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 .
Priority
This application claims priority to U.S. Patent Application 18/750,469 (Attorney Docket No. SFDCP224) by Kshirsagar et al., titled “Systems And Methods For Generative Language Model Database System Integration Architecture”, filed on June 21, 2024, which claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Patent Application 63/558,557 (Attorney Docket No. SFDCP224P) by Padmanabhan, titled “GENERATIVE LANGUAGE MODEL DATABASE SYSTEM INTEGRATION ARCHITECTURE”, filed on February 27, 2024, and to U.S. Provisional Patent Application 63/558,580 (Attorney Docket No. SFDCP225P) by Padmanabhan, titled “GENERATIVE LANGUAGE MODEL DATABASE SYSTEM INTEGRATION INTERFACE CONFIGURATION”, filed on February 27, 2024, and to U.S. Provisional Patent Application 63/558,641 (Attorney Docket No. SFDCP226P) by Padmanabhan, titled “GENERATIVE LANGUAGE MODEL DATABASE SYSTEM ACTION CONFIGURATION AND EXECUTION”, filed on February 27, 2024, and to U.S. Provisional Patent Application 63/558,653 (Attorney Docket No. SFDCP227P) by Padmanabhan, titled “GENERATIVE LANGUAGE MODEL DATABASE SYSTEM ACTION CUSTOMIZATION AND EXECUTION”, filed on February 28, 2024, all of which are incorporated herein by reference in their entirety and for all purposes. This application also claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Patent Application 63/665,455 (Attorney Docket No. SFDCP230P) by Kshirsagar et al., titled “Systems and Methods for Generative Language Model Database System Dynamic Reasoning Engine Selection and Execution”, filed on June 28, 2024, and to U.S. Provisional Patent Application 63/665,857 (Attorney Docket No. SFDCP231P) by Kshirsagar et al., titled “Systems and Methods for Generative Language Model Database System Dynamic Enrichment And Disambiguation”, filed on June 28, 2024, and to U.S. Provisional Patent Application 63/665,466 (Attorney Docket No. SFDCP232P) by Kshirsagar et al., titled “Systems and Methods for Generative Language Model Database System Interactive Action Plan Determination”, filed on June 28, 2024, and to U.S. Provisional Patent Application 63/665,995 (Attorney Docket No. SFDCP210P) by Kshirsagar et al., titled “Systems and Methods for Generative Language Model Database System Retrieval Augmented Generation for Context Retention”, filed on June 28, 2024.
Information Disclosure Statement
The information disclosure submitted on 9/8/2026 was filed after the mailing data of the first office action. The /submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
Claims 1, 13 and 17 are amended. Claims 3-5, 14-16 and 18-20 are cancelled. Claims 1-2, 6-13, and 17 are presented for examination.
Response to Arguments
The applicant’s arguments filed on 9/4/2026 have been reviewed, and the response is provided below.
Double Patenting
In light of the terminal disclaimer rejection under 35 U.S.C §101 is withdrawn.
Claim Interpretation
Claim does not include the word processor, but recites the function of a processor and hence the claim interpretation under 112 (f) is issued. Applicant can clearly recites the processor to avoid such interpretation.
35 U.S.C. §103 Rejections
Applicant’s arguments with respect to claims 1-2, 6-13, and 17 have been 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 Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Claims 1-2 and 6-12 include one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitations use a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: a database system, an application server, an orchestration and planning service, computing service environment, communication interface in claim 1, trust layer in claim 9 and 10.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f), they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof (environment include …processor ( fig 9, 11, Para 0153, 183--; Environment 910 may include user systems 912, network 914, database system 916, processor system 917, application platform 918, network interface 920, tenant data storage 922, tenant data 923, system data storage 924, system data 925, program code 926, process space 928, User Interface (UI) 930, Application Program Interface (API) 932, PL/SOQL 934, save routines 936, application setup mechanism 938, application servers 950-1 through 950-N, system process space 952, tenant process spaces 954, tenant management process space 960, tenant storage space 962, user storage 964, and application metadata 966. Some of such devices may be implemented using hardware or a combination of hardware and software and may be implemented on the same physical device or on different devices. Thus, terms such as “data processing apparatus,” “machine,” “server” and “device” as used herein are not limited to a single hardware device, but rather include any hardware and software configured to provide the described functionality.)
If applicant does not intend to have these limitation(s) interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f).
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 11 recites the limitation “the plurality of generative model" in line 1. There is insufficient antecedent basis for this limitation in the claim.
. 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
And
KSR, 550 U.S. at 418, 82 USPQ2d at 1396. Exemplary rationales that may support a conclusion of obviousness include:
(A) Combining prior art elements according to known methods to yield predictable results;
(B) Simple substitution of one known element for another to obtain predictable results;
(C) Use of known technique to improve similar devices (methods, or products) in the same way;
(D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results;
(E) "Obvious to try" – choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success;
(F) Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art;
(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
See MPEP § 2143 for a discussion of the rationales listed above along with examples illustrating how the cited rationales may be used to support a finding of obviousness. See also MPEP § 2144 - § 2144.09 for additional guidance regarding support for obviousness determination.
Claims 1-2, 6-8, 11-12, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Xu ( US 20250094465) and further in view of Abraham ( US 20240428008)
Regarding claim 1, Xu teach a computing services environment ( Fig 2) comprising: a database system storing database records for client organizations ( agents associated with enterprise, Para 0044, provisional – Para 0026) accessing computing services including a conversational chat interface ( Enterprises may use one or more bot systems to communicate with end users through a messaging application, Para 0039, provisional- Para 0022); an application server providing access to the conversational chat interface to a metadata repository storing metadata entries characterizing actions capable of being performed via the computing services environment ( multiple agents, Fig 2; instances, the agents can be developed by an enterprise and then added to a digital assistant using DABP 105. In other instances, the agents can be developed and created using DABP 105 and then added to a digital assistant created using DABP 105. In yet other instances, DABP 105 provides an online digital store (referred to as an “agent store”) that offers various pre-created agents directed to a wide range of tasks and actions. The agents offered through the agent store may also expose various cloud services. In order to add the agents to a digital assistant being generated using DABP 105, a user 110 of DABP 105 can access assets via tools 120, select specific assets for an agent, initiate a few mock chat conversations with the agent, and indicate that the agent is to be added to the digital assistant created using DABP 105, Para 0048, provisional- Para 0038; where the list may be determined by running a search, such as a semantic search, on a context and memory store that has one or more indices comprising metadata for all agents 145 available to the digital assistant 115A. Metadata for the candidate agents 145A-N in the list of candidate agents is then combined with the user input to construct an input prompt for the one or more LLMs 140., Para 0052, 0060-0061, Fig 2; provisional - Para 0040-0041, Fig 2); an orchestration service configured to execute an orchestration process based on a natural language request message received via the conversational chat interface ( input pipe line receives the message, fig 2, provisional- fig 2), the orchestration process including: determining an input prompt including (1) the natural language request message( search, Para 0059 Fig 2; provisional, - Fig 2) and (2) descriptions of actions selected from the metadata entries( Metadata for the candidate agents 145A-N in the list of candidate agents is then combined with the user input to construct an input prompt for the one or more LLMs 140, Para 0052; wherein the metadata includes The list of candidate agents includes the metadata (e.g., metadata extracted from artifacts 217 and assets 219) from the context and memory store 214 that is associated with each of the candidate agents, Para 0060 and artifacts include The artifacts 217 for the digital assistant include information on the general capabilities of the digital assistant and specific information concerning the capabilities of each of the agents 218 (e.g., actions) available to the digital assistant (e.g., agent artifacts), Para 0060, provisional – Para 0041-0043), transmitting the input prompt to a generative language model via a generative language model interface( end responses, Para 0066-0067, Fig 2, provisional Para 0043), receiving from the generative language model interface a prompt completion including: (1) a plan that includes a subset of the actions ( generating a execution plan, element 210, Fig 2) , and (2) a human-readable natural language description of the plan generated from corresponding metadata entries ( plans is based on the metadata, Fig 2) ,generating novel text responsive to the natural language request message by executing one or more actions of the plurality of actions, the one or more actions being determined based on the plan and and transmitting the novel text to the client machine via the conversational chat interface (novel text is generated to communicate the response to the user, Fig 2, Para 0066-0067; provisional- Para 0041-0044, Fig 2)
Xu does not teach transmitting the human-readable natural language description via the conversational chat interface for review, receiving natural language user input regarding the plan via the conversational chat interface indicating a requested modification to the plan, determining an updated input prompt including the natural language request message, the user input, and the plan, transmitting the updated input prompt to the generative language model and receiving an updated prompt completion including an updated plan reflecting the requested modification, generating novel text responsive to the natural language request message by executing one or more actions; the one or more actions being determined based on the updated plan and the user input, and transmitting the novel text to the client machine via the conversational chat interface
However, Abraham teaches transmitting the human-readable natural language description via the conversational chat interface for review ( if the intent is a multistep query, the LLM generates a response with a plan with steps for responding to the multistep query, Para 0028, 0072, Fig 2c-2e) , receiving natural language user input regarding the plan via the conversational chat interface indicating a requested modification to the plan (The chat area 12 includes an icon 216 for accepting the plan 18 and an icon 218 for requesting alternative steps for the plan 18. The user 104 may make modifications to the plan 18 interactively using the chat area 12., Para 0072; Another technical advantage of the methods and systems of the present disclosure is allowing modifications of the plan, interactively through chat, for responding to multistep queries prior to receiving a response from an LLM for an input message., Para 0029) , determining an updated input prompt including the natural language request message, the user input, and the plan, transmitting the updated input prompt to the generative language model ( executing the plan or the updated plan, Para 0028, Fig 2c-2e) and receiving an updated prompt completion including an updated plan reflecting the requested modification ( Fig 2c-2e) , generating novel text responsive to the natural language request message by executing one or more actions; the one or more actions being determined based on the updated plan and the user input, and transmitting the novel text to the client machine via the conversational chat interface ( generates a response and transmit to the user, Para 0096-0102)
It would have been obvious to a POSITA to further include the concept of Abraham before the effective filing date, since allowing a user to edit the plan shifts from input-response sequential chains that are limited to a fixed number of turns. Furthermore, by allowing user to change the plan ensure that plans only incorporate what is necessary to for a particular task
Regarding claim 2, Xu as above in claim 1, teach wherein the plan includes a plurality of identifiers uniquely identifying the subset of the actions ( identifier for e.g. “401k contribution” as an assets, Para 0045, Fig 2), provisional – digital assistant 106 may generate an execution plan that identifies the bot or agent to execute and perform one or more actions or operations responsive to the understood meaning or goal of the user, Para 0030, fig 2)
Regarding claim 6, Abraham as above in claim 1, teach wherein the user input includes an indication of a selection of a user interface affordance at the client machine (the copilot engine 106 receives feedback 22 for the plan 18 with a modification to the plan 18. For example, the user 104 provides the feedback 22 to the plan 18 using the user interface 10. Examples of modifications include a removal of an AI model 36 or formula 34, an addition of an AI model 36 or formula 34, a removal of a step 20, an addition of a step 20, selecting a different data source 110, 112 for the plan 18, or editing a step 20. The LLM 108 incorporates the feedback 22 into the response 16 and the response 16 includes the modifications to the plan 18., Para 0101)
Regarding claim 7, Abraham as above in claim 6, teach wherein the user interface affordance is a virtual button presented on a display screen (screen button etc., Fig 2c-2e, Para 0101)
Regarding claim 8, Xu as above in claim 1, teach wherein identifying the subset of actions comprises: determining a topic identification input prompt that includes the natural language request message and one or more natural language instructions executable by the generative language model to identify a topic based on the natural language request message ( topic for e.g. 401k or pizza) ; transmitting the topic identification input prompt to the generative language model for completion; receiving a topic identification prompt completion from the generative language model; and identifying one or more topics of a plurality of topics by parsing the topic identification prompt completion, wherein each of the plurality of topics corresponds with a respective topic-based subset of the plurality of actions, and wherein the subset of topics corresponds with the one or more topics ( different topic for e.g. 401k contribution vs. contribution limit, Fig 2)
Regarding claim 11, Abraham as above in claim 1, teaches wherein the plurality of generative language models includes a first generative language model hosted outside the computing services environment, wherein the plurality of generative language models includes a second generative language model hosted outside of the computing services environment (same or different cloud network for different AI models, Para 0032, 0065, 0101( removal or addition of AI))
Regarding claim 12, Xu as above in claim 1, teach wherein an action of the actions comprises retrieving one or more database records from the database system, the one or more database records being associated with a client organization of the plurality of client organizations ( agent associated with enterprise, Fig 2,/para 0026, Provisional - DA and agent artifacts, Fig 2)
Regarding claim 13, arguments analogous to claim 1, are applicable.
Regarding claim 17, arguments analogous to claim 1, are applicable.
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Xu ( US 20250094465) and further in view of Abraham ( US 20240428008) and further in view of Bazzo ( US 20250138986)
Regarding claim 9, Xu modified by Abraham as above in claim 1, does not teach further comprising a trust layer, wherein the trust layer is configured to mask sensitive data included the input prompt before the input prompt is transmitted to a generative language model for completion
However, Bazzo teaches a trust layer, wherein the trust layer is configured to mask sensitive data included the input prompt before the input prompt is transmitted to a generative language model for completion ( replacing PII with mask characters, thereby de-identifying or redacting the PII before it is sent to the LLM 118, Para 0078, 0035, 0111)
It would have been obvious having the teachings of Xu modified by Abraham to further include the concept of Bazzo before effective filing date to protect privacy ( Para 0035, Bazzo)
Regarding claim 10, Bazzo as above in claim 9, teach , wherein masking sensitive data includes replacing a text portion with a unique identifier, and wherein the trust layer is further configured to demask the prompt completion received from the generative language model by replacing the unique identifier with the text portion ( The response generation component 216 may postprocess “raw” responses from the LLM 118 to transform the responses into final output that can be presented to a user (e.g., at the user device 106 via the web client 112). For example, where original data items where replaced with alternative or placeholder data items in order to de-identify or shorten the input prompt, and one or more of the alternative or placeholder data items appear in the response from the LLM 118, the response generation component 216 may automatically replace them with the corresponding original data items (e.g., to re-identify the information in the response to make the output understandable or relevant to the user)., Para 0088; (] De-identification may be performed by the change data preprocessing component 208 by replacing PII with alternative data items or placeholders, such as unique identifiers (e.g., USER_NAME_1, ADDRESS_2, or CLIENT_NAME_3), or replacing PII with mask characters,, Para 0078),
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 12586578 discloses where the plan generation component 135 generates more than one task to be completed in order to perform the action responsive to the user input, the plan generation component 135 may further maintain and prioritize the list of tasks as the processing of the system 100 with respect to the user input is performed. In other words, as the system 100 processes to complete the list of tasks, the plan generation component 135 may (1) incorporate the potential responses associated with completed tasks into data provided to other components of the system 100; (2) update the list of tasks to indicate completed (or attempted, in-progress, etc.) tasks; (3) generate an updated prioritization of the tasks remaining to be completed (or tasks to be attempted again); and/or (4) determine an updated current task to be completed. The plan generation component 135 may generate and send task processing data 137 representing the selected task to be completed and various other information needed to perform further processing with respect to the task (e.g., the user input data 127, an indication of the selected task, potential responses associated with previous tasks, the remaining task(s), and context data associated with the user input data 127, as described in detail herein below with respect to FIG. 2) to the LLM shortlister component 140.( Col 8, line 30-50)
Baldua ( US 20250110957)
Cai( Low-code LLM: Graphical User Interface over Large Language Models )
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Richa Sonifrank whose telephone number is (571)272-5357. The examiner can normally be reached M-T 7AM - 5:30PM.
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/Richa Sonifrank/Primary Examiner, Art Unit 2654
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