5

 
Context-appropriate selection of the user's currently activated profile records (e.g., PEEP, PHAFUEL, etc.) is an important step. If such selection is repeatedly done incorrectly, it can drive the system into a state of repeatedly picking wrong topic nodes and repeatedly suggesting wrong chat or other forum participation opportunities. In one embodiment, a fail-safe default or checkpoint switching system 301 s (controlled by module 301 pvp in FIG. 3D) is employed. A predetermined-to-be-safe set of default or checkpoint profile selections 301 d is automatically resorted to in place of profile selections indicated by a current, but apparently erroneous, context(s)-guessing output signal 316 o of the system's context mapping mechanism 316?. More specifically, if recent feedback signals (e.g., CVi vote signals) from the user (301A?) indicate that invitations (e.g., 102 i of FIG. 1A), promotional offerings (e.g., 104 t of FIG. 1A), suggestions (102J2L of FIG. 1N) or other communications (e.g., Hot Alert 115 g? of FIG. 1N) recently made to the user by the system are meeting with negative reactions from the user (301A?), where such negativity is not the expected reaction, then the system automatically determines that it has probably guessed wrong as to current user context. In other words, if the system provided invitations and/or other suggestions are highly unwelcome, this is probably so because the system 410 has lost track of what the user's current “perceived” and/or “virtual” set of contextual states are. And as a result the system is using an inappropriate one or more profiles (e.g., PEEP, PHAFUEL etc.) and interpreting user signals (e.g., keywords, body language, etc.) incorrectly as a result. In such a case, a switch over to the fail-safe or default set is automatically carried out in response to detection of persistent negative user reactions to system provided invitations and/or other suggestions. The default profile selections 301 d may be pre-recorded to select a relatively universal or general PEEP profile for the user as opposed to one that is highly dependent on the user being in a specific mood and/or other “Perceived” and/Or “Virtual” (PoV) set of contextual states. Moreover, the default profile selections 301 d may be pre-recorded to select a relatively universal or general Domain Determining profile for the user as opposed to one that is highly dependent on the user being in a special mood or unusual PoV context state.
Additionally, the default profile selections 301 d may be pre-recorded to select relatively universal or general chat co-compatibility, PHAFUEL's (personal habits and routines logs, see FIG. 5A), and/or PSDIP's (Personal Social Dynamics Interaction Profiles, see FIG. 5B) as opposed to ones that are highly dependent on the user being in a special mood or unusual PoV context state. In one embodiment, the Conflicts and Errors Resolver module 301 pvp is coupled to receive physical context representing signals, XP. This physical context representing signals, XP are generated by one or more physical context detecting units 304. (Although not fully shown in FIG. 3D due to space limitations, the physical context detecting unit 304—shown above 298?—is to be understood to be operatively coupled to a user-adjacent GPS unit or the like such that the physical context detecting unit(s) 304 can determine current user position in space and time, current surroundings, and can generate corresponding physical context representing signals, XP for the user. The physical context detecting unit(s) 304 may include cameras, directional microphones and/or other sensing devices for visually or otherwise sensing the user's surrounding environment. The physical context detecting unit(s) 304 may include Wi-Fi™ or other wireless detecting and/or interfacing means for detecting presence of local area networks (LANs) and for interfacing with the same if possible so as to automatically determine what on-network devices are usably proximate to the user 301A?. The physical context representing signals, XP can be used by the Conflicts and Errors Resolver module 301 pvp for automatically selecting currently activated user profiles (301 p) that correspond to the current physical surroundings (301 x) of the user. Once the fail safe (e.g., default) profiles 301 d have been activated as the current profiles of the user, the system may begin to try to home in again on more definitive determinations of current state of mind for the user (e.g., top 5 now topics, most likely context states, etc.). The fail-safe mechanism 301 s/301 d (plus the module 301 pvp which module controls switches 301 s) automatically prevents the context-determining subsystem of the STAN—3 system 410 from falling into an erroneous pit or an erroneous chaotic state from which it cannot then escape from.
In one embodiment, in addition to the physical context detecting unit(s) 304, the system includes a proximate resources identifying unit 306 (shown next to 314? in FIG. 3D). The proximate resources identifying unit 306 may be configured for detecting and identifying machine resources that are proximate to the user (and thus potentially usable by the user 301A?) but which proximate resources may not at the time be powered up or operatively coupled to a network such that their presence can be detected by means of scanning a local network for presence of nearby online, on-network devices. In terms of a more specific example, one possible proximate resource may be a video teleconferencing station that is not currently turned on, but could be turned on by the user 301A? (or could be remotely turned on by the STAN—3 system) so that the respective user can then engage in a live video web conference with use of the currently turned-off station. It is envisaged here that numerous, user-proximate resources can be tagged with bar code labels (e.g., including those coded with non-visible indicia such as those that fluoresce when excited by UV rays and/or are discernable in the IR band) and/or RFID tags that can be scanned by the proximate resources identifying unit 306 and identified even though those proximate resources are not currently turned on. Then the identified proximate resources can be activated remotely or manually so that they can be used. The types of chat or other forum participation opportunities presented to the respective user 301A? by the STAN—3 system may accordingly be based not only on what already-online resources are determined by the system to be turned on and thus immediately available to the user but also based on what currently off-line (e.g., powered off) resources are determined by the system to be proximate to the user and thus perhaps available (once turned on and/or operatively coupled to a network) for use by the user when engaging in a chat or other forum participation session. Aside from video teleconferencing stations, other proximate resources that may be of value for enhancing user enjoyment of services provided by the STAN—3 system may include, but are not limited to, 3D display units, large screen, high definition display units, high fidelity sound reproduction units, haptic feedback providing units, robotic units, performance enhancement units that can enable or enhance a performance (e.g., music creation) the user may wish to engage in and so on. In accordance with one aspect of the present disclosure, the proximate resources identifying unit 306 automatically scans the user's nearby surroundings and detects potentially usable proximate resources and sends the identifications of these to the head end (e.g., cloud) of the STAN—3 system. In response, the STAN—3 system may automatically by itself, turn on and/or otherwise activate a selected one or more of the proximate resources or suggest to the user 301A? that he/she activate the one or more proximate resources so as to thereby take advantage of their capabilities when interacting with the STAN—3 system and/or other STAN users. In one embodiment, the offline proximate resources detected and identified by the proximate resources identifying unit 306 are included in the descriptions of surrounding physical context (XP) reported to the STAN—3 system by the physical context detecting unit 304. In other words, the proximate resources identifying unit 306 may be an integral part of the physical context XP detected by the physical context detecting unit 304.
In one embodiment, the physical context determining devices (e.g., 304, 306) that are proximate to the user 301A? may include means for automatically recognizing non-instrumented objects, such as for example, conventional pots, pans, plates, cups, silverware, etc. and for recognizing movement of such non-instrumented objects and sequence of movement of such objects, where the physical context determining devices are configured for reporting to the system core (e.g., the cloud) the presence and/or movement and/or order of movement of such non-instrumented objects as defining part of the physical surroundings context of, and/or activities of the user 301A?. Therefore, and as an example, the user is seated in front of his smartphone camera and the camera captures automatically recognizable images of plates, spoons, forks, cups moving in the background behind the user, the system core (e.g., cloud) may use these background captured image portions to automatically determine that perhaps the user is in a restaurant (or cafeteria, meeting hall, etc.) and is surrounded by other people who are consuming meal courses in a discernable sequence based on the order of use of their utensils. It may then be inferred by the system that the user is doing the same (mirroring the behavior of the others) at substantially the same times. Such information may be used for automatically determining a behavioral context in which the user is surrounded and/or engaged in.
Assuming that, when the user's local machine systems are initially activated, there is no specific and refined context yet established by the STAN—3 system for the respective user, and assuming further that the default profiles state 301 d for the user 301A? have been instead used for establishing during system initialization or during a user PoV state reset operation, then after this initialization process completes, switch 301 s is automatically flipped into its normal mode wherein the current context indicating signals 316 o, produced and output from the context space mapping mechanism (Xs) 316? are used for determining which next user profiles 301 p (beyond the relatively vague default ones) will become the new, currently active profiles of the user 301A?. It should be recalled that profiles can have knowledge base rules (KBR's) embedded in them (e.g., 599 of FIG. 5A) and those rules may also urge switching to yet other alternate profiles, or to yet further alternate contexts based on unique circumstances that the knowledge base rules (KBR's) are custom tailored to address (e.g., by addressing pre-specified exceptions to more general rules). In accordance with one embodiment, a weighted voting mechanism (not shown and understood to be inside module 301 pvp) is used to automatically arrive at a profile selecting decision when the current context guessing signals 316 o output by mechanism 316? conflict with knowledge base rule (KBR) decisions of currently active profiles that regard the next PoV context state that is to be assumed for the user. The weighted voting mechanism (disposed inside the Conflicts and Errors Resolver 301 pvp) may decide to not switch at all in the face of a detected conflict as to next context state or it may decide to side with the profile selection choice of one or the other of the context guessing signals 316 o and the conflicting knowledge base rules subsystem (see FIGS. 5A and 5B for example where KBR's thereof can suggest a next context state that is to be assumed). It is to be noted that the Conflicts and Errors Resolver module 301 pvp is coupled to receive the physical context representing signal, XP and thus module 301 pvp is generally aware at least of the user's current physical disposition if not of the user's current mental disposition and the Conflicts and Errors Resolver 301 pvp can therefore resolve conflicts on the basis of what is known about the user's currently detected physical disposition (XP).
It is to be also noted here that interactions between the knowledge base rules (KBR's) subsystem and the current context defining output signals 316 o of the context mapping mechanism 316? can synergistically complement each other rather than conflicting with one another. The Conflicts and Errors Resolver module 301 pvp is there for the rare occasions where conflict does arise and a fall back is made to relying on current physical context (XP) and associated safe profiles. However, a more common situation can be that where the current context defining output, 316 o of context mapping mechanism 316? is used by the knowledge base rules (KBR's) subsystem to determine a next-to-be active, and more context-appropriate profile. For example, one of the knowledge base rules (KBR's) within a currently active profile may read as follows: “IF The Current Most Probable Context(s) Determining signals 316 o include an active pointer to context space subregion XSR2 (a subregion determined by the system to be likely for the user) THEN Switch to PEEP profile number PEEP5.7 as being the currently active PEEP profile, and also Switch to CpCCp profile number PHood5.9 as being the currently active personhood profile, ELSE . . . ”. In such a case therefore, the output 316 o of the context mapping mechanism 316? is supplying the knowledge base rules (KBR's) subsystem with input signals that the latter calls for as its input parameters and the two systems synergistically complement each other rather than conflicting with one another. The dependency may flow the other way incidentally, wherein the context mapping mechanism 316? uses an output signal produced by a context resolving KBR algorithm embedded within a currently activated profile, where for example such a KBR algorithm may read as follows: “IF Current PHAFUEL profile is number PHA6.8 THEN exclude context subregion XSR3 as being likely, ELSE . . . ” Accordingly, such a profile-dependent KBR algorithm portion thereby controls how other, next activated profiles will be selected or not. In-profile knowledge base rules (KBR's) and/or other knowledge base rules used by the context mapping mechanism 316? may rely on the current physical context signal (XP) as an alternative to, or in addition to relying on the current user context defining output signal, 316 o of the context mapping mechanism 316?. More specifically, one of the knowledge base rules (KBR's) within a currently active profile may read as follows: “IF Current Physical Context signal XP indicates that the user (301A?) is at his workplace site and indicates that time is normal work hours and today is Wednesday, THEN Switch to PEEP profile number PEEP5.8 as being the currently active PEEP profile, ELSE . . . ”.
From the above, it can be seen that, in accordance with one aspect of the present disclosure, context guessing signals 316 o (which signals often represent the apparent mental or perceived context(s) of greatest likelihood(s) for the user 301A? rather than merely physical context 301 x) are produced and output from a context space mapping mechanism (Xs) 316? which mechanism (Xs) is schematically shown in FIG. 3D as having an upper input plane through which context indicative input signals 316 v (categorized CFi's 311? plus optional others, as will be detailed below) project down into an inverted-pyramid-like hierarchical structure and these input signals are used to better focus-upon or triangulate around subregions within that represented context space (316?) so as to produce better (more refined) determinations of active “perceived” and/or “virtual” (PoV) contextual states (a.k.a. context space region(s), subregions (XSR's) and nodes) of a respective user (301A?). The term “triangulating” is used here-at in a loose sense for lack of better terminology. It does not have to imply three linear vectors pointing into a hierarchical space and to a subregion or node located at an intersection point of the three linear vectors. (In a better sense it may imply that three or more cross-correlated cognitive nuggets (e.g., keywords) have been grouped together as belonging to each other and collectively indicating one context subregion as being more likely than another. But that is an understanding best left for discussion further below.) Crossing vectors and “triangulation” is one metaphorical way of understanding what happens except that such a metaphorical view chronologically pre-supposes the existence of the output 316 o of subsystem 316? ahead of its earlier in time inputs. The signals that are inputted into the illustrated mapping mechanism 316? (but this can also apply to others of the illustrated mapping mechanisms, e.g., 312? 313?, etc. of FIG. 3D) are more correctly described as including one or more of pre-grouped, pre-clustered and “pre-categorized” CFi's and CFi complexes (e.g., hybridized HyCFi signals and/or clusters of clusters) and/or one or more of physical context state descriptor signals (301 x?, which may include the current physical context signal XP) and/or algorithmic guidance signals (e.g., KBR guidances) 301 p? provided by then active user profiles. Best guess fits are then found as between the various input vector signals (e.g., 316 v, which latter signal can include signals 301 x?, 301 p? and a below described 311? signal) and corresponding points, nodes or subregions within the context space defined by the context mapping mechanism 316? in response to these various input vector signals being applied to the respective mapping mechanisms (e.g., 316?) of FIG. 3D. In other words, specific points, regions, subregions or nodes are found within the respective mapping mechanisms that best cross-correlate or most suitably fit with the then received input vector signals (e.g., 316 v). The result of such automated, best guess fittings or cross-correlation is that a “triangulation” of sorts develops around one or more regions (e.g., XSR1, XSR2) or points or nodes within the respective mapping mechanisms (e.g., 316?) and the uncertainty or nonconfidence about the best-fit subregions tends to shrink as the number of differentiating ones of “pre-categorized” CFi's, hybridized HyCFi's, and clusters of clusters of such or the like increase and cross-confirm with the most likely contexts guessed at by mechanism 316?. In hindsight, the input vector signals (e.g., 316 v) may be thought of as having operated sort of like fuzzy pointing beams or “fuzzy” pointer vectors 316 v that homed in on the one or more regions (e.g., XSR1, XSR2) in accordance with a metaphorical “triangulation” although in actuality the vector signals 316 v did not point there. Instead the automated, best guess fitting algorithms of the particular mapping mechanisms (e.g., 316?) made it seem in hindsight as if the vector signals 316 v had pointed there.
A more specific example of how a user's current mental or perceived context (as represented by result signal 316 o) may be developed is as follows. Suppose that the physical context detecting unit 304 reports to mapping mechanism 316? (by way of the XP signal) that user 310A? is physically located at address 21771 Stanley Creek Blvd., Cupertino Calif. (a hypothetical example) and the day of week for that user is Wednesday and the time of day is 10:00 AM and the biological states of the user include being awake (e.g., not asleep) and alert (e.g., not groggy). Assume that, at that instant, the system is basically using a generic (e.g., like 301 d) rather than context-based set of profiles for the user. However, in response to the GPS data and the biological state data, one or more of numerous software modules in mapping mechanism 316? fetches more up to date and currently activated and personalized and pre-specified profile records (e.g., PHAFUEL and CpCCp (the personhood demographic profile) of the specific user and from these, the software module(s) automatically determine that, in all likelihood, the user is at his/her workplace (e.g., based on habits and routines for location and time) and that the user is likely to be perceiving him/herself as being in a normal employee role (e.g., Senior Software Design Engineer—again, a hypothetical example). Additionally, suppose the one or more of numerous software modules in mapping mechanism 316? next responsively fetch data from a currently activated workplace calendaring tool (e.g., Microsoft Office™) of the user where the automatically fetched calendaring data indicates that the user (301A?) is scheduled to work on a so-called, STAN-Development-Project-3D (a hypothetical example) at this time of the current work day and week within the current month. In response to this fetched information and as yet a next step in the context-refining process, the one or more software modules in mapping mechanism 316? send instructions, by way of current output signals 316 o which connect to and drive unit 301 p, to thereby cause unit 301 p to activate a specific and more context-appropriate PEEP profile for the user and specific topic domain specifying profiles (DsCCP) that relate more closely to the scheduled STAN-Development-Project-3D. As a consequence, the profiles-produced, decision-guiding input vector signal 301 p? (which feeds from unit 301 p into the formation of input vector signal 316 v) points to a more specific subregion within context space 316? and the current context representing signal 316 o is updated to reflects this for the corresponding user 301A?. As part of the feedback loop, the produced context representing signal 316 o is next used by unit 301 p to perhaps pick yet another combination of user profiles.
In one embodiment, after new context defining signals 316 o are produced (signals representing the one or top n best guesses as to current user context(s)) the system next causes automatic loading of context-appropriate web content (e.g., 117 of FIG. 1A) or the like onto the information presenting devices (e.g., screen 111) of the user. In other words, once the user context is automatically guessed at by the STAN—3 system, the system automatically presents what it considers to be context-appropriate presentations (e.g., content and/or invitations) to the user 301A?. Subsequent CFi signals received from the corresponding user (301A?) in response to the newly presented content (and/or invitations) will next be interpreted in light of this more refined context determination (as represented by the updated 316 o signal). If the user subsequently expresses satisfaction with the supposedly on-topic invitations and/or suggestions and/or content presentations made to him/her on the basis of this state, the STAN—3 system interprets such positive voting (implicit or explicit) as a reinforcing feedback for its neural net and/or other forms of adaptive and self-correcting modeling of the user. If the user expresses dissatisfaction (by way of unexpected negative CVi's), then the STAN—3 system interprets such negative voting as constituting a detracting feedback for its neural net and/or other form of adaptive and self-corrective modeling of the user and the system then adjusts (“learns”) accordingly so as to reduce the frequency of reoccurrence of such error. Strong and prolonged dissatisfaction beyond a predetermined threshold leads to reloading of the default profiles 301 d and starting over afresh as described above.
The above example illustrated a case where one or more current contexts of the user (301A?), as represented by context(s) indicating signal 316 o, are refined and resolved by starting with a relatively coarse determination or guess of context (e.g., alive, awake, alert and at this location) and then narrowing the machine-generated result to a finer determination of more likely context(s) (e.g., in work mode and working on specific project). It is to be appreciated that, just like the having of a large number of less “fuzzy” and more informative pointer vectors 316 v (vector signals 316 v) generally helps the system to metaphorically home in or resolve down to more narrow and well bounded context states or context space subregions of smaller hierarchical scope near the base (upper surface) of the inverted pyramid; conversely, as the number of context-differentiating, input vector signals (e.g., 316 v) and the information in them decreases, the tendency is for the resolving power of the metaphorical “fuzzy” pointer vectors to decrease whereby, in hindsight, it appears as if the comparatively more “fuzzy” pointer vectors 316 v were pointing to and resolving around only coarser (less hierarchically refined) nodes and/or coarser subregions of the respective mapping mechanism space (CARS, e.g., 316?), where those coarser nodes and/or subregions are conceptually located near the more “coarsely-resolved” apex portion of the inverted hierarchical pyramids (which represent the respective CARS) rather than near the more “finely-resolved” base layers of the corresponding inverted hierarchical pyramids depicted in FIG. 3D. In other words, cruder (coarser, less refined, poorer resolution) determinations of current context space region(s) (XSR's) likely to be representative of the user's context are usually had when the metaphorical projection beams of the supplied current focus indicator signals (e.g., the raw CFi's) point to hierarchically-speaking; broader regions or domains disposed near the apex (bottom point) of the inverted pyramid (e.g., where such a coarse context indicative signal might merely say the user is alive and at a location having no known significance in his/her currently activated profiles). On the other hand, finer (higher resolution) determinations are usually had when the metaphorical projection beams are comparatively more informative and thus “triangulate” (so to speak) around hierarchically-speaking; finer regions or domains disposed nearer the base of the inverted pyramid (e.g., due to collection of context indicative signals that more informatively says the user is not only alive, but is also respectively spatially and chronologically disposed at a location that does have a known significance in his/her currently activated profiles—i.e. this is where he/she works—and at a time that does have a known significance in his/her currently activate profiles—i.e. this is the time when; according to the user's PHAFUEL record, he/she usually works on the task known as STAN-Development-Project-3D).
The above example was a simple one based on a GPS reporting of a single location (e.g., 21771 Stanley Creek Blvd., Cupertino Calif.—a hypothetical example) for the user and on a single point in time (e.g., Wednesday, 10:00 AM) for the user. However, it is within the contemplation of the present disclosure to determine the top n most likely user context(s) (where n=1, 2, 3, . . . here) based on a sequence of significant events (optionally interrupted by a sequence of none or insignificant events) such as for example, the user's GPS and/or other locater device reporting the user as hopping from one spatial location to another (in real and/or virtual world) with this occurring at respective times of day, week, month etc. (in real or virtual world time). The user's activated PHAFUEL record (habits and routines—see FIG. 5A) may then inform as to a likely specific context based on such a sequence of events and the STAN—3 system uses this additional information for automatically determining user context to a finer degree of resolution. Additionally, the user's then activated Personhood profile (a.k.a. PHood profile or CpCCp profile—see giF. 1B of the STAN-1 application incorporated here by reference) may include in a demographics portion thereof, various cross-associations as between individualized data points (e.g., street addresses, dates during the calendar year, etc.) and more generalized or normalized contextual significances such as, but not limited to, “This is my Date of Birth”, “This is my Place of Birth”, “This is my Wedding Anniversary Date”, “This is my Primary workplace Address”, and so on. These individual-to-normalized-information data pairs may be used to inform as to a likely specific context in a consensus-wise normalized and communal context space while inputting the specific recent dates or events or visited places, as well as those planned for the near future for the specific user (301A?). By way of example, if the current week is a week containing the user's 25th wedding anniversary and the user has a “special” restaurant reservation in his/her electronic calendar for the special date, then a received reminder email saying for example, “call restaurant to confirm” in its subject line can have context-augmenting data automatically attached to it by the STAN—3 system indicating that more likely than not, the ambiguous keyword, “restaurant” means, at least this week; the restaurant of the “special” restaurant reservation where the user plans to celebrate the user's 25th wedding anniversary. This is just one example of how resolved user context can be used to better inform the STAN—3 system as to probable semantic intents of ambiguous CFi's (e.g., ambiguous keywords, ambiguous URL's—those specifying only a portal page, and so on).
As explained above, the input vector signals (e.g., 316 v being input into context mapping mechanism 316?) are not actually “fuzzy” pointer vectors that of themselves point to a specific point, node or subregion in the mapped Cognitive Attention Receiving Space (e.g., context space 316?) because the results (e.g., context(s) representing output signal 316 o) arising from their being inputted into the corresponding mapping mechanism (e.g., 316?) are usually not known until after the mapping mechanism (e.g., 316?) has processed the supplied input vector signals (e.g., 316 v) in combination with other available information (e.g., currently activated profiles) and has responsively generated newer or updated state signals (e.g., new top n most likely contexts as represented by context representing signal 316 o) which then in turn may help to identify the more appropriate user profiles and the better fitting or more appropriate points, nodes or subregions in other, cross-associated Cognitive Attention Receiving Spaces such as topic space for example to which yet newer CFi's (next received CFi's) may apply. In one embodiment, the output signals (e.g., 316 o) of each, “user-is-likely-here” mapping mechanism (e.g., context mapping mechanism 316?) are output as a sorted list that provides ranked identifications of the best fitted-to and more hierarchically refined internal points, nodes and/or subregions in that space (e.g., at the top of the list and with regard to context space for example) and that also provides ranked identifications of the more poorly fitted-to and less hierarchically refined internal points, nodes and/or subregions as last (e.g., at the bottom of the list and again with regard to context space for example). The outputted resolving signals (e.g., 316 o) may also include indications of how well or poorly the internal resolution process executed (e.g., with what level of confidence). If the resolution process is indicated to have executed more poorly than a predetermined acceptable level, and as a result confidence in the results is poor; the STAN—3 system 410 may elect to not generate any invitations (and/or promotional offerings) on the basis of the subpar resolution of, or confidence in the current context determination and/or in the current other focused-upon points, nodes and/or subregions within the corresponding other spaces (e.g., topic space (Ts, 313?), keyword space, URL space, social dynamics space and so on).
The input vector signals (e.g., 316 v) that are supplied to the various nodes-mapping and space maintaining mechanisms (e.g., to context space 316?, to topic space 313?, etc.) as briefly noted above can include various context resolving signals obtained from one or more of a plurality of context indicating signals, such as but not limited to: (1) “pre-clustered” or “pre-categorized” or “pre-cross-associated” first CFi signals 3020 produced by, and stored in, a first CFi clustering/categorizing-mechanism 302? (shown in FIG. 3D as being one of an adjacent pair of pyramids), (2) pre-clustered/categorized second CFi signals 2980 produced by, and stored in, a second CFi categorizing-mechanism (298?), (3) physical context indicating signals 301 x? (representing biological states and physical surrounds) derived from sensors that sense physical surroundings and/or physical states XP of the user where unit 304 is representative of sensors that pick up physical surroundings indications and generate corresponding state signals XP such as obtained from a user-carried GPS device for example, and (4) context indicating or suggesting signals 301 p? obtained from currently active profiles 301 p of the user 301A? (e.g., from executing KBR's within those currently active profiles 301 p). This aspect is represented in FIG. 3D by the illustrated signal feeds going into input port 316 v of the context mapping mechanism 316?. However, to avoid illustrative clutter, this aspect (regarding multiple input feeds) is understood to occur for, but is not illustratively repeated for others of the illustrated mapping mechanisms including: topic space 313?, content source space 314?, emotional/behavioral states space 315?, the social dynamics subspace represented by inverted pyramid 312? and other state defining spaces (e.g., pure and hybrid spaces) as are also represented by inverted pyramid 312?.
While not shown in the drawings for all the various and possible mapping mechanisms, it is to be observed that in general, each mapping mechanism 312?-316? produces a respective mapped results output signal (e.g., 312 o) which represents mapping results (also denoted as 312 o for example) generated internally within that respective mapping mechanism (inside the pyramid). The respective mapped results output signal (e.g., 312 o, 313 o, 316 o, etc.) can define a sorted list of ranked identifications of internal points, nodes and/or subregions within the represented space of the respective mapping mechanism (e.g., 312?, 313?, 316?, etc.) where those identified internal parts which are deemed most likely for a given time period (e.g., “Now”) are ranked highest to thereby indicate which focused upon cognitions of the respective social entity (e.g., STAN user 301A?) with regard to attributes (e.g., topics, context, keywords, etc.) that are categorized within that mapped space are comparatively more or less likely. More specifically, one of the energy-consuming cognitions that a STAN user may consciously or subconsciously have (or not) can be those revolving around the question of what “topic” or “topics” best describe content being currently focused-upon by the user and being thought about by the user under a user-assumed (picked) context. More to the point, if the currently focused-upon content contains the text, “Joe-the-Throw Nebraska” (using the hypothetical Superbowl™ Sunday Party example of above), that alone may not indicate a specific topic being cross-associated in the user's mind with the hypothetical celebrity's name. The topic could be, what book does Joe recommend to his Twitter™ followers? The topic could be, what food does Joe like to eat; or it could pertain to the current state of Joe's health. And so on. A recent heat map history of where the specific STAN user (e.g., 301A?) has been recently casting a predominant amounts of his/her attention giving energies may give hints, clues and best guess answers as to which topic node(s) in system-maintained topic space is/are the more likely one(s). More specifically, if the user has been inputting health-related keywords into his utilized search engine, that may help to narrow the likely topic(s) to that or those dealing with the combination of “Joe-the-Throw's” identity and Joe's health.
It is to be understood that sometimes there is no specific “topic” yet emerged in the user's conscious or subconscious mind and instead the user is casting attention giving energies on merely a keyword or keyphrase (where herein and in the context of the disclosure of invention, the term “keyword” is to be understood as encompassing the concept of phrases or other combinations or sequences of text and/or sounds rather than merely one word taken at a time) that a user would input into a respective search engine for the purpose of retrieve corresponding search results. The user could instead be casting attention giving energies on merely a scent or a feeling. As explained above, in accordance with one aspect of the present disclosure, users of the STAN—3 system may be brought into an online and/or a real life (ReL) joinder with other users on the basis of shared cognitions or experiences including on the basis of non-topical and/or non-textual shared cognitions where the mapped cognitions of the respective users are deemed by the system to be substantially same or similar based on relative hierarchical and/or spatial distances within corresponding Cognitions-representing Spaces.
The “triangulation” wise identified points, nodes or subregions of a CFi and XP driven mapping mechanism (e.g., 302?, 312?, 313?, 316? of FIG. 3D) will often have node-to-forums links that point to chat or other forum participation opportunities that are cross-associated with that mapped-to node, or they will have node-to-social entity/-ies links that point to one or more social entities who are cross-associated with that mapped-to node. Accordingly, when the respective mapping mechanism result signals (e.g., 312 o, 313 o) output by a given one or more mapping mechanisms (e.g., 312?, 313?) correspond to specific internal nodes (or points, or subregions) of the signal outputting mechanism, such result signals (e.g., 312 o, 313 o) will also indirectly correspond to specific social entities (e.g., identified other STAN users who are co-mapped into substantially same or similar regions of the same CARS) and/or to predefined time durations and/or predefined locations that also indirectly cross-correlate with the CFi signals and/or the XP signals collected from a first user (e.g., 301A?). Therefore the result signals (e.g., 312 o) can be used to provide identification information (e.g., User-ID's, Group ID's, chat room ID's, other Forum ID's, etc.) that ultimately lead to online and/or real life (ReL) joinder as between system users and on the basis of shared cognitions or experiences that are deemed by the STAN—3 system to be substantially same or similar, where such joinders may be made on the basis of non-topical and/or non-textual shared cognitions as well as topical and/or textual cognitions that take place in identified subregions of the space and time continuum.
As a more specific example, user 301A? may be interested in locating other system users who were located in a particular geographic region (e.g., California, USA) and who focused their attention giving activities upon a specific one or more subregions of topic space (313?) while also operating in a specific context (e.g., “at work”) where this occurred in a specified time zone (e.g., last month). The various Cognitive Attention Receiving Spaces maintained by the STAN—3 system (not all shown in FIG. 3D) can be used in a cross cooperating manner to produce such a desired identification of other users. While not shown in FIG. 3D, the present disclosure contemplates the inclusion of one or more location “spaces” (e.g., geography mapping mechanisms) and one or more chronological “spaces” (e.g., history mapping mechanisms) among the numerous, system-maintained Cognitive Attention Receiving Spaces.
One of the system-maintained location “spaces” is a real life (ReL) geography mapping mechanism whose points, nodes and/or subregions cross-correlate with real life locations on the basis of a variety of designations including but not limited to, GPS coordinates; latitude, longitude, altitude coordinates; street map coordinates (e.g., postal address and street name) and so on. A user's personhood profile (e.g., CpCCp) may include logical links pointing into the system-maintained ReL geography mapping mechanism (not shown) and identifying parts thereof as being the user's “normal work place”, “normal place of residence” (a.k.a. “home”) and so on. The combination of the user's currently activated personhood profile (e.g., CpCCp) and the system-maintained ReL geography mapping mechanism (not shown) then provides a ReL location-to-context mapping. Such mapping may include use of knowledge base rules (KBR's). For example: IF Month=June-August THEN Home=GPScoords(x1,y1,z1) ELSE Home=GPScoords(x2,y2,z2). The system's context space mapping mechanism 316? does not contain specific information about most users' home address, workplace address, etc.; but instead refers abstractly to such context-oriented items as, for example, Primary Home, Secondary Home, etc. The reason is because the system's context space mapping mechanism 316? is used as a collectively shared resource among many users and not as an individualized resource. This will become clearer when FIG. 3R is described. In one embodiment, the user can section off his personhood profile (e.g., CpCCp, see giF. 1B of the STAN-1 application) into private and shareable demographics information sections where the private demographics information is blocked from being used by the STAN—3 system for routine context determination steps but may be used in special situations the user pre-agrees to. In one embodiment, the user may deploy knowledge base rules (KBR's) for determining when and to what extent his/her individualized demographics information can be used by specific ones of modules of the STAN—3 system, including by automated context determining modules of the STAN—3 system.
While real life (ReL) location is one type of spatial location that can be mapped and tracked by the STAN—3 system, it is within also within the contemplation of the present disclosure to similarly map virtual life (e.g., SecondLife™) locations, except with a separate mapping mechanism dedicated to a respective virtual life support platform.
Real life (ReL) time durations (e.g., this week, this day, this hour; last month, etc.) are similarly mapped in a system-maintained ReL time mapping mechanism (not shown). Each user's personhood profile (e.g., CpCCp) may include logical links pointing into the system-maintained ReL time mapping mechanism (not shown) and identifying parts thereof as being the user's “normal work week”, “normal time at home” and so on. The combination of the user's currently activated personhood profile (e.g., CpCCp, in its user Demographics section) and the system-maintained ReL time mapping mechanism (not shown) then provides a ReL time-to-context mapping. Such mapping may include use of knowledge base rules (KBR's). For example: IF Month=June-August THEN “Normal Work Week”=None ELSE “Normal Work Week”=Monday/9:00 AM to Friday/5:00 PM. The system's context space mapping mechanism 316? does not contain specific information about most users' normal work hours, normal vacation time, etc.; but instead refers abstractly to such context-oriented items as, for example, “Normal Work Week”, “Normal Vacation Time”, etc. Once again, the reason for this is because the system's context space mapping mechanism 316? is used as a collectively shared resource among many users and not as an individualized resource. This aspect will become clearer when FIG. 3R is described.
While real life (ReL) time periods is one type of chronological location that can be mapped and tracked by the STAN—3 system, it is within also within the contemplation of the present disclosure to similarly map virtual life (e.g., SecondLife™) chronological locations, except with a separate mapping mechanism dedicated to each respective virtual life support platform. Accordingly interactions between virtual personas or between real and virtual personas can be specified for purpose of creating chat or other forum participation opportunities just as interactions just between real life (ReL) persons can be tracked.
When an individual user's CFi signals (and/or other signals like CVi's and HyCFi's) upload into the STAN system cloud (and/or other support platform), they generally have “normalizing” data added to them or substituted for them so that they can better match with consensus-wise defined, communal cognitions and/or communal expressions. More specifically, if the uploading CFi's of user 301A? (FIG. 3D) basically say: “I am at geographic location, 21771 Stanley Creek Blvd., Cupertino Calif. and my current time is Wednesday, 10:00 AM”, that data is translated into “normalized” (less individualized, more communally understandable data) that instead basically says: “I am at the geographic location which is my “Normal Work Place” (a.k.a. “at work”) and my current time is “Normal Work Hours”. This normalized input data may then “triangulate” on a subregion of the context space (316?) which is directed to more specific context definitions dealing with being at the work place during normal work hours. For example, a more refined context specification may also add that the user has adopted a particular job role (e.g., Senior Software Design Engineer—a hypothetical example).
At this point in the discussion, an important observation that was made above is again repeated with slightly different wording. The user (e.g., 301A?) is part of his/her own context(s) from under which his or her various attention giving actions emanate and that/those individualized context(s) may be mapped to corresponding, communally understandable (e.g., more generalized) contexts that populate a communally created and communally updated context space (XS). More specifically, the user's currently “perceived” and/or “virtual” (PoV) set of contextual states (what is activated in his or her mind) is part of the individualized context from under which that user's actions emanate. So if the user is thinking to him/herself, “I am currently taking on the role of Senior Software Design Engineer” that is part of that user's overall and individually-adopted context. Often, the user's current physical surroundings (location, furniture, operational data processing devices, etc.) and/or body states (collectively denoted as 301 x) are part of the perceived context from under which the individual user's actions emanate. The user's current physical surroundings and/or current body states (301 x) can be sensed by various sensors, including but not limited to, sensors that sense, discern and/or measure: (1) current location and time (in real life (ReL) and/or in a virtual world that the user is participating within; (2) surrounding images and their locations relative to the user, (3) surrounding sounds and their locations relative to the user, (4) surrounding physical odors or chemicals, (5) presence of nearby other persons (not shown in FIG. 3D; real and/or virtual) and their locations relative to the user, (6) presence of nearby electronic devices and their current settings and/or states (e.g., on/off, tuned to what channel, button activated, etc.) as well as their locations relative to the user, (7) presence of nearby buildings, structures, vehicles, natural objects, etc. as well as their locations relative to the user; and (8) orientations and movements of various body parts of the user including his/her head, eyes, shoulders, hands, etc. Any one or more of these various contextual attributes can help to add additional semantic spin and/or other types of cognitive flavorings to otherwise ambiguous words (e.g., 301 w), facial gestures (e.g., 301 g), body orientations, gestures (e.g., blink, nod) and/or device actuations (e.g., mouse clicks, finger taps, etc.) emanating from the user 310A?. Interpretation of ambiguous or “fuzzy” user expressions (301 w, 301 g, etc.) can be augmented by lookup tables (LUTs, see 301 q of FIG. 3D) and/or knowledge base rules (KBR's) made available within the currently active and individualized profiles 301 p of the user as well as by inclusion in the lookup and/or KBR processes of dependence on the current physical surrounds and states 301 x of the user. Since the currently active profiles 301 p are selected by the context indicating output signals 316 o of context mapping mechanism 316? and since the currently active profiles 301 p also provide context-hinting clue signals 301 p? as next inputs into the context (316?) and/or various other mapping mechanisms (e.g., 312?, 313?, 315?, etc.), a feedback loop is created (where the feedback system's states should converge on a more refined contextual state and/or more refined other state of the user 301A?) whereby the progressively better-selected profiles 301 p drive the context mapping mechanism 316? (for example) and the latter contributes to selection of the next to be activated and yet better-selected profiles.
The feedback loop is not an entirely closed and isolated one because the real physical surroundings and state indicating signals 301 x? (which include the XP signal) of the user are included in the input vector signals (e.g., 316 v) that are supplied to the context mapping mechanism 316?. Thus context is usually not determined purely due to guessing about the currently activated (e.g., lit up in an fMRI sense) internal mind states (PoV's, a.k.a. “perceived” and/or “virtual” set of contextual states) of the individual user 301A? based on previously guessed-at mind states but rather also on the basis of surrounding reality. The real physical surrounding context signals 301 x? (a.k.a. the XP signals) of the user are grounded in physical reality (e.g., What are the current GPS coordinates of the user? What non-mobile devices is he proximate to? What other persons is he proximate to? What is their currently determined context? What biometric data is currently being collected from the user? and so on) and thus the output signals 316 o of the context mapping mechanism 316? are generally prevented from running amuck into purely fantasy-based determinations of the likely current mind set of the user. Moreover, fresh and newly received CFi signals (302 e? and 298 e?) are repeatedly being admixed into the input vector signals 316 v. Thus the profiles-to-context space feedback loop is not free to operate in a completely unbounded and fantasy-based manner but instead keeps being re-grounded with surrounding physical realities.
With that said, it may still be possible for the context mapping mechanism 316? to nonetheless output context representing signals 316 o that make no sense (because they point to or imply untenable nodes or subregions in other spaces as shall be explained below). In accordance with one aspect of the present disclosure and in an embodiment, the conflicts and errors resolving module 301 pvp automatically detects such untenable conditions and in response to the same, automatically forces a reversion to use of the default set of safe profiles 301 d. In that case, the context mapping mechanism 316? “learns” that its previous context-determining steps were erroneous ones and adaptively alters its neural net and/or other trainable modeling parts and then restarts from a safe broad definition of current user profile states and then tries to narrow the definition of current user context to one or more, smaller, finer subregions (e.g., XSR1 and/or XSR2) in the communally created and communally updated context space (XS) as new CFi signals 302 e?, 298 e? are received and processed by CFi categorizing-mechanisms 302? and 298? and then processed by the context mapping mechanism 316? as well as other such mapping mechanisms (e.g., 313?, 314? etc.) included within the STAN—3 system.
It will now be explained in yet more detail how input vector signals (like 316 v) for the mapping mechanisms (e.g., 316?, 313?, etc.) are generated from raw CFi signals and the like. There are at least two different kinds of energetic activities the user (301A? of FIG. 3D) can be engaged in. One is energetic paying of attention to user-receivable inputs (298?). The other is energetic outputting of user produced signals 302? (e.g., mouse click or screen tap streams, intentionally communicative head nods and facial expressions—i.e. tongue projections, etc.). A third possibility is that the user (301A? of FIG. 3D) is not paying attention and is instead day dreaming while producing meaningless and random facial expressions, grunts, screen taps and the like.
The CFi's processing portion of system 300D of FIG. 3D relies on available sensors (instruments) at the user's location for gathering data that likely indicates user context and/or what the user is focusing his/her attention giving energies upon. More specifically, a first set of sensors 298 a? (referred to here as attentive inputting tracking sensors) are provided and disposed to track various biometric indicators of the user, such as eyeball movement patterns, eye movement velocities, tongue positionings, and so on, to thereby detect if the user is actively reading text and/or focusing-upon then presented imagery, and if so what parts thereof and/or with what degree of attentiveness. (In one embodiment, the user's currently activated PEEP profile equates different kinds of tongue, mouth and/or other body part dispositions—e.g., mouth agape and tongue stuck out—with different degrees of individualized attentiveness.) The various biometric indicators may include those that are detectable in a non-visible/non-hearable wavelength band such as biometric states detectable in an IR band and/or biometric states detectable in a sub-audio or super-audio frequency band. A crude example of such biometric indicators may be simply that the user's head is facing towards a computer screen. A more refined example of such tracking of various biometric indicators could be that of keeping track of user eye blinking rates (301 g), breathing rates, exhalation temperatures and exhalation gas compositions (e.g., using absorption spectrum detecting means for example), salivation rates, salivation composition, tongue movement rates, etc. and then referring to the currently active PEEP profile of the user 301A? for translating such biometric activities into indicators that the user is in an alerted state and is actively paying attention to material being presented to him or not. As already explained in the here-incorporated STAN-1 and STAN-2 applications, STAN users may have unique ways of expressing their individual emotional and/or attentive states where these expressions and their respective meanings may vary based on mood, context and/or current topic of focus. As such, context-dependent and/or topic of focus-dependent lookup tables (LUT's) and/or knowledge base rules (KBR's) are typically included in the user's currently active PEEP profile (not explicitly shown, but understood to be part of profiles set 301 p) and used for normalizing individualized expressions into more communally understandable expressions. In other words, raw expressions of each given user are run through that individual user's then-active PEEP profile to thereby convert that individual's individualized expressions into more universally understandable (normalized) counterparts. More specifically, for one specific user, a shrug of the left shoulder and a tilt of the head to left might always mean an indication of aloofness. The normalized user state (one that is communally understandable) would then be “aloof” while the individualized gesture is an ambiguous shrug of the left shoulder and a tilt of the head to left.
Incidentally, just as each user may have one or more unique (e.g., idiosyncratic) facial expressions or the like for expressing internal emotional states (e.g., happy, sad, angry, etc.), each user may also have one or more unique other kinds of expressions or codings (e.g., unique keywords, unique topic names, etc.) that they personally use to represent things that the more general populace (the relevant community) expresses with use of other, more-universally accepted expressions (e.g., popular keywords, popular topic names, etc.). More specifically, and using the hypothetical example of the Superbowl™ Sunday Party up top, one system user may have an idiosyncratic pet name he uses in place of a more commonly, communally used name for a well known celebrity. The nonconforming user might routinely refer to “Joe-the-Throw Nebraska” as “Yo Ho Joe”. This kind of information is stored in a currently activated personhood profile of the user, under a section entitled for example, Favorite Idiosyncratic Keywords, where a translation to the more commonly used terminology (e.g., “Joe-the-Throw Nebraska”) is included and where the STAN—3 system automatically performs the translation when normalizing the raw CFi's received from that individual user. More generally and in accordance with one aspect of the disclosure, one or more of the user profiles 301 p include expression-translating lookup tables (LUT's) and/or knowledge base rules (KBR's) that provide translation from relatively idiosyncratic CFi expressions often produced by the respective individual user into more universally understood (communally understandable), normal CFi expressions. This expression normalizing process is represented in FIG. 3D by items 301 q and 302 qe?. Due to space constraints in FIG. 3D, the actual disposition of module 302 qe? (the one that replaces ‘abnormal’ CFi-transmitted expressions with more universally-accepted counterparts) could not be shown. The abnormal(a.k.a. idiosyncratic)-to-normal swap operation of module 302 qe? occurs in that part of the data flow where CFi-carried signals are coupled from raw-CFi signal generating units 302 b? and 298 a? to CFi categorizing-mechanisms 302? and 298?. In addition to replacing ‘abnormal’ or user-idiosyncratic CFi-transmitted expressions with more universally-accepted/recognized counterparts, the system includes a spell-checking and fixing module 302 qe 2? which automatically tests CFi-carried textual material for likely spelling errors and which automatically generates spelling-wise corrected copies of the textual material. (In one embodiment, the original, misspelled text is not deleted because the misspelled version can be useful for automated identification of STAN users who are focusing-upon same misspelled content. Instead, the original, misspelled text is augmented with an appending thereto of the spelling-wise corrected textual material.)
In addition to replacing and/or supplementing ‘abnormal’ (user-idiosyncratic) CFi-transmitted expressions with more universally-accepted and/or spell-corrected counterparts, the system includes a new permutations generating module 302 qe 3? which automatically tests CFi-carried material for intentional uniqueness by, for example, detecting whether plural reputable users (e.g., influential persons) have started to use a unique and previously not commonly seen pattern of CFi-carried data at about the same time. This may signal that perhaps a newly observed pattern or permutation is not an idiosyncratic aberration of one or a few non-influential users but rather that it is likely being adopted by the user community (e.g., firstly by influential early-adopter or Tipping Point Persons within that community, and later by following others) and thus it is not a misspelling or an individually unique pattern (e.g., a pet idiosyncratic name) that is used only by one or a small handful of users in place of a more universally accepted pattern. If the new-permutations generating module 302 qe 3? determines that the new pattern or permutation is being adopted by the user community, the new-permutations generating module 302 qe 3? automatically inserts a corresponding new node into the system-maintained keyword expressions space (e.g., in expressions layer 371 of FIG. 3E) and/or another such space (e.g., hybrid keyword plus context space) as may be appropriate so that the new-permutation no longer appears to modules 302 qe? and 302 qe 2? as being an idiosyncratic, abnormal or misspelled expression pattern. The node (corresponding to the early-adopted new CFi pattern) can be inserted into keyword expressions space and/or another such space (e.g., hybrid keyword plus context space) even before a topic node is optionally created for the new CFi pattern. Later, if and when a new topic node is created in topic space for a topic related to the new CFi pattern, there will already exist in the system's keyword expressions space (e.g., in expressions layer 371 of FIG. 3E) and/or another such space (e.g., hybrid keyword plus context space), a non-topic node to which the newly-created topic node can be logically linked. In other words, the system can automatically start laying down an infra-structure (e.g., keyword expression primitives; which concept will be explained in conjunction with 371 of FIG. 3E) for supporting newly emerging topics even before a large portion of the user population starts voting for the creation of such new topic nodes (and/or for the creation of associated, on-topic chat or other forum participation sessions). A further explanation of where and how the new permutations generating module 302 qe 3? fits into the overall scheme of things will be provided in conjunction with FIG. 3W.
In addition to replacing and/or supplementing ‘abnormal’ (user-idiosyncratic) CFi-transmitted expressions with more universally-accepted and/or spell-corrected counterparts, the system includes an expressions expanding or supplementing/augmenting module (not separately shown, but part of the 302 qe? complex) which optionally adds to the normalized expressions already provided by the individual user, supplemental expressions that are of similar meaning (e.g., synonyms) and/or are of opposite meaning (e.g., antonyms) and/or are of similar sound (e.g., homonyms). This may be done by referencing online Thesauruses and/or dictionaries and/or system-maintained lists that provide such augmenting information. In this way, if the user picked a non-idiosyncratic, but nonetheless not popularly used term, the system can automatically add a more popularly used term to the mix and, as a result, the context and/or other mapping mechanisms (e.g., 316?, 313? of FIG. 3D) are assisted towards more quickly finding matching nodes (and/or points or subregions) within their internal Cognitions-representing Spaces.
Sometimes, a same one system user can have multiple sensing machines (e.g., 298 a?, 302 b?, 304) reading out similar and basically duplicative CFi reporting records for uploading into the system cloud. Such redundant generating of duplicative CFi's may make it appear as if the respective user is more intensely focused-upon something than is really the case. However, each locally generated CFi signal usually has attached to it at least a time stamp if not also a location stamp and/or machine ID stamp and/or user ID stamp and/or data-type indicating stamp (e.g., image data, text data, coded data, biometric data, etc.). When a string or streamlet of CFi signals are received at the head end (e.g., cloud end) of the STAN—3 system, in one embodiment they are preprocessed by a data deduplicating module (not shown) which is configured to detect likely data duplication conditions and remove data that is likely to be duplicative from the data stream sent further upstream for yet further processing. In this way, the upstream resources are not unduly swamped with duplicative CFi data so that, for example, one person's duplicative CFi's do not unfairly swamp out (e.g., out-vote) another person's CFi's just because the latter user has a fewer number of local CFi generators than does the first user. In one embodiment, the number of CFi generating instruments that can simultaneously supply CFi reporting records on behalf of a respective individual user (e.g., 301 a?) is limited to a predefined number and hierarchical rankings are attributed to different ones of such duplicative reporting instruments whereby, if the predetermined CFi inputs per person per unit of time threshold is exceeded, the lower ranked ones among the duplicative reporting instruments are disabled or ignored first so that the higher quality, better reporting ones are the ones who contribute to the limited reporting bandwidth granted to each STAN—3 system user. (Of course, in one embodiment, users who pay for premium subscriptions are granted a higher maximum CFi's/unit-time value than are those with no or lesser subscriptions.)