DETAILED ACTION
This office action is in response to Applicant’s Request for Continued Examination (RCE), received on 10/15/2025. Claims 1, 3, 10, 12, and 19 have been amended. Claims 1-4,7-13 and 16-23 are pending and have been considered.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/15/2025 has been entered.
Response to Arguments
Applicant’s arguments, see pgs. 14-16, filed 10/15/2025, with respect to “Rejections under 35 U.S.C. 101” have been fully considered and are persuasive. The rejections of claims 1-4, 7-13, 16-23 have been withdrawn. The examiner would like to note that the amendment which introduces “utilizing an artificial neural network technique to generate at least one model that comprises a Markov machine learning model of a randomly changing system” has incorporated elements, namely, an artificial neural network and/or Markov machine learning model, to perform the claimed tasks which precludes an interpretation of these steps being performed in the mind with the aid of pen and paper. Further, as the artificial neural network and/or Markov machine learning models are not recited with specific, generic math, it would not be reasonable to assert that these are generic mathematical operations being performed with generic components. Further still, the amendment featuring “retreiv[ing]… at least one case…that provides an existing solution to a first problem that is analogous to a second problem…” sufficiently incorporates a claimed improvement, i.e. applying previously processed cases/rules to current inputs, removing the need to continuously repeat processing cycles for the same problems, reducing overall computation cost, see [0003] of instant app, into a system which relies upon the improvement as a critical part of its functionality.
Applicant's arguments filed 10/15/2025, see pgs. 16-17, with respect to “Rejections under 35 U.S.C. 103”, with specific regard to independent claims 1, 10, and 19, have been fully considered but they are not persuasive.
Applicant’s representative asserts: “None of the cited prior art references, alone or in combination, discloses or otherwise renders obvious the limitation: ‘at least one case that provides an existing solution to a first problem that is analogous to a second problem of the at least one input ....’
As set forth above, during the Interview, the Examiner acknowledged that Rahman does not explicitly disclose ‘at least one case that provides an existing solution to a first problem that is similar to a second problem of [] at least one input ....’ However, the Examiner argued that this feature may be rendered obvious by combining Rahman with Sethi.
Therefore, while not conceding to the appropriateness of this argument, but merely to overcome the prior art of record and thereby advance prosecution of the present application, Applicant notes that the foregoing feature has now been modified to require that its ‘at least one case [] provides an existing solution to a first problem that is analogous to a second problem of [] at least one input....’
None of the cited prior art references, alone or in combination, discloses or otherwise renders obvious the limitation: ‘generating. . . a constraint that comprises an if-then condition ....’
During the Interview, the Examiner also argued that the cited prior art also reads on the feature: ‘generating ... a constraint that is based on an if-then condition ... .’ Therefore, during the Interview, while not conceding to the appropriateness of the Examiner's argument, but merely to overcome the prior art of record and to thereby advance the prosecution of the present application, Applicant suggested modifying the foregoing feature to instead require ‘generating…a constraint that comprises an if-then condition ....’
In response, the Examiner acknowledged that this modification appears to overcome the prior art of record and, therefore, requires further search and consideration.”
In response, the examiner would like to refer to sections of the previously cited art which are respectfully asserted to still teach the elements of the amended independent claims. Specifically, with regard to the “at least one case that provides an existing solution to a first problem that is analogous to a second problem of the at least one input…”, the examiner would like to refer to the domain-based template matching of Rahman, [0044]-[0051]. Rahman’s disclosure of domain-specific rules, wherein the rules may include template matching rules ([0045]) and/or entity-value matching rules ([0050]), further wherein a match is determined based on a similarity score ([0046]), indicates the template rule being compared to new input for similarity is a solution to a first problem, i.e. that input/document which generated the rule/template, that is analogous to a second problem, i.e. a new input/document where a similar rule/template is applied, e.g. to determine whether entities of the new document have resolvable values ([0050]), of the at least one input. The examiner asserts that the broadest reasonable interpretation of “problem” and “solution” as currently claimed can be including a problem of needing to find a matching rule, case and/or template document to be applied to a new rule/case/document, with the solution being finding the matching rule/case/document to be appropriate/valid/etc. As such, the examiner respectfully asserts that Rahman teaches this amended element of the independent claims.
Continuing, with regard to “generating… a constraint that comprises an if-then condition…”, the examiner would again like to refer to the template rule matching of Rahman, with specific regard to [0046]: “If the computed similarity score satisfies a threshold condition, such as where the threshold condition is satisfied if the computed similarity score is equal to or greater than a threshold similarity score, then that document may be classified as valid against the evaluation of the template matching rule”. To the examiner, this tracks to a constraint comprising an if-else statement of the following form: if the similarity score of this document satisfies the threshold, then classify the document as valid, wherein the similarity, and, therefore, the constraint is based on a retrieved at least one case, i.e. template being compared to a new document.
Applicant’s arguments, see pgs. 17-18, filed 10/15/2025, with respect to the rejection(s) of claim(s) 3, 12 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Mukherjee et al. (US-12266431-B2), hereinafter Mukherjee. Mukherjee discloses tracking of classification codes corresponding to portions of text and counting the instances of the classification codes for determining term frequency-inverse document frequency scores, [Col. 9, Lines 50-67]-[Col. 10, Lines 1-5]. See updated rejections below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2, 7-11, 16-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rahman et al. (US-20230394235-A1), hereinafter Rahman, in view of Sethi et al. (US-20220374605-A1), hereinafter Sethi, further in view of Bonissone et al. (US-20150019269-A1), hereinafter Bonissone.
Regarding claim 1, Rahman discloses: a method of utilizing case-based artificial intelligence reasoning ([0018] set of domain-specific rules, [In view of Fig. 6 of the instant application, the examiner is interpreting a case to be representative of one rule, i.e. one row of the case repository. Further, the examiner would like to note that the preamble of a claim does not necessarily provide patentable weight; therefore, the “artificial intelligence reasoning” does not require a mapping. Further still, Bonissone explicitly defines “a case-based reasoning system…different from other artificial intelligence approaches” (which also indicates Bonissone’s method to be an artificial intelligence approach for case-based reasoning), [0069].]) to convert natural language data (Abstract, natural language text) into constraints ([0022] findings that indicate whether the document meets a particular domain-specific requirement defined by a respective domain-specific rule [A constraint tracks to a requirement/rule]) via memory-based processing ([Fig. 3, Document Database 172, indicating the matching rules are memory-based, i.e. rules gathered from previous documents in memory]), the method being implemented by at least one processor ([0029] one or more processors), the method comprising:
receiving, by the at least one processor via a graphical user interface ([0058] generate graphical user interface data), at least one input ([0028] an end user may submit, using client device 104, a query via the web-based interface for documents [Web-based interface indicates a graphical representation]), each of the at least one input including input wording in a natural language format ([0030] the documents may include natural language prose, numbers, letters, and the like); and,
parsing, by the at least one processor using the at least one model ([0047] An NLP similarity recognition model), the at least one input to retrieve, from a case repository ([0047] identify sentences in a document that similar to one or more previously analyzed documents, [In view of document repository 202/document database 172 indicating retrieval from these storages for analysis]), at least one case that provides an existing solution to a first problem that is analogous to a second problem of the at least one input ([0046] template matching rules 500 may determine a similarity score indicating how similar a structure of text of a document is to a template structure, [0050] Entity-Value Matching: Entity-value matching rules 510 may include rules defining requirements that a particular entity or set of entities are represented by a document, have values resolvable to those entities, have valid values for those entities…for a document determined to be related to a first domain, entities 512 may include a set of entities expected to be within all documents related to the first domain, [Disclosure of domain-specific rules, wherein the rules may include template matching rules ([0045]) and/or entity-value matching rules ([0050]), further wherein a match is determined based on a similarity score ([0046]), indicates the rule being compared to new input for similarity is a solution to a first problem, i.e. document validation for that which generated the rule, that is analogous to a second problem, i.e. a new document where a similar rule is applied, e.g. to determine whether entities of the new document have resolvable values for purposes of validation (solving the problem), ([0050]), of the at least one input]), the retrieval including identification of the at least one case based on a predetermined similarity threshold ([0047] may identify similar documents, similar portions of documents (for example, similar sections, sentences, paragraphs, etc.), by computing a similarity metric, such as a cosine similarity, where, [0046] If the computed similarity score satisfies a threshold condition… [Indicating retrieval of documents based on a similarity threshold]).
Rahman does not disclose:
utilizing an artificial neural network technique to generate at least one model that comprises a Markov machine learning model of a randomly changing system.
Sethi discloses:
utilizing an artificial neural network technique to generate at least one model that comprises a Markov machine learning model of a randomly changing system ([0106] The models may include one or more of hidden Markov models, [0127] training samples may be based on one or more of random selection, [0147] FIG. 8 illustrates an example artificial neural network (“ANN”) 800. In particular embodiments, an ANN may refer to a computational model, [Disclosing an ANN which refers to a model while also disclosing that the model may contain Markov models indicates the ANN to be comprising a Markov machine learning model of a randomly changing system, i.e. during at least a training]).
Rahman, and Sethi are considered analogous art within document analysis. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Rahman to incorporate the teachings of Sethi, because of the novel way to replace unknown (Sethi, [0134]).
Rahman in view of Sethi does not disclose:
utilizing the Markov machine learning model to adapt, by the at least one processor the retrieved at least one case to the at least one input.
Bonissone discloses:
utilizing the Markov machine learning model ([In view of the previously disclosed Markov machine learning model of Sethi]) to adapt, by the at least one processor ([In view of the previously disclosed processor of Rahman]) the retrieved at least one case to the at least one input ([0128] 5) Adapt the L refined solutions to the current case in order to derive a solution for the case, [Current case tracks to an input being adapted to retrieved cases]).
Rahman, Sethi, and Bonissone are considered analogous art within domain-specific document analysis. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Rahman in view of Sethi to incorporate the teachings of Bonissone, because of the novel way to determine documents from previous cases relevant to a current case and adapting those solutions to the current case based on a confidence, improving quality of retrieved cases for adaptation (Bonissone, [0123]).
Sethi further discloses:
wherein the utilizing of the Markov machine learning model to adapt the retrieved at least one case to the at least one input comprises utilizing the Markov machine learning model ([In view of the previously disclosed Markov learning model of Sethi]) to compute, by the at least one processor ([0165] a processor 902), a merged mapping by:
updating, by the at least one processor ([In view of the previously disclosed processor of Sethi]), an input word mapping of the at least one input with a case word mapping of the retrieved at least one case ([0134] The extracted templates may be then provided to a data generator 536, which may further generate a plurality of synthetic dialog samples based on the one or more templates. For example, these synthetic dialog samples may include “what's the broadcast in New York,” “what's the broadcast in San Francisco,” etc. for the failure case of “what's the broadcast in Seattle?” [In view of the input and retrieved cases of Rahman, identifying a failure case, i.e. an unrecognized command, as input to be expanded to other templates with different people indicates the original “what’s the broadcast in Seattle?” as a retrieved case for inputs of “what’s the broadcast in New York?”, “what’s the broadcast in San Francisco?”, etc. where the entities, i.e. mappings, of the original and input are merged, i.e. updated, to determine the task of calling an individual]).
Rahman further discloses:
based on the retrieved at least one case ([Comparing a new document to a template indicates the similarity comparison for generating constraints (see below) is based upon the retrieved template case]), generating, by the at least one processor ([In view of the previously disclosed processor of Rahman/Sethi]), a constraint that comprises an if-then condition ([0046] if the computed similarity score is equal to or greater than a threshold similarity score, then that document may be classified as valid against the evaluation of the template matching rule, [This tracks to an if: similarity score exceeds a threshold, then: classify as valid]).
Bonissone further discloses:
wherein generating the at least one constraint further comprises:
replacing, by the at least one processor ([In view of the previously disclosed processor of Sethi]), a set of text abstractions in the retrieved at least one case with information from the parsed at least one input by using the merged mapping ([Fig. 5, 508 “Adapt Retrieved Case to Current Case”], [In view of the entity merging of Sethi, adapting a retrieved case to a current case tracks to merging, i.e. replacing, the entities, i.e. text abstractions values, of the retrieved case with those parsed from the input case]).
Sethi further discloses:
generating, by the at least one processor, the at least one constraint by using a constraint template and a result of the replacing ([0134] For example, if “please call mom” is a failure case, the template extractor 534 may extract a template as “please call {sl:contact}”. This template may then be expanded to other similar utterances such as “please call dad”, “please call Jill”, etc. and added to training [Generating a constraint using a constraint template, i.e. please call {sl:contact}, and expanding to other people, i.e. “dad”, “Jill”, etc., indicates replacement of the original entity value “mom” (tracking to a retrieved case) with other known contacts for new constraints, i.e. “please call dad”]).
Rahman further discloses:
generating, by the at least one processor based on a result of the utilizing of the Markov machine learning model to adapt ([In view of the previously disclosed Markov machine learning model of Sethi]), at least one constraint that characterizes the at least one input, the at least one constraint relating to a rule that is mandated by the at least one input ([0049] In the template matching rule example, a document having a structure that is the same or similar to a template structure may produce a finding that the document complies with the template matching rule [Template matching “rule” tracks to a constraint to match pattern in order to satisfy compliance]).
Bonissone further discloses:
evaluating, by the at least one processor ([In view of the processor of Sethi/Rahman]), the at least one constraint ([0070] The retrieved, relevant cases are evaluated versus the current case, based on a confidence factor at step 510, [Applying the determined pattern/entity-value constraints of Rahman as the evaluation of confidence, i.e. confidence factor, in terms of the current case’s confidence/ability to fit the constraint/template/entity-value pattern of Rahman tracks to a constraint evaluation]).
Regarding claim 2, Rahman in view of Sethi, further in view of Bonissone discloses: the method of claim 1.
Rahman further discloses:
wherein the input wording includes at least one from among a word, a phrase, a sentence, a paragraph, and a document in the natural language format, the document including electronic data in a document file format ([0025] automatically validating unstructured documents having natural language text…Unstructured text is primarily composed of prose, and may include dates, numbers, and/or other forms of data. An example of unstructured data is unstructured text in journal articles [Journal articles are generally in the form of a document file (in view of the document database 172 indicating electronic data), in a natural language format, containing input of at least a word, phrase, sentence, and paragraph to convey the opinion of the author]).
Regarding claim 7, Rahman in view of Sethi, further in view of Bonissone discloses: the method of claim 1.
Rahman further discloses:
presenting, by the at least one processor ([In view of the previously disclosed processor of Rahman]) via the graphical user interface ([In view of the previously disclosed GUI of Rahman]), a notification to at least one user associated with the at least one input ([Fig. 9A, 902a-n], [0062] an example user interface 900 may include section 902a, section 902b, ..., section 902n, each of which relates to a particular domain-specific rule that was evaluated against a document [Looking at figure 9A, the user will clearly be notified of the results in the form of the sections corresponding to rules 902a-n]), the notification including at least one from among the at least one constraint ([Fig. 9A, Edit Rule 916], [The ability to edit rules, i.e. constraints, indicates the user is notified to the constraint when editing]), a request for user feedback ([Fig. 9, Accept 908, Reject 910, Edit 912], [All forms of user feedback]), and information that relates to retrieval of the at least one case ([Fig. 9, Expected Result 904, Finding 906], [Findings of how well a rule fits a case tracks to information relating to retrieval of the case]);
determining, by the at least one processor ([In view of the previously disclosed processor of Rahman]), whether the generated at least one constraint includes information that corresponds to the at least one input based on the user feedback ([0062] Feedback 414 may include an indication of whether a particular finding was accepted, rejected, or edited [Wherein findings represent evaluations of domain specific rules, i.e. constraints, against documents ([0022]). Accepted findings indicate the generated constraint includes information that corresponds to the input. Rejected findings indicate the generated constraint does not include information that corresponds to the input.]),
wherein the user feedback is positive when the generated at least one constraint includes information that corresponds to the at least one input ([0022] As another example, feedback may indicate that a particular finding, corresponding to a particular domain- specific rule, is correct [“Correct” represents positive feedback regarding how a rule, i.e. constraint, corresponds to a document, i.e. input]); and,
wherein the user feedback is negative when the generated at least one constraint does not include information that corresponds to the at least one input ([0022] For example, feedback may indicate that a particular finding corresponding to a particular domain-specific rule is incorrect or should be updated [“Incorrect” represents negative feedback regarding how a rule, i.e. constraint, corresponds to a document, i.e. input]).
Regarding claim 8, Rahman in view of Sethi, further in view of Bonissone discloses: the method of claim 7.
Rahman further discloses:
requesting ([Fig. 9A, 912], [A button to edit a rule based on findings indicates a request to edit as having edit on each rule finding indicates the system “requests” the user to edit each finding, but the user is not required to edit]), by the at least one processor via the graphical user interface ([In view of the previously disclosed processor and graphical user interface of Rahman]), at least one correct constraint from the at least one user when the user feedback is negative ([0065] In some embodiments, rule updater 808 may update a rule based on feedback 414 [In view of the “reject” and “edit” buttons of Fig. 9A of Rahman, further in view of Fig. 9B indicating alternate correct structures that don’t match the original rule, i.e. resulting in corrections based on original negative user feedback]).
Sethi further discloses:
aggregating, by the at least one processor ([In view of the processor of Rahman]), data that corresponds to the at least one input ([0080] The NLU module 210 may further process information from these different sources by identifying and aggregating information [In view of Fig. 2 demonstrating the information is input through ASR system 208 resulting in text, in view of the input documents of Rahman. In view of the entity-value pairs of Rahman as the information gathered from NLU module 210]), the data including the input wording and at least one related annotation ([0134] For example, if “please call mom” is a failure case, the template extractor 534 may extract a template as “please call {sl:contact}” [{sl:contact} tracks to an annotation related to the input wording]);
computing, by the at least one processor ([In view of the previously disclosed processor of Rahman]), a new annotation for each of the at least one correct constraint ([0134] This template may then be expanded to other similar utterances such as “please call dad”, “please call Jill”, etc. and added to training [New annotations track to different values of the contact entity based on the constraint of them being after “call”, that the contact is known, etc.]); and,
generating, by the at least one processor ([In view of the previously disclosed processor of Rahman]), a new case by appending the new annotation to the aggregated data ([0134] The extracted templates may be then provided to a data generator 536, which may further generate a plurality of synthetic dialog samples based on the one or more templates. For example, these synthetic dialog samples may include “what's the broadcast in New York,” “what's the broadcast in San Francisco,” etc. for the failure case of “what's the broadcast in Seattle?” [Generating a plurality of samples, i.e. new cases, by appending a “location” annotation entity-value pair to aggregated broadcast data, in view of the previous aggregating NLU module 210]).
Rahman further discloses:
indexing, by the at least one processor ([In view of the previously disclosed processor of Rahman]), the new case for storage in the case repository ([0045] The set of domain-specific rules may be stored and/or access via rules database 174 [Domain-specific rules track to matching constraints in view of Rahman’s template matching rules]).
Regarding claim 9, Rahman in view of Sethi, further in view of Bonissone discloses: the method of claim 1.
Rahman further discloses:
wherein the at least one model includes at least one from among a natural language processing model ([0061] NLP model 616), a machine learning model ([0038] Machine learning models), a mathematical model ([Machine learning models are inherently statistically, i.e. mathematically, driven]), a process model ([0072] processing model), and a data model ([Fig. 1], [Transmitting/receiving data from a network 150 indicates the computing system 102 is a data model]).
Regarding claim 10, Rahman discloses: a computing device ([0026] computing system 102) configured to implement an execution of a method of utilizing case-based artificial intelligence reasoning ([0018] set of domain-specific rules, [In view of Fig. 6 of the instant application, the examiner is interpreting a case to be representative of one rule, i.e. one row of the case repository. Further, the examiner would like to note that the preamble of a claim does not necessarily provide patentable weight; therefore, the “artificial intelligence reasoning” does not require a mapping. Further still, Bonissone explicitly defines “a case-based reasoning system…different from other artificial intelligence approaches” (which also indicates Bonissone’s method to be an artificial intelligence approach for case-based reasoning), [0069].]) to convert natural language data (Abstract, natural language text) into constraints ([0022] findings that indicate whether the document meets a particular domain-specific requirement defined by a respective domain-specific rule [A constraint tracks to a requirement/rule]) via memory-based processing ([Fig. 3, Document Database 172, indicating the matching rules are memory-based, i.e. rules gathered from previous documents in memory]), the method being implemented by at least one processor ([0029] one or more processors), the computing device comprising:
a processor ([Fig. 12, Processor 1210-1]);
a memory ([Fig. 12, Memory 1220]); and
a communication interface coupled to each of the processor and the memory ([Fig. 12, I/O Interface 1250]), wherein the processor is configured to:
receive, via a graphical user interface ([0058] generate graphical user interface data), at least one input ([0028] an end user may submit, using client device 104, a query via the web-based interface for documents [Web-based interface indicates a graphical representation]), each of the at least one input including input wording in a natural language format ([0030] the documents may include natural language prose, numbers, letters, and the like); and,
parse, by using the at least one model ([0047] An NLP similarity recognition model), the at least one input to retrieve, from a case repository ([0047] identify sentences in a document that similar to one or more previously analyzed documents, [In view of document repository 202/document database 172 indicating retrieval from these storages for analysis]), at least one case that provides an existing solution to a first problem that is analogous to a second problem of the at least one input ([0046] template matching rules 500 may determine a similarity score indicating how similar a structure of text of a document is to a template structure, [0050] Entity-Value Matching: Entity-value matching rules 510 may include rules defining requirements that a particular entity or set of entities are represented by a document, have values resolvable to those entities, have valid values for those entities…for a document determined to be related to a first domain, entities 512 may include a set of entities expected to be within all documents related to the first domain, [Disclosure of domain-specific rules, wherein the rules may include template matching rules ([0045]) and/or entity-value matching rules ([0050]), further wherein a match is determined based on a similarity score ([0046]), indicates the rule being compared to new input for similarity is a solution to a first problem, i.e. document validation for that which generated the rule, that is analogous to a second problem, i.e. a new document where a similar rule is applied, e.g. to determine whether entities of the new document have resolvable values for purposes of validation (solving the problem), ([0050]), of the at least one input]), the retrieval including identification of the at least one case based on a predetermined similarity threshold ([0047] may identify similar documents, similar portions of documents (for example, similar sections, sentences, paragraphs, etc.), by computing a similarity metric, such as a cosine similarity, where, [0046] If the computed similarity score satisfies a threshold condition… [Indicating retrieval of documents based on a similarity threshold]).
Rahman does not disclose:
utilize an artificial neural network technique to generate at least one model that comprises a Markov machine learning model of a randomly changing system.
Sethi discloses:
utilize an artificial neural network technique to generate at least one model that comprises a Markov machine learning model of a randomly changing system ([0106] The models may include one or more of hidden Markov models, [0127] training samples may be based on one or more of random selection, [0147] FIG. 8 illustrates an example artificial neural network (“ANN”) 800. In particular embodiments, an ANN may refer to a computational model, [Disclosing an ANN which refers to a model while also disclosing that the model may contain Markov models indicates the ANN to be comprising a Markov machine learning model of a randomly changing system, i.e. during at least a training]).
Rahman, and Sethi are considered analogous art within document analysis. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Rahman to incorporate the teachings of Sethi, because of the novel way to replace unknown (Sethi, [0134]).
Rahman in view of Sethi does not disclose:
utilize the Markov machine learning model to adapt the retrieved at least one case to the at least one input.
Bonissone discloses:
utilizing the Markov machine learning model ([In view of the previously disclosed Markov machine learning model of Sethi]) to adapt the retrieved at least one case to the at least one input ([0128] 5) Adapt the L refined solutions to the current case in order to derive a solution for the case, [Current case tracks to an input being adapted to retrieved cases]).
Rahman, Sethi, and Bonissone are considered analogous art within domain-specific document analysis. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Rahman in view of Sethi to incorporate the teachings of Bonissone, because of the novel way to determine documents from previous cases relevant to a current case and adapting those solutions to the current case based on a confidence, improving quality of retrieved cases for adaptation (Bonissone, [0123]).
Sethi further discloses:
wherein the processor is configured to utilize the Markov machine learning model ([In view of the previously disclosed Markov machine learning model of Sethi]) to adapt the retrieved at least one case to the at least one input by utilizing the Markov machine learning model to compute a merged mapping by:
updating an input word mapping of the at least one input with a case word mapping of the retrieved at least one case ([0134] The extracted templates may be then provided to a data generator 536, which may further generate a plurality of synthetic dialog samples based on the one or more templates. For example, these synthetic dialog samples may include “what's the broadcast in New York,” “what's the broadcast in San Francisco,” etc. for the failure case of “what's the broadcast in Seattle?” [In view of the input and retrieved cases of Rahman, identifying a failure case, i.e. an unrecognized command, as input to be expanded to other templates with different people indicates the original “what’s the broadcast in Seattle?” as a retrieved case for inputs of “what’s the broadcast in New York?”, “what’s the broadcast in San Francisco?”, etc. where the entities, i.e. mappings, of the original and input are merged, i.e. updated, to determine the task of calling an individual]).
Rahman further discloses:
based on the retrieved at least one case ([Comparing a new document to a template indicates the similarity comparison for generating constraints (see below) is based upon the retrieved template case]), generating a constraint that comprises an if-then condition ([0046] if the computed similarity score is equal to or greater than a threshold similarity score, then that document may be classified as valid against the evaluation of the template matching rule, [This tracks to an if: similarity score exceeds a threshold, then: classify as valid]).
Bonissone further discloses:
wherein generating the at least one constraint further comprises:
replacing a set of text abstractions in the retrieved at least one case with information from the parsed at least one input by using the merged mapping ([Fig. 5, 508 “Adapt Retrieved Case to Current Case”], [In view of the entity merging of Sethi, adapting a retrieved case to a current case tracks to merging, i.e. replacing, the entities, i.e. text abstractions values, of the retrieved case with those parsed from the input case]).
Sethi further discloses:
generating the at least one constraint by using a constraint template and a result of the replacing ([0134] For example, if “please call mom” is a failure case, the template extractor 534 may extract a template as “please call {sl:contact}”. This template may then be expanded to other similar utterances such as “please call dad”, “please call Jill”, etc. and added to training [Generating a constraint using a constraint template, i.e. please call {sl:contact}, and expanding to other people, i.e. “dad”, “Jill”, etc., indicates replacement of the original entity value “mom” (tracking to a retrieved case) with other known contacts for new constraints, i.e. “please call dad”]).
Rahman further discloses:
generating based on a result of the utilizing of the Markov machine learning model to adapt ([In view of the previously disclosed Markov machine learning model of Sethi]), at least one constraint that characterizes the at least one input, the at least one constraint relating to a rule that is mandated by the at least one input ([0049] In the template matching rule example, a document having a structure that is the same or similar to a template structure may produce a finding that the document complies with the template matching rule [Template matching “rule” tracks to a constraint to match pattern in order to satisfy compliance]).
Bonissone further discloses:
evaluating the at least one constraint ([0070] The retrieved, relevant cases are evaluated versus the current case, based on a confidence factor at step 510, [Applying the determined pattern/entity-value constraints of Rahman as the evaluation of confidence, i.e. confidence factor, in terms of the current case’s confidence/ability to fit the constraint/template/entity-value pattern of Rahman tracks to a constraint evaluation]).
Regarding claim 11, Rahman in view of Sethi, further in view of Bonissone discloses: the computing device of claim 10.
Rahman further discloses:
wherein the input wording includes at least one from among a word, a phrase, a sentence, a paragraph, and a document in the natural language format, the document including electronic data in a document file format ([0025] automatically validating unstructured documents having natural language text…Unstructured text is primarily composed of prose, and may include dates, numbers, and/or other forms of data. An example of unstructured data is unstructured text in journal articles [Journal articles are generally in the form of a document file (in view of the document database 172 indicating electronic data), in a natural language format, containing input of at least a word, phrase, sentence, and paragraph to convey the opinion of the author]).
Regarding claim 16, Rahman in view of Sethi, further in view of Bonissone discloses: the computing device of claim 10.
Rahman further discloses:
present, via the graphical user interface ([In view of the previously disclosed GUI of Rahman]), a notification to at least one user associated with the at least one input ([Fig. 9A, 902a-n], [0062] an example user interface 900 may include section 902a, section 902b, ..., section 902n, each of which relates to a particular domain-specific rule that was evaluated against a document [Looking at figure 9A, the user will clearly be notified of the results in the form of the sections corresponding to rules 902a-n]), the notification including at least one from among the at least one constraint ([Fig. 9A, Edit Rule 916], [The ability to edit rules, i.e. constraints, indicates the user is notified to the constraint when editing]), a request for user feedback ([Fig. 9, Accept 908, Reject 910, Edit 912], [All forms of user feedback]), and information that relates to retrieval of the at least one case ([Fig. 9, Expected Result 904, Finding 906], [Findings of how well a rule fits a case tracks to information relating to retrieval of the case]);
determine whether the generated at least one constraint includes information that corresponds to the at least one input based on the user feedback ([0062] Feedback 414 may include an indication of whether a particular finding was accepted, rejected, or edited [Wherein findings represent evaluations of domain specific rules, i.e. constraints, against documents ([0022]). Accepted findings indicate the generated constraint includes information that corresponds to the input. Rejected findings indicate the generated constraint does not include information that corresponds to the input.]),
wherein the user feedback is positive when the generated at least one constraint includes information that corresponds to the at least one input ([0022] As another example, feedback may indicate that a particular finding, corresponding to a particular domain- specific rule, is correct [“Correct” represents positive feedback regarding how a rule, i.e. constraint, corresponds to a document, i.e. input]); and,
wherein the user feedback is negative when the generated at least one constraint does not include information that corresponds to the at least one input ([0022] For example, feedback may indicate that a particular finding corresponding to a particular domain-specific rule is incorrect or should be updated [“Incorrect” represents negative feedback regarding how a rule, i.e. constraint, corresponds to a document, i.e. input]).
Regarding claim 17, Rahman in view of Sethi, further in view of Bonissone discloses: the computing device of claim 16.
Rahman further discloses:
request ([Fig. 9A, 912], [A button to edit a rule based on findings indicates a request to edit as having edit on each rule finding indicates the system “requests” the user to edit each finding, but the user is not required to edit]), via the graphical user interface ([In view of the previously disclosed graphical user interface of Rahman]), at least one correct constraint from the at least one user when the user feedback is negative ([0065] In some embodiments, rule updater 808 may update a rule based on feedback 414 [In view of the “reject” and “edit” buttons of Fig. 9A of Rahman, further in view of Fig. 9B indicating alternate correct structures that don’t match the original rule, i.e. resulting in corrections based on original negative user feedback]);
Sethi further discloses:
aggregate data that corresponds to the at least one input ([0080] The NLU module 210 may further process information from these different sources by identifying and aggregating information [In view of Fig. 2 demonstrating the information is input through ASR system 208 resulting in text, in view of the input documents of Rahman. In view of the entity-value pairs of Rahman as the information gathered from NLU module 210]), the data including the input wording and at least one related annotation ([0134] For example, if “please call mom” is a failure case, the template extractor 534 may extract a template as “please call {sl:contact}” [{sl:contact} tracks to an annotation related to the input wording]);
compute a new annotation for each of the at least one correct constraint ([0134] This template may then be expanded to other similar utterances such as “please call dad”, “please call Jill”, etc. and added to training [New annotations track to different values of the contact entity based on the constraint of them being after “call”, that the contact is known, etc.]); and,
generate a new case by appending the new annotation to the aggregated data ([0134] The extracted templates may be then provided to a data generator 536, which may further generate a plurality of synthetic dialog samples based on the one or more templates. For example, these synthetic dialog samples may include “what's the broadcast in New York,” “what's the broadcast in San Francisco,” etc. for the failure case of “what's the broadcast in Seattle?” [Generating a plurality of samples, i.e. new cases, by appending a “location” annotation entity-value pair to aggregated broadcast data, in view of the previous aggregating NLU module 210]).
Rahman further discloses:
index the new case for storage in the case repository ([0045] The set of domain-specific rules may be stored and/or access via rules database 174 [Domain-specific rules track to matching constraints in view of Rahman’s template matching rules]).
Regarding claim 18, Rahman in view of Sethi, further in view of Bonissone discloses: the computing device of claim 10.
Rahman further discloses:
wherein the at least one model includes at least one from among a natural language processing model ([0061] NLP model 616), a machine learning model ([0038] Machine learning models), a mathematical model ([Machine learning models are inherently statistically, i.e. mathematically, driven]), a process model ([0072] processing model), and a data model ([Fig. 1], [Transmitting/receiving data from a network 150 indicates the computing system 102 is a data model]).
Regarding claim 19, Rahman discloses: a non-transitory computer readable storage medium ([0090] non-transitory computer readable storage medium) storing instructions that utilize case-based artificial intelligence reasoning ([0018] set of domain-specific rules, [In view of Fig. 6 of the instant application, the examiner is interpreting a case to be representative of one rule, i.e. one row of the case repository. Further, the examiner would like to note that the preamble of a claim does not necessarily provide patentable weight; therefore, the “artificial intelligence reasoning” does not require a mapping. Further still, Bonissone explicitly defines “a case-based reasoning system…different from other artificial intelligence approaches” (which also indicates Bonissone’s method to be an artificial intelligence approach for case-based reasoning), [0069].]) to convert natural language data (Abstract, natural language text) into constraints ([0022] findings that indicate whether the document meets a particular domain-specific requirement defined by a respective domain-specific rule [A constraint tracks to a requirement/rule]) via memory-based processing, the storage medium comprising executable code which ([0090] Instructions or other program code), when executed by a processor ([Fig. 12, Processor 1210-1]), causes the processor to:
receive, via a graphical user interface ([0058] generate graphical user interface data), at least one input ([0028] an end user may submit, using client device 104, a query via the web-based interface for documents [Web-based interface indicates a graphical representation]), each of the at least one input including input wording in a natural language format ([0030] the documents may include natural language prose, numbers, letters, and the like); and,
parse, by using the at least one model ([0047] An NLP similarity recognition model), the at least one input to retrieve, from a case repository ([0047] identify sentences in a document that similar to one or more previously analyzed documents, [In view of document repository 202/document database 172 indicating retrieval from these storages for analysis]), at least one case that provides an existing solution to a first problem that is analogous to a second problem of the at least one input ([0046] template matching rules 500 may determine a similarity score indicating how similar a structure of text of a document is to a template structure, [0050] Entity-Value Matching: Entity-value matching rules 510 may include rules defining requirements that a particular entity or set of entities are represented by a document, have values resolvable to those entities, have valid values for those entities…for a document determined to be related to a first domain, entities 512 may include a set of entities expected to be within all documents related to the first domain, [Disclosure of domain-specific rules, wherein the rules may include template matching rules ([0045]) and/or entity-value matching rules ([0050]), further wherein a match is determined based on a similarity score ([0046]), indicates the rule being compared to new input for similarity is a solution to a first problem, i.e. document validation for that which generated the rule, that is analogous to a second problem, i.e. a new document where a similar rule is applied, e.g. to determine whether entities of the new document have resolvable values for purposes of validation (solving the problem), ([0050]), of the at least one input]), the retrieval including identification of the at least one case based on a predetermined similarity threshold ([0047] may identify similar documents, similar portions of documents (for example, similar sections, sentences, paragraphs, etc.), by computing a similarity metric, such as a cosine similarity, where, [0046] If the computed similarity score satisfies a threshold condition… [Indicating retrieval of documents based on a similarity threshold]).
Rahman does not disclose:
utilize an artificial neural network technique to generate at least one model that comprises a Markov machine learning model of a randomly changing system.
Sethi discloses:
utilize an artificial neural network technique to generate at least one model that comprises a Markov machine learning model of a randomly changing system ([0106] The mode