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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 4 and 19 are rejected under 35 U.S.C. 112 (b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Dependent claim 4 and 19 recite limitation “if the user query is broad”. The metes and bounds of the aforementioned claims are not clear because the limitation cannot be clearly understood. It is not clear what is the scope of the term “broad”.
For the purpose of the examination the examiner interprets the unclear limitation “if the user query is broad” as a query which requires multiple associated additional queries to fulfill the intent of the original query.
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.
Claim 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 16 is directed to statutory category process. The claim recites “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”. The process of parsing and identifying data file or table based on a query, generating sequences of queries based on the query and parsed content, searching on the identified data file/tables using sequences of generated queries to obtain the query result involve observation, judgement and evaluation and can practically be performed in human mind. Accordingly, recited limitations fall into abstract idea groupings of mental process (see MPEP 2106.04(a)(2)(III)) under Step 2A, prong 1 of the 2019 PEG. Therefore, aforementioned processes can practically be performed in the human mind and directed to an abstract idea.
At step 2A, prong 2, this judicial exception is not integrated into a practical application. In particular, the claim recites additional elements – “setting up a symbol or link in a user interface, the symbol or link representing a dataset in a cloud environment; receiving a user query initiated by a user through the user interface” above additional elements of setting up link to receiving or obtaining data and receiving user query recites insignificant extra-solution activity of data gathering as “obtaining information” as identified in MPEP 2106.05 (g). Further high-level recitations of a generic computer components represent mere instructions to apply the abstract idea on a computer as in MPEP 2106.05(f). Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application. Therefore, claim is directed to an abstract idea.
Further, At step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above the additional elements of gathering user data is mere data gathering and, is well-understood, routine or conventional activities. Sending/receiving user query related data to local and remote server is insignificant extra-solution activity of data transmission, such is also well- understood, routine, and conventional (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, see MPEP 2106.05 (f). Looking at the limitations in combination and the claim as a whole does not change this conclusion and the claim is ineligible.
Claim 1 differs from claim 16 in that the steps of the claimed method are implemented by instructions when executed by one or more processors. The invention of claim 1 is a system including one or more processors and a memory storing the instructions to perform recited steps. For reasons discussed above, the claimed steps are directed to mental steps. Use of a processor to execute instructions stored in memory constitutes use of a generic computer as a tool and does not constitute an application of significantly more than the abstract idea. Accordingly, claim 1 is not patent eligible.
Dependent claim 2, 3, 4, 5, 6, 7, 8 and 10 are directed to the same abstract idea as the independent claim from which they depend and further recite limitations – “perform a data analysis on the query results” , “if the user query is broad, 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”, “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”, “wherein the generated sequence of queries are SQL statements” and “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”. The process of performing analysis on the query results, generation of additional queries when the scope of a query is broad, searching by generated queries, defining rule for generating sequences of queries specific to service provider, creating/generating sequences of SQL queries/statement based on rules and instructions and generating error when content parsing fails involve observation, judgement and evaluation and can practically be performed in human mind. Accordingly, recited limitations fall into abstract idea groupings of mental process (see MPEP 2106.04(a)(2)(III)) under Step 2A, prong 1 of the 2019 PEG. Therefore, aforementioned processes can practically be performed in the human mind and directed to an abstract idea.
At step 2A, prong 2, this judicial exception is not integrated into a practical application. In particular, the claims recite additional elements – “render the query results in the user interface according to a predefined visualization format” and “render the data analysis in the user interface according to a predefined visualization format” above mentioned additional elements considered as insignificant extra solution activity of data output. Claims also recite –“ 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”, “specify a data service provider” and “data service provider is an SQL-compatible database service provider” which are insignificant extra-solution activity of data gathering as “obtaining information” as identified in MPEP 2106.05 (g). Further high-level recitations of a generic computer components represent mere instructions to apply the abstract idea on a computer as in MPEP 2106.05(f). Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application. Therefore, claim 2, 3, 4, 5, 6, 7, 8 and 10 are directed to an abstract idea..
At step 2B, the claims don’t include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above the additional elements recites insignificant extra-solution activity of data gathering and outputting/transmitting data such are also well- understood, routine, and conventional. Further, sending/transmitting data is insignificant extra-solution activity of data transmission, such is also well- understood, routine, and conventional (Presenting data is WURC based on OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1362-63 (Fed. Cir. 2015) (presenting offers and gathering statistics); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, see MPEP 2106.05 (f). Looking at the limitations in combination and each claim as a whole does not change this conclusion and claim 2, 3, 4, 5, 6, 7, 8 and 10 are ineligible.
Claim 17, 18, 19 and 20 differ from claim 2, 3, 4 and 20 respectively in that the steps of the claimed method are implemented by instructions when executed by one or more processors. The invention of claim 17, 18, 19 and 20 is a system including one or more processors and a memory storing the instructions to perform recited steps. For reasons discussed above, the claimed steps are directed to mental steps. Use of a processor to execute instructions stored in memory constitutes use of a generic computer as a tool and does not constitute an application of significantly more than the abstract idea. Accordingly, claim 17, 18, 19 and 20 are not patent eligible.
Dependent claim 9, 11, 12, 13, 14 and 15 are directed to the same abstract idea as the independent claim from which they depend and further recite limitations – “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; 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; and generate the sequence of queries including the classification keywords”. The process of determining the presence of a particular column, determining presence sentimental analysis and existence of classification column and its association to user queries and in the event of non-existence of classification column, creating a list of classification, adding a temporary column, populating the inserted column with values and generating sequences of queries based on classification keywords involve observation, judgement and evaluation and can practically be performed in human mind. Accordingly, recited limitations fall into abstract idea groupings of mental process (see MPEP 2106.04(a)(2)(III)) under Step 2A, prong 1 of the 2019 PEG. Therefore, aforementioned processes can practically be performed in the human mind and directed to an abstract idea.
At step 2A, prong 2, this judicial exception is not integrated into a practical application. In particular, the claims recite additional elements – “wherein the sequence of queries are generated by a machine learning model” and “wherein the machine learning model is a large language model” above mentioned additional elements of using machine learning model such as LLM is considered as using machine learning technology as a tool or generally linking the mental steps identified above to the technological area of machine learning or AI, see MPEP 2106.05(f) and/or (h). This generic high-level recitation of machine learning LLM model is nothing more than mere instructions to apply on a computer with a possible field of use limitation to machine-learning field. The claims further recite – “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”, “render the query results in one or more of a text or non-text object” and “wherein the one or more of a text or non-text object comprise one or more of a template, diagram, flow chart, or wireframe”-considered as insignificant extra solution activity of data output. Further high-level recitations of a generic computer components represent mere instructions to apply the abstract idea on a computer as in MPEP 2106.05(f). Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application. Therefore, claim 9, 11, 12, 13, 14 and 15 are directed to an abstract idea.
At step 2B, the claims don’t include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above the additional elements recites insignificant extra-solution activity of outputting/transmitting data such are also well- understood, routine, and conventional. Such is well- understood, routine, and conventional (Presenting data is WURC based on OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1362-63 (Fed. Cir. 2015) (presenting offers and gathering statistics). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, see MPEP 2106.05 (f) and for applying the machine learning as a tool is carried over and do not provide significantly more than the abstract idea. Looking at the limitations in combination and each claim as a whole does not change this conclusion and claim 2, 3, 9, 11, 12, 13, 14 and 15 are ineligible.
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 and 16-18 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 Ginter, Jonathan et al (PGPUB Document No. 20140101178 ), hereafter, referred to as “Ginter”.
Claim 1 , 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 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 receive a user query initiated by a user through the user interface; 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 user interface(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…..”); 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 bucket-based organization of storage of Ginter into handling of raw data of Mehlman to produce an expected result of making the data query and storage system to support querying data in raw format. The modification would be obvious because one of ordinary skill in the art would be motivated to broaden the data query and storing capability of a system by including its support for various low level data without transforming them into a structural form of data.
Regarding claim 2, Mehlman 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, Mehlman 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.)”).
Claim 16 , Mehlman teaches A method for handling a user query, comprising: setting up 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 receiving a user query initiated by a user through the user interface; 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 user interface (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…..”); 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 bucket-based organization of storage of Ginter into handling of raw data of Mehlman to produce an expected result of making the data query and storage system to support querying data in raw format. The modification would be obvious because one of ordinary skill in the art would be motivated to broaden the data query and storing capability of a system by including its support for various low level data without transforming them into a structural form of data.
Regarding claim 17, Mehlman 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, Mehlman 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 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 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, Mehlman 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, 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, 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, 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 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, Mehlman 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 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, Mehlman, 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, Mehlman 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 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, Mehlman, 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 (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 ”).
Regarding claim 15, Mehlman, 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, Mehlman and Ginter teach all the limitations of claim 16 but don’t explicitly teach further comprising: if the user query is broad, 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, 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 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 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 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, Mehlman 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 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 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 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, Mehlman 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 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, Mehlman, 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, Mehlman, 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 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 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, Mehlman 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 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;
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 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 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, 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, 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 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 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, Mehlman 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 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, Mehlman 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 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).
Conclusion
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/ABDULLAH A DAUD/Examiner, Art Unit 2164
/MARK E HERSHLEY/Primary Examiner, Art Unit 2164
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