8

 [8]
More will be said about FIGS. 3E and 3W later below. However, referring now to FIG. 3J (an example of a context primitive), it may be recalled that the demographic attributes of the exemplary Fifth Grade student (studying the Gettysburg Address), which is a part of the context of the user; can serve as a filtering basis for narrowing down the set of possible nodes in topic space which should be suggested in response to a vague search keyword of the form, “*lincoln*” where the latter can have many cognitive senses (e.g., the city in Nebraska, the Automobile Dealership, the 16th President, etc.). Once user context is determined, it becomes more evident to the STAN—3 system 410 that the given STAN user (e.g., Fifth Grade student) more likely intends to focus-upon the “Abraham Lincoln” cognitive sense and not on “Local Ford/Mercury/Lincoln Car Dealerships” because the user is part of his own context and the user's demographic attributes (as found for example in the user's personhood profile) are thus also part of the context. In the example, the user's education level (e.g., Fifth Grade), the user's habits-driven role (e.g., in student mode immediately after school) and the user's age group can operate as hints or clues for narrowing down the intended topic. In other words, first round cross-correlations as between received clusterings of CFi's (e.g., 30V.12 of FIG. 3V) and spatial and/or hierarchical clusterings of nodes in corresponding spaces (e.g., keyword space, URL space, etc.) are preferably not used alone but rather in conjunction with context-sensitive hybridizations of such received CFi's. Incidentally, just as was true for the case of FIG. 3Q, due to space limitations in the drawings, some details of data structure 30J.0 are left out, including for example, a set of linked list pointers similar to 30W.7 b of FIG. 3W and one or more pointers similar to 30W.7 c of FIG. 3W that point to a corresponding one or more nearest clustering center points. The below discussion re 30W.7 b and 30W.7 c of FIG. 3W are incorporated by reference here as if applied to the illustrated operator node data structure 30J.0, including the provision of a location specifier which specifies where in its respective context space, the context-representing primitive object is located.
More generally and in accordance with the present disclosure, a context data-objects organizing space (a.k.a. context space or context mapping mechanism, e.g., 316? of FIG. 3D) is provided within the STAN—3 system 410 to be composed of stored data representing context space primitive objects (e.g., 30J.0 of FIG. 3J) hierarchically and/or spatially dispersed in the space and operator node objects (e.g., 30Q.0 of FIG. 3Q) that logically link with such context primitives (e.g., 30J.0) and are also hierarchically and/or spatially dispersed within the context space and where the primitive/operator nodes are optionally clustered around respective clustering center points (see 371.0 of FIG. 3E) where such clustering center points are also hierarchically and/or spatially dispersed within the context space. In one embodiment, each context primitive (see FIG. 3J) has a data structure which includes a number of context defining fields where these included fields may comprise one or more of: (1) a first field 30J.1 indicating a formal name of a role (e.g., 5th Grade Student) that is potentially being assumed by an actor (e.g., STAN user) who may be deemed as likely to be operating under that corresponding context. Examples of roles may include socio-economic designations such as (but not limited to) full-time student (and grade level), part-time teacher (and grade levels), employee (and job title), employer, manager, subordinate, and so on. The role designation may include an active versus inactive indicating modifier such as, “retired college professor” as compared to “acting general manager” for example. Instead of, or in addition to, naming a formal role, the first field 30J.1 may indicate a formal name of an activity corresponding to the actor's context or role (e.g., managing chat room as opposed to chat room manager). A same user can be simultaneously operating under many different contexts. More specifically, the Fifth Grade Student of the Abe Lincoln example may also be a part time worker in his/her school library and/or an active member of a school sports or other such team or club. When CFi's are received from that user, the different contexts which may be operative at the moment are sorted according to likelihood (which likelihood may be based on the user's currently activate profiles and/or the user's last determined-as-more-likely contexts (represented by signal 316 o of FIG. 3D)) and the received CFi's (e.g., post-normalization CFi's) are hybridized first with the most likely context, then with the second most likely context, and so on (as represented by and ranked by data provided in signal 316 o of FIG. 3D); so that a likely context-appropriate permutation is not overlooked.
Another of the fields in each context primitive defining object 30J.0 (FIG. 3J) can be: (2) a second field 30J.2 pointing to informal role names or role states or activity names. The reason for inclusion of this second field 30J.2 is because the formal names assigned to some roles (e.g., Vice President) can often be for sake of a facade or ego rather than for reflecting actual reality. Someone can be formally referred to as Vice President or Manager of Data Reproduction when in fact they routinely operate the company's photocopying machine. Therefore cross-links 30J.2 to the informal but more accurate definitions of the actor's role may be helpful in more accurately defining the user's context for certain users, where the weighting in favor of second field 30J.2 rather than first field 30J.1 can be based on a physical locality indicating signal (the XP signal of FIG. 3D). The pointed-to informal role can simply be another context primitive defining object like 30J.0.
Assigned roles (as defined by field 30J.1) will often have one or more normally expected activities or performances that correspond to the named formal role. For example, a normally expected activity of someone in the context of being a “manager” might be “managing subordinates”. Therefore, when a user is determined (by signal 316 o) as likely to currently be in the context of being an acting manager (as defined by field 30J.1, if primitive 30Q.0 is being referenced based on the current version of output signal 316 o), corresponding third field 30J.3 may include a pointer pointing to an operator node object in context space or in an activities space (not directly shown) that combines the activity “managing” with the object of the activity, “subordinates”. Each of those primitives (“managing” and “subordinates”) may logically link to nodes in topic space and/or to nodes in other spaces. (Another example of “expected performances” 30J.3 might be “does homework immediately after school” for the case of the Fifth Grade Student working on his/her Abe-Lincoln assignment.) Although each user who operates under an assumed role (context) is “expected” to perform one or more of the expected activities of that role, it may be the case that the individual user has habits or routines wherein the individual user avoids certain of those “expected” performances. Such exceptions to the general rule are defined (in one embodiment) within the individual user's currently active PHAFUEL profile (e.g., FIG. 5A). More specifically, even if the “expected performances” 30J.3 for the average Fifth Grade Student might be “does homework immediately after school”, for the case of the specific Fifth Grade Student in the above Abe-Lincoln example, that user's PHAFUEL profile might indicate that he/she normally does it 2 hours after supper. Accordingly, if the physical context signals (XP) that accompany the user's CFi's indicate the time to be 1-3 hours after supper, that additional information will be used by the STAN—3 system to indicate increased likelihood that the user is in the doing-homework activity part of the assumed role (Fifth Grade Student).
A fourth field 30J.4 (FIG. 3J) may include pointers pointing to one or more communal-basis-wise expected cross-correlated nodes in topic space. By this it is meant that the average or normal member of the relevant community of alike users would be expected to likely be focused-upon the listed topic nodes when in the given reference. It does not necessarily mean that the current, specific user is now focused-upon those nodes. The pointers of fourth field 30J.4 may alternatively or additionally point to knowledge base rules (KBR's) that exclude or include various nodes and/or subregions of topic space. Once again, because the context space primitive object 30J.0 of FIG. 3J is part of a communally created and communally updated context space (XS), the pointed-to knowledge base rules (KBR's) are ones that apply to the average or normal member of the relevant community of alike users and they do not necessarily reflect the propensities of the current, specific user. More specifically, if the role or user context is Fifth Grade Student, one of the pointed-to KBR's may exclude or substantially downgrade in match score, topic nodes directed to purchase, driving or other uses of automobiles since the average Fifth Grade Student is not engaged in such activities. On the other hand, further knowledge base rules (KBR's) stored in one of the specific user's currently activated, personal profiles may indicate that for this particular Fifth Grade Student, the match score should not be downgraded as much.
A fifth field 30J.5 of each context primitive may include pointers to, and/or knowledge base rules (KBR's) for including and/or excluding subregions of a demographics space (not shown). The logical links between context space (e.g., 316?) and demographics space (not shown) should be bi-directional ones such that the providing of specific demographic attributes (e.g., age, gender, height, weight, income group, etc.) will link with different linkage strength values (positive or negative) to nodes and/or subregions in context space (e.g., 316?) and such that the providing of specific context attributes (e.g., role name equals normal or average “Fifth Grade Student”) link with different linkage strength values (positive or negative) to nodes and/or subregions in demographics space (e.g., age is probably less than 15 years old, height is probably less than 6 feet and so on).
A sixth field 30J.6 of each context primitive 30J.0 may include pointers to, and/or knowledge base rules (KBR's) for including and/or excluding likely subregions of a forums space (not shown, in other words, a space defining different kinds of chat or other forum participation opportunities which the in-context average or normal user is likely to be excluded from and/or included within).
A seventh field 30J.7 of each context primitive 30J.0 may include pointers to, and/or knowledge base rules (KBR's) for including and/or excluding likely subregions of a related-users space (not shown, but whose nodes would indicate other users to whom the first user is likely to be currently relating to (or vise versa) because of the currently undertaken role of the first user). More specifically, a primitive 30J.0 whose formal role is “Fifth Grade Student” may have pointers and/or KBR's in seventh field 30J.7 pointing to “Fifth Grade Teachers” and/or “Fifth Grade Tutors” and/or “Other Fifth Grade Students”. In one embodiment, the seventh field 30J.7 specifies other social entities that are likely to be currently giving attention to the person who holds the role of primitive 30J.0 (or vise versa). More specifically, a social entity with the role of “Fifth Grade Teacher” may be specified as a role of another person who is likely giving current attention to the inhabitant who holds the role of primitive 30J.0 (e.g., “Fifth Grade Student”) or vise versa where the average or normal “Fifth Grade Student” is likely giving partial focusing attention to the “Fifth Grade Teacher”. The context of a STAN user can often include a current expectation that other users (e.g., his online “Fifth Grade Teacher” and/or his “Mother” who just reminded him to do his homework) are currently casting attention on that first user. People may act differently when alone as opposed to when they believe others are watching them, auditing them, or otherwise currently paying attention to what the first user (e.g., “Fifth Grade Student”) is currently doing.
Each context primitive 30J.0 may include pointers to, and/or knowledge base rules (KBR's) for including and/or excluding likely subregions of yet other spaces (other data-objects organizing spaces) and/or other informational resources as is indicated by eighth area 30J.8 of data structure 30J.0. The pointed to other informational resources may include chat or other forum participation sessions cross-associated with the context primitive 30J.0. They may alternatively or additionally include non-forum research sources. The pointed to other informational resources may include personas or groups (expert groups, influential persons, etc.) cross-associated with the context primitive 30J.0; where once again, the results apply to the average or normal user within the relevant community but not necessarily to the given specific user. Chat rooms full of, and/or individualized users do not necessarily have to tether to, or only to a topic center (topic node). They may alternatively or additionally tether to a context node within the system's context space such as one represented by context primitive 30J.0 or one represented by an operator node that is a progeny of context node 30J.0. More specifically, and by way of example, one context node in context space may be that of pretending (e.g., as part of an online game) to take on the role of “President of the United States” (POTUS, i.e. in field 30J.1) and one of the expected performance or activities may be that of acting as Commander in Chief (e.g., in field 30J.3). There can be online chat or other forum participation sessions devoted to this contextual role-playing aspect, where for example, eighth area 30J.8 may include pointers to online forum participation sessions devoted to a corresponding online game. At the same time, there may be one or more topic nodes or subregions in topic space dedicated to the topic of pretending to be POTUS. Unlike a conventional Wikipedia™ structure, the Cognitive Attention Receiving Spaces of the STAN—3 system may each have many points, nodes or subregions that each, on the surface, appears to be directed to a same or similar cognition. More specifically, just as was true for the above exemplary case of “*lincoln*” being plurally expressed in a corresponding plurality of different hierarchical and/or spatial locations within keyword space, context space (XS)—as another example—may be filled with many copies of data structure 30J.0 each having a same formal role name and a same informal role name and yet the on-the-surface apparently same context specifications respectively overlie different cognitive senses of the specified role (e.g., pretending to be POTUS as part of a serious strategic game, pretending to be POTUS as part of a comic or mocking game, pretending to be POTUS as part of an educational Fifth Grade level exercise and so on). In one embodiment, just as keyword space may be populated by clustering center points each representing a respective cognitive sense for nearby keyword expressions, context space may be similarly populated by clustering center points each representing a respective cognitive sense for nearby context-specifying expressions (e.g., substantially same or similar copies of primitive object 30J.0). Moreover, topic space and yet others of the system-maintained Cognitions-representing Spaces may be similarly populated by clustering center points each representing a respective cognitive sense for nearby cognition-representing topic or other respective types of nodes. By providing such clustering center points in each respective space, distinctions can be made as between apparently (on the surface) same Cognitive Attention Receiving Nodes or Subregions (CARNS) where the underlying cognitive senses are actually different. Ranking and sorting according to different cognitive senses may be based on a complex set of currently activate user states that indicate likely user mood, likely user context, the user's currently chosen persona name, recent user activity history, and so on.
Referring next to FIG. 3X as well as FIG. 3Q, in one embodiment, the operator node objects and/or inter-space cross-association links (e.g., IoS-CAX 370.6?, 370.7?) emanating therefrom may be automatically generated by so-called, keyword expressions space consolidator modules (e.g., 370.8? in FIG. 3X). Such consolidator modules (e.g., 370.8?) automatically crawl through their respective spaces looking for nodes and/or logical links that can be consolidated from many into one without loss of function (basically, a deduplication function). More specifically, if keyword node 374.1 of FIG. 3E hypothetically had four cross-space links like 370.6, each pointing to a respective one of topic nodes Tn71 to Tn74 with same strength, then those four hypothetical (not shown) cross-space links are essentially superfluous duplicates of one another and they could be consolidated into and replaced by a single, wide beam projecting link (see 370.6? of FIG. 3Y) without loss of function. A consolidator module (e.g., 370.8?) automatically finds such overlap and/or redundancy during its space crawl-through operations and it then consolidates the many links into a functionally equivalent one and/or the many nodes into a functionally equivalent one node where possible. Such consolidation would reduce memory consumption and increase data processing speed because the keyword-to-topic nodes matching servers would have a fewer number of nodes and/or cross-spaces links to trace through when trying to match a received CFi's cluster (see 30U.12 of FIG. 3U) of a respective user with cross-correlating or matching nodes in topic or other spaces.
Referring to FIG. 3Y as well as FIG. 3E, in one embodiment, the automated determination of what topic nodes the logged-in user is more likely to be currently focusing-upon is carried out in a stepping stones fashion with the help of a hybrid space scanner 30Y.50 that automatically searches through hybrid spaces that have “context” as one of their hybridizing factors. Recall that the likely context(s) signal 316 o output by the context mapping mechanism 316? of FIG. 3D (see also 30Y.36 of FIG. 3Y) includes data identifying the most likely N contexts (where here N=1, 2, 3, . . . ) and data ranking and sorting these probable contexts according to likelihood that these are the current context(s). Starting with the determined-as-most-likely context, the hybrid space scanner 30Y.50 finds a first, relatively coarse subregion in hybrid space to serve as a first foothold or stepping stone; and then as more information comes in about user context and/or about user focused-upon items (e.g., keywords, URL's, sub-portions of user-perceivable content), the scanner 30Y.50 steps forward (e.g., transitions) from a respective first pointing state 30Y.51 (shown at a bottom middle portion of FIG. 3Y) to a second pointing state 30Y.52 which points to a more specific, more refined (higher resolution) subregion (e.g., 30Y.9) in the hybrid space that better indicates what the user appears to be focusing-upon given the assumption of the first picked context as being the most likely one.
In terms of further specifics, it should be recalled that often, the received CFi's of a given user (e.g., 301A? of FIG. 3A) are so-called, hybridized or HyCFi's which define a complex of physical and/or other context (e.g., biometric) representing signals as well as those defining things (e.g., sub-portions of on-screen content that the user is focusing-upon, keywords used, URL's accessed, etc.) thereby it is determined that the respective user appears to have recently been giving focused attention to a corresponding one or more topic nodes. Yet more specifically, in the case where a given set of the user's recently used keywords are received via a respective first set of CFi's that are grouped together (e.g., Kw1 AND Kw3 in the example of FIG. 3Y), the hybrid space scanner 30Y.50 is configured to responsively and automatically search through a hybrid keywords and context states space looking for a hybrid node or subregion (e.g., 30Y.8) that substantially matches (not necessarily 100%) both the grouped together keywords (e.g., Kw1 AND Kw3) and the currently resolved context states (e.g., Xsr5, which context space subregion (XSR) is initially pointed to by corresponding context output signal 30Y.36), where these currently resolved context states are those determined for the corresponding STAN user. More to the point, if the STAN user currently has the context state (e.g., Xsr5) of being in the role of a Fifth Grade student doing his/her homework soon after coming home from school; because habitually, per his/her currently active PHAFUEL profile 30Y.10 (disposed in an active profiles layer 30Y.63) that is what the user usually does at that time and/or place and/or if the STAN user is determined by the system to currently have the context state (e.g., Xsr5) of being in a studious mood because his/her currently active PEEP profile (e.g., 30Y.20, also in layer 30Y.63) so indicates, and/or if the STAN user currently is determined by the system to have the context state (e.g., Xsr5) of being a Fifth Grade student because his/her currently active Personhood/Demographics profile (e.g., 30Y.30, also in layer 30Y.63) so indicates, then the resulting, CFi-refined and profile-refined context-determining signals 30Y.36 next which are output by the mapping mechanism 316?? (which mapping mechanism is disposed in a subregions matching layer 30Y.64 of a process depicted by FIG. 3Y) will be collected by the hybrid space scanner 30Y.50 (which scanner is also disposed in layer 30Y.64) as defining to best of current resolution by the system what the user's current context is. This updated determination enables the scanner 30Y.50 to output progressively updated pointers (stored in pointers layer 30Y.65) that focus-upon a correspondingly matching (not necessarily 100%) first portion 30Y.8 of hybrid context-keywords space in a first of progressive resolving steps. When a next and newer set of one or more keyword expressions 30Y.4 (e.g., Kw6) are received under this initially refined context definition (e.g., 30Y.36), the newer set of keyword expressions (and/or newer set of other focus-indicating expressions) are automatically added to the hints of clues collected by the hybrid space scanner 30Y.50 to thereby enable the scanner to advance its hybrid-matching pointer (30Y.51) to thereby better focus (by way of updated matching pointer 30Y.52) upon a corresponding narrower portion of the hybrid context and keywords space that contains the more relevant hybrid node 30Y.9. More specifically, if the first set of keywords (e.g., Kw1 AND Kw3) are “Lincoln's” and “Address” and the first resolved context (e.g., XSR5) is “Fifth Grade Student doing homework” and then the more recently received keywords 30Y.4 are Kw6=“How Historians see it now”, then the hybrid space scanner 30Y.50 stepping-stone-wise steps forward form a first state (where it outputs pointer 30Y.51 and it is thereby pointing at a first hybrid subregion parented by hybrid node 30Y.8) to a second state (where it outputs pointer 30Y.52 and it is thereby pointing at a smaller hybrid subregion parented by hybrid node 30Y.9). Note that hybrid node 30Y.9 is hierarchically a child of node 30Y.8 and the latter operator node 30Y.8 is hierarchically a child of node 30Y.7. Nodes 30Y.7, 30Y.8 and 30Y.9 are represented by data stored in a hybrid context-plus-keywords space maintained by the STAN—3 system.
The newer found, hybrid node 30Y.9 has a cross-spaces logical link 380.9? that points to a topic space subregion 370.7? containing topic nodes Tn74? and Tn75?. In one embodiment, cross-spaces logical link 380.9? points to the center of an elliptical region 370.7? by specifying a nearby, cognitive sense representing, clustering center point 370.9?, by specifying an offset distance and offset direction from that center point 370.9? and then by specifying the two focal points of elliptical region 370.7? relative to the offset vector (the vector defined by the offset distance and offset direction). The referenced cognitive sense representing, clustering center point 370.9? defines, among other things, spatial distances such as 370.10? and 370.11? between itself and nearby topic nodes such as Tn61?, Tn74?, etc. The defined spatial distances indicate relative closeness of cognitive sense as between a central cognitive sense of the clustering center point 370.9? and respective cognitive senses of the nearby topic nodes (e.g., Tn61? and Tn74?). The linked-to elliptical region 370.7? encompasses subregions Tn74? and Tn75? within its interior and thereby references them. These, traced-to and corresponding topic nodes and/or topic subregions (e.g., Tn74? and Tn75?) in topic space may then point to a context-appropriate set of chat or other forum participation sessions (not shown) which the user will be invited to join in on where the forum participation sessions are closely related to “Lincoln's Gettysburg Address” and how historians currently view it and where the participation sessions are co-compatibility appropriate for an average or normal Fifth Grade Student. Contrastingly, the so traced-to corresponding nodes and/or subregions (e.g., Tn74? and Tn75?) will not be ones directed to a local Ford/Lincoln™ automobile dealership or to a topic directed to the city of Lincoln, Nebr. Thus the corresponding invitation(s) and/or suggestions which the Fifth Grade Student receives from the STAN—3 system will be demographics-wise appropriate and topic-wise appropriate and context-wise appropriate.
By way of contrast, had the system user been an older person who recently was searching for a new car, the keywords “Lincoln's Address” would have instead led to the system pointing to a topic or other kind of node (e.g., geography space node) directed to the local Ford/Lincoln™ automobile dealership. This would be so because under that alternate context (older user and different user history), the possibility of the user being a Fifth Grade student would have been excluded, or at least much reduced in score in terms of context and a corresponding topic likely to be then be on the user's mind. At the same time logical connections to nodes or subregions pointing to automobile dealerships would have received substantially greater scores.
Still referring to FIG. 3Y and this time also to FIG. 3D, a more specific example is provided of how the currently activated profiles (301 p, 301 p? in FIG. 3D; and layer 30Y.63 in FIG. 3Y) can work in combination with currently received indications of user physical and other contexts to progressively home in on a likely, subregion XSR5 of FIG. 3Y within the context mapping mechanism (316??).
Some of the recently received CFi's, 30Y.1 will be those indicating current physical context (e.g., geographic location and temporal positioning within a user associated calendar) where these current physical context CFi's 30Y.1 operate to identify a more likely, current PHAFUEL log(habits and routines) 30Y.10 for the user and to identify a more likely, current PEEP record (personhood and emotional expressions profile) 30Y.20 for the user. Aside from the emotional expressions profile (30Y.20), the user may have a corresponding, currently exposable other personhood profile 30Y.30 which the user has indicated as being currently exposable over the network, except that the exposed data from the personhood profile 30Y.30 may be less detailed or specific than that of the current PEEP record (30Y.20). For example, the exposed data from the personhood profile 30Y.30 may only show a rough yearly income range (e.g., “above $30K per year”) rather than the user's actual income numbers. The logged-in persona 30Y.3 of the user may point to a specific personhood profile 30Y.30 as well as to a specific (but not exposed) PEEP 30Y.20. A last determined, mental context of the user (e.g., recent user history) may also point to specific ones of the user's PHAFUEL records, PEEP profiles and personhood profile (e.g., 30Y.10, 30Y.20, 30Y.30) as being the currently most likely to use. These currently activated profile records may then match with or strongly cross-correlate with a specific subregion, XSR5 in context space 316?? (e.g., by pointing to the parent node of that subregion). The cross-correlation is represented by respective pointers 30Y.15, 30Y.25 and 30Y.35. Although not shown in FIG. 3Y, an example of a series of hierarchically organized nodes represented by data stored for the system-maintained context space 316?? may be as follows: //currently adopted role=at home/young person/student/elementary school/Fifth Grade Student/doing homework/for History class. That, context (as represented by output signal 30Y.36), when combined with recently received CFi's (e.g., keyword type CFi's 30Y.4) causes the scanner 30Y.50 to automatically point to a first subregion in a hybrid keyword/context space (having node 30Y.8 as its parent, where 30Y.7 is the parent of 30Y.8). Then when newer, context indicating CFi's (30Y.1, 30Y.2, 30Y.3) are received and newer, focus-indicating CFi's (30Y.4) are received, the updated context indicating signal 30Y.36 (and also 30Y.36? which drives the profiles) may identify a smaller (better resolved) subregion in context space (and in profiles space) and the scanner 30Y.50 may then step forward to a state in which it points to a smaller (better resolved) subregion 30Y.9 in the hybrid keyword/context space, thereby directly or indirectly pointing to context and topic appropriate chat or other forum participation opportunities which the user is to be invited into. In one embodiment, an automated link tracer 30Y.67 uses the inter-space links (e.g., 380.9?) of the pointed-to hybrid node (e.g., 30Y.9) to trace to the indirectly pointed-to subregion (e.g., topic space region 370.7?) of another Cognitive Attention Receiving Space (e.g., topic space) and it then fetches the chat room or other informational resources of the indirectly pointed-to subregion (e.g., 370.7?) for use in transmitting an invitation or other communication back to the user.
Sometimes, a user is momentarily interrupted out of one context and asked to temporarily switch into a second context with the expectation that the user will soon return to the first context. By way of example, while the Fifth Grade Student is doing his/her homework, the mother comes into the room and asks, “Sorry to interrupt, but my computer is down; can you do me a favor and print out some driving directions to my friend's house?” In this exemplary case, the student is momentarily taken out of his/her first context (e.g., researching the question about how modern historians view Abe-Lincoln's Address) and put into a different context (e.g., temporarily helping his/her mother to get driving directions). The STAN—3 system can automatically detect this sudden switch of context by, for example, detecting that the new search keywords being inputted into respective search engines (e.g., “What is the shortest driving directions to Montgomery Street?”) are incongruent with the context (30Y.36) last determined for that user (Fifth Grade Student).
In response to this determination, and in accordance with one aspect of the present disclosure, the system automatically saves the previously determined context (represented by signals 30Y.36 and 30Y.36?) into a first context swap stack (or other such history memory) 30Y.59 that is associated with recent activities of the first user. The system also automatically saves the previously determined set of activated profiles (of active profiles layer 30Y.63) into a second user's context swap stack (or other such memory) 30Y.58. Additionally, the system automatically saves the previously determined set of pointers (the pointers of active pointers layer 30Y.65) into a corresponding hybrid space pointers saving stack (or other such memory) 30Y.55 that belongs to the interrupted user. In one embodiment, a synchronizing signal is also stored that indicates which levels of the various context swap stacks belong to one another.
Once the interrupted context-development process is stored away in the swap stacks, the STAN—3 system can then begin to develop a new determination of the newly inserted and current context (e.g., helping mother get driving directions) for the same user and it can then begin making context-appropriate suggestions for that new context. When the interrupting second context completes (as evidenced by changed CFi's from the user), the system temporarily saves the parameters of that second context into the context swap stacks, 30Y.59, 30Y.58, 30Y.55, and retrieves the earlier saved parameters of the first, and temporarily interrupted context (e.g., researching the question about how modern historians view Abe-Lincoln's Address). In this way, the work done by the system in refining its understandings of the user's context for the first, temporarily interrupted task (Fifth Grade homework task) is not lost and the interrupted user can pick up where he/she last left off. It is within the contemplation of the disclosure that the context swap stacks, 30Y.59, 30Y.58, 30Y.55 may be sized and organized for swapping as between three or more interleaving tasks. In one embodiment, the user identifies to the system, one or more tasks as being long-term continuing ones and the system then understands that other intervening tasks are shorter-term ones for which the parameters do not have to be saved for a long time.
Still referring to FIG. 3Y for just a bit longer, it may be seen that hybrid matching functions depicted in this figure are subdivided into a series of pipelined machine operations, including: (a) a feedback operation (layer 30Y.60) in which a latest, other-than-purely physical, context determination representing signal 30Y.36? obtained from the context mapping mechanism 316?? is received and stored; (b) a recent CFi's and other-user-state-reporting signals receiving operation (layer 30Y.61/62) in which recent physical context reporting CFi's (XP signals) and other attention giving activities reporting signals related to the user and the user's state are received and stored; (c) a profiles updating operation (layer 30Y.63) in which selection of the currently activated profiles may be changed based on the more recently received CFi and other user-state reporting signals; (d) a subregions cross-correlating/matching operation (layer 30Y.64) in which the currently activated user profiles are used in combination with recently received, reporting signals (e.g., CFi's, CVi's) related to the user's state and recent attention giving activities of the user are used to better resolve or update the system's determination of the user's likely current and other-than-purely physical context, which context is represented by the context space output signal, 30Y.36 and in which operational layer 30Y.64 the user's current, other-than-purely physical context representing signal 30Y.36 is used to drive the hybrid space scanner 30Y.50 in combination with drives provided by recently received CFi's, CVi's (which recently received signals may be transformed/translated based on the currently activated profiles (of layer 30Y.63) before driving the scanner 30Y.50) so that the scanner 30Y.50 generates pointers (e.g., 30Y.51,52) pointing to hybrid space points, nodes or subregions (e.g., 30Y.7,8,9) that are likely to be cross-associated with what the user appears to be casting his/her attention giving energies on, given the determined, other-than-purely physical context (30Y.36) of the user; (e) a cross-space linking operation (e.g., 30Y.67) in which the identified hybrid space points, nodes or subregions are used to logically link (380.9?) to corresponding points, nodes or subregions (or clustering center points, e.g., 370.9?) in other Cognitive Attention Receiving Spaces (e.g., in topic space—as represented in FIG. 3Y by subregion 370.7?); and (f) an informational resources providing operation (not explicitly shown, see description above of tracer 30Y.67) in which the user (e.g., the Fifth Grade Student) is provided with on-topic and/or otherwise appropriate informational resources that are likely to be relevant to what the user apparently has in mind given the determinations made by the STAN—3 system regarding the user's current context (represented by signal 30Y.36) and given the determinations made by the STAN—3 system regarding the user's current attention giving activities. The provided informational resources which are transmitted to the user (e.g., to the user's mobile data processing device) may include one or more of invitations to join in on chat or other online forum participation sessions, invitations to join in real life (ReL) gathering events, suggestions of other users (e.g., topic experts) whom the first user may wish to link up with so as to obtain further relevant information and suggestions of other informational resources which the first user may wish to tap so as to obtain further relevant information, where the relevancy of the provided informational resources is based on the pointers generated by the hybrid space scanner 30Y.50 and the hybrid space points, nodes or subregions pointed to by those pointers (e.g., 30Y.51, 30Y.52).
Stated otherwise, a machine-implemented and automated process (e.g., 30Y.60-67) is provided which empowers a first user (e.g., 30R.0A) whose attention giving activities are being automatically monitored by one or more local devices (e.g., mobile wireless device 30R.00 in FIG. 3R) and being automatically reported to the ss3 core (e.g., the cloud) so as to cause his monitored activities to induce the automated informational resource lookup operations to take place in the STAN—3 system core on his/her behalf where the automated informational resource lookup operations include one or more of: (a) automatically determining one or more most likely current contexts (30Y.36) for the user; (b) automatically determining, based on the determined current context(s), one or more currently likely profiles (30Y.63) to be activated for the user; (c) automatically identifying, based on the currently activated one or more profiles and on reporting signals (e.g., 30Y.4) recently received for the user reporting recent attention giving activities of the user and/or reporting recent physical context and/or biometric states of the user, one or more points, nodes or subregions (or clustering center points, e.g., 370.9?) of a pure or hybrid Cognitive Attention Receiving Space (e.g., keyword-and-context space) to be currently pointed-to; (d) automatically identifying, based on the currently pointed-to parts of a hybrid or pure Cognitive Attention Receiving Space, one or more informational resources to be transmitted back to the user in the form, for example, of invitations to join chat or other online forum participation sessions, invitations to join real life (ReL) or virtual life events related to the currently pointed-to parts of the pure/hybrid Cognitive Attention Receiving Space, and so on. As used in this paragraph, the term “empowers” includes at least the notion that a user is enabled to log-into and/or otherwise access remote resources of the STAN—3 system core for thereby causing the system core to return to that distally located user, informational resource signals which can represent at least one of: invitations to join chat or other online forum participation sessions related to the pointed-to parts of the hybrid Cognitive Attention Receiving Space (HyCARS), invitations to join real life (ReL) or virtual life events related to the pointed-to parts of the HyCARS, suggestions to connect with one or more identified other users (e.g., experts, influencers) in regard to the pointed-to parts of the HyCARS, and suggestions to access one or more identified data resources (e.g., databases) in regard to the pointed-to parts of the hybrid Cognitive Attention Receiving Space (HyCARS).
Referring to FIG. 3F, in one embodiment, one of the data-objects organizing spaces maintained by the STAN—3 system 410 is a music-type Cognitive Attention Receiving Space (CARS) that includes as its primitives, a music primitive object 30F.0 having a data structure composed of pointers and/or descriptors including first ones defining musical melody notes and/or musical chords and/or relative volumes or strengths of the same relative to each other. It is to be understood that due to drawing space limitations some housekeeping fields are not shown in FIG. 3F, including for example fields identifying where in the local space the data object is hierarchically and/or spatially located, fields identifying the data object by serial number or other unique means and fields identifying nearby clustering center points. On the other hand, examples of such left out fields may be found for example in FIGS. 3Ta-TB and 3W as will be detailed below. The discussion later below of such housekeeping fields are to be seen as if incorporated here at by reference.
The music primitive object 30F.0 of FIG. 3F may alternatively or additionally define percussion waveforms and their interrelationships as opposed to musical melody notes. The music primitive object 30F.0 may identify associated musical instruments or types of instruments and/or mixes thereof. The music primitive object 30F.0 may identify associated nodes and/or subregions in topic space, for example those that identify a corresponding name for a musical piece having the notes and/or percussions identified by the music primitive object 30F.0 and/or identify a corresponding set of lyrics that go with the musical piece and/or identify corresponding historical or other events that are logically associated to the musical piece. The music primitive object 30F.0 may identify associated nodes and/or subregions in context space, for example those that identify a corresponding location or situation or contextual state that is likely to be associated with the corresponding musical segment. The music primitive object 30F.0 may identify associated nodes and/or subregions in multimedia space, for example those that identify a corresponding movie film or theatrical production that is likely to be associated with the corresponding musical segment. The music primitive object 30F.0 may identify associated nodes and/or subregions in emotional/behavioral state space, for example states that are likely to be present in association with the corresponding musical segment. And moreover, the music primitive object 30F.0 may identify cross-associated informational resources for its notes/percussions and/or associated nodes and/or subregions in yet other spaces where appropriate. Although not explicitly shown, the cross-associated informational resources may include one or more of cross-associated chat or other forum participation sessions, cross-associated personas and/or other such informational resources as may be useful to system users when focusing-upon the respective notes/percussions of the corresponding music primitive object 30F.0 or of respective operator nodes that inherit attributes of the music primitive object 30F.0.
Of importance, it is to be understood that the illustrated data structures of the different cognition representing data objects being introduced here-at; where the music primitive object 30F.0 of FIG. 3F is merely an example, are not limited in content or organization to that which is shown in FIG. 3F. The data structures (e.g., of music primitive object 30F.0 as a first example; and also the data structures of further data objects shown in FIGS. 3G-3Q) or other such primitive data objects not illustrated in figures but included as part of the spirit and scope of the present teachings, may include additional fields (e.g., like 30T.1 a-30T.1 d and others of FIGS. 3Ta-3Tb and like 30W.7 b-30W.7 c and others of FIG. 3W) and/or fields organized in different ways and/or ancillary other data structures with which the illustrated ones cross-cooperate. More specifically, because the concept of non-textual cognition representing data objects like 30F.0 of FIG. 3F is being elaborated on here for a relatively first time and it may be hard to simultaneously wrap one's mind around the dual ideas of what each primitive object does and then how plural ones of such cognition representing data objects (e.g., music primitives 30F.0) may be distributively placed (e.g., clustered, for example adjacent to one or more cognitive-sense-representing clustering center points—see again 370.9? of FIG. 3Y) within corresponding spatial and/or hierarchical spaces, it is to be understood that additional fields (not shown in FIG. 3F) may be provided for specifying where in such spaces the data objects virtually reside in a spatial and/or hierarchical and/or other sense (e.g., including where in the system's physical memory the data representing the data objects resides), but for the sake of simplification such additional fields are not shown (at least in FIGS. 3F-3P). On the other hand, when the yet more detailed data structure of a topic primitive object (TPO, see briefly, FIGS. 3Ta-3Tb) will be later described, the concept of primitive cognition representing data objects having spatial and/or hierarchical placements will be better explained (see briefly, fields 30T.1 a-30T.3 of FIG. 3Ta). Nonetheless, it is to be understood that data structures such as that of the above introduced music primitive object 30F.0 may include one or more additional fields which provide data indicative of where in a corresponding one or more spatial and/or hierarchical spaces the respective primitive (e.g., 30F.0) resides and/or how it is shaped or sized. This concept was already mentioned above with regard to field 30Q.1 of FIG. 3Q. The one or more additional fields (not shown in FIG. 3F) may include bi-directional pointers to ancillary, position defining data structures (not shown) where those ancillary position-defining data structures define, or assist in defining where the first data structure (e.g., 30F.0) resides in a respective one or more virtual spaces. As an example, an ancillary, position defining data structure (not shown) may identify a specific subregion (e.g., a base address) within which or near to which the respective primitive (e.g., 30F.0) resides and then the respective primitive may itself include a more detailed one or more location defining fields (e.g., an offset from a base address) which indicate where in respective spatial and/or hierarchical spaces and in corresponding subregions the respective primitive (e.g., 30F.0) is precisely located. (The notion of a primitive and/or non-primitive cognition representing data object having location was described above as part of field 30Q.1 of FIG. 3Q (operator node data object) and that notion will be explicated even further in the discussion of FIGS. 3R, 3S, 3Ta and 3Tb.)
Referring next to FIG. 3G, in one embodiment, one of the data-objects organizing spaces maintained by the STAN—3 system 410 is a sound waveforms space that includes as its primitives, a sound primitive object 30G.0 having a data structure composed of pointers and/or descriptors including first ones 30G.1 defining sound waveforms and relative magnitudes thereof as well as, or alternatively overlaps, relative timings and/or spacing apart pauses between the defined sound segments. The sound primitive object 30G.0 may include data 30G.2 identifying associated portions of a frequency spectrum that correspond with the represented sound segments. The sound primitive object 30G.0 may include stored data 30G.3 identifying associated nodes and/or subregions in topic space that correspond with the represented sound segments. The illustrated and respective links 30G.4-30G.7 to context space, multimedia space and so on may provide functions substantially similar to those described above for music space. These include stored data 30G.7 identifying cross-associated informational resources for its sound waveforms and/or stored data 30G.6 identifying cross-associated points, nodes and/or subregions in yet other spaces where appropriate. Although not explicitly shown, the cross-associated informational resources may include one or more of cross-associated chat or other forum participation sessions, cross-associated personas and/or other such informational resources as may be useful to system users when apparently giving attention energies to respective sound waveforms of the corresponding sound primitive object 30G.0 or of respective operator nodes that inherit attributes of the sound primitive object 30G.0.
Referring to FIG. 3H, in one embodiment, one of the data-objects organizing spaces maintained by the STAN—3 system 410 is a voice primitive representing object 30H.0 having a data structure composed of pointers and/or descriptors including first ones defining phoneme attributes of a corresponding voice sound segment and relative magnitudes thereof as well as, or alternatively overlaps, relative timings and/or spacing apart pauses between the defined voice segments. The voice primitive object 30H.0 may identify associated portions of a frequency spectrum that correspond with the represented voice segments. The voice primitive object 30H.0 may identify associated nodes and/or subregions in topic space that correspond with the represented voice segments. The links to context space, multimedia space and so on may provide functions substantially similar to those described above for the music and sound spaces.
Referring to FIG. 3I, in one embodiment, one of the data-objects organizing spaces maintained by the STAN—3 system 410 is a linguistics primitive(s) representing object 30 i.0 having a data structure composed of pointers and/or descriptors including first ones defining root entomological origin expressions (e.g., foreign language origins) and/or associated mental imageries corresponding to represented linguistics factors and optionally indicating overlaps of linguistic attributes, spacing aparts of linguistic attributes and/or other combinations of linguistic attributes. The linguistics primitive(s) representing object 30 i.0 may identify associated portions of a frequency spectrum that correspond with represented linguistic attributes (e.g., pattern matching with other linguistic primitives or combinations of such primitives). The linguistics primitive(s) representing object 30 i.0 may identify included linguistic types for corresponding included linguistic elements of the represented primitive such as verb(s), noun(s), adverbs, adjectives, homonyms, antonyms, negations, connectors (e.g., “and”, “or”, “as well as”, etc.), punctuations or pauses, clauses and so on. It is to be understood here that linguistic primitives are not limited to textual material and may alternatively or additionally include phonetic material and even sign language. The linguistics primitive(s) representing object 30 i.0 may further identify associated nodes and/or subregions in topic space that correspond with the represented linguistics primitive(s). Also for the linguistics primitive(s) representing object 30 i.0, the included links to context space, body gesture space, multimedia space and so on and may provide functions substantially similar to those described above for music and other such spaces. (The context primitive 30J.0 of FIG. 3J has already been discussed above.)
Referring to FIG. 3M, in one embodiment, one of the data-objects organizing spaces maintained by the STAN—3 system 410 is an image(s) representing primitive object 30M.0 having a data structure composed of pointers and/or descriptors including first ones defining a corresponding image object in terms of pixilated bitmaps and/or in terms of geometric vector-defined objects where the defined bitmaps and/or vector-defined image objects may have relative transparencies and/or line boldness factors relative to one another and/or they may overlap one another (e.g., by residing in different overlapping image planes) and/or they may be spaced apart from one another by object-defined spacing apart factors and/or they may relate chronologically to one another by object-defined timing or sequence attributes so as to form slide shows and/or animated presentations in addition to or as alternatives to still image objects. The image(s) representing primitive object 30M.0 may identify associated portions of spatial and/or color and/or presentation speed frequency spectrums that correspond with the represented image(s). The image(s) representing primitive object 30M.0 may identify associated nodes and/or subregions in topic space that correspond with the represented image(s). Also for the image(s) representing primitive object 30M.0, the included links to context space, multimedia space and so on may provide functions substantially similar to those described above for music and other such spaces.
Referring to FIG. 3N, in one embodiment, one of the data-objects organizing spaces maintained by the STAN—3 system 410 is a body and/or body parts(s) representing primitive object 30N.0 having a data structure composed of pointers and/or descriptors including first ones defining a corresponding and configured (e.g., oriented, posed, still or moving, etc.) body and/or body parts(s) object in terms of identification of the body and/or specific body part(s) and/or in terms of sizes, types, spatial dispositions of the body and/or specific body part(s) relative to a reference frame and/or relative to each other. The body and/or body parts(s) representing primitive object 30N.0 may identify associated portions of spatial and/or color and/or presentation speed frequency spectrums that correspond with the represented body or part(s). The body and/or body parts(s) representing primitive object 30N.0 may identify associated force vectors or power vectors corresponding to the represented body or part(s) as may occur for example during exercising, dancing or sports activities. The body and/or body parts(s) representing primitive object 30N.0 may identify associated nodes and/or subregions in topic space that correspond with the represented body and/or specific body part(s) and their still or moving states. Also for the body and/or body parts(s) representing primitive object 30N.0, the included links to emotion space, context space, multimedia space and so on may provide functions substantially similar to those described above for music and other such spaces. In one embodiment, keyword expressions that correspond to action verbs are logically cross linked to corresponding body motion attributes of the body and/or body parts(s) representing primitive object 30N.0. In the same or another embodiment keyword expressions (or linguistic expressions, see FIG. 3I) that correspond to computer action verbs are logically cross linked to corresponding computer action nodes in a system-maintained computer actions space (not shown). As a result, a neural and neuroplastically variable network of logical linkages is built up in the system for cross-correlating between action-representing words/linguistics or like expressions and definitions of corresponding body and/or computer actions.
Referring to FIG. 3O, in one embodiment, one of the data-objects organizing spaces maintained by the STAN—3 system 410 is a physiological, biological and/or medical condition/state representing primitive object 30 o.0 having a data structure composed of pointers and/or descriptors including first ones defining a corresponding biological entity and/or biological entity parts(s) object in terms of identification of the biological entity and/or biological entity parts(s) and/or in terms of sizes, macroscopic and/or microscopic resolution levels, systemic types, metabolic states or dispositions of the biological entity and/or biological entity parts(s) for example relative to a reference biological entity (e.g., a healthy subject) and/or relative to each other. The physiological, biological and/or medical condition/state representing primitive object 30 o.0 may identify associated condition names, degrees of attainment of such conditions (e.g., pathologies). The physiological, biological and/or medical condition/state representing primitive object 30 o.0 may identify associated dispositions within reference demographic spaces and/or associated dispositions within spatial and/or color and/or metabolism rate spectrums that correspond with the represented biological entity and/or biological entity parts(s). The physiological, biological and/or medical condition/state representing primitive object 30 o.0 may identify associated force or stress or strain vectors or energy vectors (e.g., metabolic energy flows and/or rates in or out) corresponding to the represented biological entity and/or biological entity parts(s) as may occur for example during various metabolic states including those when healthy or sick or when exercising, dancing or engaging sports activities. The physiological, biological and/or medical condition/state representing primitive object 30 o.0 may identify associated nodes and/or subregions in topic space that correspond with the represented biological entity and/or biological entity parts(s) and their still or moving states. Also for the physiological, biological and/or medical condition/state representing primitive object 30 o.0, the included links to emotion space, context space, multimedia space and so on may provide functions substantially similar to those described above for music and other such spaces.
Referring to FIG. 3P, in one embodiment, one of the data-objects organizing spaces maintained by the STAN—3 system 410 is a chemical compound and/or mixture and/or reaction representing primitive object 30P.0 having a data structure composed of pointers and/or descriptors including first ones defining a corresponding chemical compound and/or mixture and/or reaction in terms of identification of the corresponding chemical compound and/or mixture and/or reaction and/or in terms of mixture concentrations, particle sizes, structures of materials at macroscopic and/or microscopic and/or molecular/atomic/subatomic resolution levels, and/or in terms of reaction environment (e.g., presence of catalysts, enzymes, etc.), temperature, pressure, flow rates, etc. The chemical compound and/or mixture and/or reaction representing primitive object 30P.0 may identify associated condition/reaction state names, degrees of attainment of such conditions (e.g., forward and backward reaction rates). The chemical compound and/or mixture and/or reaction representing primitive object 30P.0 may identify associated other entities such as biological entities as disposed for example within reference demographic spaces (e.g., likelihood of negative reaction to pharmaceutical compound and/or mixture) and/or associated dispositions of the compound and/or reactants within spatial and/or reaction rate spectrums. The chemical compound and/or mixture and/or reaction representing primitive object 30P.0 may identify associated power vectors or energy vectors (e.g., reaction energy flows and/or rates in or out) corresponding to the represented chemical compound and/or mixture and/or reaction as may occur for example under various reaction conditions. The chemical compound and/or mixture and/or reaction representing primitive object 30P.0 may identify associated nodes and/or subregions in topic space that correspond with the represented chemical compound and/or mixture and/or reaction. Also for the chemical compound and/or mixture and/or reaction representing primitive object 30P.0, the included links to emotion space, biological condition/state space, context space, multimedia space and so on may provide functions substantially similar to those described above for music or other such spaces. (FIG. 3Q was already described above.)
Referring next to FIG. 3X, in one embodiment, the STAN—3 system 410 includes a node attributes comparing module that automatically crawls through a given data-objects organizing space (e.g., topic space) and automatically compares corresponding attributes of two or more nodes (e.g., topic nodes) in that space for various notions of sameness (e.g., duplication), degree of sameness or degree of differences, where the results are recorded into a nodes comparison database such as in the form, for example, of the illustrated nodes comparison matrix of FIG. 3X. Due to space limitations in the drawings, not all of the various notions of substantial sameness or similarity are illustrated. For example, comparison as between relative hierarchical and/or spatial distances of compared topic nodes to identified clustering center points (see 370.9? of FIG. 3Y) are not shown but are nonetheless understood to be contemplated herein. In one embodiment, the attributes that are compared may include any one or more of: hierarchical or nonhierarchical trees or graphs to which the compared nodes (e.g., Tn74? and Tn75?) belong. Note that the universal hierarchical “A” tree is not tested for, because all nodes of the given space must be members of that universal tree irrespective of where in the spatial dimensions of the topic space the nodes reside. (It is within the contemplation of the present disclosure to alternatively have a topic space and/or other Cognitions-representing Spaces that do not hierarchically organize their respective nodes or other such data object but instead place them only spatially, for example as clustered near or far to one another and/or near or far to clustering center points and in such a case the tests performed by the node attributes comparing module will be varied accordingly.) The attributes that are compared as between the two or more hierarchically organized nodes (e.g., Tn74? versus Tn75?) may further include the number of child nodes that the compared node has, the number of out-of-tree logical links that the compared node has, and if such out-of-tree logical links point to specific external spaces, an indication of what those specific external spaces are (e.g., keyword expressions space, URL space, context space, etc.) and optionally an identification of the specific nodes and/or subregions in the specific external spaces that are being pointed to. It is to be understood that this is a non-limiting set of examples of the kinds of information that is recorded into the node-versus-node comparison matrix.
In one embodiment, the STAN—3 system 410 further includes a differences/equivalences locating module that automatically crawls through the respective node-versus-node comparison matrix of each space (e.g., topic space, context space, keyword expressions space, URL expressions space, etc.) looking for nodes (or points or subregions) that are substantially the same and/or very different from one another and generating further records that identify the substantially same and/or substantially different nodes (e.g., substantially different sibling nodes of a same tree branch, or ditto for respective points or respective subregions). The generated and stored records that are automatically produced by the differences/equivalences locating module are subsequently automatically crawled through by other modules and used for generating various reports and/or for identifying unusual situations (e.g., possible error conditions that warrant further investigation). One of the other modules that crawl through the differences/equivalences records can be the local space consolidating module (e.g., 370.8? of FIG. 3 x in the case of the keyword expressions or other such textual expressions space).