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 .
The action is responsive to Applicant’s amendment filed on 1/16/2026.
Claims 1, 3-12, 14-21 are pending.
Claims 2, 13 are cancelled.
Response to Arguments
Applicant’s arguments with respect to the rejections previously made and the amended claims filed on 1/16/2026 have been fully considered. In view of the amendment filed, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made.
35 USC 102 Rejections
Applicant’s arguments on cited reference Zhang fails teach amended limitations of “receiving, from a user terminal, a first user input indicating a selection of an analysis history displayed on a user interface and a second user input that comprises the natural language query requesting the data analysis" as recited in the independent claims 1 and 11-12 have been fully considered.
In response to the arguments, it is submitted that in view of the amendment, the rejection has been withdrawn, and upon further consideration, a new ground(s) of rejection is made to properly address the amended claims; see rejection below for detail.
Applicant also argued that Zhang fail to teach “determining, by using a similarity determination model, a degree of similarity between at least one natural language query related to the selected analysis history and the received natural language query and metadata of each of the plurality of databases," as recited in claims 3 and 14.
In response to the arguments, it is submitted that the cited limitation may at least be interpreted as determine a degree of similarity between (i) at least one natural language query that is related to the selected analysis history and the received natural language query, and (ii) metadata of each of the plurality of databases.
In view of that interpretation, the at least one natural language query may be any natural language query, including a natural language query of a recently cached prompt that was successfully processed ([0031], [0039]), which is related to the user input indicating a selected analysis history and the received natural language query since all data elements as well as components of a same system are related either directly or indirectly.
Also, the metadata of each of the plurality of databases may be any data describing each database, including but not limited to data describing database data tables, table fields, query processing data for each prompt of each database, data of database AI agent, previously processed prompts with respective natural language queries and query intent for each database ([0034], [0039-0040]). A degree of similarity between a natural language query and metadata (e.g. tables, agent, related cached prompt) of each database is determined via at least semantic matching in order to select one or more AI agent on performing analysis involving a database, such that a database correspond to the target database is being determined via respective AI agent selection ([0039-0040], [0069-0073]).
Thus, for at least the reasonings stated above, limitations of claims 3 and 14 are properly addressed; see rejection below for detail.
Applicant further argued that cited art fails to disclose the newly added claim 21. In response to the arguments, it is submitted that claim 21 is properly addressed; see rejection below for detail.
Furthermore, it is submitted that all limitations in claims--including those not specifically addressed in the Applicant’s remarks--are properly addressed. The reason is set forth in the rejections; see below for detail.
Claim Objections
Claim 8 is objected to because of the following informalities: depending on a cancelled claim 2, which could cause confusion. Appropriate correction is required.
Claim 12 is objected to because of the following informalities: a structured query (SQL). It appears that the claimed structured query (SQL) is meant to be “a structured query language (SQL)” as indicated in the amended independent claims 1 & 11. The differences for such element could cause confusion. Appropriate correction is required.
Claim 19 is objected to because of the following informalities: depending on a cancelled claim 13, which could cause confusion. Appropriate correction is required.
For the purpose of examination and prosecution, it is assumed that claims 8 and 19 depend on claims 2 and 13 respectively.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3-12, 14-21 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al (Pub No. US 2025/0190449 hereinafter Zhang) in view of Telling et al (Pub No. US 2025/0094439, hereinafter Telling).
Zhang is cited in the previous office action.
With respect to claim 1, Zhang discloses a method of performing data analysis according to a natural language query from a user (abstract), the method comprising:
receiving, from a user terminal, a user input indicating a selection of an analysis history displayed on a user interface and comprises the natural language query requesting the data analysis ([0004], [0031], Fig 1 & 12: receive a user input represented by a prompt—e.g. prompt 170-- indicates a selection of analysis history display on a graphical user interface 174. The user input includes but not limited to indication of user preference for an analysis history as further described in [0044], and a natural language query represented by a natural language question requesting the data analysis via question submission);
determining, based on the user input, at least one database among a plurality of databases as a target database ([0004], [0033-0035], [0049], [0071], Fig 2-4: determine at least one database represented by at least one structured data set as a target database/data set among multiple databases/datasets, as each data set is corresponded to a database);
generating a prompt based on the user input and the target database ([0004],[0008], [0035], [0038], [0049], [0071], Fig 5-7: generate a prompt represented by a modified prompt based on user input prompt and the target data set/database according to at least structure/field of data set analysis);
inputting the prompt into a code generation model to obtain a structured query language (SQL) statement ([0040], [0053], [0055], [0077-0078], Fig 7: input the prompt into a code generation model such as and not limited to a SQL agent to obtain a SQL statement as the SQL agent processes received modified prompt via performing text to SQL query);
executing the SQL statement to generate a result of the data analysis on the target database ([0078], [0090], [0093-0094], Fig 7 & 11-12: execute the SQL statement to generate result of the data analysis as the SQL performs SQL over the SQL database/dataset to obtain response correspond to the result); and
transmitting the result of the data analysis to the user terminal, wherein the result of the data analysis is displayed on a screen of the user terminal ([0005], [0045], [0090], [0093-0094] Fig 2 & 10 & 11-12: transmitting the result of the data analysis as the obtained response is being displayed to the user via screen of a GUI, and further allowing the user to provide feedback accordingly).
Zhang does not explicitly disclose that the user input is directed to a first user input and a second user input; the at least one database determination is based on the first user input and second user input; and the prompt is generated based on the first user input, second user input and the target database as claimed.
However, Telling discloses receiving, from a user terminal, a first user input indicating a selection of an analysis history displayed on a user interface and a second user input that comprises the natural language query requesting the data analysis ([0095-0096], [0107], [0140], Fig 8: receive a first user input indicating a user selection of data set corresponding an analysis history displayed on a user interface since analysis is directed to a type of data, and receive a second input of a natural language query request data analysis via a LLM as further described in [0054] & [0058]);
determining, based on the first user input and the second user input, at least one database among a plurality of databases as a target database ([0096-0097], [0141-0142], Fig 8: determine at least a database represented by a dataset among the datasets as target database, which is correspond to the dataset, as further described in [0051], [0064]);
generating a prompt based on the first user input, the second user input, and the target database ([0096], [00140], Fig 8: generate a prompted based on the 1st & 2nd user inputs and the target database represented by at least the dataset via indication);
Since both Zhang and Telling are from the same field of because both directed to performing data analysis according to natural language query from a user, which is in the same field of endeavor as the claimed invention, it would have been obvious to one skilled in the art at the time of the invention to combine their teachings by incorporate the first input indicating a user selection and a second input comprises a user natural language query user and of Telling into Zhang for analysis result generation as claimed. The motivation to combine is to optimize user request processing with intelligently perform analytic tasks while minimize effort required by human users (]; Zhang, [0001]; Telling, [0005).
With respect to claim 12, Zhang discloses an electronic device for performing data analysis according to a natural language query from a user (Abstract, Fig 1), the electronic device comprising:
memory storing one or more instructions ([0111], Fig 1); and
at least one processor operatively coupled to the memory, wherein the one or more instructions, when executed by the at least one processor ([0109-0111], Fig 1), cause the electronic device to:
receive, from a user terminal, a user input indicating a selection of an analysis history displayed on a user interface and comprises the natural language query requesting the data analysis ([0004], [0031], Fig 1 & 12: receive a user input represented by a prompt—e.g. prompt 170-- indicates a selection of analysis history display on a graphical user interface 174. The user input includes but not limited to indication of user preference for an analysis history as further described in [0044], and a natural language query represented by a natural language question requesting the data analysis via question submission),
determine, based on the user input, at least one database among a plurality of databases as a target database ([0004], [0033-0035], [0049], [0071], Fig 2-4: determine at least one database represented by at least one structured data set as a target database/data set among multiple databases/datasets, as each data set is corresponded to a database),
generate a prompt based on the user input and the target database ([0004],[0008], [0035], [0038], [0049], [0071], Fig 5-7: generate a prompt represented by a modified prompt based on user input prompt and the target data set/database according to at least structure/field of data set analysis),
input the prompt into a code generation model to obtain a structured query (SQL) statement ([0040], [0053], [0055], [0077-0078], Fig 7: input the prompt into a code generation model such as and not limited to a SQL agent to obtain a SQL statement as the SQL agent processes received modified prompt via performing text to SQL query),
execute the SQL statement to generate a result of the data analysis on the target database ([0078], [0090], [0093-0094], Fig 7 & 11-12: execute the SQL statement to generate result of the data analysis as the SQL performs SQL over the SQL database/dataset to obtain response correspond to the result); and
transmit the result of the data analysis to the user terminal, wherein the result of the data analysis is displayed on a screen of the user terminal ([0005], [0045], [0090], [0093-0094] Fig 2 & 10 & 11-12: transmitting the result of the data analysis as the obtained response is being displayed to the user via screen of a GUI, and further allowing the user to provide feedback accordingly).
Zhang does not explicitly disclose that the user input is directed to a first user input and a second user input; the at least one database determination is based on the first user input and second user input; and the prompt is generated based on the first user input, second user input and the target database as claimed.
However, Telling discloses receive, from a user terminal, a first user input indicating a selection of an analysis history displayed on a user interface and a second user input that comprises the natural language query requesting the data analysis ([0095-0096], [0107], [0140], Fig 8: receive a first user input indicating a user selection of data set corresponding an analysis history displayed on a user interface since analysis is directed to a type of data, and receive a second input of a natural language query request data analysis via a LLM as further described in [0054] & [0058]);
determine, based on the first user input and the second user input, at least one database among a plurality of databases as a target database ([0096-0097], [0141-0142], Fig 8: determine at least a database represented by a dataset among the datasets as target database, which is correspond to the dataset, as further described in [0051], [0064]);
generate a prompt based on the first user input, the second user input, and the target database ([0096], [00140], Fig 8: generate a prompted based on the 1st & 2nd user inputs and the target database represented by at least the dataset via indication);
Since both Zhang and Telling are from the same field of because both directed to performing data analysis according to natural language query from a user, which is in the same field of endeavor as the claimed invention, it would have been obvious to one skilled in the art at the time of the invention to combine their teachings by incorporate the first input indicating a user selection and a second input comprises a user natural language query user and of Telling into Zhang for analysis result generation as claimed. The motivation to combine is to optimize user request processing with intelligently perform analytic tasks while minimize effort required by human users (]; Zhang, [0001]; Telling, [0005).
With respect to claims 3 and 14, the combined teachings of Zhang and Telling further discloses wherein the determining of the at least one database as the target database comprises:
determining, by using a similarity determination model, a degree of similarity between at least one natural language query related to the selected analysis history and the received natural language query and metadata of each of the plurality of databases (Zhang, [0034], [0039-0040], [0051], [0069-0070], [0080-0081]: determine a degree of similarity between the natural language query related to selected history, receive and the data set attribute/metadata as the received prompt is being optimized and at least one agent with respect to historical information); and
determining, based on a result of the determining of the degree of similarity, at least one of the plurality of databases as the target database (Zhang, [0071-0073], [0081]: determine at least one data set as the target based on the result of similarity determination via AI agent that accesses the target data set via score with respect to the similarity).
With respect to claims 4 and 15, the combined teachings of Zhang and Telling further discloses wherein the metadata comprises a table catalog and a table schema, the table catalog comprises a description of a table included in a database corresponding to the metadata and a description of each column in the table, and the table schema defines a structure and rules of the table included in the database corresponding to the metadata (the limitations are directed to non-functional descriptive materials that are not necessary being used to impact the functionalities of the claimed steps, and all claimed steps would be performed the same regardless; Zhang, [0035], [0041], [0049], [0059]; Telling, [0051-0052], [0071], [0090]: the metadata comprises table catalog and table schema represented by the schema descriptions having fields of tables that define the structure of the table of the database with respect rules).
With respect to claims 5 and 16, the combined teachings of Zhang and Telling further discloses wherein the prompt comprises an analysis history-related portion and a current natural language query-related portion (Zhang, [0004], [0035], [0038-0039], [0048-0049], [0056], [0071], Fig 5-7; Telling, [0082], [0096], [0140]: the modified prompt comprises different types of data included history related and query relation), and
the generating of the prompt comprises:
generating the analysis history-related portion by using metadata of a database related to the selected analysis history, a natural language query related to the selected analysis history, and an SQL statement related to the selected analysis history (Zhang, [0004],[0008], [0035], [0038], [0041], [0049], [0071], Fig 5-7; Telling, [0067], [0095-0096], [0140]: generate the history related portion by using data set schema description, and past queries, such as SQL with AI SQL agent, via prompt optimization) :; and
generating the current natural language query-related portion by using metadata of the target database and the received natural language query (Zhang, [0004],[0008], [0035], [0038], [0041], [0049], [0071], Fig 5-7: Telling, [0067], [0095-0096], [0140]: generate the current natural language portion using metadata represented by the field of data set and receive query via prompt optimization and AI agent selection).
With respect to claims 6 and 17, the combined teachings of Zhang and Telling further discloses wherein the database related to the selected analysis history is a database used in performing at least one analysis task included in the selected analysis history (the limitation is directed to non-functional descriptive material that is not necessary being used to impact the functionalities of the claimed steps, and all claimed steps would be performed the same regardless; Zhang,[0034], [0039], [0051], [0069-0070], [0080-0081]; Telling, [0051-0053], [0095-0096]: the dataset is related to the select analysis history via learning and similarity determination with respect to historical performance),
the natural language query related to the selected analysis history is a natural language query input in performing the at least one analysis task included in the selected analysis history (the limitation is directed to non-functional descriptive material that is not necessary being used to impact the functionalities of the claimed steps; every element in the system is related to others element in the same system/method either directly or indirectly, and all claimed steps would be performed the same regardless; Zhang,[0004],[0008], [0035], [0038], [0041], [0049], [0071], Fig 5- 7; Telling, [0095-0096], [0140]; Fig 8: the natural query is a query input in performing the analysis via prompt optimization and/or AI agent assignment), and
the SQL statement related to the selected analysis history is an SQL statement generated by the code generation model in performing the at least one analysis task included in the selected analysis history (the limitation is directed to non-functional descriptive material that is not necessary being used to impact the functionalities of the claimed steps; every element in the system is related to others element in the same system/method either directly or indirectly, and all claimed steps would be performed the same regardless; Zhang, [0004],[0008], [0035], [0038], [0040-0041], [0049], [0053], [0055], [0071], [0077-0078], Fig 5- 7; Telling, [0051-0053], [0067], [0095-0096]: the SQL is related to the analysis history and is a SQL generated by the code generated model represented by the selected AI SQL agent that is being selected with respect to history analysis).
With respect to claims 7 and 18, the combined teachings of Zhang and Telling further discloses wherein each of the analysis history-related portion and the current natural language query-related portion comprises an instruction to generate an SQL statement corresponding to a natural language query by referring to metadata of at least one database from the plurality of databases (the limitation is directed to non-functional descriptive material that is not necessary being used to impact the functionalities of the claimed steps and all claimed steps would be performed the same regardless; Zhang, [0004],[0008], [0035], [0038], [0040-0041], [0049], [0053], [0055], [0071], [0077-0078], Fig 5- 7; Telling, [0051-0053], [0095-0096]: the history related and the current query portion represented by the modified prompt include instruction to generate SQL statement as the modified prompt is being assigned to an AI SQL agent that access the dataset by referring to the metadata represented by the fields).
With respect to claims 8 and 19, the combined teachings of Zhang and Telling further discloses wherein the analysis history comprises at least one analysis task previously performed on at least one database among the plurality of databases (the limitation is directed to non-functional descriptive material that is not necessary being used to impact the functionalities of the claimed steps and all claimed steps would be performed the same regardless; Zhang, [0004],[0008], [0035], [0038], [0040-0041], [0049], [0053], [0055], [0071], [0077-0078], Fig 5- 7; Telling, [0067], [0095-0096] : the analysis history include analysis of past queries performance and/or performance of the dataset via prompt optimization and AI agent assignment with a learning process).
With respect to claims 9 and 20, the combined teachings of Zhang and Telling further discloses wherein the outputting of the result of the data analysis comprises
generating a prompt for visualizing the result of the data analysis by using the received natural language query and the generated SQL statement (Zhang, [0043-0045], [0083], [0090], [0093-0094]; Telling, [0096], [0140], [0143], Fig 8: generate a prompt represented by instructions for visualizing/presenting the result using the query obtained from the SQL statement execution via AI SQL agent);
obtaining a visualization code by inputting the prompt for visualizing the result of the data analysis to the code generation model (Zhang, [0043-0045], [0083], [0090], [0093-0094]; Telling, [0096], [0140], [0143], Fig 8: input the prompt for visualization as the result and visualization are being processed via AI); and
visualizing and outputting the result of the data analysis as at least one of a graph or a chart by executing the visualization code (Zhang, [0043-0045], [0083], [0090], [0093-0094]; Telling, [0140], [0143], Fig 8: visualizing and outputting the result in a particular format by executing the code/instruction).
With respect to claim 10 the combined teachings of Zhang and Telling further discloses wherein the prompt for visualizing the result of the data analysis comprises an instruction to generate the visualization code corresponding to the received natural language query by referring to the generated SQL statement (Zhang, [0043-0045], [0083], [0090], [0093-0094]; Telling, [0096], [0140], [0143], Fig 8:: configure to generate the visualization/outputting instruction/code via referring the SQL statement as the visualization agent generate a graph or other visualization output based on the SQL execution).
With respect to claim 11, Zhang discloses a non-transitory computer-readable storage medium having instructions stored therein, which when executed by a processor (abstract, [0109-0111], Fig 1), cause the processor to execute a method comprising:
receiving, from a user terminal, a user input indicating a selection of an analysis history displayed on a user interface and comprises the natural language query requesting the data analysis ([0004], [0031], Fig 1 & 12: receive a user input represented by a prompt—e.g. prompt 170-- indicates a selection of analysis history display on a graphical user interface 174. The user input includes but not limited to indication of user preference for an analysis history as further described in [0044], and a natural language query represented by a natural language question requesting the data analysis via question submission);
determining, based on the user input, at least one database among a plurality of databases as a target database ([0004], [0033-0035], [0049], [0071], Fig 2-4: determine at least one database represented by at least one structured data set as a target database/data set among multiple databases/datasets, as each data set is corresponded to a database);
generating a prompt based on the user input and the target database ([0004],[0008], [0035], [0038], [0049], [0071], Fig 5-7: generate a prompt represented by a modified prompt based on user input prompt and the target data set/database according to at least structure/field of data set analysis);
inputting the prompt into a code generation model to obtain a structured query language (SQL) statement ([0040], [0053], [0055], [0077-0078], Fig 7: input the prompt into a code generation model such as and not limited to a SQL agent to obtain a SQL statement as the SQL agent processes received modified prompt via performing text to SQL query);
executing the SQL statement to generate a result of the data analysis on the target database ([0078], [0090], [0093-0094], Fig 7 & 11-12: execute the SQL statement to generate result of the data analysis as the SQL performs SQL over the SQL database/dataset to obtain response correspond to the result); and
transmitting the result of the data analysis to the user terminal, wherein the result of the data analysis is displayed on a screen of the user terminal ([0005], [0045], [0090], [0093-0094] Fig 2 & 10 & 11-12: transmitting the result of the data analysis as the obtained response is being displayed to the user via screen of a GUI, and further allowing the user to provide feedback accordingly).
Zhang does not explicitly disclose that the user input is directed to a first user input and a second user input; the at least one database determination is based on the first user input and second user input; and the prompt is generated based on the first user input, second user input and the target database as claimed.
However, Telling discloses receiving, from a user terminal, a first user input indicating a selection of an analysis history displayed on a user interface and a second user input that comprises the natural language query requesting the data analysis ([0095-0096], [0107], [0140], Fig 8: receive a first user input indicating a user selection of data set corresponding an analysis history displayed on a user interface since analysis is directed to a type of data, and receive a second input of a natural language query request data analysis via a LLM as further described in [0054] & [0058]);
determining, based on the first user input and the second user input, at least one database among a plurality of databases as a target database ([0096-0097], [0141-0142], Fig 8: determine at least a database represented by a dataset among the datasets as target database, which is correspond to the dataset, as further described in [0051], [0064]);
generating a prompt based on the first user input, the second user input, and the target database ([0096], [00140], Fig 8: generate a prompted based on the 1st & 2nd user inputs and the target database represented by at least the dataset via indication);
Since both Zhang and Telling are from the same field of because both directed to performing data analysis according to natural language query from a user, which is in the same field of endeavor as the claimed invention, it would have been obvious to one skilled in the art at the time of the invention to combine their teachings by incorporate the first input indicating a user selection and a second input comprises a user natural language query user and of Telling into Zhang for analysis result generation as claimed. The motivation to combine is to optimize user request processing with intelligently perform analytic tasks while minimize effort required by human users (]; Zhang, [0001]; Telling, [0005).
With respect to claim 21, the combined teachings of Zhang and Telling further discloses
wherein the prompt includes the natural language query, data corresponding to the selected analysis history, and metadata of the target database (the limitation is directed to non-functional descriptive material that is not necessary being used to impact the functionalities of the claimed steps, and all claimed steps would be performed the same regardless; Zhang, [0004], [0035], [0038-0039], [0048-0049], [0056], [0071], Fig 5-7; Telling, [0096], [0140]: the prompt includes different type of data that correspond to the query, data and metadata represented by the features).
Conclusion
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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/MICHELLE N OWYANG/Primary Examiner, Art Unit 2168