Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. This Final Office action is in response to the application filed on January 31st, 2024 and in response to Applicant’s Arguments filed on September 22nd, 2025. Claims 1-20 are pending.
Priority
3. Application 18/428,323 was filed on January 31st, 2024.
Examiner Request
4. The Applicant is requested to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. §112(a) or §112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
Response to Arguments
5. Applicant’s arguments, see page 10, 1st paragraph, with respect to claims 16-20 being rejected under 35 U.S.C. 101 because the claimed invention was directed to nonstatutory subject matter have been fully considered and are persuasive in view of the amended claim language. The Step 1, 101 rejection of claims 16-20 has been withdrawn.
6. Applicant argues that the claim limitations recite “an improvement to computer functionality” and therefore do not need to undergo the full eligibility analysis. ” More specifically, Applicant argues that the claimed subject matter provides a solution to the initial problem of resolving an “incident,” such as a failure or error occurring in a managed network or computing environment. Examiner respectfully disagrees. Under the broadest reasonable interpretation, the amended claimed steps of receiving event data, generating incident workflows, assigning and adjusting priority levels, updating workflows, and executing actions based on priority collectively amount to organizing and managing incident priority, fall within certain methods of organizing human activity and managing information. Such activities are analogous to how humans prioritize and route tasks based on urgency, even if performed at a greater scale using generic computers.
Applicant’s reliance on examples involving IT infrastructure events is unpersuasive. The cited examples (e.g., CPU overutilization, server errors, system outages) appear only in the specification and are not recited in the claims. Instead, the claims are directed to using information about events to prioritize and manage incidents, regardless of the technical source of that information. The claims do not recite how such events are detected, monitored, diagnosed, or resolved in a technically specific manner, but rather focus on what is done with the information once received. As such, the claims are directed to managing information, rather than improving computer functionality or other technology.
Further, although the claim recites a machine learning model receiving a natural language prompt, the claim does not specify any particular training technique, model architecture, data structure, or algorithmic improvement. Instead the model is generically used to perform the abstract idea, i.e. adjust a priority level. There mere use of a machine learning model to automate decision-making does not constitute an improvement to computer functionality. While certain improvements to computer functionality may be non-abstract, the claims must recite the improvement with sufficient technical detail. Here, the asserted improvement relates to incident handling efficiency and prioritization, not to an improvement in computer capabilities. Improving how incidents are prioritized or resolved is an improvement to a business or operational process, not to the functioning of the computer itself.
Accordingly, Applicant’s arguments have been fully considered but are not persuasive. The amended claims continue to recite an abstract idea that is not integrated into a practical application and does not include additional elements sufficient to amount to significantly more. The rejection under 35 USC § 101 is maintained.
7. Applicant’s arguments, see pages 12-16 with respect to 35 USC § 102(a)(1) rejection of claims 1-20 have been fully considered and are persuasive in view of the amended claim language. The 35 USC § 102(a)(1) rejection of claims 1-20 has been withdrawn.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
8. Claims 10-15 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.
Claim 10 recites the limitation "the computing system" in line 12. There is insufficient antecedent basis for this limitation in the claim. Examiner notes that the claim does recite “one or more customer computing systems” however, based on the specification in addition to acknowledging that claim 12 recites similar claim limitations as claim 1, “the computing system” in line 12 does not appear to be the same as “one or more customer computing systems.” Claims 11-15 re also rejected by virtue of their dependency on claim 10.
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.
9. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-20 are directed to a system, method, or product which are/is one of the statutory categories of invention. (Step 1: YES).
Claims 1, 10, and 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites adjusting priority levels of incidents. Claims 1, 10 and 16 the limitations of (Claim 1 being representative) recites:
receiving, […], event data for one or more events detected […];
generating, […], and based on the event data, one or more incident objects for one or more incidents, wherein the one or more incident objects include incident data including a respective priority level;
generating, […], a respective incident workflow for each of the one or more incident objects, wherein each respective incident workflow includes a set of configurable actions for […] execution;
applying, […], […], a […] model to determine a respective adjusted priority level for each of the one or more incident objects, wherein the […] model is configured to receive a first […] prompt indicative of incident data included in the one or more incident objects;
receiving, […],the respective adjusted priority level for each of the one or more incident objects;
updating, […],the respective incident workflow for each of the one or more incident objects with the adjusted priority level for each of the one or more incident objects; and
[…] executing, […] and based on the respective adjusted priority level for each of the one or more incident objects, each respective incident workflow.
These limitations as drafted is process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., commercial or legal interactions and managing personal behavior including following rules or instructions) but for recitation of generic computer components. That is, other than reciting a computing system and customer computing systems (Claims 1, 10, and 16) automatic execution (Claims 1, 10, and 16), an application programming interface (Claims 1, 10, and 16), a machine learning model and natural language prompt (Claims 1, 10, and 16), a memory and one or more processors (claim 10), and a non-transitory computer-readable storage medium and processor of a computing system (claim 16) the claimed invention amounts to commercial or legal interactions and managing personal behavior including following rules or instructions. If a claim limitation, under its broadest reasonable interpretation, covers commercial or legal interactions and managing personal behavior or interactions between people but for the recitation of generic computer components (see MPEP 2106.04(a)(2)(II)), then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, Claims 1, 10, and 16 recite an abstract idea. (Step 2A- Prong 1: YES. The claims are abstract).
Alternately, as drafted, the limitations recite a process that, under the broadest
reasonable interpretation cover performance of the limitation in the mind but for
recitation of generic computer components. That is, other than reciting a computing system and customer computing systems (Claims 1, 10, and 16) automatic execution (Claims 1, 10, and 16), an application programming interface (Claims 1, 10, and 16), a machine learning model and natural language prompt (Claims 1, 10, and 16), a memory and one or more processors (claim 10), and a non-transitory computer-readable storage medium and processor of a computing system (claim 16), nothing in the claim precludes the step from practically being performed in the mind. For example, but for the generic computer components recited above, this claim encompasses receiving event data, generating an incident priority and workflow based on the event data, applying a model to adjust the priority level, receiving the adjusted priority level and updating the workflow to reflect the adjusted priority level described in the identified abstract idea, supra. If a claim limitation under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract idea. Accordingly, Claims 1, 10, and 16 recite an abstract idea.
The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes. (Step 2A- Prong 1: YES. The claims are abstract).
This judicial exception is not integrated into a practical application. Claims 1, 10, and 16 recite the additional elements of a computing system and customer computing systems (Claims 1, 10, and 16) automatic execution (Claims 1, 10, and 16), an application programming interface (Claims 1, 10, and 16), a machine learning model and natural language prompt (Claims 1, 10, and 16), a memory and one or more processors (claim 10), and a non-transitory computer-readable storage medium and processor of a computing system (claim 16). These additional elements are not described by the applicant and are recited at a high-level of generality (i.e., generic computers performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer components. Alternatively or in addition, the implementation of machine learning merely confines the use of the abstract idea to a particular technological environment or field of use (machine learning). MPEP 2106.04(d)(I) and MPEP 2106.05(A) indicate that merely “generally linking” the abstract idea to a particular technological environment or field of use cannot provide a practical application. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claims 1, 10, and 16 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO: the additional claimed elements are not integrated into a practical application).
Claims 1, 10, and 16 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a computing system and customer computing systems (Claims 1, 10, and 16) automatic execution (Claims 1, 10, and 16), an application programming interface (Claims 1, 10, and 16), a machine learning model and natural language prompt (Claims 1, 10, and 16), a memory and one or more processors (claim 10), and a non-transitory computer-readable storage medium and processor of a computing system (claim 16), to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Alternatively or in addition, the implementation of machine learning merely confines the use of the abstract idea to a particular technological environment or field of use (machine learning). MPEP 2106.04(d)(I) and MPEP 2106.05(A) indicate that merely “generally linking” the abstract idea to a particular technological environment or field of use cannot provide an inventive concept (“significantly more”). Accordingly, even when considered separately and as an ordered combination, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. Thus claims 1, 10, and 16 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more).
Dependent Claims 2, 3, 7, 11, and 17 are similarly rejected because they merely further narrow the same abstract idea of independent claims 1, 10, and 16 as discussed above and hence are abstract for at least the reasons presented above. Claims 2, 3, 11, and 17 merely describe incident data and priority level. Therefore claims 2, 3, 7, 11, and 17 are considered patent ineligible for the reasons given above.
Dependent claims 4-6, 8, 9, 12-15 and 18-20 recite limitations that further define the same abstract idea of independent claims 1, 10, and 16 as discussed above. In addition, they recite the additional elements of a second natural language prompt (claims 4 and 18), the machine learning model (claims 5, 12, and 18), computing system (claims 5, 6, 8, 9, 12-15, and 19-20), application programming interface (claims 8, 14, and 19) and the first natural language prompt (claims 8, 9, 14, 15, 19, and 20). These additional elements are again recited at a high-level of generality (i.e., generic computers performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer components. Even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. Alternatively or in addition, the implementation of machine learning (natural language prompts) merely confines the use of the abstract idea to a particular technological environment or field of use (machine learning). MPEP 2106.04(d)(I) and MPEP 2106.05(A) indicate that merely “generally linking” the abstract idea to a particular technological environment or field of use cannot provide a practical application or significantly more. Therefore claims 4-6, 8, 9, 12-15 and 18-20 are patent ineligible.
Subject Matter Distinguished from Prior Art
10. The cited prior art of record fails to expressly teach or suggest, either alone or in combination, the features found within the independent claims 1, 10, and 16
Azmoon (US 2019/0227822) discloses a user assistance system for enterprises that streamlines how agents help users (such as employees or customers) resolve issues or incidents. Instead of juggling multiple windows and copying information, the system provides a single interface where agents can chat with users, view incident records, and see AI-generated suggested responses. The system analyzes both the incident record and the ongoing conversation, using templates and relevance scoring to suggest contextually appropriate messages for the agent to send. Agents can select, edit, or drag-and-drop information from the record into messages, and the system can dynamically update records based on conversation context or sentiment analysis. The interface is designed to be efficient and reduce repetitive tasks, improving agent productivity and user satisfaction.
Dhawan et al. (US2022/0245647) discloses assessing the priority of a customer reported issue include receiving input regarding a customer issue experienced by a customer; calculating an incident grievance score by inserting the received input into a machine learning model; assigning a priority to the customer issue based on the calculated incident grievance score; receiving updated input regarding the customer issue; periodically recalculating the incident grievance score for the customer issue by inserting the received input and the updated input into the machine learning model; changing the priority of the customer issue when the recalculated incident grievance score differs from the calculated incident grievance score; and notifying a team assigned to fix the customer issue when the priority of the customer issue changes.
Ghosh et al. (US2020/0210924) discloses artificial intelligence and machine learning based incident management may include analyzing incident data related to a plurality of incidents associated with organization operations of an organization to train and test a machine learning classification model. Based on mapping of the organization operations to associated organizational key performance indicators, a corpus may be generated and used to determine an organizational key performance indicator that is impacted by each incident. New incident data related to a further plurality of incidents may be ascertained, and specified organizational key performance indicators associated with further organizational operations may be determined.
Howes et al. (US2013/0091574) discloses the incident triage engine receives information about the incident, assets, and the environment of the system to be protected. The incident triage engine may characterize the incidents based on incident, asset, and/or environment profiles. The incident triage engine prioritizes incidents based on a damage or loss forecast. The incident triage engine may use machine learning to match incidents to appropriate pre-defined remediation plans. The incident triage engine optimizes the incident priority based on the remediation time of the incidents in the queue.
In particular, the cited prior art of record fails to expressly teach or suggest all of the features in the independent claims 1, 10, and 16 and more specifically the limitations of: "generating, by the computing system, a respective incident workflow for each of the one or more incident objects, wherein each respective incident workflow includes a set of configurable actions for automatic execution by the computing system," and "automatically executing by the computing system and based on the respective adjusted priority level for each of the one or more incident objects, each respective incident workflow," as well as "applying, by the computing system, and using an application programming interface, a machine learning model to determine a respective adjusted priority level for each of the one or more incident objects, wherein the machine learning model is configured to receive a first natural language prompt indicative of incident data included in the one or more incident objects; ... updating, by the computing system, the respective incident workflow for each of the one or more incident objects with the respective adjusted priority level for each of the one or more incident objects; and automatically executing, by the computing system and based on the respective adjusted priority level for each of the one or more incident objects, each respective incident workflow," in combination with the other claim limitations, either individually or as an obvious combination.
Conclusion
11. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Azmoon (US 2019/0227822) discloses a user assistance system for enterprises that streamlines how agents help users (such as employees or customers) resolve issues or incidents. Instead of juggling multiple windows and copying information, the system provides a single interface where agents can chat with users, view incident records, and see AI-generated suggested responses. The system analyzes both the incident record and the ongoing conversation, using templates and relevance scoring to suggest contextually appropriate messages for the agent to send. Agents can select, edit, or drag-and-drop information from the record into messages, and the system can dynamically update records based on conversation context or sentiment analysis. The interface is designed to be efficient and reduce repetitive tasks, improving agent productivity and user satisfaction.
Dhawan et al. (US2022/0245647) discloses assessing the priority of a customer reported issue include receiving input regarding a customer issue experienced by a customer; calculating an incident grievance score by inserting the received input into a machine learning model; assigning a priority to the customer issue based on the calculated incident grievance score; receiving updated input regarding the customer issue; periodically recalculating the incident grievance score for the customer issue by inserting the received input and the updated input into the machine learning model; changing the priority of the customer issue when the recalculated incident grievance score differs from the calculated incident grievance score; and notifying a team assigned to fix the customer issue when the priority of the customer issue changes.
Ghosh et al. (US2020/0210924) discloses artificial intelligence and machine learning based incident management may include analyzing incident data related to a plurality of incidents associated with organization operations of an organization to train and test a machine learning classification model. Based on mapping of the organization operations to associated organizational key performance indicators, a corpus may be generated and used to determine an organizational key performance indicator that is impacted by each incident. New incident data related to a further plurality of incidents may be ascertained, and specified organizational key performance indicators associated with further organizational operations may be determined.
Howes et al. (US2013/0091574) discloses the incident triage engine receives information about the incident, assets, and the environment of the system to be protected. The incident triage engine may characterize the incidents based on incident, asset, and/or environment profiles. The incident triage engine prioritizes incidents based on a damage or loss forecast. The incident triage engine may use machine learning to match incidents to appropriate pre-defined remediation plans. The incident triage engine optimizes the incident priority based on the remediation time of the incidents in the queue.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA LEMIEUX whose telephone number is (571)270-3445. The examiner can normally be reached Monday-Friday 7AM-3PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tariq Hafiz can be reached at 571-272-5350. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JESSICA LEMIEUX/Supervisory Patent Examiner, Art Unit 3626
Prosecution Insights