DETAILED ACTION
This is a non-final, first office action on the merits. Claims 1-20 are pending. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over respective corresponding Claims 1-2, 6-13, and 17 of Application #18/817,996 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the current application claims as mapped can be anticipated by the reference application claims. This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
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.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations (See claims 1 and 13-14) 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 limitation(s) uses 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 limitation(s) is/are: a database system, an application server, an orchestration and planning service, computing service environment, communication interface in claim 1, trust layer in claims 13-14.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
The Specification describes:
(Fig 9, 11, Para 0153, 0183; 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).
According to the Specification, the generic place holders could be by hardware, software, and/or any combination of hardware, software. The Specification does not specifically point out the structure. The generic place holders are not modified by significant structure, material, or acts of performing the claimed function. Therefore the limitations of Claims 1 and 13-14 listed above have invoked 112(f).
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (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) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claims 1-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 1 and 13-14 have a number of limitations that invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Claims 1 and 13-14 do NOT state that a database system, an application server, an orchestration and planning service, computing service environment, communication interface stored in memory and executed by the processor. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Paragraphs Para 0153, 183 state that embodiments may be entirely software, hardware, or some combination. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claims 2-16 are rejected for having the same deficiencies as those set forth with respect to the claims that they depend from, independent claim 1.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1-5, 8-12, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. ( US 20250094465) (hereinafter Xu et al.) in view of Qadrud-Din et al. (US Pat No. 11,860,914) (hereinafter Qadrud-Din et al.).
Regarding claims 1, 17, and 19, Xu discloses a computing services environment comprising:
One or more non-transitory computer readable media having instructions stored thereon for performing a method implement at a computing services environment (see Xu, Fig. 11),
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 (see Xu, paras [0044] & [0026], wherein agents associated with enterprise);
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 (see Xu, paras [0038] & [0048], wherein 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);
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 (see Xu, Fig 2, paras [0032], [0040]-[0045], [0050], [0052], & [0060]-[0061], wherein 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…. For each digital assistant, a user may assemble one or more agents…..possessing extensive language understanding derived from vast datasets….Via these conversations, a user 125 can request one or more tasks to be performed by the digital assistant 115A and, in response, the digital assistant 115A is configured to perform the user-requested tasks and respond with appropriate responses to the user 125 using one or more LLMs 140…..metadata associated with candidate agent actions within the list of candidate agent actions, the one or more executable actions that provide information or knowledge for generating the response to the natural language utterance, and (ii) generating a structured output for the execution plan by creating an ordered list of the one or more executable actions and a set of dependencies among the one or more executable actions); and
an orchestration service configured to execute an orchestration process based on a natural language request message received via the conversational chat interface (see Xu, Fig 2, paras [0035], [0041], [0047], wherein the execution plan can include a set of actions to execute, an order in which to execute the set of actions, such as APIs (i.e., Orchestration Service) for interfacing with applications, files and/or documents for retrieving knowledge, data stores for interacting with data, and the like, available to the agents for the execution of actions. The execution plan can be generated by a generative model, such as a large language model, in response to the digital assistant receiving input from an end-user….End users may interact with the bot system through a conversational interaction (sometimes referred to as a conversational user interface (UI)), 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 (see Xu, Fig 2, paras [0058] and [0032]-[0033], wherein
FIG. 1) based at least in part on a series of prompts such as a conversation (e.g., action created for generating natural language text or audio response in reply to an authored natural language prompt such as the query-'What is the impact of XYZ on my 401k Contribution limit?') or an implicit one that is created when an asset is imported…..the selected agent receives the processed input in the form of a query and processes the query to generate a response. This is done by an LLM of the agent predicting the most contextually relevant and grammatically correct response based on its training data and the input (e.g., the query and configuration data) it receives. The generated response may undergo post-processing to ensure it adheres to guidelines, policies, and formatting standards),
transmitting the input prompt to a generative language model via a generative language model interface (see Xu, Fig 2, paras [0093] and [0041], wherein transmit the first prompt to the first generative artificial intelligence model to cause the first generative artificial intelligence model to output the list that includes one or more executable actions….End users may interact with the bot system through a conversational interaction (sometimes referred to as a conversational user interface (UI)),
receiving from the generative language model interface a prompt completion including a selection of the designated agent based on the plurality of agent descriptions (see Xu, Fig 2, paras [0052], [0059], and [0041], wherein the user inputs 130 can be used by the digital assistant 115A to determine a list of candidate agents 145AN. The list of candidate agents (e.g., 145A-N) includes agents configured to perform one or more actions that could potentially facilitate a response 135 to the user input 130…..Communicated to the digital assistant (e.g., via text dialogue box or microphone) and provided as input to the input pipeline 208. The input pipeline 208 is used by the digital assistant to create an execution plan 210 that identifies one or more agents to address the request and one or more actions for the one or more agents to execute for responding to the request….End users may interact with the bot system through a conversational interaction (sometimes referred to as a conversational user interface (UI)), and
generating (summarization) 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 (see Xu, Fig 2, paras [0079], [0033], [0039], [0041], and [0102], wherein a semantic action such as summarizing text or other suitable semantic action….natural-language messages (e.g., questions or comments) through a messaging application that uses natural-language messages….transmit the response to a computing device associated with the user to present the response to the user, to request…..the selected agent receives the processed input in the form of a query and processes the query to generate a response..…End users may interact with the bot system through a conversational interaction (sometimes referred to as a conversational user interface (UI)).
while Xu discloses a generating novel text (para 0079, i.e.. a semantic action such as summarizing text), Xu et al. fails to explicitly disclose generating novel text.
Analogous art Din discloses 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 (see Din, column 10, lines 25-29, wherein a request from a client machine to generate a novel text portion is received at 402. In some embodiments, the request may include a query portion. The query portion may include natural language text, one or more instructions in a query language, user input in some other format; column 7, lines 42-43, wherein transmitting one or more requests to the text generation modeling system 270; column 5, lines 32-39, wherein automated solutions for generated text in accordance with a number of specialized applications. Such applications may include, but are not limited to: simplifying language, generating correspondence, generating a timeline, reviewing documents, editing a contract clause, drafting a contract, performing legal research, preparing for a depositions, drafting legal interrogatories, drafting requests; and column 12, lines 28-32, wherein a text generation flow may involve an initial set of prompts to summarize a set of portions, and then another round of interaction with the text generation modeling system to produce a more compressed summary. Additional details regarding the generation of a novel text).
Xu directed to a system for executing an execution plan for generating a response to an utterance using a digital assistant and large language models. Din directed to identifying one or more documents based on search terms included in a search query. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Johnson, regarding the System for Executing an Execution Plan with a Digital Assistant, to have included 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 because both inventions teach improving model context to yield an improved response. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 2, Xu discloses the computing services environment recited in claim 1, wherein the designated agent is an artificial intelligence model (see Xu, Fig 2, paras [0007], wherein generative artificial intelligence model).
Regarding claims 3, 18, and 20, Xu discloses the computing services environment recited in claim 2, wherein the artificial intelligence model includes a selected generative language model, and wherein generating the novel text, as set forth above with claim 1,
further includes
transmitting a plan determination input prompt to the selected generative language model that includes a (list) of actions executable by the computing services environment (see Xu, Fig 2, paras [0012], [0067], & [0082], wherein transmitted to an output pipeline 270 for generating end-user responses 272….an output prompt 274 for input to the LLM 236…the LLM 236 has a deep generative model architecture (e.g., a reversible or autoregressive architecture with) for generating the responses 272…..generating a structured output for the execution plan by creating an ordered list of the one or more executable actions and a set of dependencies among the one or more executable actions), and
wherein generating the novel text further includes receiving from the selected generative language model a plan determination prompt completion identifying a selected (list) of the actions (see Xu, paras [0093], [0058]-[0059] & [0082], wherein a separate prompt to cause the first generative artificial intelligence model to output the list that includes the one or more executable actions…..generating the list of the one or more executable actions can include selecting the one or more executable actions from a list of candidate actions that are determined via a semantic search of a semantic index…..Communicated to the digital assistant (e.g., via text dialogue box or microphone) and provided as input to the input pipeline 208. The input pipeline 208 is used by the digital assistant to create an execution plan 210 that identifies one or more agents to address the request and one or more actions for the one or more agents to execute for responding to the request….various services and features can be used to enable a user to interact with a digital assistant (e.g., digital assistant 115A described with respect to FIG. 1) based at least in part on a series of prompts such as a conversation.…..generating a structured output for the execution plan by creating an ordered list of the one or more executable actions and a set of dependencies among the one or more executable actions), and
wherein generating the novel text further includes executing the selected (list) of the actions at the computing services environment (see Xu, paras [0093], [0058]-[0059] & [0082], wherein a separate prompt to cause the first generative artificial intelligence model to output the list that includes the one or more executable actions…..generating the list of the one or more executable actions can include selecting the one or more executable actions from a list of candidate actions that are determined via a semantic search of a semantic index…..Communicated to the digital assistant (e.g., via text dialogue box or microphone) and provided as input to the input pipeline 208. The input pipeline 208 is used by the digital assistant to create an execution plan 210 that identifies one or more agents to address the request and one or more actions for the one or more agents to execute for responding to the request….various services and features can be used to enable a user to interact with a digital assistant (e.g., digital assistant 115A described with respect to FIG. 1) based at least in part on a series of prompts such as a conversation.…..generating a structured output for the execution plan by creating an ordered list of the one or more executable actions and a set of dependencies among the one or more executable actions).
Xu et al. fails to explicitly disclose a subset of actions.
Analogous art Din discloses a subset of actions executable by the computing services environment (see Din, column 3, lines 25-29, wherein identify a subset of documents relevant to a particular search query. The text of the filtered documents may then be analyzed against the search query to produce document-level answers to the search query….the system may answer a search query that asks about which features are common in a subset of a corpus of documents that exhibit one or more characteristics).
One of ordinary skill in the art would have recognized that applying the known technique of Din would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Regarding claim 4, Xu discloses the computing services environment recited in claim 3, wherein the designated agent includes a planner service (see Xu, para [0077], wherein a planner 304 of the digital assistant 300).
Regarding claim 5, Xu discloses the computing services environment recited in claim 4, wherein the planner service is a sequential planner configured to identify a sequence for executing the selected subset of the actions (see Xu, paras [0013] & [0077], wherein a planner 304 of the digital assistant 300…determining that one or more dependencies exist between the executable action and at least one other executable action of the one or more executable actions based on the set of dependencies among the one or more executable actions).
Regarding claim 8, Xu discloses the computing services environment recited in claim 1, wherein the designated agent is a workflow that includes a sequence of operations defined within the computing services environment (see Xu, para [0097], wherein the action type may indicate a workflow, or an order or set of states to invoke, for the corresponding executable action. For example, if the corresponding executable action has a first action type, the digital assistant may use a first set of states to invoke as the workflow for executing the executable action, and if the corresponding executable action has a second action type, the digital assistant may use a second set of states to invoke as the workflow for executing the corresponding executable action in which the first set of states and the second set of states may be different from one another).
Xu et al. fails to explicitly disclose generating the novel text includes triggering the workflow to execute the sequence of actions.
Analogous art Din discloses generating the novel text includes triggering the workflow to execute the sequence of actions (see Din, column 10, lines 25-29, wherein a request from a client machine to generate a novel text portion is received at 402. In some embodiments, the request may include a query portion. The query portion may include natural language text, one or more instructions in a query language, user input in some other format; column 3, lines 32-34, wherein different workflows are provided for different tasks, and this application describes a number of examples of such workflows; and column 14, lines 21-26, wherein the text portions selected at 506 and identified at 512 may be assigned to these text chunks, for instance in a sequential order. That is, text portions near to one another in the text itself may be assigned to the same text chunk where it is possible to reduce the number of divisions between semantically similar elements of the text).
One of ordinary skill in the art would have recognized that applying the known technique of Din would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Regarding claim 9, Xu discloses the computing services environment recited in claim 1, wherein the designated agent is a human (see Xu, para [0003], wherein maintaining a live communication channel with entities through human service personnel; and para [0041], wherein end users may interact with the bot system through a conversational interaction (sometimes referred to as a conversational user interface (UI)), just as interactions between people).
Xu et al. fails to explicitly disclose wherein generating the novel text includes transmitting a message to the human.
Analogous art Din discloses generating the novel text includes transmitting a message to the human (see Din, column 12, lines 13-15, wherein if a response is to be provided to the client machine, then a client response message including a novel text passage is transmitted to the client machine at 420).
One of ordinary skill in the art would have recognized that applying the known technique of Din would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Regarding claim 10, Xu discloses the computing services environment recited in claim 1, wherein the novel text is determined based on an interaction with a generative language model (see Xu, para [0041], wherein end users may interact with the bot system through a conversational interaction (sometimes referred to as a conversational user interface (UI)), just as interactions between people; and para [0093], wherein transmit the first prompt to the first generative artificial intelligence model to cause the first generative artificial intelligence model to output the list that includes one or more executable actions).
Regarding claim 11, Xu discloses the computing services environment recited in claim 1, wherein determining the novel text 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 (see Xu, paras [0032]-[0034], wherein topic for e.g. 401k or pizza (e.g., action created for generating natural language text or audio response in reply to an authored natural language prompt such as the query-'What is the impact of XYZ on my 401k Contribution limit?'); and
transmitting the topic identification input prompt to the generative language model for completion (see Xu, paras [0032]-[0034] & [0055], wherein providing an utterance such as "I want to order a pizza." Upon receiving such an utterance, digital assistant 115A is configured to understand the meaning or goal of the utterance and take appropriate actions. The appropriate actions may involve, for example, providing responses 135 to the user with questions requesting user input on the type of pizza the user desires to order, the size of the pizza, any toppings for the pizza).
Regarding claim 12, Xu discloses the computing services environment recited in claim 11, wherein determining the novel text further comprises:
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 a plurality of actions executable at the computing services environment (see Xu, paras [0032]-[0034], [0047], [0055], [0063], [0066], & [0074], wherein topics for e.g. 401k or pizza and different topic for e.g. 401k contribution vs. contribution limit, Fig 2).
Regarding claim 16, Xu discloses the computing services environment recited in claim 1, wherein the generative language model is hosted inside the computing services environment (see Xu, para [0105], wherein a cloud computing model will require the participation of a cloud provider. An entity might also opt to deploy a private cloud, becoming its own provider of infrastructure services).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. ( US 20250094465) (hereinafter Xu et al.), in view of Qadrud-Din et al. (US Pat No. 11,860,914) (hereinafter Qadrud-Din et al.), and further in view of S Yao, J Zhao, D Yu, N Du, I Shafran, K Narasimhan, Y Cao et al. (React: Synergizing reasoning and acting in language models) arXiv preprint arXiv:2210.03629, 2022•arxiv.org (hereinafter Cao et al.).
Regarding claim 6, Xu discloses the computing services environment recited in claim 4, wherein the planner service is configured to determine an updated plan based on execution of the initial action (see Xu, para [0020], wherein updating a semantic context and memory store for a digital assistant that can execute an execution plan for
responding to an utterance from a user).
Xu et al. and Qadrud-Din et al. combined fail to explicitly disclose a ReAct planner configured to identify an initial action to perform and thought text corresponding to a portion of a virtual chain of thought.
Analogous art Din discloses a ReAct planner configured to identify an initial action to perform and thought text corresponding to a portion of a virtual chain of thought (see Din, abstract, wherein abilities for reasoning (e.g. chain-of-thought prompting) and acting (e.g. action plan generation) have primarily been studied as separate topics. In this paper, we explore the use of LLMs to generate both reasoning traces and task-specific actions in an interleaved manner, allowing for greater synergy between the two: Reasoning traces help the model induce, track, and update action plans as well as handle exceptions, while actions allow it to interface with and gather additional information from external sources such as knowledge bases or environments. We apply our approach, named ReAct, to a diverse set of language and decision-making tasks and demonstrate its effectiveness over state-of-the-art baselines…page 4, wherein we use a combination of thoughts that decompose questions (“I need to search x, find y, then find z”), extract information from Wikipedia observations (“x was started in 1844”)),
Analogous art Din discloses wherein the planner service is configured to determine an updated plan based on execution of the initial action (see Din, abstract, wherein update action plans as well as handle exceptions, while actions allow it to interface with and gather additional information from external sources such as knowledge bases or environments).
Xu directed to a system for executing an execution plan for generating a response to an utterance using a digital assistant and large language models. Cao directed to generating both reasoning traces and task-specific actions in an interleaved manner. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Johnson, regarding the System for Executing an Execution Plan with a Digital Assistant, to have included a ReAct planner configured to identify an initial action to perform and thought text corresponding to a portion of a virtual chain of thought, wherein the planner service is configured to determine an updated plan based on execution of the initial action because both inventions teach improving self-consistency to yield an improved response. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. ( US 20250094465) (hereinafter Xu et al.), in view of Qadrud-Din et al. (US Pat No. 11,860,914) (hereinafter Qadrud-Din et al.), and further in view of R Dubot, C Collet et al. (A hybrid formalism to parse Sign Languages) arXiv preprint arXiv:1403.4467, 2014•arxiv.org (hereinafter Collet et al.).
Regarding claim 7, Xu discloses the computing services environment recited in claim 4, wherein the planner service, as set forth above with claim 4.
Xu et al. and Qadrud-Din et al. combined fail to explicitly disclose a non-sequential planner configured to identify one or more of the selected subset of the actions to execute in parallel.
Analogous art Collet discloses a non-sequential planner configured to identify one or more of the selected subset of the actions to execute in parallel (see Collet, page 1, wherein the syntax SLs is complex and different from vocal languages. They use the multiplicity and the spatial abilities of the available articulators. It results non-sequential productions with complex temporal, special and articulatory synchronizations; pages 4-5, wherein the parser needs an input to parse. This input is an annotation of a subset of the units of the model. Units of this subset (they can be either pattern or alternatives) are said to be detectable. Their annotation can originate from manual annotation or third-party detectors. These detectable units appear in red in figures 4 and 3).
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Xu directed to a system for executing an execution plan for generating a response to an utterance using a digital assistant and large language models. Collet directed to both constituency-based and dependency-based models. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Johnson, regarding the System for Executing an Execution Plan with a Digital Assistant, to have included a non-sequential planner configured to identify one or more of the selected subset of the actions to execute in parallel because both inventions teach improving self-consistency to yield an improved response. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. ( US 20250094465) (hereinafter Xu et al.), in view of Qadrud-Din et al. (US Pat No. 11,860,914) (hereinafter Qadrud-Din et al.), and further in view of Gomez et al. ( US 2025/0165648) (hereinafter Gomez et al.).
Regarding claim 13, Xu discloses the computing services environment recited in claim 12.
Xu et al. and Qadrud-Din et al. combined fail to explicitly disclose further comprising a trust layer, wherein the trust layer is configured to mask sensitive data included the topic identification input prompt before the topic identification input prompt is transmitted to a generative language model for completion.
Analogous art Gomez discloses a trust layer, wherein the trust layer is configured to mask sensitive data included the topic identification input prompt before the topic identification input prompt is transmitted to a generative language model for completion (see Gomez, para [0035], wherein (a trust layer) is a security layer between an enterprise environment and the external generative AI 160, ensuring users of the enterprise environment can communicate with the generative AI 160 without leaking sensitive information; paras [0039]-[0040], wherein topic identification such as sensitive data (e.g., personal information like names, social security numbers, birthdates, addresses, credit card information, phone numbers, email addresses; business-critical information like pricing data, cost data, sales data, invoice data, specific dates; trade secrets; etc.)……the anonymizer 130 can be configured to anonymize the prompt 102 containing sensitive data; paras [0029]-[0030], wherein obtain consent from data subjects before collecting, processing, or sharing their personal data. They also grant data subjects the right to access, rectify, erase, restrict, or object to the use of their data….a privacy-preserving prompt engineering technology which enables a user to interact with generative AI while shielding sensitive data contained in user's prompt from the generative AI such that a prompt engineering technique used to extract the main themes, categories, or subjects from a given text before passing it to a generative language model for further processing).
Xu directed to a system for executing an execution plan for generating a response to an utterance using a digital assistant and large language models. Gomez directed to providing a security protocol, generating a modified prompt query which anonymizes the sensitive data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Johnson, regarding the System for Executing an Execution Plan with a Digital Assistant, to have included a trust layer, wherein the trust layer is configured to mask sensitive data included the topic identification input prompt before the topic identification input prompt is transmitted to a generative language model for completion because both inventions teach improving privacy and sentence positioning based on relationships. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 14, Xu discloses the computing services environment recited in claim 13.
Xu et al. and Qadrud-Din et al. combined fail to explicitly disclose wherein masking sensitive data includes replacing a text portion with a unique identifier, and wherein the trust layer is further configured to demask the topic identification prompt completion received from the generative language model by replacing the unique identifier with the text portion.
Analogous art Gomez discloses masking sensitive data includes replacing a text portion with a unique identifier (see Gomez, para [0042], wherein the prompt modifier 136 can be configured to modify the prompt 102 by replacing pieces of sensitive data contained in the prompt 102 with corresponding unique, non-sensitive tokens (e.g., the hash values) generated by the tokenizer 134), and
Analogous art Gomez discloses wherein the trust layer is further configured to demask the topic identification prompt completion received from the generative language model by replacing the unique identifier with the text portion (see Gomez, para [0047], wherein after receiving the reply 106, the anonymizer 130 can be configured to deanonymize the reply 106. Specifically, the reply modifier 138 can be configured to covert the anonymized sensitive data in the reply 106 back into the original sensitive data contained in the prompt 102 by retrieving the mapping or lookup table persisted in the mapping storage 125. For example, each unique, non-sensitive token contained in the reply 106 can be replaced with a corresponding piece of sensitive data by searching the lookup table stored in the mapping storage 125 ( also referred to as "inverse mapping")).
One of ordinary skill in the art would have recognized that applying the known technique of Gomez would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 13.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. ( US 20250094465) (hereinafter Xu et al.), in view of Qadrud-Din et al. (US Pat No. 11,860,914) (hereinafter Qadrud-Din et al.), and further in view of Gardner et al. ( US 2025/0061290) (hereinafter Gardner et al.).
Regarding claim 15, Xu discloses the computing services environment recited in claim 1.
Xu et al. and Qadrud-Din et al. combined fail to explicitly disclose wherein the generative language model is hosted outside the computing services environment.
Analogous art Gardner discloses the generative language model is hosted outside the computing services environment (see Gardner, para [1158], wherein the orchestration techniques allow the system to leverage an evolving ensemble of third party LLMs and AI technologies. The models themselves may be externally maintained (e.g., by partners) and continuously improved independently).
Xu directed to a system for executing an execution plan for generating a response to an utterance using a digital assistant and large language models. Gardner directed to providing novel capabilities in leveraging large language models. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Johnson, regarding the System for Executing an Execution Plan with a Digital Assistant, to have included the generative language model is hosted outside the computing services environment because both inventions teach offering high performance and low maintenance. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior arts made of record and not relied upon is considered pertinent to applicant's disclosure. (US Pub No. 2025/0315769; US Pub No. 2018/0034966; US Pub No. 2024/0020538; US Pub No. 2012/0130771; US Pat No. 2024/0028999; US Pub No. 2010/0318410; US Pat No. 2019/0182382; US Pub No. 2020/0250277; US Pub No. 2019/0340684; and US Pub No. 2022/0027837.
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/HAFIZ A KASSIM/Primary Examiner, Art Unit 3623 09/14/2026
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