DETAILED ACTION
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 .
Priority
Acknowledgement is made of applicant’s claim for priority based on foreign priority application filed on June 10, 2024.
Information Disclosure Statement
The references listed in the IDS filed on June 5, 2025 has been considered and entered into record. A copy of the signed or initialed IDS is hereby attached.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of mental process without significantly more. The claims recite “generate an analytic query for analyzing the data; generate an insight of the data based on the data and the analytic query; and generate metadata of the data based on the insight”. This judicial exception is not integrated into a practical application because the steps can be performed manually in human mind. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claim here merely uses the processor as a tool to perform the otherwise mental processes. See October Update at Section I(C)(ii). Thus, the limitations recite concepts that fall into the “mental process” grouping of abstract ideas.
ANALYSIS under Revised Guidance of 2019 PEG:
Statutory Category:
The claims 1-10 are directed to one of the four statutory category (claims 1-8 a device, claim 9 a method or a process, and claim 20 a non-transitory computer readable storage medium).
Step 2A – Prong 1: Is there a Judicial Exception (e.g. abstract idea)? (See MPEP§§2106.04(II)(A)(1), 2106.04(a)(2)).
Claim 1 recites, at its core, the limitations directed to generating an analytic query, generate an insight…, and generating metadata.... These limitations are data analysis and information processing steps, which can be characterized as mental processes and/or methods of organizing/ analyzing information. There is no technical detail regarding how these steps are performed beyond generic “processor” and “memory” components. Accordingly, claim 1 recites an abstract idea under step 2A, prong 1.
Step 2A – Prong 2: Is the abstract idea integrated into a practical application? (See MPEP§§2106.04(II)(A)(2), 2106.04(d)). To pass Prong 2, the claim must apply the abstract idea in a meaningful way (e.g., by improving computer functionality or another technology).
Claim 1 recites additional elements such as “at least one memory,” and “at least one processor”. These elements are generic computer components. The claim further recites the steps that result in additional data (e.g., insight, metadata), rather than a technical effect. The claim does not recite any improvement to computer performance, data structures, or a specific technological process. Instead, the claim merely uses a computer as a tool to perform the abstract idea, rather than improving the computer itself. Therefore, the claim does not provide meaningful integration into a practical application and fails to meet step 2A, prong 2.
Step 2B: significantly more or amounting to an incentive concept. (See MPEP§2106.05).
Claim 1 recites additional elements such as generic hardware performing routine data processing, and does not include any unconventional techniques (e.g., specific algorithm, architecture, or transformation). These are well-understood, routine, and conventional activities, and are described functionally rather than structurally. Thus, the additional elements amounts to no more than mere instructions to apply the judicial exception and do not integrate a judicial exception into a practical application or provide an inventive concept. Accordingly, the claim fails under step 2B because the mere implementation on a computer does not provide significantly more.
Dependent claim 2 recites “generate text data obtained by summarizing the data, generate, based on the text data, the metadata which includes at least one of a tag regarding the data, is an analysis type regarding the data, and/or a highlight regarding the data” abstract idea under step 2A(ii). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 3 recites “input, into a language model, as a prompt, text data which includes an outline of the data and instructions to generate a predetermined number of queries in accordance with the outline, and acquire, as the analytic queries, the predetermined number of queries output by the language model, wherein the language model is trained through machine learning to take, as input, the prompt and output an answer to the prompt” abstract idea under step 2A(ii). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 4 recites “extract some columns or rows of the table based on a degree of relevance between the analytic query and each column or row of the table and generate the insight based on the extracted columns or rows” abstract idea under step 2A(i). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 5 recites “acquire, as the insight, at least one of a chart output by a chart recommendation model based on the data and the analytic query and/or text data obtained by verbalizing the chart” abstract idea under step 2A(i). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 6 recites “acquire, as the insight, text data output by a question answering model based on data and the analytic query” abstract idea under step 2A(i). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 7 recites “wherein the data is target data of search registered in a data catalog, and wherein the at least one processor is configured to execute the instructions to update the data catalog based on the metadata” abstract idea under step 2A(i). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 8 recites “cause a display device to display the metadata together with the data” abstract idea under step 2A(ii). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Claims 9 and 10 are rejected due to the similar analysis of claim 1. Therefore, claims 1-10 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.
Claims 1-10 are rejected under 35 U.S.C. 103 as being unpatentable over Shachaf et al. (US 20250225127 A1) in view of Panuganty et al. (US 20200210647 A1).
Regarding claim 1, Shachaf discloses data analysis device (Fig.1-2, Shachaf) comprising:
at least one memory (memory 120, Fig.1, Shachaf) configured to store instructions, and
at least one processor (processor 125, Fig.1, Shachaf)configured to execute the instructions to:
generate, from data (data of step 208 of Fig. 2), an analytic query for analyzing the data (abstract; step 208 of Fig. 2, and ¶[0038] Shachaf);
generate an insight of the data based on the data and the analytic query (steps 202 and 210 of Fig. 2 and ¶[0042]-[0044], Shachaf, i.e., enter a free-text insight request into an appropriate user interface (UI) such as for example a text box in an analytics portal to generate insight data based on the data and the charts’ metadata “analytic”); and
generate metadata of the data based on the insight (step 210 of Fig. 2 and ¶[0046]-[0048], Shachaf, i.e., generate, by a GenAI or LLM component, chart metadata or metadata items based on the wrapped query).
To clarify the language of “generate an insight of the data based on the data and the analytic query,” although as stated above the Shachaf discloses the feature of generate an insight of the data based on the data and the analytic query (steps 202 and 210 of Fig. 2 and ¶[0042]-[0044], Shachaf).
However, Panuganty discloses generate an insight of the data based on the data and the analytic query (¶[0068] and [0083], Panuganty).
It would have been obvious to a person having ordinary skill in the art, before the effective filing date, to utilize the analytic system of Panuganty to generate insights from data based on an analytic query (¶[0083], Panuganty), in view of Shachaf and Panuganty. One of ordinary skill in the art would be motivated to integrate data analysis into Shachaf, with a reasonable expectation of success, in order to enhance the content output and improve capabilities related to interaction with the generative model.
Regarding claim 2, Shachaf/Panuganty combination discloses wherein the at least one processor is configured to execute the instructions to generate, as the insight, text data obtained by summarizing the data (¶[0070] and [0074], Panuganty), generate, based on the text data, the metadata which includes at least one of a tag regarding the data (¶[0078], [0138] and [0189], Panuganty), is an analysis type regarding the data, and/or a highlight regarding the data (¶[0078], [0138], [0189] and [0255], Panuganty).
Regarding claim 3, Shachaf/Panuganty combination discloses wherein the at least one processor is configured to execute the instructions to input, into a language model, as a prompt, text data which includes an outline of the data (¶[0077]-[0078], and [0084], Panuganty) and instructions to generate a predetermined number of queries in accordance with the outline (¶[0078]-[0079], Shachaf), and acquire, as the analytic queries, the predetermined number of queries output by the language model (¶[0078]-[0079], Shachaf), wherein the language model is trained through machine learning to take, as input, the prompt and output an answer to the prompt (¶[0078]-[0079], Shachaf).
Regarding claim 4, Shachaf/Panuganty combination discloses wherein the data is a table, and wherein the at least one processor is configured to execute the instructions to extract some columns or rows of the table based on a degree of relevance between the analytic query (¶[0048] and [0082], Shachaf) and each column or row of the table and generate the insight based on the extracted columns or rows (¶[0048], [0082] and [0100], Shachaf).
Regarding claim 5, Shachaf/Panuganty combination discloses wherein the at least one processor is configured to execute the instructions to acquire, as the insight, at least one of a chart output by a chart recommendation model based on the data and the analytic query and/or text data obtained by verbalizing the chart (¶[0061] and [0078], Shachaf).
Regarding claim 6, Shachaf/Panuganty combination discloses wherein the at least one processor is configured to execute the instructions to acquire, as the insight, text data output by a question answering model based on data and the analytic query (¶[0100], Shachaf).
Regarding claim 7, Shachaf/Panuganty combination discloses wherein the data is target data of search registered in a data catalog, and wherein the at least one processor is configured to execute the instructions to update the data catalog based on the metadata (¶[0080], and [0084]-[0085], Panuganty).
Regarding claim 8, Shachaf/Panuganty combination discloses wherein the at least one processor is configured to execute the instructions to cause a display device to display the metadata together with the data (¶[0044] and [0046], Shachaf).
Regarding claim 9, Shachaf discloses data analysis method executed by a computer (computer of Fig.1-2, Shachaf), comprising:
generating, from data (data of step 208 of Fig. 2), an analytic query for analyzing the data (abstract; step 208 of Fig. 2, and ¶[0038] Shachaf);
generating an insight of the data based on the data and the analytic query (steps 202 and 210 of Fig. 2 and ¶[0042]-[0044], Shachaf, i.e., enter a free-text insight request into an appropriate user interface (UI) such as for example a text box in an analytics portal to generate insight data based on the data and the charts’ metadata “analytic”); and
generating metadata of the data based on the insight (step 210 of Fig. 2 and ¶[0046]-[0048], Shachaf, i.e., generate, by a GenAI or LLM component, chart metadata or metadata items based on the wrapped query).
To clarify the language of “generating an insight of the data based on the data and the analytic query,” although as stated above the Shachaf discloses the feature of generate an insight of the data based on the data and the analytic query (steps 202 and 210 of Fig. 2 and ¶[0042]-[0044], Shachaf).
However, Panuganty discloses generate an insight of the data based on the data and the analytic query (¶[0068] and [0083], Panuganty).
It would have been obvious to a person having ordinary skill in the art, before the effective filing date, to utilize the analytic system of Panuganty to generate insights from data based on an analytic query (¶[0083], Panuganty), in view of Shachaf and Panuganty. One of ordinary skill in the art would be motivated to integrate data analysis into Shachaf, with a reasonable expectation of success, in order to enhance the content output and improve capabilities related to interaction with the generative model.
Regarding claim 10, Shachaf discloses a non-transitory computer readable storage medium storing a program executed by a computer (Fig.1-2, Shachaf) , the program causing the computer to:
generate, from data (data of step 208 of Fig. 2), an analytic query for analyzing the data (abstract; step 208 of Fig. 2, and ¶[0038] Shachaf);
generate an insight of the data based on the data and the analytic query (steps 202 and 210 of Fig. 2 and ¶[0042]-[0044], Shachaf, i.e., enter a free-text insight request into an appropriate user interface (UI) such as for example a text box in an analytics portal to generate insight data based on the data and the charts’ metadata “analytic”); and
generate metadata of the data based on the insight (step 210 of Fig. 2 and ¶[0046]-[0048], Shachaf, i.e., generate, by a GenAI or LLM component, chart metadata or metadata items based on the wrapped query).
To clarify the language of “generate an insight of the data based on the data and the analytic query,” although as stated above the Shachaf discloses the feature of generate an insight of the data based on the data and the analytic query (steps 202 and 210 of Fig. 2 and ¶[0042]-[0044], Shachaf).
However, Panuganty discloses generate an insight of the data based on the data and the analytic query (¶[0068] and [0083], Panuganty).
It would have been obvious to a person having ordinary skill in the art, before the effective filing date, to utilize the analytic system of Panuganty to generate insights from data based on an analytic query (¶[0083], Panuganty), in view of Shachaf and Panuganty. One of ordinary skill in the art would be motivated to integrate data analysis into Shachaf, with a reasonable expectation of success, in order to enhance the content output and improve capabilities related to interaction with the generative model.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Manivannan et al. (US 20250378106 A1) disclose generating insights for large datasets using prompt generation processes and generative artificial intelligence (AI) models.
Bandugula et al. (US 20250307277 A1) disclose real-time and diagnostic omnichannel interaction insights, actions, and management using.
Lee et al. (US 20240386040 A1) disclose building management system with building equipment service and parts recommendations.
Smaagard et al. (US 20230205794 A1) disclose generating search insight data.
Neckermann et al. (US 11182748 B1) disclose augmented data insight generation and provision.
Panuganty et al. (US 20200034357 A1) disclose modifying a scope of a canonical query.
Asplund et al. (US 20200034481 A1) disclose language agnostic data insight handling for user application data.
Kohlmeier et al. (US 20150100562 A1) disclose contextual insights and exploration.
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/HANH B THAI/Primary Examiner, Art Unit 2163
May 1, 2026
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