DETAILED ACTION
The following is a Final Office Action in response to communications filed December 3, 2025. Claims 1, 4, 7, 10, and 12–14 are amended; claims 9 and 20 are canceled. Currently, claims 1–8 and 10–19 are pending.
Response to Amendment/Argument
Applicant’s Response is sufficient to overcome the previous objection to claims 1 and 14 for informalities. Accordingly, the previous objection to claims 1 and 14 is withdrawn.
Applicant’s Response is sufficient to overcome the previous rejection of claims 1–20 under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Accordingly, the previous rejection of claims 1–20 under 35 U.S.C. 112(b) is withdrawn.
However, Applicant’s Response necessitates new rejections under 35 U.S.C. 112(b), and Examiner directs Applicant to the relevant explanation below.
With respect to the previous rejection of claims 1–20 under 35 U.S.C. 101, Applicant’s remarks have been fully considered but are not persuasive.
Under Step 2A Prong One, Applicant asserts that the claims are not directed to an abstract idea because the claims recite a processor and functions associated with the recited processor. Examiner disagrees. As an initial matter, Examiner notes that Step 2A Prong One identifies when a claim recites an abstract idea, whereas Step 2A, as a whole, determines when a claim is directed to an abstract idea. Here, the rejection of record does not assert that the processor is an abstract idea under Step 2A Prong One. Instead, the processor is addressed as an additional element under both Step 2A Prong Two and Step 2B. As a result, Applicant’s remarks are not persuasive.
Applicant’s remaining remarks under Step 2A Prong One are similarly unpersuasive because the remarks conflate Step 2A Prong One and Step 2A Prong Two and do not accurately address the asserted identification of abstract elements under Step 2A Prong One. As a result, Applicant’s remarks are not persuasive.
Under Step 2A Prong Two, Applicant asserts that the claims include additional elements that integrate the abstract idea into a practical application. Examiner disagrees and notes that Applicant’s remarks are not commensurate with the scope of the claim. For example, Applicant asserts that the generative AI model is not a generic computer function by indicating that the “specification states that the generative AI model (110) interprets user-provided natural language descriptions, identifies context, semantics, and intent, and transforms them into structured workflow elements (paragraphs [36–37] of published application).” However, the claims generally recite the generative AI model without disclosing any of the underlying functionality described in the Specification. As a result, Applicant’s remarks are not commensurate with the scope of the claim and are not persuasive.
To the extent that Applicant asserts that constructing the DAG and generating an executable workflow model embody an improvement to workflow automation technology, Examiner disagrees. Examiner submits that DAG construction embodies a business improvement rather than any improvement in the technology. Paragraph 66 of the Specification indicates that the “DAG construction enhances the visual representation of the workflow model, aiding users, including the workflow creators and consumers, in better grasping the logical flow and dependencies within the workflow model. This technical advantage contributes to improved user comprehension and effective management of the workflow generation process.”
Similarly, paragraph 67 of the Specification indicates that the process “enhances the accuracy” of the model and “facilitates a dynamic and user-centric model, aligning closely with the user’s intent and requirements.” In view of the above, Applicant’s Specification does not support any technical improvements in workflow automation. Instead, the recited steps embody business improvements for assisting users in the creation of a workflow. As a result, Applicant’s remarks are not persuasive.
Under Step 2B, Applicant asserts that the recited processor embodies specialized hardware rather than generic technology. Examiner disagrees. MPEP 2106(I) indicates that the “programmed computer or “special purpose computer” test of In re Alappat, 33 F.3d 1526, 31 USPQ2d 1545 (Fed. Cir. 1994) (i.e., the rationale that an otherwise ineligible algorithm or software could be made patent-eligible by merely adding a generic computer to the claim for the “special purpose” of executing the algorithm or software) was also superseded by the Supreme Court’s Bilski and Alice Corp. decisions.” Here, Applicant’s Specification expressly states that examples of the processor “may include but are not limited to” various types of processors, including generic implementations of “other processors or control circuitry.” Further, the claimed processor is recited at a high level of generality and does not embody any technical improvement when considered in view of Applicant’s Specification. As a result, Applicant’s remarks are not persuasive.
Applicant’s remaining remarks under Step 2B have been fully considered but are not persuasive for the same reasons as stated above. Accordingly, Applicant’s remarks are not persuasive, and the previous rejection of claims is reasserted below.
With respect to the previous rejection of claims under 35 U.S.C. 103, Applicant’s remarks have been fully considered but are not persuasive.
As an initial matter, Applicant’s remarks regarding the teachings of the asserted references do not comply with the requirements set forth under 37 CFR 1.111(b) because the remarks amount to no more than a “general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references”. More particularly, Applicant’s remarks broadly quote the Abstract of each cited reference without providing any argument or rationale underpinning explaining why the asserted citations do not disclose the claimed elements. As a result, Applicant’s remarks amount to no more than a general allegation and are not persuasive.
Applicant next asserts that the cited combination of references fails to provide any articulated rationale or motivation that would have led one of skill in the art to combine the references. Examiner disagrees. MPEP 2143.01 states that a “motivation to combine may be found explicitly or implicitly in market forces; design incentives; the ‘interrelated teachings of multiple patents’; ‘any need or problem known in the field of endeavor at the time of invention and addressed by the patent’; and the background knowledge, creativity, and common sense of the person of ordinary skill.” Zup v. Nash Mfg., 896 F.3d 1365, 1371, 127 USPQ2d 1423, 1427 (Fed. Cir. 2018) (quoting Plantronics, Inc. v. Aliph, Inc., 724 F.3d 1343, 1354 [107 USPQ2d 1706] (Fed. Cir. 2013) (citing Perfect Web Techs., Inc. v. InfoUSA, Inc., 587 F.3d 1324, 1328 [92 USPQ2d 1849] (Fed. Cir. 2009) (quoting KSR, 550 U.S. at 418-21)). In view of the above, the references of record need not explicitly disclose a motivation to combine because implicit motivations may be found based on the common sense of the person of ordinary skill in the art, and Examiner maintains that the teachings of Dechu are reasonably pertinent to the disclosure of Maes for the same reasons as stated below. Accordingly, Applicant’s remarks are not persuasive.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 8, 10, and 14–19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 8 and 19 recite “a second user feedback” in lines 2–3 and 3, respectively. However, claims 1 and 14, from which claims 8 and 19 depend, previously recite “a second user feedback”. As a result, the scope of claims 8 and 19 is indefinite because it is unclear whether Applicant intends for the second recitations to reference the initial recitations or intends to introduce a second, different “second user feedback”.
For purposes of examination, claims 8 and 19 are interpreted as reciting “[[a]] the second user feedback”.
In view of the above, claims 8 and 19 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 10, which depends from claim 8, inherits the deficiencies described above. As a result, claim 10 is similarly rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 12 recites “an interconnectedness” in lines 3–4. However, claim 1, from which claim 12 depends, previously recites “an interconnectedness”. As a result, the scope of claim 12 is indefinite because it is unclear whether Applicant intends for the second recitations to reference the initial recitations or intends to introduce a second, different “interconnectedness”.
For purposes of examination, the claim is interpreted as reciting “the [[an]] interconnectedness”.
In view of the above, claim 12 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 14 recites “the executable workflow model” in the element to “iteratively refine”. There is insufficient antecedent basis for this limitation in the claim.
For purposes of examination, the claim is interpreted as reciting “[[the]] an executable workflow model” in the element to “iteratively refine”.
In view of the above, claim 14 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 15–19, which depends from claim 14, inherits the deficiencies described above. As a result, claims 15–19 are similarly rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
In view of the above, Examiner respectfully requests that Applicant thoroughly review the claims for compliance with the requirements set forth under 35 U.S.C. 112(b).
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 1–8 and 10–19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1–8 and 10–19 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
With respect to Step 2A Prong One of the framework, claim 1 recites an abstract idea. Claim 1 includes elements for “receiving a multimodal input from at least one user, the multimodal input comprising a description associated with a workflow model to be generated”; “causing an elaboration of the received description”; “generating and rendering an elaborate plan based on the elaboration of the received description, wherein the elaborate plan is a representation of one or more sequential tasks, organized logically to depict an intended sequence and interdependencies of the workflow model”; “receiving a first user feedback on the elaborate plan in a natural language”; “refining the elaborate plan based on the first user feedback and re-rendering a refined elaborate plan”; “establishing a mapping between the refined elaborate plan and a plurality of sub-skills to obtain a plurality of mapped sub-skills”; “providing, by a workflow consumer, a second user feedback”; “iteratively refining the plurality of mapped sub-skills of an executable workflow model based on the second user feedback received in the natural language”; “validating the plurality of mapped sub-skills and an interconnectedness within the executable workflow model based on the second user feedback”; “constructing a directed acyclic graph (DAG) based on the first user feedback and the mapping, wherein the constructed DAG is a visual view indicative of one or more connections between the plurality of mapped sub-skills based on an order of execution of the plurality of mapped sub-skills”; and “generating, by the processor, the workflow model based on the constructed DAG and the first user feedback.”
The limitations above recite an abstract idea. More particularly, the elements above recite certain methods of organizing human activity for managing personal behavior or relationships or interactions between people because the elements describe a process for generating a workflow model based on a workflow description and user feedback. Further, the elements recite mental processes because the elements describe observations or evaluations that can be practically performed in the mind or by a human using pen and paper. As a result, claim 1 recites an abstract idea under Step 2A Prong One.
Claim 14 includes substantially similar limitations to those included with respect to claim 1. As a result, claim 14 recites an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claim 1.
Claims 2–8, 10–13 and 15–19 further describe the process for generating a workflow model based on a workflow description and user feedback and further recite certain methods of organizing human activity and/or mental processes for the same reasons as stated above. As a result, claims 2–8, 10–13 and 15–19 recite an abstract idea under Step 2A Prong One.
With respect to Step 2A Prong Two of the framework, claim 1 does not include additional elements that integrate the abstract idea into a practical application. Claim 1 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include a processor, a first user interface, a generative artificial intelligent model, a second user interface, a chat interface linked to the second user interface, and a step for generating an executable workflow model. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computer elements are generic computing components that are merely used as a tool to perform the recited abstract idea, and the generative artificial intelligent model and step for generating an executable workflow model do no more than generally link the use of the recited abstract idea to a particular technological field or environment. As a result, claim 1 does not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
As noted above, claim 14 includes substantially similar limitations to those included with respect to claim 1. Although claim 14 further includes a client device, the additional element, when considered in view of the claim as a whole, does not integrate the abstract idea into a practical application because the additional computer element is a generic computing component that is merely used as a tool to perform the recited abstract idea. As a result, claim 14 does not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 3, 10, and 16 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include the executable workflow model configured to receive an input and provide an output when executed (claims 3 and 16) and an element for compiling (claim 10). When considered in view of the claims as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional elements do no more than generally link the use of the recited abstract idea to a particular technological field or environment. As a result, claims 3, 10, and 16 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2, 4–8, 11–13, 15, and 17–19 do not include any additional elements beyond those included with respect to the claims from which claims 2, 4–8, 11–13, 15, and 17–19 depend. As a result, claims 2, 4–8, 11–13, 15, and 17–19 do not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above.
With respect to Step 2B of the framework, claim 1 does not include additional elements amounting to significantly more than the abstract idea. As noted above, claim 1 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include a processor, a first user interface, a generative artificial intelligent model, a second user interface, a chat interface linked to the second user interface, and a step for generating an executable workflow model. The additional elements do not amount to significantly more than the recited abstract idea because the additional computer elements are generic computing components that are merely used as a tool to perform the recited abstract idea, and the generative artificial intelligent model and step for generating an executable workflow model do no more than generally link the use of the recited abstract idea to a particular technological field or environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claim 1 does not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B.
As noted above, claim 14 includes substantially similar limitations to those included with respect to claim 1. Although claim 14 further includes a client device, the additional element does not amount to significantly more than the recited abstract idea because the additional computer element is a generic computing component that is merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claim 14 does not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B.
Claims 3, 10, and 16 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include the executable workflow model configured to receive an input and provide an output when executed (claims 3 and 16) and an element for compiling (claim 10). The additional elements do not amount to significantly more than the recited abstract idea because the additional elements do no more than generally link the use of the recited abstract idea to a particular technological field or environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 3, 10, and 16 do not include additional elements that amount to significantly more than the recited abstract idea under Step 2B.
Claims 2, 4–8, 11–13, 15, and 17–19 do not include any additional elements beyond those included with respect to the claims from which claims 2, 4–8, 11–13, 15, and 17–19 depend. As a result, claims 2, 4–8, 11–13, 15, and 17–19 do not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B for the same reasons as stated above.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1–8 and 10–19 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1–3, 5–6, 8, 10, and 12–19 are rejected under 35 U.S.C. 103 as being unpatentable over Maes et al. (U.S. 2020/0192975) in view of Dechu et al. (U.S. 2024/0161025).
Claims 1 and 14: Maes discloses a method for automatic visual workflow model generation and management, the method comprising:
receiving, by a processor (See paragraph 32), a multimodal input via a first user interface from at least one user, the multimodal input comprising a description associated with a workflow model to be generated (See FIG. 1A and paragraph 37, in view of paragraphs 35–36, wherein an intent input is received via a GUI, and wherein the input may be received as an uploaded file or manual entry);
causing, by the processor, an elaboration of the received description using a generative artificial intelligent (AI) model that supports natural language understanding (NLU) and natural language generation (NLG) (See FIG. 2 and paragraphs 55–56, wherein a sequence of statements are generated from the received intent using natural language processing and natural language generation; see also paragraphs 39, 41, and 48, wherein the natural language processing model is an artificial neural network employing natural language processing and generation);
generating and rendering, by the processor, an elaborate plan via a second user interface based on the elaboration of the received description, wherein the elaborate plan is a representation of one or more sequential tasks, organized logically to depict an intended sequence and interdependencies of the workflow model (See FIG. 2 and paragraph 40, wherein candidate orchestration workflows are displayed to the user in an editing interface; see also paragraphs 55–56);
receiving, by the processor, a first user feedback on the elaborate plan in a natural language via a chat interface linked to the second user interface (See paragraph 40, wherein the user may refine or edit the orchestration workflow in an editing interface);
refining, by the processor, the elaborate plan based on the first user feedback and re-rendering a refined elaborate plan via the second user interface (See paragraph 40, wherein the user may refine or edit the orchestration workflow in an editing interface); and
generating, by the processor, the executable workflow model based on the constructed workflow and the first user feedback (See FIG. 2 and paragraphs 39–40 and 47, wherein an orchestration workflow is generated based on previously generated workflows and user feedback). Maes does not expressly disclose the remaining claim elements.
Dechu discloses establishing, by the processor, a mapping between the elaborate plan and a plurality of sub-skills to obtain a plurality of mapped sub-skills (See paragraphs 62–63, wherein skills are mapped to tasks for a given incident remediation sequence; see also paragraph 85);
providing, by a workflow consumer, a second user feedback (See paragraph 53, in view of paragraphs 51–52, wherein the skills catalog includes a description describing an output goal and required inputs, and wherein skills are learned from descriptions in NLP documents and knowledge articles, and see paragraph 44, wherein knowledge articles include user feedback);
iteratively refining, by the processor, the plurality of mapped sub-skills of an executable workflow model based on the second user feedback received in the natural language (See paragraphs 56 and 87, in view of paragraphs 44 and 65, wherein skill mapping updates are performed each time a knowledge article is updated, and wherein knowledge articles include both task performance results and user feedback; see also paragraphs 51–52);
validating, by the processor, the plurality of mapped sub-skills and an interconnectedness within the executable workflow model based on the second user feedback (See paragraphs 56 and 87, in view of paragraphs 44 and 65, wherein skill mapping updates are performed each time a knowledge article is updated, and wherein knowledge articles include both task performance results and user feedback such that the skills mapping is based on execution success; see also paragraphs 51–52);
constructing, by the processor, a directed acyclic graph (DAG) based on the first user feedback and the mapping, wherein the constructed DAG is a visual view indicative of one or more connections between the plurality of mapped sub-skills based on an order of execution of the plurality of mapped sub-skills (See paragraphs 89–91, in view of paragraph 54, wherein a graph data structure is generated for a remediation workflow; see also paragraphs 44, 47, and 49–50, wherein skill-task relationships are determined using user feedback); and
generating, by the processor, the executable workflow model based on the constructed DAG and the first user feedback (See paragraphs 98–99, in view of paragraphs 54 and 89–91, wherein an automated workflow is generated and executed, and wherein the workflow is generated based on the constructed graph; see also paragraphs 44, 47, and 49–50, wherein skill-task relationships are determined using user feedback).
Maes discloses a system directed to translating a natural language intent into an orchestration workflow. Dechu discloses a system directed to generating, executing, and orchestrating task workflows. Each reference discloses a system directed to generating and orchestrating workflows. The technique of generating a workflow based on establishing a graph of mapped skills is applicable to the system of Maes as they each share characteristics and capabilities; namely, they are directed to generating and orchestrating workflows.
One of ordinary skill in the art would have recognized that applying the known technique of Dechu would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Dechu to the teachings of Maes would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate generating and orchestrating workflows into similar systems. Further, applying workflow generation by establishing a graph of mapped skills to Maes would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results.
With respect to claim 14, Maes further discloses a client device (See paragraph 12).
Claims 2 and 15: Maes discloses the method of claim 1, wherein the elaboration of the received description is a process of enriching the received description provided in the natural language (See FIG. 2 and paragraphs 55–56, wherein the initial intent is enriched into a sequence of statements).
Claims 3 and 16: Maes discloses the method of claim 1, wherein the executable workflow model is configured to receive an input dataset and provide a workflow output by performing the one or more sequential tasks of the refined elaborate plan when executed (See FIG. 2, in view of paragraph 47, wherein the orchestration workflow is a series of tasks, and wherein the input is the instance of the virtual machine on DataCenter 1).
Claims 5 and 17: Maes discloses the method of claim 1, wherein the multimodal input comprises at least one of: a text, a code, an image, an audio and a video (See paragraph 25, wherein text inputs are disclosed).
Claims 6 and 18: Maes does not expressly disclose the elements of claim 6.
Dechu discloses the method of claim 1, wherein each of the plurality of mapped sub-skills comprises a goal of each mapped sub-skill, one or more requirements to achieve the goal, and an input and an output for each mapped sub-skill (See paragraph 53, wherein the skills catalog includes a description describing an output goal and required inputs).
One of ordinary skill in the art would have recognized that applying the known technique of Dechu would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claims 8 and 19: Maes does not expressly disclose the elements of claim 8.
Dechu discloses the method of claim 6, further comprising verifying and editing, by the processor, the goal, the one or more requirements, and the input and the output for each sub-skill based on a second user feedback received in the natural language (See paragraph 53, in view of paragraphs 51–52, wherein the skills catalog includes a description describing an output goal and required inputs, and wherein skills are learned from descriptions in NLP documents and knowledge articles, and see paragraph 44, wherein knowledge articles include user feedback).
One of ordinary skill in the art would have recognized that applying the known technique of Dechu would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 10: Maes does not expressly disclose the elements of claim 10.
Dechu discloses the method of claim 8, wherein the generating of the executable workflow model based on the constructed DAG comprises compiling, by the processor, the executable workflow model from associated sub-skills, interconnections, and the second user feedback (See paragraphs 98–99, in view of paragraphs 54 and 89–91, wherein an automated workflow is generated and executed, and wherein the workflow is generated based on the constructed graph; see also paragraphs 44, 47, and 49–50, wherein skill-task relationships are determined using user feedback).
One of ordinary skill in the art would have recognized that applying the known technique of Dechu would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 12: Maes does not expressly disclose the elements of claim 12.
Dechu discloses the method of claim 1, further comprising validating, by the processor, the executable workflow model by verifying if a workflow output of the executable workflow model matches the description, the refined elaborate plan, and an interconnectedness of the DAG (See paragraphs 56 and 87, in view of paragraphs 44 and 65, wherein remediation task sequences are validated by updating skills mappings each time a knowledge article is updated, and wherein knowledge articles include task performance results such that the validation is based on execution success; see also paragraphs 51–52).
One of ordinary skill in the art would have recognized that applying the known technique of Dechu would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 13: Maes does not expressly disclose the elements of claim 13.
Dechu discloses the method of claim 1, further comprising validating, by the processor, each of the plurality of mapped sub-skills by verifying if an output of each mapped sub-skill matches a goal (See paragraphs 56 and 87, in view of paragraphs 44 and 65, wherein skills are validated by updating skills mappings each time a knowledge article is updated, and wherein knowledge articles include task performance results such that the validation is based on execution success; see also paragraphs 51–52).
One of ordinary skill in the art would have recognized that applying the known technique of Dechu would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Maes et al. (U.S. 2020/0192975) in view of Dechu et al. (U.S. 2024/0161025), and in further view of RINTEL et al. (U.S. 2024/0185534).
Claim 4: As disclosed above, Maes and Dechu disclose the elements of claim 1. Although Maes discloses a refined elaborated plan (See citations above), Maes does not expressly disclose the remaining elements of claim 4.
Dechu discloses wherein the mapping of the refined elaborate plan comprises: selecting, by the processor, at least one of a set of predefined applications for execution of the workflow model (See paragraphs 46 and 57, wherein automated tools are selected for each task for of the remediation sequence); and
determining one or more actions for execution of the workflow model based on at least one selected application (See paragraphs 46 and 57, in view of paragraphs 58–59, wherein alternative tools and skills/actions are determined prior to execution or dynamically in response to task performance monitoring).
One of ordinary skill in the art would have recognized that applying the known technique of Dechu would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1. Maes and Dechu do not expressly disclose the remaining claim elements.
Rintel discloses identifying, by the processor, a modality and a domain of the multimodal input associated with the workflow model (See paragraphs 22–23, in view of paragraphs 39–40, wherein audio, video, and image inputs are captured, and wherein audio inputs are identified and converted to textual information using a text-to-speech engine).
As disclosed above, Maes discloses a system directed to translating a natural language intent into an orchestration workflow, and Dechu discloses a system directed to generating, executing, and orchestrating task workflows. Rintel discloses a system directed to generating workflows by analyzing data that defines the workflow. Each reference discloses a system directed to generating and orchestrating workflows. The technique of identifying input modalities is applicable to the systems of Maes and Dechu as they each share characteristics and capabilities; namely, they are directed to generating and orchestrating workflows.
One of ordinary skill in the art would have recognized that applying the known technique of Rintel would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Rintel to the teachings of Maes and Dechu would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate generating and orchestrating workflows into similar systems. Further, applying input modality identification to Maes and Dechu would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Maes et al. (U.S. 2020/0192975) in view of Dechu et al. (U.S. 2024/0161025), and in further view of Amamou (U.S. 2024/0127617).
Claim 7: As disclosed above, Maes and Dechu disclose the elements of claim 1. Although Dechu discloses learning techniques for each of the plurality of mapped sub-skills (See paragraph 51) and instructions or queries aimed at refining functionality and accuracy of each sub-skill (See paragraphs 56 and 87, in view of paragraphs 44 and 65, wherein skills are validated by updating skills mappings each time a knowledge article is updated, and wherein knowledge articles include task performance results such that the validation is based on execution success), Maes and Dechu do not expressly disclose the remaining elements of claim 7.
Amamou discloses generating, by the processor, prompts along with few-shot examples for each of the plurality of mapped elements, wherein the prompts include detailed instructions or queries (See paragraphs 25–26, in view of paragraph 66, wherein prompts are generated using few-shot LLM learning techniques).
As disclosed above, Maes discloses a system directed to translating a natural language intent into an orchestration workflow using machine learning processing, and Dechu discloses a system directed to generating, executing, and orchestrating task workflows using a machine learning model. Amamou discloses a system directed to labeling text data in machine learning models. Each reference discloses a system directed to data analytics using machine learning. The technique of applying few-shot prompting is applicable to the systems of Maes and Dechu as they each share characteristics and capabilities; namely, they are directed to data analytics using machine learning.
One of ordinary skill in the art would have recognized that applying the known technique of Amamou would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Amamou to the teachings of Maes and Dechu would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate data analytics using machine learning into similar systems. Further, applying few-shot prompting to Maes and Dechu would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Maes et al. (U.S. 2020/0192975) in view of Dechu et al. (U.S. 2024/0161025), and in further view of Shetty et al. (U.S. 2022/0292416).
Claim 11: As disclosed above, Maes and Dechu disclose the elements of claim 1. Although Dechu discloses validating the executable workflow model using a third user feedback received in the natural language (See paragraphs 56 and 87, in view of paragraphs 44 and 65, wherein remediation task sequences are validated by updating skills mappings each time a knowledge article is updated, and wherein knowledge articles include task performance results and user feedback such that the validation is based on execution success), Maes and Dechu do not expressly disclose the remaining elements of claim 11.
Shetty discloses generating, by the processor, a set of test/validation examples using the workflow model and validating, by the processor, the executable workflow model using the set of generated test/validation examples with a third user feedback received (See paragraph 37, wherein test scripts are generated and executed for each user-selected step in the workflow; see also paragraphs 47 and 49).
As disclosed above, Maes discloses a system directed to translating a natural language intent into an orchestration workflow using machine learning processing, and Dechu discloses a system directed to generating, executing, and orchestrating task workflows using a machine learning model. Shetty discloses a system directed to designing and validating workflows. Each reference discloses a system directed to generating workflows. The technique of applying validation testing is applicable to the systems of Maes and Dechu as they each share characteristics and capabilities; namely, they are directed to generating workflows.
One of ordinary skill in the art would have recognized that applying the known technique of Shetty would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Shetty to the teachings of Maes and Dechu would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate workflow generation into similar systems. Further, applying validation testing to Maes and Dechu would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/WILLIAM S BROCKINGTON III/Primary Examiner, Art Unit 3623