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 .
Status of the Claims
Claims 1, 9, 15 have been amended. Claims 1-9, 11-16, 18-20 are pending.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-9, 11-16, 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guan et al. (US 20240427742) in view of Jones et al. (US 12,321,791) and in further view of Hoang et al. (US 20250068627).
Regarding claim 1, Guan teaches a system comprising: a memory storing program code: and one or more processing units (F1) to execute the program code to cause the system to:
generate an application-specific data dictionary for each application ([0027]-[0028], [0031]- [0032], [0037], [0040], [0173], [0189], [0202] “dictionary definition ( e.g., on a target program or a database”), the application-specific data dictionary including for each of a plurality of objects: an object name, a description, a data type, a size, a classification and a relationship with other data sets ([0032] “specific data context that is presented by a specific database, a database schema, and/or a database architecture”; [0046] “"data dictionary" which is a collection of metadata such as object name, data type, size, classification, and relationships with other data assets”), wherein the description includes terms used to describe the object and generate at least a first Structured Query Language (SQL) query ([0037] “get a database dictionary related to the NL query and input them to LLM to generate SQL code”);
receive a natural language query ([0026], [0155]);
generate a first SQL query based on the received natural language query and using a large language model (LLM) ([0026] “generate structured query language code based on a query entered as a text input to a query prompt”; “SQL code is generated … the LLM provides limited or no options to tailor the produced SQL query to a target database”) trained with the application-specific data dictionary ([0027]-[0029], [0046] [0178], [0202]), the generated SQL query including an endpoint and one or more fields based on the application- specific data dictionary trained LLM ([0046], [0054]-[0099], [0160], [0179]);
receive a response to the first SQL query from a data source ([0033]-[0034])
determine the natural language query is answerable with the received response ([0033] “LLM processes the natural language text input to produce query code that may return the result requested”);
generate a second SQL query in a case it is determined the natural language query is determined unanswerable ([0033] “database being queried which may have data tables that do not match the produced code. … if the database targeted does not have a product name data table … will produce errors or no output”, [0136] as in conflicts in the query), wherein the second SQL query is one of: a new query and an update to the first SQL query ([0121] “refine any generated code”, [0136] “system will then produce a query so that the criteria … used in SQL query instead of OrderStatusName='Paid'”, [0137] as in a second query is generated based on the initial queries shown in [0131]);
generate a natural language response from the response to the SQL query in a case the natural language query is determined answerable ([0035] “user asks ChatGPT to summarize a document that they provide as part of the request, the input words allow the output produced by the model to reflect the action requested”, [0036], [0053]); and transmit the natural language response to an entity ([0033]-[0034]) (see NOTE).
Guan does not explicitly teach however Jones discloses determine, via a no contract-based Application Programming Interface (API) (C6L44 -50 “API is not specified using predefined or preprogramed software”; “the API may be a codeless API that is flexibly adapted”), the endpoint and an API call from data included in the generated SQL query (C8L48-53, C28L65-66, C30L44-54); invoke the no contract-based API (C36L32-37); receive a response to the first SQL query from a data source via the no contract-based API (C28L65-67 – C29L1-5, C30L44-54, C35L1-22).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Guan to include no contract-based Application Programming Interface as disclosed by Jones. Doing so would allow data in database to be efficiently, flexibly and robustly accessed in real time in a wide variety of applications (Jones C42L43-45).
NOTE Guan teaches in a respond to a user’s natural language query a response is returned by the LLM in a form of a structural code (as opposed to a “natural language response”), which seems to be a desired, requested output. However, in other embodiments and paragraphs Guan teaches in a respond to a user’s natural language query providing a summary (aka natural language) response. It is well-known that LLM models, such as GPT3, GPT4 shown in Fig.3 are more than capable to produce a natural language response when prompted. Thus, it is obvious and reasonable to conclude that such natural language response surely be provided to the user when such format is requested.
However, to merely obviate such reasoning, Hoang discloses generate a natural language response ([0039] "formulate a response to the NL utterance (e.g., an answer to the user's question) for review by a user", [0047], [0050] "generate a natural language response", [0055]) from the response to the SQL query ([0038], [0130], [0137]); and transmit the natural language response to an entity ([0166] "eventually returns to the users/customers (e.g., as part of a dialog or response to the user)", [0177]).
NOTE Hoang further discloses application-specific data ([0188] “data repositories used by applications may be of different types such as, for example, a key-value store repository, an object store repository, or a general storage repository supported by a file system”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Guan to include natural language response and application-specific data as disclosed by Hoang. Doing so would improve conversational experience (Hoang [0003]).
Claims 9 and 15 recite substantially the same limitations as claim 1, and is rejected for substantially the same reasons.
Regarding claim 2, Guan as modified teaches the system of claim 1, further comprising program code to cause the system to:
extract one or more intents from the natural language query (Hoang [0052], [0080], [0104]); and
transmit the extracted one or more intents to a text generation tool for generation of the SQL query (Hoang [0122], [0126], [0165]).
Regarding claims 3 and 16, Guan as modified teaches the system and the media wherein the one or more intents are extracted based on action verbs in the natural language query (Hoang [0103]-[0104], [0106]).
Regarding claim 4, Guan as modified teaches the system of claim 2, wherein the text generation tool is the large language model (LLM) (Guan [0028], Hoang [0131]).
Regarding claims 5, 11 and 18, Guan as modified teaches the system, the method and the media, wherein the LLM is trained with two or more data dictionaries (Guan F1:1010, [0028], [0046] [0113], [0130], [0133], Hoang [0085], [0103] [0113], [0130], [0133]).
Regarding claims 6, 12 and 19, Guan as modified teaches the system, the method and the media, wherein each data dictionary is generated for a respective application-specific database (Guan [0027]-[0028], [0031]-[0032], [0189], [0196] Hoang [0187] “data repositories 1414, 1416 may be used to store information such as information related to chatbot”, [0225] “communication subsystem may be used to communicate with a chatbot system selected for an application”, Jones C31L4-6, C20L15-31).
Regarding claims 7 and 13, Guan as modified teaches the system and the method, wherein the SQL query includes a data source identifier (Hoang [0158], [0167] Table 5, [0187], [0208], Jones C27L56-67, C30L1-8, C44L44-50, C46L10-13).
Regarding claim 8, Guan as modified teaches the system of claim 1, wherein the API call provides security to the data source (Hoang [0203], [0210], Jones C9L13-14, C16L22-23, C17L66-67, C19L1-5).
Regarding claims 14 and 20, Guan as modified teaches the method and the media, wherein the natural language response is transmitted as a text response or a voice response (Hoang [0056], [0064], Jones C31L7-8)).
Claims 7-8, 13 is/are additionally rejected under 35 U.S.C. 103 as being unpatentable over Guan as modified and in further view of Zhao et al. (US 20250077511).
Regarding claims 7 and 13, if Guan as modified does not explicitly teach, however Zhao discloses the system and the method, wherein the SQL query includes a data source identifier ([0022], [0167] Table 5, [0187], [0208]).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Guan as modified to include data source identifier as disclosed by Zhao. Doing so providing tenants with a robust system for retrieving information (Zhao [0012]).
Regarding claim 8, if Guan as modified does not explicitly teach, however Zhao discloses the system of claim 1, wherein the API call provides security to the data source ([0015], [0029], [0036], [0038]).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Guan as modified to include API call that provides security as disclosed by Zhao. Doing so providing tenants with information security across a variety of knowledge sources from a single interface via natural language interactions (Zhao [0012]).
Response to Arguments
Applicant’s arguments, filed 07/07/2026, in regard to the presently amended claims, are addressed in the updated rejections to the claims above.
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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/POLINA G PEACH/Primary Examiner, Art Unit 2165 July 21, 2026
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