DETAILED ACTION
This communication is in response to the Application filed on December 9, 2024.
Claims 1 - 10 are pending and have been examined.
Claims 1, 9 and 10 are independent.
Foreign priority JP2024-006921: 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 statements (IDS) submitted on December 9, 2024 and January 2, 2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Drawings
The drawings filed on February 13, 2025 have been accepted and considered by the Examiner.
Claim Objections
Claim 9 is objected to because of the following informalities: In claim 9, line 15: “generated at the generating.” is an incomplete sentence.
Appropriate correction is required.
Claim 10 is objected to because of the following informalities: In claim 10, line 17: “generated at the generating.” is an incomplete sentence.
Appropriate correction is required.
Double Patenting Note
The Examiner notes that previously patent application publication U.S. 2025/0238445 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 - 7 and 9 - 10 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 Ji et al., (CN117610580A), hereinafter referred to as Ji.
Regarding Claims 1 and 9 - 10, Paliwal teaches:
1. An information processing apparatus comprising, 9. An information processing method implemented by a computer, the information processing method comprising, and 10. A non-transitory computer readable storage medium having stored therein an information processing program that causes a computer to execute a process, the process comprising:
an estimation unit that estimates degrees of importance of a plurality of items based on evaluation information that is information indicating evaluation on a service using generation information that is information generated by using generative AI based on prompts that include pieces of information on the plurality of items; [Paliwal, “the knowledge data store 150 can access persona information and query history rank (i.e., the claimed “degrees of importance”) information (i.e., the claimed “plurality of items”),” Col. 11:5-7; “In some implementations, the semantic layer management engine 250 can enable prompt augmentation (e.g., table prioritization (i.e., the claimed “degrees of importance”), variable prioritization (i.e., the claimed “degrees of importance”)). The semantic layer management engine (i.e., the claimed “estimation unit”) 250 can utilize intelligent ranking algorithms (i.e., the claimed “evaluation”) (such as TF-IDF, BM25, learning-to-rank models, or neural ranking architectures) that prioritize (i.e., the claimed “degrees of importance”) database tables and columns based on multiple factors including user access patterns (tracked through usage analytics and query logs), data freshness (measured by last update timestamps or data ingestion metrics), business criticality scores (assigned through manual curation or automated importance scoring (i.e., the claimed “degrees of importance”)), and semantic relevance to the current query context (i.e., the claimed “degrees of importance”) (computed using embedding similarity or keyword matching).” Col. 15:12-22; 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 generation unit that generates a prompt that includes pieces of information on one or more items that are selected based on the degrees of importance of the plurality of items that are estimated by the estimation unit; and [Paliwal, “A computer system (i.e., the claimed “generation unit”) can generate a prompt,” Col. 36: 8; Paliwal, rank (i.e., the claimed “degrees of importance”) information (i.e., the claimed “plurality of items”),” Col. 11:5-7; “In some implementations, the semantic layer management engine 250 can enable prompt augmentation (i.e., the claimed “generate a prompt that includes pieces of information on one or more items that are selected based on the degrees of importance”) (e.g., table prioritization (i.e., the claimed “degrees of importance”), variable prioritization (i.e., the claimed “degrees of importance”)). The semantic layer management engine 250 can utilize intelligent ranking algorithms (i.e., the claimed “evaluation”) (such as TF-IDF, BM25, learning-to-rank models, or neural ranking architectures) that prioritize (i.e., the claimed “degrees of importance”) database tables and columns based on multiple factors including user access patterns (tracked through usage analytics and query logs), data freshness (measured by last update timestamps or data ingestion metrics), business criticality scores (assigned through manual curation or automated importance scoring (i.e., the claimed “degrees of importance”)), and semantic relevance to the current query context (i.e., the claimed “degrees of importance”) (computed using embedding similarity or keyword matching).” Col. 15:12-22; “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 (i.e., the claimed “generate a prompt”) (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 service using generation information that is generated by using the generative AI based on the prompt that is generated by the generation unit. [Paliwal, “The conversational BI platform (i.e., the claimed “providing unit”) 100 can utilize a SQL query stub store 290 (implemented as a repository using databases, file systems, or in-memory caches) (e.g., by cross-referencing code segments 272c to code snippets 290a-2, 290b-2 and/or by searching for a persona 290a-1, 290b-1), which can provide computer-executable instructions (e.g., AI generative instructions, prompts, query templates) tailored to specific business intelligence tasks, such as sales analysis or customer segmentation (i.e., the claimed “provides the service”).” Col. 4: 22 - 29;“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, 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, “A computer system (i.e., the claimed “generation unit”) can generate a prompt,” Col. 36: 8;]
Paliwal fails to teach plurality of items.
However, Ji teaches:
an estimation unit that estimates degrees of importance of a plurality of items based on evaluation information that is information indicating evaluation on a service using generation information that is information generated by using generative AI based on prompts that include pieces of information on the plurality of items; [Ji, “In this disclosure, the input statement may contain multiple intents (i.e., the claimed “plurality of items”). After the input statement and target prompt information are input into the target big model, the target big model can first identify the number of intents (i.e., the claimed “plurality of items”) contained in the input statement based on the set of intents in the target prompt information, and then output a set of slot values based on each intent (i.e., the claimed “pieces of information on the plurality of items”).” Par. n0064]
Paliwal and Ji 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 “automated importance scoring (i.e., the claimed “degrees of importance”)” (Paliwal, Col. 15:12-22), “rank (i.e., the claimed “degrees of importance”) information (i.e., the claimed “plurality of items”)” (Paliwal, Col. 11:5-7) and “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 “multiple intents (i.e., the claimed “plurality of items”)” and “slot values based on each intent (i.e., the claimed “pieces of information on the plurality of items”)” (Ji, Par. n0064) taught by Ji in order to “improve efficiency and accuracy of instruction recognition and further enhance the performance of applications” (Ji, Par. n0004).
Regarding Claim 2, Paliwal in view of Ji has been discussed above. The combination further teaches:
a selection unit that selects information on the one or more items to be included in a prompt among the pieces of information on the plurality of item based on the degrees of importance of the plurality of items that are estimated by the estimation unit, [Paliwal, “In some implementations, the semantic layer management engine (i.e., management engine performs the functions of the claimed “selection unit” and “estimation unit”) 250 can enable prompt augmentation (e.g., table prioritization (i.e., the claimed “degrees of importance”), variable prioritization (i.e., the claimed “degrees of importance”)). The semantic layer management engine (management engine performs the functions of the i.e., the claimed “estimation unit” and “selection unit) 250 can utilize intelligent ranking algorithms (i.e., the claimed “evaluation”) (such as TF-IDF, BM25, learning-to-rank models, or neural ranking architectures) that prioritize (i.e., the claimed “degrees of importance”) database tables and columns based on multiple factors including user access patterns (tracked through usage analytics and query logs), data freshness (measured by last update timestamps or data ingestion metrics), business criticality scores (assigned through manual curation or automated importance scoring (i.e., the claimed “degrees of importance”)), and semantic relevance to the current query context (i.e., the claimed “degrees of importance”) (computed using embedding similarity or keyword matching).” Col. 15:12-22; “history rank information (i.e., the claimed “degrees of importance of the plurality of items”) to resolve the user’s current persona and provide relevant results (i.e., the claimed “select information”).” Col. 11:6-8; 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”.]
wherein the generation unit generates a prompt that includes the one or more items that are selected by the selection unit. [Paliwal, “A computer system (i.e., the claimed “generation unit”) can generate a prompt,” Col. 36: 8; Paliwal, “history rank information (i.e., the claimed “degrees of importance of the plurality of items”) to resolve the user’s current persona and provide relevant results (i.e., the claimed “select information”).” Col. 11:6-8; 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”; “In some implementations, the semantic layer management engine (i.e., management engine performs the functions of the claimed “selection unit” and “estimation unit”) 250 can enable prompt augmentation (i.e., the claimed “generate a prompt that includes pieces of information on one or more items that are selected based on the degrees of importance”) (e.g., table prioritization (i.e., the claimed “degrees of importance”), variable prioritization (i.e., the claimed “degrees of importance”)). The semantic layer management engine 250 can utilize intelligent ranking algorithms (i.e., the claimed “evaluation”) (such as TF-IDF, BM25, learning-to-rank models, or neural ranking architectures) that prioritize (i.e., the claimed “degrees of importance”) database tables and columns based on multiple factors including user access patterns (tracked through usage analytics and query logs), data freshness (measured by last update timestamps or data ingestion metrics), business criticality scores (assigned through manual curation or automated importance scoring (i.e., the claimed “degrees of importance”)), and semantic relevance to the current query context (i.e., the claimed “degrees of importance”) (computed using embedding similarity or keyword matching).” Col. 15:12-22]
Regarding Claim 3, Paliwal in view of Ji has been discussed above. The combination further teaches:
wherein the estimation unit estimates a degree of importance of each of the items. [Paliwal, “In some implementations, the semantic layer management engine 250 can enable prompt augmentation (e.g., table prioritization (i.e., the claimed “degrees of importance”), variable prioritization (i.e., the claimed “degrees of importance”)). The semantic layer management engine (i.e., management engine performs functions of the claimed “estimation unit”) 250 can utilize intelligent ranking algorithms (i.e., the claimed “evaluation”) (such as TF-IDF, BM25, learning-to-rank models, or neural ranking architectures) that prioritize (i.e., the claimed “degrees of importance”) database tables and columns based on multiple factors including user access patterns (tracked through usage analytics and query logs), data freshness (measured by last update timestamps or data ingestion metrics), business criticality scores (assigned through manual curation or automated importance scoring (i.e., the claimed “degrees of importance”)), and semantic relevance to the current query context (i.e., the claimed “degrees of importance”) (computed using embedding similarity or keyword matching).” Col. 15:12-22]
Regarding Claim 4, Paliwal in view of Ji has been discussed above. The combination further teaches:
wherein the estimation unit estimates a degree of importance of each combination of the plurality of items. wherein the estimation unit estimates a degree of importance of each combination of the plurality of items. [Paliwal, “In some implementations, the semantic layer management engine 250 can enable prompt augmentation (e.g., table prioritization (i.e., the claimed “degrees of importance”), variable prioritization (i.e., the claimed “degrees of importance”)). The semantic layer management engine (i.e., management engine performs functions of the claimed “estimation unit”) 250 can utilize intelligent ranking algorithms (i.e., the claimed “evaluation”) (such as TF-IDF, BM25, learning-to-rank models, or neural ranking architectures) that prioritize (i.e., the claimed “degrees of importance”) database tables and columns based on multiple factors including user access patterns (tracked through usage analytics and query logs), data freshness (measured by last update timestamps or data ingestion metrics), business criticality scores (assigned through manual curation or automated importance scoring (i.e., the claimed “degrees of importance”)), and semantic relevance to the current query context (i.e., the claimed “degrees of importance”) (computed using embedding similarity or keyword matching).” Col. 15:12-22; Ji, “The determination module is used to determine the target instruction corresponding to the input statement based on the at least one set of slot values (i.e., the claimed “combination of the plurality of items”).” Par. n0014]
Regarding Claim 5, Paliwal in view of Ji has been discussed above. The combination further teaches:
wherein the estimation unit estimates degrees of importance of the plurality of items based on evaluation information that is information indicating evaluation on a service using the plurality pieces of generation information each being generated by using generative AI based on a corresponding prompt among the plurality of prompts each including a different combination of pieces of information on a plurality of items. [Paliwal, “In some implementations, the semantic layer management engine 250 can enable prompt augmentation (e.g., table prioritization (i.e., the claimed “degrees of importance”), variable prioritization (i.e., the claimed “degrees of importance”)). The semantic layer management engine (i.e., management engine performs functions of the claimed “estimation unit”) 250 can utilize intelligent ranking algorithms (i.e., the claimed “evaluation”) (such as TF-IDF, BM25, learning-to-rank models, or neural ranking architectures) that prioritize (i.e., the claimed “degrees of importance”) database tables and columns based on multiple factors including user access patterns (tracked through usage analytics and query logs), data freshness (measured by last update timestamps or data ingestion metrics), business criticality scores (assigned through manual curation or automated importance scoring (i.e., the claimed “degrees of importance”)), and semantic relevance to the current query context (i.e., the claimed “degrees of importance”) (computed using embedding similarity or keyword matching).” Col. 15:12-22; Ji, “The determination module is used to determine the target instruction corresponding to the input statement based on the at least one set of slot values (i.e., the claimed “combination of the plurality of items”).” Par. n0014; Claim is directed to repeating the subject matter for more prompts. However, more prompts/repeating steps known from prior art is straightforward, amounts to the normal use of the teachings of Paliwal in view of Ji and are rejected under similar rationale.]
Regarding Claim 6, Paliwal in view of Ji has been discussed above. The combination further teaches:
wherein the estimation unit estimates the degrees of importance of the plurality of items for each context of a user who uses the service based on the evaluation information for each context of the user, and [Paliwal, “In some implementations, the semantic layer management engine 250 can enable prompt augmentation (e.g., table prioritization (i.e., the claimed “degrees of importance”), variable prioritization (i.e., the claimed “degrees of importance”)). The semantic layer management engine (i.e., management engine performs functions of the claimed “estimation unit”) 250 can utilize intelligent ranking algorithms (i.e., the claimed “evaluation”) (such as TF-IDF, BM25, learning-to-rank models, or neural ranking architectures) that prioritize (i.e., the claimed “degrees of importance”) database tables and columns based on multiple factors including user access patterns (tracked through usage analytics and query logs), data freshness (measured by last update timestamps or data ingestion metrics), business criticality scores (assigned through manual curation or automated importance scoring (i.e., the claimed “degrees of importance”)), and semantic relevance to the current query context (i.e., the claimed “degrees of importance”) (computed using embedding similarity or keyword matching).” Col. 15:12-22; Paliwal, “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]
the selection unit selects information on an item that is to be included in a new prompt from among the plurality of items for each context of the user. [Paliwal, “In some implementations, the semantic layer management engine (i.e., management engine performs the functions of the claimed “selection unit” and “estimation unit”) 250 can enable prompt augmentation (e.g., table prioritization (i.e., the claimed “degrees of importance”), variable prioritization (i.e., the claimed “degrees of importance”)). The semantic layer management engine (management engine performs the functions of the i.e., the claimed “estimation unit” and “selection unit) 250 can utilize intelligent ranking algorithms (i.e., the claimed “evaluation”) (such as TF-IDF, BM25, learning-to-rank models, or neural ranking architectures) that prioritize (i.e., the claimed “degrees of importance”) database tables and columns based on multiple factors including user access patterns (tracked through usage analytics and query logs), data freshness (measured by last update timestamps or data ingestion metrics), business criticality scores (assigned through manual curation or automated importance scoring (i.e., the claimed “degrees of importance”)), and semantic relevance to the current query context (i.e., the claimed “degrees of importance”) (computed using embedding similarity or keyword matching).” Col. 15:12-22; Paliwal, “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; “A computer system (i.e., the claimed “generation unit”) can generate a prompt (i.e., the claimed “new prompt”),” Col. 36: 8]
Regarding Claim 7, Paliwal in view of Ji has been discussed above. The combination further teaches:
wherein the evaluation on the service is at least one of evaluation that is made by the user who uses the service and evaluation that is based on a behavior of the user who uses the service. [Paliwal, “behavior patterns (i.e., the claimed “behavior of the user”),” Col. 23:37; “The user interface engine can then retrieve…browsing behavior (i.e., the claimed “behavior of the user”)… The model can then generate a report (i.e. ,the claimed “evaluation on the service is at least one of evaluation that is made by the user who uses the service and evaluation that is based on a behavior of the user who uses the service”),” Col. 8:3]
Claim 8 is rejected under 35 U.S.C. 103(a) as being unpatentable over Paliwal in view of Ji as applied in claim 1 above, and in further view of Lee, (KR20240078387A).
Regarding Claim 8, Paliwal in view of Ji has been discussed above. The combination fails to teach service is an advertisement distribution service, and the generation information is an advertisement content that is distributed by the advertisement distribution service.
However, Lee teaches:
wherein the service is an advertisement distribution service, and the generation information is an advertisement content that is distributed by the advertisement distribution service. [Lee, “The advertiser terminal (110) selects the advertising topic, the form of the product, the profit sharing method, and the evaluation method between the artificial intelligence service provider (e.g., artificial intelligence service provider (i.e., the claimed “advertisement distribution service”)) and the consumer of the digital product (including the advertiser), and inputs them to the competition operation server (130) via wired/wireless internet. Here, the digital product may include at least one of 'Text 2 Image', 'Text 2 Video', 'Text 2 Audio', 'Text 2 Music', language completion or chatting using LLM (Large Language Model), and the creation of advertisements (i.e., the claimed “generation information is an advertisement content”), audio tracks, storybooks, chatbots, and images through the combination of these technologies.” Par. 0047]
Paliwal, Ji and Lee 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 “automated importance scoring (i.e., the claimed “degrees of importance”)” (Paliwal, Col. 15:12-22), “rank (i.e., the claimed “degrees of importance”) information (i.e., the claimed “plurality of items”)” (Paliwal, Col. 11:5-7) and “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 “multiple intents (i.e., the claimed “plurality of items”)” and “slot values based on each intent (i.e., the claimed “pieces of information on the plurality of items”)” (Ji, Par. n0064) taught by Ji and “creation of advertisements” (Lee, Par. 0047) taught by Lee in order to “improve efficiency and accuracy of instruction recognition and further enhance the performance of applications” (Ji, Par. n0004) and “improve profitability of AI users” (Lee, Par. 0006).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gupta et al., (U.S. Patent Application Publication 2025/0131024) teaches generating a prompt for a generative artificial intelligence (AI) models with users’ context information.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EUNICE LEE whose telephone number is 571-272-1886. The examiner can normally be reached M-F 8:00 AM - 5:00 PM.
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/EUNICE LEE/Examiner, Art Unit 2656
/EDGAR X GUERRA-ERAZO/ Primary Examiner, Art Unit 2656
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