DETAILED ACTION
This communication is in response to the Application filed on December 9, 2024.
Claims 1 - 13 are pending and have been examined.
Claims 1, 12 and 13 are independent.
Foreign priority: January 19, 2024.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on December 9, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings filed on February 13, 2025 have been accepted and considered by the Examiner.
Claim Objections
Claim 7 is objected to because of the following informalities: In claim 7, line 5 and line 8: “information processing apparatus” should read “first information processing apparatus” to differentiate from “another information processing apparatus” on line 7.
Appropriate correction is required.
Double Patenting Note
The Examiner notes that previously patent application publication U.S. 2025/0238661 was analyzed for Double Patenting. However, based on the current claim scope no Double patenting was found.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 1 - 6, 12 and 13 are rejected under 35 U.S.C. 103(a) as being unpatentable over Paliwal et al., (U.S. Patent 12,675,260), hereinafter referred to as Paliwal, in view of Yamashita et al., (WO 2020080375 A1), hereinafter referred to as Yamashita.
Regarding Claims 1, 12 and 13, Paliwal teaches:
1. An information processing apparatus comprising, 12. An information processing method executed by a computer, the information processing method comprising, and 13. A non-transitory computer readable storage medium having stored therein an information processing program that causes a computer to execute a process comprising:
a reception unit that receives a query including a prompt sent from a user or a query for obtaining the prompt; [Paliwal, “user interface engine (i.e., the claimed “reception unit”), Col. 7:29; Figure 3B, “prompting the artificial intelligence model with the at least a portion of the natural language query”; “For example, a user can ask (i.e., the claimed “query including a prompt sent from a user”), “What are the top 5 product categories by gross margin in the Western region for the last 12 months, and how do they compare to the same period last year?” For example, a user can edit a generated query to change the aggregation level from monthly to quarterly, or to add a filter for specific product SKUs. For example, a user can adjust a model parameter to change the forecasting algorithm from ARIMA to Prophet (or other time series models such as LSTM, GRU, or seasonal decomposition methods), or to tune hyperparameters such as seasonality or trend sensitivity. For example, a user can provide an AI/ML model prompt such as “Develop a predictive model to identify high-value customer segments based on demographics, purchase history, and browsing behavior, and estimate the potential revenue impact of targeted marketing campaigns.” ” Col. 7:43-58]
a selection unit that selects, based on information on a plurality of items included in the prompt, AI that is used to generate response information indicating a response to the prompt from among a plurality of pieces of AI and [Paliwal, “The automated query engine 141 of the code unit personalization engine 140 can include the generated embeddings, persona information, tokens (e.g., portions (i.e., the claimed “plurality of items”) or derivatives of the user question), table definition (in formats such as JSON schema, XML schema, or database metadata objects), query snippets (i.e., the claimed “plurality of items”) (stored as text templates, parameterized queries, or code fragments), and other information in a prompt (i.e., the claimed “plurality of items”) (formatted as structured text, JSON objects, or domain-specific prompt templates) invokable by the SQL query generation engine 142 to generate a query (as described, for example, in reference to FIG. 6).” Col. 11:17-27; “slot-filling” Col. 16:63; Referring to the Specification Pg. 9 of the instant Application, “The information processing apparatus 1 extracts the information on the plurality of items included in the new prompt by using a technology of slot filtering”.]
a providing unit that provides the response information that has been generated by using the Al selected by the selection unit to the user. [ “applying a generative artificial intelligence model” Col. 18:14; “The knowledge data store 150 can work in conjunction with the RAG (Retrieval-Augmented Generator) framework 160 and/or AI framework 170 (i.e., the claimed “providing unit”), which provide advanced natural language processing and machine learning capabilities, enabling the conversational BI (i.e., the claimed AI”) platform 100 to generate accurate and relevant responses to user queries (i.e., the claimed “response information that has been generated by using the Al selected by the selection unit to the user”).” Col. 10:47-52]
Paliwal fails to teach plurality of items.
However, Yamashita teaches:
a selection unit that selects, based on information on a plurality of items included in the prompt, AI that is used to generate response information indicating a response to the prompt from among a plurality of pieces of AI and [Referring to the Specification Pg. 9 of the instant Application, “The information processing apparatus 1 extracts the information on the plurality of items included in the new prompt by using a technology of slot filtering”; Yamashita, “selection unit,” Par. 0012; “desired information (i.e., the claimed “plurality of items”) and create a report (slot filtering) based on freely spoken utterances without the user having to be conscious of keywords.” Par. 0117; “extraction unit extracts keywords that correspond to predefined semantic classes from the text information converted from speech and user information.” Par. 0007; “prompt the user,” Par. 0152]
Paliwal and Yamashita pertain to natural language processing systems and are analogous to the instant application. Accordingly, it would have been obvious to one of ordinary skill in the natural language processing art to modify Paliwal’s teachings of “BI (i.e., the claimed AI”) platform 100 to generate accurate and relevant responses to user queries (i.e., the claimed “response information that has been generated by using the Al selected by the selection unit to the user”)” (Paliwal, Col. 10:47-52) with the teachings of “selection unit” and “slot filtering” (Yamashita, Par. 0117) taught by Yamashita in order to improve “the accuracy of slot filtering” and “enable creation of more reliable reports” (Yamashita, Par. 0119).
Regarding Claim 2, Paliwal in view of Yamashita has been discussed above. The combination further teaches:
wherein the plurality of pieces of AI include a plurality of pieces of generative AI [Paliwal, see mapping applied to claim 1; Yamashita, see mapping applied to claim 1; “applying a generative artificial intelligence” Col. 18:14; “The AI/ML framework 514 can include an open-source library (such as TensorFlow, PyTorch (i.e., the claimed “plurality of pieces of generative AI”),” Col. 22:4-6; “deep learning toolkit (such as Keras, PyTorch Lightning, or Hugging Face Transformers) (i.e., the claimed “plurality of pieces of generative AI”)” Col. 22:10-12]
Regarding Claim 3, Paliwal in view of Yamashita has been discussed above. The combination further teaches:
wherein the selection unit selects the generative Al that is used to generate the response information from among the plurality of pieces of generative AI based on a comparison result between information on the plurality of items associated with the plurality of respective pieces of generative AI and the information on the plurality of items included in the prompt. [Paliwal, see mapping applied to claims 1-2; Yamashita, see mapping applied to claims 1-2; “applying a generative artificial intelligence” Col. 18:14; “The AI/ML framework 514 can include an open-source library (such as TensorFlow, PyTorch (i.e., the claimed “plurality of pieces of generative AI”),” Col. 22:4-6; “deep learning toolkit (such as Keras, PyTorch Lightning, or Hugging Face Transformers) (i.e., the claimed “plurality of pieces of generative AI”)” Col. 22:10-12; “The personalization engine of the conversational BI platform offers substantial technical improvements over conventional systems through its sophisticated user persona identification and code unit (e.g., query) customization capabilities (selecting options/customizing is the claimed “select based on comparison result”). In some aspects, the platform employs advanced natural language processing techniques (such as transformer models, BERT, or GPT-based architectures (i.e., the claimed “plurality of pieces of generative AI”)) to analyze natural language user queries (i.e., the claimed “plurality of items included in the prompt”) and automatically determine (i.e., the claimed “select”) the most appropriate (i.e., the claimed “comparison”) persona based on contextual clues, historical interactions, or organizational role information... Furthermore, the personalization engine can utilize collaborative filtering and recommendation algorithms (such as matrix factorization, deep learning models (i.e., the claimed “plurality of pieces of generative AI”), or graph-based approaches) to suggest relevant queries and insights based on similar user behaviors and organizational patterns (e.g., for users with similar personas).” Col. 2:22-64]
Regarding Claim 4, Paliwal in view of Yamashita has been discussed above. The combination further teaches:
wherein the selection unit selects the generative AI that is used to generate the response information from among the plurality of pieces of generative AI based on a context of the user and the information on the plurality of items included in the prompt. [Paliwal, see mapping applied to claims 1-3; Yamashita, see mapping applied to claims 1-3; “The personalization engine of the conversational BI platform offers substantial technical improvements over conventional systems through its sophisticated user persona identification and code unit (e.g., query) customization capabilities (selecting options/customizing is the claimed “select based on comparison result”). In some aspects, the platform employs advanced natural language processing techniques (such as transformer models, BERT, or GPT-based architectures (i.e., the claimed “plurality of pieces of generative AI”)) to analyze natural language user queries (i.e., the claimed “plurality of items included in the prompt”) and automatically determine (i.e., the claimed “select”) the most appropriate (i.e., the claimed “comparison”) persona based on contextual clues (i.e., the claimed “context of the user”), historical interactions, or organizational role information... Furthermore, the personalization engine can utilize collaborative filtering and recommendation algorithms (such as matrix factorization, deep learning models (i.e., the claimed “plurality of pieces of generative AI”), or graph-based approaches) to suggest relevant queries and insights based on similar user behaviors and organizational patterns (e.g., for users with similar personas).” Col. 2:22-64; “context about the user’s role,” Col. 4:55; “produce (i.e., the claimed “generate”) contextually appropriate responses,” Col. 16:37-38]
Regarding Claim 5, Paliwal in view of Yamashita has been discussed above. The combination further teaches:
wherein the selection unit selects the generative AI that is used to generate the response information from among the plurality of pieces of generative AI based on a comparison result between a combination of information on the plurality of items associated with the plurality of respective pieces of generative AI and the context and a combination of the information on the plurality of items included in the prompt and the context of the user. [Paliwal, see mapping applied to claims 1-4; Yamashita, see mapping applied to claims 1-4; “prompting the artificial intelligence model with the at least a portion of the natural language query and at least two of: (i) the user role information, (ii) the historical interaction pattern information, or (iii) the determined user persona (i.e., the claimed “combination of information on the plurality of items associated with the plurality of respective pieces of generative AI and the context and a combination of the information on the plurality of items included in the prompt and the context of the user”);” Col. 39:43-47]
Regarding Claim 6, Paliwal in view of Yamashita has been discussed above. The combination further teaches:
wherein the selection unit includes an extraction model that extracts the information on the plurality of items included in the prompt received by the reception unit, and [Paliwal, see mapping applied to claims 1-5; Yamashita, see mapping applied to claims 1-5; Yamashita, “extraction unit (i.e., the claimed “extraction model”) extracts keywords (i.e., the claimed “information on the plurality of items”) that correspond to predefined semantic classes from the text information converted from speech and user information.” Par. 0007; Paliwal, “In some implementations, the chatbot interface invokes a user input parser (i.e., the claimed “extraction model”) (implemented using natural language processing libraries such as spaCy, NLTK, or Stanford CoreNLP) to analyze (i.e., the claimed “extract”) and tokenize the natural language query, identifying key entities (i.e., the claimed “extracts the information on the plurality of items included in the prompt received”), intent, and context.” Col. 4:13-17]
extracts the information on the plurality of items included in the prompt by using the extraction model. [Paliwal, see mapping applied to claims 1-5; Yamashita, see mapping applied to claims 1-5; Yamashita, see mapping applied to claims 1-5; Yamashita, “extraction unit (i.e., the claimed “extraction model”) extracts keywords (i.e., the claimed “information on the plurality of items”) that correspond to predefined semantic classes from the text information converted from speech and user information.” Par. 0007; Paliwal, “In some implementations, the chatbot interface invokes a user input parser (i.e., the claimed “extraction model”) (implemented using natural language processing libraries such as spaCy, NLTK, or Stanford CoreNLP) to analyze (i.e., the claimed “extract”) and tokenize the natural language query, identifying key entities (i.e., the claimed “extracts the information on the plurality of items included in the prompt received”), intent, and context.” Col. 4:13-17]
Claim 7 - 11 are rejected under 35 U.S.C. 103(a) as being unpatentable over Paliwal in view of Yamashita as applied in claim 1 above, and in further view of Chalkley et al., (U.S. Patent 12,699,738), hereinafter referred to as Chalkley.
Regarding Claim 7, Paliwal in view of Yamashita has been discussed above. The combination further teaches:
wherein the plurality of pieces of generative AI includes first generative AI that is the generative Al included in the information processing apparatus and second generative Al that is generative Al included in another information processing apparatus that is different from the information processing apparatus [Paliwal, see mapping applied to claims 1-2; Yamashita, see mapping applied to claims 1-2]
The combination fails to explicitly teach first generative AI and second generative AI.
However, Chalkley teaches:
wherein the plurality of pieces of generative AI includes first generative AI that is the generative Al included in the information processing apparatus and second generative Al that is generative Al included in another information processing apparatus that is different from the information processing apparatus [Chalkley, “first generative AI model,” Col. 2:3; “second generative AI model,” Col. 2:10]
Paliwal, Yamashita and Chalkley pertain to natural language processing systems and are analogous to the instant application. Accordingly, it would have been obvious to one of ordinary skill in the natural language processing art to modify Paliwal’s teachings of “BI (i.e., the claimed AI”) platform 100 to generate accurate and relevant responses to user queries (i.e., the claimed “response information that has been generated by using the Al selected by the selection unit to the user”)” (Paliwal, Col. 10:47-52) with the teachings of “selection unit” and “slot filtering” (Yamashita, Par. 0117) taught by Yamashita and the teachings of “first generative AI model” (Chalkley, Col. 2:3) and “second generative AI model” (Chalkley, Col. 2:10) taught by Chalkley in order to improve “the accuracy of slot filtering” and “enable creation of more reliable reports” (Yamashita, Par. 0119) and “enrich categorizations” (Chalkley, Col. 1:30).
Regarding Claim 8, Paliwal in view of Yamashita and Chalkley has been discussed above. The combination further teaches:
a determination unit that determines, when the second generative AI has been selected by the selection unit as the generative Al that is used to generate the response information, whether or not the response to the prompt is available by using past generated information that is information generated by using the second generative AI in the past, and [Paliwal, see mapping applied to claims 1-2,7; Yamashita, see mapping applied to claims 1-2,7; Chalkley, see mapping applied to claim 7; Chalkley, “prior user interactions,” Col. 2:17; Paliwal, “historical interactions,” Col. 2:54; Paliwal, “past conversations,” Col. 14:58; Paliwal, “history of user interactions,” Col. 4:33; Yamashita, “determination unit,” Par. 0012; Yamashita, “past report information (i.e., the claimed “past generated information”),” Par. 0053]
a providing unit that provides, when it is determined by the determination unit that the response to the prompt is available by using the past generated information, the past generated information or information based on the past generated information to the user as the response information indicating the response to the prompt. [Paliwal, see mapping applied to claims 1-2,7; Yamashita, see mapping applied to claims 1-2,7; Chalkley, see mapping applied to claim 7; Chalkley, “prior user interactions,” Col. 2:17; Paliwal, “historical interactions,” Col. 2:54; “past conversations,” Col. 14:58; Paliwal, “history of user interactions,” Col. 4:33; “determination unit,” Par. 0012; Yamashita, “past report information (i.e., the claimed “past generated information”),” Par. 0053]
Regarding Claim 9, Paliwal in view of Yamashita and Chalkley has been discussed above. The combination further teaches:
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wherein the providing unit includes an acquisition processing unit that inputs, when it is determined by the determination unit that the response to the prompt is not available by using the past generated information, information including the prompt to the second generative AI as input information, and [The combination clearly teaches past generated information. The combination clearly teaches generating new information. It is obvious to one skilled in the art that when past generated information is not available, new information is generated; Paliwal, see mapping applied to claims 1-2,7-8; Yamashita, see mapping applied to claims 1-2,7-8; Chalkley, see mapping applied to claim 7-8; Paliwal, “Responsive to determining that at least a portion of the natural language query does not match a data store entry (i.e., the claimed “not available by using past generated information”),” Figure 3B “370”]
that acquires the response information indicating the response to the prompt or information for generating the response information from the second generative AI as new generated information, and [The combination clearly teaches past generated information. The combination clearly teaches generating new information. It is obvious to one skilled in the art that when past generated information is not available, new information is generated; Paliwal, see mapping applied to claims 1-2,7-8; Yamashita, see mapping applied to claims 1-2,7-8; Chalkley, see mapping applied to claim 7-8; Paliwal, “Responsive to determining that at least a portion of the natural language query does not match a data store entry (i.e., the claimed “not available by using past generated information”), apply an artificial intelligence model (i.e., the claimed “second generative AI”) to generate a new (i.e., the claimed “new generated information”) code unit” Figure 3B “370”; “enabling highly tailored responses.” Col. 2:58]
a providing processing unit that provides the new generated information acquired by the acquisition processing unit or information based on the new generated information to the user as the response information. [The combination clearly teaches past generated information. The combination clearly teaches generating new information. It is obvious to one skilled in the art that when past generated information is not available, new information is generated; [The combination clearly teaches past generated information. The combination clearly teaches generating new information. It is obvious to one skilled in the art that when past generated information is not available, new information is generated; Paliwal, see mapping applied to claims 1-2,7-8; Yamashita, see mapping applied to claims 1-2,7-8; Chalkley, see mapping applied to claim 7-8; Paliwal, “Responsive to determining that at least a portion of the natural language query does not match a data store entry (i.e., the claimed “not available by using past generated information”), apply an artificial intelligence model (i.e., the claimed “second generative AI”) to generate a new (i.e., the claimed “new generated information”) code unit” Figure 3B “370”; “enabling highly tailored responses.” Col. 2:58]
Regarding Claim 10, Paliwal in view of Yamashita and Chalkley has been discussed above. The combination further teaches:
a determination unit that determines, when a single piece of generative Al has been selected from among the plurality of pieces of generative Al by the selection unit as the generative AI that is used to generate the response information, whether or not the response to the prompt is available by using past generated information that is information generated by using the single piece of generative AI in the past, and [Paliwal, see mapping applied to claims 1-2,7-9; Yamashita, see mapping applied to claims 1-2,7-9; Chalkley, see mapping applied to claim 7-9]
a providing unit that provides, when it is determined by the determination unit that the response to the prompt is available by using the past generated information, the past generated information or information based on the past generated information to the user as the response information indicating the response to the prompt. [Paliwal, see mapping applied to claims 1-2,7-9; Yamashita, see mapping applied to claims 1-2,7-9; Chalkley, see mapping applied to claim 7-9]
Regarding Claim 11, Paliwal in view of Yamashita and Chalkley has been discussed above. The combination further teaches:
wherein the providing unit includes an acquisition processing unit that inputs, when it is determined by the determination unit that the response to the prompt is not available by using the past generated information, information including the prompt to the single piece of generative Al as input information, and [Paliwal, see mapping applied to claims 1-2,7-10; Yamashita, see mapping applied to claims 1-2,7-10; Chalkley, see mapping applied to claim 7-10]
that acquires the response information indicating the response to the prompt or information for generating the response information from the single piece of generative AI as new generated information, and [Paliwal, see mapping applied to claims 1-2,7-10; Yamashita, see mapping applied to claims 1-2,7-10; Chalkley, see mapping applied to claim 7-10]
a providing processing unit that provides the new generated information acquired by the acquisition processing unit or information based on the new generated information to the user as the response information. [Paliwal, see mapping applied to claims 1-2,7-10; Yamashita, see mapping applied to claims 1-2,7-10; Chalkley, see mapping applied to claim 7-10]
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Baba et al., (JP 2025104448 A) teaches slot filtering.
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/EUNICE LEE/Examiner, Art Unit 2656
/BHAVESH M MEHTA/ Supervisory Patent Examiner, Art Unit 2656
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