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 .
DETAILED ACTION
This action is response to the Remarks filed 06/17/2026.
Claims 1-18 stand rejected, objected to and are pending in this Office Action. Claims 1, 4 and 5 are independent claims.
Priority
Applicant’s claim for the benefit of a prior-filed application a continuation of 18181280, filed 03/09/2023, now U.S. Patent #12229195 issued 02/182025 to which the instant application is a divisional, under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged.
Claim Rejections - 35 USC § 103
The following is a quotation of - 35 USC § 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 4-5, 1 and 6-7 are rejected under 35 U.S.C. § 103 as being unpatentable over
Di Fabbrizio et al.: “QUESTION-ANSWER EXPANSION” (United States Patent Application Publication 20230106590 A1, DATE PUBLISHED 2023-04-06; and DATE FILED 2021-10-04, hereafter “Di Fabbrizio”) in view of
SHLOMOV et al.: “COMPUTER-IMPLEMENTED METHOD, COMPUTER SYSTEM AND COMPUTER PROGRAM (MESSAGE MAPPING AND COMBINATION FOR INTENTIONAL CLASSIFICATION)” (Japan Patent Application Publication JP 2023129389 A, DATE PUBLISHED 2023-09-14; and DATE FILED 2023-03-03, hereafter “SHLOMOV”).
As per claim 4, Di Fabbrizio teaches a method for automatically generating a response to an input query, the method comprising:
receiving, by a computer, an input query (See [0019], a user provided query is received);
automatically identifying a topic cluster associated with the input query based on one or both of a first topic prediction model and second topic prediction model (See [0004], [0020] and [0036], extracting a topic of the query and determining a topic of a user provided query associated with an item included in an item catalog; and a predictive model can be trained using question-answer pair data associated with items in an item catalog to determine the topic of the query and plural trained prediction models are generated for use in the QA expansion system. Here determining a topic of a query associated with an item in an item category reads on identifying a topic associated with an item, the item reads on the entity and the topic associated item in item category reads on topic cluster, in other words, topic associated item category reads on topic cluster),
wherein the topic cluster comprises a plurality of topic entities (See [0020], determined topic of a user provided query associated with an item included in an item catalog. Here determining a topic of a query associated with items in an item category reads on the topic cluster comprises a plurality of topic entities);
directing the input query to a data structure associated with the identified topic cluster (See [0054], a user's queries can refer to a category (e.g., dresses, pants, skirts, etc.) identified by a taxonomy label and the catalog-to-dialog (CTD) modules can extract category labels from the catalog taxonomy. Here the taxonomy label of topic cluster [the topic associated item category] teaches the data structure),
wherein the data structure comprises a plurality of nodes, each node representing one of the topic entities in the topic cluster (See [0082], a data structure 500 used by the QA expansion system can represent a taxonomy tree structure, intermediate nodes of the taxonomy tree structure can represent product categories and leaf nodes can represent products belonging to a particular product category), and
wherein at least one of the nodes in the topic cluster is linked by at least one edge to one or more of the other nodes in the topic cluster (See [0082], a data structure 500 used by the QA expansion system can represent a taxonomy tree structure, intermediate nodes of the taxonomy tree structure can represent product categories and leaf nodes can represent products belonging to a particular product category. Each leaf node is linked to an intermediate node by a link or "edge". Here both the intermediate and the leaf nodes are plural).
Concerning "the at least one edge being formed by at least one linguistic modality identified in input data from which the topic entities are extracted", Di Fabbrizio teaches edges, links, being formed by taxonomy tree data structure identified in input data from which the topic entities are extracted as described above and further at [0055], (the CTD module can also automatically generate query responses based on the tenant catalog and the lexicon of natural language units), Di Fabbrizio teaches natural language units and taxonomy tree data structure, instead of linguistic modality for the limitation "the at least one edge being formed by at least one linguistic modality identified in input data from which the topic entities are extracted" .
On the other hand, SHLOMOV teaches message may be expressed as different language modalities than the original message (See Page 4, the predicted message may have a different language modality than the original message has, e.g., a different wording. Linguistic modality may be expressed as a lengthening of arranged requests for humans. Linguistic modalities can express arranged requests for humans in more detail and with more words. Having a different linguistic modality for a first word group (e.g., a sentence or a paragraph) requires that the first word group have at least one different word than the second word group).
It would have been obvious to one having ordinary skill in the art at the time the Applicant’s application was filed to combine SHLOMOV’s teaching with Di Fabbrizio because Di Fabbrizio is dedicated to using a predictive model trained using question-answer pair data for determining an attribute of the content item and SHLOMOV is dedicated to supporting automated agents that can interact with humans via web chat, voice conversation, or some other manner, the combined teaching of Di Fabbrizio and SHLOMOV references would have enabled Di Fabbrizio to utilize different linguistic modalities to express question-answer pair for understanding and responding to the information-rich enriched question-answer pair data domain.
Di Fabbrizio in view of SHLOMOV further teaches the following:
the at least one linguistic modality defining a logical relationship linking the respective nodes (See SHLOMOV: Page 26, in natural language processing, a sentence can be expressed as a vector of numbers based on the semantic meaning of each word in the sentence and the relationship between each word in the sentence and other words); and
generating a response to the input query (See Di Fabbrizio: [0055], the CTD module can also automatically generate query responses based on the tenant catalog and the lexicon of natural language units)).
As per claim 5, the claim recites a non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of an electronic device, cause the electronic device (See Di Fabbrizio: [0007], the non-transitory computer program products (i.e., physically embodied computer program products) are also described that store instructions, which when executed by one or more data processors of one or more computing systems, causes at least one data processor) to perform operations recited as the method steps of the claim 4 and rejected above under 35 U.S.C. § 103 as being unpatentable over Di Fabbrizio in view of SHLOMOV.
Accordingly, claim 5 is rejected along the same rationale that rejected claim 4.
As per claim 1, the claim recites a system for automatically generating a response to an input query, the system comprising one or more processors configured to cause the system (See [0027], the QA processing platform 120 also includes at least one processor 124 configured to execute instructions, which when executed cause the processor 124 to perform expansion of QA data for a plurality of items or products identified in an item catalog and to generate contextually specific query responses to user queries) to perform operations recited as the method steps of the claim 4 and rejected above under 35 U.S.C. § 103 as being unpatentable over Di Fabbrizio in view of SHLOMOV.
Accordingly, claim 1 is rejected along the same rationale that rejected claim 4.
As per claim 6, Di Fabbrizio in view of SHLOMOV teaches the system of claim 1, wherein the first prediction model is a trained semantic classification model (See Di Fabbrizio: [0036]-[0037], the machine learning platform 165 can include a number of components configured to generate one or more trained prediction models suitable for use in the QA expansion system and the generated training models, e.g., classification algorithms and models included in the NLU of the NLA ensemble 145, are then capable of receiving user query data and to output predicted query responses including at least one attribute value associated with the product or item that was the subject of the user's query).
As per claim 7, Di Fabbrizio in view of SHLOMOV teaches the system of claim 6, wherein the second prediction model is a semantic embedding model (See Di Fabbrizio: [0032] and [0037], the NLA ensembles 145 can include a natural language understanding (NLU) module implementing a number of classification algorithms trained in a machine learning process to classify the text string into a semantic interpretation; and The generated training models, e.g., classification algorithms and models included in the NLU of the NLA ensemble 145, are then capable of receiving user query data and to output predicted query responses including at least one attribute value associated with the product or item that was the subject of the user's query).
Claims 2- 3, are rejected under 35 U.S.C. § 103 as being unpatentable over
Di Fabbrizio in view of SHLOMOV, as applied to claims 4-5, 1 and 6-7 above, and further in view of
Sivakumar et al.: “CACHING OF TEXT ANALYTICS BASED ON TOPIC DEMAND AND MEMORY CONSTRAINTS” (United States Patent Application Publication US 20240095270 A1, DATE PUBLISHED 2024-03-21; and DATE FILED 2022-09-21, hereafter “Sivakumar”).
As per claim 2, Di Fabbrizio in view of SHLOMOV does not explicitly teach the system of claim 1, wherein generating a response to the input query comprises selecting, based on the data structure comprising the identified topic cluster, a response from a predefined group of responses.
However, Sivakumar teaches the system of claim 1, wherein generating a response to the input query comprises selecting, based on the data structure comprising the identified topic cluster, a response from a predefined group of responses (See Sivakumar: [0025] and [0086], the process applies a clustering algorithm to the lines of text based on the identified topics and sentiments. In some embodiments, the process uses a known technique that determines sets of topics and sentiments that form clusters; and the process constructs a hierarchical topic model based on the topic and sentiment clusters. In some embodiments, the hierarchical topic model has nodes that are associated with respective topics and sentiments that are arranged in a hierarchical manner. Here the set of topics teaches topic cluster and identified and arranged hierarchically suggests pre-defined).
It would have been obvious to one having ordinary skill in the art at the time the Applicant’s application was filed to combine Sivakumar’s teaching with Di Fabbrizio and SHLOMOV because Di Fabbrizio is dedicated to using a predictive model trained using question-answer pair data for determining an attribute of the content item and Sivakumar is dedicated to caching of text analytics based on topic demand and memory constraints, SHLOMOV is dedicated to supporting automated agents that can interact with humans via web chat, voice conversation, or some other manner, the combined teaching of Di Fabbrizio and SHLOMOV references would have enabled Di Fabbrizio in view of SHLOMOV to consider memory constraints and to cache of question-answer pair data and memory constraints for better system performance.
As per claim 3, Di Fabbrizio in view of SHLOMOV and further in view of Sivakumar teaches the system of claim 1, wherein generating a response to the input query comprises generating, using the associated nodes of the data structure, a response to the input query (See Sivakumar: [0027], the demand data includes counts of how many times each node has been used to generate a response to a user query).
As per claim 9, Di Fabbrizio in view of SHLOMOV and further in view of Sivakumar teaches the system of claim 1, wherein the one or more processors are configured to cause the system to:
identify the topic cluster based on a prediction by both the first and second topic prediction model (See Sivakumar: [0003], identifying the first topic cluster as a first topic-cache candidate based on the query demand data. The embodiment also includes comparing a first required amount of memory required for storing text associated with the first topic cluster to available cache memory in a database cache. The embodiment also includes storing, responsive to identifying the first topic cluster as the first topic-cache candidate and determining that the available cache memory is greater than the first required amount of memory, the text associated with the first topic cluster in the database cache).
As per claim 10, Di Fabbrizio in view of SHLOMOV and further in view of Sivakumar teaches the system of claim 1, wherein the input query is a natural language input (See Sivakumar: [0003], analyzing text content of a first user query to identify via natural language processing a first query topic defined by words of the text content ).
As per claim 11, Di Fabbrizio in view of SHLOMOV and further in view of Sivakumar teaches the system of claim 1, wherein the input query is extracted from structured or unstructured textual data (See Sivakumar: [0003], analyzing text content of a first user query to identify via natural language processing a first query topic defined by words of the text content).
As per claim 12, Di Fabbrizio in view of SHLOMOV and further in view of Sivakumar teaches the system of claim 11, wherein the input query is extracted from a predefined set of questions and answers (See Sivakumar: [0018] A typical information retrieval system performs several NLP tasks, including NLP tasks on ingested documents and NLP tasks on user queries. Hereinafter, a request for information presented in any correct or incorrect, complete or incomplete, colloquial or formal, grammatical form of a natural language, during a conversation occurring with an illustrative embodiment described herein, is interchangeably referred to as a “question” or “query” unless expressly disambiguated where used.).
As per claim 13, Di Fabbrizio in view of SHLOMOV and further in view of Sivakumar teaches the system of claim 1, wherein generating the response to the input query comprises: generating, using the interconnected nodes of the data structure, a response to the input query (See Sivakumar: [0027], the process generates demand data indicative of the demand for topics and sentiments in hierarchical topic model based on the user queries. In some embodiments, the demand data includes counts of how many times each node has been used to generate a response to a user query.).
As per claim 14, Di Fabbrizio in view of SHLOMOV and further in view of Sivakumar teaches the method of claim 1, wherein generating the response to the input query comprises:
traversing between the at least one node in the topic cluster and the one or more other associated nodes using one or more edges connecting the nodes (See Sivakumar: [0028], the process traverses the hierarchical topic model for topic matching queries to nodes, the process maintains statistical data that includes a count for each node indicating the number of times that node has been used to answer a query); and
generating a response to the input query based on the traversed nodes and edges (See Sivakumar: [0028], the process traverses the hierarchical topic model for topic matching queries to nodes, the process maintains statistical data that includes a count for each node indicating the number of times that node has been used to answer a query).
As per claim 15, Di Fabbrizio in view of SHLOMOV and further in view of Sivakumar teaches the system of claim 1, wherein generating the response to the input query comprises:
selecting, based on the data structure comprising the identified topic cluster, a response from a predefined group of responses (See Sivakumar: [0025] and [0086], the process applies a clustering algorithm to the lines of text based on the identified topics and sentiments. In some embodiments, the process uses a known technique that determines sets of topics and sentiments that form clusters; and the process constructs a hierarchical topic model based on the topic and sentiment clusters. In some embodiments, the hierarchical topic model has nodes that are associated with respective topics and sentiments that are arranged in a hierarchical manner.. Here the identified and arranged hierarchically suggests pre-defined).
As per claim 16, Di Fabbrizio in view of SHLOMOV and further in view of Sivakumar teaches the system of claim 1, wherein the generated response comprises at least one of:
a natural language description of an accounting topic, a natural language description of a business entity, a natural language description of an audit method, a natural language description of a mathematical relationship, and a natural language explanation of the generated response to the input query (See ).
Claims 8 and 17, are rejected under 35 U.S.C. § 103 as being unpatentable over
Di Fabbrizio in view of SHLOMOV, as applied to claims 4-5, 1 and 6-7 above, and further in view of
Tappin; Isabella: “STATEFUL, REAL-TIME, INTERACTIVE, AND PREDICTIVE KNOWLEDGE PATTERN MACHINE” (United States Patent Application Publication US 20220188661 A1, DATE PUBLISHED 2022-06-16; and DATE FILED 2022-03-02, hereafter “Tappin”).
As per claim 8, Di Fabbrizio in view of SHLOMOV teaches the system of claim 7, wherein the semantic embedding model is configured to:
extract a plurality of query entities from the input query (See SHLOMOV: Page 24; and Natural language processing may include entity extraction, which extracts named entities from text and categorizes them into predetermined categories; and Di Fabbrizio: [0004], extracting a topic of the query and determining the attribute based on the topic of the query); and
apply a clustering process to generate one or more clusters of query nodes (See SHLOMOV: Page 24; and Natural language processing may include entity extraction, which extracts named entities from text and categorizes them into predetermined categories. Here the entity categorizing teaches clustering query entities as query nodes).
Di Fabbrizio in view of SHLOMOV does not explicitly teach [to] compute an average semantic embedding for one or more of the generated clusters of query nodes.
However, Tappin teaches [to] compute an average semantic embedding for one or more of the generated clusters of query nodes (See [0057], semantic embedding techniques may be used to focus more on semantic information (i.e. the meaning of the words) and embedding that information (e.g. using pre-trained language models to find the query embedding). The matching of the input query and the pre-computed signal may be based on the syntactic embedding, the semantic embedding, or a combination (or hybrid) thereof).
It would have been obvious to one having ordinary skill in the art at the time the Applicant’s application was filed to combine Tappin’s teaching with Di Fabbrizio and SHLOMOV because Di Fabbrizio is dedicated to using a predictive model trained using question-answer pair data for determining an attribute of the content item and Tappin is dedicated to intelligent interactive data analytics in a real-time predictive knowledge pattern machine, the combined teaching of Tappin and Di Fabbrizio in view of SHLOMOV references would have enabled Di Fabbrizio in view of SHLOMOV’s to predictive model to utilize syntactic embedding, the semantic embedding, or a combination (or hybrid) thereof to better match the input query and the query result.
Fabbrizio in view of SHLOMOV and further in view of Tappin further teaches the following:
wherein each average semantic embedding represents a generated cluster of query nodes (See Tappin: [0057], semantic embedding techniques may be used to focus more on semantic information (i.e. the meaning of the words) and embedding that information (e.g. using pre-trained language models to find the query embedding). The matching of the input query and the pre-computed signal may be based on the syntactic embedding, the semantic embedding, or a combination (or hybrid) thereof.);
compute an average semantic embedding for one or more topic clusters of a plurality of topic clusters (See Tappin: [0057], the matching of the input query and the pre-computed signal may be based on the syntactic embedding, the semantic embedding, or a combination (or hybrid) thereof. Here the matching reads on computing),
wherein each average semantic embedding represents a topic cluster (See Tappin: [0113] and [0057], data sources may be characterized by various reliability levels (e.g., by confidence interval, and/or degree of confidence and accuracy through vector embedding and/or clustering); and
select a topic cluster for the input query based on a comparison of at least one average semantic embedding representing a generated cluster and at least one average semantic embedding representing a topic cluster (See Tappin: [0101] and [0057], The user may further be allowed to modify configuration parameters related to the recommender system, and the user may be provided first with recommended options of types/topics/categories of questions for selection and then provided with specific recommended questions in a subsequent query recommendation interface after a specific type/topic/category is selected by the use; and both syntactic and semantic embedding could be used simultaneously, and the better result may be adopted or combined. Alternatively, both embedding techniques could be run sequentially. The embedding of a processed input query, may be generated by an embedding model (either a syntactic embedding model, a semantic embedding model, or a hybrid model). Such a model may be trained based using a natural language model and is capable of providing embedding vectors for any input query).
As per claim 17, Fabbrizio in view of SHLOMOV and further in view of Tappin further teaches the system of claim 1, wherein the data structure is a knowledge graph (See Tappin: [0020], The pattern machine may be further configured to automatically identify relevant information entities in the formalized query and expand to additional relevant information entities and correlations, or a lack thereof, between all the different relevant information entities based on one or more natural language understanding module(s), one or more query database(s), one or more knowledge graph(s), signals database(s), and/or events database(s). The knowledge graphs, signals databases, and events databases may be precomputed but automatically and continuously updated).
Claim 18 is rejected under 35 U.S.C. § 103 as being unpatentable over
Di Fabbrizio in view of SHLOMOV, as applied to claims 4-5, 1 and 6-7 above, and further in view of
ANJUM et al.: “INVESTIGATING POWER IN ENGLISH EDUCATIONAL LANGUAGE POLICY AND ITS IMPLEMENTATION: A CRITICAL DISCOURSE ANALYSIS” (Jahan-e-Tahqeeq, https:// Jahan-e-Tahqeeq.com>article>download, hereafter “ANJUM”).
As per claim 18, Di Fabbrizio in view of SHLOMOV does not explicitly teach the system of claim 1, wherein the one or more linguistic modalities comprise at least one of a deontic linguistic modality and an epistemic linguistic modality.
However, ANJUM teaches the system of claim 1, wherein the one or more linguistic modalities comprise at least one of a deontic linguistic modality and an epistemic linguistic modality (See Page 19, lines 2, using deontic linguistic modality support the power in discourse produced by policy makers and textbook designers in the English textbook; and the logical connectivity in the text to emphasize the saying by implied use of power both lexically and grammatically and the use of modal auxiliary "could" in this example represents the possibility that also reflects the epistemic modality).
It would have been obvious to one having ordinary skill in the art at the time the Applicant’s application was filed to combine ANJUM’s teaching with Di Fabbrizio and SHLOMOV because Di Fabbrizio is dedicated to using a predictive model trained using question-answer pair data for determining an attribute of the content item and is dedicated to intelligent interactive data analytics in a real-time predictive knowledge pattern machine, ANJUM is dedicated to evaluate the implementation of ELPP (Educational Language Policy and Planning) theoretically and of curriculum framework (CF) according to ELPP, in addition to this, strived to fulfill the purpose by checking the power representation in textbook basing CF and ELPP, the combined teaching of ANJUM and Di Fabbrizio in view of SHLOMOV references would have enabled Di Fabbrizio in view of SHLOMOV’s to fully appreciate the benefits of deontic linguistic modality and an epistemic linguistic modality for the predictive model trained using question-answer pair data expression.
Related Prior Arts
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the PTO-892 Notice of Reference Cited.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. SEE MPEP 2141.02 [R-5] VI. PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS: A prior art reference must be considered in its entirety, i.e., as a whole, including portions that would lead away from the claimed invention. W.L. Gore & Associates, Inc. v. Garlock, Inc., 721 F.2d 1540, 220 USPQ 303 (Fed. Cir. 1983), cert. denied, 469 U.S. 851 (1984) In re Fulton, 391 F.3d 1195, 1201, 73 USPQ2d 1141, 1146 (Fed. Cir. 2004). >See also MPEP §2123.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KUEN S LU whose telephone number is (571)272-4114. The examiner can normally be reached on M-F, 8-19, Mid-Flex 2 hours.
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KUEN S LU /Kuen S Lu/
Art Unit 2165
Primary Patent Examiner
July 8, 2026
Prosecution Insights