DETAILED ACTION
This is Final Office Action in response to amendment filed on August 7, 2026. Claim 3 has been canceled. Claim 11 is newly added. Claims 1-2 and 4-11 are pending.
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive. Applicant argues that the claimed features “inputting, into a language model, as a prompt, first text data which comprises an outline of the data and instructions to generate a predetermined number of queries,” and “acquiring …the predetermined number of queries output by the language model ,” including “generating the insight based on a degree of similarity in vector embeddings between the analytic query and serial data in which elements of a column or a row of the data are connected in series” cannot be perform by human mind and represent an improvement in a technical field (response 8/7/2026, pages 1-3). The examiner respectfully disagrees. The recitations “inputting… first text data which comprises an outline of the data and instructions to generate a predetermined number of queries” and “acquiring …the predetermined number of queries output by the language model” amount to abstract idea because these operations involve collecting, analyzing, evaluating, and generating information. Even though these operations are performed using generic computer component, “a processor” (see MPEP 2106.05(f)), and involve a language model, the underlying activities remain directed to obtaining, analyzing, organizing, and generating information. Furthermore, the claim recitation of “generating the insight based on a degree of similarity in vector embeddings between the analytic query and serial data in which elements of a column or a row of the data are connected in series” adds mere insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)). The claim does not recite a particular technological mechanism for performing those operations (see MPEP 2106.04(d)) and fail to recite any unconventional steps per MPEP 2106.05(a). Merely apply the abstract information processing activities using a generic computer and language model is not enough to integrate the exception into a practical application. Therefore, the rejection under 35 USC § 101 is maintained.
Applicant argues regarding the amended limitations that are add to independent claims 1, 9 and 10 “generate an analytic query for analyzing the data, wherein generating the analytic query comprises inputting, into a language model, as a prompt, first text data which comprises an outline of the data and instructions to generate a predetermined number of queries in accordance with the outline; and acquiring, as the analytic query, the predetermined number of queries output by the language model” have been considered but are moot the new ground of rejections.
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 1-2 and 4-11 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 amount 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 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.
Dependent claim 11 recites “generating the insight based on a degree of similarity in vector embeddings between the analytic query and serial data in which elements of a column or a row of the data are connected in series” adds mere insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
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 1-2 and 4-11 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) and further in view of Hamid et al. (US 20250217371 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.
The modified Shachaf/Panuganty, however, does not explicitly disclose the amended limitations “wherein generating the analytic query comprises inputting, into a language model, as a prompt, first text data which comprises an outline of the data and instructions to generate a predetermined number of queries in accordance with the outline; and acquiring, as the analytic query, the predetermined number of queries output by the language model.”
Hamid discloses inputting, into a language model (¶[0052], Hamid), as a prompt, first text data which comprises an outline of the data and instructions to generate a predetermined number of queries in accordance with the outline (¶[0052]-[0054] and [0240], Hamid); and acquiring, as the analytic query, 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 (¶[0052]-[0054] and [0240], Hamid).
It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to utilize the generated query as input prompt for Hamid (¶[0052] and [0240], Hamid) and to generate output answer to the prompt, in view of modified Shachaf/Panuganty reference. One of ordinary skill in the art would be motivated to integrate data analysis into the modified Shachaf with a reasonable expectation of success, in order to enhance the content output and improve capabilities related to interaction with the generative content service with multi-path content pipeline.
Regarding claim 2, Shachaf/Panuganty/Hamid combination discloses wherein the at least one processor is configured to execute the instructions to generate, as the insight, second text data obtained by summarizing the data (¶[0070] and [0074], Panuganty), generate, based on the second text data, the metadata which comprises 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 4, Shachaf/Panuganty/Hamid 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/Hamid 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 second text data obtained by verbalizing the chart (¶[0061] and [0078], Shachaf).
Regarding claim 6, Shachaf/Panuganty/Hamid combination discloses wherein the at least one processor is configured to execute the instructions to acquire, as the insight, second text data output by a question answering model based on data and the analytic query (¶[0100], Shachaf).
Regarding claim 7, Shachaf/Panuganty/Hamid 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/Hamid 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.
The modified Shachaf/Panuganty, however, does not explicitly disclose the amended limitations “wherein generating the analytic query comprises inputting, into a language model, as a prompt, first text data which comprises an outline of the data and instructions to generate a predetermined number of queries in accordance with the outline; and acquiring, as the analytic query, the predetermined number of queries output by the language model.”
Hamid discloses inputting, into a language model (¶[0052], Hamid), as a prompt, first text data which comprises an outline of the data and instructions to generate a predetermined number of queries in accordance with the outline (¶[0052]-[0054] and [0240], Hamid); and acquiring, as the analytic query, 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 (¶[0052]-[0054] and [0240], Hamid).
It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to utilize the generated query as input prompt for Hamid (¶[0052] and [0240], Hamid) and to generate output answer to the prompt, in view of modified Shachaf/Panuganty reference. One of ordinary skill in the art would be motivated to integrate data analysis into the modified Shachaf with a reasonable expectation of success, in order to enhance the content output and improve capabilities related to interaction with the generative content service with multi-path content pipeline.
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.
The modified Shachaf/Panuganty, however, does not explicitly disclose the amended limitations “wherein generating the analytic query comprises inputting, into a language model, as a prompt, first text data which comprises an outline of the data and instructions to generate a predetermined number of queries in accordance with the outline; and acquiring, as the analytic query, the predetermined number of queries output by the language model.”
Hamid discloses inputting, into a language model (¶[0052], Hamid), as a prompt, first text data which comprises an outline of the data and instructions to generate a predetermined number of queries in accordance with the outline (¶[0052]-[0054] and [0240], Hamid); and acquiring, as the analytic query, 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 (¶[0052]-[0054] and [0240], Hamid).
It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to utilize the generated query as input prompt for Hamid (¶[0052] and [0240], Hamid) and to generate output answer to the prompt, in view of modified Shachaf/Panuganty reference. One of ordinary skill in the art would be motivated to integrate data analysis into the modified Shachaf with a reasonable expectation of success, in order to enhance the content output and improve capabilities related to interaction with the generative content service with multi-path content pipeline.
Regarding claim 11, Shachaf/Panuganty/Hamid combination discloses wherein the insight is generated based on a degree of similarity in vector embeddings between the analytic query and serial data in which elements of a column or a row of the data are connected in series (¶[0238]-[0240], Hamid).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wilson et al. (US 20260003481 A1) disclose issue tracking platform having bulk action operations using a generative output engine.
Huang (US 12259895 B1) disclose behavior driven query similarity prediction based on language model for database search.
TCA et al. (US 20250077582 A1) disclose proactive determination of data insights.
Mansour et al. (US 20250005528 A1) disclose automated content creation and content services for collaboration platforms.
Chen et al. (US 20240403313 A1) disclose generating data insights.
Sadasivam et al. (US 20240004879 A1) disclose dynamically substituting a modified query based on performance analysis.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/HANH B THAI/Primary Examiner, Art Unit 2163
October 5, 2026
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