Patent Publication Number: US-8127002-B2

Title: Hypothesis development based on user and sensing device data

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application is related to and claims the benefit of the earliest available effective filing date(s) from the following listed application(s) (the “Related Applications”) (e.g., claims earliest available priority dates for other than provisional patent applications or claims benefits under 35 USC §119(e) for provisional patent applications, for any and all parent, grandparent, great-grandparent, etc. applications of the Related Application(s)). All subject matter of the Related Applications and of any and all parent, grandparent, great-grandparent, etc. applications of the Related Applications is incorporated herein by reference to the extent such subject matter is not inconsistent herewith. 
     RELATED APPLICATIONS 
     For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/313,659, entitled CORRELATING SUBJECTIVE USER STATES WITH OBJECTIVE OCCURRENCES ASSOCIATED WITH A USER, naming Shawn P. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors, filed 21 Nov. 2008, now U.S. Pat. No. 8,046,455 which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. 
     For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/315,083, entitled CORRELATING SUBJECTIVE USER STATES WITH OBJECTIVE OCCURRENCES ASSOCIATED WITH A USER, naming Shawn P. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors, filed 26 Nov. 2008, now U.S. Pat. No. 8,005,948 which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. 
     For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/319,135, entitled CORRELATING DATA INDICATING AT LEAST ONE SUBJECTIVE USER STATE WITH DATA INDICATING AT LEAST ONE OBJECTIVE OCCURRENCE ASSOCIATED WITH A USER, naming Shawn P. Firminger; Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C. Leuthardt; Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D. Rinaldo, Jr.; Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood, Jr. as inventors, filed 31 Dec. 2008, now U.S. Pat. No. 7,937,465 which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. 
     For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/319,134, entitled CORRELATING DATA INDICATING AT LEAST ONE SUBJECTIVE USER STATE WITH DATA INDICATING AT LEAST ONE OBJECTIVE OCCURRENCE ASSOCIATED WITH A USER, naming Shawn P. Firminger; Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C. Leuthardt; Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D. Rinaldo, Jr.; Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood, Jr. as inventors, filed 31 Dec. 2008, now U.S. Pat. No. 7,945,632 which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. 
     For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/378,162, entitled SOLICITING DATA INDICATING AT LEAST ONE OBJECTIVE OCCURRENCE IN RESPONSE TO ACQUISITION OF DATA INDICATING AT LEAST ONE SUBJECTIVE USER STATE, naming Shawn P. Firminger; Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C. Leuthardt; Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D. Rinaldo, Jr.; Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood, Jr. as inventors, filed 9 Feb. 2009, now U.S. Pat. No. 8,028,063 which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. 
     For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/378,288, entitled SOLICITING DATA INDICATING AT LEAST ONE OBJECTIVE OCCURRENCE IN RESPONSE TO ACQUISITION OF DATA INDICATING AT LEAST ONE SUBJECTIVE USER STATE, naming Shawn P. Firminger; Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C. Leuthardt; Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D. Rinaldo, Jr.; Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood, Jr. as inventors, filed 11 Feb. 2009, now U.S. Pat. No. 8,032,628 which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. 
     For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/380,409, entitled SOLICITING DATA INDICATING AT LEAST ONE SUBJECTIVE USER STATE IN RESPONSE TO ACQUISITION OF DATA INDICATING AT LEAST ONE OBJECTIVE OCCURRENCE, naming Shawn P. Firminger; Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C. Leuthardt; Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D. Rinaldo, Jr.; Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood, Jr. as inventors, filed 25 Feb. 2009, now U.S. Pat. No. 8,010,662 which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. 
     For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/380,573, entitled SOLICITING DATA INDICATING AT LEAST ONE SUBJECTIVE USER STATE IN RESPONSE TO ACQUISITION OF DATA INDICATING AT LEAST ONE OBJECTIVE OCCURRENCE, naming Shawn P. Firminger; Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C. Leuthardt; Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D. Rinaldo, Jr.; Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood, Jr. as inventors, filed 26 Feb. 2009, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. 
     For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/383,581, entitled CORRELATING DATA INDICATING SUBJECTIVE USER STATES ASSOCIATED WITH MULTIPLE USERS WITH DATA INDICATING OBJECTIVE OCCURRENCES, naming Shawn P. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors, filed 24 Mar. 2009, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. 
     For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/383,817, entitled CORRELATING DATA INDICATING SUBJECTIVE USER STATES ASSOCIATED WITH MULTIPLE USERS WITH DATA INDICATING OBJECTIVE OCCURRENCES, naming Shawn P. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors, filed 25 Mar. 2009, now U.S. Pat. No. 8,010,663 which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. 
     For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/384,660, entitled HYPOTHESIS BASED SOLICITATION OF DATA INDICATING AT LEAST ONE SUBJECTIVE USER STATE, naming Shawn P. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors, filed 6 Apr. 2009, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. 
     For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/384,779, entitled HYPOTHESIS BASED SOLICITATION OF DATA INDICATING AT LEAST ONE SUBJECTIVE USER STATE, naming Shawn P. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors, filed 7 Apr. 2009, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. 
     For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/387,487, entitled HYPOTHESIS BASED SOLICITATION OF DATA INDICATING AT LEAST ONE OBJECTIVE OCCURRENCE, naming Shawn P. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors, filed 30 Apr. 2009, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. 
     For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/387,465, entitled HYPOTHESIS BASED SOLICITATION OF DATA INDICATING AT LEAST ONE OBJECTIVE OCCURRENCE, naming Shawn P. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors, filed 30 Apr. 2009, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. 
     For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/455,309, entitled HYPOTHESIS DEVELOPMENT BASED ON SELECTIVE REPORTED EVENTS, naming Shawn P. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors, filed 28 May 2009, now U.S. Pat. No. 8,010,664 which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. 
     For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/455,317, entitled HYPOTHESIS DEVELOPMENT BASED ON SELECTIVE REPORTED EVENTS, naming Shawn P. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors, filed 29 May 2009, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. 
     For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/456,249, entitled HYPOTHESIS SELECTION AND PRESENTATION OF ONE OR MORE ADVISORIES, naming Shawn P. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors, filed 12 Jun. 2009, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. 
     For purposes of the USPTO extra-statutory requirements, the present application constitutes a continuation-in-part of U.S. patent application Ser. No. 12/456,433, entitled HYPOTHESIS SELECTION AND PRESENTATION OF ONE OR MORE ADVISORIES, naming Shawn P. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, Eric C. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D. Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L. Wood, Jr., as inventors, filed 15 Jun. 2009, which is currently co-pending, or is an application of which a currently co-pending application is entitled to the benefit of the filing date. 
     The United States Patent Office (USPTO) has published a notice to the effect that the USPTO&#39;s computer programs require that patent applicants reference both a serial number and indicate whether an application is a continuation or continuation-in-part. Stephen G. Kunin,  Benefit of Prior - Filed Application , USPTO Official Gazette Mar. 18, 2003, available at http://www.uspto.gov/web/offices/com/sol/og/2003/week11/patbene.htm. The present Applicant Entity (hereinafter “Applicant”) has provided above a specific reference to the application(s) from which priority is being claimed as recited by statute. Applicant understands that the statute is unambiguous in its specific reference language and does not require either a serial number or any characterization, such as “continuation” or “continuation-in-part,” for claiming priority to U.S. patent applications. Notwithstanding the foregoing, Applicant understands that the USPTO&#39;s computer programs have certain data entry requirements, and hence Applicant is designating the present application as a continuation-in-part of its parent applications as set forth above, but expressly points out that such designations are not to be construed in any way as any type of commentary and/or admission as to whether or not the present application contains any new matter in addition to the matter of its parent application(s). 
     All subject matter of the Related Applications and of any and all parent, grandparent, great-grandparent, etc. applications of the Related Applications is incorporated herein by reference to the extent such subject matter is not inconsistent herewith. 
    
    
     SUMMARY 
     A computationally implemented method includes, but is not limited to acquiring a first data indicating at least one reported event as originally reported by a user and a second data indicating at least a second reported event as originally reported by one or more sensing devices; and developing a hypothesis based, at least in part, on the first data and the second data. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure. 
     In one or more various aspects, related systems include but are not limited to circuitry and/or programming for effecting the herein-referenced method aspects; the circuitry and/or programming can be virtually any combination of hardware, software, and/or firmware configured to effect the herein-referenced method aspects depending upon the design choices of the system designer. 
     A computationally implemented system includes, but is not limited to: means for acquiring a first data indicating at least one reported event as originally reported by a user and a second data indicating at least a second reported event as originally reported by one or more sensing devices; and means for developing a hypothesis based, at least in part, on the first data and the second data. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure. 
     A computationally implemented system includes, but is not limited to: circuitry for acquiring a first data indicating at least one reported event as originally reported by a user and a second data indicating at least a second reported event as originally reported by one or more sensing devices; and circuitry for developing a hypothesis based, at least in part, on the first data and the second data. In addition to the foregoing, other system aspects are described in the claims, drawings, and text forming a part of the present disclosure. 
     A computer program product including a signal-bearing medium bearing one or more instructions acquiring a first data indicating at least one reported event as originally reported by a user and a second data indicating at least a second reported event as originally reported by one or more sensing devices; and one or more instructions for developing a hypothesis based, at least in part, on the first data and the second data. In addition to the foregoing, other computer program product aspects are described in the claims, drawings, and text forming a part of the present disclosure. 
     The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
         FIGS. 1   a  and  1   b  show a high-level block diagram of a computing device  10  operating in a network environment. 
         FIG. 2   a  shows another perspective of the events data acquisition module  102  of the computing device  10  of  FIG. 1   b.    
         FIG. 2   b  shows another perspective of the hypothesis development module  104  of the computing device  10  of  FIG. 1   b.    
         FIG. 2   c  shows another perspective of the action execution module  106  of the computing device  10  of  FIG. 1   b.    
         FIG. 2   d  shows another perspective of the one or more sensing devices  35   a  and/or  35   b  of  FIGS. 1   a  and  1   b.    
         FIG. 3  is a high-level logic flowchart of a process. 
         FIG. 4   a  is a high-level logic flowchart of a process depicting alternate implementations of the data acquisition operation  302  of  FIG. 3 . 
         FIG. 4   b  is a high-level logic flowchart of a process depicting alternate implementations of the data acquisition operation  302  of  FIG. 3 . 
         FIG. 4   c  is a high-level logic flowchart of a process depicting alternate implementations of the data acquisition operation  302  of  FIG. 3 . 
         FIG. 4   d  is a high-level logic flowchart of a process depicting alternate implementations of the data acquisition operation  302  of  FIG. 3 . 
         FIG. 4   e  is a high-level logic flowchart of a process depicting alternate implementations of the data acquisition operation  302  of  FIG. 3 . 
         FIG. 4   f  is a high-level logic flowchart of a process depicting alternate implementations of the data acquisition operation  302  of  FIG. 3 . 
         FIG. 4   g  is a high-level logic flowchart of a process depicting alternate implementations of the data acquisition operation  302  of  FIG. 3 . 
         FIG. 4   h  is a high-level logic flowchart of a process depicting alternate implementations of the data acquisition operation  302  of  FIG. 3 . 
         FIG. 4   i  is a high-level logic flowchart of a process depicting alternate implementations of the data acquisition operation  302  of  FIG. 3 . 
         FIG. 4   j  is a high-level logic flowchart of a process depicting alternate implementations of the data acquisition operation  302  of  FIG. 3 . 
         FIG. 4   k  is a high-level logic flowchart of a process depicting alternate implementations of the data acquisition operation  302  of  FIG. 3 . 
         FIG. 4   l  is a high-level logic flowchart of a process depicting alternate implementations of the data acquisition operation  302  of  FIG. 3 . 
         FIG. 4   m  is a high-level logic flowchart of a process depicting alternate implementations of the data acquisition operation  302  of  FIG. 3 . 
         FIG. 5   a  is a high-level logic flowchart of a process depicting alternate implementations of the hypothesis development operation  304  of  FIG. 3 . 
         FIG. 5   b  is a high-level logic flowchart of a process depicting alternate implementations of the hypothesis development operation  304  of  FIG. 3 . 
         FIG. 5   c  is a high-level logic flowchart of a process depicting alternate implementations of the hypothesis development operation  304  of  FIG. 3 . 
         FIG. 6  is a high-level logic flowchart of another process. 
         FIG. 7   a  is a high-level logic flowchart of a process depicting alternate implementations of the action execution operation  606  of  FIG. 6 . 
         FIG. 7   b  is a high-level logic flowchart of a process depicting alternate implementations of the action execution operation  606  of  FIG. 6 . 
         FIG. 7   c  is a high-level logic flowchart of a process depicting alternate implementations of the action execution operation  606  of  FIG. 6 . 
         FIG. 7   d  is a high-level logic flowchart of a process depicting alternate implementations of the action execution operation  606  of  FIG. 6 . 
     
    
    
     DETAILED DESCRIPTION 
     In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented here. 
     A recent trend that is becoming increasingly popular in the computing/communication field is to electronically record one&#39;s feelings, thoughts, and other aspects of the person&#39;s everyday life onto an open diary. One place where such open diaries are maintained are at social networking sites commonly known as “blogs” where users may report or post their latest status, personal activities, and various other aspects of the users&#39; everyday life. The process of reporting or posting blog entries is commonly referred to as blogging. Other social networking sites may allow users to update their personal information via, for example, social networking status reports in which a user may report or post for others to view their current status, activities, and/or other aspects of the user. 
     A more recent development in social networking is the introduction and explosive growth of microblogs in which individuals or users (referred to as “microbloggers”) maintain open diaries at microblog websites (e.g., otherwise known as “twitters”) by continuously or semi-continuously posting microblog entries. A microblog entry (e.g., “tweet”) is typically a short text message that is usually not more than 140 characters long. The microblog entries posted by a microblogger may report on any aspect of the microblogger&#39;s daily life. Typically, such microblog entries will describe the various “events” associated with or are of interest to the microblogger that occurs during a course of a typical day. The microblog entries are often continuously posted during the course of a typical day, and thus, by the end of a normal day, a substantial number of events may have been reported and posted. 
     Each of the reported events that may be posted through microblog entries may be categorized into one of at least three possible categories. The first category of events that may be reported through microblog entries are “objective occurrences” that may or may not be associated with the microblogger. Objective occurrences that are associated with a microblogger may be any characteristic, incident, happening, or any other event that occurs with respect to the microblogger or are of interest to the microblogger that can be objectively reported by the microblogger, a third party, or by a device. Such events would include, for example, intake of food, medicine, or nutraceutical, certain physical characteristics of the microblogger or by others such as blood sugar level or blood pressure that can be objectively measured, activities of the microblogger objectively observable by the microblogger, by others, or by a device, activities of others that may be objectively observed by the microblogger, by others, or by a device, external events such as performance of the stock market (which the microblogger may have an interest in), performance of a favorite sports team, and so forth. 
     In some cases, objective occurrences may not be at least directly associated with a microblogger. Examples of such objective occurrences include, for example, external events such as the local weather, activities of others (e.g., spouse or boss), the behavior or activities of a pet or livestock, the characteristics or performances of mechanical or electronic devices such as automobiles, appliances, and computing devices, and other events that may directly or indirectly affect the microblogger. 
     A second category of events that may be reported or posted through microblog entries include “subjective user states” of the microblogger. Subjective user states of a microblogger may include any subjective state or status associated with the microblogger that can only be typically reported by the microblogger (e.g., generally cannot be directly reported by a third party or by a device). Such states including, for example, the subjective mental state of the microblogger (e.g., happiness, sadness, anger, tension, state of alertness, state of mental fatigue, jealousy, envy, and so forth), the subjective physical state of the microblogger (e.g., upset stomach, state of vision, state of hearing, pain, and so forth), and the subjective overall state of the microblogger (e.g., “good,” “bad,” state of overall wellness, overall fatigue, and so forth). Note that the term “subjective overall state” as will be used herein refers to those subjective states that may not fit neatly into the other two categories of subjective user states described above (e.g., subjective mental states and subjective physical states). 
     A third category of events that may be reported or posted through microblog entries include “subjective observations” made by the microblogger. A subjective observation is similar to subjective user states and may be any subjective opinion, thought, or evaluation relating to any external incidence (e.g., outward looking instead of inward looking as in the case of subjective user states). Thus, the difference between subjective user states and subjective observations is that subjective user states relates to self-described subjective descriptions of the user states of one&#39;s self while subjective observations relates to subjective descriptions or opinions regarding external events. Examples of subjective observations include, for example, a microblogger&#39;s perception about the subjective user state of another person (e.g., “he seems tired”), a microblogger&#39;s perception about another person&#39;s activities (e.g., “he drank too much yesterday”), a microblogger&#39;s perception about an external event (e.g., “it was a nice day today”), and so forth. Although microblogs are being used to provide a wealth of personal information, thus far they have been primarily limited to their use as a means for providing commentaries and for maintaining open diaries. 
     Another potential source for valuable but not yet fully exploited data is the data provided by sensing devices that are used to sense and/or monitor various aspects of everyday life. Currently there are a number of sensing devices that can detect and/or monitor various user related and nonuser related events. For example, there are presently a number of sensing devices that can sense various physical or physiological characteristics of a person or an animal (e.g., a pet or livestock). Examples of such devices include commonly known and used monitoring devices such as blood pressure devices, heart rate monitors, blood glucose sensors (e.g., glucometers), respiration sensor devices, temperature sensors, and so forth. Other examples of devices that can monitor physical or physiological characteristics include more exotic and sophisticated devices such as functional magnetic resonance imaging (fMRI) device, functional Near Infrared (fNIR) devices, blood cell-sorting sensing device, and so forth. Many of these devices are becoming more compact and less expensive such that they are becoming increasingly accessible for purchase and/or self-use by the general public. 
     Other sensing devices may be used in order to sense and monitor activities of a person or an animal. These would include, for example, global positioning systems (GPS), pedometers, accelerometers, and so forth. Such devices are compact and can even be incorporated into, for example, a mobile communication device such a cellular telephone or on the collar of a pet. Other sensing devices for monitoring activities of individuals (e.g., users) may be incorporated into larger machines and may be used in order to monitor the usage of the machines by the individuals. These would include, for example, sensors that are incorporated into exercise machines, automobiles, bicycles, and so forth. Today there are even toilet monitoring devices that are available to monitor the toilet usage of individuals. 
     Other sensing devices are also available that can monitor general environmental conditions such as environmental temperature sensor devices, humidity sensor devices, barometers, wind speed monitors, water monitoring sensors, air pollution sensor devices (e.g., devices that can measure the amount of particulates in the air such as pollen, those that measure CO 2  levels, those that measure ozone levels, and so forth). Other sensing devices may be employed in order to monitor the performance or characteristics of mechanical and/or electronic devices. All the above described sensing devices may provide useful data that may indicate objectively observable events (e.g., objective occurrences). 
     In accordance with various embodiments, robust methods, systems, and computer program products are provided to, among other things, acquiring events data indicating multiple events as originally reported by multiple sources including acquiring at least a first data indicating at least one reported event as originally reported by a user and a second data indicating at least a second reported event as originally reported by one or more sensing devices. The methods, systems, and computer program products may then develop a hypothesis based, at least in part, on the first data and the second data. In some embodiments, one or more actions may be executed based, at least in part, on the developed hypothesis. Examples of the types of actions that may be executed include, for example, the presentation of the developed hypothesis or advisories relating to the developed hypothesis. Other actions that may be executed include the prompting of mechanical and/or electronic devices to execute one or more operations based, at least in part, on the developed hypothesis. 
     The robust methods, systems, and computer program products may be employed in a variety of environments including, for example, social networking environments, blogging or microblogging environments, instant messaging (IM) environments, or any other type of environment that allows a user to, for example, maintain a diary. 
     In various implementations, a “hypothesis,” as referred to herein, may define one or more relationships or links between different types of events (i.e., event types) including at least a first event type (e.g., a type of event such as a particular type of subjective user state including, for example, a subjective mental state such as “happy”) and a second event type (e.g., another type of event such as a particular type of objective occurrence, for example, favorite sports team winning a game). In some cases, a hypothesis may be represented by an events pattern that may indicate spatial or sequential relationships between different event types (e.g., different types of events such as subjective user states and objective occurrences). In some embodiments, a hypothesis may be further defined by an indication of the soundness (e.g., strength) of the hypothesis. 
     Note that for ease of explanation and illustration, the following description will describe a hypothesis as defining, for example, the sequential or spatial relationship between two different event types, for example, a first event type and a second event type. However, those skilled in the art will recognize that such a hypothesis may also identify the relationships between three or more event types (e.g., a first event type, a second event type, a third event type, and so forth). 
     In some embodiments, a hypothesis may, at least in part, be defined or represented by an events pattern that indicates or suggests a spatial or a sequential (e.g., time/temporal) relationship between different event types. Such a hypothesis, in some cases, may also indicate the strength or weakness of the link between the different event types. That is, the strength or weakness (e.g., soundness) of the correlation between different event types may depend upon, for example, whether the events pattern repeatedly occurs and/or whether a contrasting events pattern has occurred that may contradict the hypothesis and therefore, weaken the hypothesis (e.g., an events pattern that indicates a person becoming tired after jogging for thirty minutes when a hypothesis suggests that a person will be energized after jogging for thirty minutes). 
     As briefly described above, a hypothesis may be represented by an events pattern that may indicate spatial or sequential (e.g., time or temporal) relationship or relationships between multiple event types. In some implementations, a hypothesis may merely indicate temporal sequential relationships between multiple event types that indicate the temporal relationships between multiple event types. In alternative implementations a hypothesis may indicate a more specific time relationship between multiple event types. For example, a sequential pattern may represent the specific pattern of events that occurs along a timeline that may indicate the specific time intervals between event types. In still other implementations, a hypothesis may indicate the spatial (e.g., geographical) relationships between multiple event types. 
     In various embodiments, the development of a hypothesis may be particularly useful to a user (e.g., a microblogger or a social networking user) that the hypothesis may or may not be directly associated with. That is, in some embodiments, a hypothesis may be developed that directly relates to a user. Such a hypothesis may relate to, for example, one or more subjective user states associated with the user, one or more activities associated with the user, or one or more characteristics associated with the user. In other embodiments, however, a hypothesis may be developed that may not be directly associated with a user. For example, a hypothesis may be developed that may be particularly associated with an acquaintance of the user, a pet, or a device operated or used by the user. 
     In some embodiments, the development of a hypothesis may assist a user in modifying his/her future behavior, while in other embodiments, such a hypothesis may be useful to third parties such as other users or nonusers, or even to advertisers in order to assist the advertisers in developing a more targeted marketing scheme. In still other situations, the development of a hypothesis relating to a user may help in the treatment of ailments associated with the user. 
     In some embodiments, a hypothesis may be developed (e.g., creating and/or further refinement of a hypothesis) by determining a pattern of reported events that repeatedly occurs and/or to compare similar or dissimilar reported pattern of events. For example, if a user such as a microblogger reports repeatedly that after each visit to a particular restaurant, the user always has an upset stomach, then a hypothesis may be created and developed that suggests that the user will get an upset stomach after visiting the particular restaurant. Note that such events may be based on reported data originally provided by two different sources, the user who reports having a stomach ache, and a sensing device such as a GPS device that reports data that indicates the user&#39;s visit to the restaurant just prior to the user reporting the occurrence of the stomach ache. 
     If, on the other hand, after developing such a hypothesis, the GPS device reports data that indicates that the user visited the same restaurant again but after the second visit the user reports feeling fine, then the reported data provided by the GPS device and the data provided by the user during and/or after the second visit may result in the weakening of the hypothesis (e.g., the second visit contradicts the hypothesis that a stomach ache is associated with visiting the restaurant). Alternatively, if after developing such a hypothesis, the GPS device and the user reports that in a subsequent visit to the restaurant, the user again got an upset stomach, then such reporting, as provided by both the user and the GPS device, may result in a confirmation of the soundness of the hypothesis. 
     In various embodiments, other types of hypothesis may be developed that may not be directly related to a user. For instance, a user (e.g., a person) and one or more sensing devices may report on the various characteristics, activities, and/or behaviors of a friend, a spouse, a pet, or even a mechanical or electronic device that the user may have an interest in. Based on such reported data, one or more hypothesis may be developed that may not be directly related to the user. 
     Thus, in accordance with various embodiments, robust methods, systems, and computer program products are provided that may be designed to, among other things, acquire events data indicating multiple events as originally reported by multiple sources including at least a first data indicating at least one reported event as originally reported by a user and a second data indicating at least a second reported event originally reported by one or more sensing devices. Based on the at least one reported event as indicated by the acquired first data and the at least second reported event as indicated by the second data, a hypothesis may be developed. In various embodiments, such a hypothesis may be related to, for example, the user, a third party (e.g., another user or nonuser, or a nonhuman living organism such as a pet or livestock), a mechanical and/or electronic device, the environment, or any other entity or item that may be relevant to the user. Note that the phrase “as originally reported” is used herein since the first data and the second data indicating the at least one reported event and the at least second reported event may be obtained from other sources other than their original sources (e.g., the user and the one or more sensing devices). 
       FIGS. 1   a  and  1   b  illustrate an example environment in accordance with various embodiments. In the illustrated environment, an exemplary system  100  may include at least a computing device  10  (see  FIG. 1   b ). The computing device  10 , which may be a server (e.g., network server) or a standalone device, may be designed to, among other things, acquire events data that indicates multiple reported events originally reported by different sources. For example, in some implementations, the events data to be acquired by the computing device  10  may include at least a first data  60  indicating at least one reported event as originally reported by a user  20 * and a second data  61  indicating at least a second reported event as originally reported by one or more sensing devices  35 *. In some embodiments, the computing device  10  may further acquire a third data  62  indicating at least a third reported event as originally reported by a third party  50  and/or a fourth data  63  indicating at least a fourth reported event as originally reported by another one or more sensing devices  35 *. 
     Based at least on the reported events as indicated by the acquired first data  60  and the second data  61  (and in some cases, based further on the reported events indicated by the third data  62  and/or the fourth data  63 ), a hypothesis may be developed by the computing device  10 . In some embodiments, one or more actions may be executed by the computing device  10  in response at least in part to the development of the hypothesis. In the following, “*” indicates a wildcard. Thus, references to user  20 * may indicate a user  20   a  or a user  20   b  of  FIGS. 1   a  and  1   b . Similarly, references to sensing devices  35 * may be a reference to sensing devices  35   a  or sensing devices  35   b  of  FIGS. 1   a  and  1   b.    
     As indicated earlier, in some embodiments, the computing device  10  may be a server while in other embodiments the computing device  10  may be a standalone device. In the case where the computing device  10  is a network server, the computing device  10  may communicate indirectly with a user  20   a , one or more third parties  50 , and one or more sensing devices  35   a  via wireless and/or wired network  40 . The wireless and/or wired network  40  may comprise of, for example, a local area network (LAN), a wireless local area network (WLAN), personal area network (PAN), Worldwide Interoperability for Microwave Access (WiMAX), public switched telephone network (PTSN), general packet radio service (GPRS), cellular networks, and/or other types of wires or wired networks. In contrast, in embodiments where the computing device  10  is a standalone device, the computing device  10  may communicate directly at least with a user  20   b  (e.g., via a user interface  122 ) and one or more sensing devices  35   b . In embodiments in which the computing device  10  is a standalone device, the computing device  10  may also communicate indirectly with one or more third parties  50  and one or more sensing devices  35   a  via a wireless and/or wired network  40 . 
     In embodiments in which the computing device  10  is a network server (or simply “server”); the computing device  10  may communicate with a user  20   a  through a wireless and/or wired network  40  and via a mobile device  30 . A network server, as will be described herein, may be in reference to a server located at a single network site or located across multiple network sites or a conglomeration of servers located at multiple network sites. The mobile device  30  may be a variety of computing/communication devices including, for example, a cellular phone, a personal digital assistant (PDA), a laptop, a desktop, or other types of computing/communication devices that can communicate with the computing device  10 . In some embodiments, the mobile device  30  may be a handheld device such as a cellular telephone, a smartphone, a Mobile Internet Device (MID), an Ultra Mobile Personal Computer (UMPC), a convergent device such as a personal digital assistant (PDA), and so forth. 
     In embodiments in which the computing device  10  is a standalone device that may communicate directly with a user  20   b , the computing device  10  may be any type of portable device (e.g., a handheld device) or non-portable device (e.g., desktop computer or workstation). For these embodiments, the computing device  10  may be any one of a variety of computing/communication devices including, for example, a cellular phone, a personal digital assistant (PDA), a laptop, a desktop, or other types of computing/communication devices. In some embodiments, in which the computing device  10  is a handheld device, the computing device  10  may be a cellular telephone, a smartphone, an MID, an UMPC, a convergent device such as a PDA, and so forth. In various embodiments, the computing device  10  may be a peer-to-peer network component device. In some embodiments, the computing device  10  and/or the mobile device  30  may operate via a Web 2.0 construct (e.g., Web 2.0 application  268 ). 
     In some implementations, in order to acquire the first data  60  and/or the second data  61 , the computing device  10  may be designed to prompt the user  20 * and/or the one or more sensing devices  35 * (e.g., transmitting or indicating a request or an inquiry to the user  20 * and/or the one or more sensing device  35 *) to report occurrences of the first reported event and/or the second reported event as indicated by refs.  22  and  23 . In alternative implementations, however, the computing device  10  may be designed to, rather than prompting the user  20 * and/or the one or more sensors  35 *, prompt one or more network devices such as the mobile device  30  and/or one or more network servers  36  in order to acquire the first data  60  and/or the second data  61 . That is, in some cases, the user  20 * and/or the one or more sensors  35 * may already have previously provided the first data  60  and/or the second data  61  to one or more of the network devices (e.g., mobile device  30  and/or network servers  36 ). 
     Each of the reported events indicated by the first data  60  and/or the second data  61  may or may not be directly associated with a user  20 *. For example, although each of the reported events may have been originally reported by the user  20 * or by the one or more sensing devices  35 *, the reported events (e.g., at least the one reported event as indicated by the first data  60  and the at least second reported event as indicated by the second data  61 ) may be, in some implementations, related or associated with one or more third parties (e.g., another user, a nonuser, or a nonhuman living organism such as a pet dog or livestock), one or more devices  55  (e.g., electronic and/or mechanical devices), or one or more aspects of the environmental (e.g., the quality of the local drinking water, local weather conditions, and/or atmospheric conditions). For example, when providing the first data  60 , a user  20 * may report on the perceptions made by the user  20 * regarding the behavior or activities of a third party (e.g., another user or a pet) rather than the behavior or activities of the user  20 * him or herself. 
     As previously described, a user  20 * may at least be the original source for the at least one reported event as indicated by the first data  60 . The at least one reported event as indicated by the first data  60  may indicate any one or more of a variety of possible events that may be reported by the user  20 *. For example, and as will be explained in greater detail herein, the at least one reported event as indicated by the first data  60  may relate to at least a subjective user state (e.g., a subjective mental state, a subjective physical state, or a subjective overall state) of the user  20 *, a subjective observation (e.g., the perceived subjective user state of a third party  50  as perceived by user  20 *, the perceived activity of a third party  50  or the user  20 * as perceived by the user  20 *, the perceived performance or characteristic of a device  55  as perceived by the user  20 *, the perceived occurrence of an external event as perceived by the user  20 * such as the weather, and so forth), or an objective occurrence (e.g., objectively observable activities of the user  20 *, a third party  50 , or a device  55 ; objectively observable physical or physiological characteristics of the user  20 * or a third party  50 ; objective observable external events including environmental events or characteristics of a device  55 ; and so forth). 
     In contrast, the at least second reported event as originally reported by one or more sensing devices  35 * and indicated by the second data  61  may be related to an objective occurrence that may be objectively observed by the one or more sensing devices  35 *. Examples of the type of objective occurrences that may be indicated by the second data  61  includes, for example, physical or physiological characteristics of the user  20 * or a third party  50 , selective activities of the user  20 * or a third party  50 , some external events such as environmental conditions (e.g., atmospheric temperature and humidity, air quality, and so forth), characteristics and/or operational activities of a device  35 , geographic location of the user  20 * or a third party  50 , and so forth.  FIGS. 1   a  and  1   b  show the one or more sensing device  35 * detecting or sensing various aspects of a user  20 *, one or more third parties  50 , or one or more device  55  as indicated by ref.  29 . As will be described in greater detail herein, the one or more sensing devices  35 * may include one or more different types of sensing devices (see  FIG. 2   d ) that are capable of sensing objective occurrences. 
     After acquiring the events data including the first data  60  indicating the at least one reported event as originally reported by a user  20 * and the second data  61  indicating the at least second reported event as originally reported by one or more sensing devices  35 *, the computing device may be designed to develop a hypothesis. In various embodiments, the computing device  10  may develop a hypothesis by creating a new hypothesis based on the acquired events data and/or by refining an already existing hypothesis  80 , which in some cases, may be stored in a memory  140 . 
     After developing a hypothesis, the computing device  10  may be designed to execute one or more actions in response, at least in part, to the development of the hypothesis. One such action that may be executed is to present (e.g., transmit via a wireless and/or wired network  40  and/or indicate via user interface  122 ) one or more advisories  90  that may be related to the developed hypothesis. For example, in some implementations, the computing device  10  may present the developed hypothesis itself, or present an advisory such as a alert regarding reported past events or a recommendation for a future action to a user  20 *, to one or more third parties  50 , and/or to one or more remote network devices (e.g., network servers  36 ). In other implementations, or in the same implementations, the computing device  10  may prompt (e.g., as indicated by ref.  25 ) one or more devices  55  (e.g., an automobile or a portion thereof, a household appliance or a portion thereof, a computing or communication device or a portion thereof, and so forth) to execute one or more operations. 
     Turning now to  FIG. 1   b , the computing device  10  may include one or more components and/or sub-modules. As those skilled in the art will recognize, these components and sub-modules may be implemented by employing hardware (e.g., in the form of circuitry such as application specific integrated circuit or ASIC, field programmable gate array or FPGA, or other types of circuitry), software, a combination of both hardware and software, or may be implemented by a general purpose computing device executing instructions included in a signal-bearing medium. In various embodiments, computing device  10  may include an events data acquisition module  102 , a hypothesis development module  104 , an action module  106 , a network interface  120  (e.g., network interface card or NIC), a user interface  122  (e.g., a display monitor, a touchscreen, a keypad or keyboard, a mouse, an audio system including a microphone and/or speakers, an image capturing system including digital and/or video camera, and/or other types of interface devices), one or more applications  126  (e.g., a web 2.0 application  268 , one or more communication applications  267  including, for example, a voice recognition application, and/or other applications), and/or memory  140 . In some implementations, memory  140  may include an existing hypothesis  80  and/or historical data  81 . Note that although not depicted, in various implementations, one or more copies of the one or more applications  126  may be included in memory  140 . 
     The events data acquisition module  102  of  FIG. 1   b  may be configured to, among other things, acquire events data indicating multiple reported events as reported by different sources. The events data to be acquired by the events data acquisition module  102  may include at least a first data  60  indicating at least one reported event as originally reported by a user  20 * and a second data  61  indicating at least a second reported event as originally reported by one or more sensing devices  35 *. In some implementations, the events data acquisition module  102  may be configured to further acquire a third data indicating at least a third reported event as originally reported by one or more third parties  50  and/or a fourth data indicating at least a fourth reported event as originally reported by another one or more sensing devices  35 *. 
     Referring now to  FIG. 2   a  illustrating particular implementations of the events data acquisition module  102  of the computing device  10  of  FIG. 1   b . The events data acquisition module  102  may include at least a first data acquisition module  201  configured to, among other things, acquire a first data  60  indicating at least one reported event that was originally reported by a user  20 * and a second data acquisition module  215  configured to, among other things, acquire a second data  61  indicating at least a second reported event that was originally reported by one or more sensing devices  35 *. In some implementations, the events data acquisition module  102  may further include a time element acquisition module  228  configured to acquire time elements associated with the reported events (e.g., the at least one reported event and the at least second reported event) and/or a spatial location indication acquisition module  234  configured to acquire spatial locations associated with reported events. 
     In various implementations, the first data acquisition module  201  may include one or more sub-modules. For example, in some implementations, such as in the case where the computing device  10  is a server, the first data acquisition module  201  may include a network interface reception module  202  configured to interface with a wireless and/or wired network  40  in order to receive the first data from a wireless and/or a wired network  40 . In some implementations, such as when the computing device  10  is a standalone device, the first data acquisition module  201  may include a user interface reception module  204  configured to receive the first data  60  through a user interface  122 . 
     In some instances, the first data acquisition module  201  may include a user prompting module  206  configured to prompt a user  20 * to report occurrence of an event. Such an operation may be needed in some cases when, for example, the computing device  10  is missing data (e.g., first data  60  indicating the at least one reported event) that may be needed in order to develop a hypothesis (e.g., refining an existing hypothesis  80 ). In order to implement its operations, the user prompting module  206  may include a requesting module  208  that may be configured to indicate (e.g., via a user interface  122 ) or transmit (e.g., via a wireless and/or wired network  40 ) a request to a user  20 * to report the occurrence of the event. The requesting module  208  may, in turn, include an audio requesting module  210  configured to audioally request (e.g., via one or more speakers) the user  20 * to report the occurrence of the event and/or a visual requesting module  212  configured to visually request (e.g., via a display monitor) the user  20 * to report the occurrence of the event. In some implementations, the first data acquisition module  201  may include a device prompting module  214  configured to, among other things, prompt a network device (e.g., a mobile device  30  or a network server  36 ) to provide the first data  60 . 
     Turning now to the second data acquisition module,  215 , the second data acquisition module  215  in various implementations may include one or more sub-modules. For example, in some implementations, the second data acquisition module  215  may include a network interface reception module  216  configured to interface with a wireless and/or wired network  40  in order to, for example, receive the second data  61  from at least one of a wireless and/or a wired network  40  and/or a sensing device reception module  218  configured to receive the second data  61  directly from the one or more sensing devices  35   b . In various implementations, the second data acquisition module  215  may include a device prompting module  220  configured to prompt the one or more sensing devices  35 * to provide the second data  61  (e.g., to report the second reported event). 
     In order to implement its functional operations, the device prompting module  220  in some implementations may further include one or more sub-modules including a sensing device directing/instructing module  222  configured to direct or instruct the one or more sensing devices  35 * to provide the second data  61  (e.g., to report the second reported event). In the same or different implementations, the device prompting module  220  may include a sensing device configuration module  224  designed to configure the one or more sensing devices  35 * to provide the second data  61  (e.g., to report the second reported event). In the same or different implementations, the device prompting module  220  may include a sensing device requesting module  226  configured to request the one or more sensing devices  35 * to provide the second data  61  (e.g., to report the second reported event). 
     In various implementations, the time element acquisition module  228  of the events data acquisition module  102  may include one or more sub-modules. For example, in some implementations, the time element acquisition module  228  may include a time stamp acquisition module  230  configured to acquire a first time stamp associated with the at least one reported event and a second time stamp associated with the at least second reported event. In the same or different implementations, the time element acquisition module  228  may include a time interval indication acquisition module  232  configured to acquire an indication of a first time interval associated with the at least one reported event and an indication of second time interval associated with the at least second reported event. 
     Referring back to  FIG. 1   b , the hypothesis development module  104  of  FIG. 1   b  may be configured to, among other things, develop a hypothesis based, at least in part, on the first data  60  and the second data  61  (e.g., the at least one reported event and the at least second reported event) acquired by the events data acquisition module  102 . In some embodiments, the hypothesis development module  104  may develop a hypothesis by creating a new hypothesis based, at least in part, on the acquired first data  60  (e.g., at least one reported event as indicated by the first data  60 ) and the second data  61  (e.g., at least a second reported event as indicated by the second data  61 ). In other embodiments, however, a hypothesis may be developed by refining an existing hypothesis  80  based, at least in part, on the acquired first data  60  (e.g., at least one reported event as indicated by the first data  60 ) and the second data  61  (e.g., at least a second reported event as indicated by the second data  61 ). 
       FIG. 2   b  illustrates particular implementations of the hypothesis development module  104  of  FIG. 1   b . In various implementations, the hypothesis development module  104  may include a hypothesis creation module  236  configured to create a hypothesis based, at least in part, on the first data  60  (e.g., at least one reported event as indicated by the first data  60 ) and the second data  61  (e.g., at least a second reported event as indicated by the second data  61 ) acquired by the events data acquisition module  102 . In the same or different implementations, the hypothesis development module  104  may include an existing hypothesis refinement module  244  configured to refine an existing hypothesis  80  based, at least in part, on the at least one reported event (e.g., as indicated by the first data  60 ) and the at least reported event (e.g., as indicated by the second data  61 ). 
     The hypothesis creation module  236  may include one or more sub-modules in various implementations. For example, in some implementations, the hypothesis creation module  236  may include an events pattern determination module  238  configured to determine an events pattern based, at least in part, on occurrence of the first reported event and occurrence of the second reported event. The determined events pattern may then facilitate the hypothesis creation module  236  in creating a hypothesis. In some implementations, the events pattern determination module  238 , in order to for example facilitate the hypothesis creation module  236  to create a hypothesis, may further include a sequential events pattern determination module  240  configured to determine a sequential events pattern based, at least in part, on the time or temporal occurrence of the at least one reported event and the time or temporal occurrence of the at least second reported event and/or a spatial events pattern determination module  242  configured to determine a spatial events pattern based, at least in part, on the spatial occurrence of the at least one reported event and the spatial occurrence of the at least second reported event. 
     The existing hypothesis refinement module  244 , in various implementations, may also include one or more sub-modules. For example, in various implementations, the existing hypothesis refinement module  244  may include an events pattern determination module  246  configured to, for example, facilitate the existing hypothesis refinement module  244  in refining the existing hypothesis  80  by determining at least an events pattern based, at least in part, on occurrence of the at least one reported event and occurrence of the at least second reported event. In some implementations, the events pattern determination module  246  may further include a sequential events pattern determination module  248  configured to determine a sequential events pattern based, at least in part, on the time or temporal occurrence of the at least one reported event and the time or temporal occurrence of the at least second reported event and/or a spatial events pattern determination module  250  configured to determine a spatial events pattern based, at least in part, on the spatial occurrence of the at least one reported event and the spatial occurrence of the at least second reported event. Note that in cases where both the hypothesis creation module  236  and the existing hypothesis refinement module  244  are present in the hypothesis development module  104 , one or more of the events pattern determination module  246 , the sequential events pattern determination module  248 , and the spatial events pattern determination module  250  of the existing hypothesis refinement module  244  may be the same modules as the events pattern determination module  238 , the sequential events pattern determination module  240 , and the spatial events pattern determination module  242 , respectively, of the hypothesis creation module  236 . 
     In some cases, the existing hypothesis refinement module  244  may include a support determination module  252  configured to determine whether an events pattern, as determined by the events pattern determination module  246 , supports an existing hypothesis  80 . In some implementations, the support determination module may further include a comparison module  254  configured to compare the determined events pattern (e.g., as determined by the events pattern determination module  246 ) with an events pattern associated with the existing hypothesis  80  to facilitate in the determination as to whether the determined events pattern supports the existing hypothesis  80 . 
     In some cases, the existing hypothesis refinement module  244  may include a soundness determination module  256  configured to determine soundness of an existing hypothesis  80  based, at least in part, on a comparison made by the comparison module  254 . In some cases, the existing hypothesis refinement module  244  may include a modification module  258  configured to modify an existing hypothesis  80  based, at least in part, on a comparison made by the comparison module  254 . 
     Referring back to  FIG. 1   b , the action execution module  106  of the computing device  10  may be designed to execute one or more actions (e.g., operations) in response, at least in part, to the development of a hypothesis by the hypothesis development module  104 . The one or more actions to be executed may include, for example, presentation (e.g., transmission or indication) of one or more advisories related to the hypothesis developed by the hypothesis development module  104  and/or prompting one or more local or remote devices  55  to execute one or more actions or operations. 
     Referring now to  FIG. 2   c  illustrating particular implementations of the action execution module  106 . In various embodiments, the action execution module  106  may include one or more sub-modules. For example, in various implementations, the action execution module  106  may include an advisory presentation module  260  configured to present one or more advisories relating to a hypothesis developed by, for example, the hypothesis development module  104  and/or a device prompting module  277  configured to prompt (e.g., as indicated by ref  25 ) one or more devices  55  to execute one or more operations (e.g., actions) based, at least in part, on a hypothesis developed by, for example, the hypothesis development module  104 . 
     The advisory presentation module  260 , in turn, may further include one or more additional sub-modules. For instance, in some implementations, the advisory presentation module  260  may include an advisory indication module  262  configured to indicate, via a user interface  122 , the one or more advisories related to the hypothesis developed by, for example, the hypothesis development module  104 . In the same or different implementations, the advisory presentation module  260  may include an advisory transmission module  264  configured to transmit, via at least one of a wireless network or a wired network, the one or more advisories related to the hypothesis developed by, for example, the hypothesis development module  104 . 
     In the same or different implementations, the advisory presentation module  260  may include a hypothesis presentation module  266  configured to, among other things, present (e.g., either transmit or indicate) at least a form of a hypothesis developed by, for example, the hypothesis development module  104 . In various implementations, the hypothesis presentation module  266  may include one or more additional sub-modules. For example, in some implementations, the hypothesis presentation module  266  may include an event types relationship presentation module  268  configured to present an indication of a relationship between at least a first event type and at least a second event type as referenced by the hypothesis developed by, for example, the hypothesis development module  104 . 
     In the same or different implementations, the hypothesis presentation module  266  may include a hypothesis soundness presentation module  270  configured to present an indication of soundness of the hypothesis developed by, for example, the hypothesis development module  104 . In the same or different implementations, the hypothesis presentation module  266  may include a temporal/specific time relationship presentation module  271  configured to present an indication of a temporal or specific time relationship between the at least first event type and the at least second event type as referenced by the hypothesis developed by, for example, the hypothesis development module  104 . In the same or different implementations, the hypothesis presentation module  266  may include a spatial relationship presentation module  272  configured to present an indication of a spatial relationship between the at least first event type and the at least second event type as referenced by the hypothesis developed by, for example, the hypothesis development module  104 . 
     In various implementations, the advisory presentation module  260  may include a prediction presentation module  273  configured to present an advisory relating to a predication of one or more future events based, at least in part, on the hypothesis developed by, for example, the hypothesis development module  104 . In the same or different implementations, the advisory presentation module  260  may include a recommendation presentation module  274  configured to present a recommendation for a future course of action based, at least in part, on the hypothesis developed by, for example, the hypothesis development module  104 . In some implementations, the recommendation presentation module  274  may further include a justification presentation module  275  configured to present a justification for the recommendation presented by the recommendation presentation module  274 . 
     In various implementations, the advisory presentation module  260  may include a past events presentation module  276  configured to present an indication of one or more past events based, at least in part, on the hypothesis developed by, for example, the hypothesis development module  104 . 
     The device prompting module  277  in various embodiments may include one or more sub-modules. For example, in some implementations, the device prompting module  277  may include a device instruction module  278  configured to instruct one or more devices  55  to execute one or more operations (e.g., actions) based, at least in part, on the hypothesis developed by, for example, the hypothesis development module  104 . In the same or different implementations, the device prompting module  277  may include a device activation module  279  configured to activate one or more devices  55  to execute one or more operations (e.g., actions) based, at least in part, on the hypothesis developed by, for example, the hypothesis development module  104 . In the same or different implementations, the device prompting module  277  may include a device configuration module  280  designed to configure one or more devices  55  to execute one or more operations (e.g., actions) based, at least in part, on the hypothesis developed by, for example, the hypothesis development module  104 . 
     Turning now to  FIG. 2   d  illustrating particular implementations of the one or more sensing devices  35 * (e.g., one or more sensing devices  35   a  and/or one or more sensing devices  35   b ). In some implementations, the one or more sensing devices  35 * may include one or more physiological sensor devices  281  designed to sense one or more physical or physiological characteristics of a subject such as a user  20 * or a third party  50  (e.g., another user, a nonuser, or a nonhuman living organism such as a pet or livestock). In various implementations, the one or more physiological sensor devices  281  may include, for example, a heart rate sensor device  282 , blood pressure sensor device  283 , a blood glucose sensor device  284 , a functional magnetic resonance imaging (fMRI) device  285 , a functional near-infrared (fNIR) device  286 , a blood alcohol sensor device  287 , a temperature sensor device  288  (e.g., to measure a temperature of the subject), a respiration sensor device  289 , a blood cell-sorting sensor device  322  (e.g., to sort between different types of blood cells), and/or other types of devices capable of sensing one or more physical or physiological characteristics of a subject (e.g., a user  20 *). 
     In the same or different implementations, the one or more sensing devices  35 * may include one or more imaging system devices  290  for capturing various types of images of a subject (e.g., a user  20 * or a third party  50 ). Examples of such imaging system devices  290  include, for example, a digital or video camera, an x-ray machine, an ultrasound device, and so forth. Note that in some instances, the one or more imaging system devices  290  may also include an fMRI device  285  and/or an fNIR device  286 . 
     In the same or different implementations, the one or more sensing devices  35 * may include one or more user activity sensing devices  291  designed to sense or monitor one or more user activities of a subject (e.g., a user  20 * or a third party  50  such as another person or a pet or livestock). For example, in some implementations, the user activity sensing devices  291  may include a pedometer  292 , an accelerometer  293 , an image capturing device  294  (e.g., digital or video camera), a toilet monitoring device  295 , an exercise machine sensor device  296 , and/or other types of sensing devices capable of sensing a subject&#39;s activities. 
     In the same or different implementations, the one or more sensing devices  35 * may include a global position system (GPS)  297  to determine one or more locations of a subject (e.g., a user  20 * or a third party  50  such as another user or an animal), an environmental temperature sensor device  298  designed to sense or measure environmental (e.g. atmospheric) temperature, an environmental humidity sensor device  299  designed to sense or measure environmental (e.g. atmospheric) humidity level, an environmental air pollution sensor device  320  to measure or sense various gases such as CO 2 , ozone, xenon, and so forth in the atmosphere or to measure particulates (e.g., pollen) in the atmosphere, and/or other devices for measuring or sensing various other characteristics of the environment (e.g., a barometer, a wind speed sensor, a water quality sensing device, and so forth). 
     In various implementations, the computing device  10  of  FIG. 1   b  may include one or more applications  126 . The one or more applications  126  may include, for example, one or more communication applications  267  (e.g., text messaging application, instant messaging application, email application, voice recognition system, and so forth) and/or Web 2.0 application  268  to facilitate in communicating via, for example, the World Wide Web. In some implementations, copies of the one or more applications  126  may be stored in memory  140 . 
     In various implementations, the computing device  10  may include a network interface  120 , which may be a device designed to interface with a wireless and/or wired network  40 . Examples of such devices include, for example, a network interface card (NIC) or other interface devices or systems for communicating through at least one of a wireless network or wired network  40 . In some implementations, the computing device  10  may include a user interface  122 . The user interface  122  may comprise any device that may interface with a user  20   b . Examples of such devices include, for example, a keyboard, a display monitor, a touchscreen, a microphone, a speaker, an image capturing device such as a digital or video camera, a mouse, and so forth. 
     The computing device  10  may include a memory  140 . The memory  140  may include any type of volatile and/or non-volatile devices used to store data. In various implementations, the memory  140  may comprise, for example, a mass storage device, a read only memory (ROM), a programmable read only memory (PROM), an erasable programmable read-only memory (EPROM), random access memory (RAM), a flash memory, a synchronous random access memory (SRAM), a dynamic random access memory (DRAM), and/or other memory devices. In various implementations, the memory  140  may store an existing hypotheses  80  and/or historical data  81  (e.g., historical data including, for example, past events data or historical events patterns related to a user  20 *, related to a subgroup of the general population that the user  20  belongs to, or related to the general population). 
     The various features and characteristics of the components, modules, and sub-modules of the computing device  10  presented thus far will be described in greater detail with respect to the processes and operations to be described herein. 
       FIG. 3  illustrates an operational flow  300  representing example operations related to, among other things, acquisition of events data from multiple sources including at least a first data indicating at least one reported event as originally reported by a user and a second data indicating at least a second reported event as originally reported by one or more sensing devices, and the development of a hypothesis based, at least in part, on the acquired first and second data. In some embodiments, the operational flow  300  may be executed by, for example, the computing device  10  of  FIG. 1   b , which may be a server or a standalone device. 
     In  FIG. 3  and in the following figures that include various examples of operational flows, discussions and explanations may be provided with respect to the above-described exemplary environment of  FIGS. 1   a  and  1   b , and/or with respect to other examples (e.g., as provided in  FIGS. 2   a - 2   c ) and contexts. However, it should be understood that the operational flows may be executed in a number of other environments and contexts, and/or in modified versions of FIGS.  1   a ,  1   b , and  2   a - 2   d . Also, although the various operational flows are presented in the sequence(s) illustrated, it should be understood that the various operations may be performed in different sequential orders other than those which are illustrated, or may be performed concurrently. 
     Further, in the following figures that depict various flow processes, various operations may be depicted in a box-within-a-box manner. Such depictions may indicate that an operation in an internal box may comprise an optional example embodiment of the operational step illustrated in one or more external boxes. However, it should be understood that internal box operations may be viewed as independent operations separate from any associated external boxes and may be performed in any sequence with respect to all other illustrated operations, or may be performed concurrently. 
     In any event, after a start operation, the operational flow  300  may move to a data acquisition operation  302  for acquiring a first data indicating at least one reported event as originally reported by a user and a second data indicating at least a second reported event as originally reported by one or more sensing devices. For instance, the events data acquisition module  102  of the computing device  10  acquiring a first data  60  (e.g., in the form of a blog entry, a status report, an electronic message, or a diary entry) indicating at least one reported event (e.g., a subjective user state, a subjective observation, or an objective occurrence) as originally reported by a user  20 * and a second data  61  indicating at least a second reported event (e.g., objective occurrence) as originally reported by one or more sensing devices  35 *. 
     Next, operational flow  300  may include hypothesis development operation  304  for developing a hypothesis based, at least in part, on the first data and the second data. For instance, the hypothesis development module  104  of the computing device  10  developing a hypothesis (e.g., creating a new hypothesis or refining an existing hypothesis) based, at least in part, on the first data  60  and the second data  61 . Note that in the following description and for ease of illustration and understanding the hypothesis to be developed through the hypothesis development operation  304  may be described as linking together two types of events (i.e., event types). However, those skilled in the art will recognize that such a hypothesis  80  may alternatively relate to the association of three or more types of events in various implementations. 
     In various implementations, the first data  60  to be acquired during the data acquisition operation  302  of  FIG. 3  may be acquired through various means in various forms. For example, in some implementations, the data acquisition operation  302  may include an operation  402  for receiving the first data from at least one of a wireless network and a wired network as depicted in  FIG. 4   a . For instance, when the computing device  10  of  FIG. 1   b  is a server, the network interface reception module  202  of the computing device  10  may receive the first data  60  from at least one of a wireless network and a wired network  40 . 
     In some alternative implementations, the data acquisition operation  302  may include an operation  403  for receiving the first data via a user interface as depicted in  FIG. 4   a . For instance, when the computing device  10  is a standalone device, such as a handheld device, the user interface reception module  204  of the computing device  10  may receive the first data  60  via a user interface  122  (e.g., a touch screen, a microphone, a mouse, and/or other input devices). 
     In the same or different implementations, the data acquisition operation  302  may include an operation  404  for prompting the user to report an occurrence of an event as depicted in  FIG. 4   a . For instance, when the computing device  10  is either a server or a standalone device, the user prompting module  206  of the computing device  10  prompting (as indicated by ref.  22  in  FIGS. 1   a  and  1   b ) the user  20 * (e.g., by generating a simple “ping,” or generating a more specific request) to report an occurrence of an event (e.g., the reported event may be a subjective user state, a subjective observation, or an objective occurrence). 
     In various implementations, operation  404  may comprise an operation  405  for requesting the user to report the occurrence of the event as depicted in  FIG. 4   a . For instance, the requesting module  208  of the computing device  10  requesting (e.g., transmitting a request or indicating a request via the user interface  122 ) the user  20 * to report the occurrence of the event. 
     In some implementations, operation  405  may further comprise an operation  406  for requesting audioally the user to report the occurrence of the event as depicted in  FIG. 4   a . For instance, audio requesting module  210  of the computing device  10  requesting audioally (e.g., via the user interface  122  in the case where the computing device  10  is a standalone device or via a speaker system of the mobile device  30  in the case where the computing device  10  is a server) the user  20 * to report the occurrence of the event. 
     In some implementations, operation  405  may further comprise an operation  407  for requesting visually the user to report the occurrence of the event as depicted in  FIG. 4   a . For instance, visual requesting module  212  of the computing device  10  requesting visually (e.g., via the user interface  122  in the case where the computing device  10  is a standalone device or via a display system of the mobile device  30  in the case where the computing device  10  is a server) the user  20 * to report the occurrence of the event. 
     In some implementations, the data acquisition operation  302  may include an operation  408  for prompting a network device to provide the first data as depicted in  FIG. 4   a . For instance, the device prompting module  214  of the computing device  10  prompting (as indicated by ref.  24  in  FIG. 1   a ) a network device such as the mobile device  30  or a network server  36  to provide the first data  60 . 
     The first data  60  to be acquired through the data acquisition operation  302  may be in a variety of different forms. For example, in some implementations, the data acquisition operation  302  may include an operation  409  for acquiring, via one or more electronic entries, a first data indicating at least one reported event as originally reported by the user as depicted in  FIG. 4   a . For instance, the first data acquisition module  201  of the computing device  10  acquiring (e.g., acquiring through the user interface  122  or receiving through the wireless and/or wired network  40 ) a first data  60  indicating at least one reported event as originally reported by the user  20 *. 
     In some implementations, operation  409  may comprise an operation  410  for acquiring, via one or more blog entries, a first data indicating at least one reported event as originally reported by the user as depicted in  FIG. 4   a . For instance, the first data acquisition module  201  of the computing device  10  acquiring (e.g., receiving through the wireless and/or wired network  40 ), via one or more blog entries (e.g., microblog entries), a first data  60  indicating at least one reported event as originally reported by the user  20   a.    
     In some implementations, operation  409  may include an operation  411  for acquiring, via one or more status report entries, a first data indicating at least one reported event as originally reported by the user as depicted in  FIG. 4   a . For instance, the first data acquisition module  201  of the computing device  10  acquiring (e.g., receiving through the wireless and/or wired network  40 ), via one or more status report entries, a first data  60  indicating at least one reported event as originally reported by the user  20   a.    
     In some implementations, operation  409  may include an operation  412  for acquiring, via one or more electronic messages, a first data indicating at least one reported event originally reported by the user as depicted in  FIG. 4   a . For instance, the first data acquisition module  201  of the computing device  10  acquiring (e.g., receiving through the wireless and/or wired network  40 ), via one or more status electronic messages (e.g., text messages, email messages, IM messages, and so forth), a first data  60  indicating at least one reported event as originally reported by the user  20   a.    
     In some implementations, operation  409  may include an operation  413  for acquiring via one or more diary entries, a first data indicating at least one reported events originally reported by the user as depicted in  FIG. 4   a . For instance, the first data acquisition module  201  of the computing device  10  acquiring (e.g., acquiring through the user interface  122 ), via one or more diary entries, a first data  60  indicating at least one reported event as originally reported by the user  20   b.    
     As will be further described herein, the first data  60  acquired during the data acquisition operation  302  of  FIG. 3  may indicate a variety of reported events. For example, in various implementations, the data acquisition operation  302  may include an operation  414  for acquiring a first data indicating at least one subjective user state of the user as originally reported by the user as depicted in  FIG. 4   b . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least one subjective user state (e.g., fatigue, happiness, sadness, nauseous, alertness, energetic, and so forth) of the user  20 * as originally reported by the user  20 *. 
     Various types of subjective user states may be indicated by the first data  60  acquired through operation  414 . For example, in some implementations, operation  414  may include an operation  415  for acquiring a first data indicating at least one subjective mental state of the user as originally reported by the user as depicted in  FIG. 4   b . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least one subjective mental state (e.g., fatigue, happiness, sadness, nauseous, alertness, energetic, and so forth) of the user  20 * as originally reported by the user  20 *. 
     In some implementations, operation  414  may include an operation  416  for acquiring a first data indicating at least one subjective physical state of the user as originally reported by the user as depicted in  FIG. 4   b . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least one subjective physical state (e.g., headache, stomach ache, sore back, sore or stiff ankle, overall fatigue, blurry vision, and so forth) of the user  20 * as originally reported by the user  20 *. 
     In some implementations, operation  414  may include an operation  417  for acquiring a first data indicating at least one subjective overall state of the user as originally reported by the user as depicted in  FIG. 4   b . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least one subjective overall state (e.g., “good,” “bad,” “well,” “available,” and so forth) of the user  20 * as originally reported by the user  20 *. 
     In various alternative implementations, the first data  60  acquired during the data acquisition operation  302  of  FIG. 3  may indicate at least one subjective observation. For example, in some implementations, the data acquisition operation  302  of  FIG. 3  may include an operation  418  for acquiring a first data indicating at least one subjective observation made by the user as depicted in  FIG. 4   b . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least one subjective observation (e.g., a subjective observation regarding an external event, a subjective observation regarding an activity executed by the user or by a third party, a subjective observation regarding the subjective user state of a third party as perceived by the user  20 *, and so forth) made by the user  20 *. 
     A variety of subjective observations may be indicated by the first data  60  acquired during operation  418 . For example, in various implementations, operation  418  may include an operation  419  for acquiring a first data indicating at least one subjective observation made by the user regarding a third party as depicted in  FIG. 4   b . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least one subjective observation made by the user  20 * regarding a third party  50  (e.g., subjective user state of the third party  50  or demeanor of the third party  50  as perceived by the user  20 *). A third party  50 , as will be described herein, may be in reference to a person such as another user or a non-user, or a non-human living creature or organism such as a pet or livestock. 
     As will be further described herein, various types of subjective observations may be made by the user  20 * regarding a third party. For example, in various implementations, operation  419  may include an operation  420  for acquiring a first data indicating at least one subjective observation made by the user regarding subjective user state of the third party as perceived by the user as depicted in  FIG. 4   b . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least one subjective observation made by the user  20 * regarding subjective user state (e.g., subjective mental state, subjective physical state, or subjective overall state) of the third party  50  as perceived by the user  20 *. 
     In some implementations, operation  420  may include an operation  421  for acquiring a first data indicating at least one subjective observation made by the user regarding subjective mental state of the third party as perceived by the user as depicted in  FIG. 4   b . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least one subjective observation made by the user  20 * regarding subjective mental state (e.g., distracted, indifferent, angry, happy, nervous, alert, and so forth) of the third party  50  as perceived by the user  20 *. 
     In some implementations, operation  420  may include an operation  422  for acquiring a first data indicating at least one subjective observation made by the user regarding subjective physical state of the third party as perceived by the user as depicted in  FIG. 4   b . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least one subjective observation made by the user  20 * regarding subjective physical state (e.g., in pain) of the third party  50  as perceived by the user  20 *. 
     In some implementations, operation  420  may include an operation  423  for acquiring a first data indicating at least one subjective observation made by the user regarding subjective overall state of the third party as perceived by the user as depicted in  FIG. 4   b . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least one subjective observation made by the user  20 * regarding subjective overall state (e.g., “available”) of the third party  50  as perceived by the user  20 *. 
     In various implementations, operation  419  of  FIG. 4   b  may include an operation  424  for acquiring a first data indicating at least one subjective observation made by the user regarding one or more activities performed by the third party as perceived by the user as depicted in  FIG. 4   c . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least one subjective observation made by the user  20 * regarding one or more activities (e.g., demeanor or facial expression) performed by the third party  50  (e.g., another user or a pet) as perceived by the user  20 *. 
     In various implementations, operation  418  of  FIG. 4   c  may include an operation  425  for acquiring a first data indicating at least one subjective observation made by the user regarding occurrence of one or more external activities as depicted in  FIG. 4   c . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least one subjective observation made by the user  20 * regarding occurrence of one or more external activities (e.g., “my car is poorly running”). 
     In some implementations, operation  418  may include an operation  426  for acquiring a first data indicating at least one subjective observation made by the user relating to an external event as depicted in  FIG. 4   c . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least one subjective observation made by the user  20 * relating to an external event (e.g., “it is a hot day”). 
     The data acquisition operation  302  of  FIG. 3  may acquire a first data that indicates at least one objective occurrence. For example, in various implementations, the data acquisition operation  302  may include an operation  427  for acquiring a first data indicating at least one objective occurrence as originally reported by the user as depicted in  FIG. 4   d . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least one objective occurrence (e.g., an activity executed by the user  20 * or by a third party  50 *) as originally reported by the user  20 *. 
     In some cases, operation  427  may involve acquiring a first data  60  that indicates an objective occurrence related to the user  20 *. For example, in various implementations, operation  427  may include an operation  428  for acquiring a first data indicating at least one activity executed by the user as originally reported by the user as depicted in  FIG. 4   d . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least one activity (e.g., an activity participated by the user  20 * such as eating or exercising) executed by the user  20 * as originally reported by the user  20 *. 
     In some instances, the first data  60  to be acquired may indicate an activity involving the consumption of an item by the user  20 *. For example, in some implementations, operation  428  may comprise an operation  429  for acquiring a first data indicating at least a consumption of an item by the user as originally reported by the user as depicted in  FIG. 4   d . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least a consumption of an item (e.g., alcoholic beverage) by the user  20 * as originally reported by the user  20 *. 
     In these implementations, the first data  60  to be acquired may indicate the user  20 * consuming any one of a variety of items. For example, in some implementations, operation  429  may include an operation  430  for acquiring a first data indicating at least a consumption of a food item by the user as originally reported by the user as depicted in  FIG. 4   d . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least a consumption of a food item (e.g., spicy food) by the user  20 * as originally reported by the user  20 *. 
     In some implementations, operation  429  may include an operation  431  for acquiring a first data indicating at least a consumption of a medicine by the user as originally reported by the user as depicted in  FIG. 4   d . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least a consumption of a medicine (e.g., aspirin) by the user  20 * as originally reported by the user  20 *. 
     In some implementations, operation  429  may include an operation  432  for acquiring a first data indicating at least a consumption of a nutraceutical by the user as originally reported by the user as depicted in  FIG. 4   d . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least a consumption of a nutraceutical (e.g., Kava, Ginkgo, Sage, and so forth) by the user  20 * as originally reported by the user  20 *. 
     The first data  60  acquired in operation  428  may indicate other types of activities executed by the user  20 * in various alternative implementations. For example, in some implementations, operation  428  may include an operation  433  for acquiring a first data indicating at least a social or leisure activity executed by the user as originally reported by the user as depicted in  FIG. 4   d . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least a social or leisure activity (e.g., eating dinner with friends or family or playing golf) executed by the user  20 * as originally reported by the user  20 *. 
     In some implementations, operation  428  may include an operation  434  for acquiring a first data indicating at least a work activity executed by the user as originally reported by the user as depicted in  FIG. 4   d . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least a work activity (e.g., arriving at work at 6 AM) executed by the user  20 * as originally reported by the user  20 *. 
     In some implementations, operation  428  may include an operation  435  for acquiring a first data indicating at least an exercise activity executed by the user as originally reported by the user as depicted in  FIG. 4   d . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least an exercise activity (e.g., walking, jogging, lifting weights, swimming, aerobics, treadmills, and so forth) executed by the user  20 * as originally reported by the user  20 *. 
     In some implementations, operation  428  may include an operation  436  for acquiring a first data indicating at least a learning or educational activity executed by the user as originally reported by the user as depicted in  FIG. 4   d . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least a learning or educational activity (e.g., reading, attending a class or lecture, and so forth) executed by the user  20 * as originally reported by the user  20 *. 
     In various implementations, the first data  60  that may be acquired through operation  427  of  FIG. 4   d  may indicate other types of activities or events that may not be directly related to the user  20 *. For example, in various implementations, operation  427  may include an operation  437  for acquiring a first data indicating at least one activity executed by a third party as originally reported by the user as depicted in  FIG. 4   e . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least one activity executed by a third party  50  (e.g., another user, a nonuser, or a nonhuman living organism such as a pet or livestock) as originally reported by the user  20 *. 
     Various types of activities executed by the third party  50  may be indicated by the first data  60  acquired through operation  437 . For example, in some implementations, operation  437  may further include an operation  438  for acquiring a first data indicating at least a consumption of an item by the third party as originally reported by the user as depicted in  FIG. 4   e . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least a consumption of an item by the third party  50 * as originally reported by the user  20 *. 
     For these implementations, the first data  60  acquired through operation  438  may indicate the third party  50  consuming at least one item from a variety of edible items. For example, in some implementations, operation  438  may include an operation  439  for acquiring a first data indicating at least a consumption of a food item by the third party as originally reported by the user as depicted in  FIG. 4   e . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least a consumption of a food item (e.g., ice cream) by the third party  50  (e.g., pet dog) as originally reported by the user  20 *. 
     In alternative implementations, however, operation  438  may include an operation  440  for acquiring a first data indicating at least a consumption of a medicine by the third party as originally reported by the user as depicted in  FIG. 4   e . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least a consumption of a medicine (e.g., beta blocker) by the third party  50  (e.g., a spouse of the user  20 *) as originally reported by the user  20 *. 
     In still other alternative implementations, operation  438  may include an operation  441  for acquiring a first data indicating at least a consumption of a nutraceutical by the third party as originally reported by the user as depicted in  FIG. 4   e . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least a consumption of a nutraceutical (e.g., Gingko) by the third party (e.g., co-worker) as originally reported by the user  20 *. 
     The first data  60  acquired through operation  437  may indicate other types of activities associated with a third party  50  other than a consumption of an item in various alternative implementations. For example, in some implementations, operation  437  may include an operation  442  for acquiring a first data indicating at least a social or leisure activity executed by the third party as originally reported by the user as depicted in  FIG. 4   e . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least a social or leisure activity (e.g., attending a family function) executed by the third party  50  (e.g., another user such as a friend or a family member) as originally reported by the user  20 *. 
     In some implementations, operation  437  may include an operation  443  for acquiring a first data indicating at least a work activity executed by the third party as originally reported by the user as depicted in  FIG. 4   e . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least a work activity (e.g., arriving for work late at 10 AM) executed by the third party  50  (e.g., co-worker or a supervisor) as originally reported by the user  20 *. 
     In some implementations, operation  437  may include an operation  444  for acquiring a first data indicating at least an exercise activity executed by the third party as originally reported by the user as depicted in  FIG. 4   e . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least an exercise activity (e.g., going for a walk) executed by the third party (e.g., pet dog) as originally reported by the user  20 *. 
     In some implementations, operation  437  may include an operation  445  for acquiring a first data indicating at least a learning or educational activity executed by the third party as originally reported by the user as depicted in  FIG. 4   e . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least a learning or educational activity (e.g., attending a class) executed by the third party (e.g., an off-spring) as originally reported by the user. 
     Referring back to  FIG. 4   d , the first data  60  acquired through operation  427  may indicate other types of objective occurrences in various alternative implementations. For example, in some implementations, operation  427  may include an operation  446  for acquiring a first data indicating at least a location associated with the user as originally reported by the user as depicted in  FIG. 4   f . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least a location (e.g., geographic location) associated with the user  20 * as originally reported by the user  20 *. 
     In some implementations, operation  427  may include an operation  447  for acquiring a first data indicating at least a location associated with a third party as originally reported by the user as depicted in  FIG. 4   f . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least a location (e.g., home of the user  20 *) associated with a third party  50  (e.g., in-laws) as originally reported by the user  20 *. 
     In some implementations, operation  427  may include an operation  448  for acquiring a first data indicating at least an external event as originally reported by the user as depicted in  FIG. 4   f . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least an external event (e.g., a sports event or the atmospheric pollution level on a particular day) as originally reported by the user  20 *. 
     In some implementations, operation  427  may include an operation  449  for acquiring a first data indicating one or more physical characteristics of the user as originally reported by the user as depicted in  FIG. 4   f . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least one or more physical characteristics (e.g., blood pressure or skin color) of the user  20 * as originally reported by the user  20 *. 
     In some implementations, operation  427  may include an operation  450  for acquiring a first data indicating one or more physical characteristics of a third party as originally reported by the user as depicted in  FIG. 4   f . For instance, the first data acquisition module  201  of the computing device  10  acquiring a first data  60  indicating at least one or more physical characteristics (e.g., blood shot eyes) of a third party (e.g., another user such as a friend) as originally reported by the user  20 *. 
     Referring back to the data acquisition operation  302  of  FIG. 3 , the second data  61  indicating at least a second reported event as acquired in the data acquisition operation  302  may be acquired through various means and in various different forms. For example, in some implementations, the data acquisition operation  302  may include an operation  451  for receiving the second data from at least one of a wireless network and a wired network as depicted in  FIG. 4   g . For instance, the network interface reception module  216  (which may be the same as the network interface reception module  202 ) of the computing device  10  receiving the second data  61  (e.g., as originally provided by a sensing device  35   a ) from at least one of a wireless network and a wired network  40 . 
     Alternatively, in some implementations, the data acquisition operation  302  may include an operation  452  for receiving the second data directly from the one or more sensing devices as depicted in  FIG. 4   g . For instance, the sensing device reception module  218  of the computing device  10  receiving the second data  61  directly from the one or more sensing devices  35   b.    
     In some implementations, the data acquisition operation  302  may include an operation  453  for acquiring the second data by prompting the one or more sensing devices to provide the second data as depicted in  FIG. 4   g . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  by the device prompting module  220  prompting (e.g., as indicated by ref.  23 ) the one or more sensing devices  35 * to provide the second data  61 . 
     Various approaches may be employed in operation  453  in order to prompt the one or more sensing devices  35  to provide the second data  61 . For example, in some implementations, operation  453  may include an operation  454  for acquiring the second data by directing or instructing the one or more sensing devices to provide the second data as depicted in  FIG. 4   g . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  60  by the sensing device directing/instructing module  222  directing or instructing the one or more sensing devices  35 * to provide the second data  61 . 
     In some implementations, operation  453  may include an operation  455  for acquiring the second data by configuring the one or more sensing devices to provide the second data as depicted in  FIG. 4   g . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  60  by the sensing device configuration module  224  configuring the one or more sensing devices  35 * to provide the second data  61 . 
     In some implementations, operation  453  may include an operation  456  for acquiring the second data by requesting the one or more sensing devices to provide the second data as depicted in  FIG. 4   g . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  60  by the sensing device requesting module  226  requesting (e.g., transmitting a request) the one or more sensing devices  35 * to provide (e.g., to have access to or to transmit) the second data  61 . 
     The second data  61  acquired through the data acquisition operation  302  of  FIG. 3  may indicate a wide variety of objective occurrences that may be detected by a sensing device  35  including, for example, the objectively observable physical characteristics of the user  20 *. For example, in various implementations, the data acquisition operation  302  may include an operation  457  for acquiring the second data including data indicating one or more physical characteristics of the user as originally reported by the one or more sensing devices as depicted in  FIG. 4   h . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including data indicating one or more physical characteristics of the user  20 * as originally reported by the one or more sensing devices  35 *. 
     In some implementations, operation  457  may include an operation  458  for acquiring the second data including data indicating one or more physiological characteristics of the user as originally reported by the one or more sensing devices as depicted in  FIG. 4   h . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including data indicating one or more physiological characteristics of the user  20 * as originally reported by the one or more sensing devices (e.g., physiological sensor devices  281 ). 
     Various types of physiological characteristics of the user  20 * may be indicated by the second data  61  acquired through operation  458  in various alternative implementations. For example, in some implementations, operation  458  may include an operation  459  for acquiring the second data including heart rate sensor data relating to the user as depicted in  FIG. 4   h . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including heart rate sensor data relating to the user  20 * as at least originally provided by, for example, a heart rate sensor device  282 . 
     In some implementations, operation  458  may include an operation  460  for acquiring the second data including blood pressure sensor data relating to the user as depicted in  FIG. 4   h . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including blood pressure sensor data relating to the user  20 * as at least originally provided by, for example, a blood pressure sensor device  283 . 
     In some implementations, operation  458  may include an operation  461  for acquiring the second data including glucose sensor data relating to the user as depicted in  FIG. 4   h . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including glucose sensor data relating to the user  20 * as at least originally provided by, for example, a blood glucose sensor device  284  (e.g., glucometer). 
     In some implementations, operation  458  may include an operation  462  for acquiring the second data including blood cell-sorting sensor data relating to the user as depicted in  FIG. 4   h . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including blood cell-sorting sensor data relating to the user  20 * as provided by, for example, a blood cell-sorting sensor device  322 . 
     In some implementations, operation  458  may include an operation  463  for acquiring the second data including sensor data relating to blood oxygen or blood volume changes of a brain of the user as depicted in  FIG. 4   h . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including sensor data relating to blood oxygen or blood volume changes of a brain of the user  20 * as at least originally provided by, for example, an fMRI device  285  and/or an fNIR device  286 . 
     In some implementations, operation  458  may include an operation  464  for acquiring the second data including blood alcohol sensor data relating to the user as depicted in  FIG. 4   h . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including blood alcohol sensor data relating to the user  20 * as at least originally provided by, for example, a blood alcohol sensor device  287 . 
     In some implementations, operation  458  may include an operation  465  for acquiring the second data including temperature sensor data relating to the user as depicted in  FIG. 4   h . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including temperature sensor data relating to the user  20 * as at least originally provided by, for example, temperature sensor device  288 . 
     In some implementations, operation  458  may include an operation  466  for acquiring the second data including respiration sensor data relating to the user as depicted in  FIG. 4   h . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including respiration sensor data relating to the user  20 * as at least originally provided by, for example, a respiration sensor device  289 . 
     In various implementations, operation  457  of  FIG. 4   h  for acquiring the data indicating one or more physical characteristics of the user  20 * may include an operation  467  for acquiring the second data including imaging system data relating to the user as depicted in  FIG. 4   h . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including imaging system data relating to the user  20 * as at least originally provided by, for example, one or more image system devices  290  (e.g., a digital or video camera, an x-ray machine, an ultrasound device, an fMRI device, an fNIR device, and so forth). 
     Referring back to the data acquisition operation  302  of  FIG. 3 , in various implementations, the second data  61  acquired through the data acquisition operation  302  may indicate one or more activities executed by the user  20 * as originally reported by one or more sensing devices  35 *. For example, in some implementations, the data acquisition operation  302  may include an operation  468  for acquiring the second data including data indicating one or more activities of the user as depicted in  FIG. 4   i . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including data indicating one or more activities of the user  20 * as at least originally provided by, for example, one or more user activity sensing devices  291 . 
     The data indicating the one or more activities of the user  20 * acquired through operation  468  may be acquired from any one or more of a variety of different sensing devices  35 * capable of sensing the activities of the user  20 *. For example, in some implementations, operation  468  may include an operation  469  for acquiring the second data including pedometer data relating to the user as depicted in  FIG. 4   i . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including pedometer data relating to the user  20 * as at least originally provided by, for example, a pedometer  292 . 
     In some implementations, operation  468  may include an operation  470  for acquiring the second data including accelerometer device data relating to the user as depicted in  FIG. 4   i . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including accelerometer device data relating to the user  20 * as at least originally provided by, for example, an accelerometer  293 . 
     In some implementations, operation  468  may include an operation  471  for acquiring the second data including image capturing device data relating to the user as depicted in  FIG. 4   i . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including image capturing device data relating to the user  20 * as at least originally provided by, for example, an image capturing device  294  (e.g. digital or video camera to capture user movements). 
     In some implementations, operation  468  may include an operation  472  for acquiring the second data including toilet monitoring device data relating to the user as depicted in  FIG. 4   i . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including toilet monitoring device data relating to usage of a toilet by the user  20 * as at least originally provided by, for example, a toilet monitoring device  295 . 
     In some implementations, operation  468  may include an operation  473  for acquiring the second data including exercising machine sensor data relating to the user as depicted in  FIG. 4   i . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including exercising machine sensor data relating to exercise machine activities of the user  20 * as at least originally provided by, for example, an exercise machine sensor device  296 . 
     Various other types of events related to the user  20 *, as originally reported by one or more sensing devices  35 *, may be indicated by the second data  61  acquired in the data acquisition operation  302 . For example, in some implementations, the data acquisition operation  302  may include an operation  474  for acquiring the second data including global positioning system (GPS) data indicating at least one location of the user as depicted in  FIG. 4   i . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including global positioning system (GPS) data indicating at least one location of the user  20 * as at least originally provided by, for example, a GPS  297 . 
     In some implementations, the data acquisition operation  302  may include an operation  475  for acquiring the second data including temperature sensor data indicating at least one environmental temperature associated with a location of the user as depicted in  FIG. 4   i . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including temperature sensor data indicating at least one environmental temperature associated with a location of the user  20 * as at least originally provided by, for example, an environmental temperature sensor device  298 . 
     In some implementations, the data acquisition operation  302  may include an operation  476  for acquiring the second data including humidity sensor data indicating at least one environmental humidity level associated with a location of the user as depicted in  FIG. 4   i . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including humidity sensor data indicating at least one environmental humidity level associated with a location of the user  20 * as at least originally provided by, for example, an environmental humidity sensor device  299 . 
     In some implementations, the data acquisition operation  302  may include an operation  477  for acquiring the second data including air pollution sensor data indicating at least one air pollution level associated with a location of the user as depicted in  FIG. 4   i . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including air pollution sensor data indicating at least one air pollution level (e.g., ozone level, carbon dioxide level, particulate level, pollen level, and so forth) associated with a location of the user  20 * as at least originally provided by, for example, an environmental air pollution sensor device  320 . 
     In various implementations, the second data  61  acquired through the data acquisition operation  302  of  FIG. 3  may indicate events originally reported by one or more sensing devices  35 * that relates to a third party  50  (e.g., another user, a nonuser, or a nonhuman living organism such as a pet or livestock). For example, in some implementations, the data acquisition operation  302  may include an operation  478  for acquiring the second data including data indicating one or more physical characteristics of a third party as originally reported by the one or more sensing devices as depicted in  FIG. 4   j . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including one or more physical characteristics of a third party  50  as originally reported by one or more sensing devices  35   a.    
     In various implementations, operation  478  may further include an operation  479  for acquiring the second data including data indicating one or more physiological characteristics of the third party as originally reported by the one or more sensing devices as depicted in  FIG. 4   j . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including data indicating one or more physiological characteristics of the third party  50  as originally reported by the one or more sensing devices  35   a  (e.g., a physiological sensor device  281 ). 
     In various implementations, the second data  61  acquired through operation  479  may indicate at least one of a variety of physiological characteristics that may be associated with the third party  50 *. For example, in some implementations, operation  479  may include an operation  480  for acquiring the second data including heart rate sensor data relating to the third party as depicted in  FIG. 4   j . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including heart rate sensor data relating to the third party  50  as at least originally provided by, for example, a heart rate sensor device  282 . 
     In some implementations, operation  479  may include an operation  481  for acquiring the second data including blood pressure sensor data relating to the third party as depicted in  FIG. 4   j . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including blood pressure sensor data relating to the third party  50  as at least originally provided by, for example, a blood pressure sensor device  283 . 
     In some implementations, operation  479  may include an operation  482  for acquiring the second data including glucose sensor data relating to the third party as depicted in  FIG. 4   j . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including glucose sensor data relating to the third party  50  as at least originally provided by, for example, a blood glucose sensor device  284 . 
     In some implementations, operation  479  may include an operation  483  for acquiring the second data including blood cell-sorting sensor data relating to the third party as depicted in  FIG. 4   j . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including blood cell-sorting sensor data relating to the third party  50  as at least originally provided by, for example, a blood cell-sorting sensor device  322 . 
     In some implementations, operation  479  may include an operation  484  for acquiring the second data including sensor data relating to blood oxygen or blood volume changes of a brain of the third party as depicted in  FIG. 4   j . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including sensor data relating to blood oxygen or blood volume changes of a brain of the third party  50  as at least originally provided by, for example, an fMRI device  285  and/or an fNIR device  286 . 
     In some implementations, operation  479  may include an operation  485  for acquiring the second data including blood alcohol sensor data relating to the third party as depicted in  FIG. 4   j . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including blood alcohol sensor data relating to the third party  50  as at least originally provided by, for example, a blood alcohol sensor device  287 . 
     In some implementations, operation  479  may include an operation  486  for acquiring the second data including temperature sensor data relating to the third party as depicted in  FIG. 4   j . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including temperature sensor data relating to the third party  50  as at least originally provided by, for example, temperature sensor device  288 . 
     In some implementations, operation  479  may include an operation  487  for acquiring the second data including respiration sensor data relating to the third party as depicted in  FIG. 4   j . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including respiration sensor data relating to the third party  50  as at least originally provided by, for example, a respiration sensor device  289 . 
     In various implementations, operation  478  of  FIG. 4   j  for acquiring the data indicating one or more physical characteristics of the third party  50  may include an operation  488  for acquiring the second data including imaging system data relating to the third party as depicted in  FIG. 4   j . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including imaging system data relating to the third party  50  as at least originally provided by, for example, one or more image system devices  290  (e.g., a digital or video camera, an x-ray machine, an ultrasound device, an fMRI device, an fNIR device, and so forth). 
     Referring back to the data acquisition operation  302  of  FIG. 3 , in various implementations the second data  61  acquired through the data acquisition operation  302  may indicate one or more activities executed by a third party  50  as originally reported by one or more sensing devices  35   a . For example, in some implementations, the data acquisition operation  302  may include an operation  489  for acquiring the second data including data indicating one or more activities of a third party as depicted in  FIG. 4   k . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including data indicating one or more activities of a third party  50  as at least originally provided by, for example, one or more user activity sensing devices  291 . 
     The data indicating the one or more activities of the third party  50  acquired through operation  489  may be acquired from any one or more of a variety of different sensing devices  35 * capable of sensing the activities of the user  20 *. For example, in some implementations, operation  489  may include an operation  490  for acquiring the second data including pedometer data relating to the third party as depicted in  FIG. 4   k . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including pedometer data relating to the third party  50  as at least originally provided by, for example, a pedometer  292 . 
     In some implementations, operation  489  may include an operation  491  for acquiring the second data including accelerometer device data relating to the third party as depicted in  FIG. 4   k . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including accelerometer device data relating to the third party  50  as at least originally provided by, for example, an accelerometer  293 . 
     In some implementations, operation  489  may include an operation  492  for acquiring the second data including image capturing device data relating to the third party as depicted in  FIG. 4   k . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including image capturing device data relating to the third party  50  as at least originally provided by, for example, an image capturing device  294  (e.g. digital or video camera to capture user movements). 
     In some implementations, operation  489  may include an operation  493  for acquiring the second data including toilet monitoring sensor data relating to the third party as depicted in  FIG. 4   k . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including toilet monitoring sensor data relating to usage of a toilet by the third party  50  as at least originally provided by, for example, a toilet monitoring device  295 . 
     In some implementations, operation  489  may include an operation  494  for acquiring the second data including exercising machine sensor data relating to the third party as depicted in  FIG. 4   k . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including exercising machine sensor data relating to exercise machine activities of the third party  50  as at least originally provided by, for example, an exercise machine sensor device  296 . 
     Various other types of events related to a third party  50 , as originally reported by one or more sensing devices  35 *, may be indicated by the second data  61  acquired in the data acquisition operation  302 . For example, in some implementations, the data acquisition operation  302  may include an operation  495  for acquiring the second data including global positioning system (GPS) data indicating at least one location of a third party as depicted in  FIG. 4   k . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including global positioning system (GPS) data indicating at least one location of a third party  50  as at least originally provided by, for example, a GPS  297 . 
     In some implementations, the data acquisition operation  302  may include an operation  496  for acquiring the second data including temperature sensor data indicating at least one environmental temperature associated with a location of a third party as depicted in  FIG. 4   k . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including temperature sensor data indicating at least one environmental temperature associated with a location of a third party  50  as at least originally provided by, for example, an environmental temperature sensor device  298 . 
     In some implementations, the data acquisition operation  302  may include an operation  497  for acquiring the second data including humidity sensor data indicating at least one environmental humidity level associated with a location of a third party as depicted in  FIG. 4   k . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including humidity sensor data indicating at least one environmental humidity level associated with a location of a third party  50  as at least originally provided by, for example, an environmental humidity sensor device  299 . 
     In some implementations, the data acquisition operation  302  may include an operation  498  for acquiring the second data including air pollution sensor data indicating at least one air pollution level associated with a location of the third party as depicted in  FIG. 4   k . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including air pollution sensor data indicating at least one air pollution level (e.g., ozone level, carbon dioxide level, particulate level, pollen level, and so forth) associated with a location of a third party  50  as at least originally provided by, for example, an environmental air pollution sensor device  320 . 
     In various alternative implementations, the second data  61  acquired through the data acquisition operation  302  of  FIG. 3  may indicate at least a second reported event that may be related to a device or an environmental characteristic. For example, in some implementations, the data acquisition operation  302  may include an operation  499  for acquiring the second data including device performance sensor data indicating at least one performance indication of a device as depicted in  FIG. 4   l . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including device performance sensor data indicating at least one performance indication (e.g., indication of operational performance) of a device (e.g., household appliance, automobile, communication device such as a mobile phone, medical device, and so forth). 
     In some alternative implementations, the data acquisition operation  302  may include an operation  500  for acquiring the second data including device characteristic sensor data indicating at least one characteristic of a device as depicted in  FIG. 4   l . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including device characteristic sensor data indicating at least one characteristic (e.g., air pressure) of a device (e.g., tires). 
     In some alternative implementations, the data acquisition operation  302  may include an operation  501  for acquiring the second data including environmental characteristic sensor data indicating at least one environmental characteristic as depicted in  FIG. 4   l . For instance, the second data acquisition module  215  of the computing device  10  acquiring the second data  61  including environmental characteristic sensor data indicating at least one environmental characteristic. Such an environmental characteristic sensor data may indicate, for example, air pollution levels or water purity levels of a local drinking water supply. 
     In some implementations, the data acquisition operation  302  of  FIG. 3  may include an operation  502  for acquiring a third data indicating a third reported event as originally reported by a third party as depicted in  FIG. 4   l . For instance, the events data acquisition module  102  of the computing device  10  acquiring a third data  62  indicating a third reported event as originally reported by a third party  50 . As an illustration, suppose a user  20 * provides a first data  60  that indicates that the user  20 * felt nauseous in the morning (e.g., subjective user state) and a sensing device  35 *, such as a blood alcohol sensor device  287 , provides a second data  61  indicating that the user  20 * had a slightly elevated blood alcohol level, then a third party  50  (e.g., spouse) may provide a third data  62  that indicates that the third party  50  observed the user  20 * staying up late the previous evening. This may ultimately result in a hypothesis being developed that indicates that there is a link between moderate alcohol consumption and staying up late with feeling nauseous. 
     In alternative implementations, the data acquisition operation  302  may include an operation  503  for acquiring a third data indicating a third reported event as originally reported by another one or more sensing devices as depicted in  FIG. 4   l . For instance, the events data acquisition module  102  of the computing device  10  acquiring a third data (e.g., fourth data  63  in  FIG. 1   a  and  1   b ) indicating a third reported event as originally reported by another one or more sensing devices  35 *. 
     In still other alternative implementations, the data acquisition operation  302  may include an operation  504  for acquiring a third data indicating a third reported event as originally reported by a third party and a fourth data indicating a fourth reported event as originally reported by another one or more sensing devices as depicted in  FIG. 4   m . For instance, the events data acquisition module  102  of the computing device  10  acquiring a third data  62  indicating a third reported event as originally reported by a third party  50  and a fourth data  63  indicating a fourth reported event as originally reported by another one or more sensing devices  35 . 
     In order to facilitate the development of a hypothesis, the data acquisition operation  302  of  FIG. 3  may involve the acquisition of time or spatial data related to the first reported event and the second reported event. For example, in various implementations, the data acquisition operation  302  may include an operation  505  for acquiring a first time element associated with the at least one reported event and a second time element associated with the at least second reported event as depicted in  FIG. 4   m . For instance, the time element acquisition module  228  of the computing device  10  acquiring a first time element associated with the at least one reported event (e.g., angry exchange with boss) and a second time element associated with the at least second reported event (e.g., elevated blood pressure). 
     In some implementations, operation  505  may comprise an operation  506  for acquiring a first time stamp associated with the at least one reported event and a second time stamp associated with the at least second reported event as depicted in  FIG. 4   m . For instance, the time stamp acquisition module  230  of the computing device  10  acquiring (e.g., receiving or self-generating) a first time stamp (e.g., 9 PM) associated with the at least one reported event (e.g., upset stomach) and a second time stamp (e.g., 7 PM) associated with the at least second reported event (e.g., visiting a particular restaurant as indicated by data provided by a GPS  297  or an accelerometer  293 ). 
     In some implementations, operation  505  may comprise an operation  507  for acquiring an indication of a first time interval associated with the at least one reported event and an indication of second time interval associated with the at least second reported event as depicted in  FIG. 4   m . For instance, the time interval indication acquisition module  232  of the computing device  10  acquiring (e.g., receiving or self-generating) an indication of a first time interval (e.g., 2 PM to 4 PM) associated with the at least one reported event (e.g., neighbor&#39;s dog being let out) and an indication of a second time interval (e.g., 3 PM to 4:40 PM) associated with the at least second reported event (e.g., user&#39;s dog staying near fence line as indicated by a GPS  297  coupled to the user&#39;s dog). 
     In some implementations, the data acquisition operation  302  may comprise an operation  508  for acquiring an indication of a first spatial location associated with the at least one reported event and an indication of a second spatial location associated with the at least second reported event as depicted in  FIG. 4   m . For instance, the spatial location indication acquisition module  234  of the computing device  10  acquiring (e.g., receiving or self-generating) an indication of a first spatial location (e.g., place of employment) associated with the at least one reported event (e.g., boss is out of office) and an indication of a second spatial location (e.g., place of employment) associated with the at least second reported event (e.g., reduced blood pressure). 
     Referring back to  FIG. 3 , the hypothesis development operation  304  may be executed in a number of different ways in various alternative implementations. For example, in some implementations, the hypothesis development operation  304  may include an operation  509  for developing a hypothesis by creating the hypothesis based, at least in part, on the at least one reported event and the at least second reported event as depicted in  FIG. 5   a . For instance, the hypothesis development module  104  of the computing device  10  developing a hypothesis based on the hypothesis creation module  236  creating the hypothesis based, at least in part, on the at least one reported event and the at least second reported event. 
     In some instances, operation  509  may include an operation  510  for creating the hypothesis based, at least in part, on the at least one reported event, the at least second reported event, and historical data as depicted in  FIG. 5   a . For instance, the hypothesis creation module  236  of the computing device  10  creating the hypothesis based, at least in part, on the at least one reported event, the at least second reported event, and historical data  81  (e.g., past reported events or historical events pattern). 
     In some implementations, operation  510  may further include an operation  511  for creating the hypothesis based, at least in part, on the at least one reported event, the at least second reported event, and historical data that is particular to the user or a sub-group of a general population that the user belongs to as depicted in  FIG. 5   a . For instance, the hypothesis creation module  236  of the computing device  10  creating the hypothesis based, at least in part, on the at least one reported event, the at least second reported event, and historical data  81  that is particular to the user  20 * or a sub-group of a general population that the user belongs to. Such a historical data  81  may include historical events pattern that may be associated with the user  20 * or the sub-group of the general population. 
     In various implementations, the hypothesis created through operation  509  may be implemented by determining an events pattern. For example, in some instances, operation  509  may include an operation  512  for creating the hypothesis by determining an events pattern based, at least in part, on occurrence of the at least one reported event and occurrence of the at least second reported event as depicted in  FIG. 5   a . For instance, the hypothesis creation module  236  of the computing device  10  creating the hypothesis based on the events pattern determination module  238  determining an events pattern based, at least in part, on occurrence (e.g., time or spatial occurrence) of the at least one reported event and occurrence (e.g., time or spatial occurrence) of the at least second reported event. 
     In some implementations, operation  512  may include an operation  513  for creating the hypothesis by determining a sequential events pattern based at least in part on time occurrence of the at least one reported event and time occurrence of the at least second reported event as depicted in  FIG. 5   a . For instance, the hypothesis creation module  236  of the computing device  10  creating the hypothesis based on the sequential events pattern determination module  240  determining a sequential events pattern based at least in part on time occurrence of the at least one reported event and time occurrence of the at least second reported event. 
     In some implementations, operation  512  may include an operation  514  for creating the hypothesis by determining a spatial events pattern based at least in part on spatial occurrence of the at least one reported event and spatial occurrence of the at least second reported event as depicted in  FIG. 5   a . For instance, the hypothesis creation module  236  of the computing device  10  creating the hypothesis based on the spatial events pattern determination module  242  determining a spatial events pattern based at least in part on spatial occurrence of the at least one reported event and spatial occurrence of the at least second reported event. 
     In various implementations, the hypothesis development operation  302  of  FIG. 3  may involve the refinement of an already existing hypothesis. For example, in some implementations, the hypothesis development operation  302  may include an operation  515  for developing a hypothesis by refining an existing hypothesis based, at least in part, on the at least one reported event and the at least second reported event as depicted in  FIG. 5   b . For instance, the hypothesis development module  104  of the computing device  10  developing a hypothesis by the existing hypothesis refinement module  244  refining (e.g., further defining or developing) an existing hypothesis  80  based, at least in part, on the at least one reported event and the at least second reported event. 
     Various approaches may be employed in order to refine an existing hypothesis  80  in operation  515 . For example, in some implementations, operation  515  may include an operation  516  for refining the existing hypothesis by at least determining an events pattern based, at least in part, on occurrence of the at least one reported event and occurrence of the at least second reported event as depicted in  FIG. 5   b . For instance, the existing hypothesis refinement module  244  of the computing device  10  refining the existing hypothesis  80  by the events pattern determination module  246  at least determining an events pattern based, at least in part, on occurrence of the at least one reported event and occurrence of the at least second reported event. 
     Operation  516 , in turn, may further comprise an operation  517  for refining the existing hypothesis by at least determining a sequential events pattern based, at least in part, on time occurrence of the at least one reported event and time occurrence of the at least second reported event as depicted in  FIG. 5   b . For instance, the existing hypothesis refinement module  244  of the computing device  10  refining the existing hypothesis  80  by the sequential events pattern determination module  248  at least determining a sequential events pattern based, at least in part, on time occurrence of the at least one reported event and time occurrence of the at least second reported event. 
     In some alternative implementations, operation  516  may include an operation  518  for refining the existing hypothesis by at least determining a spatial events pattern based, at least in part, on spatial occurrence of the at least one reported event and spatial occurrence of the at least second reported event as depicted in  FIG. 5   b . For instance, the existing hypothesis refinement module  244  of the computing device  10  refining the existing hypothesis  80  by the spatial events pattern determination module  250  at least determining a sequential events pattern based, at least in part, on spatial occurrence of the at least one reported event and spatial occurrence of the at least second reported event. 
     In some implementations, operation  516  may include an operation  519  for refining the existing hypothesis by determining whether the determined events pattern supports the existing hypothesis as depicted in  FIG. 5   b . For instance, the existing hypothesis refinement module  244  of the computing device  10  refining the existing hypothesis  80  by the support determination module  252  determining whether the determined events pattern supports (or contradicts) the existing hypothesis  80  (e.g., the determined events pattern at least generally matches or is at least generally in-line with the existing hypothesis  80 ). 
     In various implementations, operation  519 , in turn, may include an operation  520  for comparing the determined events pattern with an events pattern associated with the existing hypothesis to determine whether the determined events pattern supports the existing hypothesis as depicted in  FIG. 5   b . For instance, the comparison module  254  of the computing device  10  comparing he determined events pattern with an events pattern associated with the existing hypothesis  80  to determine whether the determined events pattern supports (or contradicts) the existing hypothesis  80 . 
     In some implementations, operation  520  may further include an operation  521  for determining soundness of the existing hypothesis based on the comparison as depicted in  FIG. 5   b . For instance, the soundness determination module  256  of the computing device  10  determining soundness of the existing hypothesis  80  (e.g., whether the existing hypothesis  80  is a weak or a strong hypothesis) based on the comparison made, for example, by the comparison module  254 . Note that the determination of “soundness” in operation  521  appears to be relatively close to the determination of “support” in operation  520 . However, these operations may be distinct as it may be possible to have, for example, a determined events that does not support (e.g., contradicts) the existing hypothesis  80  (as determined in operation  520 ) while still determining that the existing hypothesis  80  is sound when there is, for example, strong historical data (e.g., a number of past events pattern) that supports the existing hypothesis  80 . In such a scenario, the determination of a contradictory events pattern (e.g., operation  520 ) may result in a weaker hypothesis. 
     In some implementations, operation  520  may further include an operation  522  for modifying the existing hypothesis based on the comparison as depicted in  FIG. 5   b . For instance, the modification module  258  of the computing device  10  modifying the existing hypothesis  80  based on the comparison made, for example, by the comparison module  254 . As an illustration, suppose an existing hypothesis  80  links the consumption of ice cream and coffee with increased toilet use. Suppose further that the events pattern determined by the events pattern determination module  246  (e.g., determined based on the first reported event and the second reported event) indicates that increased toilet use (e.g., as reported by the toilet monitoring device  295 ) occurred after only consuming ice cream (e.g., as reported by the user  20 *). Then the modification module  258  may modify the existing hypothesis  80  to link increased toilet use with only the consumption of ice cream. 
     In various implementations, the hypothesis to be developed in the hypothesis development operation  304  of  FIG. 3  may be related to any one or more of a variety of different entities. For example, in some implementations, the hypothesis development operation  304  may include an operation  523  for developing a hypothesis that relates to the user as depicted in  FIG. 5   c . For instance, the hypothesis development module  104  of the computing device  10  developing a hypothesis (e.g., creating a new hypothesis or refining an existing hypothesis  80 ) that relates to the user  20 *. 
     In some alternative implementations, the hypothesis development operation  304  may include an operation  524  for developing a hypothesis that relates to a third party as depicted in  FIG. 5   c . For instance, the hypothesis development module  104  of the computing device  10  developing a hypothesis (e.g., creating a new hypothesis or refining an existing hypothesis  80 ) that relates to a third party  50  (e.g., another user, a nonuser, a pet, a livestock, and so forth). 
     In some implementations, operation  524  may include an operation  525  for developing a hypothesis that relates to a person as depicted in  FIG. 5   c . For instance, the hypothesis development module  104  of the computing device  10  developing a hypothesis (e.g., creating a new hypothesis or refining an existing hypothesis  80 ) that relates to a person (e.g., another user or nonuser). 
     In some implementations, operation  524  may include an operation  526  for developing a hypothesis that relates to a non-human living organism as depicted in  FIG. 5   c . For instance, the hypothesis development module  104  of the computing device  10  developing a hypothesis (e.g., creating a new hypothesis or refining an existing hypothesis  80 ) that relates to a non-human living organism (e.g., a pet such as a dog, a cat, or a bird, a livestock, or other types of living creatures). 
     In various implementations, the hypothesis development operation  304  may include an operation  527  for developing a hypothesis that relates to a device as depicted in  FIG. 5   c . For instance, the hypothesis development module  104  of the computing device  10  developing a hypothesis (e.g., creating a new hypothesis or refining an existing hypothesis  80 ) that relates to a device  55  (e.g., an automobile or a part of the automobile, a household appliance or a part of the household appliance, a mobile communication device, a computing device, and so forth). 
     In some implementations, the hypothesis development operation  304  may include an operation  528  for developing a hypothesis that relates to an environmental characteristic as depicted in  FIG. 5   c . For instance, the hypothesis development module  104  of the computing device  10  developing a hypothesis (e.g., creating a new hypothesis or refining an existing hypothesis  80 ) that relates to an environmental characteristic (e.g., weather, water quality, air quality, and so forth). 
     Referring now to  FIG. 6  illustrating another operational flow  600  in accordance with various embodiments. In some embodiments, operational flow  600  may be particularly suited to be performed by the computing device  10 , which may be a network server or a standalone computing device. Operational flow  600  includes operations that mirror the operations included in the operational flow  300  of  FIG. 3 . For example, operational flow  600  may include a data acquisition operation  602  and a hypothesis development operation  604  that corresponds to and mirror the data acquisition operation  302  and the hypothesis development operation  304 , respectively, of  FIG. 3 . 
     In addition, and unlike operational flow  300 , operational flow  600  may further include an action execution operation  606  for executing one or more actions in response at least in part to the developing (e.g., developing of a hypothesis performed in the hypothesis development operation  604  of operational flow  600 ). For instance, the action execution module  106  of the computing device  10  executing one or more actions in response at least in part to the developing of the hypothesis (e.g., developing of the hypothesis as in the hypothesis development operation  604 ). 
     Various types of actions may be executed in the action execution operation  606  in various alternative implementations. For example, in some implementations, the action execution operation  606  may include an operation  730  for presenting one or more advisories relating to the hypothesis as depicted in  FIG. 7   a . For instance, the advisory presentation module  260  of the computing device  10  presenting one or more advisories relating to the hypothesis. 
     The presentation of the one or more advisories in operation  730  may be performed in various ways. For example, in some implementations, operation  730  may include an operation  731  for indicating the one or more advisories related to the hypothesis via a user interface as depicted in  FIG. 7   a . For instance, the advisory indication module  262  of the computing device  10  indicating the one or more advisories related to the hypothesis via a user interface  122  (e.g., a display monitor, a touchscreen, a speaker system, and so forth). 
     In same or different implementations, operation  730  may include an operation  732  for transmitting the one or more advisories related to the hypothesis via at least one of a wireless network or a wired network as depicted in  FIG. 7   a . For instance, the advisory transmission module  264  of the computing device  10  transmitting (e.g., via a network interface  120 ) the one or more advisories related to the hypothesis via at least one of a wireless network or a wired network  40 . 
     In some implementations, operation  732  may further include an operation  733  for transmitting the one or more advisories related to the hypothesis to the user as depicted in  FIG. 7   a . For instance, the advisory transmission module  264  of the computing device  10  transmitting (e.g., via a network interface  120  and to mobile device  30 ) the one or more advisories related to the hypothesis to the user  20   a.    
     In the same or different implementations, operation  732  may include an operation  734  for transmitting the one or more advisories related to the hypothesis to one or more third parties as depicted in  FIG. 7   a . For instance, the advisory transmission module  264  of the computing device  10  transmitting (e.g., via a network interface  120 ) the one or more advisories related to the hypothesis to one or more third parties  50  (e.g., other users or nonusers, content providers, advertisers, network service providers, and so forth). 
     In operation  730  of  FIG. 7   a , various types of advisories may be presented in various alternative implementations. For example, in some implementations, operation  730  may include an operation  735  for presenting at least one form of the hypothesis as depicted in  FIG. 7   b . For instance, the hypothesis presentation module  266  of the computing device  10  presenting (e.g., transmitting via a wireless and/or wired network  40  or indicated via a user interface  122 ) at least one form (e.g., audio, graphical, or text form) of the hypothesis. 
     In various instances, operation  735  may further comprise an operation  736  for presenting an indication of a relationship between at least a first event type and at least a second event type as referenced by the hypothesis as depicted in  FIG. 7   b . For instance, the event types relationship presentation module  268  of the computing device  10  presenting an indication of a relationship between at least a first event type (e.g., a type of event such as a subjective user state, a subjective observation, or an objective occurrence) and at least a second event type (e.g., a type of event such as an objective occurrence) as referenced by the hypothesis. For example, a hypothesis may hypothesize that a person may feel tense (e.g., subjective user state) or appear to be tense (e.g., subjective observation by another person) whenever the user blood pressure is high (e.g., objective occurrence). Note that a hypothesis does not need to indicate a cause/effect relationship, but instead, may merely indicate a linkage between different event types. 
     In some implementations, operation  736  may include an operation  737  for presenting an indication of soundness of the hypothesis as depicted in  FIG. 7   b . For instance, the hypothesis soundness presentation module  270  of the computing device  10  presenting an indication of soundness (e.g., strength or weakness) of the hypothesis. As an illustration, one way that the soundness of a hypothesis may be presented is to provide a number between, for example, 1 and 10, where 10 indicates maximum soundness (e.g., confidence). Another way to provide an indication of soundness of the hypothesis is to provide a percentage of past reported events that actually supports the hypothesis (e.g., “in the past when you have eaten ice cream, you have gotten a stomach ache within two hours of consuming the ice cream 70 percent of the time”). Of course many other ways of presenting an indication of soundness of the hypothesis may be implemented in various other alternative implementations. 
     In some implementations, operation  736  may include an operation  738  for presenting an indication of a temporal or specific time relationship between the at least first event type and the at least second event type as depicted in  FIG. 7   b . For instance, the temporal/specific time relationship presentation module  271  of the computing device presenting (e.g., transmitting via a network interface  120  or indicating via a user interface  122 ) an indication of a temporal or more specific time relationship between the at least first event type and the at least second event type (e.g., as referenced by the hypothesis). For example, presenting a hypothesis that indicates that a pet dog will go to the backyard (e.g., a first event type) to relieve himself after (e.g., temporal relationship) eating a bowl of ice cream. 
     In some implementations, operation  736  may include an operation  739  for presenting an indication of a spatial relationship between the at least first event type and the at least second event type as depicted in  FIG. 7   b . For instance, the spatial relationship presentation module  272  of the computing device  10  presenting an indication of a spatial relationship between the at least first event type (e.g., boss on vacation) and the at least second event type (e.g., feeling of happiness at work). 
     Various types of events may be linked together by the hypothesis to be presented through operation  736  of  FIG. 7   b . For instance, in some implementations, operation  736  may include an operation  740  for presenting an indication of a relationship between at least a subjective user state type and at least an objective occurrence type as indicated by the hypothesis as depicted in  FIG. 7   b . For instance, the event types relationship presentation module  268  of the computing device  10  presenting an indication of a relationship between at least a subjective user state type (e.g., overall feeling of fatigue) and at least an objective occurrence type (e.g., high blood glucose level) as indicated by the hypothesis. 
     In some implementations, operation  736  may include an operation  741  for presenting an indication of a relationship between at least a first objective occurrence type and at least a second objective occurrence type as indicated by the hypothesis as depicted in  FIG. 7   b . For instance, the event types relationship presentation module  268  of the computing device  10  presenting an indication of a relationship between at least a first objective occurrence type (e.g., consumption of white rice) and at least a second objective occurrence type (e.g., high blood glucose level) as indicated by the hypothesis. 
     In some implementations, operation  736  may include an operation  742  for presenting an indication of a relationship between at least a subjective observation type and at least an objective occurrence type as indicated by the hypothesis as depicted in  FIG. 7   b . For instance, the event types relationship presentation module  268  of the computing device  10  presenting an indication of a relationship between at least a subjective observation type (e.g., and at least an objective occurrence type (e.g., high blood glucose level) as indicated by the hypothesis. 
     Other types of advisories other than the hypothesis itself may also be presented through operation  730  of  FIGS. 7   a  and  7   b  in various alternative implementations. For example, in some implementations, operation  730  may include an operation  743  for presenting an advisory relating to a predication of one or more future events based, at least in part, on the hypothesis as depicted in  FIG. 7   c . For instance, the prediction presentation module  273  of the computing device presenting an advisory relating to a predication of one or more future events (e.g., “you will have a stomach ache since you ate an ice cream an hour ago”) based, at least in part, on the hypothesis. 
     In various implementations, operation  730  may include an operation  744  for presenting a recommendation for a future course of action based, at least in part, on the hypothesis as depicted in  FIG. 7   c . For instance, the recommendation presentation module  274  of the computing device  10  presenting a recommendation for a future course of action (e.g., “you should take antacid now”) based, at least in part, on the hypothesis. 
     In some implementations, operation  744  may further include an operation  745  for presenting a justification for the recommendation as depicted in  FIG. 7   c . For instance, the justification presentation module  275  of the computing device  10  presenting a justification for the recommendation (e.g., “you just ate at your favorite Mexican restaurant, and each time you have gone there, you ended up with a stomach ache”). 
     In some implementations, operation  730  may include an operation  746  for presenting an indication of one or more past events based, at least in part, on the hypothesis as depicted in  FIG. 7   c . For instance, the past events presentation module  276  of the computing device  10  presenting an indication of one or more past events (e.g., “did you know that the last time you went to your favorite restaurant, you subsequently had a stomach ache?”) based, at least in part, on the hypothesis. 
     Referring back to the action execution operation  606  of  FIG. 6 , in various implementations, the one or more actions to be executed in the action execution operation  606  may involve the prompting of one or more devices (e.g., sensing devices  35 * or devices  55 ) to execute one or more actions. For example, in some implementations, the action execution operation  606  may include an operation  747  for prompting one or more devices to execute one or more actions as depicted in  FIG. 7   d . For instance, the device prompting module  277  of the computing device  10  prompting (e.g. as indicated by ref.,  25  of  FIG. 1   a ) one or more devices (e.g., one or more sensing devices  35 * or one or more devices  55  such as an automobile or a portion thereof, a household appliance or a portion thereof, a computing device, a communication device, and so forth) to execute one or more actions. Note that the word “prompting” does not require the immediate or real time execution of one or more actions. Instead, the one or more actions may be executed by the one or more devices at some later point in time from the point in time in which the one or more devices was directed or instructed to execute the one or more actions. 
     In some implementations, operation  747  may include an operation  748  for instructing the one or more devices to execute one or more actions as depicted in  FIG. 7   d . For instance, the device instruction module  278  of the computing device  10  instructing the one or more devices (e.g., one or more sensing devices  35 * or one or more devices  55  such as an automobile or a portion thereof, a household appliance or a portion thereof, a computing device, a communication device, and so forth) to execute one or more actions. For example, instructing a GPS to provide a current location for a user  20 *. 
     In some implementations, operation  747  may include an operation  749  for activating the one or more devices to execute one or more actions as depicted in  FIG. 7   d . For instance, the device activation module  279  of the computing device  10  activating the one or more devices (e.g., home air conditioner/heater) to execute one or more actions (e.g., cooling or heating the home). 
     In some implementations, operation  747  may include an operation  750  for configuring the one or more devices to execute one or more actions as depicted in  FIG. 7   d . For instance, the device configuration module  280  of the computing device  10  configuring the one or more devices (e.g., automatic lawn sprinkler system) to execute one or more actions. 
     In some implementations, operation  747  may include an operation  751  for prompting one or more environmental devices to execute one or more actions as depicted in  FIG. 7   d . For instance, the device prompting module  277  of the computing device  10  prompting one or more environmental devices (e.g., air conditioner, heater, humidifier, air purifier, and/or other environmental devices) to execute one or more actions. 
     In some implementations, operation  747  may include an operation  752  for prompting one or more household devices to execute one or more actions as depicted in  FIG. 7   d . For instance, the device prompting module  277  of the computing device  10  prompting one or more household devices (e.g., coffee maker, television, lights, and so forth) to execute one or more actions. 
     In some implementations, operation  747  may include an operation  753  for prompting one or more of the sensing devices to execute one or more actions as depicted in  FIG. 7   d . For instance, the device prompting module  277  of the computing device  10  prompting one or more of the sensing devices  35 * (e.g., environmental temperature sensor device  298 ) to execute one or more actions. 
     In some implementations, operation  747  may include an operation  754  for prompting a second one or more sensing devices to execute one or more actions as depicted in  FIG. 7   d . For instance, the device prompting module  277  of the computing device  10  prompting a second one or more sensing devices  35 * (e.g., environmental humidity sensor device  299 ) to execute one or more actions. 
     In some implementations, operation  747  may include an operation  755  for prompting the one or more devices including one or more network devices to execute one or more actions as depicted in  FIG. 7   d . For instance, the device prompting module  277  of the computing device  10  prompting the one or more devices  55  including one or more network devices (e.g., when one or more of the devices  55  are linked to the wireless and/or wired network  40 ) to execute one or more actions. 
     Those having skill in the art will recognize that the state of the art has progressed to the point where there is little distinction left between hardware and software implementations of aspects of systems; the use of hardware or software is generally (but not always, in that in certain contexts the choice between hardware and software can become significant) a design choice representing cost vs. efficiency tradeoffs. Those having skill in the art will appreciate that there are various vehicles by which processes and/or systems and/or other technologies described herein can be effected (e.g., hardware, software, and/or firmware), and that the preferred vehicle will vary with the context in which the processes and/or systems and/or other technologies are deployed. For example, if an implementer determines that speed and accuracy are paramount, the implementer may opt for a mainly hardware and/or firmware vehicle; alternatively, if flexibility is paramount, the implementer may opt for a mainly software implementation; or, yet again alternatively, the implementer may opt for some combination of hardware, software, and/or firmware. Hence, there are several possible vehicles by which the processes and/or devices and/or other technologies described herein may be effected, none of which is inherently superior to the other in that any vehicle to be utilized is a choice dependent upon the context in which the vehicle will be deployed and the specific concerns (e.g., speed, flexibility, or predictability) of the implementer, any of which may vary. Those skilled in the art will recognize that optical aspects of implementations will typically employ optically-oriented hardware, software, and or firmware. 
     The foregoing detailed description has set forth various embodiments of the devices and/or processes via the use of block diagrams, flowcharts, and/or examples. Insofar as such block diagrams, flowcharts, and/or examples contain one or more functions and/or operations, it will be understood by those within the art that each function and/or operation within such block diagrams, flowcharts, or examples can be implemented, individually and/or collectively, by a wide range of hardware, software, firmware, or virtually any combination thereof. In one embodiment, several portions of the subject matter described herein may be implemented via Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), digital signal processors (DSPs), or other integrated formats. However, those skilled in the art will recognize that some aspects of the embodiments disclosed herein, in whole or in part, can be equivalently implemented in integrated circuits, as one or more computer programs running on one or more computers (e.g., as one or more programs running on one or more computer systems), as one or more programs running on one or more processors (e.g., as one or more programs running on one or more microprocessors), as firmware, or as virtually any combination thereof, and that designing the circuitry and/or writing the code for the software and or firmware would be well within the skill of one of skill in the art in light of this disclosure. In addition, those skilled in the art will appreciate that the mechanisms of the subject matter described herein are capable of being distributed as a program product in a variety of forms, and that an illustrative embodiment of the subject matter described herein applies regardless of the particular type of signal bearing medium used to actually carry out the distribution. Examples of a signal bearing medium include, but are not limited to, the following: a recordable type medium such as a floppy disk, a hard disk drive, a Compact Disc (CD), a Digital Video Disk (DVD), a digital tape, a computer memory, etc.; and a transmission type medium such as a digital and/or an analog communication medium (e.g., a fiber optic cable, a waveguide, a wired communications link, a wireless communication link, etc.). 
     In a general sense, those skilled in the art will recognize that the various aspects described herein which can be implemented, individually and/or collectively, by a wide range of hardware, software, firmware, or any combination thereof can be viewed as being composed of various types of “electrical circuitry.” Consequently, as used herein “electrical circuitry” includes, but is not limited to, electrical circuitry having at least one discrete electrical circuit, electrical circuitry having at least one integrated circuit, electrical circuitry having at least one application specific integrated circuit, electrical circuitry forming a general purpose computing device configured by a computer program (e.g., a general purpose computer configured by a computer program which at least partially carries out processes and/or devices described herein, or a microprocessor configured by a computer program which at least partially carries out processes and/or devices described herein), electrical circuitry forming a memory device (e.g., forms of random access memory), and/or electrical circuitry forming a communications device (e.g., a modem, communications switch, or optical-electrical equipment). Those having skill in the art will recognize that the subject matter described herein may be implemented in an analog or digital fashion or some combination thereof. 
     Those having skill in the art will recognize that it is common within the art to describe devices and/or processes in the fashion set forth herein, and thereafter use engineering practices to integrate such described devices and/or processes into data processing systems. That is, at least a portion of the devices and/or processes described herein can be integrated into a data processing system via a reasonable amount of experimentation. Those having skill in the art will recognize that a typical data processing system generally includes one or more of a system unit housing, a video display device, a memory such as volatile and non-volatile memory, processors such as microprocessors and digital signal processors, computational entities such as operating systems, drivers, graphical user interfaces, and applications programs, one or more interaction devices, such as a touch pad or screen, and/or control systems including feedback loops and control motors (e.g., feedback for sensing position and/or velocity; control motors for moving and/or adjusting components and/or quantities). A typical data processing system may be implemented utilizing any suitable commercially available components, such as those typically found in data computing/communication and/or network computing/communication systems. 
     The herein described subject matter sometimes illustrates different components contained within, or connected with, different other components. It is to be understood that such depicted architectures are merely exemplary, and that in fact many other architectures can be implemented which achieve the same functionality. In a conceptual sense, any arrangement of components to achieve the same functionality is effectively “associated” such that the desired functionality is achieved. Hence, any two components herein combined to achieve a particular functionality can be seen as “associated with” each other such that the desired functionality is achieved, irrespective of architectures or intermedial components. Likewise, any two components so associated can also be viewed as being “operably connected”, or “operably coupled”, to each other to achieve the desired functionality, and any two components capable of being so associated can also be viewed as being “operably couplable”, to each other to achieve the desired functionality. Specific examples of operably couplable include but are not limited to physically mateable and/or physically interacting components and/or wirelessly interactable and/or wirelessly interacting components and/or logically interacting and/or logically interactable components. 
     While particular aspects of the present subject matter described herein have been shown and described, it will be apparent to those skilled in the art that, based upon the teachings herein, changes and modifications may be made without departing from the subject matter described herein and its broader aspects and, therefore, the appended claims are to encompass within their scope all such changes and modifications as are within the true spirit and scope of the subject matter described herein. Furthermore, it is to be understood that the invention is defined by the appended claims. 
     It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases “at least one” and “one or more” to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim recitation to inventions containing only one such recitation, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or “an” should typically be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations. 
     In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, typically means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). 
     In those instances where a convention analogous to “at least one of A, B, or C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, or C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” will be understood to include the possibilities of “A” or “B” or “A and B.”