# US 12,762,210 B2 · System, method, and product for transparently encapsulated artificial intelligence > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,762,210 B2 - Document ID: US12762210B2 - Status: Granted - Kind: B2 - Application number: 19/306,535 - Filed: 2025-08-21 - Published: 2026-06-11 - Granted: 2026-10-13 - Earliest claimed: 2023-12-13 - Applicants: Alan Bennett Sherr - Inventors: Alan Bennett Sherr - Web page: https://patentgrep.com/patent/US12762210B2 ## CPC - G06F 18/241: relating to the classification model, e.g. parametric or non-parametric approaches - G06V 10/454: {Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Artificial Intelligence systems, methods, and products identify significant patterns in data and persist those patterns and/or their constituents as data entries in databases or as transitory patterns of activation. The constituents associated with significant patterns include data constructs representing concepts or filter/exemplars. Those concepts or filters/exemplars themselves represent significant patterns. Recursive identification of significant patterns of significant patterns, and significant patterns of those patterns, and so on to any degree of recursiveness desired, facilitates the leveraging of combinatorial expansion to categorize, recognize, associate, and predict complex combinations of information while maintaining practical control over resources. In preferred implementations, the elements of the systems, methods, and products described herein are transparently encapsulated, and their behaviors are monitored and enforced, to provide alignment with human-determined objectives and rules. ## Claims ### Claim 1 A computer-implemented method for processing a plurality of data entries in a computer data bank persisted in at least one hardware medium, each data entry representing a respective significant pattern and comprising at least one activation attribute and at least one linking attribute, the method comprising: (a) priming a first subset of the plurality of data entries based at least in part on a first objective of a plurality of objectives and based at least in part on at least one linking attribute of at least one data entry associated with the first objective, the first objective comprising identifying a first significant pattern selected from the group consisting of: a first significant pattern represented by a first data entry of the plurality of data entries, a first significant pattern maintained in a transitory pattern of activation, and combinations thereof, the priming comprising altering, in the computer data bank, at least one activation attribute of each first-subset data entry to at least one distinguishing state of activation; (b) identifying, within processed data, a first set of at least one processed significant pattern, the identifying being based at least in part on the primed first subset; (c) priming a second subset of the plurality of data entries, each data entry in the second subset representing a respective one of the first set of at least one processed significant pattern, the priming the second subset comprising altering, in the computer data bank, at least one activation attribute of each second-subset data entry to at least one distinguishing state of activation; (d) determining whether the first objective has been accomplished, based at least in part on: (i) states of activation attributes of data entries belonging to the first subset; (ii) states of activation attributes of data entries belonging to the second subset; and (iii) at least one relationship among at least two data entries, the at least one relationship being specified by at least one item selected from the group consisting of: a filter/exemplar represented by at least one data entry, a linking attribute of at least one data entry, and combinations thereof, the at least one relationship being with respect to at least one dimension of at least one space; (e) in response to determining that the first objective has been accomplished, performing at least one operation, with respect to the computer data bank, selected from the group consisting of: creating a data entry in the computer data bank representing the first significant pattern and comprising at least one linking attribute associating the created data entry with at least one constituent data entry of the first significant pattern, altering at least one attribute of an existing data entry in the computer data bank to persist the first significant pattern, maintaining the first significant pattern in a transitory pattern of activation by maintaining activation attributes of constituent data entries of the first significant pattern in distinguishing states of activation, and combinations thereof; and (f) in response to determining that the first objective has not been accomplished, recursively processing significant patterns of significant patterns by: (i) recursively identifying, within processed data, additional sets of processed significant patterns; (ii) recursively priming additional subsets of the plurality of data entries, each additional-subset data entry representing a respective processed significant pattern of a respective additional set of processed significant patterns, the priming comprising altering, in the computer data bank, at least one activation attribute of each additional-subset data entry to at least one distinguishing state of activation; (iii) recursively determining whether the first objective has been accomplished, based at least in part on states of activation attributes of data entries belonging to the first subset, the second subset, and at least one additional subset, and at least one relationship among at least two data entries belonging to at least two of the first subset, the second subset, and the at least one additional subset, the at least one relationship being specified by at least one item selected from the group consisting of: a filter/exemplar represented by at least one data entry, a linking attribute of at least one data entry, and combinations thereof, the at least one relationship being with respect to at least one dimension of at least one space; and (iv) terminating the recursive processing in response to determining that at least one condition selected from the group consisting of: the first objective has been accomplished, the first objective is to be reformulated, the first objective is to be reassessed, the first objective is to be postponed, the first objective is to be abandoned, and combinations thereof, has been satisfied. ### Claim 2 (depends on 1) The method of claim 1, wherein determining whether anticipated identification of the first significant pattern is confirmed is based at least in part on conformity, within at least one permitted deviation, to at least one canonical form associated with the first significant pattern. ### Claim 3 (depends on 1) The method of claim 1, wherein the at least one distinguishing state of activation comprises at least one activation-intensity attribute. ### Claim 4 (depends on 1) The method of claim 1, wherein the at least one relationship is with respect to at least one dimension of a first space and at least one dimension of a second space different than the first space. ### Claim 5 (depends on 1) The method of claim 1, wherein the first processed data is selected from the group consisting of: internally generated processed data, externally generated processed data, and combinations thereof. ### Claim 6 (depends on 1) The method of claim 1, wherein the data bank is hierarchically organized, at least in part by levels, and wherein, when the first significant pattern is represented by the first data entry, the first data entry is in a first level, at least one data entry of the first set of constituents is in at least one second level lower than the first level, and at least one data entry of the second set of constituents is in at least one third level lower than the second level, continuing recursively with respect to levels for the additional sets of constituents of constituents of the first significant pattern. ### Claim 7 (depends on 1) The method of claim 1, wherein the recursive processing of significant patterns of significant patterns comprises incrementing by hierarchical level. ### Claim 8 (depends on 1) The method of claim 1, wherein the recursive processing of significant patterns of significant patterns comprises decrementing by hierarchical level. ### Claim 9 (depends on 1) The method of claim 1, wherein the recursive processing is terminated in response to determining that the first objective is to be reassessed, wherein the reassessment comprises changing composition of the first subset by selecting at least one different data entry of the plurality of data entries, and wherein the method of claim 1 is repeated using the changed composition of the first subset. ### Claim 10 (depends on 9) The method of claim 9, wherein the data bank is hierarchically organized, at least in part by levels, and wherein, when the first significant pattern is represented by the first data entry, the first data entry is in a first level, at least one data entry of the first set of constituents is in at least one second level lower than the first level, and at least one data entry of the second set of constituents is in at least one third level lower than the second level, continuing recursively with respect to levels for the additional sets of constituents of constituents of the first significant pattern, and wherein composition of the first subset is changed, at least in part, by selecting from at least one first lower level below the first level and not from levels above the at least one first lower level. ### Claim 11 (depends on 10) The method of claim 10, wherein the at least one first lower level is determined based at least in part on a desired resolution. ### Claim 12 (depends on 9) The method of claim 9, wherein the data bank is hierarchically organized, at least in part by levels, and wherein, when the first significant pattern is represented by the first data entry, the first data entry is in a first level, at least one data entry of the first set of constituents is in at least one second level lower than the first level, and at least one data entry of the second set of constituents is in at least one third level lower than the second level, continuing recursively with respect to levels for the additional sets of constituents of constituents of the first significant pattern, and wherein composition of the first subset is changed, at least in part, by selecting from at least one first higher level above the lowest level of the hierarchy and not from levels below the at least one first higher level. ### Claim 13 (depends on 12) The method of claim 12, wherein the at least one first higher level is determined based at least in part on a desired resolution. ### Claim 14 (depends on 1) The method of claim 1, wherein the recursive processing is terminated in response to determining that the first objective is to be reassessed, wherein the reassessment comprises changing preferences for linking among the first subset of the plurality of data entries, and wherein the method of claim 1 is repeated using the changed preferences. ### Claim 15 (depends on 1) The method of claim 1, wherein the recursive processing is terminated in response to determining that the first objective is to be reformulated, wherein the reformulation comprises anticipating identification of a second significant pattern selected from the group consisting of: a second significant pattern represented by a second data entry of the plurality of data entries, a second significant pattern maintained in a transitory pattern of activation, and combinations thereof, and wherein the method of claim 1 is repeated using the reformulated objective with the second significant pattern substituted for the first significant pattern. ### Claim 16 (depends on 1) The method of claim 1, wherein the recursive processing is terminated in response to determining that the first objective is to be reformulated based at least in part on a deviation of at least one of the recursively processed significant patterns from a canonical form associated with the first significant pattern. ### Claim 17 (depends on 1) The method of claim 1, wherein anticipated identification of the first significant pattern is based at least in part on a world model. ### Claim 18 (depends on 17) The method of claim 17, wherein the world model comprises a first script comprising the first significant pattern. ### Claim 19 (depends on 17) The method of claim 17, wherein the world model conditions the first processed data with respect to at least one item selected from the group consisting of: a world orientation reference, scale, translation, and combinations thereof. ### Claim 20 (depends on 1) The method of claim 1, wherein the recursive processing is terminated in response to determining that the first objective is to be postponed, and the postponement comprises maintaining in a transitory pattern a first plurality of attributes selected from the group consisting of: the at least one attribute of at least one data entry of the first subset, the at least one attribute of at least one data entry of the second subset, the at least one attribute of at least one data entry of the additional subset, and combinations thereof. ### Claim 21 (depends on 20) The method of claim 20, wherein the maintaining comprises setting the first plurality of attributes to a state of resonance. ### Claim 22 (depends on 1) The method of claim 1, wherein: (i) the data bank is further organized by space; (ii) at least one data entry of a collection of data entries including the first subset of the plurality of data entries, the second subset of the plurality of data entries, and the additional subset represents at least one significant pattern of a first concept space; (iii) at least one other data entry of the collection of data entries represents at least one significant pattern of a second concept space different than the first concept space; and (iv) the first concept space and the second concept space are each selected from the group consisting of: word space, physical space, temporal space, people space, emotion space, mathematical space, more-than-three-dimensional space, objectives space, scripts space, and scripts-for-objectives space. ### Claim 23 (depends on 22) The method of claim 22, wherein at least one relationship among at least two data entries of the collection of data entries is specified by at least one item selected from the group consisting of: a filter/exemplar represented by at least one data entry of the collection of data entries, a linking attribute of at least one data entry of the collection of data entries, and combinations thereof, the at least one relationship being along a dimension of a concept space. ### Claim 24 (depends on 22, 23) The method of claim 22, wherein the recursive processing is terminated in response to determining that the first objective is to be reformulated, wherein the reformulation comprises changing composition of the collection of data entries to comprise at least one data entry representing a significant pattern of a third concept space different than the first concept space and the second concept space, and further comprising repeating the method of claim 23 using the reformulated objective. ### Claim 25 A computer system for processing a plurality of data entries, comprising: a computer data bank persisted in at least one hardware medium and storing a plurality of data entries, each data entry representing a respective significant pattern and comprising at least one activation attribute and at least one linking attribute; a data collector and processor constructed and arranged to provide processed data; and an execution manager constructed and arranged to: (a) prime a first subset of the plurality of data entries based at least in part on a first objective of a plurality of objectives and based at least in part on at least one linking attribute of at least one data entry associated with the first objective, the first objective comprising identifying a first significant pattern selected from the group consisting of: a first significant pattern represented by a first data entry of the plurality of data entries, a first significant pattern maintained in a transitory pattern of activation, and combinations thereof, the priming comprising altering, in the computer data bank, at least one activation attribute of each first-subset data entry to at least one distinguishing state of activation; (b) identify, within processed data, a first set of at least one processed significant pattern, the identifying being based at least in part on the primed first subset; (c) prime a second subset of the plurality of data entries, each data entry in the second subset representing a respective one of the first set of at least one processed significant pattern, the priming the second subset comprising altering, in the computer data bank, at least one activation attribute of each second-subset data entry to at least one distinguishing state of activation; (d) determine whether the first objective has been accomplished, based at least in part on: (i) states of activation attributes of data entries belonging to the first subset; (ii) states of activation attributes of data entries belonging to the second subset; and (iii) at least one relationship among at least two data entries, the at least one relationship being specified by at least one item selected from the group consisting of: a filter/exemplar represented by at least one data entry, a linking attribute of at least one data entry, and combinations thereof, the at least one relationship being with respect to at least one dimension of at least one space; (e) in response to determining that the first objective has been accomplished, perform at least one operation, with respect to the computer data bank, selected from the group consisting of: creating a data entry in the computer data bank representing the first significant pattern and comprising at least one linking attribute associating the created data entry with at least one constituent data entry of the first significant pattern, altering at least one attribute of an existing data entry in the computer data bank to persist the first significant pattern, maintaining the first significant pattern in a transitory pattern of activation by maintaining activation attributes of constituent data entries of the first significant pattern in distinguishing states of activation, and combinations thereof; and (f) in response to determining that the first objective has not been accomplished, recursively process significant patterns of significant patterns by: (i) recursively identifying, within processed data, additional sets of processed significant patterns; (ii) recursively priming additional subsets of the plurality of data entries, each additional-subset data entry representing a respective processed significant pattern of a respective additional set of processed significant patterns, the priming comprising altering, in the computer data bank, at least one activation attribute of each additional-subset data entry to at least one distinguishing state of activation; (iii) recursively determining whether the first objective has been accomplished, based at least in part on states of activation attributes of data entries belonging to the first subset, the second subset, and at least one additional subset, and at least one relationship among at least two data entries belonging to at least two of the first subset, the second subset, and the at least one additional subset, the at least one relationship being specified by at least one item selected from the group consisting of: a filter/exemplar represented by at least one data entry, a linking attribute of at least one data entry, and combinations thereof, the at least one relationship being with respect to at least one dimension of at least one space; and (iv) terminating the recursive processing in response to determining that at least one condition selected from the group consisting of: the first objective has been accomplished, the first objective is to be reformulated, the first objective is to be reassessed, the first objective is to be postponed, the first objective is to be abandoned, and combinations thereof, has been satisfied.