DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-20 were previously pending and subject to a non-final rejection dated January 28, 2026. In the Response, submitted on April 21, 2026, claims 1, 9-10, 13, 17 and 20 were amended. Therefore, claims 1-20 are currently pending and subject to the following final rejection.
Response to Arguments
Applicant’s Remarks on Pages 9-15 of the Response, regarding the previous rejection of the claims 35 U.S.C. 101, have been fully considered but are not found persuasive or are moot in view of the amended rejection.
On Page 11 of the Response, Applicant argues “claim 1 includes claim limitations that encompass AI (e.g., machine learning) in a technical way that differentiates the claims from certain methods of organizing human activity. Specifically, claim 1 recites…training, by the computing device, a multivariate classification model to categorize a plurality of resolution resources using information and availability associated with the plurality of resolution resources… identifying, by the computing device, patterns of IT operation requests using a clustering machine learning model, based on historical IT operation requests in the knowledge bank, respective solutions, and associated results provided by the plurality of resolution resources…determining, by the computing device, a set of resolution resources of the plurality of resolution resources using the multivariate classification model, based on the identified patterns of IT operation requests, the success rate, and the confidence score…and updating, by the computing device, an IT operations management database with data on the assigned resolution resource, using a reinforcement learning model. These steps, individually and in combination, differentiate the claims from being part of any method of organizing human activity, including commercial interactions, as alleged in the Office Action…”
Examiner respectfully disagrees and notes that “categorize a plurality of resolution resources using information and availability associated with the plurality of resolution resources… identifying…patterns of IT operation requests…based on historical IT operation requests in the knowledge bank, respective solutions, and associated results provided by the plurality of resolution resources…determining…a set of resolution resources of the plurality of resolution resources… based on the identified patterns of IT operation requests, the success rate, and the confidence score…and updating…data on the assigned resolution resource…” recite a certain method of organizing human activity, such as fundamental economic principles or managing personal behavior or relationships or interactions between people. The additional “ecompass[ed] AI” merely amounts to apply it or generally links the use of the abstract idea to a particular technological environment or field of use (i.e., machine learning). Thus, Applicant’s arguments regarding the claims reciting an abstract idea of a certain method of organizing human activity, such as commercial interactions are moot in view of the amended rejection.
On Page 12 of the Response, Applicant further argues “claim 1 recites training and using artificial intelligence models to identify patterns of IT operation requests and determine resolution resources for dynamically infusion of intrinsic pressure that expedites responses through triaging, prioritizing, and optimal assigning of the IT operations requests. Such limitations are not interpretable as human activities in any way, but are rather technology-driven IT operations management.”
Examiner respectfully disagrees and as discussed above, the high-level recitation of training and using AI models amounts to apply it or generally links the use of the abstract idea to a particular technological environment or field of use (i.e., machine learning). “to identify patterns of IT operation requests and determine resolution resources for dynamically infusion of intrinsic pressure that expedites responses through triaging, prioritizing, and optimal assigning of the IT operations requests” reflects the abstract idea itself. Thus, Applicant’s arguments are not found persuasive.
On Pages 13-14 of the Response, Applicant further argues “independent claim 1 is directed towards an improvement in the technical field of IT operations management, and more specifically to provision of a technical solution that provides a method and a system that can (i) identify patterns of IT operation requests using a clustering machine learning model, based on historical IT operation requests in the knowledge bank, respective solutions, and associated results provided by the plurality of resolution resources, (ii) determine a set of resolution resources of the plurality of resolution resources using the multivariate classification model, based on the identified patterns of IT operation requests, the success rate, and the confidence score, (iii) determine a resolution resource for completing the IT operation request, by continuously polling for availability of each resolution resource in the set of resolution resources, and (iv) update an IT operations management database with data on the assigned resolution resource, using a reinforcement learning model. Applicant's specification provides a technical explanation as to how to implement the invention with sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement in the technical field of IT operations management.” Applicant further cites to Para. 20 of the specification to “allow an expedited operation resolution system to better assign and schedule resolution of IT operation request with resolution resources and optimally utilize resources without compromising quality and standards” and argues that “this method overcomes limitations of conventional techniques of IT operations management, and provides users with expedited resolution of IT issues… The solution to this technical problem is recited in the features of claim 1, which integrate any alleged exception, therefore, into a practical application by providing an improvement to the technical field of IT operations management that is necessarily rooted in computer-based technology.”
Examiner respectfully disagrees and discussed above, “identify[ing] patterns of IT operation request…based on historical IT operation requests in the knowledge bank, respective solutions, and associated results provided by the plurality of resolution resources, (ii) determin[ing] a set of resolution resources of the plurality of resolution resources … based on the identified patterns of IT operation requests, the success rate, and the confidence score, (iii) determin[ing] a resolution resource for completing the IT operation request, by continuously polling for availability of each resolution resource in the set of resolution resources, and (iv) update[ing]….data on the assigned resolution resource“ reflects the abstract idea discussed above. Additionally, the high-level recitation of “using a clustering machine learning model”, “using the multivariate classification model” and “updating and IT operations management database” amounts to apply it or generally links the use of the abstract idea to a particular technological environment or field of use (i.e., machine learning).
Lastly, similar to Trading Tech, it appears Applicant is addressing solutions to a business process of IT operations management (i.e., “allow an expedited operation resolution system to better assign and schedule resolution of IT operation request with resolution resources and optimally utilize resources without compromising quality and standards” and “overcomes limitations of conventional techniques of IT operations management, and provides users with expedited resolution of IT issues” rather than an improvement to any underlying IT technology itself. See Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), where the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology. Nothing in the claims or specification discloses a technical improvement in the “IT” aspect of the claims or “operations management that is necessarily rooted in computer-based technology” as alleged. Rather, “better assign and schedule resolution…with resolution resources and to optimally utilize resources without compromising quality and standards” is being done on “developers or subject matter experts”, i.e., people. (See Para. 65 of Applicant’s specification). Thus, Applicant’s arguments are not found persuasive.
On Pages 14-15 of the Response, Applicant further argues “the claims do not need to recite an improvement; they only need to recite elements that result in the improvement of technology….limitations in claim 1 related to training and using artificial intelligence models (multivariate classification model, clustering machine learning model, reinforcement learning model) do, in fact, recite a technical improvement for expedited resolution of IT operation requests. The Office Action's allegation in pages 5 and 12 that the multivariate classification model and reinforcement learning model are merely generic computer components used in applying an alleged judicial exception does not consider that these artificial intelligence models are customized and trained for the purpose of the invention described in the disclosure, rather than being a generic off-the-shelf model that is procured for the invention. In particular, training of the multivariate classification model is explicitly recited in claim 1, and represents a clear technical contribution to the invention recited in claim 1.”
Examiner respectfully disagrees and notes that in Recentive Analytics, Inc. v Fox Corp., the court held that “We see no merit to Recentive’s argument that its patents are eligible because they apply machine learning to this new field of use. We have long recognized that ‘[a]n abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment.’..” Similarly here, that the “artificial intelligence models are customized and trained for the purpose of the invention described” are of no merit because the abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment. Thus, Applicant’s arguments are not found persuasive.
On Page 15 of the Response, Applicant further argues “improvement of technology can pertain to any field of technology (e.g., IT operations management as in the instant case), rather than necessarily be related to computing technology…The word ‘any’ in that section heading and the description in the same section the improvement can be to ‘any other technology’ means that the interpretation of ‘technology or technical field’ should be broad, and there is no reason why IT operations management cannot be considered a ‘technology or technical field.’ Therefore, based at least on being an improvement to the technical field of IT operations management, claim 1 integrates any alleged judicial exception into a practical application and satisfies Step 2A Prong 2 of the § 101 analysis. Accordingly, independent claim 1 should be deemed patent-eligible.”
Examiner respectfully disagrees that the claims or specification describe anything related to the technology of the IT operations themselves. Rather, the claims recite receiving an IT operations request and a determined resolution resource for completing the IT operations request is assigned; and para. 18 and of the specification, describes that “implementations of the invention provide optimal assignment and scheduling of resolution resources to complete an IT operation request.” Nothing in the claims or specification describes “any technology” that is being improved in the request and assignment of determined resources. Thus, Applicant’s arguments are not found persuasive.
Applicant’s Remarks on Pages 15-20 of the Response, regarding the previous rejection of the claims 35 U.S.C. 103, have been fully considered and are persuasive in view of the amended claims.
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.
Claims 1-12 are directed to a method (i.e., process); claims 13-16 are directed a computer program product comprising one or more computer readable storage media (i.e., a machine) as interpreted based on the disclaimer in Para. 25 of the specification “A computer readable storage medium or media, as used herein, is not to be construed as being transitory signals per se”, and claims 17-20 are directed to system comprising a processor (i.e., a machine). Therefore, claims 1-20 all fall within one of the four statutory categories of invention.
Step 2A, Prong One
Claim 1 recites a series of steps of: receiving an information technology (IT) operation request; collecting information associated with the IT operation request from a bank; using a multivariate classification model to categorize a plurality of resolution resources using information and availability associated with the plurality of resolution resources; identifying patterns of IT operation requests using a clustering model, based on historical IT operation requests in the bank, respective solutions, and associated results provided by the plurality of resolution resources; determining a success rate and a confidence score associated with each resolution resource of the plurality of resolution resources for resolving the IT operation request; determining a set of resolution resources of the plurality of resolution resources using the multivariate classification model, based on the identified patterns of IT operation requests, the success rate, and the confidence score; determining a resolution resource for completing the IT operation request, by continuously polling for availability of each resolution resource in the set of resolution resources; assigning the resolution resource to resolve the IT operation request; and updating data on the assigned resolution resource, using a reinforcement learning model.
Claim 13 recites a series of steps of: receiving an information technology (IT) operation request; collecting information associated with the IT operation request from a bank; using a multivariate classification model to categorize a plurality of resolution resources using information and availability associated with the plurality of resolution resources; identifying patterns of IT operation requests using a clustering model, based on historical IT operation requests in the bank, respective solutions, and associated results provided by the plurality of resolution resources; determining a success rate and a confidence score associated with each resolution resource of the plurality of resolution resources for resolving the IT operation request; determining a set of resolution resources of the plurality of resolution resources using the multivariate classification model, based on the identified patterns of IT operation requests, the success rate, and the confidence score; determining a resolution resource for completing the IT operation request, by continuously polling for availability of each resolution resource in the set of resolution resources; assigning the resolution resource to resolve the IT operation request; scheduling a solution to the IT operation request with the resource request; and updating data on the assigned resolution resource, using a reinforcement learning model.
Claim 17 recites functions of receiving an information technology (IT) operation request; collecting information associated with the IT operation request from a bank; using a multivariate classification model to categorize a plurality of resolution resources using information and availability associated with the plurality of resolution resources; identifying patterns of IT operation requests using a clustering model, based on historical IT operation requests in the bank, respective solutions, and associated results provided by the plurality of resolution resources; determining a success rate and a confidence score associated with each resolution resource of the plurality of resolution resources for resolving the IT operation request; generating a current state of environment of the IT operation request based on the information in the bank; determining a set of resolution resources of the plurality of resolution resources using the multivariate classification model, based on the identified patterns of IT operation requests, the success rate, and the confidence score; determining a resolution resource for completing the IT operation request, by continuously polling for availability of each resolution resource in the set of resolution resources; assigning the resolution resource to resolve the IT operation request; and updating data on the assigned resolution resource, using a reinforcement learning model.
The claims as a whole recite a certain method of organizing human activity. The limitations recited above, under broadest reasonable interpretation, recite the abstract idea of a certain method of organizing human activity, e.g., fundamental economic principles or managing personal behavior or relationships or interactions between people. Therefore, the claims recite an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claims 1, 13, and 17 as a whole amount to: merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract, or “apply it”; or generally link the use of the abstract idea to a particular technological environment or field of use (i.e., machine learning),
The claims recite the additional elements of: (i) a computing device (claim 1); (ii) a computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media (claim 13); (iii) a system comprising: a processor, a computer readable memory, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media (claim 17), (iv) a knowledge bank (claims 1, 13, and 17), (v) updating an IT operations management database (using a reinforcement learning model) (claims 1, 13, and 17); and (vi) training a multivariate classification model (claims 1, 13, and 17).
The additional element of (i) a computing device (claim 1), is recited at a high-level of generality (See Para.51 of Applicant’s Specification disclosing computer system/server is shown in the form of a general-purpose computing device), such that, when viewed as whole/ordered combination, it amounts to no more than mere instructions to apply the judicial exception using generic computer components (See MPEP 2106.05(f)).
The additional elements of (ii) a computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media (claim 13), are recited at a high-level of generality (See Paras. 22-25 of Applicant’s Specification disclosing a computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon), such that, when viewed as whole/ordered combination, it amounts to no more than mere instructions to apply the judicial exception using generic computer components (See MPEP 2106.05(f)).
The additional elements of (iii) a system comprising: a processor, a computer readable memory, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media (claim 17), are recited at a high-level of generality (See Paras 22-25 of Applicant's Specification disclosing a computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon) such that, when viewed as whole/ordered combination, it amounts to no more than mere instructions to apply the judicial exception using generic computer components (See MPEP 2106.05(f)).
The additional element of (iv) a knowledge bank (claims 1, 13, and 17) is recited at a high-level of generality (See Paras. 64 and 81 of Applicant’s Specification disclosing the knowledge bank), such that, when viewed as whole/ordered combination, it amounts to no more than mere instructions to apply the judicial exception using generic computer components (See MPEP 2106.05(f)).
The additional elements of (v) updating an IT operations management database, (using a reinforcement learning model) (claims 1, 13, and 17), are recited at a high-level of generality (See Para. 79 of Applicant’s Specification disclosing a reinforcement learning model and Paras. 96-97 disclosing an IT operations management database), such that, when viewed as whole/ordered combination, it amounts to no more than mere instructions to apply the judicial exception using generic computer components (See MPEP 2106.05(f)).
The addition element of (vi) training a multivariate classification model (claims 1, 13, and 17), is recited at a high-level of generality (See Paras. 67-68 and 80 of Applicant’s Specification disclosing the ML model is a multivariate classification model), such that, it generally links the use of the abstract idea to a particular technological environment or field of use (i.e., machine learning),
Accordingly, these additional elements, when viewed as a whole/ordered combination (e.g., Fig. 1 and 9) do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus, the claims are directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements amount to no more than: merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract, or “apply it”, or generally link the use of the abstract idea to a particular technological environment or field of use (i.e., machine learning), and are not a practical application of the abstract idea. The same analysis applies here in Step 2B, i.e., merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract, or “apply it” (See MPEP 2106.05(f)), or generally linking the use of the abstract idea to a particular technological environment or field of use (i.e., machine learning), does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional elements discussed above do not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination, nothing in the claims add significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claims are ineligible.
Dependent claims 2-11, 14-16, and 18-20 further recite details which merely narrow the previously recited abstract idea limitiaitions. For these reasons, as described above with respect to claims 1, 13 and 17, these judicial exceptions are not meaningfully integrated into a practical application or significantly more than the abstract idea. Thus, claims 2-11, 14-16, and 18-20 are also ineligible.
Claim 12 recites substantially the same abstract idea as claim 1 and is rejected for substantially the same reasons.
The additional elements unencompassed by the abstract idea include software provided as a service in a cloud environment. The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include limitations sufficient, either alone or in combination, to amount to significantly more than the claimed abstract idea because the aforementioned additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
Allowable over the Prior Art
The claims are allowable over the prior art, but subject to the above rejections under 35 U.S.C. 101. None of the current or previously cited prior art teaches the independent claims limitations as a whole, and in particular “determining, by the computing device, a set of resolution resources of the plurality of resolution resources using the multivariate classification model, based on the identified patterns of IT operation requests, the success rate, and the confidence score; determining, by the computing device, a resolution resource for completing the IT operation request, by continuously polling for availability of each resolution resource in the set of resolution resources” in combination with the other claim limitations of the independent claims.
The closest prior art includes:
U.S. Patent Application Publication No. 2018/0336485 to Bikumala et al. (hereinafter "Bikumala"). Bikumala discloses intelligent ticket assignment through self-categorizing the problems and self-rating the analysts.
U.S. Patent Application Publication No. 2016/0048514 to Allen (hereinafter "Allen"). Allen discloses a question-answering (QA) system first receives input questions. Each question is then assigned to a first question category of a plurality of question categories. The QA system then identifies a set of candidate answers to each question using a core information source. A set of confidence scores, including a confidence score for each candidate answer, is then calculated. The QA system then determines that the first set of confidence scores fails to satisfy confidence criteria. In response to this determination, an updated information source is ingested.
U.S. Patent No. 8,429,097 to Sivasubramanian et al. (hereinafter "Sivasubramanian"). Sivasubramanian discloses a resource isolation mechanism applied in a shared storage system, or database service, that limits the resource utilization of each namespace to its specified allocation.
U.S. Patent Application Publication No. 2021/0182606 to Maroo et al. (hereinafter "Maroo"). Maroo discloses generating labeled training data for training a learning algorithm.
U.S. Patent Application Publication No. 2023/016145 to Srtivastava et al. (hereinafter “Srivastava”). Srivatsava discloses classification of events by pattern recognition in multivariate time series data associated with one or more assets.
The following is prior art not cited but considered relevant:
“TaDaa: real time Ticket Assignment Deep learning Auto Advisor for customer support, help desk, and issue ticketing systems” by Feng et al., dated July 18, 2022 (hereinafter “Feng”). Feng discloses using machine learning techniques to assign issues within an organization, like customer support, help desk and alike issue ticketing systems. Feng provides functionality to 1) assign an issue to the correct group, 2) assign an issue to the best resolver, and 3) provide the most relevant previously solved tickets to resolvers.
U.S. Patent Application Publication No. 2025/0104089 to Jaiswal et al. (hereinafter “Jaiswal”). Jaiswal discloses assigning a ticket to an agent and includes he method also includes searching for at least one candidate agent by querying a roster table.
U.S. Patent Application Publication No. 2022/0270019 to Mujumdar et al. (hereinafter “Majumdar”). Majumdar discloses ticket-agent matching and agent skillset development.
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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/RUPANGINI SINGH/
Primary Examiner, Art Unit 3628
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