DETAILED ACTION
Notice of 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
2. Amendment filed 07/14/2026 has been considered by Examiner. Claims 1-7, and 9-20 have been amended. Claims 1-20 are pending, and likewise Claims 1- 20 have been examined.
Response to Arguments
Applicant’s amendments and arguments filed 07/14/2026, with respect to claim(s) 1-20 have been fully considered.
Claim Objections for claims 1, 2, 4- 7, and 9- 20 have been withdrawn in view of the amended claims filed on 07/14/2026.
Applicant’s arguments in pages 7-11, filed 07/14/2026, with respect to 35 U.S.C 101 rejections of Claims 1-20 have been fully considered but they are not persuasive. Applicant argued that “even if the limitations of the present claims could be seen to recite a judicial exception under Prong One of Step 2A, which is not conceded, the present claims clearly integrate the exception into a practical application under Prong Two of Step 2A and are therefore patent-eligible”. Examiner respectfully disagrees. Applicant cited different steps of generating syntactically and semantically correct queries from a defective input query. But didn’t recite any practical application where the corrected queries will be needed and executed. Applicant mentioned using “machine learning model” to generate the corrected query, which examiner identified as an additional element. In the specification, in page 9, para. 3, page 10, para.6, specifies “The machine learning model could be a general-purpose large language model”, “machine learning model 171 may be provided by OpenAI, e.g., running the large language model ChatGPT, e.g., based on GPT-4, or GPT-3.5, e.g., the August 3 version of 2023, or an earlier or later version”, which is generic and not sufficient to amount to significantly more than the judicial exception. Applicant cited precedential September 26, 2025 Decision on Request for Rehearing in U.S. Patent Application No. 16/319,040, where the note says, "under the panel's reasoning, many AI innovations are potentially unpatentable-even if they are adequately described and nonobvious because the panel essentially equated any machine learning with an unpatentable "algorithm" and the remaining additional elements as "generic computer components," without adequate explanation. Examiners and panels should not evaluate claims at such a high level of generality." Applicant further cited another letter from August 4th, 2025 , which says, "examiners are cautioned not to oversimplify claim limitations and expand the application of the "apply it" consideration….. or whether the claim purports to improve computer capabilities or to improve an existing technology." Examiner would like to point out that the specification already stated in the above mentioned pages and paragraphs that the “machine learning model” is generic. Throughout the amended claims, no specialized or unique or unconventional technology or steps have been mentioned to improve any technology or system. The use of a computer or generic machine learning model does not preclude performance of the invention via pen and paper or in a person’s mind. Applicant cited MPEP2106.04(a) and 2106.05(a) and further argued that the specification identifies core problem and provide improvements by obtaining "technical data reflecting the technical state of the computer system" using "defective" queries and that this "streamlines decision-making by eliminating the need to learn a particular query language" and "reduces the time to obtain data". Applicant mentioned that “the as-filed specification is replete with "sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement" in technology”, but the claims and only the claims define the metes and bounds of the invention. Claims should reflect the assertions made by the applicant with respect to the improvements. The use of a computer or other machinery in its ordinary capacity to perform a task or simply adding a general purpose computer to an abstract idea, does not integrate a judicial exception into a practical application. Here the computer is the machine that is merely an object on which the method operates, which does not integrate the exception into a practical application or provide significantly more. Thus, 35 U.S.C 101 rejections of Claims 1-20 have been maintained.
Applicant’s arguments in pages 11-13, filed 07/14/2026, with respect to claim(s) 1-20, under 35 U.S.C. 102/103 have been fully considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 therefore, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an
abstract idea without significantly more.
The Independent claims 1, 11 and 16 recite “accessing a plurality of query examples, the query examples in the plurality being syntactically correct according to a query language and being configured to correctly retrieve data from the data source when executed at the data source”, “obtaining an input query requiring information from the data source, the input query being noncompliant with the query language and inexecutable at the data source”, “selecting from the plurality of query examples at least one query example related to the input query”, “inputting and the input query to a machine learning model to prompt the machine learning model to generate the executable data query by adjusting and submitting the executable query to the data source”. The limitations above as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process, as this could be performed in the human mind or with the aid of pen and paper.
The limitation of " accessing ... ", " obtaining ... ", " selecting ... ", “ inputting…”, “submitting..” as drafted covers mental activities. More specifically, a person can gather/store a plurality of questions/query examples according to certain rules, where questions/queries examples are based on some syntax rules, can obtain an input query but which doesn’t comply with the predefined stored query examples. The person can select one saved query example similar to the input query, can adjust the selected query according to the input query to make it similar to the predefined saved example query and can use that query to find out the answer from a data source. The above steps, as drafted, is a process that under its broadest reasonable interpretation, covers performance of the limitation in the mind. There is nothing in the claim element precludes the step from practically being performed in the human mind. Additionally, the mere nominal recitation of a generic computer appliance does not take the claim limitations out of the mental processes grouping. Thus, the claims recite a mental process.
The claims recite the additional limitation of “machine learning model”, for performing the method, which is recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. The current specification in page 9, para. 3, page 10, para.6, specifies “The machine learning model could be a general-purpose large language model”, “machine learning model 171 may be provided by OpenAI, e.g., running the large language model ChatGPT, e.g., based on GPT-4, or GPT-3.5, e.g., the August 3 version of 2023, or an earlier or later version”, which is generic and not sufficient to amount to significantly more than the judicial exception. Claims 11 and 16 recite additional limitation of “processor” and claim 16 recites additional limitation of “ non-transitory computer readable medium “. All those are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. This is no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds
nothing that is not already present when looking at the elements taken individually. There is no indication
that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Claims 1, 11 and 16 are therefore not drawn to eligible subject matter as this are directed to an abstract idea without
significantly more than the abstract idea.
Claims 2, 12, 17 recite “wherein the data source adheres to a schema defining
Claims 3, 13, 18 recite “wherein adjusting the selected at least one query example
Claims 4, 14, 19 recite “wherein selecting at least one query example comprises: identifying keywords, from a predetermined list of keywords, applying to the input query”, “scoring each query example of the plurality of query examples for the identified keywords”, “and selecting at least one high scoring query example from the plurality of query examples”, where to find out a similar query example of the input query, identifying a keyword from the input query, selecting all the examples with the keyword and scoring and selecting the one with highest score, could be performed in the human mind or with the aid of pen and paper. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claims 4, 14 and 19 do not recite any additional limitations. The claims as drafted, are not patent eligible.
Claims 5, 15, 20 recite “wherein selecting at least one query example comprises converting the input query into an input embedding vector”, “obtaining for the plurality of query examples, a plurality of corresponding embedding vectors”, “ and selecting a query example from the plurality of query examples according to a vector similarity between the input embedding vector and embedding vectors in the plurality of embedding vectors”, where converting the input query and the plurality of query examples into embedding vectors, which can be a number representation and comparing the vectors and selecting the similar vector, could be performed with the aid of pen and paper. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claims 5, 15 and 20 do not recite any additional limitations. The claims as drafted, are not patent eligible.
Claim 6 recites “wherein the input query is a technical state query requesting information regarding a technical state of a technical system, and/or the input query is a natural language query”, to determine that input query is a technical query regarding a technical state of the system or a natural language query, is an observation, evaluation, could be performed in the human mind or with the aid of pen and paper. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claim 6 does not recite any additional limitations. The claim as drafted, is not patent eligible.
Claim 7 recites “comprising executing the executable query at the data source, and if the data source responds with an error, adjusting the executable data query with a machine learning model”, where adjusting the data source which has plurality of query examples, in response of an error , could be performed with the aid of pen and paper. The claim recites additional limitation of machine learning model. The current specification in page 9, para. 3, page 10, para.6, specifies “The machine learning model could be a general-purpose large language model”, “machine learning model 171 may be provided by OpenAI, e.g., running the large language model ChatGPT, e.g., based on GPT-4, or GPT-3.5, e.g., the August 3 version of 2023, or an earlier or later version”, which is generic and not sufficient to amount to significantly more than the judicial exception. The claim 7 as drafted, is not patent eligible.
Claim 8 recites “wherein the query language is one of: GraphQL, SQL”, to determine the query language if it’s SQL or GraphQL, is an observation, could be performed in the human mind. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claim 8 does not recite any additional limitations. The claim as drafted, is not patent eligible.
Claim 9 recites “wherein the machine learning model comprises a sequence-based model configured to receive a sequence of tokens as input and to produce a sequence of tokens as output, and/or a transformer model, and/or a generative model, in particular a text generative model”, which is stating well known models, is an observation, could be performed in the human mind. The claim recites additional limitation of machine learning model, transformer model, generative model. The current specification in page 9, para. 3, page 10, para.6, specifies “The machine learning model could be a general-purpose large language model”, “machine learning model 171 may be provided by OpenAI, e.g., running the large language model ChatGPT, e.g., based on GPT-4, or GPT-3.5, e.g., the August 3 version of 2023, or an earlier or later version”, which is generic. The current specification in page 21, para. 3, transformer is just mentioned, in page 11, para. 1 and in page 21, para.3, generative model is mentioned. Both were mentioned generically. None of them are sufficient to amount to significantly more than the judicial exception. The claim 9 as drafted, is not patent eligible.
Claim 10 recites “wherein applying the machine learning model to the selected at least one query example comprises, applying the machine learning model to the selected at least one query example and at least part of a schema defining version of 2023, or an earlier or later version”, which is generic and not sufficient to amount to significantly more than the judicial exception. The claim 10 as drafted, is not patent eligible.
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 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 7-12, 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Maheshwari et al. ( US 20220197900 A1), hereinafter referenced as Maheshwari, in view of Trummer et al. (US 20240281222 A1), hereinafter referenced as Trummer.
Regarding Claim 1, Maheshwari teaches a computer-implemented method for generating an executable data query, wherein said query is configured for execution at a data source for the purpose of data retrieval therefrom the method comprising:
accessing a plurality of query examples, the query examples in the plurality being syntactically correct according to a query language and being configured to correctly retrieve data from the data source when executed at the data source ( Maheshwari: Para.[0034]-[0036],[0039], Fig. 1 illustrates system 100 for automatically generating, editing, and optimizing queries using machine learning. System 100 includes client 102a and client 102b, query editor 104, query engine 112, database 120, and machine learning (ML) engine 122. Query editor 104 comprises logic to facilitate the composition and optimization of queries and include editor interface 106, autocomplete engine 108, and recommendation engine 110. Client 102a and client 102b are applications or application components that submit requests to store and retrieve data to/from database 120. The requests may include or be used to construct a database query that conforms to a structured language, such as SQL. Recommendation engine 110 may identify queries that satisfy a similarity threshold, such as the top n most similar queries from a list of performant queries);
obtaining an input query requiring information from the data source, the input query being noncompliant with the query language and inexecutable at the data source (Maheshwari: Para.[0058],[0059], Fig. 2 illustrates model 200 including multiple layers for processing an incomplete query which is obtained at 202 and, in response, generates output 212. Incomplete query 202 may comprise one or more query tokens, such as SQL commands, clauses, and/or object references. Output 212 may include suggestions for similar queries that have been identified as performant ( executable));
selecting from the plurality of query examples at least one query example related to the input query (Maheshwari: Para.[0060]-[0063],Fig. 2, recommendation layer 208 receives the autocompleted query from NLP prediction layer 204 and/or its numerical vector representation from embedding layer 206 for the incomplete query 202. Recommendation layer 208 may use the numerical vector representation to identify one or more of query vectors 210 that satisfy a similarity threshold. Recommendation layer 208 may be configured to identify the top n most similar query as determined by cosine similarity or another similarity measurement, where n is a positive integer) ,
Maheshwari while teaching the method of claim 1, fails to explicitly teach the claimed, inputting and the input query to a machine learning model to prompt the machine learning model to generate the executable data query by adjusting and submitting the executable query to the data source.
However , Trummer does teach the claimed, inputting and the input query to a machine learning model to prompt the machine learning model to generate the executable data query by adjusting (Trummer: Para.[0076],[0077], Fig. 1, users can provide code samples, in addition to or instead of natural language instructions, as input. Based on such code samples, the AI system 110 can learn what the desired code looks like and can generate database code for a corresponding database management system in that style );
and submitting the executable query to the data source ( Trummer: Para.[0046], Fig. 1, providing the generated database code to at least one of the database management systems 106 associated with the one or more databases for execution).
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 Trummer’s teaching of artificial intelligence (AI) system to generate database code from natural language input, into the system and method of intelligent query editor using neural network based machine learning, taught by Maheshwari, because, by providing an AI framework configured to generate customized code for data processing in a database management system or other code execution environment based at least in part on user inputs provided in natural language, the database code generation could be more efficient.(Trummer, Para.[0003]-[0007]).
Claim 11 is system claim comprising: one or more processors; and one or more storage devices storing instructions that, when executed by the one or more processors, cause the one or more processors ( Maheshwari: Para.[0151],[0152], Fig. 8, Computer system 800 includes main memory 806, such as a random access memory (RAM) or other dynamic storage device, coupled to bus 802 for storing information and instructions to be executed by processor 804), performing the steps in method claim 1 above and as such, claim 11 is similar in scope and content to claim 1 and therefore, claim 11 is rejected under similar rationale as presented against claim 1 above.
Claim 16 is non-transitory computer readable medium claim comprising data representing instructions, which when executed by a processor system, cause the processor system ( Maheshwari: Para.[0155],[0156], Fig. 8, non-transitory storage medium store data and/or instructions that cause a machine to operate in a specific fashion. Execution of the sequences of instructions contained in storage medium causes processor 804 to perform the process steps described herein), performing the steps in method claim 1 above and as such, claim 16 is similar in scope and content to claim 1 and therefore, claim 16 is rejected under similar rationale as presented against claim 1 above.
Regarding Claim 2, Maheshwari in view of Trummer, teach the method of claim 1. Maheshwari further teaches, wherein the data source adheres to a schema defining ( Maheshwari: Para.[0035], Fig. 1, the request to store or retrieve data to/from the database 120 may include or be used to construct a database query that conforms to a structured language, such as SQL. A client may submit a data definition language (DDL) command to define, modify, and remove the data structures, such as tables and schemas, to control how the underlying data is stored and related).
Claim 12 is system claim performing the steps in method claim 2 above and as such, claim 12 is similar in scope and content to claim 2 and therefore, claim 12 is rejected under similar rationale as presented against claim 2 above.
Claim 17 is non-transitory computer readable medium claim performing the steps in method claim 2 above and as such, claim 17 is similar in scope and content to claim 2 and therefore, claim 17 is rejected under similar rationale as presented against claim 2 above.
Regarding Claim 7, Maheshwari in view of Trummer, teach the method of claim 1. Maheshwari further teaches comprising: executing the executable query at the data source; and if the data source responds with an error, adjusting the executable data query with a machine learning model ( Maheshwari: Para.[0081], [0082], Fig.4, at 416, process 400 adjusts weights and biases of one or more nodes within the NLP neural network model to minimize an error function when applying the model to a test dataset. Once the error has been minimized, at 418, process 400 stores the set of weights and biases and apply to new queries, either complete or incomplete, to perform next token predictions ( execution)).
Regarding Claim 8, Maheshwari in view of Trummer, teach the method of claim 1. Maheshwari further teaches, wherein the query language is one of: GraphQL, SQL ( Maheshwari: Para.[0035], the requests may include or be used to construct a database query that conforms to a structured language, such as SQL).
Regarding Claim 9, Maheshwari in view of Trummer, teach the method of claim 1. Maheswari further teaches, wherein the machine learning model comprises [a sequence-based model configured to receive a sequence of tokens as input and to produce a sequence of tokens as output,] and/or a transformer model, [and/or a generative model, in particular a text generative model] ( Maheswari: Para.[0070], [0071], Fig. 3, neural network architecture 300 includes transformer encoder layer 306, transformer decoder layer 308).
Regarding Claim 10, Maheshwari in view of Trummer, teach the method of claim 1. Maheshwari further teaches, wherein applying the machine learning model to the selected at least one query example comprises, applying the machine learning model to the selected at least one query example and at least part of a schema defining ( Maheshwari: Para.[0046],[0047], Fig.1, Machine learning (ML) engine 122 provides components through which inferences about query performance and adjustments to database queries may be automatically made during query composition rather than relying on static instruction sets to perform tasks. ML engine 122 may be configured to automatically learn and infer query patterns associated with performant queries. ML engine 122 includes tokenizer 124, which receives a set of queries or query expressions as input and automatically extracts a set of query tokens. a query token may correspond to a database command, a clause, a predicate, and/or a reference to a database object (e.g., a table, view, or database schema name)).
Claims 3, 6, 13 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Maheshwari et al. ( US 20220197900 A1), hereinafter referenced as Maheshwari, in view of Trummer et al. (US 20240281222 A1), hereinafter referenced as Trummer, further in view of Marks et al. (US 12248502 B1), hereinafter referenced as Marks.
Regarding Claim 3, Maheshwari in view of Trummer, teach the method of claim 1. Maheswari in view of Trummer, fail to explicitly teach the claimed, wherein adjusting the selected at least one query example
However, Marks does teach the claimed, wherein adjusting the selected at least one query example(Marks: Column 26, lines 10-12, structured query is reduced to a threshold byte size for encapsulation, the threshold byte size being processable by the generative AI model).
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 Marks’s teaching of structured filtering for a natural language input based on generative artificial intelligence, into the system and method, taught by Maheshwari in view of Trummer, because, this would improve user experience by increasing search speed and accuracy.(Marks, Column 6, lines 47-67, column 9, lines 31-47).
Claim 13 is system claim performing the steps in method claim 3 above and as such, claim 13 is similar in scope and content to claim 3 and therefore, claim 13 is rejected under similar rationale as presented against claim 3 above.
Claim 18 is non-transitory computer readable medium claim performing the steps in method claim 3 above and as such, claim 18 is similar in scope and content to claim 3 and therefore, claim 18 is rejected under similar rationale as presented against claim 3 above.
Regarding Claim 6, Maheshwari in view of Trummer, teach the method of claim 1. Maheshwari in view of Trummer, fail to explicitly teach the claimed, wherein the input query is a technical state query requesting information regarding a technical state of a technical system, and/or the input query is a natural language query.
However, Marks does teach the claimed, wherein [the input query is a technical state query requesting information regarding a technical state of a technical system, ] and/or the input query is a natural language query (Marks: Column 19, lines 3-7, Fig. 8, A search interface that can receive a natural language question/input 802 and utilize generative artificial intelligence (AI) to select the appropriate filters for the search based on the natural language question/input 802 may provide a more user-friendly search interface).
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 Marks’s teaching of structured filtering for a natural language input based on generative artificial intelligence, into the system and method, taught by Maheshwari in view of Trummer, because, this would improve user experience by increasing search speed and accuracy.(Marks, Column 6, lines 47-67, column 9, lines 31-47).
Claims 4, 5, 14, 15, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Maheshwari et al. ( US 20220197900 A1), hereinafter referenced as Maheshwari, in view of Trummer et al. (US 20240281222 A1), hereinafter referenced as Trummer, further in view of Kelsey et al. (US 20180260472 A1), hereinafter referenced as Kelsey.
Regarding Claim 4, Maheshwari in view of Trummer, teach the method of claim 1. Maheshwari in view of Trummer, fail to explicitly teach the claimed, wherein selecting at least one query example comprises: identifying keywords, from a predetermined list of keywords, applying to the input query, scoring each query example of the plurality of query examples for the identified keywords, selecting at least one high scoring query example from the plurality of query examples.
However, Kelsey does teach the claimed, wherein selecting at least one query example comprises: identifying keywords, from a predetermined list of keywords, applying to the input query ( Kelsey: Para.[0039],[0076], Words or phrases that are closest to the weighted average can be determined to be keywords (i.e. closest to the subject of the text fragment), and can be selected as candidates ),
scoring each query example of the plurality of query examples for the identified keywords ( Kelsey: Para.[0085], Ranking of questions can be based on their semantic relatedness to a set of key words or phrases generated for the source document overall. The key words and phrases can be determined by one or more of the following techniques: a syntactic approach, Latent Dirichlet Allocation, or a technical keyword generator that identifies domain-specific technical terms ),
selecting at least one high scoring query example from the plurality of query examples ( Kelsey: Para.[0085], Questions that closely match the most important keywords and phrases from the overall document can be assigned high ranks ( and can be selected), while questions having a poor match to the set of key words and phrases can be assigned low ranks. Para.[0096], questions with high rank has higher yield of acceptance ).
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 Kelsey’s teaching of automated tool for question generation, into the system and method, taught by Maheshwari in view of Trummer, because, this would improve the percentage of grammatically correct sentences, questions, answers, and can be higher than obtainable by conventional techniques. The subject matter relevance of questions, answers, to the most important sections of the source document can be judged improved as compared to conventional techniques.(Kelsey, Para.[0031]).
Claim 14 is system claim performing the steps in method claim 4 above and as such, claim 14 is similar in scope and content to claim 4 and therefore, claim 14 is rejected under similar rationale as presented against claim 4 above.
Claim 19 is non-transitory computer readable medium claim performing the steps in method claim 4 above and as such, claim 19 is similar in scope and content to claim 4 and therefore, claim 19 is rejected under similar rationale as presented against claim 4 above.
Regarding Claim 5, Maheshwari in view of Trummer, further in view of Kelsey teach the method of claim 4. Maheshwari further teaches, wherein selecting at least one query example comprises, converting the input query into an input embedding vector ( Maheshwari: Para.[0061],[0062], Fig. 2, NLP prediction layer 204 may provide an internal suggestion to autocomplete incomplete query 202 to embedding layer 206 by generating numerical vector representation),
obtaining for the plurality of query examples, a plurality of corresponding embedding vectors ( Maheshwari: Para.[0064], Fig. 2, query vectors 210 include numerical vector representations for a set of performant database queries),
selecting a query example from the plurality of query examples according to a vector similarity between the input embedding vector and embedding vectors in the plurality of embedding vectors ( Maheshwari: Para. [0113],Fig. 7, process 700 identifies one or more additional queries that satisfy a similarity threshold relative to completed query based on numerical query representations of performant queries ( operation 706). Process 700 may identify the queries by converting the autocompleted query output by the NLP model into a numerical query vector. A cosine similarity, Euclidean distance, and/or other similarity measure may then be computed between the vector representation of the autocompleted query and vector representations for the set of performance queries).
Claim 15 is system claim performing the steps in method claim 5 above and as such, claim 15 is similar in scope and content to claim 5 and therefore, claim 15 is rejected under similar rationale as presented against claim 5 above.
Claim 20 is non-transitory computer readable medium claim performing the steps in method claim 5 above and as such, claim 20 is similar in scope and content to claim 5 and therefore, claim 20 is rejected under similar rationale as presented against claim 5 above.
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant's disclosure.
Cunningham et al. (US 20240394249 A1) teaches systems and methods for natural language query processing and visualization. In embodiments, a structure associated with a dataset and a natural language question are obtained and provided to an AI model to request, from the AI model, a query that may be used for retrieving data from the dataset responsive to the natural language question. The query is received from the AI model and executed against the data in the dataset to retrieve data responsive to the natural language question. In embodiments, the data responsive to the natural language question is analyzed to determine one or more structural characteristics of the data responsive to the natural language question, and a graphical visualization of the data responsive to the natural language question is generated based on the one or more structural characteristics of the data responsive to the natural language question.
Li et al. (Query from examples: an iterative, data driven approach to query construction, Proceedings of the VLDB Endowment, Vol. 8, No. 13, 2015) teaches Query from Examples (QFE) process, to help non-expert database users to construct SQL queries, which is designed for users who might be unfamiliar with SQL, only requires that the user is able to determine whether a given output table is the result of his or her intended query on a given input database. To kick-start the construction of a target query Q, the user first provides a pair of inputs: a sample database D and an output table R which is the result of Q on D. As there will be many candidate queries that transform D to R, QFE knows this collection by presenting the user with new database-result pairs that distinguish these candidates. Unlike previous approaches that use synthetic data for such pairs, QFE strives to make these distinguishing pairs as close to the original (D,R) pair as possible. By doing so, it seeks to minimize the effort needed by a user to determine if a new database-result pair is consistent with his or her desired query.
Kandukuri et al. (US 11954128 B2) teaches methods, systems, and apparatuses for generating notifications corresponding to queries submitted for execution by virtual warehouses. A request to execute a query may be received. An execution plan, for the query, may be identified. A processing complexity for the query may be predicted based on the query and the execution plan. A notification may be generated based on the processing complexity meeting an alert threshold. A user device may display the notification. A response to the notification comprising a selection of a first virtual warehouse, of a plurality of virtual warehouses, to execute the query may be received.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NADIRA SULTANA whose telephone number is (571)272-4048. The examiner can normally be reached M-F,7:30 am-5:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Paras D. Shah can be reached on (571) 270-1650. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/NADIRA SULTANA/Examiner, Art Unit 2653
/Paras D Shah/Supervisory Patent Examiner, Art Unit 2653
09/16/2026
Prosecution Insights