12

 
Representations of the various types, sizes and numbers of the empty chat or other forum participation opportunities are automatically recorded into launching area 565. Each of the empty forum descriptions in launching area 565 is next to be populated with a socially “interesting” mix of co-compatible personalities (with identifications of those personas) so that a socially “interesting” interchange will likely develop when invitees (those waiting in pool 504) are accordingly invited to join into the, soon-to be launched forums (565) and when a statistically predictable subpopulation of them subsequently accept the invitations. To this end, an automated social dynamics, recipe assigning engine 555 is deployed. The recipe assigning engine 555 has access to predefined room-filling recipes 555 i 4 (a.k.a. social-mix recipes) which respectively define different mixes of personality types that usually (based on earlier collected statistical data and survey results) can be invited into a chat room or other forum participation session where that mixture of personality types will usually produce well-received results for the participants. In one embodiment, promoters (e.g., vendors) who plan to make promotional offerings later downstream in the process, get to supply some of their preferences as requested mixes or mix modification 555 i 2 into the recipe assigning/formulating engine 555. In one embodiment, a listing of the current top topics identified by module 551 (or other current top N points, nodes or subregions (PNOS's) in other Cognitive Attention Receiving Spaces) are fed into recipe assigning/formulating engine 555 as input 555 i 3 so that assigning/formulating engine 555 can pick out or formulate recipes based on those current top topics (or other PNOS's). As the recipe assigning/formulating engine 555 begins to generate corresponding room make-up recipes, it will start to detect that certain participant personality types are more desired (e.g., more in short supply) than others and it will feed this information as signal 555 o 2 to one or more bottleneck traits identifying engines 577.
The bottleneck traits identifying engines 577 compare what they have (551 o 3) in the waiting pool 504 versus what is called-for by the initially generated recipes. The bottleneck traits identifying engines 577 then responsively transmit bottleneck warning signals 557 i 2 to a next-in-the-assembly line, recipes modifying engine 557. As in the case, for example, of high production restaurant kitchen, the inventory of raw materials on hand (in 504) may not always perfectly match what an idealized recipe calls for; and the chef (or in this case, the automated recipes modifying engine 557) has to make adjustments to the recipes so that a good-enough result is produced from ingredients on hand as opposed to the ideally desired ingredients (pool of available users). In the instant case, the ingredients on hand are the entity identifications waiting in pool area 504. The automated recipes modifying engine 557 has been warned by signal 557 i 2 that certain types of social entities (e.g., potential room leaders or top influencers) are in short supply. So the recipes modifying engine 557 has to make adjustments accordingly.
The recipe assigning module 555 assigns an idealized recipe from its recipes compilation storage area 555 i 4 to the pre-sized and otherwise pre-designed empty chat rooms or empty other forums flowing out of staging area 565 to thereby produce corresponding forums 567 (rolling out on the assembly line) having idealized recipes logically attached to them. The automated recipes modifying engine 557 then looks into the ingredients pool 504 then on hand and makes adjustments to the recipes as necessary to compensate for expected bottlenecks or shortages in desired personality types. More specifically, a recipe may call for two leaders and two influencers, but these personas are in currently short supply in pool 504. So the recipes modifying engine 557 automatically trims the recipe to one of each for example. The on-assembly-line rooms 568 with correspondingly modified recipes attached to them are then output assembly line wise along a data flow storing path (delaying and buffering path) to await acceptances of corresponding invitations to these rooms by respective entities in pool 504. The invitations are sent to the pooled personas (504) by the automated recipes modifying and invitations sending engine 557.
In an alternate or supplemental embodiment, the output signal from bottleneck traits identifying engines 577 is also transmitted to the recipe assigning module 555. In response, the recipe assigning module 555 curtails its selections to those that do not overdraw on the identified scarce ingredients. In other words, even though the currently identified top N topics (555 i 3—or top N? other PNOS's of another CARS) and/or the received vendor requests (555 i 2) point to a first subset of the stock recipes 555 i 4 as being ideal ones for the currently hot topics (or hot other ‘touchings’); if the bottleneck traits identifying engines 577 indicate that the called-for personas are not present in sufficient quantities (or at all) inside the current waiting pool 504, then the recipe assigning module 555 adjusts accordingly, making do with the available people, or better yet with the people who have actually accepted the chat invitations rather than picking recipes first and then trying to produce room participant populations in accordance to the pre-picked recipes.
Next in the assembly line, an RSVP receiving engine 559 automatically receives acceptances (or not) from the invited potential participants of pool 504. Some chat rooms or other forums will receive an insufficient number of the right kinds of acceptances (e.g., a critically needed and scarce room leader does not sign up). If that happens, the RSVP receiving engine 559 automatically trashes the room (removal flow 569) and sends apologies to the invitees indicating that the party had to be canceled due to unforeseen circumstances. On the other hand, with regard to rooms for which a sufficient number of the right kinds of acceptances (e.g., critically needed room leaders and/or rebels and/or social butterflies and/or Tipping Point Persons) are received so as to allow the intent of the room recipe to substantially work as intended, those rooms (or other forums) 570 continue flowing down the assembly buffer line (memory system that functions as if it were a conveyor belt) for processing next by engine 561. At the same time, a feedback signal, FB4 is output from the RSVP's receiving engine 559 and transmitted to a recipes perfecting engine (not shown) that is operatively coupled to the holding area of the social-mix recipes 555 i 4. The FB4 feedback signal (e.g., percentage of acceptances and/or types of acceptances) are used by the recipes perfecting engine (of holding module 555 i 4) to tweak the existing recipes so they better conform to actual results (what is observed in the field) as opposed to theoretical predictions of results (e.g., which room recipes are most successful in getting the right kinds and numbers of positive RSVP's). The recipes perfecting engine (which tweaks one or more recipes in holding module 555 i 4) receives yet other feedback signals (e.g., FB3, 575 o 3-described below) which it can use alone or in combination with FB4 for tweaking the existing recipes and thus improving them based on obtained in-field data (on FB4, etc.).
Engine 561 is referred to as the demographics reporting and new social dynamics predicting engine. It collects the demographics data of the social entities (e.g., people) who actually accepted the invitations and forwards the same to auctioning engine 562. It also predicts the new social dynamics that are expected to occur within the chat room (or other forum) based on who actually joined as opposed who was earlier expected to join (expected by upstream engine 557).
The auctioning engine 562 is referred to as a post-RSVP auctioning engine 562 because it tries to auction off (or sell off) populated rooms to potential promotion offerors (vendors) 560 p based on who actually joined the room and on what social dynamics are predicted to occur within the room by predicting engine 561. By auctioning off (or selling off), it is meant here that the winning/buying promotion offeror(s) will correspondingly receive a chance to post a promotional offering (e.g., discounted pizza) to participants of the corresponding chat or other forum participation session. Naturally, chat or other forum participation sessions that have influential Tipping Point Persons or the like joined in to them and/or are predicted to have very entertaining or otherwise “interesting” social dynamics taking place in them, can be put up for auction or sale at minimum bid amounts that are higher than chat rooms or the like that are expected to be less “interesting”. The potential promotion offerors (vendors) 560 p transmit their bids or sale acceptances to engine 562 after having received the demographics and/or social dynamics predicting reports from engine 562. Identifications of the auction winners or accepting buyers (from among buying/bidding population 560 p) are transmitted to access awarding engine 563.
As an alternative to bidding or buying exclusive or non-exclusive access rights to post-RSVP forums that have already begun to have active participation therein, the potential promotion offerors (vendors) 560 p may instead interact with a pre-RSVP's engine 560 that allows them to buy exclusive or non-exclusive access rights for making promotional offerings to spawned rooms even before the RSVP's are accepted. In one embodiment, the system 410 establishes fixed prices for such pre-RSVP purchases of rights. Since the potential promotion offerors (vendors) 560 p take a bigger risk in the case where RSVP's are not yet received (e.g., because the room might get trashed 569), the pre-RSVP purchase prices are typically lower than the minimum bid prices established for post-RSVP rooms.
In one embodiment, influential Tipping Point Personas (e.g., 501 a) present within the waiting pool 504 are identified before the auctioning off of promotional access takes place (in engine 562). Special preliminary invitations are sent to these identified TPP personas. The special preliminary invitations indicate to the targeted Tipping point people that, if they join, and the afterwards joining participants are happy with the chat (as indicated by fedback CVi data), then the early-wise committing TPP will be rewarded, for example with discount coupons offered by a corresponding promotion offeror (vendor) 560 p. This mechanism can encourage certain people to establish themselves as happy-room-makers or as other forms of system-recognized, influential people (e.g., Tipping Point Persons) since they typically know they have personalities for making other people happy (as will be objectively reported by automatically collected CVi signals) and thus they are likely to win the promised rewards if they perform as expected of them. The result is a win-win for all involved because the other chat or forum participants perceive a more enjoyable chat or other forum participation experience thanks to the extra energies exerted by the happy-room-makers (the system-recognized, influential people (e.g., Tipping Point Persons)) to make the sessions enjoyable ones. The enjoyment factor induces pleased participants to return again for more such sessions. The enjoyment factor also induces the pleased participants to associate the promotional offerings of the winning promotion offeror (vendor) 564 with goodwill feelings which can lead to increased sales. Over time, as positive influence casting results are collected via fedback CVi signals obtained from the other forum participants, the STAN—3 system can automatically rank and thus determine who among the happy-room-makers are best at performing their task (of making the in-room experience more enjoyable for the other participants) for different categories of topics or other such classes of chat rooms; and the rewards offered to these identified TPP personas may be increased accordingly.
In one embodiment, the auction winners 564 can first test-pitch their promotional offerings to one or a few in-room representatives (e.g., the room discussion leader) in private before attempting to pitch the same to the general population of the chat room or other forum. Feedback (FB1) from the test run of the pitch (564 a) on the room representative (e.g., leader) is sent to the access-rights owning promoters (564). They can use the feedback signals (FB1) to determine whether or not to pitch the same promotional presentation to the room's general population (with risk of losing goodwill if the pitch is poorly received) and/or to determine when to pitch the same to the room's general population and/or to determine whether modifying tweaks are to be made to the pitch before it is broadcast (564 b) to the room's general population. It is to be noted that as time progresses while the instantiated forum advances on the room assembly-and-conveying line, various room participants may drop out and/or new ones may join the room. Thus the makeup and social dynamics of the room at a time period represented by 574 (when the pitch is made or thereafter) may not be the same as at a time period represented by test run 573.
In one embodiment, a further engine 575 (referred to here as the ongoing social dynamics and demographics following and reporting engine) periodically checks in on the in-process chat rooms (or other forums) 571, 573, 574 and it generates various feedback signals that can be used elsewhere in the system for improving system reliability and performance. One such feedback signal (FB2, a.k.a. signal 575 o 2) indicates the way that participants actually behave in the rooms as opposed to what was expected of them, for example based on their currently activated profiles. These actual behavior reports are transmitted to another engine (not shown) which compares the actual behavior reports 575 o 2 against the traits and habits recorded in the respective user's currently activate profiles 501 p (See also PHAFUEL log 501? of FIG. 5A.) The profiles versus actual behavior comparing engine (not shown, associated with signals 575 o 2) either reports variances as between actual behavior and profile-predicted behavior or automatically tweaks the profiles 501 p of the respective users to better reflect the observed actual behavior patterns under corresponding contextual background. Another feedback signal (FB3) sent back from engine 575 to the variance reporting/correcting engine (not shown) is one relating to the verification of the alleged street credentials of certain Tipping Point Persons or the like. These credential verification signals are derived from votes (e.g., CVi's) cast by in-room participants other than the persons whose credentials are being verified. Another feedback signal (575 o 3) sent back from engine 575 goes to the recipes tweaking engine (not shown) associated with holding area 555 i 4. These downstream feedback signals (575 o 3) indicate how the spawned room performs later downstream, long after it has been launched but before it fades out (576) for example due to loss of participants and/or interest. The downstream feedback signals (575 o 3) may be used to improve recipes for longevity as opposed to good performance merely soon after launch (570) of the rooms (of the TCONEs).
The statistics developed by the ongoing social dynamics and demographics following and reporting engine 575 may be used to signal (564) the best timings for pitching promotional offerings to respective rooms. By properly timing when a promotional offering is made and to whom, the promotional offering can be caused to be more often welcomed than not by those who receive it (e.g., “Pizza: Big Neighborhood Discount Offer, While it lasts, First 10 Households, Press here for more”). In one embodiment, the ongoing social dynamics and demographics following and reporting engine 575 is operatively coupled to receive context state reports generated by the context space mapping mechanism (316? of FIG. 3D) for indicating the most appropriate generalized context node(s) for each of potential recipients of promotional offerings. Accordingly, the engine 575 can better predict when is the best timing 564 c to pitch the offering based on latest reports about the user's contextual state (and/or other mapped states, e.g., physiological/emotional/habitual states=hungry and in mood for pizza).
The present disclosure is to be taken as illustrative rather than as limiting the scope, nature, or spirit of the subject matter claimed below. Numerous modifications and variations will become apparent to those skilled in the art after studying the disclosure, including use of equivalent functional and/or structural substitutes for elements described herein, use of equivalent functional couplings for couplings described herein, and/or use of equivalent functional steps for steps described herein. Such insubstantial variations are to be considered within the scope of what is contemplated here. Moreover, if plural examples are given for specific means, or steps, and extrapolation between and/or beyond such given examples is obvious in view of the present disclosure, then the disclosure is to be deemed as effectively disclosing and thus covering at least such extrapolations.
In terms of some of the novel concepts that are presented herein, the following recaps are provided:
Per FIG. 1A, an automated and machine-implemented mechanism is provided for allowing the inviting together of, or the automatic bringing together of, people or groups of people based on machine automated determinations of more likely cognitions within those users' minds, for example based on uncovering what topics (or other points, nodes or subregions (PNOS's) of other Cognitive Attention Receiving Spaces) are currently most likely relevant to them and by presenting them with appropriately categorized invites; where the determination of currently relevant topics and/or other PNOS's, the determination of currently appropriate times and places to present the invites and/or hold the gatherings are based on one or more of: automatically determining user location and/or other context by means of embedded GPS sensors or the like, automatically determining proximity with other people and/or proximity of their computers, and/or wireless communicating devices automatically determining what virtually or physically proximate people are allowing broadcast of their Top 5 Now Topics to others where at least one matches with that of a potential invitee. In such a machine-implemented and automation driven bringing together of co-compatible people (or driven directing of people to on-topic events), the current levels of attention giving energies and their focus upon corresponding topic nodes or subregions (or focus upon corresponding other PNOS's of other CARSs) is detected by means of received CFi signals, and/or heats of CFi's, and/or keyword usages, and/or hyperlink usages, and/or perused online material, and/or environmental clues (odors, pictures, physiological responses, music, etc.) that can indicate user context.
Also per FIG. 1A, an automated and machine-implemented mechanism is provided for allowing the inviting together or the automatic bringing together of people or groups of people based on currently determined attention giving activities where the latter can include automatically detected choices or actions made by the users or based on currently determined other indicators that can be implied from their choices or actions and/or interactions as combined with currently activated profiles.
In one embodiment, each STAN user can designate a top 5 topics of that user as broadcast-able topic identifications. The identifications are broadcast on a peer to peer basis and/or by way of a central server. As a result, if a first user is in proximity of other people who have one or more of their broadcast-able topic identifications matching at least one of the first user's broadcast-able topic identifications, then the system automatically alerts the respective users of this condition. In one embodiment, the system allows the matched and proximate persons to identify themselves to the others by, for example, showing the others via wireless communication a recent picture of themselves and/or their relative locations to one another (which resolution of location can be tuned by the respective users). This feature allows users who are in a crowded room to find other users who currently have same focus in topic space and/or other spaces supported by the STAN—3 system 410. Current focus is to be distinguished from reported “general interest” in a given topic. Just because someone has general interest, that does not mean they are currently focused-upon that topics and/or on specific nodes and/or subregions in other spaces maintained by the STAN—3 system 410. More specifically, just because a first user is a fisherman by profession, and thus it's a key general interest of his when considered over long periods of time, in a given moment and given context, it might not be one of his Top 5 Now Topics of focus and therefore the fisherman may not then be in a mood or disposition to want to engage in online or in person exchanges regarding the fishing profession at that moment and/or in that context. It is to be understood that the present disclosure arbitrarily calls it the top 5 now, but in reality it could instead be the top 3 or the top 7. The number N in the designation of top N Now (or then) topics may be a flexible one that varies based on context and most recent CFi's having substantial heat attached to them. In one embodiment, the broadcastable top 5 topic focuses can be put in a status message transmitted via the user's instant messenger program, and/or it can be posted on the user's Facebook™ or other alike platform profile.
In one embodiment, the system 410 supports automated scanning of NearFiledCodes and/or 2D barcodes as part of up or in-loaded CFi's where the automatically scanned codes demonstrate that the user is in range of corresponding merchandise or the like and thus “can” scan the 2d barcode, or any other object-identifying code (2d optical or not) that will show he or she is proximate to and thus probably focused on an object or environment in which the barcode or other scannable information is available.
In one embodiment, the system 410 automatically provides offers and notifications of events occurring now or soon which are triggered by socio-topical acts and/or proximity to corresponding locations.
In one embodiment, the system 410 automatically provides various Hot topic indicators, such as, but not limited to, showing each user's favorite groups of hot topics, showing personal group hot topics. In one embodiment, each user can give the system permission to automatically update the person's broadcastable or shareable hot topics whenever a new hot topic is detected as belonging to the user's current top 5. In one embodiment, the user needs to give permission to show, how long he will share this interest in the new hot topic (e.g., if more or less than the life of the CFi detections period), and/or the user needs to give permission with regard to who the broadcastable information will be broadcast or multi-cast or uni-cast to (e.g., individual person(s), group(s), or all persons or no persons (i.e. hide it)). If a given hot topic falls off the user's top 5 hot topic broadcastables list, it won't show in permitted broadcast. In one embodiment, an expansion tool (e.g., starburst+) is provided under each hot topic graphing bar and the user can click, tap or otherwise activate it to see the corresponding broadcast settings.
In one embodiment, the system 410 automatically provides for showing intersections of heat interests, and thus provides a quick way of finding out which groups have same CFi's, or which CFi's they have in common.
In one embodiment, the system 410 automatically provides for showing topic heat trending data, where the user can go back in time, and see how top hot topics heats trended or changed over given time frames.
In one embodiment, the system 410 automatically provides for use of a single thumb's up icon as an indicator of how the corresponding others in a chat or other forum participation session are looking at the user of the computer 100. If the perception of the others is neutral or good, the thumb icon points up, if its negative, the thumb icon points down and optionally it reciprocates up and down in that configuration show more negative valuation. Similarly, positive valuation by the group can be indicated with a reciprocating thumb's up configuration. So if a given user is not deemed to be rocking the boat (so to speak), then the system shows him a thumb's up icon. On the other hand, if the user is generating a negative raucous in the forum then the thumb points down. The thumb icon doesn't have to operate on a binary up or down basis. Instead, in one embodiment, it acts like a dial on a metered background scale, where if it's up 90 degrees it's good, down its bad, and in the middle it's a varying degree of good or bad or neutral.
In one embodiment, the system 410 automatically scans a local geographic area of predetermined scope surrounding a first user and automatically designates STAN users within that local geographic area as a relevant group of users for the first user. Then the system can display to the first user the top N now topics and/or the top N now other nodes and/or subregions of other spaces of the so designated group, thereby allowing the first user to see what is “hot” in his/her immediate surroundings. The system can also identify within that designated group, people in the immediate surroundings that have similar recent CFi's to the first user's top 5 CFi's and/or compatible personhood compatibility profiles. The geographic clusterings shown in FIG. 4E can be used for such purposes.
Referring to FIG. 4E, in one embodiment 400.E, a spatial and/or hierarchical clusterings map 40E.1 for a selected one or more subregions of topic space (or of another CARS, including hybrid ones of such CARS) is displayed on a user display device (e.g., tablet computer) where the selected subregion(s) may be cross-correlated with, for example, a user-defined geographic area (in real life (ReL) or in virtual life) and/or a user-defined demographic sector (e.g., age/occupation; also optionally in virtual life rather than ReL) and/or other user defined or specified subregion specifications, where an icon representing recent ‘touchings’ by the user (a.k.a. first user, e.g., 431?) to whom the clusterings map is displayed may optionally be shown located somewhere on that map and his/her recent ‘touching’ positions may be displayed relative to significant ‘touchings’ made by other people (e.g., a selected subset of other people) in that spatial clusterings map 40E.1, Therefore, the user (a.k.a. first user, e.g., 431?) may easily see how his ‘touchings’ in his selected one or more subregions (see divider line 40E.1X, discussed below) of topic space relates to recent ‘touchings’ (e.g., above threshold ‘touchings’) by other users in those selected subregions. In one embodiment, the displayed spatial clusterings map 40E.1 may also indicate relative distances within the selected spatial subregion(s) as between the ‘touchings’ of the first user and clusters of significant (above threshold) and recent ‘touchings’ made by the other people. In the same or another embodiment, the displayed spatial clusterings map 40E.1 may indicate significant (above threshold) and recent ‘touchings’ made by non-personal “events” within the selected subregion(s) of topic space. Those non-personal “events” may include organizational announcements, for example that an on-topic conference or lecture will be held at a geographically nearby conference hall where the topic nodes or subregions ‘touched’ (or to be ‘touched’) by the conference are relatively close within the displayed topic subregion (e.g., one relating to a specific geographic area and/or a specific demographic class of people) to the ‘touchings’ made by the first user (e.g., 431?). In this way the first user (e.g., 431?) can see which significant ‘touchings’ by other people and/or by non-person “events” are close to his in the displayed spatial clusterings map 40E.1.
In one embodiment, the map presenting system 400.E automatically indicates which persons or groups in the selected geographic/demographic specific subregion(s) (40E.6 i—to be explained shortly—being one of them) of topic space whose clustered ‘touchings’ are displayed have shared a Top 5 Now Topics with the first user and moreover, if they have co-compatible personhood attributes. If such other users are present, the system may then automatically put up a suggestive invite (e.g., an invitation icon) for the first user to join with the others if the others have current “availability” for such suggested joinder. In other words, rather than starting with a predefined one user or group of users and asking what are these pre-identified social entities focusing-upon (as was disclosed for example by pyramid 101 rb of FIG. 1A), the clustered ‘touchings’ map 40E.1 of FIG. 4E may start with a set of pre-specified subregions (e.g., 40E.6 i—only one shown) in topic space and first ask what are the top N topics being focused-upon (being significantly ‘touched’ in each of the selected areas (e.g., 40E.6 i). Then it may ask as a follow-up question, which social entities are performing the displayed significant ‘touchings’ in the displayed subregion(s) are also social entities who share a top N topics with the first user? The map presenting system 400.E may also be configured to automatically ask and answer the question regarding which of these shared top N topics are receiving the most attention? The system may also display in the displayed ‘touchings’ map 40E.1 (or elsewhere) an availability score for each of the displayed nearby other users who are focusing-upon the identified top N topics of the selected topic subregion, e.g., 40E.6 i (where N can be 1, 2, 3, . . . etc. here).
As mentioned, the number of displayed subregions can be more than one. Plane 40E.1 can be composed of a collage of selected subregions. Dividing line 40E.1?3 for example, may represent a collage or puzzle-pieces amalgamation line where a first cluster of significant ‘touchings’ 40E.1 a from a first selected subregion (e.g., 40E.6 i) is joined in displayed plane 40E.1 with a second cluster of significant ‘touchings’ 40E.1 b taken from a different second selected subregion (not shown) of topic space mapping mechanism 413?. The number of stitched together subregions can be more than two. The user is given access to a subregions selecting tool with which the user can specify the one or more subregions of a selected space that are to be displayed in plane 40E.1 and how they should be organized in that displayed i40E.1.
As a more specific example, let's say the first user has a top-5-now topics set and a first selected topic subregion (e.g., 40E.6 i) contains topic nodes corresponding to his top-5-now topics. Also say that the first user is publicly broadcasting a definition of this set as being his top 5. Let's say the co-compatible other users (whose currently significant ‘touchings’ are taking place in the same topic subregion, e.g., 40E.6 i) cannot now meet physically (in real life (ReL) or meet as avatars in virtual life if the latter is in effect), but they can remotely chat with the first user; perhaps only by means of a short (e.g., 5 minute) chat. In that case, the availability score will indicate the limited way in which the other users are each available for the first user. In other words, there are different types of availabilities that can be indicated on a spectrum extending from real life (ReL) meeting availability for long chats to only virtual availability for short chats and perhaps only in a virtual life context. A significant ‘touchings’ clustering map such as 40E.1 can indicate all this. More specifically, if the first user used tool 40E.6 (explained shortly) to choose his selected topic subregions (e.g., 40E.6 i) wisely, the displayed other users (or more specifically those whose significant ‘touchings’ are being displayed) will inherently be a in geographic area that the first user is also in and/or the other users will inherently belong to a demographic subgroup in which the first user is interested. As a result, even though the first user does not know the identities of these other users beforehand, the first user can find them (provided they are allowing themselves to be found) by virtue of the others having significant ‘touchings’ within the selected topic subregions (e.g., 40E.6 i) that the first user has asked the system (400.E) to display to him.
The displayed clusterings map 40E.1 (which in this example displays clusterings of now-on-topic-touchings by other personas within at least topic subregion 40E.6 i; but in other here-contemplated versions may display clusterings relative to a defined other subregions or more in a defined other spatial space, e.g., URL's space—see FIG. 4F) can be modified by user operation of various display control tools: 40E.5-40E.9 to reveal many different kinds of clusterings. The format of the displayed clustering map need not be a plane (40E.1) in a 3-dimensional spatial space 40E.0 as shown in the example of FIG. 4E. Instead the format could be one mimicking a cylindrical topic space branch (see 30R.10 of FIG. 3R) or the spatial geometry (e.g., conical) of yet another subregion of topic space or of other subregions of other Cognitive Attention Receiving Spaces (see for example FIG. 3E). More generally, the displayed clusterings do not have to be those of touchings of specified people; or only topic-space touchings by people and/or in real life (ReL) ‘touchings’, and may alternatively or additionally be displayed clusterings of event-based ‘touchings’ (e.g., on-topic event announcements, tweets etc.) and/or displayed clusterings of other CARS-related and available resources (e.g., university laboratory facilities that logically cross-correlate with a respective subregion of a respective Cognitive Attention Receiving Space (CARS) that is being selected (alone or with selected others) as a mapping source. A bottom right corner portion of FIG. 4E is intended to indicate that the reported clusterings of ‘touchings’ can identify the users who did the ‘touching’ and/or can identify the forums in which they performed the touch and/or identify the points, nodes or subregions in respective Cognitive Attention Receiving Spaces (CARS's) that were ‘touched’, where ‘touchings’ can have respective locations and times in real life (ReL) and/or virtual life and the touched PNOS's can be those of textual types of CARS's (e.g., keywords, URL's, meta-tags, etc.) and/or of nontextual types of CARS's (e.g., visuals, audibles, emotional or other feelings, biological or other states of the users and so on). Stated otherwise, mapped clusterings do not have to start with a specific identification of clustered personas (e.g., a pre-specified “group” of uniquely identified users—see again My Family 101 b of FIG. 1A) and then proceed to identifying what subregions of topic space (and/or of another space) they are focusing-upon. Instead a clusterings mapping (e.g., 40E.1) can be automatically generated by starting with a pre-specified one or more geographic areas in a geography space and/or with a pre-specified one or more areas (subregions) in other kinds of spaces (e.g., topic space, keyword space, URL space, social dynamics space and so on) and by thereafter asking open ended or criteria limited questions as to which geographic and/or other areas are receiving the hottest amounts of attention and as directed to what in the respective area; where here hotness can mean most number of people giving attention and/or a geographic and/or other area receiving the most emotionally charged of attention giving energies and if, so; what other spaces and subregions (e.g., topic space subregions) thereof are these hottest amounts of attention being directed to?
The layout of the displayed first clusterings map 40E.1 can be varied to suit user preferences. More specifically, the system provides a user-operable, map format selector module 40E.3 that determines a format for a corresponding, virtual reference frame 40E.0 according to which the clusterings map 40E.1 will be displayed. As indicated in a non-limited way, user selectable input parameters for the map format selector module 40E.3 may designate a 3-dimensional format or a 2D format or a 4D format (e.g., animated or color coded) or even a higher dimensionality and also a reference frame geometry such as rectangular, cylindrical, spherical and so on. The quantitative parameters of the axes of the chosen reference frame 40E.0 may vary and may include one or more members of the set comprising: time, location, trending rate or trending acceleration, distance within a cognition space subregion from main-stream cognitions (see radius RTsBr of FIG. 3R for example) and so on. In the illustrated example of overlaid 3D planes 40E.1/40E.2, the user has chosen a rectangular reference frame 40E.0 whose Z-axis represents time. The upper displayed layer or plane 40E.1 shows clusterings (e.g., of significant touchings) during a first pre-specified time duration (e.g., within the last 30 days) while the lower displayed layer or plane 40E.2 shows clusterings during a second pre-specified time duration (e.g., within the previous 335 days). One or both of overlaid maps 40E.1 and 40E.2 may be translucent so that clusterings of both can be seen simultaneously. In this way, the user not only sees how the clustered items (e.g., touchings) are distributed in the selected XY plane over the most recent month (or day or other such first time period), but also how such clusterings were distributed over an earlier time period (40E.2). In one example the illustrated X and Y coordinates can represent latitude and longitude of a real life (ReL) geographic map. In a second example they can be latitude and longitude of a virtual life world. In a third example they can correspond to the X and Y coordinates (or other coordinates, e.g., cylindrical) of a selected subregion 413 xyz of topic space or of a subregion of another Cognitive Attention Receiving Space (e.g., URL's space). The map format selector module 40E.3 drives a display controller module 40E.4, where the latter is configured to match with display capabilities of the display device (e.g., smartphone) then being used by the respective system user (e.g., 431?). It is within the contemplation of the disclosure that clusterings information can be presented to a system user alternatively or additionally in audible form; particularly if the user is sight impaired or cannot at the time safely view his/her screen (e.g., because they are driving a vehicle). The audibly relayed clusterings information may be of a narrower type than the visually relayed information. For example, the audibly relayed clusterings information may indicate, “The following top 3 most promising contacts are clustered within 1 mile of you and all are now focusing-upon the following two of your Top 5 Now Topics: users B, C and D for topics 2 and 3; do you want to make contact with any of them?”. A yes answer will then be followed by further audio menu choices and the contact that is established may, in some cases, be an audio only communicative session because at least the first user has been predetermined to not be able to then use or safely use visually-based communicative modes.
Still referring to FIG. 4E, another module 40E.5 used in generating the displayed map or overlaid maps (e.g., 40E.1, 40E.2; or optionally the automatically audibly described map) is a data-objects organizing spaces selector module 40E.5. In the illustrated example, the organizing spaces selector module 40E.5 is selecting topic space (413?) and user-to-user spaces (U2U 411?) as two primary input source spaces for generating the map(s) 40E.1 (and optionally the underlain 40E.2 plane). Therefore, a first data source pointer 40E.5 a of selector module 40E.5 points to the system-maintained topic space and a second data source pointer 40E.5 b points to the system-maintained users space. However, in other variations, the first source pointer 40E.5 a could have instead pointed to another Cognitive Attention Receiving Space (CARS) such as, but not limited to, a real life (ReL) geography space, a real life (ReL) hybrid geography and chronology space, the system-maintained keywords space, URL's space, ERL's spaces, a music space, a microblogs space (e.g., tweets), a hybrid space (e.g., context-plus-another), a social dynamics space, and so on; where points, nodes or subregions in any such CARS can be receiving significant ‘touchings’ (e.g., hot emotional ‘touchings’) from users and/or user groups and where clusterings of such significant ‘touchings’ can be occurring in one or more specific subregions (e.g., 40E.6 i) of the selected CARS while being optionally directed to subregions of other CARS (e.g., of topic space).
As further shown in FIG. 4E, yet another module, namely, a first subregions filtering module 40E.6 is configured (e.g., by user selectable options) to identify one or more subregions (e.g., 40E.6 i) of the space pointed to by the first source data pointer 40E.5 a (topic space) as regions to be investigated for presence of clustered significant ‘touchings’. The first subregions filtering module 40E.6 may also control where in displayed map 40E.1 the results of different subregions are to be placed. For example, the first subregions filtering module 40E.6 may be used to draw collage joinder lines like 40E.1X, where in the final version of the displayed clustering map 40E.1, collage forming lines like 40E.1X are rendered invisible.
As yet further shown in FIG. 4E, another module, namely, a second subregions filtering module 40E.7 is configured (e.g., by user selectable options) to identify one or more subregions (e.g., 40E.7 i) of the space pointed to by the second source data pointer 40E.5 b (e.g., pointing to users space) as regions to be selectively used when generating the map that reports (e.g., displays) clusterings of significant user ‘touchings’. The second subregions filtering module 40E.7 may be pre-configured to include and/or exclude various kinds of entities in the system-maintained users space such as specifically identified individual users, specifically identified groups of users, users who satisfy a predefined search criteria (e.g., geographically nearby users who have top-N-now-topics sets strongly cross-correlating with the first user's top-N-now-topics set and are chat-wise co-compatible with the first user).
Referring to both of FIGS. 4E and 3K, in one embodiment, the STAN—3 system automatically generates so-called, entity focus defining objects (EFDO's) 30K.0 for respective ones of social entities monitored by the system. The so-monitored social entities may include individual users and/or predefined groups of such users. Each individual user may have plural “personas” associated with him/her, where each such persona (e.g., Tom, Tommy, Thomas) is assigned a unique user identification (social entity ID) and the latter is recorded as, or pointed to by data stored in a first section 30K.1 a of the illustrated EFDO data structure 30K.0. Each monitored group similarly is assigned a unique entity ID. Accordingly, the EFDO data structure 30K.0 can be ubiquitously used for defining respective focusing-upon activities of individual users and/or predefined groups. A second section 30K.1 b of the EFDO data structure stores code uniquely identifying the corresponding entity focus defining object. A given social entity (identified by 30K.1 a) may have many entity focus defining objects (EFDO's) generated for that entity at different times and stored in system memory for later recall and re-use. By providing at least the unique EFDO identifying code 30K.1 b (and optionally also the unique user identifying code 30K.1 a) a specific one EFDO may be called out. Although not shown, in one embodiment, the EFDO data structure 30K.0 may include addition fields indicating when (in what time range) and/or where (in what geographic sector) and/or with what emotional intensity (“heat”) and/or under what context the associated user performed the corresponding focusing activity.
A further section 30K.2 of the EFDO data structure stores code identifying a type of focusing activity being defined by the respective EFDO data structure 30K.0. As illustrated in example block 30K.2 a, the respective EFDO may be defining a set of top-N-now topics being focused-upon by the identified social entity (30K.1) where the latter is provided by a sorted list of N pointers (e.g., 30K.4 a) in section 30K.4 that respectively point to respectively ranked topic nodes or topic subregions of the system's topic space. So if the code in the second section 30K.2 specifies the EFDO of the respective social entity (30K.1) as being directed to the top N topics now being focused-upon (or focused-upon in a previous time period), then section 30K.4 will include a sorted listing of pointers pointing to the corresponding nodes or subregions of topic space.
On the other hand, if the code in section 30K.2 specifies the EFDO of the respective social entity (30K.1) as being directed to a “diversified” top N now topics, the corresponding and pre-sorted pointers of the section 30K.4 will point to a ranked set of such “diversified” topic nodes or subregions. In one embodiment, it is permissive to have complex combinations of focus sets; indicating for example that the respective social entity is simultaneously focusing-upon a top K keywords AND a top N topics; or an undiversified Top 5 Now Topics plus a diversified next 3 topics, and so on. Accordingly, the illustrated EFDO data structure 30K.0 includes pointer storing sections like 30K.4-30K.7 for respectively each storing one or more sets of pre-sorted (and/or pre-ranked) pointers pointing to respectively pre-ranked ones of points, nodes or subregions in respective Cognitive Attention Receiving Spaces (CARS) that satisfy a corresponding subset definition (e.g., “diversified” topic nodes).
One of the sections, 30K.5 included in the EFDO data structure 30K.0 identifies the most probable current context of the respective social entity by pointing to (30K.5 a) corresponding points, nodes or subregions (XSR) in the system-maintained context space. As with other examples provided herein, the system does not know for sure that the pointed to PNOS's are indeed the top ones currently receiving cognitive attention from the respective user of group of users and the exact order of attention giving energies directed to each. These are just best guess modelings of what probably is going on inside the users' minds based on collected CFi's telemetry and the clustering and categorizing of such telemetry in accordance with, for example, the process described herein for FIG. 3U. Hence the illustrated EFDO data structure 30K.0 is to be understood as indicating the “probable” mindset of the identified social entity based on collected telemetry. The system cannot know for sure what is inside the respective users' heads.
Another of the sections, 30K.6 included in the EFDO data structure 30K.0 identifies (30K.6 a) the most probable current hybrids of context-plus-topic nodes then determined by the system to be most likely receiving attention giving energies from the identified social entity.
Although the descriptions above focused-upon the “current” time period, yet another section 30K.3 of the illustrated EFDO data structure 30K.0 identifies the covered time period for the entity focus defining object (EFDO) and the corresponding physical context associated with the EFDO and/or other filtering attributes (e.g., real life (ReL) geographic location, temperature, humidity, wind velocity, biological status, etc.) associated with the EFDO. Accordingly, a plurality of different EFDO's (30K.0, 30K.0?) may be generated and stored by the system where the different EFDO's cover respective different time periods and/or different user contexts and/or different focus type (30K.2) and/or different user personas (30K.1) or different user groups, and so on. The generated and stored entity focus defining objects (EFDO's) may then be accessed by the map generating modules (e.g., 40E.7, 40E.6) of FIG. 4E for determining which focusings and/or significant ‘touchings’ of a filtered subset of users or groups are clustered where, geographically, temporally or in other terms.
Referring yet a bit more to FIG. 3K, in one embodiment the STAN—3 system comprises one or more entity focus defining objects (EFDO's) generating modules 30K.10. These may be tasked to run in the background as system data processing bandwidth permits and to follow monitored ones of individual users and to automatically generate “primitive” EFDO's for these users; such as for example, primitive EFDO's for all contexts, for a most recent time period and for just the top N topics of that user, or for just the top K keywords, the top L URL's and so on (where N, K and L are integers here representing an expected maximum value of ‘tops’ for each category). After the primitives have been generated, the EFDO's generating module(s) 30K.10 can use these as recursive inputs 30K.11 for generating more complex EFDO's 30K.12; for example those identifying concurrent focus-upon both of a top L URL's and a top K keywords and/or those with limited contexts (30K.3) such as being ‘at work’, ‘at home’, and so on. The first rounds of complex and generated EFDO's may then serve as inputs 30K.11 for generating yet more complex EFDO's 30K.12 and so on. In one embodiment, the system includes further modules (not shown) for predicting which types (30K.2) of focuses will be most in demand by the user population for the purpose of generating clustering maps (e.g., 40E.1, 40E.2 of FIG. 4E) and/or for other purposes. These prediction signals are fed to the EFDO's generating modules 30K.10 for prioritizing the background tasks of the latter modules 30K.10 (e.g., which types of to-be-generated EFDO's take precedence over other types).
Returning to FIG. 4E, the EDFO's of FIG. 3K are one way in which clusterings of significant ‘touchings’ can be identified and mapped. Additionally, or alternatively, the topic node primitives 30T.0 of FIGS. 3Ta-3Tb may be used (more specifically, at least sections 30T.6 and 30T.12 thereof) for determining which users, user groups and/or forums are currently focusing-upon various nodes or subregions in topic space and to what degree. Individualized and recently updated user profiles (not shown in 4E, see instead FIGS. 5A-5B as examples) may also be used for determining which users, user groups and so on are currently focusing-upon various points, nodes or subregions in respective ones of different Cognitive Attention Receiving Spaces (CARS's) and to what extent. Aside from identifying individualized users and user groups who are casting significant ‘touchings’ on different subregions of topic space, the clusterings mapping subsystem 400.E of FIG. 4E may automatically identify which real life (ReL) gathering events or the like are receiving significant ‘touchings’ from corresponding system users and where those events are clustered geographically or within a subregion of topic space or of another CARS. Additionally, the clusterings mapping subsystem 400.E of FIG. 4E may automatically identify which real life (ReL) or virtual life facilities (e.g., university lecture halls, laboratories, informational resource repositories, etc.) are receiving significant ‘touchings’ from corresponding system users and where those other resources are clustered geographically or within a subregion of topic space or of another CARS.
Aside from filtering on the basis of user types (e.g., 40E.7 i) and/or subregions (e.g., 40E.6 i) of the CARS (e.g., topic space) under consideration, the clusterings mapping subsystem 400.E of FIG. 4E may automatically filter according to different kinds of ‘touching’ heats and/or degrees of the same (e.g., those above or below a predefined threshold value) as is indicated by module 40E.8 and according to different kinds of time or place and/or other context criteria as is indicated by module 40E.9. Additionally; and as inherently indicated by the above mention of trending velocities or accelerations, the clustering mappings provided by the clusterings mapping subsystem 400.E of FIG. 4E may automatically filter according to different rates of trendings so that system users who use the clustering mappings may easily perceive which subregions of a topic space region they are focused-upon are experiencing the fastest growth rates in significant ‘touchings’ from all or a predefined subset (40E.7 i) of users and under the conditions of all or a predefined subset (40E.9) of contexts. In one embodiment, trending velocities may be indicated by use of color codings and/or directional vector lines (e.g., red for hottest growth spots, blue for cooling off regions) in the generated clusterings maps while current clustering dispositions are indicated by black dots or other such means and relative distance from a center of gravity for weighted ones of the points (e.g., black dots) are indicated by concentric circles. With this kind of information, the user may quickly see where the center of action is or which central area the ‘touchings’ actions are heading to (if red hot) or running away from (if cold blue) in geographic terms and/or in other spatial and/or temporal terms.
Referring briefly to FIG. 3L, it is within the contemplation of the disclosure to have entity focus defining objects (EFDO's; e.g., 30K.0?) which point (e.g., by way of pointer 30K.6 b) to complex operator nodes such as 30L.8. The complex operator nodes (e.g., 30L.8) may in turn point to yet other operator nodes; for example 30L.5, 30L.6, 30L.7 so as to thereby define a complex combination of likely cognitions that are cross-associated with an input set of background context specifications (30L.3; e.g., geographic location, time of day, day of week), an input set of background music specifications (30L.2; e.g., melodies) and an input set of topic specifications (30L.9). As explained above, the operator node 30L.5 that points to the input set of music primitives 30L.2, may additionally be pre-configured to also point to a likely, or ‘expected’ set of augmenting topic nodes 30L.1 a and/or to also point to a likely, or ‘expected’ set of augmenting context nodes 30L.1 b by virtue of respective augmentation pointers 30L.5 b and 30L.5 c; where incorporation pointers 30L.5 a are the main rather than augmentation type incorporation pointers. Similarly operator node 30L.6 drags in with it, the augmenting set of 30L.4 of expected topic nodes for that context set 30L.3. As a result, the second level operator node 30L.8 incorporates into its pulled in set of topic nodes, not only its main topic nodes 30L.9 but also the augmentation-wise supplied topic nodes 30L.1 a and 30L.4. Then, by virtue of this pulled-in complex of different topic nodes as well as context and music nodes, the second level operator node 30L.8 points to (via pointer 30L.8 f) a finely-resolved cross-correlated set of pre-ranked online chat rooms 30L.10 that are related to the combination of original input sets, 30L.2, 30L.3 and 30L.9. In other words, the EFDO data structure 30K.0? which points (via 30L.8 f) to the second level operator node 30L.8 thereby indirectly points to the highly specific set of online chat rooms 30L.10, which chat rooms may have geographically or otherwise closely clustered users participating in them. And therefore, the clusterings map 40E.1 provided in FIG. 4E on the basis of culled-through EFDO's may identify closely clustered users of a given chat room where those closely clustered users are focusing-upon a finely (rather than coarsely) defined set of points, nodes or subregions of different Cognitive Attention Receiving Spaces as if they were overlapping in a Venn diagram (e.g., 30L.7.Venn of FIG. 3L). More specifically, Venn diagram 30L.7.Venn is intended to indicate that the chat or other forum participation opportunities 30L.10 pointed to by operator node 30L.8 will have exchanges focusing-upon an overlap of plural topic nodes or subregions and plural context nodes and plural music space nodes such as for example the topic nodes of group 30L.1 a, the topic nodes of groups 30L.4 and 30L.9, the music nodes of group 30L.2 and the context nodes of group 30L.3.
Referring next to FIG. 4F, shown is another possible set of clustering mappings 40F.1, 40F.2 that can be displayed by the clusters-representing subsystems of the STAN—3 system. Where practical, reference numbers in the 40F.nn series are used to correspond to those of the 40E.nn series of FIG. 4E so that illustrated modules such as 40F.3, 40F.4, . . . 40F.9, etc. do not have to be re-described in detail again. Instead, focus is directed here upon the alternative presentations of clustering information that may be provided to the user. More specifically, an upper displayed mapping 40F.1 has been selected by the user (or by default by a system-provided template) to be displayed as a 2D plane disposed in a 3D reference frame 40F.0. At the same time, a lower displayed mapping 40F.2 (second mapping) has been selected by the user (or by default by a system-provided template) to be displayed as a 3D translucent cube having a substantially opaque bottom floor 40F.2M. A 2D map of a predefined geographic area (in real life (ReL) or virtual life) is painted on the bottom floor 40F.2M. Additionally, sets of concentric clustering radius rings 40F.2 a, 40F.2 b, etc. are overlaid on top of the bottom floor map 40F.2M where the center most ring of each set signifies an area of maximum concentration of ‘touchings’ while the peripheral rings each contain within their mutually exclusive areas (those not including areas of yet more inward circles) ‘touchings’ concentrations of a relatively lesser degree. A tear-drop like reporting tool, e.g., 40F.2Ta can be moved by the user such that the bottom tip of the tear-drop shape touches one of the peripheral ring areas rather than touching by default the central ring of a respective set of concentric clustering radius rings; and then in that case, a color coded (and/or texture coded) set of proportionality areas change inside the moved tear-drop (e.g., 40F.2Ta) to show how the proportionality and/or absolute magnitude of ‘touchings’ concentrations have changed as one moves from the inner most or core ring to the outer or peripheral regions. Although not shown, the peripheral rings may optionally be broken up into sectors; in which case the moveable tear-drop tool (e.g., 40F.2Ta) reports on ‘touchings’ distributions in each pointed to sector.
For sake of convenience, a first of the tear-drop tools (40F.2Ta) is shown in enlarged form 40F.Ta? on the exterior side of the symbolic magnifier. The largest of the color coded (and/or texture coded) areas 40F.2Ta1 represents the cognition subregion (in this a topic node or topic subregion) of greatest popularity within the core ring (or within another area if the tip of the tear drop is moved there) while the next inward area, 40F.2Ta2 represents the cognition subregion (e.g., topic) of next greatest popularity and the third inward area, 40F.2Ta3 represents the cognition subregion (e.g., topic) of yet lesser concentration and popularity within the tipped-at ring area. A legend 40F.2TL may be automatically displayed adjacent to the lower clusters mapping 40F.2 for indicating which cognition subregion (e.g., topic) is represented by each respective color coded (and/or texture coded) area, 40F.2Ta1-a3 inside the displayed tear-drops (e.g., 40F.2Ta, 40F.2Tb, 40F.2Tc). In one embodiment, an expansion tool (e.g., starburst+) is provided adjacent to each named cognition subregion (e.g., topics TSR5.9, TSR5.917) for allowing the user to learn more about that represented cognition point, node or subregion if so desired.
Although not shown for all clustering ring sets (40F.1 a, 40F.1 b, 40F.1 c) of the exemplary URL's map 40F.1, in one embodiment, translucent connection bands or tubes 40F.3Ta (optionally of different colors or textures) are made visible upon user request as between clusterings of URL expressions in a URL-expressions clustering space (e.g., mapped by 40F.1) and a geographic or other such space (e.g., mapped by 40F.2) having ‘touchings’ thereto cross-mapped to a third space (e.g., to topic space) by tear-drop display tools such as 40F.2Ta or the like. More specifically, primitive URL expressions (see 391.2 of FIG. 3E for example and also 30W.0 of FIG. 3W) and operator nodes (e.g., 394.1 of FIG. 3E) that draw on them may be clustered in a corresponding URL's space according to one or both of geographic preferences (e.g., which URL's are most ‘touched’ or most intensely ‘touched’ by system users in respective pre-specified geographic sectors) and demographic preferences (e.g., which URL's are most ‘touched’ or most intensely ‘touched’ by system users in respective and pre-specified demographic sectors—i.e. as more specifically delineated for example by occupation, age group, income group and so on). In FIG. 4F, the clustered URL expressions are represented by dark dots of respective diameters placed within clustering ring sets 40F.1 a, 40F.1 b and 40F.1 c. The wider or darker the dot, the greater are the represented ‘touchings’ in terms of number of users and/or their intensities of ‘touching’ upon the corresponding URL expression (primitive or operator node defined).
People of like propensities (e.g., of like demographic preferences) tend to congregate or cluster together geographically and/or in other ways (e.g., in terms of their top N topic, keyword and/or URL's ‘touchings’ in respective other spaces) and as a consequence, cross-space connection tubes (e.g., 40F.3Ta) may often be generated and drawn by the STAN—3 system to indicate machine-found cross-correlations between, say URL expression clusterings (of significant ‘touchings’) in a URL's space (mapped by 40F.1) and geographic space clusterings (of significant ‘touchings’) into a corresponding subregion (e.g., represented by 40F.2Ta1 of magnified tear drop) of say, topic space. These cross-correlations may run bi-directionally. By activating a respective expansion tool (e.g., starburst+) in the corresponding legend area, map area or connecting tube area (e.g., 40F.2TL+, 40F.2Ta1+, 40F.1 a+, 40F.3Ta+), the user is empowered to being presented with additional information, including that indicating who the ‘touching’ users are, when did they touch and how intensely (e.g., emotionally) did they touch and so on. In some instances, the respective expansion tools (e.g., starbursts+) are not visible until the user zooms in with a viewing zoom-in/zoom-out tool (not shown) to see an enlarged view of the displayed object that contains its respective, information expansion tool. If the activated expansion tool (e.g., starburst+) is within an inter-space connecting tube (e.g., tool 40F.3Ta+), the user is automatically given an option of learning more information about users for the Boolean AND of the interconnected clusterings (e.g., 40F.1 a AND 40F.2 a) or learning more information resulting from the Boolean OR of the interconnected clustering or from the Boolean XOR (exclusive OR).