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 .
Claims 1, 3-5, 7, 11, 13, 14, and 19 are currently amended. No new claims have been added. Claims 2 and 12 have been canceled. Claims 1, 3-11, and 13-20 are currently pending for examination.
Response to Arguments
As per applicant’s argument, pgs.9-10, that paragraph [0065] of the applicant’s disclosure provides structure (i.e., ‘software components executed by processing circuitry’) to the terms “engine”, “module”, and “requester” the examiner has carefully considered these arguments and respectfully disagrees. It is conceded that the terms “engine” and “module” are indeed provided adequate structure by paragraph 0065, however, no mention is made of the “requester”. As such, the rejection of claims 1 and 2 under 35 USC 112(f) is withdrawn, and the rejection of claims 5 and 7 under 35 USC 112(f) is maintained.
In response to the argument, pg.11, that neither Tripathy, Gardner, nor Li disclose the amended limitations of the independent claims, specifically ; “the workflow instances and the respective scores generated by the workflow instances are stored in a performance database; and the performance database is configured to determine the recommended workflow definitions based on the scores by comparing the predicted task to workflow instances stored in the performance database”, the examiner has considered this argument. This limitation is newly presented and therefore was not mapped in the previous office action, and thus, none of these references were used to disclose this limitation.
In response to the argument, pg.11, that Chang does not disclose “the workflow instances and the respective scores generated by the workflow instances are stored in a performance database ; and the performance database is configured to determine the recommended workflow definitions based on the scores by comparing the predicted task to workflow instances stored in the performance database”, the examiner concedes. Chang was not relied upon to disclose a performance database or recommended workflow definition and does not disclose the amended limitations of the independent claims.
In response to the applicant’s argument, pgs.10-11, that Radha does not disclose the limitation in question, the examiner notes that this limitation is currently amended- i.e., newly presented- and thus, it is conceded that Radha does not disclose this limitation.
In response to the applicant’s argument, pg.11, that Rafferty does not disclose the determination of a recommended workflow definition based on the scores- Rafferty was only relied upon to disclose the limitation involving the generation of the scores. As such, it is conceded that Rafferty also does not disclose this limitation.
In summary, the contended limitation of the independent claims is rejected under Ramachandran (US 20220309413 A1).
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 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 limitations are:
“a workflow definition requester configured to…” in claim 5; interpreted as computer program capable of providing the functionality described in paragraphs [0025], [0029], and [0069]
“the workflow recommendation requester is configured to…” in claim 7; interpreted as a computer program capable of providing the functionality described in paragraphs [0025], [0029], and [0069]
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.
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 § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Tripathy (US 20250139417 A1) in view of Gardner (US 20210124610 A1) in further view of Li (US 20250071191 A1) in further view of Chang (US 20190252061 A1) in further view of Radha (US 20240394414 A1) in further view of Ramachandran (US 20220309413 A1).
As per claim 1, Tripathy discloses:
A computing system, comprising: a machine cognition workflow engine configured to: receive a plurality of prompts; extract a message and a context of each of the plurality of prompts ("The workflow system 120 can generate workflows", 0026 ; "Moreover, the workflows can be generated from templates that are automatically generated based on a natural language description of a problem. Continuing with the previous example, a user can state that they are having trouble with bills being paid on time. The workflow assistant system 130 can utilize natural language processing to determine a problem statement and match a bill reminder template to the problem statement.", 0027 ; "The method of Clauses 1-4, further comprising determining context data from the problem statement", 0081 ; Examiner Note: the problem statement equates to a message)
For each prompt, generate a workflow instance according to a recommended workflow definition (“According to one aspect, a method of workflow generation includes receiving a problem statement specified by a user in a natural language, inferring a workflow template that maps to the problem statement by at least one machine learning model… and triggering generation of the workflow with a workflow system based on the workflow template.”, 0003 ; Examiner Note: the inferred workflow template equates to a recommended workflow definition)
a machine cognition workflow evaluation and recommendation subsystem including: a response evaluation module configured to evaluate the responses to generate a score for each response generated by the workflow instances (“In the depicted example, the workflow assistant system 130 includes map component 210, score component 220, data acquisition component 230, and entity identification component 240.”, 0028 ; ”The score component 220 is configured to evaluate the confidence score associated with a matching template and a problem statement.”, 0030 ; “In one particular instance, parameters needed to build context can be retrieved, tuned, and built as a vector that can be passed to a model (e.g., chat GPT) to fetch a response and a score. Actions can be taken based on the value of the score. For example, if the score equals 100, the workflow can be built and enabled. If the confidence score equals 50, the workflow can be built but in a disabled state (e.g., not enabled/inactive).”, 0032)
generate responses for the plurality of prompts (“As an example, a user can specify a problem statement 342 by way of the customer layer 340, which is received by the assistance system 332 of the workflow plugin layer 330. The assistance system 332 can identify a template associated with a workflow that addresses the problem statement 342. The assistance system 332 initiates generation of the workflow template by invoking the generator 322 of the generative AI layer 320 with a built prompt. The generator 322 can then invoke the context builder to determine template parameters to be generated… In accordance with one embodiment, the parameters can be specified and built into a vector that is passed to Chat GPT to fetch a response and score.”, 0034)
Tripathy may disclose the above limitations of claim 1, but does not disclose the receipt of a request for a recommended workflow for a current prompt.
However, Gardner discloses:
a workflow recommendation module configured to receive a request from the machine cognition workflow engine for a recommended workflow definition for a current prompt, and output the recommended workflow definition to the machine cognition workflow engine based on the scores of the responses generated by the workflow instances. (“The process 300 optionally includes receiving a query from the computer system, determining a workflow of the multiple workflows based on the query, and providing, to the computer system, the workflow or a recommendation for the workflow in response to receiving the query. As an example, a query may be or include a request for a catalog of available workflows, a request for one or more identified workflows, a request for compatible or recommended workflows, etc. The query may include, for example, an indication of one or more specific workflows. The query may include, for example, one or more criteria for workflows that are to be used in identifying one or more workflows to be sent to the computer system in response.”, 0124)
The combination of Tripathy in view of Gardner would provide a system capable of recommending a workflow based upon the scores generated by Tripathy’s scoring module. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Tripathy with those of Gardner in order provide the system with the ability to specify the instructions of a workflow in a form that is not compiled for a specific operating system or architecture, but nevertheless indicates operations to perform (Gardner, [0079]).
Tripathy in view of Gardner may disclose the above limitations of claim 1, but does not explicitly disclose the generation and execution of a workflow which performs calls to components during execution.
However, Li discloses:
a recommended workflow definition that specifies a plurality of calls to one or more components; and for each prompt, execute the generated workflow instance to perform the calls to the one or more components ("In such examples where the monolithic software functionality is divided between the monolith and one or more microservices, the microservices need to communicate with each other and the monolith by remote call. Accordingly, one workflow will contain several remote calls across multiple microservices and/or the monolithic application. For example, an example workflow can include a request to the e-commerce application (monolithic application), which issues a remote call to the supplier service and a remote call to the delivery service. In this example, the delivery service issues a remote call to the billing service and the billing service issues a remote call to the e-commerce application.", 0018 ; " As described in further detail herein, each of the monolithic application and the microservices has an APIClient executing therewith, which enables remote calls therebetween during execution of workflows.", 0024 ; Examiner Note: the services of a microservice application equate to components of a workflow)
The system of Tripathy in view of Gardner in further view of Li would be capable of providing a recommended workflow definition which specifies a plurality of calls to one or more components/microservices. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Tripathy, Gardner, and Li in order to enable continuous enhancement of functionality without re-implementation within the workflow/microservice orchestration system (Li, [0016]).
Tripathy in view of Gardner in further view of Li fully discloses the above limitations of claim 1, but does not disclose the generation of a predicted task to be accomplished by the prompt.
However, Chang discloses:
Generate a predicted task to be accomplished by the prompt ("Once the user's contextual cues have produced a predicted desired action of the user, the user may modify the results, accept the results or reject the results. Accepting or rejecting the results may indicate to the server 102 whether the workflow orchestration engine 118 properly determined/predicted the desired action of the user.", 0035)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Tripathy, Garnder, and Li with those of Chang in order to improve the workflow efficiency of the system (Chang, [0001]).
Tripathy in view of Gardner in further view of Li in further view of Chang fully discloses the above limitations of claim 1 but does not explicitly disclose a performance database storing respective scores of workflows.
However, Radha discloses:
the workflow instances and the respective scores generated by the workflow instances are stored in a performance database ("If the validation is successful, the validated workflow is processed by the planner module 218. It will be appreciated that copies of the workflow, and other related data (e.g., validation results, invalidation errors, version IDs, status updates, etc.) are stored on the workflow database 221. ", 0075 ; Examiner Note: the copies of the workflows equate to predicted tasks, the validation results equate to scores, and the workload version IDs equate to workload identifiers)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Tripathy, Garnder, Li, and Chang with those of Radha in order to provide the system with the ability to partially execute workloads, thereby allowing the system to better satisfy the original constraints (Radha, [0128]).
Tripathy in view of Gardner in further view of Li in further view of Chang in further view of Radha fully discloses the above limitations of claim 1, but does not explicitly disclose the performance database being configured to determine a recommended workflow definition.
However, Ramachandran discloses:
The performance database is configured to determine the recommended workflow definition based on the scores by comparing the predicted task to workflow instances stored in the performance database (“The workflow guidance module (WGM) 120 is an AI/ML module or an algorithmic module, which matches one or more of call context, call metadata or historical data with various workflows stored in a workflow repository, for example, the workflow repository 136, to identify at least one recommended workflow. The Workflow guidance module WGM 120 receives call metadata from the CMS 138, for example, in response to a request sent from the WGM 120 to the CMS 138, or automatically.”, 0025)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Tripathy, Garnder, Li, Chang, and Radha with those of Ramachandran in order to improve the matching and recommendation of the workflow through the use of agent and customer feedback data (Ramachandran, [0015]).
As per claim 11, it is a method claim with substantially the same limitations as claim 1, and as such, it is rejected for substantially the same reasons.
Claims 3, 4, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Tripathy (US 20250139417 A1) in view of Gardner (US 20210124610 A1) in further view of Li (US 20250071191 A1) in further view of Chang (US 20190252061 A1) in further view of Radha (US 20240394414 A1) in further view of Ramachandran (US 20220309413 A1) in further view of Rafferty (US 20220004954 A1).
As per claim 3, Tripathy in view of Gardner in further view of Li in further view of Chang in further view of Radha in further view of Ramachandran fully discloses the limitations of claim 1, but does not disclose the generation of a score representing the degree to which a response accomplished a task from an input comprising a prompt, predicted task, and response.
However, Rafferty discloses:
the response evaluation module includes a trained machine learning scoring model that has been trained to generate a score which scores a degree to which the response accomplished the task, the trained machine learning scoring model receiving as input the prompt, the predicted task, and the response, and generating as output the score which scores the degree to which the task was completed by the response. ("The confidence score may indicate a level of confidence that the workflow is accurate and appropriate (e.g., likely to work as intended and/or expected by the particular user). The machine learning model may determine the confidence score based on the communication data, the workflow data, and/or the response data described above.", 0038 ; Examiner Note: the communication data equates to the prompt, the workflow data equates to the predicted task, and the response data equates to the response.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Tripathy, Garnder, Li, Chang, Radha, and Ramachandran with those of Rafferty in order to virtualize the workload engine, thereby increasing efficiency, lowering power consumption, increasing reliability, increasing availability, increasing flexibility, and lowering the cost as compared to using separate computing devices (Rafferty, [0068]).
As per claim 4, Tripathy in view of Gardner in further view of Li in further view of Chang in further view of Radha in further view of Ramachandran in further view of Rafferty fully discloses the limitations of claim 3.
Furthermore, Radha discloses:
a workflow identifier associated with the workflow instance is stored along with the predicted task and the score as performance data in a performance database ("If the validation is successful, the validated workflow is processed by the planner module 218. It will be appreciated that copies of the workflow, and other related data (e.g., validation results, invalidation errors, version IDs, status updates, etc.) are stored on the workflow database 221. ", 0075 ; Examiner Note: the copies of the workflows equate to predicted tasks, the validation results equate to scores, and the workload version IDs equate to workload identifiers)
As per claim 14, it is a method claim with substantially the same limitations as claim 4, and as such, it is rejected for substantially the same reasons.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable Tripathy (US 20250139417 A1) in view of Gardner (US 20210124610 A1) in further view of Li (US 20250071191 A1) in further view of Chang (US 20190252061 A1) in further view of Radha (US 20240394414 A1) in further view of Ramachandran (US 20220309413 A1) in further view of Rafferty (US 20220004954 A1) in further view of Kulkarni (US 20240134682 A1).
As per claim 5, Tripathy in view of Gardner in further view of Li in further view of Chang in further view of Radha in further view of Ramachandran in further view of Rafferty fully discloses the limitations of claim 4, but does not disclose the sending of a request for a recommended workflow.
However, Kulkarni discloses:
the machine cognition workflow engine includes a workflow definition requester configured to receive the predicted task from the task prediction module and send a request to the performance database of the workflow recommendation module for the recommended workflow definition for the predicted task. (“For example, when the workflow management system 104 receives a new process for automation via a custom workflow, the workflow management system 104 may predict a workflow template that can be used as a baseline for the custom workflow.”, 0020 ; "For example, a workflow management system may train a recommendation engine (e.g., an artificial intelligence model) to recommend workflow templates (e.g., a template predictor) from which to build a workflow for task completion.", 0013 ; "The method may include outputting, by the device, workflow data associated with the workflow recommendation.", 0003 ; “ The device may be configured to generate a workflow recommendation relating to the one or more workflow templates.”, 0002)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Tripathy, Garnder, Li, Chang, Radha, Ramachandran, and Rafferty with those of Kulkarni in order to provide the system with a method for workload optimization which improves the scale of the tasks, as well as the accuracy of the completion of those tasks (Kulkarni, [0013]).
Claims 6, 7, 15, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Tripathy (US 20250139417 A1) in view of Gardner (US 20210124610 A1) in further view of Li (US 20250071191 A1) in further view of Chang (US 20190252061 A1) in further view of Radha (US 20240394414 A1) in further view of Ramachandran (US 20220309413 A1) in further view of Rafferty (US 20220004954 A1) in further view of Kulkarni (US 20240134682 A1) in further view of Abraham (US 20150242786 A1).
As per claim 6, Tripathy in view of Gardner in further view of Li in further view of Chang in further view of Radha in further view of Ramachandran in further view of Kulkarni fully discloses the limitations of claim 5, but does not disclose the returning of a workflow identifier for a recommended workflow definition.
However, Abraham discloses:
the performance database is configured to respond by returning the workflow identifier for the recommended workflow definition or with an indication that no recommendation is able to be made ("Recommendation 352 can include, but is not limited to, a recommended action, a user identifier, a workflow identifier, and the like. Recommendation 352 can include one or more proposed optimization.", 0055)
The system of Tripathy in view of Gardner in further view of Li in further view of Chang in further view of Radha in further view of Ramachandran in further view of Kulkarni in further view of Abraham would be capable of responding to a request for a recommended workflow by returning a workflow identifier. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Tripathy, Garnder, Li, Chang, Radha, Ramachandran, Rafferty, Kulkarni, and Abraham, in order to provide a method for skipping or merging unnecessary activities within a workflow to improve the performance of the workflow (Abraham, [0002]).
As per claim 7, Tripathy in view of Gardner in further view of Li in further view of Chang in further view of Radha in further view of Ramachandran in further view of Kulkarni in further view of Abraham fully discloses the limitations of claim 6.
Furthermore, Kulkarni discloses:
when the performance database responds with the indication that no recommendation is able to be made, the workflow recommendation requester is configured to request a generative model of the workflow recommendation module to generate the recommended workflow definition, and in response receive the generated workflow definition. ("In some implementations, the workflow management system 104 may fail to identify or successfully generate a workflow. For example, the workflow management system 104 may determine that there is not a workflow template that satisfies a threshold similarity score or other scoring in relation to a task or process for which the workflow management system 104 is to generate a workflow recommendation. In this case, the workflow management system 104 may automatically request that additional workflow templates be provided or may monitor user creation of a workflow and generate a new workflow template based on the user creation of the workflow to avoid subsequent failures.", 0030)
As per claim 15, it is a method claim with substantially the same limitations as claim 6, and as such, it is rejected for substantially the same reasons.
As per claim 16, it is a method claim with substantially the same limitations as claim 7, and as such, it is rejected for substantially the same reasons.
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Tripathy (US 20250139417 A1) in view of Gardner (US 20210124610 A1) in further view of Li (US 20250071191 A1) in further view of Chang (US 20190252061 A1) in further view of Radha (US 20240394414 A1) in further view of Ramachandran (US 20220309413 A1) in further view of Li.2 (US 20140337085 A1).
As per claim 8, Tripathy in view of Gardner in further view of Li in further view of Chang in further view of Radha in further view of Ramachandran fully discloses the limitations of claim 1, but does not disclose the workflow definition including a sequence of the components to be executed.
However, Li.2 discloses:
the workflow definition includes a sequence of the components to be executed by the workflow engine, ("A service workflow template is a sequence of services with interrelated dependencies that can fulfill a specific service goal.", 0017 ; Examiner Note: a service workflow template equates to a workflow definition, services equate to components)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Tripathy, Gardner, Li, Chang, Radha, Ramachandran, and Li.2 in order to provide a user trust network and a service trust network for service workflow recommendation, thereby allowing the system to obtain reasonable workflow results/responses with well-rated workflows (Li.2, [0018]).
Furthermore, Li discloses:
the context of the prompt being passed to each component in the sequence of the components in the workflow instance. ("More particularly, implementations of the present disclosure are directed to a middleware client, referred to herein as an application programming interface (API) client (APIClient), that is deployed at each of the monolithic application and the microservices. In some examples, the APIClient includes a uniform resource locator (URL) register for each interface, a request context holder for a current workflow, a hypertext transfer protocol (HTTP) client for remote calls, and a resilience configuration”, 0015)
As per claim 17, it is a method claim with substantially the same limitations as claim 8, and as such, it is rejected for substantially the same reasons.
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Tripathy (US 20250139417 A1) in view of Gardner (US 20210124610 A1) in further view of Li (US 20250071191 A1) in further view of Chang (US 20190252061 A1) in further view of Radha (US 20240394414 A1) in further view of Ramachandran (US 20220309413 A1) in further view of Sakalkar (US 20250106322 A1).
As per claim 9, Tripathy in view of Gardner in further view of Li in further view of Chang in further view of Radha in further view of Ramachandran fully discloses the limitations of claim 1, but does not disclose at least one of the one or more components interfacing with a generative model via an agent service.
However, Sakalkar discloses:
at least one of the one or more components interfaces with a generative model via an agent service ("For example, the workflow module 208 may receive an input related to the attitudinal negativity scores 220 from the detection module 202, and the flow designer nodes 264 may cause the workflow module 208 to effectuate operation of the actions module 204 based on the input. ", 0033 ; "The illustrated detection module 202 may include a generative artificial intelligence (AI) model 469 that may help determine the emotion via the emotion inference model 454 and/or determine the attitudinal negativity score via the attitudinal negativity model 468.", 0048 ; "In some embodiments, the workflow module 208 is communicatively coupled to the agent device(s) 140, the supervisor device(s) 244, and/or the reporting and analytics device(s) 258 and is configured to receive inputs therefrom to perform a suitable operation (e.g., based on the flow designer nodes 264).", 0033)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Tripathy, Gardner, Li, Chang, Radha, Ramachandran, and Sakalkar in order to provide a method for improved call routing (Sakalkar, [0012]).
As per claim 18, it is a method claim with substantially the same limitations as claim 9, and as such, it is rejected for substantially the same reasons.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Tripathy (US 20250139417 A1) in view of Gardner (US 20210124610 A1) in further view of Li (US 20250071191 A1) in further view of Chang (US 20190252061 A1) in further view of Radha (US 20240394414 A1) in further view of Ramachandran (US 20220309413 A1) in further view of Kulkarni (US 20240134682 A1).
As per claim 10, Tripathy in view of Gardner in further view of Li in further view of Chang in further view of Radha in further view of Ramachandran fully discloses the limitations of claim 1, but does not disclose at least one of the one or more components being configured to emit an event to notify a progress of the workflow instance.
However, Kulkarni discloses:
at least one of the one or more components is configured to emit an event to notify a progress of the workflow instance ("In a third implementation, process 500 includes monitoring a data stream of workflow completion data regarding completion of a new workflow associated with the workflow recommendation, and updating the new workflow during completion of the new workflow based on monitoring the data stream.”, 0084 ; “In a fourth implementation, process 500 includes detecting an event associated with the new workflow, and updating the new workflow includes updating the new workflow based on detecting the event.", 0085)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Tripathy, Garnder, Li, Chang, Radha, and Ramachandran with those of Kulkarni in order to provide the system with a method for workload optimization which improves the scale of the tasks, as well as the accuracy of the completion of those tasks (Kulkarni, [0013]).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Tripathy (US 20250139417 A1) in view of Gardner (US 20210124610 A1) in further view of Li (US 20250071191 A1) in further view of Chang (US 20190252061 A1) in further view of Radha (US 20240394414 A1) in further view of Ramachandran (US 20220309413 A1) in further view of Li.2 (US 20140337085 A1).
As per claim 13, Tripathy in view of Gardner in further view of Li in further view of Chang in further view of Radha in further view of Ramachandran fully discloses the limitations of claim 11, but does not disclose determining the recommended workflow definition by comparing the predicted task to existing tasks.
However, Li.2 discloses:
determining the recommended workflow definition by comparing the predicted task to existing tasks in a database storing the existing tasks associated with respective workflow definitions ("According to an embodiment, a computer readable medium comprising instructions, which, when executed by at least one processor cause the at least one processor to perform the following steps is disclosed: obtaining a user's service goal, comparing the service goal to a plurality of service workflow templates in a stored set of service workflow templates, selecting a matching service workflow template from the stored set of service workflow templates based on the comparing.", 0009 ; Examiner Note: the services to which the service workflows are associated equates to existing tasks associated with respective workflow definitions.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Tripathy, Gardner, Li, Chang, Radha, Ramachandran, and Li.2 in order to provide a user trust network and a service trust network for service workflow recommendation, thereby allowing the system to obtain reasonable workflow results/responses with well-rated workflows (Li.2, [0018]).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Tripathy (US 20250139417 A1) in view of Chang (US 20190252061 A1) in further view of Li.2 (US 20140337085 A1) in further view of Li (US 20250071191 A1) in further view of Dhandhania (US 11366660 B1) in further view of Radha (US 20240394414 A1) in further view of Ramachandran (US 20220309413 A1).
As per claim 19, Tripathy discloses:
A computing system, comprising: processing circuitry; and a storage device storing a program executable by the processing circuitry to: receive a prompt; extract a message and a context of the prompt ("Moreover, the workflows can be generated from templates that are automatically generated based on a natural language description of a problem. Continuing with the previous example, a user can state that they are having trouble with bills being paid on time. The workflow assistant system 130 can utilize natural language processing to determine a problem statement and match a bill reminder template to the problem statement.", 0027 ; "The method of Clauses 1-4, further comprising determining context data from the problem statement", 0081 ; “Processor(s) 1002 are generally configured to retrieve and execute instructions stored in one or more memories, including local memories like the computer-readable medium 1012, as well as remote memories and data stores.”, 0065 ; Examiner Note: the problem statement equates to a message)
generate a workflow instance for the prompt according to the recommended workflow definition (“According to one aspect, a method of workflow generation includes receiving a problem statement specified by a user in a natural language, inferring a workflow template that maps to the problem statement by at least one machine learning model… and triggering generation of the workflow with a workflow system based on the workflow template.”, 0003 ; Examiner Note: the inferred workflow template equates to a recommended workflow definition)
evaluate the response to generate a score for the response generated by the workflow instance (“In the depicted example, the workflow assistant system 130 includes map component 210, score component 220, data acquisition component 230, and entity identification component 240.”, 0028 ; ”The score component 220 is configured to evaluate the confidence score associated with a matching template and a problem statement.”, 0030 ; “In one particular instance, parameters needed to build context can be retrieved, tuned, and built as a vector that can be passed to a model (e.g., chat GPT) to fetch a response and a score. Actions can be taken based on the value of the score. For example, if the score equals 100, the workflow can be built and enabled. If the confidence score equals 50, the workflow can be built but in a disabled state (e.g., not enabled/inactive).”, 0032)
Tripathy discloses the above limitations of claim 19, but does not disclose generating a predicted task based on a message and context of a prompt.
However, Chang discloses:
generate a predicted task based on the message and the context of the prompt ("Once the user's contextual cues have produced a predicted desired action of the user, the user may modify the results, accept the results or reject the results. Accepting or rejecting the results may indicate to the server 102 whether the workflow orchestration engine 118 properly determined/predicted the desired action of the user.", 0035)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Tripathy with those of Chang in order to improve the workflow efficiency of the system (Chang, [0001]).
Tripathy in view of Chang discloses the above limitations of claim 19, but does not disclose comparing the predicted task to existing tasks in a database storing the existing tasks to determine a recommended workflow definition.
However, Li.2 discloses:
determine a recommended workflow definition by comparing the predicted task to existing tasks in a database storing the existing tasks associated with respective workflow definitions ("According to an embodiment, a computer readable medium comprising instructions, which, when executed by at least one processor cause the at least one processor to perform the following steps is disclosed: obtaining a user's service goal, comparing the service goal to a plurality of service workflow templates in a stored set of service workflow templates, selecting a matching service workflow template from the stored set of service workflow templates based on the comparing.", 0009 ; Examiner Note: the services to which the service workflows are associated equates to existing tasks associated with respective workflow definitions.)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Tripathy, Chang, and Li.2 in order to provide a user trust network and a service trust network for service workflow recommendation, thereby allowing the system to obtain reasonable workflow results/responses with well-rated workflows (Li.2, [0018]).
Tripathy in view of Chang in further view of Li.2 discloses the above limitations of claim 19, but does not disclose a recommended workflow definition that specifies a plurality of calls to one or more components.
However, Li discloses:
The recommended workflow definition that specifies a plurality of calls to one or more components; (“Implementations can include actions of receiving. from a first entity, a first call, the call being received by a middleware client of a second entity, storing a set of headers associated with a workflow in a request context holder of the middleware client, and determining that a second call is to be executed from the middleware client of the second entity for the workflow, and in response: reading, by a client of the middleware client, the set of headers associated with the workflow, including the headers in a request of the second call, determining an endpoint for the second call, and transmitting the second call for receipt at the endpoint.”, 0015 ; Examiner Note: the set of headers correspond to a workflow definition)
The system of Tripathy in view of Chang in further view of Li.2 in further view of Li would be capable of providing a recommended workflow definition which specifies a plurality of calls to one or more components/microservices. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Tripathy, Chang, Li.2, and Li in order to enable continuous enhancement of functionality without re-implementation within the workflow/microservice orchestration system (Li, [0016]).
Tripathy in view of Chang in further view of Li.2 in further view of Li fully discloses the above limitations of claim 19, but does not disclose the outputting of a generated response for the prompt.
However, Dhandhania discloses:
execute the generated workflow instance to perform the calls to the one or more components to thereby generate a response for the prompt; and output the generated response for the prompt (“Subcomponent 307 fulfills a first subtask of a workflow of a task of API service request 305-1 and provides an output 305-1 to subcomponent 308, which fulfills a second subtask of the workflow and provides an output 305-2 to subcomponent 309. Subcomponent 309 fulfills third subtask of the workflow and provides a response 311 to API service 304 to the client.”, col.9, lines 27-33)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Tripathy, Chang, Li.2, Li and Dhandhania in order to improve service request performance by improving accuracy in the detection of malfunctioning components, improve identification of components that limit the overall output of a complex service, and provide for latency estimates with improved reliabilities to be determined for complex multiple component inferencing systems where the latency depends on the path taken by the request (Dhandhania, [col.2, lines 21-27]).
Tripathy in view of Chang in further view of Li.2 in further view of Li in further view of Dhandhania discloses the above limitations of claim 19, but does not explicitly disclose a performance database.
However, Radha discloses:
the workflow instances and the respective scores generated by the workflow instances are stored in a performance database ("If the validation is successful, the validated workflow is processed by the planner module 218. It will be appreciated that copies of the workflow, and other related data (e.g., validation results, invalidation errors, version IDs, status updates, etc.) are stored on the workflow database 221. ", 0075 ; Examiner Note: the copies of the workflows equate to predicted tasks, the validation results equate to scores, and the workload version IDs equate to workload identifiers)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Tripathy, Chang, Li.2, Li, Dhandhania, and Radha in order to provide the system with the ability to partially execute workloads, thereby allowing the system to better satisfy the original constraints (Radha, [0128]).
Tripathy in view of Chang in further view of Li.2 in further view of Li in further view of Dhandhania in further view of Radha fully discloses the above limitations of claim 19, but does not explicitly disclose the performance database being configured to determine a recommended workflow definition.
However, Ramachandran discloses:
The performance database is configured to determine the recommended workflow definition based on the scores by comparing the predicted task to workflow instances stored in the performance database (“The workflow guidance module (WGM) 120 is an AI/ML module or an algorithmic module, which matches one or more of call context, call metadata or historical data with various workflows stored in a workflow repository, for example, the workflow repository 136, to identify at least one recommended workflow. The Workflow guidance module WGM 120 receives call metadata from the CMS 138, for example, in response to a request sent from the WGM 120 to the CMS 138, or automatically.”, 0025)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Tripathy, Chang, Li.2, Li, Dhandhania, and Radha with those of Ramachandran in order to improve the matching and recommendation of the workflow through the use of agent and customer feedback data (Ramachandran, [0015]).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Tripathy (US 20250139417 A1) in view of Chang (US 20190252061 A1) in further view of Li.2 (US 20140337085 A1) in further view of Li (US 20250071191 A1) in further view of Dhandhania (US 11366660 B1) in further view of Radha (US 20240394414 A1) in further view of Ramachandran (US 20220309413 A1) in further view of Liu (US 20210117479 A1).
As per claim 20, Tripathy in view of Chang in further view of Li.2 in further view of Li in further view of Dhandhania in further view of Radha in further view of Ramachandran fully discloses the limitations of claim 19, but does not disclose a prompt including multi-modal inputs.
However, Liu discloses:
the prompt includes multi-modal inputs or content ("In particular embodiments, the assistant system 140 may support multi-modal user requests for controlling a task flow. As an example and not by way of limitation, the first, second, or third user requests may be based on one or more of a textual input, an image input, a video input, an audio input, or a gesture input.", 0100)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of Tripathy, Chang, Li.2, Li, Dhandhania, Radha, Ramachandran, and Liu in order to enhance user control of task execution, thereby improving user experience with the assistant system (Liu, [0098]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Abebe (US 20170212928 A1) – discloses a system which responds to a query in a cognitive decision-making system. In one embodiment, a query is received and a plurality of analytical tools defined in a knowledge base that are to be used to produce a response are identified. A workflow sequence is constructed based on a dependency graph of the identified analytical tools.
Yeleshwarapu (US 20220300812 A1) – discloses a method for managing execution of a workflow comprising a set of subworkflows, comprising optimizing the set of subworkflows using a deep neural network.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/R.M.V./
Examiner, Art Unit 2196
/APRIL Y BLAIR/Supervisory Patent Examiner, Art Unit 2196
