218  of an individual  102  who frequently refers to and/or spontaneously raises the fact  112  in conversations, status messages, or content items such as written articles, or who generates sound, images, or video recordings that are related to the fact  112 , than an individual  102  who seldom refers to and/or spontaneously raises the fact  112 . 
         [0050]    As a third variation of this second aspect, the significance  204  of the fact  112  to the identity  218  of the individual  102  may be determined by detecting activities according to the activity conformity of the activities  116  performed by the individual  102 . For example, for the respective activities  116  performed by the individual  102 , an embodiment may determine whether the activity  116  conforms with the fact  116 , and the significance  204  may be determined proportionally with the activity conformity frequency of the conforming activities  116 . As a further variation, the activities  116  so assessed may be distinguished between activities  116  that the individual  102  performs out of significant choices, and the activities  116  that the individual  102  performs out of obligation, habit, or convenience. That is, the determination of significance  204  may focus on the activities  116  that the individual  102  chooses when presented with a selection of viable options, and may factor out the activities  116  for which the individual  102  does not have a choice (e.g., forgoing an opportunity to watch a soccer game due to a conflicting school or work obligation may not be construed as diminishing the significance  204  of the interest  108  of the individual  102  in the sport of soccer). 
         [0051]      FIG. 6  presents an illustration of an example scenario  600  featuring the determination of the significance  204  of various facts  112  to the identity  218  of an individual  102 . In this example scenario  600 , the facts  112  relates to the interest of the individual  102  in various activities  116 , such as golfing, hiking, and rock climbing. An embodiment may detect that among the expressions  602  of the individual  102  (e.g., messages exchanged with the individual&#39;s acquaintances in a social network), the individual  102  references golfing with a high frequency  604 , references golfing with a lower frequency  604 , and never references rock climbing. The embodiment may also detect that, when presented with opportunities to perform activities  116 , the individual  102  frequently chooses hiking  116 , but never chooses golfing, and instead chooses opportunities to engage in other activities  116  over the activity  116  of golfing. An embodiment may interpret such frequencies  604  in a variety of ways. With respect to hiking, since the individual both references hiking in expressions  602  and performs activities  116  that conform with the fact  112 , the fact  112  of an interest  108  in hiking may be determined to have a high significance  204  to the identity  218  of the individual. However, with respect to rock climbing and golfing, an embodiment may determine that the discrepancy between the frequency  604  of references to the fact  112  in the expressions of the individual  102  and the frequency  604  of the activity conformity of the activities  116  with the fact  112  may indicate that the fact  112  does not have high significance  204  to the individual  102 . An embodiment may further evaluate whether such discrepancy is due to a low significance  204  of the fact  112  to the individual  102  (e.g., the individual frequently discusses golfing as a business development opportunity, but does not choose to participate in golfing because the individual does not actually like golfing) or whether the discrepancy is due to limitations that are not related to the significance  204  of the fact  112  (e.g., whether the individual would engage in golfing  602  more frequently, but is unable to do so because of an injury or the unavailability of nearby golf courses). 
         [0052]    When presented with conflicting information about the significance  204  of a fact  112 , embodiments may utilize a variety of techniques to identify the significance  204  of the fact  112  to the identity  218  of the individual  102 . In particular, techniques involving learning algorithms may be well-suited for reconciling such conflicting information. As one example, an artificial neural network may be trained to determine the significance  204  of a fact  112  using a training data set that identifies, for a set of facts  112  pertaining to a set of individuals  102 , the frequencies  604  of expressions  602  and activities  116  performed by the individuals  102 , and the significance  204  of the facts  112  to the identity  218  of each individual  102  as self-reported by the individuals  102 . Such self-identification may enable the learning network to assess which qualities reflect the significance  204  of each fact  112  to the identity  218  of an individual  102 . For example, a first fact  112  that represents an interest  108  in a “spectator sport,” such as professional football, may be readily determined by the frequency  604  of expressions  602 , and less proportional with the performance of related activities  116  such as actually playing football; whereas a second fact  112  that involves interests  108  that are performance-based, such as yoga, may be more readily assessed by the frequency  604  of the individual&#39;s performance of the activity  116  than by the frequency  604  with which the individual  102  references the activity  116  in expressions  602 . A learning algorithm, such as an artificial neural network, may be able to determine the factors about a fact  112  that most consistently relate to the self-reported significance  204  of the fact  112  to the identity  218  of the individual  102 , and once trained using a training data set, may be applied to the expressions  602  and activities  116  of individuals  102  to determine the significance  204  of such facts  112  to the identities thereof, in accordance with the techniques presented herein. 
       E3. Supplemental Information 
       [0053]    A third aspect that may vary among embodiments of the techniques presented herein relates to the use of supplemental information that, together with the determination of the significance  204  of a fact  112  to the identity  218  of an individual  102 , enables a determination of whether to add the fact  112  to the individual profile  110  of the individual  102 . 
         [0054]    As a first variation of this third aspect, the significance  204  of the fact  112  to the identity  218  of the individual  102  may involve a consideration of the sensitivity of the fact  112  to the individual  102 . In some scenarios, a fact  112  may be accurate and even significant to the individual  102 , but may also be considered by the individual  102  to be private and/or sensitive, and therefore not having significance  204  to the public identity  218  of the individual  102 . Accordingly, an embodiment may predict the sensitivity of the individual to acknowledging a fact  112 , and may discard facts  112  where the predicted sensitivity of the individual  102  exceeds a sensitivity threshold. Such prediction may be based on particular facts  112  (e.g., determining that a particular fact  112  is generally regarded as a “guilty pleasure,” such as an interest  108  in an unpopular musical group), and may evaluate the fact  112  as having low significance  204  to the identity  218  of the individual  102  unless the individual  102  expressly acknowledges the fact  112 . Alternatively or additionally, the sensitivity of individuals  102  may be determined on a cultural basis. For example, a fact  112  may be considered sensitive to individuals  102  of a first demographic, but not sensitive to individuals  102  of a second demographic (e.g., individuals  102  of a first age range may openly appreciate a particular television show, but individuals  102  of a second age range who appreciate the same television show may be reluctant to admit such interest  108 ). Accordingly, the significance  204  of a fact  112  to the identity  218  of an individual  102  may be predicted by determining a demographic trait of the individual  102 , and determining a demographic sensitivity to the fact  112  among an individual set of individuals  102  that exhibit the demographic trait. 
         [0055]      FIG. 7  presents an illustration of an example scenario  700  wherein the individual sensitivity  702  of the individual is taken into consideration while determining whether to add a fact  112  to the individual profile  110  of an individual  102 . In this example scenario  700 , a fact  112  is evaluated as having either a high, medium, or low significance  204  to the identity  218  of an individual  102 . Additionally, the individual  102  is predicted as having either high, medium, or low individual sensitivity  702  to acknowledging the fact  112  as significant to the identity  218  of the individual  102 . Accordingly, an embodiment (such as an individual profile manager  512 ) may take into consideration both the significance  204  and the individual sensitivity  702  of the fact  112  to the identity  218  of the individual  102 , and may therefore determine  704  whether to discard the fact  112  or add the fact  112  to the individual profile  110  of the individual  102 . 
         [0056]    As a second variation of this third aspect, in addition to considering the significance  204  of a fact  112  to the identity  218  of an individual, an embodiment may take into consideration the inference confidence of an inference  118  upon which the fact  112  is based. Contrasting with the example scenario  100  of  FIG. 1  in which the inference confidence of the inference  118  is the primary determinant of adding the fact  112  to the individual profile  110 , in this third variation, the inference confidence of the inference  118  may be considered together with the significance  204  of the resulting fact  112  to the identity  218  of the individual  102 . 
         [0057]      FIG. 8  presents an illustration of an example scenario  800  featuring a first technique for determining an inference confidence  802  indicating whether an inference  118  accurately reflects a fact  112  about an individual  102 . In this example scenario  800 , a first fact  112  kept in storage  206  (and not yet included in the individual profile  110 ) reflects an interest  108  of the individual  102  in a particular activity  116 , such as fishing. However, an inference confidence  802  of the inference  118  may be comparatively low, e.g., only 50% confidence that the fact  112  is accurate about the individual  102 . Because such an inference confidence  802  may be “borderline,” i.e., not sufficient either to add the fact  112  to the individual profile  110  or to discard the fact  112 , an embodiment may directly query the individual  102  about his or her interest  108  in the activity  116 , e.g., presenting a fact query  804  such as “do you like fishing?” A detection of an individual acknowledgment  810  of the fact  804  may enable an adjustment  808  of the inference confidence  802  that, in addition to the determination of the significance  204  of the fact  112  to the identity  218  of the individual  102 , enables a determination of whether to add the fact  112  to the individual profile  110  or to discard the fact  112 . As a second such example, rather than directly querying the individual  102 , an embodiment may subtly prompt the individual  102  for an expression of individual interest  810 . For example, an embodiment may present to the individual  102  a fact detail  812  about the fact  112  (e.g., a link to reviews of hiking gear), and may detect whether or not the individual  102  exhibits individual interest  810  in the fact detail  812 . A detection of individual interest  810  in the fact detail  812  may enable an adjustment  808  of the inference confidence  802  that, in addition to the determination of the significance  204  of the fact  112  to the identity  218  of the individual  102 , enables a determination of whether to add the fact  112  to the individual profile  110  or to discard the fact  112 . As a third such example, an embodiment may endeavor to determine an interference confidence  802  in a selected fact  112  (e.g., an inference that the individual  102  is interested in hiking) by presenting an option set including an option that is associated with the selected fact  112  (e.g., a link to reviews of hiking gear), and other options associated with alternative facts  112  in which the individual  102  has not expressed an individual interest  810  (e.g., interests in fishing and golfing). A detection of individual interest  810  in the option associated with the selected fact  112  that exceeds the options associated with the alternative facts may enable an adjustment  808  of the inference confidence in the selected fact  112 . Many such forms of supplemental information may be utilized together with the significance  204  of the fact  112  to the identity  218  of the individual  102  while determining whether or not to add the fact  112  to the individual profile  110  of the individual  102  in accordance with the techniques presented herein. 
       E4. Inclusion of Fact in Individual Profile 
       [0058]    A fourth aspect that may vary among embodiments of the techniques presented herein involves the determination of whether to add a fact  112  to the individual profile  110  of the individual  102 , or to discard the fact  112  and/or exclude the fact  112  from the individual profile  110  of the individual  102 . 
         [0059]    As a first variation of this fourth aspect, for a fact  112  in storage  206  and under evaluation to determine its significance  204  to the identity  218  of the individual  102 , the evaluation time  210  may be terminated if the individual  102  expressly indicates that the fact  112  has significance  204  to the identity  218  of the individual  102 , and/or if the individual  102  expresses a disavowal of the fact  112  as having significance  204  to the identity  218  of the individual  102 . The fact  112  may be accordingly added to the individual profile  110  and/or discarded from storage  206 , even if the evaluation duration  210  from the detection time  202  has not yet elapsed. Alternatively or additionally, after a fact  112  has been added to the individual profile  110 , the fact  112  may be excluded from the individual profile  110  if the individual  102  expresses a disavowal of the fact  112 . 
         [0060]    As a second variation of this fourth aspect, an embodiment may adjust the evaluation duration  210  according to a confidence of the significance  204  of the fact  112  to the identity  218  of the individual  102 . For example, if a fact  112  in storage  206  is determined to have a significance  204  that remains consistently low during the evaluation period, the evaluation duration  210  may be shortened. Conversely, if a fact  112  in storage  206  is determined to have a steadily progressing significance  204  that is approaching the significance threshold  208 , or has a significance  204  that is near the significance threshold  208  as the evaluation duration  210  nears completion, the evaluation duration  210  may be extended to provide additional evaluation time. 
         [0061]    As a third variation of this fourth aspect, the individual profile  110  may include a fact limit (e.g., the presentation of no more than ten facts  112  about the individual  102 ). If a fact count of facts  112  in the individual profile  110  exceeds the fact limit, an embodiment may remove one or more facts having a lower significance  204  to the identity  218  of the individual  102  than other facts  112  of the individual profile  110 . This variation may enable facts  112  having high significance  204  to the identity  218  of the individual  102  to replace facts  112  having lower significance  204  to the identity  218  of the individual  102 , e.g., as the identity  218  of the individual  102  changes and/or as new information about the individual  102  is detected. 
         [0062]    As a fourth variation of this fourth aspect, after adding a fact  112  to the individual profile  110  of an individual  102 , an embodiment may continue to monitor the significance  204  of the fact  112  to the identity  218  of the individual  102 . Upon detecting that the significance  204  of the fact  112  to the identity  218  of the individual  102  has diminished below the significance threshold  208  (e.g., determining that an interest  108  of the individual  102  has faded to the point where the interest  108  no longer has significance  204  to the identity  218  of the individual  102 ), the embodiment may remove the fact  112  from the individual profile  110  of the individual  102 . 
         [0063]      FIG. 9  presents an illustration of an example scenario  900  wherein the significance  204  of an activity  116  to an identity  218  of an individual  102  is monitored over time. In this example scenario  900 , at a first time  904 , a frequency  604  of the activity  116  performed by the individual  102  is determined (e.g., the individual  102  hikes during 60% of free weekends), and may be construed as a high significance  204  of the fact  112  that the individual  102  enjoys the activity  116  of hiking. Accordingly, the fact  112  may be added to the individual profile  110  of the individual  102 . At a second time  906  after the first time  904 , the frequency  604  of the activity  116  may be determined to be diminishing and therefore indicating a lower significance  204  of the fact  112  to the identity  218  of the individual  102 . At a third time  908  after the second time  906 , the frequency  604  of the activity  116  may be determined to have diminished to a point where the individual  102  seldom performs the activity  116 , indicating a low significance  204  of the fact  112  to the identity  218  of the individual  102 , and prompting an exclusion  902  of the fact  112  from the individual profile  110  of the individual  102 . 
         [0064]    As a fifth variation of this fourth aspect, techniques may be utilized to reconcile conflicting facts  112  about the individual  102 . For example, an embodiment may detect that an individual  102  has expressed interest  108  in each of two rival sports teams, or in each of two opposite political parties. An embodiment may initiate monitoring the significance  204  of each fact  112  to determine which fact  112  is more representative of the identity  218  of the individual  102 , and may adjust the individual profile  110  according to such determination. As one such example, an embodiment simply present to the individual a request to confirm the conflicting fact  112  that conflicts with a first fact  112 , and upon receiving confirmation of the conflicting fact  112 , the embodiment may exclude the first fact  112  from the individual profile  110  of the individual  102 . Many such techniques may be utilized to determine which facts  112  to include in the individual profile  110  of the individual  102  in accordance with the techniques presented herein. 
       F. Computing Environment 
       [0065]      FIG. 10  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. 10  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. 
         [0066]    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. 
         [0067]      FIG. 10  illustrates an example of a system  1000  comprising a computing device  1002  configured to implement one or more embodiments provided herein. In one configuration, computing device  1002  includes at least one processing unit  1006  and memory  1008 . Depending on the exact configuration and type of computing device, memory  1008  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. 10  by dashed line  1004 . 
         [0068]    In other embodiments, device  1002  may include additional features and/or functionality. For example, device  1002  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. 10  by storage  1010 . In one embodiment, computer readable instructions to implement one or more embodiments provided herein may be in storage  1010 . Storage  1010  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  1008  for execution by processing unit  1006 , for example. 
         [0069]    The term “computer readable media” as used herein includes computer-readable memory devices that exclude other forms of computer-readable media comprising communications media, such as signals. Such computer-readable 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. Memory  1008  and storage  1010  are examples of computer storage media. Computer-storage devices include, but are not limited to, 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. 
         [0070]    Device  1002  may also include communication connection(s)  1016  that allows device  1002  to communicate with other devices. Communication connection(s)  1016  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  1002  to other computing devices. Communication connection(s)  1016  may include a wired connection or a wireless connection. Communication connection(s)  1016  may transmit and/or receive communication media. 
         [0071]    The term “computer readable media” may include communication media. 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 a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. 
         [0072]    Device  1002  may include input device(s)  1014  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)  1012  such as one or more displays, speakers, printers, and/or any other output device may also be included in device  1002 . Input device(s)  1014  and output device(s)  1012  may be connected to device  1002  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)  1014  or output device(s)  1012  for computing device  1002 . 
         [0073]    Components of computing device  1002  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  1002  may be interconnected by a network. For example, memory  1008  may be comprised of multiple physical memory units located in different physical locations interconnected by a network. 
         [0074]    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  920  accessible via network  1018  may store computer readable instructions to implement one or more embodiments provided herein. Computing device  1002  may access computing device  920  and download a part or all of the computer readable instructions for execution. Alternatively, computing device  1002  may download pieces of the computer readable instructions, as needed, or some instructions may be executed at computing device  1002  and some at computing device  920 . 
       G. Usage of Terms 
       [0075]    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. 
         [0076]    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. 
         [0077]    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. 
         [0078]    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 computer readable media, which if executed by a computing device, will cause 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. 
         [0079]    Any aspect or design described herein as an “example” is not necessarily to be construed as advantageous over other aspects or designs. Rather, use of the word “example” is intended to present one possible aspect and/or implementation that may pertain to the techniques presented herein. Such examples are not necessary for such techniques or intended to be limiting. Various embodiments of such techniques may include such an example, alone or in combination with other features, and/or may vary and/or omit the illustrated example. 
         [0080]    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. 
         [0081]    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 example 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.”

Technology Classification (CPC): 6