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 .
DETAILED ACTION
The office action sent in response to Applicant’s communication received on 8/28/2024 for the application number 18817996. The office hereby acknowledges receipt of the following placed of record in the file: Specification, Abstract, Oath/Declaration and claims.
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.
Status of the claims
Claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure submitted on 8/28/2024 were before 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.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of Co-pending U.S. Application No. 18750469 . Although the claims at issue are not identical, they are not patentably distinct from each other.
Regarding claim 1, claim 1 of Co-pending U.S. Application No. 18/750469 claims all the limitations set forth in the application claim 1. Although the claims are not exactly same the co-pending application reads on the current application and has additional limitations. ( italicized portions)
US Application No. 18817976 Claim 1
Co-pending U.S. Application No. 18750649 Claim 1
1. A computing services environment comprising: a database system storing a plurality of database records for a plurality of client organizations accessing computing services via the computing services environment, the computing services including a conversational chat interface; an application server providing access to the conversational chat interface to a plurality of client machines associated with one or more of the plurality of client organizations; a metadata repository storing a plurality of metadata entries for a plurality of agents, a metadata entry of the plurality of metadata entries including a description of a designated agent of the plurality of agents, the metadata entry defining interaction data for interacting with the designated agent; and an orchestration service configured to execute an orchestration process based on a natural language request message received via the conversational chat interface, wherein executing the orchestration process includes: determining an input prompt including (1) the natural language request message and (2) a plurality of agent descriptions selected from some or all of the plurality of metadata entries, transmitting the input prompt to a generative language model via a generative language model interface, receiving from the generative language model interface a prompt completion including a selection of the designated agent based on the plurality of agent descriptions, and generating novel text responsive to the natural language request message by transmitting a request to the designated agent based on the natural language request message and the interaction data.
1. A computing services environment comprising: a database system storing a plurality of database records for a plurality of client organizations accessing computing services via the computing services environment, the computing services including a plurality of conversational chat assistants; an application server receiving natural language user input for [[the]] a conversational chat assistantcorresponding to a client organization of the plurality of client organizations; a generative language model interface providing access to one or more generative language models; a metadata framework including a plurality of action definitions corresponding to a plurality of actions performed via the computing services environment and defining inputs and outputs to the plurality of actions, each of the action definitions being identified by a respective unique identifier; an orchestration and planning service configured to; 1. analyze the natural language user input via a generative language model of the one or more generative language models to identify a subset of the unique identifiers corresponding to a subset of the action definitions to fulfill an intent expressed in the natural language user input, and (2) execute a subset of the plurality of actions corresponding to the subset of the action definitions to determine a natural language response message; and a communication interface configured to transmit the natural language response message to a client machine via the application server.
Regarding claim 14 and 19 , claim 17 and 20 of copending application claims all the limitations set forth in the application claims 14 and 18 respectively.
Regarding claims 2-13, 15-18 and 20, claims 1-20 of the copending application claims all the limitations set forth in the application claims 2-13, 15-18 and 20 respectively.
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 1-12 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).
Examiner’s Note regarding 101
The claims recites a practical application and hence the rejection under 101 is not applicable.
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 1-8 and 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over Xu ( US 20250094465) and further in view of Baldua ( US 20250110957) and further in view of Cai( Low-code LLM: Graphical User Interface over Large Language Models )
Regarding claim 1, Xu teaches a computing services environment ( fig. 2) comprising: a database system storing a plurality of database records for a plurality of client organizations ( agents associated with enterprise, Para 0044, provisional – Para 0026) accessing computing services via the computing services environment, the 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 plurality of client machines associated with one or more of the plurality of client organizations ( multiple agents, Fig 2; nstances, 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) ; a metadata repository storing metadata entries characterizing a plurality of actions capable of being performed via the computing services environment ( 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): wherein executing the orchestration process includes : 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 ( prompting LLM – DA input pipeline, Fig 2, where there are plural digital assistant associated the agents ( plural agents), Para 0047, 0050, provisional - Para 0026-0026) , receiving from the generative language model interface a prompt completion including: (1) a plan that includes a subset of the actions ( executing plan, Fig 2, Para 0059; provisional – Para 0041) , and (2) a natural language description of the plan ( natural language description in the plan, Fig 2, provisional -fig 2) , transmitting the natural language description to the client machine via the conversational chat interface ( end responses, Para 0066-0067, Fig 2, provisional Para 0043) , 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 explicitly teach transmitting the input prompt to a generative language model of a plurality of generative language models via a generative language model interface
Baldua teaches transmitting the input prompt to a generative language model of a plurality of generative language models via a generative language model interface ( embodiments can dynamically configure a prompt to include instructions to cause one or more generative artificial intelligence models (e.g., one or more large language models) to generate and output a plan for executing a query. In accordance with the instructions set forth in the prompt, the large language model is to generate a query execution plan that includes a set of functions, where the set of functions are executable using a set of data resources to create a modified version of the initial query, Para 0025, 0054)
It would have been obvious having the teachings of Xu to further include the concept of Baldua before effective filing date to address latency and/or other performance issues associated with a computing system or network ( Para 0033, Baldua)
Xu modified by Baldua does not explicitly teach receiving user input regarding the plan via the conversational chat interface, 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 the user input
However, Cai teach receiving user input regarding the plan via the conversational chat interface ( user can edit the workflow plan, Page 1-2, Fig1) 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 the user input ( LLM response based on the workflow and user edits, Fig 1)
It would have been obvious having the teachings of Xu and Baldua to further include the concept of Cai before effective filing date since there are at least three advantages of the low-code LLM:- controllable generation results, user-friendly human-LLM interaction, and broadly applicable scenarios ( Abstract, Cai)
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 3, Xu as above in claim 1, teach wherein the natural language description of the plan is human-readable ( fig 2)
Regarding claim 4, Cai as above in claim 1, teach wherein the user input includes a natural language clarification indicating a requested modification to the plan (modifying the step names etc., 2.1, 2.3)
Regarding claim 5, Cai as above in claim 1, t, wherein executing the orchestration process further includes: determining an updated input prompt including (1) the natural language request message, (2) the natural language clarification, and (3) the descriptions of the actions selected from the metadata entries, transmitting the updated input prompt to the generative language model via the generative language model interface, receiving from the generative language model interface an updated prompt completion including: (1) an updated plan that includes the one or more actions, and (2) an updated natural language description of the updated plan ( editing the workflow and executing, Step 2.1-2.5, Fig 2)
Regarding claim 6, Cai 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 (modifying buttons, 2.3)
Regarding claim 7, Xu as above in claim 6, teach wherein the user interface affordance is a virtual button presented on a display screen (modifying like adding deleting etc., 2.3, Fig 2)
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, Baldua 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 ( fig 1—outside of the network )
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 14, arguments analogous to claim 3, are applicable.
Regarding claim 15, arguments analogous to claim 4, are applicable.
Regarding claim 16, arguments analogous to claim 5, are applicable.
Regarding claim 17, arguments analogous to claim 1, are applicable.
Regarding claim 18, arguments analogous to claim 3, are applicable.
Regarding claim 19, arguments analogous to claim 4, are applicable.
Regarding claim 20, arguments analogous to claim 5, are applicable.
Claims 1-8 and 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over Xu ( US 20250094465) and further in view of Baldua ( US 20250110957) and further in view of Cai( Low-code LLM: Graphical User Interface over Large Language Models ) and further in view of Bazzo ( US 20250138986)
Regarding claim 9, Xu modified by Baldua and Cai 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 Baldua and Cai 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)
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Phan Hai can be reached at (571)272-6338. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Richa Sonifrank/Primary Examiner, Art Unit 2654
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