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 a responsive to the application filed on 09/29/2023.
Claims 1-20 are pending.
Claims 1-20 are rejected.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 13, and 17 are respectively drawn to a system, method, and non-transitory computer readable storage medium, hence each falls under one of four categories of statutory subject matter (Step 1). Nonetheless, the claims are directed to a judicially recognized exception of an abstract idea without significantly more.
Claim 1 recites the following limitations “receiving…a query about a chart that includes information related to a domain; generating…a response to the query based on the chart and a corpus of documents in the domain, wherein the response includes information from the corpus of documents; and providing…at least a portion of the response to the content provider”; claim 13 recites “obtaining, using a training component, training data including a corpus of documents from a domain, chart data from the domain, a training query, and a ground-truth response to the training query”; and claim 17 recites “receive a query about a chart that includes information related to a domain; …trained to generate a response to the query based on the chart and a corpus of documents in the domain, wherein the response includes information from the corpus of documents”. These limitations, as claimed, under its broadest reasonable interpretation, can be evaluated in a human mind, except for the recitation of generic computer components (using artificial intelligence/machine learning, a computer including one or more microprocessors, and a non-transitory computer readable storage medium) (Step 2A). Other than reciting “user interface”, “using a machine learning model”, “processor”, “memory”, “training, using the training component, a machine learning model to answer domain-specific questions in the domain using the training data”, “and a machine learning model including machine learning parameters stored in the at least one memory” to perform the exceptions, nothing in the claims preclude the steps from practically being performed in the human mind. For example, a human expert can:
mentally/with the aid of pen and paper receiving…a query about a chart that includes information related to a domain (claims 1 and 17) (e.g. by thinking of/writing out a remembered question of semantic topics)
mentally/with the aid of pen and paper generating…a response to the query based on the chart and a corpus of documents in the domain, wherein the response includes information from the corpus of documents (claim 1) (e.g. by thinking of/writing out an answer with a remembered article on a topic to the question based on semantic topics and remembered articles on the topic)
mentally/with the aid of pen and paper and providing…at least a portion of the response to the content provider (claim 1) (e.g. by thinking of/writing out the answer for personal review)
mentally/with the aid of pen and paper obtaining, using a training component, training data including a corpus of documents from a domain, chart data from the domain, a training query, and a ground-truth response to the training query (claim 13) (e.g. by thinking of/writing out remembered information of articles on a topic, semantically connected topics, questions on the articles, and reference question-answer pairs)
mentally/with the aid of pen and paper …trained to generate a response to the query based on the chart and a corpus of documents in the domain, wherein the response includes information from the corpus of documents (claim 17) (e.g. by thinking of/writing out a calculation able to output an answer to the question based on the semantically connected topics and articles)
Thus, the claims recite a mental process (Step 2A, Prong 1).
Claims 1, 13, and 17 include additional elements, “user interface”, “using a machine learning model”, “processor”, “memory”, “training, using the training component, a machine learning model to answer domain-specific questions in the domain using the training data”, “and a machine learning model including machine learning parameters stored in the at least one memory”, however the recitations of these elements are at a high level of generality, and adding the words “apply it” (or an equivalent) with the judicial exception (i.e., “training a machine learning model”, “training the machine learning model depending on the determined temporal distances”, “by a computing system”, and “the user-generated text entered by a user through an electronic user interface”), or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (i.e., “user interface”, “processor”, “memory”, “parameters stored in the at least one memory”) (see MPEP 2106.05(f)); and generally link the use of the judicial exception to a particular technological environment or field of use (i.e., “using a machine learning model” and “machine learning”) (see MPEP 2106.05(h)). Hence, each of the additional limitations or in combination do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (Step 2A, Prong 2). The additional elements in the claim do not amount to significantly more than an abstract idea. Furthermore, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of using “user interface”, “using a machine learning model”, “processor”, “memory”, “training, using the training component, a machine learning model to answer domain-specific questions in the domain using the training data”, “and a machine learning model including machine learning parameters stored in the at least one memory” to perform the steps of the independent claims amounts to no more than mere adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, and generally link the use of the judicial exception to a particular technological environment or field of use, as these cannot provide an inventive concept. (STEP 2B). As such, claims 1, 13, and 17 are not patent eligible.
Dependent claims 2-12, 14-16, and 18-20 are also ineligible for the same reasons given with respect to claims 1, 13, and 17. The dependent claims describe additional mental processes:
mentally/with the aid of pen and paper encoding…the query to obtain a query embedding, wherein the response is generated based on the query embedding (claim 3) (e.g. by mentally/writing out a calculation for converting data from one formant to another and converting the question to a different format)
mentally/with the aid of pen and paper encoding the chart…to obtain a chart embedding, wherein the response is generated based on the chart embedding (claim 4) (e.g. by mentally/writing out a calculation for converting data from one formant to another and converting the semantically connected topics to a different format)
mentally/with the aid of pen and paper generating…a visual element corresponding to the response; and displaying…the visual element to the content provider in response to the query (claim 5) (e.g. by mentally/writing out determining an answer and recording it for review)
mentally/with the aid of pen and paper identifying…chart data associated with the chart, wherein the response is based on the chart data (claim 6) (e.g. by mentally/writing out determining semantically connected topics associated with the answer)
mentally/with the aid of pen and paper identifying…a data trend in the domain; generating…a prompt based on the data trend, wherein the prompt comprises information included in the corpus of documents; generating…an initial response based on the prompt; and displaying…at least a portion of the initial response (claim 7) (e.g. by mentally/writing out determining an information direction regarding the topic, determining a title from the direction including article data, determining a first answer from the title, and recording the answer for review)
mentally/with the aid of pen and paper generating…a subsequent prompt based on the query and the initial response; and generating the response based on the subsequent prompt (claim 8) (e.g. by mentally/writing out a second title from the question and first answer, and determining a second answer from the second title)
mentally/with the aid of pen and paper the subsequent prompt further comprises one or more of content provider data for the content provider and user data for one or more users associated with the content provider (claim 9) (e.g. by mentally/writing out the second title includes personal data)
mentally/with the aid of pen and paper wherein: the portion of the initial response indicates a source of information from the corpus of documents. (claim 10) (e.g. by mentally/writing out the first answer includes a remembered article used in the calculation)
mentally/with the aid of pen and paper wherein: the portion of the initial response comprises a natural language response describing the data trend (claim 11) (e.g. by mentally/writing out the first answer includes text used in the calculation)
mentally/with the aid of pen and paper wherein: the portion of the response suggests one or more content provider actions (claim 12) (e.g. by mentally/writing out the first answer includes recommendations of recording the answer for review)
Again, the dependent claims continued to cover the performance of the limitation in the mind as inherited from the independent claims (Step 2A, Prong 1). The dependent claim 2 recitation of “the machine learning model is trained to answer questions in the domain using the corpus of documents as training data”, claims 3-4 recitation of “using a multimodal encoder”, claims 5-8 recitation of “using a user experience platform”, claims 5 and 7 recitation of “via the user interface”, claim 14 recitation of “training, using the training component, the machine learning model to answer the domain-specific questions based on a query embedding”, claim 15 recitation of “training, using the training component, the machine learning model to answer the domain-specific questions based on a chart embedding”, claim 16 recitation of “training, using the training component, the machine learning model to generate a chart using the training data”, claim 19 recitation of “…parameters stored in the at least one memory…”, claim 20 recitation of “a user experience platform configured to…”, are again recited at a high level and amount to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)); dependent claim 7 recitation of “using the machine learning model”, dependent claim 18 recitation of “the machine learning model comprises a transformer”, claim 19 recitation of “a multimodal encoder including multimodal encoder parameters…trained to encode the chart to obtain a chart embedding”, claim 20 recitation of “…generate a prompt for the machine learning model”, are again recited at a high level and amount to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)); and these do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (Step 2A, Prong 2). The additional element in the claims do not amount to significantly more than an abstract idea. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements to perform the steps of in the dependent claims and perform the steps of the claims amount to no more than mere instructions to apply the exception using generic computer components and adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, and generally link the use of the judicial exception to a particular technological environment or field of use; however these cannot provide an inventive concept. (STEP 2B). As such, dependent claims 2-12, 14-16, and 18-20 do not amount to significantly more than an abstract idea nor provide any inventive concept, therefore are not patent eligible.
Claim Rejections - 35 USC § 103
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.
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 nonobviousness.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Prat et al (US Pub 20230290114) hereinafter Prat, in view of Iu et al (US Pub 20240273286) hereinafter Iu.
Regarding claim 1, Prat teaches a method for data processing, comprising: receiving, from a content provider via a user interface, a query about a chart that includes information related to a domain (paragraph 0105 teaches “a user interface through which pharmaceutical research may be performed by making queries and receiving responses. The queries are sent to a data analysis engine 113 which uses the knowledge graph 111 (chart) to determine a response…the user may submit a query for identification of molecules (information related to a domain) likely to have similar bioactivity to a molecule with known bioactivity”);
generating, using a machine learning model, a response to the query based on the chart and a corpus of documents in the domain, wherein the response includes information from the corpus of documents (paragraph 0105 teaches “the data analysis engine 113 comprises one or more graph-based neural networks (graph neural networks, or GNNs) (machine learning model) to process the information contained in the knowledge graph 111 (based on the chart) to determine a response to the user's query”; wherein the knowledge graph is generated form and representative of retrieved “medical information such as published medical literature, clinical trials, dissertations, conference papers, and databases of known pharmaceuticals and their effects” in databases (response includes information from the corpus of documents)); and
providing, via the user interface, at least a portion of the response to the content provider (paragraph 0105 teaches “The queries are sent to a data analysis engine 113 which uses the knowledge graph 111 to determine a response, which is then provided to the user through the EDA interface 112”).
Prat at least implies generating, using a machine learning model, a response to the query based on the chart and a corpus of documents in the domain (see mappings above); however, Iu teaches generating, using a machine learning model, a response to the query based on the chart and a corpus of documents in the domain (paragraphs 0029, 0054, 0068, 0086, 0094, and 0123 teach a neural network transformer with model parameters that are stored, converting input format for an embedded input for processing of a submitted question and “knowledge graph 236 can be used to generate and export content and entity-level embeddings that can be used to discover new interrelationships between entities and/or concepts, which then can be used to identify related topics or phrases” in specific “domains”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to implement Iu’s teachings of machine learning model training, storage, and knowledge graph embedding domain predictions into Prat‘s teaching of user query processing with neural network knowledge graph models of database document data in order to increase reliability of model predictions and reduce “time-consuming experimentation” (Iu, paragraphs 0029, 0033, 0054, 0068, 0086, 0094, and 0123).
Regarding claim 2, the combination of Prat and Iu teach all the claim limitations of claim 1 above; and further teach the machine learning model is trained to answer questions in the domain using the corpus of documents as training data (Iu, paragraphs 0055-0056, 0123, and 0170 teach “generative language model 106 is trained on a large dataset of natural language text. For example, training samples of natural language text extracted from publicly available data sources are used to train the generative language model 106”, and “the generative language model 706 is pre-trained on a large corpus (e.g., millions of training examples) and can be re-trained or fine-tuned for particular applications or domains”).
Prat and Iu are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 3, the combination of Prat and Iu teach all the claim limitations of claim 1 above; and further teach encoding, using a multimodal encoder, the query to obtain a query embedding, wherein the response is generated based on the query embedding (Prat, paragraphs 0105, 0111, 0114, 0169, 0177, 0200 teach processing user queries and data inputs in different formats of “ligand” as “learned query vectors” to output responses, wherein the processing includes transformer encoder operations).
Prat at least implies encoding, using a multimodal encoder, the query to obtain a query embedding, wherein the response is generated based on the query embedding (see mappings above); however, Iu teaches encoding, using a multimodal encoder, the query to obtain a query embedding, wherein the response is generated based on the query embedding (paragraphs 0029, 0054 teach converting input format for an embedded input for processing of a submitted question using a “multimodal neural network” including a transformer).
Prat and Iu are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 4, the combination of Prat and Iu teach all the claim limitations of claim 1 above; and further teach encoding the chart using a multimodal encoder to obtain a chart embedding, wherein the response is generated based on the chart embedding (Prat, paragraphs 0097, 0111-0113, 0169, and Fig. 41 teach processing “vector extraction[s] and embedding[s]” of knowledge graph data to output responses, wherein the processing includes transformer encoder operations).
Prat at least implies encoding the chart using a multimodal encoder to obtain a chart embedding, wherein the response is generated based on the chart embedding (see mappings above); however, Iu teaches encoding the chart using a multimodal encoder to obtain a chart embedding, wherein the response is generated based on the chart embedding (paragraphs 0029, 0054, and 0086 teach “knowledge graph 236 can be used to generate and export content and entity-level embeddings that can be used to discover new interrelationships between entities and/or concepts, which then can be used to identify related topics or phrases” by processing with a “multimodal neural network” including a transformer).
Prat and Iu are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 5, the combination of Prat and Iu teach all the claim limitations of claim 1 above; and further teach generating, using a user experience platform, a visual element corresponding to the response; and displaying, via the user interface, the visual element to the content provider in response to the query (Prat, paragraphs 0105, 0217, and 0231 teach “the knowledge graph 111 to determine a response, which is then provided to the user (content provider) through the EDA interface 112” including displayed results and “a 3D model” (visual element)).
Regarding claim 6, the combination of Prat and Iu teach all the claim limitations of claim 1 above; and further teach identifying, using a user experience platform, chart data associated with the chart, wherein the response is based on the chart data (Prat, paragraph 0105 teaches using “the knowledge graph 111 to determine a response…the data analysis engine 113 comprises one or more graph-based neural networks (graph neural networks, or GNNs) to process the information contained in the knowledge graph 111 (identifying…chart data associated with the chart) to determine a response to the user's query (wherein the response is based on the chart data)”).
Regarding claim 7, the combination of Prat and Iu teach all the claim limitations of claim 1 above; and further teach identifying, using a user experience platform, a data trend in the domain; generating, using the user experience platform, a prompt based on the data trend, wherein the prompt comprises information included in the corpus of documents; generating, using the machine learning model, an initial response based on the prompt; and displaying, via the user interface, at least a portion of the initial response (Iu, paragraph 0077, 0100, 0110, and 0214 teach “Search optimization data can be used, for example, to identify currently upwardly or downwardly trending topics (identifying…a data trend in the domain) and search terms. In some implementations, the generative collaborative publishing system interfaces with search engine optimization system 290 in the process of generating prompts (generating…a prompt based on the data trend)…[an] article title may or may not be used to formulate a prompt for the generative language model (machine learning model) to generate a document (response) based on the article title (wherein the prompt comprises information included in the corpus of documents)”; and displaying the output to the user).
Prat and Iu are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 8, the combination of Prat and Iu teach all the claim limitations of claim 7 above; and further teach generating, using the user experience platform, a subsequent prompt based on the query and the initial response; and generating the response based on the subsequent prompt (Iu, paragraphs 0130-0131 and 0158-0161 teach “a contribution to a published document 322 is used by prompt generation subsystem 302 to select a subsequent prompt template or to modify an existing prompt template”; further context data (query and the initial response) and feedback are used for creating subsequent prompts to send to the user).
Prat and Iu are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 9, the combination of Prat and Iu teach all the claim limitations of claim 8 above; and further teach the subsequent prompt further comprises one or more of content provider data for the content provider and user data for one or more users associated with the content provider (Iu, paragraphs 0130-0131, 0158-0161, and 0194 teach generating prompts with context data for a user iteratively that routed to a user’s network (data)).
Prat and Iu are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 10, the combination of Prat and Iu teach all the claim limitations of claim 7 above; and further teach the portion of the initial response indicates a source of information from the corpus of documents (Iu, paragraph 0040, 0058, 0077, 0100, 0110, 0208-0210, and 0214 teach determining decision criterion including topic trend activities and displaying the output to the user for optional review of the generated document including reference document titles).
Prat and Iu are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 11, the combination of Prat and Iu teach all the claim limitations of claim 7 above; and further teach the portion of the initial response comprises a natural language response describing the data trend (Iu, paragraph 0077, 0100, 0110, 0208-0210, and 0214 teach determining decision criterion including topic trend activities and displaying the output to the user for optional review of the generated document).
Prat and Iu are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 12, the combination of Prat and Iu teach all the claim limitations of claim 1 above; and further teach the portion of the response suggests one or more content provider actions (Iu, paragraph 0077, 0100, 0110, 0208-0210, and 0214 teach outputting results to the user for optional review and editing (content provider actions) of the generated document (response)).
Prat and Iu are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 13, Prat teaches a method for data processing, comprising:
obtaining, using a training component, training data including a corpus of documents from a domain, chart data from the domain, a training query, and a ground-truth response to the training query (paragraph 0105 teaches “the data analysis engine 113 comprises one or more graph-based neural networks (graph neural networks, or GNNs) (machine learning model) to process the information contained in the knowledge graph 111 (based on the chart) to determine a response to the user's query”; wherein the knowledge graph is generated from and representative of retrieved “medical information such as published medical literature, clinical trials, dissertations, conference papers, and databases of known pharmaceuticals and their effects” in databases (training data including a corpus of documents from a domain, chart data from the domain); wherein paragraphs 0112-0114, 0147-0148, and 0169 teach training the neural network parameters with “ground-truth” data); and
training, using the training component, a machine learning model to answer domain-specific questions in the domain using the training data (paragraph 0105 teaches “the data analysis engine 113 comprises one or more graph-based neural networks (graph neural networks, or GNNs) (machine learning model) to process the information contained in the knowledge graph 111 to determine a response to the user's query” (answer domain-specific questions in the domain); wherein the knowledge graph is generated from and representative of retrieved “medical information such as published medical literature, clinical trials, dissertations, conference papers, and databases of known pharmaceuticals and their effects (domain)” in databases; wherein paragraphs 0112-0114, 0147-0148, and 0169 teach training the neural network parameters with “ground-truth” data).
Prat at least implies training, using the training component, a machine learning model to answer domain-specific questions in the domain using the training data (see mappings above); however, Iu teaches training, using the training component, a machine learning model to answer domain-specific questions in the domain using the training data (paragraphs 0029, 0054, 0068, 0086, 0094, and 0123 teach training a neural network transformer with model parameters that are stored, converting input format for an embedded input for processing of a submitted question and “knowledge graph 236 can be used to generate and export content and entity-level embeddings that can be used to discover new interrelationships between entities and/or concepts, which then can be used to identify related topics or phrases” in specific “domains”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to implement Iu’s teachings of machine learning model training, storage, and knowledge graph embedding domain predictions into Prat‘s teaching of user query processing with neural network knowledge graph models of database document data in order to increase reliability of model predictions and reduce “time-consuming experimentation” (Iu, paragraphs 0029, 0033, 0054, 0068, 0086, 0094, and 0123).
Regarding claim 14, the combination of Prat and Iu teach all the claim limitations of claim 13 above; and further teach training, using the training component, the machine learning model to answer the domain-specific questions based on a query embedding (Iu, paragraphs 0029, 0054-0056, 0123, and 0170 teach “generative language model 106 is trained on a large dataset of natural language text. For example, training samples of natural language text extracted from publicly available data sources are used to train the generative language model 106”, and “the generative language model 706 is pre-trained on a large corpus (e.g., millions of training examples) and can be re-trained or fine-tuned for particular applications or domains”. Further the input format is converted to an embedded input for processing of a submitted question using a “multimodal neural network” including a transformer.).
Prat and Iu are combinable for the same rationale as set forth above with respect to claim 13.
Regarding claim 15, the combination of Prat and Iu teach all the claim limitations of claim 13 above; and further teach training, using the training component, the machine learning model to answer the domain-specific questions based on a chart embedding (Iu, paragraphs 0029, 0054-0056, 0123, and 0170 teach “generative language model 106 is trained on a large dataset of natural language text. For example, training samples of natural language text extracted from publicly available data sources are used to train the generative language model 106”, and “the generative language model 706 is pre-trained on a large corpus (e.g., millions of training examples) and can be re-trained or fine-tuned for particular applications or domains”. Further “knowledge graph 236 can be used to generate and export content and entity-level embeddings that can be used to discover new interrelationships between entities and/or concepts, which then can be used to identify related topics or phrases” by processing with a “multimodal neural network” including a transformer.).
Prat and Iu are combinable for the same rationale as set forth above with respect to claim 13.
Regarding claim 16, the combination of Prat and Iu teach all the claim limitations of claim 13 above; and further teach training, using the training component, the machine learning model to generate a chart using the training data (Prat, paragraphs 0105-0107, 0113, 0140-0141, 0217, and 0231 teach training neural networks for processing 3D knowledge graph data to output 3D model results “which is then provided to the user (content provider) through the EDA interface 112” including displayed results and “a 3D model” (generate a chart)).
Regarding claim 17, Prat teaches an apparatus for data processing, comprising: at least one processor; at least one memory storing instructions executable by the at least one processor (paragraphs 0017 and 0241-0242 teach processor and memories to perform the embodiments of the disclosure);
a user interface configured to receive a query about a chart that includes information related to a domain (paragraph 0105 teaches “a user interface through which pharmaceutical research may be performed by making queries and receiving responses. The queries are sent to a data analysis engine 113 which uses the knowledge graph 111 (chart) to determine a response…the user may submit a query for identification of molecules (information related to a domain) likely to have similar bioactivity to a molecule with known bioactivity”);
and a machine learning model including machine learning parameters stored in the at least one memory and trained to generate a response to the query based on the chart and a corpus of documents in the domain, wherein the response includes information from the corpus of documents (paragraph 0105 teaches “the data analysis engine 113 comprises one or more graph-based neural networks (graph neural networks, or GNNs) (machine learning model) to process the information contained in the knowledge graph 111 (based on the chart) to determine a response to the user's query”; wherein the knowledge graph is generated from and representative of retrieved “medical information such as published medical literature, clinical trials, dissertations, conference papers, and databases of known pharmaceuticals and their effects” in databases (response includes information from the corpus of documents); wherein paragraphs 0112-0114 and 0147-0148 teach training the neural network parameters and storing its data).
Prat at least implies and a machine learning model including machine learning parameters stored in the at least one memory and trained to generate a response to the query based on the chart and a corpus of documents in the domain (see mappings above); however, Iu teaches and a machine learning model including machine learning parameters stored in the at least one memory and trained to generate a response to the query based on the chart and a corpus of documents in the domain (paragraphs 0029, 0054, 0068, 0086, 0094, and 0123 teach a neural network transformer with model parameters that are stored, converting input format for an embedded input for processing of a submitted question and “knowledge graph 236 can be used to generate and export content and entity-level embeddings that can be used to discover new interrelationships between entities and/or concepts, which then can be used to identify related topics or phrases”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to implement Iu’s teachings of machine learning model training, storage, and knowledge graph embedding domain predictions into Prat‘s teaching of user query processing with neural network knowledge graph models of database document data in order to increase reliability of model predictions and reduce “time-consuming experimentation” (Iu, paragraphs 0029, 0033, 0054, 0068, 0086, 0094, and 0123).
Regarding claim 18, the combination of Prat and Iu teach all the claim limitations of claim 17 above; and further teach the machine learning model comprises a transformer (Prat, paragraphs 0105, 0131, 0169, 0177, 0200, and 0221 teach processing the data inputs with transformer encoder operations).
Regarding claim 19, the combination of Prat and Iu teach all the claim limitations of claim 17 above; and further teach a multimodal encoder including multimodal encoder parameters stored in the at least one memory and trained to encode the chart to obtain a chart embedding (Iu, paragraphs 0029, 0054, 0068, 0086, and 0094 teach a “multimodal neural network”, including a transformer with model parameters that are stored, converting input format for an embedded input for processing of a submitted question and “knowledge graph 236 can be used to generate and export content and entity-level embeddings that can be used to discover new interrelationships between entities and/or concepts, which then can be used to identify related topics or phrases”).
Prat and Iu are combinable for the same rationale as set forth above with respect to claim 17.
Regarding claim 20, the combination of Prat and Iu teach all the claim limitations of claim 17 above; and further teach a user experience platform configured to generate a prompt for the machine learning model (Iu, paragraph 0077, 0100, 0110, and 0214 teach “the generative collaborative publishing system interfaces with search engine optimization system 290 in the process of generating prompts (generate a prompt)…[an] article title may or may not be used to formulate a prompt for the generative language model (machine learning model) to generate a document based on the article title”).
Prat and Iu are combinable for the same rationale as set forth above with respect to claim 17.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Stanley et al (US Pub 20240311664) teach training machine learning models on document data for chatbot user interactions.
Conclusion
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/C.M./Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123
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