DETAILED ACTION
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 .
This Final Office Action is responsive to Applicant's amendment filed on 27 April 2026. Applicant’s amendment on 27 April 2026 amended Claims 1-3, 6-9, and 11-20. Currently Claims1-20 are pending and have been examined. The Examiner notes that the 101 rejection regarding the signal per se argument has been withdrawn, however the 101 Alice rejection is maintained.
Response to Arguments
Applicant's arguments filed 27 April 2026 have been fully considered but they are not persuasive.
The Applicant argues on page 12-13 that the amended claims (reciting "executable code" that defines "execution order" and "endpoint invocation," and "workflow execution logic" that executes that code) shift the focus away from a mental process or method of organizing human activity, because a human mind is not equipped to "execute executable code," function as "workflow execution logic," or "invoke a computing endpoint" as claimed analogizing to SRI Int'l, Inc. v. Cisco Sys., 930 F.3d 1295 (Fed. Cir. 2019).
The Examiner respectfully disagrees.
With respect to the argument the Examiner notes that this argument is not persuasive as to the claims as a whole, though it has some force limited to the newly added execution limitations. Under the Aug. 4, 2025 memo and MPEP 2106.04(a)(2), the eligibility analysis must consider the claim "as a whole" rather than parsing out only the most technical-sounding phrase. The core of independent claims 1, 13, and 20 receiving an initial prompt indicating a task, causing inquiries to be generated based on that prompt, providing the inquiries to the user, receiving response prompts, and using those responses to incrementally construct/modify a workflow remains a claim to gathering information through a dialog and using the answers to decide what steps a workflow should contain. That is squarely the kind of "observation, evaluation, judgment" activity that the mental-processes grouping covers under MPEP 2106.04(a)(2) and is analogous to In re Killian, 45 F.4th 1373 (Fed. Cir. 2022) (collecting information from multiple sources and evaluating it to reach a determination) and Trinity Info Media v. Covalent, 72 F.4th 1355 (Fed. Cir. 2023) (receiving information, comparing it, and generating an output based on the comparison). A person can ask a series of clarifying questions about a task, note the answers, and use them to decide, add, or reorder the steps of a plan nothing in the claim forecloses that. Unlike SRI Int'l, where the human mind is not equipped to parse raw network packets at scale to detect suspicious activity, nothing about "soliciting information regarding a task" and "incrementally constructing a workflow" from that information is beyond ordinary human cognitive capability. The claims can additionally be characterized as a method of managing interactions between people (organizing human activity), since the substance of the claimed inquiries/response exchange (e.g., "would you like to automate these actions," "which email tool would you like to use") mirrors interactions a human coordinator would have with a colleague to define a business process. As to the newly added "store... as executable code" and "execute... by workflow execution logic" language, that limitation is addressed below as an additional element under Step 2A Prong Two rather than as negating the presence of an abstract idea altogether, because a claim can recite generic implementing hardware/software (which itself is not a judicial exception) while still being "directed to" the mental process it automates. Accordingly, claims 1-20 continue to recite an abstract idea at Prong One. The rejection is therefore maintained.
The Applicant argues on pages 14-17 that requiring the workflow to be stored "as executable code" and "executed... by workflow execution logic" that "defines execution order and endpoint invocation" reflects a specific execution-centric architecture that improves computer functionality, analogous to Enfish (self-referential table), McRO (specific animation rules), Data Engine (specific navigation solution), and Ancora (software enforcement logic in memory).
The Examiner respectfully disagrees.
With respect to the arguments the Examiner notes that this argument is not persuasive on the current record, and the additional elements do not integrate the exception into a practical application. Under the Aug. 4, 2025 memo, MPEP 2106.04(d)(1)/2106.05(a), and the Dec. 5, 2025 Desjardins advance notice, an improvement to computer functionality requires that the specification describe, and the claim reflect, a particular technical solution to a technical problem not merely an automated version of a process a human or generic system could otherwise perform. The cases Applicant cites each involved a specific, disclosed technical mechanism: Enfish's self-referential logical table structure improving database search/storage flexibility; McRO's specific rule set for setting morph-weight transitions that had not previously been automatable; Data Engine's specific notebook-tab interface solving a navigation problem unique to electronic spreadsheets; and Ancora's specific technique of storing a license-verification structure in BIOS non-volatile memory to solve software piracy. By contrast, claims 1, 13, and 20 recite only that the workflow is "stored... as executable code" and "executed" by "workflow execution logic" that "define[s] execution order and endpoint invocation" without reciting any particular data structure, algorithm, or technical mechanism by which execution order or endpoint invocation is determined or carried out differently from how any general-purpose workflow/orchestration engine already stores and runs a sequence of steps that call out to external systems (as the cited Peter reference itself illustrates with pre-built workflow modules and hooks/endpoints). Storing instructions in memory and having a processor execute them to invoke other software components is one of the most basic functions of any programmed computer; MPEP 2106.05(d) expressly lists "storing and retrieving information in memory" and "receiving or transmitting data over a network" as generic computer functions that, absent more, do not integrate an exception into a practical application. Nor does the specification identify a technical improvement comparable to Desjardins' disclosed solution to "catastrophic forgetting" in machine-learning training the specification here (par. [0020]–[0022]) describes only that the invention may "reduce the amount of time and/or resources" a user consumes and may increase the accuracy of the generated workflow, which speaks to the quality of the abstract output (a better plan), not to an improvement in how the computer itself operates, and is therefore analogous to In re Bd. of Trs. of Leland Stanford Jr. Univ. (Stanford I/II), where an improvement in the abstract result itself (a more accurate calculation/output) was held not to be a technological improvement. Consistent with the Desjardins reminder not to dismiss additional elements at too high a level of generality, the Examiner has considered "executable code," "workflow execution logic," "execution order," and "endpoint invocation" individually and in combination, but none of these terms is tied in the claim or specification to any non-conventional technical arrangement; they merely restate, at a high level of generality, that a computer runs the generated plan and calls out to the services the plan identifies. This remains classic "apply it" using generic computer components to implement the outcome of the abstract mental/organizational process and a general linking of the abstract idea to the field of workflow automation software, which MPEP 2106.05(f)/(h) confirm does not integrate a judicial exception into a practical application. The rejection is therefore maintained.
The Applicant argues on pages 18-19 that, even if the claims are directed to a judicial exception at Step 2A, the specific ordered combination of causing inquiries, receiving responses, incrementally constructing/modifying the workflow, storing it as executable code, and executing it via workflow execution logic is not well-understood, routine, or conventional, and reflects "a specific application of AI to a particular technological field" under the July 2024 AI Update.
The Examiner respectfully disagrees.
With respect to the arguments the Examiner notes that this argument is not persuasive. The Step 2A Prong Two analysis above carries forward into Step 2B: an additional element found to amount to no more than mere instructions to apply the exception on a generic computer, or to constitute insignificant extra-solution/generic data-handling activity, does not become "significantly more" merely because it is evaluated again at Step 2B. See MPEP 2106.05, subsection I.A; Examples 47–49 (reaching the same "no inventive concept" conclusion at Step 2B for "receiving," "outputting," and generic "using [a] computer/ANN" limitations that were found to be mere instructions to apply the exception at Prong Two). Here, "storing" a generated plan and "executing" it by invoking downstream systems in a defined order are, at the level of generality claimed, the same kind of generic "storing and retrieving information in memory" and "receiving/transmitting data" functions that MPEP 2106.05(d)(II) and Versata Dev. Group v. SAP recognize as well-understood, routine, and conventional computer functions when recited generically; the claims do not specify any unconventional arrangement of these generic components analogous to BASCOM's non-generic, specifically located filtering architecture. As to the AI Update's "particular solution to a problem" language, that consideration presupposes that the claim recites a specific application of AI to a technical field (as in Example 47's network-intrusion claim or Example 48's speech-signal-processing claims, each of which recited concrete technical operations on signal/network data). Here, the claimed "specific application" is simply using a generative AI model to have a conversational dialog and then run the resulting list of steps an application to the field of business/workflow automation, not a technical field, and one that (per Peter) is already how conversational workflow-builder products operate. Because the ordered combination does no more than take the abstract mental/organizational process of Step 2A and apply it using generic prompting, storage, and execution steps, the claim does not supply the requisite inventive concept, and the 101 rejection of claims 1-20 as directed to an abstract idea without significantly more should be maintained, updated to address the amended "executable code"/"workflow execution logic" limitations as set forth above. The rejection is therefore maintained.
Additionally, the remaining Applicant's arguments filed 27 April 2026 have been fully considered but they are moot in view of new grounds of rejection as necessitated by amendment.
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. When considering claims 1-20 as a whole and all claim elements both individually and in combination, these claim(s) 1-20 are directed to the abstract idea of generating a workflow using artificial intelligence generated inquiries to an artificial intelligence model without significantly more than the judicial exception itself.
Step 1
Regarding Step 1 of the Subject Matter Eligibility Test for Products and Processes, claim(s) (1-12) is/are directed to a system, claim(s) (13-19) is/ are directed to a method, and therefore the claims are viewed as falling in statutory categories.
Step 2A Prong 1
The independent claims 1, 13, and 20 recite a mental process as drafted, the claim recites the limitation of generating a workflow which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting a processor, nothing in the claim precludes the determining step from practically being performed in the human mind. For example, but for a processor language, the claim encompasses the user manually:
receive an initial prompt from a user, the initial prompt indicating that a task is to be completed;
cause a plurality of inquiries based at least on the initial prompt, in a first successive order to the user, the plurality of inquiries soliciting information regarding the task;
receive a plurality of response prompts, in a second successive order that corresponds to the first successive order; and
generate a workflow, which is configured to achieve the task, based at least on inputs by providing the plurality of response prompts as the inputs
cause, based on the plurality of response prompts, incremental construction and modification of the workflow.
Store the workflow…
Execute… the executable code of the workflow.
The mere nominal recitation of a generic processor does not take the claim limitation out of the mental processes grouping. It has been established by ongoing guidance that claims that contain a generic processor are still viewed as mental process when they contain limitations that can practically be performed in the human mind, however this is different for instance when the human mind is not equipped to perform the claim limitations (network monitoring, data encryption for communication, and rendering images). Therefore, these limitations are viewed a mental process.
Additionally, the claims could additionally be construed as the claim reciting a method of organizing human activity. The claimed invention is a method that allows for users to:
receive an initial prompt from a user, the initial prompt indicating that a task is to be completed;
cause a plurality of inquiries based at least on the initial prompt, in a first successive order to the user, the plurality of inquiries soliciting information regarding the task;
receive a plurality of response prompts, w, in a second successive order that corresponds to the first successive order; and
generate a workflow, which is configured to achieve the task, based at least on inputs by providing the plurality of response prompts as the inputs
cause, based on the plurality of response prompts, incremental construction and modification of the workflow.
Store the workflow…
Execute… the executable code of the workflow.
… which is a method of managing interactions between people. Thus, the claim recites an abstract idea.
Step 2A Prong 2
Specifically, the determined judicial exception is not integrated into a practical application because the generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer and additionally the data providing and receiving steps required to use the generating do not add a meaningful limitation to the method as they are insignificant extra-solution activity (including post solution activity).
The claim recites the additional element(s): that a processor is used to perform the generative AI model (as an input to a generative AI model; which are generated by the generative AI model to automatically generate workflow; The processor in the steps is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing data (generating workflows). This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
The claim recites the additional element(s): providing a prompt, providing inquiries, and receiving response prompts performs the generative step. The providing and receiving steps are recited at a high level of generality (i.e., as a general means of gathering data for use in the generating step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The processor that performs the generating step is also recited at a high level of generality, and merely automates the generating step. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component (the processor).
The Examiner has further determined that the claims as a whole does not integrate a judicial exception into a practical application in order to provide an improvement in the functioning of a computer or an improvement to other technology or technical field. It has been determined that based on the disclosure does not provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. It has not been provided clearly in the disclosure that the alleged improvement would be apparent to one of ordinary skill in the art, but is instead in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art, and therefore does not improve the technology. Second, in the instance, where it is not clear that the specification sets forth an improvement in technology, the claim must reflect the disclosed improvement (the claims must include components or steps of the invention that provide the improvement described in the specification).
Note to the Applicant from the MPEP 2106.05(a): Generally, examiners are not expected to make a qualitative judgment on the merits of the asserted improvement. If the examiner concludes the disclosed invention does not improve technology, the burden shifts to applicant to provide persuasive arguments supported by any necessary evidence to demonstrate that one of ordinary skill in the art would understand that the disclosed invention improves technology. Any such evidence submitted under 37 C.F.R. § 1.132 must establish what the specification would convey to one of ordinary skill in the art and cannot be used to supplement the specification. For example, in response to a rejection under 35 U.S.C. § 101, an applicant could submit a declaration under § 1.132 providing testimony on how one of ordinary skill in the art would interpret the disclosed invention as improving technology and the underlying factual basis for that conclusion.
For further clarification the Examiner points out that the claim(s) 1-20 recite(s) providing an initial prompt, providing a plurality of inquiries, receiving a plurality of response prompts, and generate a workflow which are viewed as an abstract idea in the form of a mental process. This judicial exception is not integrated into a practical application because the use of a computer for providing, receiving, and generating which is the abstract idea steps of valuing an idea (generating a workflow) in the manner of “apply it”.
Thus, the claims recites an abstract idea directed to a mental process (i.e. to generate a workflow). Using a computer to providing, receiving, and generating the data resulting from this kind of mental process merely implements the abstract idea in the manner of “apply it” and does not provide 'something more' to make the claimed invention patent eligible. The claimed limitations of a computing device is not constraining the abstract idea to a particular technological environment and do not provide significantly more.
The generating a workflow would clearly be to a mental activity that a company would go through in order to generate a workflow. The specification makes it clear that the claimed invention is directed to the mental activity data gathering and data analysis to determine a workflow:
The dependent claims recite elements that narrow the metes and bounds of the abstract idea but do not provide ‘something more’.
The dependent claims do not remedy these deficiencies.
Claims 3, 6, 11, and 16-18 recite limitations which further limit the claimed analysis of data.
Claims 2, 7-9, 12, 14, 15, and 19 recites limitations directed to claim language viewed insignificantly extra solution activity.
Using a computer to perform the data processing as claimed is merely implementing the abstract idea in the manner of “apply it” and does not provide significantly more. Additionally with respect to the Berkheimer the Examiner points out that the steps of the claim are viewed to be to nothing more than spell out what it means to apply it on a computer and cannot confer patent-eligibility as there are no additional limitations beyond applying an abstract idea, restricted to a computer. As the claims are merely implementing the abstract idea in the manner of “Apply It” the need for a Berkheimer analysis does not apply and is not required. With respect to the currently filed claims the implementing steps can be found in Peter which discloses how the claims alone and in combination are viewed to be well understood, routine and conventional based on point 3 of the Berkheimer memo and subsequent evidence, complying with and providing evidence.
Claims 4, 5, and 10 recites limitations directed to claim language viewed non-functional data labels.
Thus, the problem the claimed invention is directed to answering the question based on gathered and analyzed information to generate a workflow. This is not a technical or technological problem but is rather in the realm of workflow creation and therefore an abstract idea.
Step 2B
The claim(s) 1-20 does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed with respect to Step 2A Prong Two, the additional element in the claim amounts to no more than mere instructions to apply the exception using a generic computer component.
The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. This is the case because in order for the claims to be viewed as significantly more the claims must incorporate the integral use of a machine to achieve performance of a method, in contrast to where the machine is merely an object on which the method operates, which does not provide significantly more in order for a machine to add significantly more, it must play a significant part in permitting the claimed method to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly. Whether its involvement is extra-solution activity or a field-of-use, i.e., the extent to which (or how) the machine or apparatus imposes meaningful limits on the claim. Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more. Additionally, another consideration when determining whether a claim recites significantly more is whether the claim effects a transformation or reduction of a particular article to a different state or thing. "[T]ransformation and reduction of an article ‘to a different state or thing’ is the clue to patentability of a process claim that does not include particular machines. All together the above analysis shows there is not improvement in computer functionality, or improvement to any other technology or technical field. The claim is ineligible.
Additionally, with respect to the Berkheimer as noted above the same analysis applies to the 2B where the claims are viewed as applying it and as such no further analysis is required. However, with respect to the current claims providing and receiving that are viewed as extra solution or post solution activity the Examiner notes that the claims are viewed as well-understood, routine, and conventional because a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s). An appropriate publication such as the currently cited prior art Peter et al. (U.S. Patent Publication 2025/0104017A1) provides those extra solution activities and is viewed as a form of publication which also includes a book, manual, review article, or other source that describes the state of the art and discusses what is well-known and in common use in the relevant industry. The claim is ineligible.
The dependent claims recite elements that narrow the metes and bounds of the abstract idea but do not provide ‘something more’. Specifically, the dependent claims do not remedy these deficiencies of the independent claims.
With respect to the legal concept of prima facie case being a procedural tool of patent examination, which allocates the burdens going forward between the examiner and the applicant. MPEP § 2106.07 discusses the requirements of a prima facie case of ineligibility. In particular, the initial burden was on the Examiner and believed to be properly provided as to explain why the claim(s) are ineligible for patenting because of the above provided rejection which clearly and specifically points out in accordance with properly providing the requirement satisfying the initial burden of proof based on the Guidance from the United States Patent and Trademark Office and the burden now shifts to the applicant.
Therefore, based on the above analysis as conducted based on the Guidance from the United States Patent and Trademark Office the claims are viewed as a court recognized abstract idea, are viewed as a judicial exception, does not integrate the claims into a practical application, and does not provide an inventive concept, therefore the claims are ineligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of pre-AIA 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 13, and 20 is/are rejected under pre-AIA 35 U.S.C. 102(a)(2) as being anticipated by Mico et al. (U.S. Patent Publication 2024/0428260 A1) (hereafter Mico) (provisional filed 27 November 2023).
Referring to Claim 1, Peter teaches a system comprising: a processor system;
a memory that stores computer-executable instructions that are executable by the processor system to at least (see; par. [0007]] of Mico teaches memory and processors).
receive an initial prompt from a user device as an input to a generative artificial intelligence model, the initial prompt indicating that a task is to be completed (see; par. [0068] of Mico teaches an artificial intelligence controlled by prompts to manage tasks).
cause a plurality of inquiries, to be generated by execution of the generative artificial intelligence model based at least on the initial prompt, (see; par. [0068] and par. [0029] of Mico teaches causing inquiries to be created by an artificial intelligence based on prompts to gather information).
provide the plurality of inquiries in a first successive order to the user device, the plurality of inquiries soliciting information to be used in construction of a workflow to achieve the task (see; par. [0029] and par. [0036] of Mico teaches an example of inquires collected by artificial intelligence prompts, par. [0075] in order to solicit information that is then used tasks).
receive a plurality of response prompts, in a second successive order that corresponds to the first successive order, the plurality of response prompts being responses to respective inquiries of the plurality of inquiries (see; par. [0005]-[0006] of Mico teaches step by step action in the workflow path using generative artificial intelligence prompts).
cause, based on the plurality of response prompts, incremental construction and modification of the workflow by the generative artificial intelligence model, wherein the plurality of response prompts automatically trigger modification of the workflow by adding, selecting, or configuring operations to be included in the workflow (see; par. [0027]-[0028] of Mico teaches creating service workflows step by step incremental construction, par. [0054]-[0055] using a process engine to modify the workflow and unique workflows by AI, par. [0029] adapt the customer journey based on AI prompts).
execute, by workflow exception logic of the system, the executable code of the workflow, the executable code defining execution order and endpoint invocation performed during execution of the workflow to perform the task (see; par. [0006] of Mico teaches executing the workflow based on the context to manage the customer flow).
Referring to Claim 13, Mico teaches a method implemented by a computing system. Claim 13 recites the same or similar limitations as those addressed above in claim 1, Claim 13 is therefore rejected for the same reasons as set forth above in claim 1.
Referring to Claim 20, Mico teaches a computer program product comprising a computer readable storage medium. Claim 20 recites the same or similar limitations as those addressed above in claim 1, Claim 20 is therefore rejected for the same reasons as set forth above in claim 1.
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 may not be obtained through the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2-4, 6-8, 10-12, and 14-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mico et al. (U.S. Patent Publication 2024/0428260 A1) (hereafter Mico) in view of Peter et al (U.S. Patent Publication 2025/0104017 A1) (hereafter Peter).
Referring to Claim 2, see discussion of claim 1 above, while Mico teaches the system above, Mico further discloses a system having the limitations of:
artificial intelligence (see; par. [0006] of Mico teaches the use of an artificial intelligence to manage workflows).
Mico does not explicitly disclose the following limitation, however,
Peter teaches cause the model to incrementally modify the workflow to include a plurality of incremental changes in the second successive order (see; par. [0034] of Peter teaches a workflow generation in the context of generative flow builder, involves a smart chart widget that interacts with the end user or citizen developer in a multi-turn conversational manner to collect information, define guidelines, and determine the purpose of the workflow. The chat widget then automatically selects the appropriate modules and their attributes, integrates them, and produces a complete workflow ready for use by the end user (i.e. the muti-turn conversational manner involves incremental building of workflow as each turn adds information), par. [0044] an example where the generated workflow data is based on prompts and corresponding responses (e.g.. the pris indicated as prompt and response… prompt and response… etc. (i.e. the sequential prompt response pairs demonstrate successive/incremental workflow construction)), and
wherein the plurality of incremental changes are triggered by the plurality of respective response prompts (see; par. [0034] of Peter teaches the chat widget interacts with the end user or citizen developer in a multi-turn conversational manner to collect information and then automatically selects the appropriate modules and their attributes, integrates them (i.e. each response from the user triggers the next incremental addition to the workflow), par. [0047] and an example of how a user is generated that will first do a greeting, then post an open ended response to inquire on what the user is interested in and present services such as filing a claim, checking the status of the claim as options, and allow the user to navigate among these options and when done with the existing services will be able to get some feedback on the overall experience of this engagement (i.e. the composite prompt examples show sequential/incremental workflow steps that are triggered by user input)).
The Examiner notes that Mico teaches similar to the instant application teaches an artificial intelligence powered contextual customer service that controls responses. Specifically, Mico discloses the artificial intelligence powered service automation solution using logic trees enhanced with mission and corporate value based generative AI prompts it is therefore viewed as analogous art in the same field of endeavor. Additionally, Peter teaches a generative customer experience automation and as it is comparable in certain respects to Mico which an artificial intelligence powered contextual customer service that controls responses as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Mico discloses the artificial intelligence powered service automation solution using logic trees enhanced with mission and corporate value based generative AI prompts. However, Mico fails to disclose cause the model to incrementally modify the workflow to include a plurality of incremental changes in the second successive order, and wherein the plurality of incremental changes are triggered by the plurality of respective response prompts.
Peter discloses cause the model to incrementally modify the workflow to include a plurality of incremental changes in the second successive order, and wherein the plurality of incremental changes are triggered by the plurality of respective response prompts.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Mico the cause the model to incrementally modify the workflow to include a plurality of incremental changes in the second successive order, and wherein the plurality of incremental changes are triggered by the plurality of respective response prompts as taught by Peter since 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. Additionally, Mico, and Peter teach the collecting and analysis of data in order to manage customer experience using AI and they do not contradict or diminish the other alone or when combined.
Referring to Claim 3, see discussion of claim 1 above, while Mico in view of Peter teaches the system above, Mico further discloses a system having the limitations of:
artificial intelligence (see; par. [0006] of Mico teaches the use of an artificial intelligence to manage workflows).
Mico does not explicitly disclose the following limitation, however,
Peter teaches the computer-executable instructions are executable by the processor system further to: for each response prompt of the plurality of response prompts, cause a next successive inquiry of the plurality of inquiries to be generated by the generative model by providing the respective response prompt together with contextual information, which includes context regarding the respective response prompt, as inputs to the generative model, the contextual information including at least a preceding prompt that precedes the respective response prompt, the preceding prompt including the initial prompt or another response prompt of the plurality of response prompts (see; par. [0033] of Peter teaches a prompt generation in the context of generative flow builder (i.e. mutli-turn conversational format means each response generates a next inquiry), par. [0034] workflow generation that interacts with the end-user or citizen developer in a multi-turn conversational manner (i.e. the multi-turn interaction explicitly involves successive inquiries generated based on responses), par. [0051] task specific fine tuned LLM relies on accurate and relevant input data this includes workflow variables which outline the essential parameters and elements of the workflow including previous conversation history which ensures continuity and relevance in the interaction (i.e. explicitly states that previous conversation history is provided as input to the LLM to ensure continuity and relevance), Figure 6, the figure explicitly shows conversation history necessarily includes preceding prompts to maintain continuity), par. [0051] where the previous conversation history necessarily includes preceding prompts to maintain continuity and includes both initial and subsequent prompts/responses, par. [0044] the structure shows that the conversation history includes the initial prompt and subsequent response prompts in sequence,
The Examiner notes that Mico teaches similar to the instant application teaches an artificial intelligence powered contextual customer service that controls responses. Specifically, Mico discloses the artificial intelligence powered service automation solution using logic trees enhanced with mission and corporate value based generative AI prompts it is therefore viewed as analogous art in the same field of endeavor. Additionally, Peter teaches a generative customer experience automation and as it is comparable in certain respects to Mico which an artificial intelligence powered contextual customer service that controls responses as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Mico discloses the artificial intelligence powered service automation solution using logic trees enhanced with mission and corporate value based generative AI prompts. However, Mico fails to disclose the computer-executable instructions are executable by the processor system further to: for each response prompt of the plurality of response prompts, cause a next successive inquiry of the plurality of inquiries to be generated by the generative AI model by providing the respective response prompt together with contextual information, which includes context regarding the respective response prompt, as inputs to the generative AI model, the contextual information including at least a preceding prompt that precedes the respective response prompt, the preceding prompt including the initial prompt or another response prompt of the plurality of response prompts.
Peter discloses the computer-executable instructions are executable by the processor system further to: for each response prompt of the plurality of response prompts, cause a next successive inquiry of the plurality of inquiries to be generated by the generative AI model by providing the respective response prompt together with contextual information, which includes context regarding the respective response prompt, as inputs to the generative AI model, the contextual information including at least a preceding prompt that precedes the respective response prompt, the preceding prompt including the initial prompt or another response prompt of the plurality of response prompts.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Mico the computer-executable instructions are executable by the processor system further to: for each response prompt of the plurality of response prompts, cause a next successive inquiry of the plurality of inquiries to be generated by the generative AI model by providing the respective response prompt together with contextual information, which includes context regarding the respective response prompt, as inputs to the generative AI model, the contextual information including at least a preceding prompt that precedes the respective response prompt, the preceding prompt including the initial prompt or another response prompt of the plurality of response prompts as taught by Peter since 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. Additionally, Mico, and Peter teach the collecting and analysis of data in order to manage customer experience using AI and they do not contradict or diminish the other alone or when combined.
Referring to Claim 4, see discussion of claim 3 above, while Mico in view of Peter teaches the system above, Mico does not explicitly disclose a system having the limitations of, however.
Peter teaches for each response prompt of the plurality of response prompts, the contextual information regarding the respective response prompt includes the initial prompt and each response prompt of the plurality of response prompts that precedes the respective response prompt (see; par. [0051] of Peter teaches conversation history would include the initial prompt to maintain full context, par. [0044] the sequence explicitly begins with prompt (the initial prompt), and maintains the full chain, par. [0044] the sequential structure prompt – response, prompt – response indicates that all the preceding prompts and responses are maintained as part of the data structure, par. [0051] where continuity requires maintaining the full conversation history including all preceding exchanges, and par. [0052] the LLM integrates all available context to generate the workflow).
The Examiner notes that Mico teaches similar to the instant application teaches an artificial intelligence powered contextual customer service that controls responses. Specifically, Mico discloses the artificial intelligence powered service automation solution using logic trees enhanced with mission and corporate value based generative AI prompts it is therefore viewed as analogous art in the same field of endeavor. Additionally, Peter teaches a generative customer experience automation and as it is comparable in certain respects to Mico which an artificial intelligence powered contextual customer service that controls responses as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Mico discloses for each response prompt of the plurality of response prompts, the contextual information regarding the respective response prompt includes the initial prompt and each response prompt of the plurality of response prompts that precedes the respective response prompt. Mico fails to disclose for each response prompt of the plurality of response prompts, the contextual information regarding the respective response prompt includes the initial prompt and each response prompt of the plurality of response prompts that precedes the respective response prompt.
Peter discloses for each response prompt of the plurality of response prompts, the contextual information regarding the respective response prompt includes the initial prompt and each response prompt of the plurality of response prompts that precedes the respective response prompt.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Mico for each response prompt of the plurality of response prompts, the contextual information regarding the respective response prompt includes the initial prompt and each response prompt of the plurality of response prompts that precedes the respective response prompt as taught by Peter since 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. Additionally, Mico, and Peter teach the collecting and analysis of data in order to manage customer experience using AI and they do not contradict or diminish the other alone or when combined.
Referring to Claim 6, see discussion of claim 1 above, while Mico teaches the system above, Mico further discloses a system having the limitations of:
based at least on the identified endpoint being selected from the plurality of endpoints, cause the incremental construction and modification of the workflow by the generative artificial intelligence model by using the identified endpoint (see; par. [0027]-[0028] of Mico teaches creating service workflows step by step incremental construction, par. [0054]-[0055] using a process engine to modify the workflow and unique workflows by generative AI, par. [0029] adapt the customer journey based on AI prompts, par. [0007] while also identifying the workflow path (i.e. all the way to the endpoint)).
Mico does not explicitly disclose the following limitations, however,
Peter teaches determine a plurality of endpoints that are capable of being used to achieve the task (see; par. [0045] of one or more hooks to leverage when each of these options is generated, the hooks and webhooks that connect to databases and enterprise systems are endpoints. Multiple hooks/endpoints are available for each option), par. [0046] the multiple database lookups and hooks (endpoints) are available to fulfill tasks), and
select an identified endpoint from the plurality of endpoints based at least on a historical pattern of use of the identified endpoint (see; par. [0045] of Peter teaches the system is trained meaning it has learned from historical data which hooks/endpoints to use, par. [0046] previously trained on indicates learning from historical patterns of database/endpoint usage), par. [0042] progressive tuning based on enterprise needs reflects learning from historical usage patterns, Abstract learning from historical patterns of user interactions).
The Examiner notes that Mico teaches similar to the instant application teaches an artificial intelligence powered contextual customer service that controls responses. Specifically, Mico discloses the artificial intelligence powered service automation solution using logic trees enhanced with mission and corporate value based generative AI prompts it is therefore viewed as analogous art in the same field of endeavor. Additionally, Peter teaches a generative customer experience automation and as it is comparable in certain respects to Mico which an artificial intelligence powered contextual customer service that controls responses as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Mico discloses for each response prompt of the plurality of response prompts, the contextual information regarding the respective response prompt includes the initial prompt and each response prompt of the plurality of response prompts that precedes the respective response prompt. Mico fails to disclose explicitly disclose determine a plurality of endpoints that are capable of being used to achieve the task, and select an identified endpoint from the plurality of endpoints based at least on a historical pattern of use of the identified endpoint.
Peter discloses explicitly disclose determine a plurality of endpoints that are capable of being used to achieve the task, and select an identified endpoint from the plurality of endpoints based at least on a historical pattern of use of the identified endpoint.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Mico explicitly disclose determine a plurality of endpoints that are capable of being used to achieve the task, and select an identified endpoint from the plurality of endpoints based at least on a historical pattern of use of the identified endpoint as taught by Peter since 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. Additionally, Mico, and Peter teach the collecting and analysis of data in order to manage customer experience using AI and they do not contradict or diminish the other alone or when combined.
Referring to Claim 7, see discussion of claim 1 above, while Mico teaches the system above, Mico does not explicitly disclose a system having the limitations of, however,
Peter teaches provide a first subset of the plurality of inquiries during a first user session of a user (see; par. [0034] of Peter teaches a multi-turn conversation involves providing inquiries during a session), and
receive a first subset of the plurality of response prompts, which corresponds to the first subset of the plurality of inquiries, during the first user session of the user (see; par. [0034] of Peter teaches a multi-turn conversational manner to collect information, define guidelines, and determine the purpose of the workflow which indicates responses are received during the conversation session), and
provide a second subset of the plurality of inquiries during a second user session of the user that temporally follows the first user session, the first user session and the second user session being temporally noncontiguous (see; par. [0044] of Peter teaches a scheduled deployment implies temporal separation between sessions, par. [0050] the scheduler enables future deployment which necessarily involves temporally noncontiguous sessions. Database records persist data between sessions), and
receive a second subset of the plurality of response prompts, which corresponds to the second subset of the plurality of inquiries, during the second user session of the user (see; par. [0050] of Peter teaches workflow scheduler schedules for future deployment, where additional user interaction would occur), and
wherein an inquiry in the second subset of the plurality of inquiries, which is provided during the second user session of the user, is based at least on a response prompt in the first subset of the plurality of response prompts, which is received during the first user session of the user (see; par. [0051] of Peter teaches conversation history persists across sessions to inform future inquiries, par. [0050] database records store information from first session that can be used in second session, par. [0048] an example that shows a first interaction (checking policy) that informs a later interaction (renewal flow) at a future time when the policy is expiring).
The Examiner notes that Mico teaches similar to the instant application teaches an artificial intelligence powered contextual customer service that controls responses. Specifically, Mico discloses the artificial intelligence powered service automation solution using logic trees enhanced with mission and corporate value based generative prompts it is therefore viewed as analogous art in the same field of endeavor. Additionally, Peter teaches a generative customer experience automation and as it is comparable in certain respects to Mico which an artificial intelligence powered contextual customer service that controls responses as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Mico discloses for each response prompt of the plurality of response prompts, the contextual information regarding the respective response prompt includes the initial prompt and each response prompt of the plurality of response prompts that precedes the respective response prompt. Mico fails to disclose provide a first subset of the plurality of inquiries during a first user session of a user, receive a first subset of the plurality of response prompts, which corresponds to the first subset of the plurality of inquiries, during the first user session of the user, provide a second subset of the plurality of inquiries during a second user session of the user that temporally follows the first user session, the first user session and the second user session being temporally noncontiguous, receive a second subset of the plurality of response prompts, which corresponds to the second subset of the plurality of inquiries, during the second user session of the user, and wherein an inquiry in the second subset of the plurality of inquiries, which is provided during the second user session of the user, is based at least on a response prompt in the first subset of the plurality of response prompts, which is received during the first user session of the user.
Peter discloses provide a first subset of the plurality of inquiries during a first user session of a user, receive a first subset of the plurality of response prompts, which corresponds to the first subset of the plurality of inquiries, during the first user session of the user, provide a second subset of the plurality of inquiries during a second user session of the user that temporally follows the first user session, the first user session and the second user session being temporally noncontiguous, receive a second subset of the plurality of response prompts, which corresponds to the second subset of the plurality of inquiries, during the second user session of the user, and wherein an inquiry in the second subset of the plurality of inquiries, which is provided during the second user session of the user, is based at least on a response prompt in the first subset of the plurality of response prompts, which is received during the first user session of the user.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Mico provide a first subset of the plurality of inquiries during a first user session of the user, receive a first subset of the plurality of response prompts, which corresponds to the first subset of the plurality of inquiries, during the first user session of the user, provide a second subset of the plurality of inquiries during a second user session of the user that temporally follows the first user session, the first user session and the second user session being temporally noncontiguous, receive a second subset of the plurality of response prompts, which corresponds to the second subset of the plurality of inquiries, during the second user session of the user, and wherein an inquiry in the second subset of the plurality of inquiries, which is provided during the second user session of the user, is based at least on a response prompt in the first subset of the plurality of response prompts, which is received during the first user session of the user as taught by Peter since 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. Additionally, Mico, and Peter teach the collecting and analysis of data in order to manage customer experience using AI and they do not contradict or diminish the other alone or when combined.
Referring to Claim 8, see discussion of claim 1 above, while Mico teaches the system and Mico further discloses a system having the limitations of,
wherein the computer-executable instructions are executable by the processor system to: based at least on the identified response prompt identifying the trigger action, cause the incremental construction and modification of the workflow by the generative artificial intelligence model such that performance of the trigger action triggers the execution of the workflow (see; par. [0027]-[0028] of Mico teaches creating service workflows step by step incremental construction, par. [0054]-[0055] using a process engine to modify the workflow and unique workflows by generative AI, par. [0029] adapt the customer journey based on AI prompts, par. [0007] while also identifying the workflow path (i.e. all the way to the endpoint), par. [0115] where trigger options are utilized to provided automated responses).
Mico does not explicitly disclose the following limitations, however,
Peter teaches the plurality of inquiries includes an identified inquiry that requests identification of a trigger action that is to trigger execution of the workflow (see; par. [0034] of Peter teaches collecting information through multi-turn conversation including inquiring about when/how the workflow should be triggered, par. [0048] the system understand and processes trigger conditions from user input), and
wherein the plurality of response prompts includes an identified response prompt that is a response to the identified inquiry, wherein the identified response prompt identifies the trigger action that is to trigger the execution of the workflow (see; par. [0048] of Peter teaches the user’s response identifies the trigger action (i.e. if it is going to expire soon) and the system interprets this internet, par. [0050] where the user responses identify whether trigger is immediate or scheduled for future)
The Examiner notes that Mico teaches similar to the instant application teaches an artificial intelligence powered contextual customer service that controls responses. Specifically, Mico discloses the artificial intelligence powered service automation solution using logic trees enhanced with mission and corporate value based generative AI prompts it is therefore viewed as analogous art in the same field of endeavor. Additionally, Peter teaches a generative customer experience automation and as it is comparable in certain respects to Mico which an artificial intelligence powered contextual customer service that controls responses as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Mico discloses for each response prompt of the plurality of response prompts, the contextual information regarding the respective response prompt includes the initial prompt and each response prompt of the plurality of response prompts that precedes the respective response prompt. Mico fails to disclose the plurality of inquiries includes an identified inquiry that requests identification of a trigger action that is to trigger execution of the workflow, and wherein the plurality of response prompts includes an identified response prompt that is a response to the identified inquiry, wherein the identified response prompt identifies the trigger action that is to trigger the execution of the workflow.
Peter discloses the plurality of inquiries includes an identified inquiry that requests identification of a trigger action that is to trigger execution of the workflow, and wherein the plurality of response prompts includes an identified response prompt that is a response to the identified inquiry, wherein the identified response prompt identifies the trigger action that is to trigger the execution of the workflow.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Mico the plurality of inquiries includes an identified inquiry that requests identification of a trigger action that is to trigger execution of the workflow, and wherein the plurality of response prompts includes an identified response prompt that is a response to the identified inquiry, wherein the identified response prompt identifies the trigger action that is to trigger the execution of the workflow as taught by Peter since 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. Additionally, Mico, and Peter teach the collecting and analysis of data in order to manage customer experience using AI and they do not contradict or diminish the other alone or when combined.
Referring to Claim 10, see discussion of claim 8 above, while Mico in view of Peter teaches the system above, Mico does not explicitly disclose a system having the limitations of, however,
Peter teaches wherein the identified response prompt identifies the trigger action to be an asynchronous event of a designated type (see; par. [0048] of Peter teaches the trigger “policy expiring” is an asynchronous event (it happens independently of user action, at an indeterminate future time) of a designated type (policy expiration event), par. [0030] these communication channels (SMS, email) are inherently asynchronous messages arrive at indeterminate times), par. [0051] where SMS, email and voice messages are asynchronous event types).
The Examiner notes that Mico teaches similar to the instant application teaches an artificial intelligence powered contextual customer service that controls responses. Specifically, Mico discloses the artificial intelligence powered service automation solution using logic trees enhanced with mission and corporate value based generative AI prompts it is therefore viewed as analogous art in the same field of endeavor. Additionally, Peter teaches a generative customer experience automation and as it is comparable in certain respects to Mico which an artificial intelligence powered contextual customer service that controls responses as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Mico discloses for each response prompt of the plurality of response prompts, the contextual information regarding the respective response prompt includes the initial prompt and each response prompt of the plurality of response prompts that precedes the respective response prompt. Mico fails to disclose wherein the identified response prompt identifies the trigger action to be an asynchronous event of a designated type.
Peter discloses wherein the identified response prompt identifies the trigger action to be an asynchronous event of a designated type.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Mico wherein the identified response prompt identifies the trigger action to be an asynchronous event of a designated type as taught by Peter since 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. Additionally, Mico, and Peter teach the collecting and analysis of data in order to manage customer experience using AI and they do not contradict or diminish the other alone or when combined.
Referring to Claim 11, see discussion of claim 1 above, while Mico teaches the system above, Mico further discloses a system having the limitations of:
artificial intelligence (see; par. [0006] of Mico teaches the use of an artificial intelligence to manage workflows).
Mico does not explicitly disclose the following limitation, however,
Peter teaches cause the generative model to configure the workflow to require human approval as a prerequisite for performance of an operation in the workflow (see; par. [0049] of Peter teaches the enterprise persona has the capability to review end-user generated workflows at a later point. It can decide to approve or reject some service, par. [0049] and Fig. 3 provides the figure explicitly shows human approval as a prerequisite in the workflow lifecycle), and
by providing information about the operation as an input to the generative model (see; par. [0049] of Peter teaches “approved workflows are now part of the enterprise service” information about the workflow/operation is used to configure its status (approved vs. rejected), par. [0043] where multiple checks are performed on workflow operations before deployment), and
the information about the operation providing context regarding a response prompt in the plurality of response prompts (see; par. [0049] of Peter teaches the JSON elements (operation information) are derived from the user’s response prompts, providing context, par. [0052] the operation information incorporates context from user inputs/response prompts).
The Examiner notes that Mico teaches similar to the instant application teaches an artificial intelligence powered contextual customer service that controls responses. Specifically, Mico discloses the artificial intelligence powered service automation solution using logic trees enhanced with mission and corporate value based generative AI prompts it is therefore viewed as analogous art in the same field of endeavor. Additionally, Peter teaches a generative customer experience automation and as it is comparable in certain respects to Mico which an artificial intelligence powered contextual customer service that controls responses as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Mico discloses for each response prompt of the plurality of response prompts, the contextual information regarding the respective response prompt includes the initial prompt and each response prompt of the plurality of response prompts that precedes the respective response prompt. Mico fails to disclose cause the generative model to configure the workflow to require human approval as a prerequisite for performance of an operation in the workflow, by providing information about the operation as an input to the generative model, and the information about the operation providing context regarding a response prompt in the plurality of response prompts.
Peter discloses cause the generative model to configure the workflow to require human approval as a prerequisite for performance of an operation in the workflow, by providing information about the operation as an input to the generative model, and the information about the operation providing context regarding a response prompt in the plurality of response prompts.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Mico cause the generative model to configure the workflow to require human approval as a prerequisite for performance of an operation in the workflow, by providing information about the operation as an input to the generative model, and the information about the operation providing context regarding a response prompt in the plurality of response prompts as taught by Peter since 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. Additionally, Mico, and Peter teach the collecting and analysis of data in order to manage customer experience using AI and they do not contradict or diminish the other alone or when combined.
Referring to Claim 12, see discussion of claim 1 above, while Peter teaches the system above, Peter further discloses a system having the limitations of:
artificial intelligence (see; par. [0006] of Mico teaches the use of an artificial intelligence to manage workflows).
Mico does not explicitly disclose the following limitation, however,
Peter teaches the plurality of inquiries includes an identified inquiry that identifies a plurality of operations that are capable of being added to the workflow (see; par. [0045] of Peter teaches the system presents multiple operations (file a claim, check status, leave feedback) that can be added to the workflow, par. [0054]-[0055] the workflow JSON shows multiple from input and modules which shows operations/modules are identified as options), and
wherein the identified inquiry requests an indication of which operation of the plurality of operations is to be added to the workflow (see; par. [0045] of Peter teaches the inquiry asks user to select from available options, par. [0055] the inquiry presents options (Yes/No) that determine which operation is added), and
wherein the plurality of response prompts includes an identified response prompt that is a response to the identified inquiry, wherein the identified response prompt indicates selection of a designated operation from the plurality of operations; and wherein the computer-executable instructions are executable by the processor system to (see; par. [0045] of Peter teaches “the system is trained to use one or more hooks to leverage when each of theses options is generated” (i.e. users selects an option (e.g. file a claim”) which triggers the corresponding operation, par. [0047] user selects/navigates among the presented operations), and
based at least on the identified response prompt indicating the selection of the designated operation from the plurality of operations, cause the generative model to add the designated operation to the workflow (see; par. [0045] of Peter teaches when user selects an option, the corresponding modules/operations are added to the workflow, par. [0034] the AI automatically adds the selected modules/operations to the workflow).
The Examiner notes that Mico teaches similar to the instant application teaches an artificial intelligence powered contextual customer service that controls responses. Specifically, Mico discloses the artificial intelligence powered service automation solution using logic trees enhanced with mission and corporate value based generative AI prompts it is therefore viewed as analogous art in the same field of endeavor. Additionally, Peter teaches a generative customer experience automation and as it is comparable in certain respects to Mico which an artificial intelligence powered contextual customer service that controls responses as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Mico discloses for each response prompt of the plurality of response prompts, the contextual information regarding the respective response prompt includes the initial prompt and each response prompt of the plurality of response prompts that precedes the respective response prompt. Mico fails to disclose the plurality of inquiries includes an identified inquiry that identifies a plurality of operations that are capable of being added to the workflow, and wherein the identified inquiry requests an indication of which operation of the plurality of operations is to be added to the workflow, wherein the plurality of response prompts includes an identified response prompt that is a response to the identified inquiry, wherein the identified response prompt indicates selection of a designated operation from the plurality of operations; and wherein the computer-executable instructions are executable by the processor system to, and based at least on the identified response prompt indicating the selection of the designated operation from the plurality of operations, cause the generative model to add the designated operation to the workflow.
Peter discloses the plurality of inquiries includes an identified inquiry that identifies a plurality of operations that are capable of being added to the workflow, and wherein the identified inquiry requests an indication of which operation of the plurality of operations is to be added to the workflow, wherein the plurality of response prompts includes an identified response prompt that is a response to the identified inquiry, wherein the identified response prompt indicates selection of a designated operation from the plurality of operations; and wherein the computer-executable instructions are executable by the processor system to, and based at least on the identified response prompt indicating the selection of the designated operation from the plurality of operations, cause the generative model to add the designated operation to the workflow.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Mico the plurality of inquiries includes an identified inquiry that identifies a plurality of operations that are capable of being added to the workflow, and wherein the identified inquiry requests an indication of which operation of the plurality of operations is to be added to the workflow, wherein the plurality of response prompts includes an identified response prompt that is a response to the identified inquiry, wherein the identified response prompt indicates selection of a designated operation from the plurality of operations; and wherein the computer-executable instructions are executable by the processor system to, and based at least on the identified response prompt indicating the selection of the designated operation from the plurality of operations, cause the generative model to add the designated operation to the workflow as taught by Peter since 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. Additionally, Mico, and Peter teach the collecting and analysis of data in order to manage customer experience using AI and they do not contradict or diminish the other alone or when combined.
Referring to Claim 14, see discussion of claim 13 above, while Mico teaches the method above, Mico does not explicitly disclose a method having the limitations of, however,
Peter teaches providing a graphical representation of the workflow to the user (see; par. [0007] of Peter teaches a visual workflow builder inherently provides graphical representation, par. [0009] visual interfaces are provided for workflow creation/viewing, par. [0043] a visual adapter converts the workflow to be presentable (graphical) format, [0012] visual interface is provided to the user, and Figure 2 which depicts visual workflow components).
The Examiner notes that Mico teaches similar to the instant application teaches an artificial intelligence powered contextual customer service that controls responses. Specifically, Mico discloses the artificial intelligence powered service automation solution using logic trees enhanced with mission and corporate value based generative AI prompts it is therefore viewed as analogous art in the same field of endeavor. Additionally, Peter teaches a generative customer experience automation and as it is comparable in certain respects to Mico which an artificial intelligence powered contextual customer service that controls responses as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Mico discloses for each response prompt of the plurality of response prompts, the contextual information regarding the respective response prompt includes the initial prompt and each response prompt of the plurality of response prompts that precedes the respective response prompt. Mico fails to disclose providing a graphical representation of the workflow to the user.
Peter discloses providing a graphical representation of the workflow to the user.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Mico providing a graphical representation of the workflow to the user as taught by Peter since 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. Additionally, Mico, and Peter teach the collecting and analysis of data in order to manage customer experience using AI and they do not contradict or diminish the other alone or when combined.
Referring to Claim 15, see discussion of claim 13 above, while Mico teaches the method above Claim 15 recites the same or similar limitations as those addressed above in claim 2, Claim 15 is therefore rejected for the same or similar limitations as set forth above in claim 2.
Referring to Claim 16, see discussion of claim 13 above, while Mico teaches the method above, Peter further discloses a method having the limitations of:
artificial intelligence (see; par. [0006] of Mico teaches the use of an artificial intelligence to manage workflows).
Mico does not explicitly disclose the following limitation, however,
Peter teaches receiving the plurality of response prompts comprises: receiving a plurality of spoken prompts that include respective instances of a voice of a user (see; par. [0024] of Peter teaches a micro-engagement engine to provide users with various modes of communication including voice calls, par. [0051] voice call/message is explicitly a supported channel), and
wherein the plurality of spoken prompts are the responses to the plurality of respective inquiries (see; par. [0024] and par. [0051] of Peter teaches users can respond via voice), and
converting the plurality of spoken prompts to a plurality of respective textual prompts (see; par. [0036] of Peter the system converts unstructured input (including voice) into structured/text form for processing, par. [0035] where the system processes natural language from various channels including voice), and
wherein the generative model is caused to automatically generate the workflow based at least on the plurality of textual prompts (see; par. [0034] of Peter teaches LLMs (which process text) generate the workflow, par. [0052] the LLM generates workflows from textual inputs).
The Examiner notes that Mico teaches similar to the instant application teaches an artificial intelligence powered contextual customer service that controls responses. Specifically, Mico discloses the artificial intelligence powered service automation solution using logic trees enhanced with mission and corporate value based generative AI prompts it is therefore viewed as analogous art in the same field of endeavor. Additionally, Peter teaches a generative customer experience automation and as it is comparable in certain respects to Mico which an artificial intelligence powered contextual customer service that controls responses as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Mico discloses for each response prompt of the plurality of response prompts, the contextual information regarding the respective response prompt includes the initial prompt and each response prompt of the plurality of response prompts that precedes the respective response prompt. Mico fails to disclose receiving the plurality of response prompts comprises: receiving a plurality of spoken prompts that include respective instances of a voice of a user, wherein the plurality of spoken prompts are the responses to the plurality of respective inquiries, converting the plurality of spoken prompts to a plurality of respective textual prompts, and wherein the generative model is caused to automatically generate the workflow based at least on the plurality of textual prompts.
Peter discloses receiving the plurality of response prompts comprises: receiving a plurality of spoken prompts that include respective instances of a voice of a user, wherein the plurality of spoken prompts are the responses to the plurality of respective inquiries, converting the plurality of spoken prompts to a plurality of respective textual prompts, and wherein the generative model is caused to automatically generate the workflow based at least on the plurality of textual prompts.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Mico receiving the plurality of response prompts comprises: receiving a plurality of spoken prompts that include respective instances of a voice of a user, wherein the plurality of spoken prompts are the responses to the plurality of respective inquiries, converting the plurality of spoken prompts to a plurality of respective textual prompts, and wherein the generative model is caused to automatically generate the workflow based at least on the plurality of textual prompts as taught by Peter since 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. Additionally, Mico, and Peter teach the collecting and analysis of data in order to manage customer experience using AI and they do not contradict or diminish the other alone or when combined.
Referring to Claim 17, see discussion of claim 16 above, while Mico in view of Peter teaches the method above, Mico further discloses a method having the limitations of:
artificial intelligence (see; par. [0006] of Mico teaches the use of an artificial intelligence to manage workflows).
Mico does not explicitly disclose the following limitation, however,
Peter teaches converting the plurality of inquiries from a textual format, which is generated by the generative AI model, to a voice format (see; par. [0009] of Peter teaches generate an effective engagement text (that can be converted into other mediums such as voice, explicitly states that engagement text can be converted into other mediums such as voice, par. [0051] voice is a support output channel), and
wherein providing the plurality of inquiries comprises: causing generation of the plurality of spoken prompts by providing the plurality of inquiries in the voice format (see; par. [0009] of Peter teaches the system outputs inquires in voice format, par. [0024] a micro engagement engine handles with an enterprise persona voice calls are used to engage with users (including delivering inquiries)).
The Examiner notes that Mico teaches similar to the instant application teaches an artificial intelligence powered contextual customer service that controls responses. Specifically, Mico discloses the artificial intelligence powered service automation solution using logic trees enhanced with mission and corporate value based generative AI prompts it is therefore viewed as analogous art in the same field of endeavor. Additionally, Peter teaches a generative customer experience automation and as it is comparable in certain respects to Mico which an artificial intelligence powered contextual customer service that controls responses as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Mico discloses for each response prompt of the plurality of response prompts, the contextual information regarding the respective response prompt includes the initial prompt and each response prompt of the plurality of response prompts that precedes the respective response prompt. Mico fails to disclose converting the plurality of inquiries from a textual format, which is generated by the generative AI model, to a voice format, and wherein providing the plurality of inquiries comprises: causing generation of the plurality of spoken prompts by providing the plurality of inquiries in the voice format.
Peter discloses converting the plurality of inquiries from a textual format, which is generated by the generative AI model, to a voice format, and wherein providing the plurality of inquiries comprises: causing generation of the plurality of spoken prompts by providing the plurality of inquiries in the voice format.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Mico converting the plurality of inquiries from a textual format, which is generated by the generative AI model, to a voice format, and wherein providing the plurality of inquiries comprises: causing generation of the plurality of spoken prompts by providing the plurality of inquiries in the voice format as taught by Peter since 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. Additionally, Mico, and Peter teach the collecting and analysis of data in order to manage customer experience using AI and they do not contradict or diminish the other alone or when combined.
Referring to Claim 18, see discussion of claim 13 above, while Mico teaches the method above Claim 18 recites the same or similar limitations as those addressed above in claim 3, Claim 18 is therefore rejected for the same or similar limitations as set forth above in claim 3.
Referring to Claim 19, see discussion of claim 13 above, while Mico teaches the method above, Mico does not explicitly disclose a method having the limitations of, however,
Peter teaches the plurality of inquiries includes an identified inquiry that requests which endpoint of a plurality of endpoints is to be used to achieve the task, wherein each of the plurality of endpoints is capable of being used to achieve the task (see; par. [0045] of Peter teaches generating workflow that can initiate a welcome with a greeting to the claim center and present a few options such as file a claim, check status of a claim and leave feedback (multiple hooks (endpoints) are available for each option, and the user selects which option/endpoint to use, par. [0046] utilizing multiple database/hook endpoints are available), and
wherein the plurality of response prompts includes an identified response prompt that is a response to the identified inquiry, wherein the identified response prompt indicates that a designated endpoint of the plurality of endpoints is to be used to achieve the task (see; par. [0045] of Peter a hook can be a connection to a database. A specific configured webhook calls into enterprise system (i.e. each hook/endpoint is capable of being used for workflow tasks)), and
wherein causing the generative AI model to automatically generate the workflow comprises (see; par. [0045] of Peter teaches a user selects options like file a claim which designates which endpoint/hook to use (i.e. user response designates the endpoint), and par. [0047] a user selects from available endpoint options), and
based at least on the identified response prompt indicating that the designated endpoint is to be used to achieve the task, causing the generative AI model to automatically generate the workflow, which is configured to achieve the task by using the designated endpoint (see; par. [0045] of Peter teaches the underlying actions are the hooks and the system is already trained to leverage the correct underlying module that would use the hooks (i.e. when a user selects an option, the workflow is generated using the corresponding endpoint/hook), and par. [0034] the chat widget then automatically selects the appropriate modules and their attribute, integrates them (i.e. the AI generates workflow using the selected modules/endpoints)).
The Examiner notes that Mico teaches similar to the instant application teaches an artificial intelligence powered contextual customer service that controls responses. Specifically, Mico discloses the artificial intelligence powered service automation solution using logic trees enhanced with mission and corporate value based generative AI prompts it is therefore viewed as analogous art in the same field of endeavor. Additionally, Peter teaches a generative customer experience automation and as it is comparable in certain respects to Mico which an artificial intelligence powered contextual customer service that controls responses as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Mico discloses for each response prompt of the plurality of response prompts, the contextual information regarding the respective response prompt includes the initial prompt and each response prompt of the plurality of response prompts that precedes the respective response prompt. Mico fails to disclose the plurality of inquiries includes an identified inquiry that requests which endpoint of a plurality of endpoints is to be used to achieve the task, wherein each of the plurality of endpoints is capable of being used to achieve the task, wherein the plurality of response prompts includes an identified response prompt that is a response to the identified inquiry, wherein the identified response prompt indicates that a designated endpoint of the plurality of endpoints is to be used to achieve the task, wherein causing the generative AI model to automatically generate the workflow comprises, and based at least on the identified response prompt indicating that the designated endpoint is to be used to achieve the task, causing the generative AI model to automatically generate the workflow, which is configured to achieve the task by using the designated endpoint.
Peter discloses the plurality of inquiries includes an identified inquiry that requests which endpoint of a plurality of endpoints is to be used to achieve the task, wherein each of the plurality of endpoints is capable of being used to achieve the task, wherein the plurality of response prompts includes an identified response prompt that is a response to the identified inquiry, wherein the identified response prompt indicates that a designated endpoint of the plurality of endpoints is to be used to achieve the task, wherein causing the generative AI model to automatically generate the workflow comprises, and based at least on the identified response prompt indicating that the designated endpoint is to be used to achieve the task, causing the generative AI model to automatically generate the workflow, which is configured to achieve the task by using the designated endpoint.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Mico the plurality of inquiries includes an identified inquiry that requests which endpoint of a plurality of endpoints is to be used to achieve the task, wherein each of the plurality of endpoints is capable of being used to achieve the task, wherein the plurality of response prompts includes an identified response prompt that is a response to the identified inquiry, wherein the identified response prompt indicates that a designated endpoint of the plurality of endpoints is to be used to achieve the task, wherein causing the generative AI model to automatically generate the workflow comprises, and based at least on the identified response prompt indicating that the designated endpoint is to be used to achieve the task, causing the generative AI model to automatically generate the workflow, which is configured to achieve the task by using the designated endpoint as taught by Peter since 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. Additionally, Mico, and Peter teach the collecting and analysis of data in order to manage customer experience using AI and they do not contradict or diminish the other alone or when combined.
Claim 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mico et al. (U.S. Patent Publication 2024/0428260 A1) (hereafter Mico) in view of Peter et al (U.S. Patent Publication 2025/0104017 A1) (hereafter Peter) in further view of Ouyang et al. (U.S. Patent Publication 2025/0094728 A1) (hereafter Ouyang).
Referring to Claim 5, see discussion of claim 3 above, while Mico in view of Peter teaches the system above, Mico does not explicitly disclose the following system having the limitations of, however,
Peter teaches for each response prompt of the plurality of response prompts, the contextual information regarding the respective response prompt includes the initial prompt and each response prompt of the plurality of response prompts that precedes the respective response prompt (see; par. [0051] of Peter teaches conversation history would include the initial prompt to maintain full context, par. [0044] the sequence explicitly begins with prompt (the initial prompt), and maintains the full chain, par. [0039] where the phrase “few short customized JSON prompts which implies a limited/maximum number of prompts used, par. [0043] the selection based on similarity implies a limited subset chose not all possible context).
The Examiner notes that Mico teaches similar to the instant application teaches an artificial intelligence powered contextual customer service that controls responses. Specifically, Mico discloses the artificial intelligence powered service automation solution using logic trees enhanced with mission and corporate value based generative AI prompts it is therefore viewed as analogous art in the same field of endeavor. Additionally, Peter teaches a generative customer experience automation and as it is comparable in certain respects to Mico which an artificial intelligence powered contextual customer service that controls responses as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Mico discloses for each response prompt of the plurality of response prompts, the contextual information regarding the respective response prompt includes the initial prompt and each response prompt of the plurality of response prompts that precedes the respective response prompt. Mico fails to disclose for each response prompt of the plurality of response prompts, the contextual information regarding the respective response prompt includes the initial prompt and each response prompt of the plurality of response prompts that precedes the respective response prompt.
Peter discloses for each response prompt of the plurality of response prompts, the contextual information regarding the respective response prompt includes the initial prompt and each response prompt of the plurality of response prompts that precedes the respective response prompt.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Mico for each response prompt of the plurality of response prompts, the contextual information regarding the respective response prompt includes the initial prompt and each response prompt of the plurality of response prompts that precedes the respective response prompt as taught by Peter since 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. Additionally, Mico, and Peter teach the collecting and analysis of data in order to manage customer experience using AI and they do not contradict or diminish the other alone or when combined.
Mico in view of Peter does not explicitly disclose the following limitation, however,
Ouyang teaches up to a maximum number of the response prompts (see; Abstract of Ouyang teaches the aggregating of a subset of reviews from the plurality of reviews into a input prompt, the subset of reviews selected based on the relevancy values assigned to reviews (i.e. selecting a limited subset of content to include in a prompt based on criteria, analogous to limiting conversation history).
The Examiner notes that Mico teaches similar to the instant application teaches an artificial intelligence powered contextual customer service that controls responses. Specifically, Mico discloses the artificial intelligence powered service automation solution using logic trees enhanced with mission and corporate value based generative AI prompts it is therefore viewed as analogous art in the same field of endeavor. Additionally, Peter teaches a generative customer experience automation and as it is comparable in certain respects to Mico which an artificial intelligence powered contextual customer service that controls responses as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Ouyang teaches a summary of reviews generated by a generative language model and as it is comparable in certain respects to Peter and Mico which a generative customer experience automation as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Peter and Mico discloses a visual interface as well underlying methods and systems for brining generative customer experience automation to the enterprise and their end user. However, Peter and Mico fails to disclose up to a maximum number of the response prompts.
Ouyang discloses up to a maximum number of the response prompts.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Peter and Mico the up to a maximum number of the response prompts as taught by Ouyang since 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. Additionally, Peter, Mico and Ouyang teach the collecting and analysis of data in order to generate customer experience automation using associated tasks and they do not contradict or diminish the other alone or when combined.
Claim 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mico et al. (U.S. Patent Publication 2024/0428260 A1) (hereafter Mico) in view of Peter et al (U.S. Patent Publication 2025/0104017 A1) (hereafter Peter) in further view of Kasha et al. (U.S. Patent 10,733,010 B2) (hereafter Kasha).
Referring to Claim 9, see discussion of claim 1 above, while Mico teaches the system above, Mico does not explicitly disclose a system having the limitations of, however,
Peter teaches the identified response prompt indicates that the trigger action is to be performed on a periodic schedule, which is based on a period that is equal to a specified fixed amount of time (see; par. [0050] of Peter teaches scheduling at a specified time implies periodic or fixed time triggers, par. [0048] a predetermined time period is specified fixed amount of time), and
wherein the plurality of inquiries includes a follow-up inquiry, which is generated by the generative AI model based at least on the period indicated by the identified response not satisfying a criterion (see; par. [0033] of Peter teaches the system customizes and optimizes based on requirement/criteria, par. [0034] where the multi-turn conversation allows for follow-up inquiries when initial responses need refinement), and
the follow-up inquiry proposing that the periodic schedule be based on an alternative period that satisfies the criterion (see; par. [0033] of Peter teaches the generative AI optimizing the module prompts accordingly (the AI optimizes proposes alternatives based on requirements)).
Mico in view of Peter does not explicitly disclose the following limitation, however,
Kasha teaches alternative period proposal (see; col. 39, line (47) – col. 40, line (6) of Kasha teaches an alternative implementation of time for scheduling), and
The Examiner notes that Mico teaches similar to the instant application teaches an artificial intelligence powered contextual customer service that controls responses. Specifically, Mico discloses the artificial intelligence powered service automation solution using logic trees enhanced with mission and corporate value based generative AI prompts it is therefore viewed as analogous art in the same field of endeavor. Additionally, Peter teaches a generative customer experience automation and as it is comparable in certain respects to Mico which an artificial intelligence powered contextual customer service that controls responses as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Kasha teaches methods and systems that verify endpoints and external tasks in release pipeline prior to execution and as it is comparable in certain respects to Peter which a generative customer experience automation as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Mico and Peter discloses a visual interface as well underlying methods and systems for brining generative customer experience automation to the enterprise and their end user. However, Mico and Peter fails to disclose alternative period proposal.
Kasha discloses up to an alternative period proposal.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Mico and Peter alternative period proposal as taught by Kasha since 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. Additionally, Mico, Peter and Kasha teach the collecting and analysis of data in order to generate customer experience automation using associated tasks and they do not contradict or diminish the other alone or when combined.
Conclusion
The prior art made of record and not relied upon considered pertinent to Applicant’s disclosure.
SHARMA et al. (U.S. Patent Publication 2024/0419487 A1) discloses a proactive execution system.
Singh et al. (U.S. Patent Publication 2025/0078089 A1) discloses an intelligent call analysis and orchestration using machine learning, generative artificial intelligence (AI), and distributed ledger.
Williams et al. (U.S. Patent Publication 2024/0331043 A1) discloses a generative artificial intelligence as an automated investment advising system.
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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/SSS/
Patent Examiner, Art Unit 3623
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625
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