DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Application
The following is a Final Office Action. In response to Examiner's communication on 10/24/2025, Applicant on 01/22/2026, amended Claims 1, 8, and 15. Claims 1-20 are now pending in this application and have been rejected below.
Response to Amendment
Applicants’ amendments are insufficient to overcome the 35 USC 101 rejections set forth in the previous action. The rejections have been updated to address Applicant’s amendments and maintained below.
Applicants’ amendments are insufficient to overcome the 35 USC 103 rejections set forth in the previous action. The rejections have been updated to address Applicant’s amendments and are maintained below.
Response to Arguments – 35 USC § 101
Applicant's arguments with respect to the 35 USC 101 rejections have been fully considered but they are not persuasive.
Applicant’s arguments assert a conceptualization of the invention that imply what is claimed is a system of workflow management and agent orchestration, with particular software integrations that render the characterization of a mental process inapplicable. Examiner respectfully disagrees.
For such a conceptualization to hold, it must be the case that the structure of the workflows and management of said workflows are necessarily software integrations and that what is claimed is the specific architecture that facilitates said operations. The broad recitation of “workflow comprises a plurality of decision points that determine a direction…”, “generating a prompt based on the request, the workflow, and additional contextual data” are not necessarily means of interfacing with a specific AI agent architecture; these are all steps that could be taken to guide a human operator and able to be performed mentally, and Applicant merely includes a generic “generative AI model agent” to execute it instead. In other words, these additional limitations amount to no more than a general link to a particular technological environment.
As a representative example of this, see Page 3 of Applicant’s Remarks filed 01/22/2026, “This technical orchestration infrastructure that enables pausing and resuming during workflow executions cannot be performed by a human with pen and paper”. The language of the claims, where the steps of “providing the prompt...”, wherein “the prompt is to guide the generative AI model agent through the plurality of the decision points” merely serves as a general link to a particular technological environment, namely that of interfacing with an AI agent, and the specification that the agent “performs tool response interpretation to determine a subsequent step" cannot be said to integrate into a practical application or amount to significantly more by analogous reasoning as above. That said judgements may be predicated on electronic databases from a “PMSS” or broadly claimed “tools”, whether understood as electronic software integrations or not, does not preclude construing the step of “receiving a tool response” to be a general link to the generically specified AI agent.
For the sake of advancing prosecution, Examiner notes that bridging the gap between Applicant’s conceptualization of the claims, as a multi agentic orchestration system, and what is currently claimed could be arrived at through clarification as to the mechanics of how a generative AI model agent specifically is utilized, subject to support from Applicant’s specification. If the prompt is not broadly recited as serving to “guide the generative AI model Agent through the plurality of the decision points of the workflow”, or broadly executing broadly claimed “tools” that may or may not be electronic software integrations, Applicant’s understanding of the invention could surmount the characterization of the invention as mental processes of receiving data and performing judgement on its basis.
Examiner respectfully notes the rejections have been updated to address the amendments and maintained below.
Claim Rejections - 35 USC § 112(b)
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 1-20 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 8, and 15 recite “definitions of tools available to a generative AI model agent to retrieve data relevant to the request…providing the prompt as an input to a generative AI model agent”. Because “a” is used to refer to two instances of generative AI model agents, it becomes ambiguous as to whether future instances of said generative AI model agent intend to refer to the generative AI model agent invoked in the former or latter recitation. For purpose of examination, these limitations will be interpreted as “definitions of tools available to a generative AI model agent to retrieve data relevant to the request…providing the prompt as an input to the [[a]] generative AI model agent”.
Claims 2-7, 9-14, 16-20 are rejected under 35 USC 112(b) due to their dependency from rejected Claims 1, 8, and 15.
Response to Arguments – 35 USC § 103
Applicant' s arguments with respect to the rejections under 35 USC 103 have been considered but are not found to be persuasive.
Applicant firstly assert that there is insufficient motivation to combine Thielges and Rose. Examiner respectfully disagrees.
Applicant argues under B. The Proposed Combination Does Not
Render the Claimed Invention Obvious that there is no apparent reason to combine references from distinct fields. Examiner respectfully disagrees and notes MPEP 2141.01(a), Analogous and Nonanalogous art, “A reference is analogous art to the claimed invention if: (1) the reference is from the same field of endeavor as the claimed invention (even if it addresses a different problem); or (2) the reference is reasonably pertinent to the problem faced by the inventor (even if it is not in the same field of
endeavor as the claimed invention)”. That the fields be disparate need not
preclude the combination of the references as cited, as we are able to use
the second standard of “reasonable pertinence”. The shared problem of Thielges and Rose is that of streamlining and execution of business processes and functions – whether the specific nature of the workflows are pertaining to property management, as in Thielges, or the wide ambit of functionalities supported in Rose, see [0018] of Rose, “For example, the trained LMs may create content libraries for specific brand personas, build and deploy content publishing campaigns across one or more publishing channels, generate creative assets to target specific audience segments, construct charts and other data visualizes displaying trends one or more performance metrics, troubleshoot errors, and the like”, that shared problem of workflow management renders them analogous art.
Further regarding the motivation to combine, citing MPEP 2143, “The motivation to combine may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. Id. at 1366, 80 USPQ2d at 1649. “[A]n implicit motivation to combine exists not only when a suggestion may be gleaned from the prior art as a whole, but when the ‘improvement’ is technology-independent and the combination of references results in a product or process that is more desirable, for example because it is stronger, cheaper, cleaner, faster, lighter, smaller, more durable, or more efficient. Because the desire to enhance commercial opportunities by improving a product or process is universal—and even common-sensical—we have held that there exists in these situations a motivation to combine prior art references even absent any hint of suggestion in the references themselves. In such situations, the proper question is whether the ordinary artisan possesses knowledge and skills rendering him capable of combining the prior art references.” Id. at 1368, 80 USPQ2d at 1651”. As of the filing date of Applicant’s invention, the usage of agentic systems is a well established method to execute complex workflows and would be readily apparent to one of ordinary skill in the art as a means to improve software implementations.
Applicant’s remaining arguments are directed to the deficiencies of Thielges combined with Rose in teaching newly added limitations. Examiner respectfully disagrees and notes the updated rejection under 35 USC 103 below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
The claims are directed to a method and apparatus. Therefore, the claims are directed to at least one of the four statutory categories.
101 Analysis – Step 2A
Regarding Prong 1 of the Step 2A analysis in the MPEP, the claims are to be analyzed to determine whether they recite subject matter that is directed to a judicial expectation, namely a law of nature, a natural phenomenon, or one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent Claim 1 includes limitations that recite [exception type] and will henceforth be used as a representative claim for the 101 rejection until otherwise noted. Claim 1 recites:
A method comprising: receiving, from a client device connected to a property management software system (PMSS), a request associated with a property management task; identifying a workflow corresponding to the property management task, the workflow comprising a sequence of operations, wherein the workflow comprises a plurality of decision points that determine a direction of the workflow based on conditional logic; generating a prompt based on the request, the workflow, and additional contextual data from the PMSS, wherein generating the prompt comprises dynamically constructing the prompt to include information requirements corresponding to a current step of the workflow and definitions of tools available to a generative Al model agent to retrieve data relevant to the request; and providing the prompt as an input to a generative Al model agent, the generative Al model agent to execute the sequence of operations to perform the property management task, wherein the prompt is to guide the generative Al model agent through the plurality of the decision points of the workflow based on the additional contextual data from the PMSS; and receiving a tool response resulting from an execution of at least one tool of the tools available, wherein the generative Al model agent performs tool response interpretation to determine a subsequent step of the workflow.
The examiner submits that the foregoing bolded limitation(s) constitute an abstract idea because under its broadest reasonable interpretation, the claim covers a mental process.
“receiving…a request associated with a property management task”, “identifying a workflow…”, “generating a prompt…”, recite abstract ideas - namely, mental processes that could be performed by a human with a pen and paper, per the MPEP, merely
adapting them into the context of a technological environment with computing parts does not preclude them from being abstract.
Accordingly, the claim recites at least one abstract idea.
Claims 8, 15 recite abstract ideas by presenting substantially similar limitations as Claim 1.
Claims 2-7, 9-14, 16-20 recite abstract ideas by virtue of their dependency on independent Claims 1, 8, 15 respectively.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the MPEP, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into practical application. As noted in the MPEP, it must be determined whether any additional elements in the claim beyond the judicial exception integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements, such as merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A method comprising: receiving, from a client device connected to a property management software system (PMSS), a request associated with a property management task; identifying a workflow corresponding to the property management task, the workflow comprising a sequence of operations, wherein the workflow comprises a plurality of decision points that determine a direction of the workflow based on conditional logic; generating a prompt based on the request, the workflow, and additional contextual data from the PMSS, wherein generating the prompt comprises dynamically constructing the prompt to include information requirements corresponding to a current step of the workflow and definitions of tools available to a generative Al model agent to retrieve data relevant to the request; and providing the prompt as an input to a generative Al model agent, the generative Al model agent to execute the sequence of operations to perform the property management task, wherein the prompt is to guide the generative Al model agent through the plurality of the decision points of the workflow based on the additional contextual data from the PMSS; and receiving a tool response resulting from an execution of at least one tool of the tools available, wherein the generative Al model agent performs tool response interpretation to determine a subsequent step of the workflow.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
As it pertains to Claim 1, the additional elements in the claims include “from a client device connected to a property management software system (PMSS)”, “providing the prompt as an input to a generative AI model agent, the generative AI model agent to execute the sequence of operations to perform the property management task”, and “an execution of at least one tool…”, “receiving a tool response resulting from an execution of at least one tool of the tools available, wherein the generative Al model agent performs tool response interpretation to determine a subsequent step of the workflow”. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional elements are generic computing components that are merely used as a tool to perform the recited abstract idea and/or do no more than generally link the use of the recited abstract idea to a particular technological environment or field of use under Step 2A Prong Two.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05).
Accordingly, the additional limitation(s) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing an abstract idea.
Claims 8, 15 do not integrate the abstract idea into a practical application by analogous reasoning as above.
Claim 3, 10, 17 additionally recite “…via at least one of a graphical user interface or an application programming interface”. This does not integrate the abstract idea into a practical application by analogous reasoning as above.
Claims 2, 4-7, 9, 11-14, 16, 18-20 do not recite additional elements beyond those found in claims from which they are dependent, and therefore do not integrate the abstract idea into a practical application.
101 Analysis – Step 2B
Regarding Step 2B of the MPEP, representative independent Claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to generic computing components that are merely used
as a tool to perform the recited abstract idea and/or do no more than
generally link the use of the recited abstract idea to a particular
technological environment or field of use. Further, looking at the additional
elements as an ordered combination adds nothing that is not already
present when considering the additional elements individually.
Claims 8, 15 do not integrate the abstract idea into a practical application or amount to significantly more by analogous reasoning as above.
Claim 3, 10, 17 additionally recite “…via at least one of a graphical user interface or an application programming interface”. This does not integrate the abstract idea into a practical application or amount to significantly more by analogous reasoning as above.
Claims 2, 4-7, 9, 11-14, 16, 18-20 do not recite additional elements beyond those found in claims from which they are dependent, and therefore do not integrate the abstract idea into a practical application or amount to significantly more.
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 (i.e., changing from AIA to pre-AIA ) 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, 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.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Thielges(US 20020138289 A1) in view of Rose(US 20240378389 A1).
Claims 1, 8, 15
As to Claim 1,
Thielges teaches:
A method comprising: receiving, from a client device connected to a property management software system (PMSS), a request associated with a property management task;
In [0033], "In one embodiment of the present invention, service requests are initiated when a registered user determines that service is needed. In the case where the service request initiator is a tenant, the tenant begins by using a browser to access the Internet and reach a web site supporting the present invention".
…property management task, …property management task,
In [0032], "As described in detail herein, in one embodiment, the present invention mitigates or overcomes all of the forgoing limitations by providing an Internet-accessible property management system for creating and tracking events, also described as "incidents" herein, such as service requests, maintenance reminders and other events associated with managing property".
…PMSS, …PMSS;
See [0032] as outlined above.
Thielges does not expressly disclose the remaining limitations.
However, Rose teaches:
identifying a workflow corresponding to the ... task, the workflow comprising a sequence of operations, wherein the workflow comprises a plurality of decision points that determine a direction of the workflow based on conditional logic;
In [0050], "The model training service 630 may parse the user queries 612A, . . . , 612N using one or more pre-trained LMs 651 (e.g., one or more general purpose LMs trained to perform natural language processing tasks such as interpreting and generating text or code). The pre-trained LMs 651 may interpret the user queries 612A, . . . , 612N to identify one or more requests included in each request". In [0018], "The trained LMs may chain two or more actions together to execute complex workflows for completing multistep tasks". Note the iterative, sequential nature of the workflow execution, in [0066], “After the agent LM completes an action, the action, context details, prompt, and completion for the action be appended to the chain history 646A. To complete the next action in the action chain, the model training service may determine a new training prompt including the updated chain history 646A. The model training service may select an LM (e.g., a agent LM or pre-trained LM) to complete the next task and pass the new training prompt to the selected LM to train the selected LM to perform the next action”. Regarding conditional logic in [0062], “Other aspects of the training instructions including, for example, agent instructions, model tasks, input and/or output data formats, resource tasks, task parameters, and/or validation instructions may also be varied according to the type of agent LM trained”.
generating a prompt based on the request, the workflow, and additional contextual data from the ..., wherein generating the prompt comprises dynamically constructing the prompt to include information requirements corresponding to a current step of the workflow and definitions of tools available to a generative AI model agent to retrieve data relevant to the request; providing the prompt as an input to a generative AI model agent,
In [0062], "The model training service may determine custom training prompts for each agent LM based on the type of agent and the actions and/or action chains the agent performs. The custom training prompts for each LM agent may include agent specific combinations of resources and/or instructions for interacting with the resources that are unique to each agent LM...Other aspects of the training instructions including, for example, agent instructions, model tasks, input and/or output data formats, resource tasks, task parameters, and/or validation instructions may also be varied according to the type of agent LM trained, the tasks performed by the agent, and/or the characteristics of the pre-trained LM (e.g., size, amount of parameters, composition of training data, latency time, inference costs, and the like) that is trained as the agent.". For tools to retrieve data, see [0064], “The training prompt 640A may also include one or more tools 644A that may be used by the agent LMs to interact with resources”. Regarding the information and parameters of tools, see [0064], “The training prompt 640A may include a list of tools 644A available to the agent LMs to use to perform one or more actions and/or action chains. For each of the tools 644A, the training prompt 640A may include a tool name, a tool description, and one or more tool parameters. The tool description may identify the type of tool, for example, utility, software package, application, API, API wrapper, a shell or terminal that executes commands written in a computer language (e.g., Python, Node.js, SQL, and the like), LM, machine learning model, and the like. The tool parameters may train the agent LM to use each of the tools 644A”.
the generative AI model agent to execute the sequence of operations to perform the … task,
In [0066], "The model training service may select an LM (e.g., a agent LM or pre-trained LM) to complete the next task and pass the new training prompt to the selected LM to train the selected LM to perform the next action".
wherein the prompt is to guide the generative Al model agent through the plurality of the decision points of the workflow based on the additional contextual data from the …;
See [0062] above. Given the support for a plurality of model instructions, tasks and actions, we consider this plurality to correspond to the plurality of decision points.
and receiving a tool response resulting from an execution of at least one tool of the tools available, wherein the generative Al model agent performs tool response interpretation to determine a subsequent step of the workflow.
See [0066], “The training prompt 640A may also include a chain history 646A comprising a list of actions, prompts, and completions generated by agent LMs. The chain history 646A may also include context details about each action performed including the tool used to perform the action, the resource invoked by the tool, and/or a description of the data retrieved and/or action performed using the resource. The prompts and completions for each action may include one or more agent LM prompts input into a tool, one or more responses received from the tool, and one or more responses generated by the agent LMs. The chain history 646A for an initial prompt in an action chain may be empty and include no historical data. After the agent LM completes an action, the action, context details, prompt, and completion for the action be appended to the chain history 646A. To complete the next action in the action chain, the model training service may determine a new training prompt including the updated chain history 646A”.
Thielges discloses a system for streamlining the reporting and resolution of property management tasks. Rose discloses a system meant to parse user queries and perform actions on the basis of commands, streamlining workflows in various applications. Each reference discloses means to optimize workflows. Extending the agentic automation as recorded in Rose to the system of Thielges is applicable as both are directed to means of streamlining the execution of business processes and functions.
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to combine the agentic functionality as taught in Rose and apply that to the system of Thielges. Motivation to do so comes from the fact that the claim is plainly directed to the predictable result of combining known items in the prior art, with the expected benefit of adopting the agentic automation of Rose found in [0019], "The actions engine may improve the versatility, speed, efficiency, and reliability of software applications by reducing the number of manual programming tasks and API endpoints required to access and operate platform functionality. The technology further improves the availability and interoperability of software applications and enhances user experience by exposing a multitude of software components using a simplified access endpoint. The actions engine provides a simple natural language interface that enables users to readily interact with software components that were previously inaccessible because the components were reachable only through a technical interface (e.g., an API)".
Claims 8, 15 are rejected as disclosing substantially similar limitations as Claim 1.
Claim 8 discloses, "A system... the system comprising: memory; and a processing device coupled to the memory to perform operations".
Claim 15 discloses ". A non-transitory computer readable storage medium storing instructions which, when executed by a processing device, cause the processing device to perform operations comprising".
This is taught in [0020] of Rose, "A networked system 116 provides server-side functionality via a network 110 (e.g., the Internet or WAN) to a client device 108. A web client 102 and a programmatic client, in the example form of a client application 104, are hosted and executed on the client device 108.".
Claims 2, 9, 16
As to Claim 2, Thielges combined with Rose teaches all the limitations of Claim 1 as discussed above.
Thielges does not expressly disclose the remaining limitations.
However, Rose teaches:
The method of claim 1, wherein the workflow comprises: a plurality of actions; a plurality of flow paths connecting the plurality of actions; and a plurality of textual descriptions describing each action of the plurality of actions.
For the historical record of previous actions in the workflow, in [0066], "The training prompt 640A may also include a chain history 646A comprising a list of actions, prompts, and completions generated by agent LMs. The chain history 646A may also include context details about each action performed including the tool used to perform the action, the resource invoked by the tool, and/or a description of the data retrieved and/or action performed using the resource. The prompts and completions for each action may include one or more agent LM prompts input into a tool, one or more responses received from the tool, and one or more responses generated by the agent LMs". For the next action, in [0067], "LMs selected for the next action in an action chain can review the chain history 646A in the training prompt to determine the required next action in the action chain. The selected LMs can then use the resources and tools in the training prompt to complete the next action".
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to apply the agentic functionality of Rose and apply that to the system of Thielges. Motivation to do so comes from the same rationale as outlined above with respect to Claim 1.
Claims 9, 16 are rejected as disclosing substantially similar limitations as Claim 2.
Claims 3, 10, 17
As to Claim 3, Thielges combined with Rose teaches all the limitations of Claim 2 as discussed above.
Thielges teaches:
PMSS
In [0032], "As described in detail herein, in one embodiment, the present invention mitigates or overcomes all of the forgoing limitations by providing an Internet-accessible property management system for creating and tracking events, also described as "incidents" herein, such as service requests, maintenance reminders and other events associated with managing property".
Thielges does not expressly disclose the remaining limitations.
However, Rose teaches:
The method of claim 2, wherein the workflow is defined for the ... in response to input received via at least one of a graphical user interface or an application programming interface.
In [0050], "The actions engine 230 may receive request data 520 from a client device. Request data 520 may include one or more natural language user queries 612A, . . . , 612N that may be entered into a UI having a text box or other component that receives natural language input". Elaborating on components, [0091], "Component” (also referred to as a “module”) refers to a device, physical entity, or logic having boundaries defined by function or subroutine calls, branch points, application programming interfaces (APIs), or other technologies that provide for the partitioning or modularization of particular processing or control functions".
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to apply the agentic functionality of Rose and apply that to the system of Thielges. Motivation to do so comes from the same rationale as outlined above with respect to Claim 1.
Claims 10, 17 are rejected as disclosing substantially similar limitations as Claim 3.
Claims 4, 11, 18
As to Claim 4, Thielges combined with Rose teaches all the limitations of Claim 2 as discussed above.
Thielges does not expressly disclose the remaining limitations.
However, Rose teaches:
The method of claim 2, wherein generating the prompt comprises identifying a textual description describing at least one action of the plurality of actions.
In [0066], "The training prompt 640A may also include a chain history 646A comprising a list of actions, prompts, and completions generated by agent LMs. The chain history 646A may also include context details about each action performed including the tool used to perform the action, the resource invoked by the tool, and/or a description of the data retrieved and/or action performed using the resource. The prompts and completions for each action may include one or more agent LM prompts input into a tool, one or more responses received from the tool, and one or more responses generated by the agent LMs". In [0067], "LMs selected for the next action in an action chain can review the chain history 646A in the training prompt to determine the required next action in the action chain. The selected LMs can then use the resources and tools in the training prompt to complete the next action".
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to apply the agentic functionality of Rose and apply that to the system of Thielges. Motivation to do so comes from the same rationale as outlined above with respect to Claim 1.
Claims 11, 18 are rejected as disclosing substantially similar limitations as Claim 4.
Claims 5, 12, 19
As to Claim 5, Thielges combined with Rose teaches all the limitations of Claim 4 as discussed above.
Thielges does not expressly disclose the remaining limitations.
However, Rose teaches:
The method of claim 4, wherein the generative AI model agent is part of a hierarchy comprising a plurality of agents, wherein each agent is associated with one or more individual actions of the plurality of actions.
In [0058], "To train agent LMs 652A, . . . , 652N to perform one or more actions and/or action chains, the model training service 630 may determine training data for each of the agent LMs 652A, . . . , 652N. The training data may include one or more training prompts 640A, . . . , 640N and a set of test cases 648. The training service 630 may generate unique training data for each agent LM 652A, . . . , 652N to simplify the training process and enable the agent LMs 652A, . . . , 652N to specialize in specific subsets of tasks (e.g., information retrieval, campaign configuration, campaign monitoring and optimization, content generation, and the like).".
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to apply the agentic functionality of Rose and apply that to the system of Thielges. Motivation to do so comes from the same rationale as outlined above with respect to Claim 1.
Claims 12, 19 are rejected as disclosing substantially similar limitations as Claim 5.
Claims 6, 13, 20
As to Claim 6, Thielges combined with Rose teaches all the limitations of Claim 5 as discussed above.
Thielges does not expressly disclose the remaining limitations.
However, Rose teaches:
The method of claim 5, wherein to execute the sequence of operations, the generative AI model agent is to perform the at least one action of the plurality of actions.
In [0058], "To train agent LMs 652A, . . . , 652N to perform one or more actions and/or action chains, the model training service 630 may determine training data for each of the agent LMs 652A, . . . , 652N. The training data may include one or more training prompts 640A, . . . , 640N and a set of test cases 648. The training service 630 may generate unique training data for each agent LM 652A, . . . , 652N to simplify the training process and enable the agent LMs 652A, . . . , 652N to specialize in specific subsets of tasks (e.g., information retrieval, campaign configuration, campaign monitoring and optimization, content generation, and the like).".
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to apply the agentic functionality of Rose and apply that to the system of Thielges. Motivation to do so comes from the same rationale as outlined above with respect to Claim 1.
Claims 13, 20 are rejected as disclosing substantially similar limitations as Claim 6.
Claims 7, 14
As to Claim 7, Thielges combined with Rose teaches all the limitations of Claim 6 as discussed above.
Thielges does not expressly disclose the remaining limitations.
However, Rose teaches:
The method of claim 6, further comprising: obtaining an output of the generative AI model agent, the output comprising a result of the at least one action of the plurality of actions.
In [0077], "Some present examples also include methods. FIG. 9 is a block diagram of a process 900 for training an agent model (e.g., an agent LM) to generate responses to actions. The trained agent model may generate responses to actions that are included in action chains used to generate responses to user requests".
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to apply the agentic functionality of Rose and apply that to the system of Thielges. Motivation to do so comes from the same rationale as outlined above with respect to Claim 1.
Claim 14 is rejected as disclosing substantially similar limitations as Claim 7.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THEODORE L XIE whose telephone number is (571)272-7102. The examiner can normally be reached M-F 9-5.
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/THEODORE XIE/Examiner, Art Unit 3623
/WILLIAM S BROCKINGTON III/Primary Examiner, Art Unit 3623