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 .
Response to Amendment
This Office action is in response to Applicant's amendment filed on 1/27/2026.
Claim 1-21 are pending. Claim 1, 4, 9, 14, 16 and 19 are amended. Claim 21 is new. Claim 1-21 are rejected.
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.
Claim 1-3, 16-18 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Mehlman, Dan et al (PGPUB Document No. 20220365944), hereafter referred as to “Mehlman”, in view of Wen, Fang et al (PGPUB Document No. 20090254539 ), hereafter, referred to as “Wen”, in further view of Ginter, Jonathan et al (PGPUB Document No. 20140101178 ), hereafter, referred to as “Ginter”.
Claim 1(Currently Amended), Mehlman teaches A system for handling a user query, the system comprising: a processor; and a memory, coupled to the processor, configured to store executable instructions that, when executed by the processor(Mehlman, Fig. 7 and para 0068 discloses a system with processor, memory, storages etc. ), cause the processor to: set up and display a symbol or link in a user interface, the symbol or link representing a dataset in a cloud environment(Mehlman, para 0034 discloses setting up a dataset connection in the cloud from user interface “a user may use a user interface supported by the system to generate the connector 250. In some examples, the user may configure the connector to link the new database instance 235-b and the database instances that correspond to the cloud platform instances……”);
Using the broadest reasonable interpretation consistent with the specification (paragraph 0031) as it would be interpreted by one of ordinary skill in the art, examiner is interpreting the limitation “set up a symbol or link in a user interface, the symbol or link representing a dataset in a cloud environment” to mean establishing a connection data source in cloud.
But Mehlman does not explicitly teach wherein the user interface is a part of a drawing and art design application that includes a set of text tools and drawing tools for generating one or more text and non-text objects, and wherein the dataset representation is a persistent, selectable on-canvas object that anchors subsequent querying; receive a user query initiated by a user through the same user interface that displays the symbol or link representing the dataset in the cloud environment; parse the dataset to identify a data file associated with the user query; generate a sequence of queries based on the user query and content parsed from the data file; and search against the data file using the sequence of queries to obtain a set of query results.
However, in the same field of endeavor of querying contents Wen teaches wherein the user interface is a part of a drawing and art design application that includes a set of text tools and drawing tools for generating one or more text and non-text objects(Wen, para 0035 discloses querying interface consisting of drawing tools for providing search input “The intention refinement engine 238 includes a visual feature selection UI 250, which in one implementation receives graphic input such as drawing, pencil, stylus, or paintbrush strokes that specify the visual aspects and features in example images that the user intends to find in an image being retrieved. In other words, the user can designate features across one or more images that should be present in the image(s) that the user is searching for”), and wherein the dataset representation is a persistent, selectable on-canvas object that anchors subsequent querying(Wen, Fig 2-3 para 0042 disclose retrieved result is persistent (initial result) and further refinement inputs can be provided via feedback option “the relevant images designator 256 uses a collector, such as an “I like these” basket to enable the user to conduct relevance feedback naturally and effortlessly. As the user collects more subjectively “good” images to the basket via the feedback iterator 240, the image retrieval system 108 incrementally improves the retrieved image results 210” );
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of providing visual query inputs via tools on the UI of Wen into cloud-based query interface of Mehlman to produce an expected result of retrieving query results from the cloud. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the retrieved search results incrementally by utilizing user feedback(Wen, para 0042).
But Mehlman and Wen don’t explicitly teach receive a user query initiated by a user through the same user interface that displays the symbol or link representing the dataset in the cloud environment; parse the dataset to identify a data file associated with the user query; generate a sequence of queries based on the user query and content parsed from the data file; and search against the data file using the sequence of queries to obtain a set of query results.
However, in the same field of endeavor of querying data in files Ginter teaches receive a user query initiated by a user through the same user interface that displays the symbol or link representing the dataset in the cloud environment (Ginter, para 0007 discloses receiving a data query “a method may include receiving, from a user device, a data query request that includes one or more search parameters to be searched for within a plurality of files…..”; where prior art Mehlman in para 0034 discloses an interface to receive user input to cloud environment); parse the dataset to identify a data file associated with the user query(Ginter, para 0007 further discloses parsing files to identify/determine files associated with search parameters “The method may further include parsing the candidate files to determine which, if any, records included by the respective candidate files meet the search parameters”);
generate a sequence of queries based on the user query and content parsed from the data file(Ginter, para 0113 discloses generating plurality of queries to search in different files “parsing may include generating a plurality of query processing threads or the plurality of query processing threads …… parsing may include parsing a sub-portion of the candidate files by each of a portion of the plurality of query processing threads…”); and search against the data file using the sequence of queries to obtain a set of query results(Ginter, para 0113 further discloses obtaining results from the queries “each query processing thread generates resultant data, providing the resultant data to a result analyzer….”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of parsing contents associated to query contents of Ginter into cloud-based query environment of Mehlman and Wen to produce an expected result of retrieving query results. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the file parsing using file associated metadata(Ginter, para 0073).
Regarding claim 2 (Original), Mehlman, Wen and Ginter teach all the limitations of claim 1 and Ginter further teaches wherein the executable instructions further include instructions that, when executed by the processor, cause the processor to: render the query results in the user interface according to a predefined visualization format(Ginter, para 0026 discloses displaying query results on user interface “the results analyzer 145 may be configured to summarize the resultant data 188 from the query engine 144 inside or as part of the information management system 104 and then send the summarized results 189 to the application 118 for display”; para 0028 discloses formatting of display data “each results analyzer 145 may be specialized to pre-process the query resultant data 188 into a different form of display data (e.g., Top N lists, topology graphs, etc.)”).
Regarding claim 3(Original), Mehlman, Wen and Ginter teach all the limitations of claim 1 and Ginter further teaches wherein the executable instructions further include instructions that, when executed by the processor, cause the processor to: perform a data analysis on the query results; and render the data analysis in the user interface according to a predefined visualization format(Ginter, para 0026 discloses performing analysis on query results and displaying results “the results analyzer 145 may be configured to summarize the resultant data 188 from the query engine 144 inside or as part of the information management system 104 and then send the summarized results 189 to the application 118 for display”; para 0028 discloses formatting of display data “each results analyzer 145 may be specialized to pre-process the query resultant data 188 into a different form of display data (e.g., Top N lists, topology graphs, etc.)”).
Regarding claim 21 (New), Mehlman, Wen and Ginter teach all the limitations of claim 1 and Ginter further teaches wherein the executable instructions further include instructions that, when executed by the processor, cause the processor to: receive one or more follow-up queries from the user, wherein the one or more follow-up queries are contextualized with on-canvas existing content including previous queries and responses to these previous queries (Wen, Fig 6 para 0066 disclose providing further follow up inputs from the user on-canvas on existing search result “the user can also use easy-to-access buttons in the mini-toolbar shown in FIG. 6 to place images in the “I like these” image basket………..labeling an image as “not wanted” to remove it from the current search, putting the image into the “I like these” ).
Claim 16 (Currently Amended), Mehlman teaches A method for handling a user query, comprising: setting up and displaying a symbol or link in a user interface, the symbol or link representing a dataset in a cloud environment(Mehlman, para 0034 discloses setting up a dataset connection in the cloud from user interface “a user may use a user interface supported by the system to generate the connector 250. In some examples, the user may configure the connector to link the new database instance 235-b and the database instances that correspond to the cloud platform instances……”);
Using the broadest reasonable interpretation consistent with the specification (paragraph 0031) as it would be interpreted by one of ordinary skill in the art, examiner is interpreting the limitation “setting up a symbol or link in a user interface, the symbol or link representing a dataset in a cloud environment” to mean establishing a connection data source in cloud.
But Mehlman does not explicitly teach wherein the user interface is a part of a drawing and art design application that includes a set of text tools and drawing tools for generating one or more text and non-text objects, and wherein the dataset representation is a persistent, selectable on-canvas object that anchors subsequent querying; receiving a user query initiated by a user through the same user interface that displays the symbol or link representing the dataset in the cloud environment; parsing the dataset to identify a data file associated with the user query; generating a sequence of queries based on the user query and content parsed from the data file; and searching against the data file using the sequence of queries to obtain a set of query results.
However, in the same field of endeavor of querying contents Wen teaches wherein the user interface is a part of a drawing and art design application that includes a set of text tools and drawing tools for generating one or more text and non-text objects(Wen, para 0035 discloses querying interface consisting of drawing tools for providing search input “The intention refinement engine 238 includes a visual feature selection UI 250, which in one implementation receives graphic input such as drawing, pencil, stylus, or paintbrush strokes that specify the visual aspects and features in example images that the user intends to find in an image being retrieved. In other words, the user can designate features across one or more images that should be present in the image(s) that the user is searching for”), and wherein the dataset representation is a persistent, selectable on-canvas object that anchors subsequent querying(Wen, Fig 2-3 para 0042 disclose retrieved result is persistent (initial result) and further refinement inputs can be provided via feedback option “the relevant images designator 256 uses a collector, such as an “I like these” basket to enable the user to conduct relevance feedback naturally and effortlessly. As the user collects more subjectively “good” images to the basket via the feedback iterator 240, the image retrieval system 108 incrementally improves the retrieved image results 210” );
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of providing visual query inputs via tools on the UI of Wen into cloud-based query interface of Mehlman to produce an expected result of retrieving query results from the cloud. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the retrieved search results incrementally by utilizing user feedback(Wen, para 0042).
But Mehlman and Wen don’t explicitly teach receiving a user query initiated by a user through the same user interface that displays the symbol or link representing the dataset in the cloud environment; parsing the dataset to identify a data file associated with the user query; generating a sequence of queries based on the user query and content parsed from the data file; and searching against the data file using the sequence of queries to obtain a set of query results.
However, in the same field of endeavor of querying data in files Ginter teaches receiving a user query initiated by a user through the same user interface that displays the symbol or link representing the dataset in the cloud environment (Ginter, para 0007 discloses receiving a data query “a method may include receiving, from a user device, a data query request that includes one or more search parameters to be searched for within a plurality of files…..”; where prior art Mehlman in para 0034 discloses an interface to receive user input to cloud environment); parsing the dataset to identify a data file associated with the user query (Ginter, para 0007 further discloses parsing files to identify/determine files associated with search parameters “The method may further include parsing the candidate files to determine which, if any, records included by the respective candidate files meet the search parameters”);
generating a sequence of queries based on the user query and content parsed from the data file(Ginter, para 0113 discloses generating plurality of queries to search in different files “parsing may include generating a plurality of query processing threads or the plurality of query processing threads …… parsing may include parsing a sub-portion of the candidate files by each of a portion of the plurality of query processing threads…”); and searching against the data file using the sequence of queries to obtain a set of query results (Ginter, para 0113 further discloses obtaining results from the queries “each query processing thread generates resultant data, providing the resultant data to a result analyzer….”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of parsing contents associated to query contents of Ginter into cloud-based query environment of Mehlman and Wen to produce an expected result of retrieving query results. The modification would be obvious because one of ordinary skill in the art would be motivated to improve the file parsing using file associated metadata(Ginter, para 0073).
Regarding claim 17 (Original), Mehlman, Wen and Ginter teach all the limitations of claim 16 and Ginter further teaches further comprising: rendering the query results in the user interface according to a predefined visualization format(Ginter, para 0026 discloses displaying query results on user interface “the results analyzer 145 may be configured to summarize the resultant data 188 from the query engine 144 inside or as part of the information management system 104 and then send the summarized results 189 to the application 118 for display”; para 0028 discloses formatting of display data “each results analyzer 145 may be specialized to pre-process the query resultant data 188 into a different form of display data (e.g., Top N lists, topology graphs, etc.)”).
Regarding claim 18(Original), Mehlman, Wen and Ginter teach all the limitations of claim 16 and Ginter further teaches further comprising: performing a data analysis on the query results; and rendering the data analysis in the user interface according to a predefined visualization format (Ginter, para 0026 discloses performing analysis on query results and displaying results “the results analyzer 145 may be configured to summarize the resultant data 188 from the query engine 144 inside or as part of the information management system 104 and then send the summarized results 189 to the application 118 for display”; para 0028 discloses formatting of display data “each results analyzer 145 may be specialized to pre-process the query resultant data 188 into a different form of display data (e.g., Top N lists, topology graphs, etc.)”).
Claim 4, 11-15 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Mehlman, Dan et al (PGPUB Document No. 20220365944), hereafter referred as to “Mehlman”, in view of Wen, Fang et al (PGPUB Document No. 20090254539 ), hereafter, referred to as “Wen”, in view of Ginter, Jonathan et al (PGPUB Document No. 20140101178 ), hereafter, referred to as “Ginter”, in further view of Guo, Xiaofei et al (US Patent No. 12368745), hereafter, referred to as “Guo”.
Regarding claim 4 (Currently Amended), Mehlman, Wen and Ginter teach all the limitations of claim 1 but don’t explicitly teach wherein the executable instructions further include instructions that, when executed by the processor, cause the processor to: if the user query is broad by lacking specific parameters or subject details, generate an additional sequence of queries; and search against the data file using the additional sequence of queries to obtain an additional set of query results.
However, in the same field of endeavor of querying data in files Guo teaches wherein the executable instructions further include instructions that, when executed by the processor, cause the processor to: if the user query is broad by lacking specific parameters or subject details, generate an additional sequence of queries; and search against the data file using the additional sequence of queries to obtain an additional set of query results (Guo, the element 506 of Fig. 5 and 72:48-57 disclose generation of additional queries to cover the various scope of the initial query “based on the first query, one or more second queries each directed to a different table of the one or more tables. In other words, instead of simply issuing the first query generated by the large language model to access or present some data, the first query will be used as a basis for generating individual queries for each of the one or more tables targeted by the first query”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of generating additional queries from user input using learning model of Guo into generation of multiple queries based on parsed dataset of Mehlman, Wen and Ginter to produce an expected result of need specific multiple query generation. The modification would be obvious because one of ordinary skill in the art would be motivated to use large language model which can be trained specifically for generation of SQL queries(Guo, col 71:46-58) .
Regarding claim 11 (Original), Mehlman, Wen and Ginter teach all the limitations of claim 1 but don’t explicitly teach wherein the sequence of queries are generated by a machine learning model.
However, in the same field of endeavor of querying data in files Guo teaches wherein the sequence of queries are generated by a machine learning model (Guo, element 506 of Fig. 5 and 72:48-57 disclose generation of series of queries using machine learning model “based on the first query, one or more second queries each directed to a different table of the one or more tables. In other words, instead of simply issuing the first query generated by the large language model to access or present some data, the first query will be used as a basis for generating individual queries for each of the one or more tables targeted by the first query”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of generating multiple queries from user input using learning model of Guo into generation of multiple queries based on parsed dataset of Mehlman, Wen and Ginter to produce an expected result of need specific multiple query generation. The modification would be obvious because one of ordinary skill in the art would be motivated to use large language model which can be trained specifically for generation of SQL queries(Guo, col 71:46-58) .
Regarding claim 12(Original), Mehlman, Wen, Ginter and Guo teach all the limitations of claim 11 and Guo further teaches wherein the machine learning model is a large language model(Guo, element 506 of Fig. 5 and col 72:48-57 disclose generation of series of queries using large language model “based on the first query, one or more second queries each directed to a different table of the one or more tables. In other words, instead of simply issuing the first query generated by the large language model to access or present some data, the first query will be used as a basis for generating individual queries for each of the one or more tables targeted by the first query”).
Regarding claim 13 (Original), Mehlman, Wen and Ginter teach all the limitations of claim 2 but don’t explicitly teach wherein the sequence of queries are generated by a machine learning model.
However, in the same field of endeavor of querying data in files Guo teaches wherein the user interface is a part of a drawing and art design application that includes a set of text tools and drawing tools for generating one or more text and non-text objects (Guo, Fig. 4H text and non-text objects (graphs) are being drawn);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of generating text and non-text objects of Guo into generation of multiple queries based on parsed dataset of Mehlman, Wen and Ginter to produce an expected result of need specific multiple query generation. The modification would be obvious because one of ordinary skill in the art would be motivated to use large language model which can be trained specifically for generation of SQL queries(Guo, col 71:46-58) .
Regarding claim 14 (Currently Amended), Mehlman, Wen Ginter and Guo teach all the limitations of claim 13 and Guo further teaches wherein the executable instructions further include instructions that, when executed by the processor, cause the processor to: render the query results in one or more of a text or non-text object, wherein the text or non-text objects for the query results are rendered as on-canvas visual objects and remain associated with the on-canvas object for the dataset (Guo, Fig. 4H and col 53:35-46 disclose text and non-text objects (graphs) are being generated as search result “Fig. 4H……….entering text into search boxes, navigating between tabs (e.g., tab 455 vs. 465)), such interactions act as triggers that cause query service 166 to continue to obtain information from data store 30 as needed ”; where Wen in para 0034-0035 further teaches query results getting returned on the UI with image tools for providing further input/feedback “a query image input 242 to receive an image that can be used as a search criterion………. in one implementation receives graphic input such as drawing, pencil, stylus, or paintbrush strokes that specify the visual aspects and features in example images that the user intends to find in an image being retrieved”).
Regarding claim 15 (Original), Mehlman, Wen, Ginter and Guo teach all the limitations of claim 13 and Guo further teaches wherein the one or more of a text or non-text object comprise one or more of a template, diagram, flow chart, or wireframe (Guo, Fig. 2L and Fig. 4H disclose generation of flow chart and templates for displaying various graphs “based on the first query, one or more second queries each directed to a different table of the one or more tables. In other words, instead of simply issuing the first query generated by the large language model to access or present some data, the first query will be used as a basis for generating individual queries for each of the one or more tables targeted by the first query”).
Regarding claim 19 (Currently Amended), Mehlman, Wen and Ginter teach all the limitations of claim 16 but don’t explicitly teach further comprising: if the user query is broad by lacking specific parameters or subject details, generating an additional sequence of queries; and searching against the data file using the additional sequence of queries to obtain an additional set of query results.
However, in the same field of endeavor of querying data in files Guo teaches further comprising: if the user query is broad by lacking specific parameters or subject details, generating an additional sequence of queries; and searching against the data file using the additional sequence of queries to obtain an additional set of query results (Guo, in light of unclear claim limitation the element 506 of Fig. 5 and 72:48-57 disclose generation of additional queries to cover the various scope of the initial query “based on the first query, one or more second queries each directed to a different table of the one or more tables. In other words, instead of simply issuing the first query generated by the large language model to access or present some data, the first query will be used as a basis for generating individual queries for each of the one or more tables targeted by the first query”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of generating additional queries from user input using learning model of Guo into generation of multiple queries based on parsed dataset of Mehlman, Wen and Ginter to produce an expected result of need specific multiple query generation. The modification would be obvious because one of ordinary skill in the art would be motivated to use large language model which can be trained specifically for generation of SQL queries(Guo, col 71:46-58) .
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Mehlman, Dan et al (PGPUB Document No. 20220365944), hereafter referred as to “Mehlman”, in view of Wen, Fang et al (PGPUB Document No. 20090254539 ), hereafter, referred to as “Wen”, in further view of Ginter, Jonathan et al (PGPUB Document No. 20140101178 ), hereafter, referred to as “Ginter”, in further view of Kumar, Vishwajeet et al (PGPUB Document No. 20240160634), hereafter, referred to as “Kumar”.
Regarding claim 5 (Original), Mehlman, Wen and Ginter teach all the limitations of claim 1 but don’t explicitly teach wherein the dataset includes one or more data files, and the data file associated with the user query is identified from the one or more data files based on contextual information identified from the user query and the content parsed from the data file.
However, in the same field of endeavor of content determination based on query context Kumar teaches wherein the dataset includes one or more data files, and the data file associated with the user query is identified from the one or more data files based on contextual information identified from the user query and the content parsed from the data file(Mont-Reynaud, para 0027 discloses if identifying table/datafile from a dataset based on query context “In response to receiving a query …..the table retriever component 102 can retrieve, from a corpus (C) 108 of tables (T) 110 and passage data items 112 (e.g., passages (P)), a group of tables, comprising respective content,…..that can be determined to be at least potentially relevant to responding to the query, based on analysis of query data of the query and a context of the query determined from the analysis.”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of contextual analysis of queries of Kuman into generation of multiple queries based on parsed dataset of Mehlman, Wen and Ginter to produce an expected result of obtaining correct answer to queries. The modification would be obvious because one of ordinary skill in the art would be motivated to provide correct answer to questions by considering context of the queries(Kuman, abstract).
Claim 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over Mehlman, Dan et al (PGPUB Document No. 20220365944), hereafter referred as to “Mehlman”, in view of Wen, Fang et al (PGPUB Document No. 20090254539 ), hereafter, referred to as “Wen”, in further view of Ginter, Jonathan et al (PGPUB Document No. 20140101178 ), hereafter, referred to as “Ginter”, in further view of Jacob, Kristen et al (PGPUB Document No. 20230127572), hereafter, referred to as “Kristen”.
Regarding claim 6 (Original), Mehlman, Wen and Ginter teach all the limitations of claim 1 but don’t explicitly teach wherein, to generate the sequence of quires, the executable instructions further include instructions that, when executed by the processor, cause the processor to: specify a data service provider; define one or more rules and instructions for generating the sequence of queries specific to the database service provider; and create the sequence of queries following the one or more rules and instructions.
However, in the same field of endeavor of query statement generation Jacob teaches wherein, to generate the sequence of quires, the executable instructions further include instructions that, when executed by the processor, cause the processor to: specify a data service provider(Jacob, para 0035 discloses based on rules and database type appropriate service provider is getting selected for written query generation “”); define one or more rules and instructions for generating the sequence of queries specific to the database service provider; and create the sequence of queries following the one or more rules and instructions (Jacob, para 0035 discloses based on rules and instructions, different database/structure specific query generation “depending upon rules or instructions provided by a user as received by API 204 and display module 218. In still further examples, proxy/endpoint server 206 may include multiple instantiations, each of which is configured to generate multiple rewritten queries for different types, formats, structures, and/or data schemas for various databases (i.e., multiple versions of rewritten query 244, where each version may be generated for different types of databases……”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of generation of various database specific query statement of Jacob into generation of multiple queries based on parsed dataset of Mehlman, Wen and Ginter to produce an expected result of generation of query statements for various types for database providers. The modification would be obvious because one of ordinary skill in the art would be motivated to convert search queries in various database specific queries to work with different type of sources without having to know about other query languages (Jacob, para 0035).
Regarding claim 7 (Original), Mehlman, Wen, Ginter and Jacob teach all the limitations of claim 6 and Jacob further teaches wherein the data service provider is an SQL-compatible database service provider (Jacob, para 0035 discloses SQL-compatible database service provider “query engine 216 are configured to generate rewritten query 244 for each target database (not shown) on which dataset 242 is stored (e.g., as originally programmed using, for example, a SELECT statement in SQL)”).
Regarding claim 8 (Original), Mehlman, Wen, Ginter and Jacob teach all the limitations of claim 7 and Jacob further teaches wherein the generated sequence of queries are SQL statements (Jacob, para 0035 discloses multiple query generation with SQL statements “query engine 216 are configured to generate rewritten query 244 for each target database (not shown) on which dataset 242 is stored (e.g., as originally programmed using, for example, a SELECT statement in SQL)”).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Mehlman, Dan et al (PGPUB Document No. 20220365944), hereafter referred as to “Mehlman”, in view of Wen, Fang et al (PGPUB Document No. 20090254539 ), hereafter, referred to as “Wen”, in further view of Ginter, Jonathan et al (PGPUB Document No. 20140101178 ), hereafter, referred to as “Ginter”, in view of Qiao, Liang (PGPUB Document No. 20130018894), hereafter, referred to as “Qiao”, in further view of Doyle, William (US Patent No. 5233513), hereafter, referred to as “Doyle”.
Regarding claim 9 (Currently Amended), Mehlman, Wen and Ginter teach all the limitations of claim 1 and Ginter further teaches wherein, to generate the sequence of quires, the executable instructions further include instructions that, when executed by the processor, cause the processor to: and generate the sequence of queries including the classification keywords(Ginter, para 0113 discloses generating plurality of queries to search in different files “parsing may include generating a plurality of query processing threads or the plurality of query processing threads …… parsing may include parsing a sub-portion of the candidate files by each of a portion of the plurality of query processing threads…”).
But Mehlman, Wen and Ginter don’t explicitly teach determine whether the user query and a column in the data file indicate an existence of sentimental analysis; if there is an existence of sentimental analysis, determine whether there is a classification column in the data file associated with the user query; if there is no classification column associated with the user query, create a list of classifications and use a text classification model to create a temporary column for the data file and insert classification keywords into the temporary column;
However, in the same field of endeavor of sentiment analysis Qiao teaches determine whether the user query and a column in the data file indicate an existence of sentimental analysis (Qiao, Fig. 5 and para 0067 discloses if querying document associated with sentiment and further discloses determining sentiment classification such as “Positive”, “Negative”, “Neutral” etc. “data structure 500 may be used to access or determine data associated with sentiment of at least one document (e.g., by indexing a database or index including data structure 500)……….” ); if there is an existence of sentimental analysis, determine whether there is a classification column in the data file associated with the user query(Qiao, Fig. 5 and para 0067 discloses if querying document associated with sentiment and further discloses determining sentiment classification such as “Positive”, “Negative”, “Neutral” etc. “The sentiment data (e.g., positive sentiment data in column 520, negative sentiment data in column 530, neutral sentiment data in column 540, etc.) may be used, for example, in combination with a search (e.g., to generate search results including one or more documents listed in column 510 of data structure 500) to determine the sentiment of something (e.g., identified in the query for the search)” );
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of identifying documents based on query sentiment of Qiao into generation of multiple queries based on parsed dataset of Mehlman, Wen and Ginter to produce an expected result of identifying documents which are in-line with query sentiment. The modification would be obvious because one of ordinary skill in the art would be motivated to advantageously represent sentiment of a large amount of data more concise or comprehensible manner using columns sentiment of documents (Qiao, para 0067).
But Mehlman, Wen, Ginter and Qiao don’t explicitly teach and if there is no classification column associated with the user query, create a list of classifications and use a text classification model to create a temporary column for the data file and insert classification keywords into the temporary column;
However, in the same field of endeavor of data analysis Doyle teaches and if there is no classification column associated with the user query, create a list of classifications and use a text classification model to create a temporary column for the data file and insert classification keywords into the temporary column(Doyle, col 10:66~11:5 discloses adding a new column and inserting values for the column fields “Change column 82 is added to temporary table 80 to keep track of the type of changes found. A change code is written in column 82 for each section when a match is found or not found” ; where Qiao in para 0067 discloses sentiment classification);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of adding columns to table of Doyle into generation of multiple queries based on parsed dataset of Mehlman, Wen, Ginter and Qiao to produce an expected result of adding table column/field when need for operation. The modification would be obvious because one of ordinary skill in the art would be motivated to add a column to a table on a temporarily basis for the ease of operation (Doyle, col 10:66~11:5).
Claim 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mehlman, Dan et al (PGPUB Document No. 20220365944), hereafter referred as to “Mehlman”, in view of Wen, Fang et al (PGPUB Document No. 20090254539 ), hereafter, referred to as “Wen”, in further view of Ginter, Jonathan et al (PGPUB Document No. 20140101178 ), hereafter, referred to as “Ginter”, in further view of Mont-Reynaud, Bernard (PGPUB Document No. 20210174794 ), hereafter, referred to as “Mont-Reynaud”.
Regarding claim 10 (Original), Mehlman, Wen and Ginter teach all the limitations of claim 1 but don’t explicitly teach wherein the executable instructions further include instructions that, when executed by the processor, cause the processor to: if parsing the dataset does not lead to an identification of a data file associated with the user query, return an error message in response to the user query.
However, in the same field of endeavor of content parsing Mont-Reynaud teaches wherein the executable instructions further include instructions that, when executed by the processor, cause the processor to: if parsing the dataset does not lead to an identification of a data file associated with the user query, return an error message in response to the user query (Mont-Reynaud, para 0052 discloses if identifying any specific content/element fails then returning error messages “if both parsers 155 and 166 fail to recognize a query, the device will return to the idle state—most likely after issuing an appropriate error message. A failure may occur, for example, if speech recognizer 162 cannot reliably determine a transcription of the query”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of returning error message of Mont-Reynaud into generation of multiple queries based on parsed dataset of Mehlman, Wen and Ginter to produce an expected result of alerting user about error condition. The modification would be obvious because one of ordinary skill in the art would be motivated to have a process to indicate seizure of the operation in the event of an error occurrence(Mont-Reynaud, para 0052).
Regarding claim 20(Original), Mehlman, Wen and Ginter teach all the limitations of claim 16 but don’t explicitly teach wherein generating the sequence of queries further comprises: if parsing the dataset does not lead to an identification of a data file associated with the user query, returning an error message in response to the user query.
However, in the same field of endeavor of content parsing Mont-Reynaud teaches wherein generating the sequence of queries further comprises: if parsing the dataset does not lead to an identification of a data file associated with the user query, returning an error message in response to the user query (Mont-Reynaud, para 0052 discloses if identifying any specific content/element fails then returning error messages “if both parsers 155 and 166 fail to recognize a query, the device will return to the idle state—most likely after issuing an appropriate error message. A failure may occur, for example, if speech recognizer 162 cannot reliably determine a transcription of the query”);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the feature of returning error message of Mont-Reynaud into generation of multiple queries based on parsed dataset of Mehlman, Wen and Ginter to produce an expected result of alerting user about error condition. The modification would be obvious because one of ordinary skill in the art would be motivated to have a process to indicate seizure of the operation in the event of an error occurrence(Mont-Reynaud, para 0052).
Response to Arguments
I. 35 U.S.C §112 (b)
112(b) rejection to claim 4 and 19 has been withdrawn in light of respective claim amendments.
II. 35 U.S.C §101 Abstract Idea
35 U.S.C §101 abstract idea rejection to claim 1-20 has been withdrawn in light of applicant argument consideration and claim amendments.
II. 35 U.S.C §103
Applicant’s arguments filed on 1/27/2026 have been fully considered but are
moot because the independent claim 1 and 16 have been amended with newly added
features which applicant’s arguments are directed towards. Since claims have been amended with new features, a new ground of rejection is presented.
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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/ABDULLAH A DAUD/Examiner, Art Unit 2164
/AMY NG/Supervisory Patent Examiner, Art Unit 2164
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