11

 [11]
Referring to section 30U.4 of FIG. 3U, here a collection of pointers is stored each pointing to the highest level, clusters of clusters holder (in this example 30U.14) allowed in ranges section 30U.3 c. Section 30U.4 therefore defines the highest level of clusters of clusters for the given collecting node 30U.0.1 t is within the contemplation of the present disclosure that there can be a super collecting node (not shown) which points to a collection of plural collecting nodes like 30U.0.
Referring to section 30U.5, after raw ones of received CFi payloads have been reformatted (and/or re-coded) to conform with the normative codes and formats section 30U.3 a, the raw keywords, URL's, etc. defined by the reformatted (and/or re-coded) data may still be idiosyncratic (not normal) relative to a predetermine set of “normalized” keywords, keyword expressions, URL's, URL expressions and so on associated with the current collecting node 30U.0. Section 30U.5 contains pointers pointing to such CFi normalizing and/or augmenting sets for respective CFi's clustering holders 30U.12, 30U.13, 30U.14, etc. Because the clustered CFi's of holders 30U.12, 30U.13, 30U.14, etc. are so-clustered initially on only a trial and error basis, the per-cluster pointers to CFi normalizing and/or augmenting sets are also taken as being on a trial and error basis. The inferencing engines (310?) may use the normalizing/augmenting pointers of section 30U.5 for aiding in performing sanity checks. The tested against PNOS's in system-maintained Cognitive Attention Receiving Spaces will be already normalized and/or augmented. Therefore it may be necessary to normalize and/or augment the raw CFi data of the currently clustered CFi's (e.g., #1, #2, . . . , etc.).
Referring next to section 30U.6, it again should be remembered that the clustered CFi's of holders 30U.12, 30U.13, 30U.14, etc. are so-clustered initially on only a trial and error basis. Nonetheless, initial matchings can be made for each level one cluster, each level two cluster (e.g., 30U.14), etc., for matching chat rooms. Section 30U.6 may contain respective pointers to such trial and error basis matched chat rooms. The data stored in section 30U.6 may be used to invite two or more system users to a same chat room based on trial and error basis clustered CFi's alone. Section 30U.7 provides substantially the same function for other forum participation sessions. Section 30U.8 provides substantially the same function for other informational resources that currently cross-correlate on a trial and error basis with the currently clustered CFi's of the given collecting node 30U.0.
Referring to FIG. 3V, the format of special purpose collecting nodes, e.g., 30V.0 can be slightly different than that described for the general purpose CFi's collecting node 30U.0 shown in FIG. 3U. The latter is a template, but need not be strictly adhered to. In FIG. 3V, the collecting node 30V.0 is specialized for textual content containing CFi's such as those containing keywords, focused-upon sub-portions of content that the user was exposed to, URL's, meta-tags and so on. In this case, section 30V.1 may assign corresponding textual types to the textual CFi to indicate for example that is coded as ASCII plain text, as Rich text, as MS Word™ text, as HTML encoded text, XML encoded text and so on. Section 30V.2 may assign various ones of different subtypings to the typed textual material such as a neo-cortical subtype, temporal spatial subtype and so on. Each pointed-to subtype node may have an associated sanity check score. Furthermore in this case, an additional section 30V.3 a may be included in the data structure 30V.0 for defining regular expression control codes such as multi-symbol wild cards (e.g., “*”), single-symbol wild cards (e.g., “?”), antonym specifiers (e.g., “!”), and so on. Another additional section 30V.3 b may be included for defining special purpose delimiter codes as may be used in HTML or otherwise coded meta-tags and the like. Aside from that, the data structure of textual collecting node 30V.0 may be substantially similar to that of general purpose CFi's collecting node 30U.0 shown in FIG. 3U.
FIG. 3V additionally shows an illustrative example of how the level one and level two cluster holder data objects may be used. In this example, the following raw CFi parameters are present: CFi#1=“Lincoln??”, CFi#2=“Gettysburg*”, CFi#3=“Address”, CFi#4=“How”, CFi#5=“Histor*”, and CFi#6=“See it”. Therefore the first level one cluster holder data object 30V.12 defines on a trial and error basis, the test clause: CFi#1+CFi#2+CFi#3=“Lincoln's Gettysburg Address” as shown in dashed block 30V.12?. Similarly, the second level one cluster holder data object 30V.13 defines on a trial and error basis, the test clause: CFi#4+CFi#5+CFi#6=“How Historians See-it” as shown in dashed block 30V.13?. Although a human observer can almost instantly see that each of these 3-word clauses makes sense, the automated machine system performs the aforementioned sanity check runs and scores the results so as to determine which permutations and combinations are more likely valid and which are less illustrated to be sensible. Once the automated sanity checks have been run on the short run clusterings of the first and second level one cluster holder data object 30V.12, 30V.13 and the returned scores have been determined to be adequate (e.g., above a predefined threshold) and ranked or sorted, the level one cluster holder data object 30V.14 is automatically assembled by the machine system on a trial and error basis, where one high-scoring permutation turns out to be: “Lincoln's Gettysburg Address, How Historians See-it”. In that case, pointer 30V.14 d is updated to point to corresponding points, nodes or subregions in topic space, in image space, in sounds space, in context space and so on; where these pointed to, trial and error PNOS's (30V.24) can then indirectly point to chat or other forum participation opportunities corresponding to the topic of “Lincoln's Gettysburg Address, How Historians See-it”. Therefore, the exemplary data structure 30V.0 may serve as a basis for the STAN—3 system automatically sending invitations to students doing research on the question (“Lincoln's . . . How Historians See-it”) so as to automatically bring such students (e.g., Fifth Grade Students) together into same online chat rooms or the like.
For the case of the exemplary, level one clustering of CFi-delivered keywords: “How Historians See-it” (30V.13?), FIG. 3V additionally shows how pointer 30V.13 d (understood to emanate from holder 30V.13?) can point to a collection 30V.23 of further pointers that point to respective nodes and/or cognitive-sense-representing clustering center points (e.g., pointers 374.2) in keyword space that have similar semantic meanings or cognitive senses. As was explained above, keyword expressions may be clustered in a keyword expressions layer (371, FIG. 3E) of keyword space where the clustering is according a semantic sense (e.g., a Thesaurus sense) or another such cognitive sense and where clusterings may be on or around cognitive-sense-representing clustering center points in some cases. In one embodiment, the calculated distance of a first keyword expression away from a second keyword expression in hierarchical and/or spatial keyword space, where the second keyword expression is most representative (in a communal popularity sense) of an underlying cognitive sense, indicates how same or similar the first keyword expression is relative to the second keyword expression and/or relative to a cognitive-sense-representing clustering center point over which the second keyword expression directly lies. Accordingly, the keyword string, “How Historians See-it” might be, in one hypothetical example, closely clustered in keyword space adjacent to other expressions that match (per the appropriate matching rules—see 30W.3 c of FIG. 3W as will be discussed below) the text strings: “How Historians Perceive-it”, “How Historians View-it”, and/or “The Historical Perspective” 374.2 where all these differently phrased keyword strings are shown to the machine system to be different manifestations of a same neo-cortical cognition (a same communal cognitive sense of what the strings imply for that clustering subregion of keyword space). The so clustered together, but different keyword expressions and/or strings may have respective further pointers to subregions of topic space that address the concept of “Historical Perspective” (e.g., 374.2 of FIG. 3V). These sub-topic pointers (which point to a sub-topic under “Lincoln's Gettysburg Address, How Historians See-it” (30V.14) can serve as a basis for the STAN—3 system making suggestions to the students (the monitored STAN—3 system users) for further research on the topic they are apparently currently focusing-upon. In other words, it may be automatically suggested to the students that they learn how a “Historical Perspective” (e.g., 374.2) occurring some 10, 20 or 100 years after the event may differ from a concurrent perspective. The portions of topic space that keyword expression 374.2 points to may provide such relevant material. Therefore, to summarize, the progressive build up of small clusters of received (and optionally normalized) CFi's into apparently sensible combinations of such CFi's (with some being selectively masked out) and the further build up of these level one clusterings (e.g., 30V.12, 30V.13) into level two clusters of clusters (e.g., 30V.14) and so on; not only can generate ranked and sorted lists of pointers (e.g., those in memory area 30V.24) to specific topic nodes for the narrowed level two clustering (e.g., “Lincoln's Gettysburg Address, How Historians See-it” (30V.14)), but they can also at the same time generate ranked and sorted lists of pointers (e.g., those in memory area 30V.23) to subtopics that the user (e.g., student) may wish to explore. Therefore the machine generated result signals may simultaneously provide answers cross-correlating to very specific and narrow cognitions that are probably there in the user's mind or should be there (e.g., as a time-pressed Fifth Grade Student, where one ancillary topic might be: How do I get my homework task done as quickly and efficiently as possible?) as well as answers or suggestions cross-correlating to broader understandings that the user may wish to follow up on (e.g., What is the difference between Historical Perspective 100 years after the fact and perspective at the time an event happens?).
The data structure shown in FIG. 3V is not to be confused with the similar-looking one 30W.0 shown in FIG. 3W. FIG. 3V shows a CFi's collecting node 30V.0. On the other hand, FIG. 3W shows a counterpart Textual Expression primitive object (TexPO) 30W.0. TexPO 30W.0 would be an example of a simple keyword or another such textual expression that result pointers (e.g., 30V.22) of FIG. 3V may point to. It is to be understood that while keywords have been used here as an easy to appreciate example of textual content, the focused-upon sub-portions of content (e.g., web content) presented to the user are another example of textual expression content for which the system tries to automatically locate best-matching and representative textual expression primitives or operator-node-defined complexes in a corresponding content space. Like keyword expressions that have a same underlying cognitive sense, many different ones of textual content nodes may be clustered together with each other and/or near to a common cognitive-sense-representing clustering center point in the corresponding content space. There is no clear and absolute distinction between keyword expressions and content space expressions except that keywords tend to be shorter in length and keywords, rather than raw sub-portions of focused-upon textual content, are what users more normally input into their search engines.
Referring to FIG. 3W, a first section 30W.1 a of the illustrated TexPO data structure 30W.0 provides typing information (and optionally subtyping information) indicative of a type of textual data (e.g., a textual string or textual regular expression) provided in second section 30W.2 and optionally about its relative size and optionally about one or more system-maintained Cognitive Attention Receiving Spaces with which it may be best associated. In the instant example, the second section 30W.2 contains a textual regular expression formed of a combination of control codes (wildcards, match rule control codes and delimiters) as well as alphanumeric symbols that define a keyword expression: “*Ab*^Lincoln*” where here the quotation marks are delimiters indicating start and end of the regular keyword expression; the asterisks (*) are wildcards allowing for replacement by a string of any length and content including a zero length one, the up carrot (^) represents a required white space character and the two underlined letters (A and L) are indicative of a requirement that their case (in this instance, upper case lettering) is required. Accordingly, a text sequence such as “President Abraham Lincoln” will match and so too will “Mr. Abe Lincoln” and “Honest Abe Lincoln's” (assuming that there are no special rules in the match rules section 30W.3 c that indicate otherwise). Although not shown in FIG. 3W, one embodiment includes the use of so-called, “within N” (w/N) words wildcard specifications and “not within N” (!w/N) words wildcard specifications as well as before or after sequence specifications and Boolean logic specifications (e.g., “Ab*” before AND w/5 “Lincoln*”) thereby allowing for different levels of flexibility beyond just the unlimited length wildcard (*) and the single symbol length wildcard (?).
The illustrated TexPO data object 30W.0 is deemed to reside at a respective anchored location in a textual primitives layer 30W.71 (see also 371 of FIG. 3E) having logically linked other data objects and having a virtual spatial framework (which framework is also denoted as 30W.71). The residence location of data object 30W.0 in its respective hierarchical and/or spatial organizing and Cognitions-representing Space may be specified in data field 30W.1 b. As seen in FIG. 3W, the other exemplary textual primitive objects: TexPO2 (30W.12), TexPO3 (30W.13) and TexPO4 (30W.14) define in their respective second sections (like the detailed 30W.2) corresponding keyword expressions that can strongly tether with the concept of Abe-Lincoln, for example: the USA Civil War and the Gettysburg Address. In other words, the various textual primitive objects, TexPO, TexPO2, TexPO3 may closely cluster with one another, hierarchically and/or spatially because they have a common cognitive sense related to Abe-Lincoln, the USA Civil War and the Gettysburg Address. Indeed there may be one or more cognitive-sense-representing clustering center points (see 30W.7 p) that represent the common cognitive sense or something closely aligned thereto (in a cognitive sense). Each TexPO may have a respective, anchoring strength factor (e.g., 30W.2 a, 30W.12 a, 30W.14 a) associated with its respective virtual position within the virtual spatial framework 30W.71 of its subregion of keyword expressions space (or of another textual content space). Those strongly together and/or closely together TexPO's that have relatively strongest anchoring strength factors (e.g., 30W.2 a) are deemed to be the core of, or hard-to-move foundational stones of the clustering area while those that have substantially weaker anchoring strength factors and weak clustering strengths (e.g., s.0.12, or even negative clustering strengths if repulsion is intended) are deemed to be easier-to-move nonfoundational stones of the clustering area. (As will be explained soon, so-called, update engines 30W.37 can move the primitives or operator nodes logically linked to them according to a reciprocal function of anchoring strength and/or clustering strength.) The decision as to which other TexPO's (e.g., 30W.12, 30W.14) most strongly tether (anchor) into the current region of a textual primitive object layer (see 371 of FIG. 3E) and most strongly cluster with one another happens by chance and evolution rather than by pre-design. First, one textual primitive object (TexPO) is placed (hierarchically and/or spatially) into its position (30W.1 b) in the corresponding textual expression space (e.g., keyword space) and then another near it, and then another. It is left up to the large number of users who reference the current region 30W.71 (e.g., like layer 371 of FIG. 3E) of the corresponding textual expression space and who then indicate favor for one variation of clustering in that subregion over another by means of their positive and/or negative focusing energies that the subregion evolves to have its organization of clustered together textual primitive objects (TexPO's). More specifically and for example, if most users (or the more influential users) cast their focusing energies more so upon TexPO 30W.0 as opposed to on TexPO 30W.15 (as a mere example) that automatically gives one TexPO (e.g., 30W.0) a greater anchoring strength 30W.2 a (because it is more favored by users) than that of the regionally less favored TexPO (e.g., 30W.15). Similarly, by the general population user usage favoring a referencing onto TexPO 30W.12 second most often over TexPO 30W.14, where the latter is the third most often referenced one of the local textual cognition primitive objects that each of those gets its respective and proportional anchoring weights and proportional (according to popularity of joint usage) clustering strength factors (e.g., s.0.12, s.0.14; discussed below). In one embodiment, rather than relying merely on general population preferences for which TexPO will most strongly anchor in this subregion (30W.71) of a corresponding textual expression space and which will most strongly and attractively tether one to the other (as opposed to repulsion) and thus reinforce their effective anchoring strengths, the system also relies more heavily on respective focusings by expert and/or reputable users on such TexPO's of the given region for thereby increase their anchoring scores (30W.2 a) by a greater degree based on the level of expertise or reputation of the visiting expert/reputable user. Attractive or repulsive clustering strengths (e.g., s.0.12) are similarly increased in absolute magnitude based on the more heavily weighted activities of experts and/or reputable or influential users.
TexPO data objects may have respective directional distances associated with their intra-space cross-linkages (e.g., d.0.14 and d.14.0) for purpose of visually displaying a corresponding 2D or 3D map of how the TexPO's cluster closely together or more far apart and/or how they anchor (30W.2 a) strongly or weakly to their respective spots in the textual cognition primitive or other layer (see again 371). Distance values may be computed as combined functions of map room needs for squeezing in other TexPO's and on attractive or repulsive clustering strengths. However, before discussing these co-clustering factors, first some additional discussion for tertiary sections 30W.3 a, 3 b and 3 c of the detailed data structure 30W.0 is provided here. The textual expression code stored in second section 30W.2 can have various control codes associated with it, including but not limited to, various predefined wildcard codes (30W.3 a), various predefined delimiter codes (30W.3 b), and various predefined expression matching rules (30W.3 c). The expression matching rules (30W.3 c) may include specialized knowledge base rules (KBR's) indicating which symbols in the expression specification (30W.2) may require an exact match in terms of specialized formatting (e.g., font, bold, underline, italicized, capitalized-only, lower-case only, etc.). The expression matching rules (30W.3 c) may define special case exceptions to more general rules for match scoring. The expression matching rules (30W.3 c) may include rules that allow for less than perfect matching; for example a 75% cross-correlation factor being enough in place of a 100% cross-correlation factor. The expression matching rules (30W.3 c) may further include more sophisticated matching rule specifications directed to anchoring strength requirements (see 30W.2 a), effective distances (see d.0.14) from other TexPO's and so on. When the STAN—3 system tries to match (or otherwise cross-correlate) a user-supplied CFi (e.g., 30V.10 g of FIG. 3V) or a system-generated clustering of CFi's (e.g., 30V.12? of FIG. 3V) with a counterpart textual expression (e.g., 30W.2) defined within a respective TexPO (e.g., 30W.0, the Abe-Lincoln example), the system may use the expression matching rules (30W.3 c) of the trial TexPO for generating a corresponding matching or cross-correlation score to the test clustering of CFi's. In one embodiment, the system tests for matching or cross-correlation against several trial TexPO's and then picks the higher scoring ones for further processing as against a trial clustering of CFi's while tossing out the comparatively lower scoring TexPO's. Therefore, the expression matching rules (30W.3 c) may function as an important filtering mechanism for determining which CFi's cross-correlate strongly with which counterpart textual expressions (30W.2 of TexPO 30W.0 for example) in keyword space, or in URL's space or in meta-tags space, or in focused-upon sub-portions content space, or the like.
Referring next to section 30W.4 of the illustrated data structure 30W.0 (the first TexPO), each such textual primitive object may logically link to other TexPO's in its respective region 30W.71 of its respective textual expression space (e.g., in keyword space—see also link 370.12 of FIG. 3E; in URL's space—see also 391.2 of FIG. 3E; in meta-tags space see also 395 of FIG. 3E; in a hybrid space—see also 384.1 of FIG. 3E; and so on). The logical linkages between spatially nearby TexPO's may be in the form of absolute or relative location pointers (which relative ones associate with a base absolute location such as for example a cognitive-sense-representing clustering center point, see again 370.0 and 370.12 of FIG. 3E). These intra-space logical linkages may have virtual distance (e.g., d.0.12) and/or virtual strength values (e.g., s.0.12, positive or negative) logically attached to them. In one embodiment, virtual distance also partially determines virtual strength of the intra-space logical linkages and thus TexPO's that are farther apart in the corresponding virtual spatial framework (30W.71) are deemed to be more weakly clustered together while TexPO's that are comparatively closer together (e.g., Abe-Lincoln 30W.0 and Gettysburg Address 30W.14) are deemed to be more strongly clustered together and their respective anchoring factors synergistically reinforce one another so that together, these closely co-clustered TexPO's each have a greater effective anchoring factor than if it were not closely allied (by distance and/or linkage strength) to the other TexPO. For example, the linkage virtual strength value could be s=f(1/d); meaning that strength is a function of the reciprocal of virtual distance. With use of such synergistically reinforcing, directional linkages (e.g., d.0.14 from TexPO 30W.0 to TexPO 30W.14 and d.14.0 from TexPO 30W.14 to TexPO 30W.0), a foundational clustering of key TexPO's may be established, where less influential TexPO's (e.g., 30W.16) then weakly tag along to the strongly anchored foundational TexPO's of the clustered area. As mentioned above it is by happenstance (chance) usage of system users that a determination is made as to which TexPO's form the foundational anchor points of the given local region 30W.71 and for a corresponding cognitive sense. In another region of keyword or another textual expression space, the weaker expressions of first region 30W.71 may be duplicated where however, in that other region, the duplicated TexPO's are the more important, more strongly anchored one and thus kings of their realm (the other region—not shown). It is basically by user voting through usage that some TexPO's become dominant over others in one subregion and vice versa in another subregion. In other words, an example expression such as “Abe-Lincoln” might be a relatively unmovable keystone of its subregion 30W.71 in its subregion of expression space (e.g., keyword space) while the same example expression, “Abe-Lincoln” may be a relatively weakly implanted and an unimportant expression for a clustered expressions other subregion that focuses in on; for example, different styles of beards or top hats. In one embodiment, a zero-ith pointer (not shown) of ranked lists section 30W.4 points forward and/or backwards in linked list style to the next or previous instance of the same example expression, “Abe-Lincoln” and if the current region (30W.71) is determined to not be the one matching what is sought, a searching bot (e.g., 30W.11 b—to be described) or other search module follows that zero-ith pointer(s) linked list (not shown) to get to the next instance and test that one for match criteria satisfaction.
In one embodiment, the relative and/or absolute logical links stored in section 30W.4 are ranked and sorted according to effective anchoring strength (e.g., 30W.12 a) and/or relative clustering strength (e.g., s0.12). For example, the most central and foundational other TexPO for the current TexPO (e.g., 30W.0, Abe-Lincoln) might be 30W.12 (=Civil War) and the pointer to it would then be listed first in the pre-ranked and sorted list of section 30W.4; and then the next most important one (e.g., 30W.14=Gettysburg Address) would have the pointer to it listed and so on. Accordingly, when a user-launched automated search bot 30W.11 b comes across a TexPO data structure such as 30W.0, the pre-ranked and pre-sorted listing in intra-space links section 30W.4 will already have an indication of relative importance of other TexPO's (e.g., 30W.12, 30W.14) to the given TexPO (e.g., 30W.0) based on relative anchoring strengths and/or relative clustering strengths. If the automated search bot 30W.11 b has respective search instructions 30W.11 si containing search criteria directed to relative importance of other TexPO's relative to the being-considered TexPO (e.g., 30W.0) serving as a base, then the computational work of determining the strength and/or distance and/or rankings of the other TexPO's relative to the being-considered TexPO will already have been done by section 30W.4. Thus the data processing workload of the automated search bot 30W.11 b is reduced. More specifically, the pre-specified search instructions 30W.11 si of the bot may include an instruction to find a TexPO whose top N most important other TexPO's relate to: (1) the Civil War, (2) Gettysburg and (3) Washington D.C. (last TexPO not shown); N being a predefined number here. In such a case, an automated testing of a sorted list provided in pre-ranked and pre-sorted section 30W.4 will indicate to the search bot 30W.11 b how well the given TexPO under consideration (e.g., 30W.0) satisfies that part of the bot's search criteria (30W.11 si).
Before moving on to description of next section 30W.5, first a word about launched user search bot's like 30W.11 b is in order here. Like topic space, the textual cognition spaces of the STAN—3 system (e.g., keyword space, focused-upon content sub-portions space, etc.) can be constantly changing in response to the fluctuating attention giving activities of the user population. New catch phrases may come into vogue while others fade away. So the anchoring and/or clustering strengths of respective TexPO's may change over time in response to changing preferences of the user population pool. (In one embodiment, the re-direction aspect of the cognitive-sense-representing clustering center points is used to create more up to date, replacement subregions of the given textual expression space to replace the older and gone stale subregions while retaining a legacy history of the older versions.) Sophisticated users; and in particular market research specialists might want to keep track of trending changes among general population pools and their uses of various subregions of various textual expression spaces where those changes are reflected in how the organizing of TexPO's in a corresponding textual cognition space changes or in another expressed cognition space. Eventually, many such changes show up as corresponding changes in topic space. However, they may first appear as a new catch phrase (e.g., “If you love me, pass my bill”—President Obama Sep. 14, 2011) in a corresponding textual cognition space or as a catchy new other expression (e.g., a visual cartoon) in another type of expressed cognition space. Sophisticated users may wish to launch space-crawling, automated bots like 30W.11 b which virtually crawl through respective areas of specified expression cognition spaces in search of tell tale signs of changing user mood and changing usages of language or other forms of expression. Such may be signaled by the appearance of a new catch expression and/or by changes of relative rankings as between pre-established catch phrases or as between other such expressed cognitions. Search instructions (30W.1 si) that the sophisticated user formulates on his/her own or with the aid of search templates provided by the STAN—3 system are inserted into scripted code that search bot obeys. An example of a scripted code might say, “Alert me if Gettysburg Address (30W.14) becomes more highly ranked than Civil War (30W.12) in section 30W.4 of TexPO 30W.0, otherwise keep crawling”. In other words, if no important changes occur, the user does not want to be bothered by his/her in-the-background crawling around search bot 30W.111 b. The user is not focusing his/her current attention giving energies on the possible change of organization within the crawled through textual or other expressed cognition space. The user launched crawl bot 30W.11 b keeps doing this as system bandwidth allows and as long as the respective user does not cancel a subscribed to crawl service (if such subscribing is needed). Specifics regarding how to create an in-the-background crawling bot (e.g., 30W.11 b), how to program it the first time and/or how to recall it for change of search and alert instructions (e.g., 30W.11 si) may be provided by tutorial web pages or the like provided by the STAN—3 system.
Referring to section 30W.5 of the illustrated data structure 30W.0 (the first TexPO under consideration), each such textual primitive object may include logical links to normalization, augmentation and/or translation dictionaries. This concept has been discussed above. Briefly, the textual expression in section 30W.2 (assume for this explanation it says “Yo Ho Joe” rather than Abe-Lincoln) may be a relatively nonconforming one that only a small subset of system users use while the majority of users routinely refer to the referenced target as “Joe-the-Throw Nebraska” rather than as “Yo Ho Joe”. In this case, a first pointer in section 30W.5 may point to the more normal naming of the targeted cognition (e.g., Joe-the-Throw Nebraska?). The normalization pointers in section 30W.5 may be pre-ranked and/or pre-sorted according to most popular to least popular normalized alternatives. Accordingly, when a user's automated search bot 30W.11 b comes across a TexPO data structure such as 30W.0, the pre-ranked and pre-sorted listing in the normalized alternatives part of section 30W.5 will already have an indication of alternative other ways that the targeted textual cognition can be expressed. In one embodiment, the normalized alternative pointers may point to expressions in respective sections 30W.2 of respective other textual primitive objects (other TexPO's, for example TexPO2, TexPO3, etc.).
Another subsection of part 30W.5 may contain a pre-ranked and pre-sorted listing of pointers pointing to other TexPO's whose expressions are not substitutes for the textual cognition of the current TexPO (e.g., 30W.0) but rather are expansions, extensions of the given textual cognition (e.g., Abe-Lincoln). Such expansion/extension lists may be used when the system user does not have at the tip of his/her tongue the exact expression he/she is trying to grasp. For example, the user may say to themselves (or others), “It's got something to do with Abe-Lincoln (or with “Yo Ho Joe” as another example), but I can't pull the exact naming of it out of mind at the moment”. The expansion/extension lists may be pre-ranked and/or sorted according to current popularity scores or according to other, additional criteria (e.g., expert user's preferences). More specifically, as an example, if there a popular joke circulating among system users relating to the Abe-Lincoln example (e.g., “Other than that Mrs. Lincoln, how did you enjoy the show?”), one of the expansion/extension pointers may point to an intra-space node or subregion related to that currently popular joke. Often, if the textual cognition represented by section 30W.2 is a living celebrity, the number 1 popular expansion/extension pointer will point to an intra-space node or subregion related to a current events textual cognition that is currently “hot” or most popular.
Another subsection of part 30W.5 may contain a pre-ranked and pre-sorted listing of pointers pointing to other TexPO's whose expressions are substitutes for the textual cognition underlying the textual expression of the current TexPO (e.g., 30W.0) but are expressed in a different language (e.g., Spanish, French, Chinese) or with use of very different words. For example, the expression, “sixteenth president of the USA” may be a way of expressing the concept of Abe-Lincoln but with very different words. In one embodiment, a language conversion that is most often called for (most popular) at the time is automatically listed first. Two uses may be derived from such a configuration. First, because most users who need a translation will be asking for that number 1 most popular translation, it will be most readily available at the top of the pre-sorted list. Secondly, for people doing market or other research regarding the textual cognition (e.g., Abe-Lincoln) represented by section 30W.2 and which language based demographic groups are accessing it most, such information will be readily given by the pre-sorted list in the translations part of section 30W.5.
Referring to section 30W.6 of the illustrated data structure 30W.0 (the first TexPO under consideration), each such textual primitive object may include logical links to points, nodes or subregions (and/or cognitive-sense-representing clustering center points) in topic space that strongly cross-correlate with the textual cognition (e.g., Abe-Lincoln) represented by section 30W.2. This concept has been discussed above. Briefly, one or more pre-ranked and pre-sorted listings of pointers pointing to topic space may be provided. These may be ranked according to current “hotness”, according to long-term popularity, according to co-related topics that experts users currently consider to be most related, and so on. Accordingly, when a user's automated search bot 30W.11 b comes across a TexPO data structure such as 30W.0, the pre-ranked and pre-sorted listing in the topic space pointers section 30W.6 will already have indications of which topic nodes and/or cognitive-sense-representing clustering center points are most currently “hot” in relation to the textual expression and corresponding cognition of section 30W.2, which are most popular over a long term duration (e.g., last 2 years), which are most currently popular among expert users, among users having pre-specified demographic attributes, and so on.
Referring to section 30W.7 a of the illustrated data structure 30W.0 (the first TexPO under consideration), each such textual primitive object may include logical links to chat or other forum participation sessions that strongly cross-correlate with the textual cognition (e.g., Abe-Lincoln) represented by section 30W.2. This concept has been discussed above. Briefly, these sessions may be ranked according to current “hotness”, according to long-term popularity, according to participation by known expert and/or influential users currently consider to be most related to the textual cognition represented by section 30W.2, and so on. Accordingly, when a user's automated search bot 30W.11 b comes across a TexPO data structure such as 30W.0, the pre-ranked and pre-sorted listing in the cross-associated forum pointers section 30W.7 a will already have indications of which forums are most currently “hot” in relation to the textual cognition of section 30W.2, which are most popular over a long term duration (e.g., last 6 months), which are the most currently popular among expert users who are cross-associated with the textual cognition of section 30W.2, which are currently focusing-upon the textual cognition of section 30W.2 while at the same time being most currently popular or hottest among users having pre-specified demographic attributes, and so on.
Referring to section 30W.7 b of the illustrated data structure 30W.0 (the first TexPO under consideration), this functionality has also been briefly mentioned above. A same one textual expression (e.g., “Best USA President ever”) may have very different meanings or cognitive senses to different groups of users. More specifically, one group of users may consider Abe-Lincoln to be the “Best USA President ever” and thus they routinely equate the textual expression, “Best USA President ever” with Abe-Lincoln as well as that sense of Abe-Lincoln that deals with the Civil War and the Gettysburg Address for example. On the other hand, another group of users may consider Ronald Reagan or FDR to be the “Best USA President ever” for their respective various reasons. Of course the present disclosure is not picking one over the other but rather providing a means by way of which these different interpretations of the exemplary textual expression, “Best USA President ever” may be logically linked one to the next. That is what the linked list pointers of section 30W.7 b do. In one embodiment, each pointer also includes a relative ranking indication such as this next cognitive sense of the same textual expression is ranked number 3 out of the top 100. A search bot can use this linked list to locate, for example, the top 3 current understandings of what the exemplary textual expression, “Best USA President ever” means to system users.
Referring to section 30W.7 c of the illustrated data structure 30W.0 (the first TexPO under consideration), this functionality has also been briefly mentioned above. Textual primitive objects (TexPO's) such as 30W.0 may be deemed to lay directly over a specific cognitive-sense-representing clustering center point (e.g., 30W.7 p) or to be clustered near to that clustering center point (e.g., 30W.7 p) where such distance (in a hierarchical and/or spatial sense) may be calculated based on the literal locations (e.g., 30W.1 b) given respectively for the TexPO 30W.0 and its nearby clustering center point (e.g., 30W.7 p) or where such distance may be calculated based on one or more distance recalculation rules provided for the corresponding clustering center point (one of the three pointers represented by pointers trio, 30W.ERR). Although due to drawing space limitations, FIG. 3W shows just one nearby clustering center point (e.g., 30W.7 p), it is within the contemplation of the present disclosure to have section 30W.7 c storing a ranked and presorted list of the nearest N, cognitive-sense-representing clustering center points, where here N can be pre-specified as 2, 3, . . . , etc. Each clustering center point (e.g., 30W.7 p) may optionally include as part of its data structure, a time stamped re-direction pointer, a time stamped expansion pointer and/or a time stamped distance-recalculation pointer. These three optional pointers are collectively referenced by reference symbol, 30W.7ERR.
Referring next to section 30W.8 of the illustrated data structure 30W.0 (the first TexPO under consideration), each such textual primitive object may include logical links to points, nodes or subregions in other system-maintained Cognitive Attention Receiving Spaces (CARSs) besides topic space, forum space or the textual space (e.g., keyword space) of the first TexPO 30W.0. These other logical links (e.g., pointers) may be pre-ranked and pre-sorted according to appropriate ranking and sorting algorithms that serve popular desires of the user population. The other system-maintained CARSs that are referenced by section 30W.8 of the data structure may include representations of non-textual cognitions such as, for example those directed to sights, sounds, tastes, smells, emotions and so on. A more specific example of non-textual cognitions may be a plurality of image sequences relating to Abe-Lincoln giving his famous Gettysburg Address at Gettysburg. The image sequences may not have any text immediately linked to them but rather they may be simply raw image sequences as stated. However, even though there is no textual expression immediately linked to them, each of the plural image sequences may share a consensus-wise agreed to cognitive sense with the others of the plural image sequences. These plural image sequences may be clustered about a cognitive-sense-representing clustering center point in a respective, images-only space. A cross-spaces pointer such as one in field 30W.8 can point to the clustering center point in the respective, images-only space and thus logically link textual primitive object (TexPO) 30W.0 to the images-only center point in the other Cognitions-representing Space.
Referring to section 30W.9, the textual space (e.g., keyword space) of the first TexPO 30W.0 will typically have operator nodes such as 374.1? pointing back to (e.g., via pointer 370.4?) textual primitive objects such as TexPO 30W.0, where the to-primitive pointers (e.g., 370.4?) function to define a more complex, less primitive textual cognition of the respective operator node 374.1?. In its turn, the pointed-to TexPO 30W.0 can have pre-ranked and pre-sorted pointers stored in section 30W.9 that point to the back referencing operator nodes (e.g., 370.4?). Stated otherwise, section 30W.9 points to the hierarchical child nodes of node 30W.0. The pointers of section 30W.9 may have respective distance and/or strength values (e.g., d.0.74, s.0.74) logically attributed to them for indicating, in similar manner to the primitive layer links (section 30W.4) how strongly and/or closely clustered or not the more complex textual cognitions of the operator nodes are to the primitive textual cognition 30W.2 of data structure 30W.0. In one embodiment, the pointers of section 30W.9 may comprise a pointer to a specific cognitive-sense-representing clustering center point plus a relative offset from that center point to the intended operator node. In this way, each pointer of section 30W.9 may simultaneously identify the co-related center point as well as the child node (e.g., operator node) which is ultimately being pointed to.
In one embodiment, system users have the option of seeing the clustering distance and/or strength values between primitive nodes (e.g., TexPO 30W.0) and/or between selected ones of more complex nodes (e.g., 370.4?) and/or between selected ones of cognitive-sense-representing clustering center points (if used in the respective space) visually displayed to them on a screen in similar manner to the way that topic or other space nodes of FIG. 3S may be displayed. The visually displayed information may be formatted onto a 2D plane or displayed with a 3D or higher format including relying on color coding to represent alternate dimensions and/or different coupling strengths or distances (e.g., d.0.74, s.0.74) and/or different levels of “hotness” being currently associated with respective nodes or subregions of the displayed space.
The pointers of section 30W.9 may be pre-ranked and pre-sorted according to appropriate ranking and sorting algorithms, including for example, according to which operator nodes are most frequently in recent times (e.g., last day, week or month) referenced by all system users, which are most frequently in recent times referenced by system recognized experts or influential persons, which are most frequently in recent times referenced by chat or other forum participation sessions that have hotness scores exceeding predetermined threshold values, and so on. Accordingly, when a user's automated search bot 30W.11 b comes across a TexPO data structure such as 30W.0, the pre-ranked and pre-sorted listing in the cross-associated operator nodes section 30W.9 will already have indications for exploitation by the bot including indications of which more complex (less primitive) textual cognitions (as represented by respective operator nodes like 370.4?) are most currently “hot”, which are most popular over a long term duration (e.g., last 3 months), which are most currently popular among expert users who are cross-associated with the primitive textual cognition of section 30W.2, which users are currently focusing-upon a textual cognition having that of section 30W.2 as its primitive, and so on. The automated search bot 30W.11 b may use the results for purposes of market research or other purposes.
Referring to section 30W.10 of the illustrated data structure 30W.0 (the first TexPO under consideration), each such textual primitive object may include logical links pointing into user-to-user associations (U2U) space (see for example 30T.6 b of FIG. 3Ta) and thereby identifying specific users who are strongly cross-associated with the TexPO under consideration (e.g., 30W.0) where the basis for such strong cross-association may be specified and may include one or more of bases such as, being a highly influential persona with respect to the textual cognition of section 30W.2; being a well regarded expert persona with respect to the textual cognition of section 30W.2; and so on. The pointers to influential and other types of personas may be pre-ranked and pre-sorted according to appropriate predetermined and machine-implemented algorithms. Accordingly, when a user's automated search bot 30W.11 b comes across a TexPO data structure such as 30W.0, the pre-ranked and pre-sorted listing in the cross-associated users section 30W.10 will already have indications for exploitation by the bot as may be deemed appropriate by the predetermined search instructions 30W.11 si given to the bot 30W.11 b.
Referring to section 30W.11, in addition to strongly cross-associated users (of section 30W.10), listings of pre-ranked and pre-sorted pointers may be provided in section 30W.11 for logically linking to other informational resources which are cross-associated with the textual cognition of section 30W.2. These other informational resources may include cross-correlated conference events, research facilities, non-public database resources and so on. The list sortings may indicate which are most preferred by lay or expert users, which are currently the most “hotly” referenced ones and so on.
FIG. 3W additionally shows the presence of two kinds of automated engines that are associated with primitive (e.g., 30W.0) or more complex nodes (e.g., 374.1?) of the corresponding textual or other cognition space. One of the engines is a space populating engine 30W.30 that automatically adds new nodes to the space. The other is an automated space updating engine 30W.37 that automatically updates the pre-existing nodes and logical linkages of the respective cognition space (e.g., keywords space, URL's space, etc.). The automated space updating engine 30W.37 may also from time to time, update the cognitive-sense-representing clustering center points (e.g., 30W.7 p) by for example creating an expanded space subregion that contains a mirror copy of the first center point but at a different location and pointing to different nearby PNOS's in its respective subregion. In one embodiment, when mirror copies of a cognitive-sense-representing clustering center point are created by use of expansion pointers (“Expand” in FIG. 3W), each such pointer includes a time-stamped forward pointer pointing to the more recently created expansion subregion and indicating the date of the expansion and a time-stamped backward pointer pointing from the more recently created copy of the center point back to the earlier-in-time one (e.g., 30W.7 p) and indicating the creation date of the earlier-in-time one (e.g., 30W.7 p). In one variation the back and forth pointers also indicate a relative hotness ranking (e.g., number 3 out of 100) for at least some of the pointed to center points. In this way a linked list is formed that allows users or an automated bot to navigate from one expansion subregion to the next and to determine which of the subregions is the most often referenced one (e.g., the hottest) among system users and which is next most popular and so on.
The automated space updating engine 30W.37 may also from time to time, update the cognitive-sense-representing clustering center points (e.g., 30W.7 p) by for example creating a substitute (replacement) subregion that contains a copy of the first center point but at a different location and pointing to different nearby PNOS's in its respective subregion. In one embodiment, when such a replacement copy of a cognitive-sense-representing clustering center point is created, it is done by use of a redirect pointer (“Redirect” in FIG. 3W). Each redirection pointer includes a time-stamped forward pointer pointing to the more recently created substitute subregion and indicating the date of the substitution and a time-stamped backward pointer pointing from the more recently created, substitute copy of the center point back to the earlier-in-time one (e.g., 30W.7 p) and indicating the creation date of the earlier-in-time one (e.g., 30W.7 p).
The automated space updating engine 30W.37 may additionally from time to time, update the distance recalculation algorithms (“ReCalc” in FIG. 3W) of respective center points.
When each new subregion in a textual space or in another cognition space is created and initially populated, it may be manually or automatically pre-seeded with information obtained from one or more listings of expert or influential users who are strongly cross-associated with that new space or new subregion of the space. In one embodiment, various hierarchical and/or spatial dimension ranges of each Cognitions-representing Space are designated as “reserved for future expansion needs” and these are released for populating with new points, nodes or sub-subregions as the need arises. When a new subregion is opened up for homesteading by new nodes or other such data objects, a rough city plan for the new area may be defined by sparse seeding with expert-created and placed nodes and/or with expert-created and placed cognitive-sense-representing clustering center points. Consider by way of an example the creation of a new textual cognition subregion directed to the concepts of “Abe-Lincoln” (30W.0) and “The Civil War” (30W.12). At the time of creation of the new textual cognition subregion, there already may exist various bibliographic databases or the like which contain listings of renowned scholars or experts who wrote books, treatises or the like that are logically cross-associated with the given primitive textual cognitions taken alone or as more complex combinations (e.g., 374.1?). More specifically, the title of a newly released paper written by a renowned scholar might be, “Abe-Lincoln, the Civil War years” (a hypothetical example). The release of the newly published paper may alone be sufficient reason for seeding an empty and correspondingly newly released or created area of a textual cognition region (e.g., 30W.71) devoted to that paper. The releasing or creation of the new (sub)area may be automatically accompanied by a sparse seeding thereof with TexPO's like the illustrated 30W.0, 30W.12 and 30W.15. When system monitored ones of such expert or influential users directly or indirectly induce the introduction a new textual subregion or of a new textual expression (or a different expression) in a pre-existing subregion because they released a new treatise, a new talk/lecture or other form of communication, the STAN—3 system automatically searches for and seeds within the newly introduced subregion or around the newly introduced expression, additional cognition-representing nodes or clustering center points that are obvious variations of first seeds implanted into the new subregion. In other words, the STAN—3 system (or more specifically an automated space populating engine 30W.30 thereof) automatically creates one or more respective new nodes (e.g., 30W.0, 30W.12 and 30W.15 for “Abe-Lincoln, the Civil War years”) in that newly spawned subregion; where the new TexPO nodes are weakly cross linked (e.g., with a pointer such as one in 30W.4 or 30W.9) to/from a corresponding, less complex node (which could be a root node of keyword space for example—not shown or a pre-existing other node like 30W.13 (“How Historians See It”) for example). In other words, the automated space populating engine 30W.30 keeps track of system monitored ones of expert or influential users (30W.31), and it automatically tests for novelty of expressions or other works they generate regarding a corresponding subregion of an expressible Cognitive Attention Receiving Space (e.g., keyword space), and it automatically inserts a new one or more nodes (and/or cross clustering connectors, e.g., s.0.12; d.14.15) when the generated expression or other work is determined to be novel and optionally a hot or catchy one.
The automated space populating engine 30W.30 keeps track of system monitored chat or other forum participation sessions that are strongly cross-associated with respective subregions assigned to the space populating engine 30W.30, testing for newly trending usages 30W.32 in such forums of expressions not otherwise found in the assigned subregions. When usage in a tracked one or more forums exceeds a predetermined threshold in terms of “hotness” and/or popularity, the space populating engine 30W.30 automatically adds a corresponding new node into the assigned subregion where a textual or other cognition storing section (e.g., 30W.2) of the newly added node stores a respective digital representation of the new expression. Aside from system-spawned or supported forums (e.g., system generated online chat rooms), the STAN—3 system may monitor other informational resources such as Twitter™ feeds for trending new expressions (e.g., new or hot turns of phrase; for example an actor's novel line in a new movie (i.e. ‘make my day’—Clint Eastwood; ‘I'll be back’—Arnold Schwarzenegger, etc.) and the respective space populating engine 30W.30 may then insert the new textual or other cognition node (e.g., 374.1?) as trending developments warrant. The same can be done for trending catch phrases 30W.34 found on parts of the internet (e.g., micro-blogs, news headlines consolidating sites, movie reviews, which may not be directly driven by the STAN—3 system and in other (30W.35) such informational resources. New cognitions for which new nodes are generated and inserted into a respective subregion of a system-maintained Cognitive Attention Receiving Space need not be limited to digitally-represented-by-text cognitions (e.g., 30W.2). They can be new musical cognitions (see again FIG. 3F), new linguistic cognitions (see again FIG. 3I), new cognitions respecting user contexts (see again FIG. 3J), new cognitions respecting visual attributes (see again FIG. 3M), new cognitions respecting biological attributes (see again FIG. 3O), new cognitions respecting topic space (see again FIGS. 3Ta-3Tb), and so on.
After the automated space populating engine 30W.30 has added a new textual or other cognition representing node or subregion into a respective, system-maintained Cognitive Attention Receiving Space (CARS), the new node or subregion is tracked by an automated space update engine 30W.37 assigned to that subregion of the given CARS. The assigned automated space update engine 30W.37 is assigned with various consolidation and update tasks. An example of a consolidation task may be as follows. One chat room shows excited trending (e.g., great hotness) for a first version of a celebrity's novel expression (example: ‘make my day’—Clint Eastwood) and a new node is created for that version. Then another forum (e.g., a web blog) shows excited trending (e.g., great popularity) for a second version of the same celebrity's novel expression (example: ‘Go ahead, make my day’—Clint Eastwood) and a separate new node is created for that version. After a while, the automated space update engine 30W.37 assigned to that subregion of expression space automatically realizes that the two versions are actually referring to a substantially a same cognitive expression. One basis for so realizing by automated machine means is that same users are found by the automated machine means to be interchangeably referring to both. In that case the update engine 30W.37 automatically consolidates the two nodes into one or makes one the hierarchical child of the other. When making one node the parent of the other node or consolidating two nodes into one, the update engine 30W.37 may generate a wild-cards filled version of the expression that covers both versions. For example, ‘Go ahead, make my day’—Clint Eastwood may be consolidated into the wild card padded expression: ‘*make my day*’—C* Eastwood*; where here the asterisk (*) denotes any additional or no symbol string. Thus the parent node expression covers the varied versions of the child node expressions.
Another of the assigned tasks of the automated space update engine 30W.37 is to update the rankings and optional sorted listings in the various pointer storing sections (e.g., 30W.4-30W.11) of the primitive of more complex nodes in its assigned subregion of the Cognitive Attention Receiving Space. For example, a usage that was most popular last week may suddenly drop into second or third place this week while a new usage takes over the number spot. Such change in rankings is handled by the automated space update engine 30W.37.
Referring to FIG. 5C, in one embodiment, the STAN—3 system 410 includes a chat or other forum participation sessions generating service 503? that automatically sends out invitations for, and thus tries to populate corresponding ones of chat or other online forum participation sessions with “interesting” mixtures of participants. More specifically, and referring to social entities—identifying module 551, social entities that have a same topic node and/or topic space region (TSR) being currently focused-upon (or other specified points, nodes or subregions of other specified CARS spaces being currently focused-upon) are automatically identified by module 551. The commonality isolating function of module 551 need not be limited to sameness of topic nodes and/or topic space subregions in a current time period. The commonality isolating function of module 551 can alternatively or additionally group STAN using social entities according to personhood co-compatibilities for now joining with each other in chat or other online forum participation sessions or even in real life (ReL) meeting sessions. The commonality isolating function of module 551 can alternatively or additionally group STAN using social entities according to substantial sameness of currently received CFi's and/or according to substantial sameness of currently focused-upon nodes and/or subregions in various other spaces (CARS's), including but not limited to, music space, emotion space, context space, keyword expressions space, URL expressions space, linguistics space, image space, body or biological state spaces, and chemical substance and/or mixture and/or reaction space. More specifically, if two or more people (or other social entities) are listening to substantially same music pieces at substantially same times and having similar emotional reactions to the music (as indicated by substantial similarity of identified nodes and/or subregions in emotions/behavior state space) and/or they are experiencing the substantially same music pieces in substantially similar contextual settings (as indicated by substantial similarity of nodes and/or subregions in context space) and/or those social entities are otherwise having substantially similar and sharable experiences which they may wish to then exchange notes or observations about, then the commonality isolating module 551 may automatically group them (or more specifically, their identifications) into corresponding pooling bins (504). Although FIG. 5C shows just one such pooling bin 504, in general there will be a plurality of such corresponding pooling bins 504 formed; one for each of shared points, nodes or subregions (PNOS's) in a corresponding system-maintained first cognitions representing space (e.g., topic space) where the shared PNOS's of the respective bin cross-correlate with received ones of the reporting signals (CFi's) received for respective ones of the pooled together system users.
Once the identifications (e.g., signals 551 o 2) of the identified social entities are pooled together into respective pooling areas (e.g., 504) based on one or more specified commonalities, another module 553 fetches a copy of the identifications (as signals 551 o 1) and uses the same to scan the currently active, preferences profiles (e.g., 501 p) of those social entities where the fetched preferences profiles (501 p) include indications of currently active preferences of the pooled persons (or other social entities) for being invited or not into different kinds of chat or other forum participation sessions. The indications may include, for example, indications of the maximum or minimum size of a chat room that they would be willing to participate in (in terms of how many other participants are invited into and join that chat room), of the level of expertise or credentials of other participants that they desire to be present or not within the forum, of the personality types of other participants whom they wish to avoid or whom they wish to join with, and so on. The fetched preferences profiles (501 p) should include indications of social dynamic propensity attributes to be expected of the respective users if and when they are invited into and participate in a respective chat or other forum participation session directed to the topic and/or other PNOS's of a respective Cognitive Attention Receiving Space. In other words, the social dynamic propensity attributes indicate which users are likely to be room leaders or respected room participants or social-discourse facilitating members relative to the topic and/or other PNOS's of a respective CARS of the corresponding waiting pool 504. The preferences collecting module 553 forwards its results (of the aggregate desires and/or the social dynamic propensity attributes of the currently pooled (504) users) to a chat rooms spawning engine 552. The spawning engine 552 then uses the combination of the preferences collected by module 553 and the demographic data obtained for the identified social entities collected in the waiting pool 504 to predict what sizes and how many of each of now-empty, chat or other forum participation opportunities are probably needed to satisfy the wishes (preferences) of gathered identifications in the waiting pool 504.