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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on December 22, 2025 has been entered.
Status of the Claims
Claims 1-20 were previously pending and subject to a final rejection dated 9-24-2025. In the RCE, submitted on December 24, 2025, claims 1, 6, 13, and 17 were amended. Therefore, claims 1-20 are currently pending and subject to the following non-final rejection.
Response to Arguments
Applicant’s Remarks on Pages 8-14 of the RCE, regarding the previous rejection of the claims 35 U.S.C. 101, have been fully considered but are not found persuasive.
On Page 10 of the Response, in arguing Step 2A, Prong 1, Applicant states “Applicant disagrees and submits that the claims do not recite any commercial interaction because they do not fall under any of the categories of commercial interactions-contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations-defined in MPEP §2106.04(a)(2)(II)(B). In fact, the systems and methods of expedited resolution of IT issues, as recited in the claims, do not relate in any way to any commercial interaction. The claims recite how optimal assignment and scheduling of resolution resources for expedited resolution of IT issues is achieved through technology-driven patterning of IT operation requests, the resolution resources, and other information associated with IT operation requests as well as use of artificial intelligence to dynamically infuse expedited responses through triaging, prioritizing, and assigning of IT operations requests. See paragraphs [0018]-[0019] of the Specification.”
Examiner respectfully disagrees and notes the “optimal assignment and scheduling of resolution resources for expedited resolution” reflects the claim’s recitation of a certain method of organizing human activity (e.g., fundamental economic principles or practices, or commercial or legal interactions) – as reflected in the cited Paras. 18-19 of the specification that further clarify that the invention is related to “….operations management…In this manner, implementations of the invention provide optimal assignment and scheduling of resource resolution to recomplete an IT operation request… Implementations of the invention include a method and system for expediting the turnaround time of an IT operation request”. The recitation of the “issues” and “operation requests” being IT related does not take the claims out of the grouping of a certain method of organizing human activity. Thus, Applicant’s arguments are not found persuasive.
On Pages 12-13 of the Response, in discussing Step 2A, Prong 2, Applicant argues “independent claim 1 is directed towards an improvement in the technical field of IT operations management within computer technology, and more specifically to provision of a technical solution ‘to a problem of addressing second day operations (e.g., maintenance, monitoring, optimization of products in a product lifecycle) in an efficient manner without compromising quality and standards of the work product (i.e., work of the resolution resource such as coding that may be used to solve the IT operation request)’. …the claims improve the technical field of IT operations management by providing a method and a system that can (i) identify patterns of IT operation requests using a multivariate classification model trained on information from the knowledge bank including historical IT operation requests, respective solutions from resolution resources, and information associated with the resolution resources, (ii) determine a resolution resource for completing the IT operation request, based on the identified patterns, the success rate and the confidence score, (iii) schedule the resolution resource to resolve the IT operation request based on whether the resolution resource can take on additional work, and/or whether some or all of the current work bandwidth of the resolution resource can be re-prioritized, 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.”
Examiner respectfully disagrees and notes, as discussed above, the recitation of the “operation requests” and subsequent “resolution resource” being IT related does not provide a technical improvement “in the…field of….operations management”, as alleged. Specifically, nothing in the claims or specification describes a technical resolution resource such that “the claims improve the technical field of IT operations management” as alleged. As explained in Para. 65 of the Specification, the resolution resources are “developers or subject matter experts”, i.e., people.
Therefore, Examiner notes the limitations to “identify patterns of IT operation requests using… the knowledge bank including historical IT operation requests, respective solutions from resolution resources, and information associated with the resolution resources, (ii) determine a resolution resource for completing the IT operation request, based on the identified patterns, the success rate and the confidence score, (iii) schedule the resolution resource to resolve the IT operation request based on whether the resolution resource can take on additional work, and/or whether some or all of the current work bandwidth of the resolution resource can be re-prioritized” are all limitations that recite the abstract idea of a certain method of organizing human activity. As will be discussed further below, the use of a “multivariate classification model trained on information” does not more than generally link the use of the abstract idea to a particular technological environment or field of use (i.e., machine learning), and updating an IT operations management database, using a reinforcement learning model (claims 1, 13, and 17), amounts to no more than mere instructions to apply the judicial exception using generic computer components. Thus, Applicant’s arguments are not found persuasive.
On Pages 13-14 of the Response, Applicant further argues “The subject matter of claim 1 recites a method 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’. …Accordingly, 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...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….”
Examiner respectfully disagrees and notes similar to Trading Tech, Applicant appears to address solutions to a business process of IT operations management (i.e., “allow an expedited operation resolution system to better assign and schedule resolution of [a] request with resolution resources and optimally utilize resources without compromising quality and standards” 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.
As discussed above, 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.
Applicant’s Remarks on Pages 14-18 of the Response, regarding the previous rejection of the claims 35 U.S.C. 103, have been fully considered but are not found persuasive or are moot in view of the amended claims and amended rejection.
On Page 15 of the Response, Applicant argues “…Bikumala teaches assigning analysts to a problem ticked based on the analyst’s workload…Bikumala makes no mention of scheduling resolution of IT operation requests based on ‘whether a resource resolution can take on additional work’…as recited in independent claim 1.
Examiner respectfully disagrees and notes, as explained in Paragraph [0065] of Applicant’s specification explains that “resolution resources (e.g., developers or subject matter experts (SMEs)) that will be utilized by the expedited operation management device 404 to resolve the IT operation request.” Therefore, the analysts of Bikumala necessarily reads on the resolution resource (developers or SMEs) recited in the independent claims. Thus, assigning the ticket to the analyst based on the analyst’s workload, necessarily reads on “scheduling…the resolution resource to resolve the IT request based on…(i) whether the resolution resource can take on additional work”.
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) operations request; collecting information associated with the IT operation request from a knowledge bank; identifying patterns of IT operation request from the knowledge bank including historical IT operation requests, respective solutions from resolution resources, and information associated with the resolution resources; determining a success rate and a confidence score associated with each resolution resource of a plurality of resolution resources for resolving the IT operation request; determining a resolution resource for completing the IT operation request based on the identified patterns, the success rate and the confidence score; assigning the resolution resource to resolve the IT operation request; scheduling the resolution resource to resolve the IT operation request based on at least one of: (i) whether the resolution resource can take on additional work, and (ii) whether some or all of the current work bandwidth of the resolution resource can be re- prioritized; and updating data on the assigned resolution resource.
Claim 13 recites a series of functions of: receive an information technology (IT) operations request; collect information associated with the IT operation request from a knowledge bank; identify patterns of IT operation request from the knowledge bank including historical IT operation requests, respective solutions from resolution resources, and information associated with the resolution resources; determine a success rate and a confidence score associated with each resolution resource of a plurality of resolution resources for resolving the IT operation request; determine a resolution resource for completing the IT operation request, based on the identified patterns, the success rate and the confidence score; assign the resolution resource to resolve the IT operation request; schedule a solution to the IT operation request with the resolution resource; and update data on the assigned resolution resource.
Claims 17 recite a series of functions of: receive an information technology (IT) operations request; collect information associated with the IT operation request from a knowledge bank; determine a success rate and a confidence score associated with each resolution resource of a plurality of resolution resources for resolving the IT operation request; generate a current state of environment of the IT operation request based on the information in the knowledge bank; determine a resolution resource for completing the IT operation request based on at least availability of the resolution resource, the success rate, the confidence score, and the current state; assign the resolution resource to resolve the IT operation request; schedule the resolution resource to resolve the IT operation request based on at least one of: (i) whether the resolution resource can take on additional work, and (ii) whether some or all of the current work bandwidth of the resolution resource can be re-prioritized; and update data on the assigned resolution resource.
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., commercial interactions or fundamental economic principles . 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) using a multivariate classification model trained on information (claims 1 and 13).
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) using a multivariate classification model trained on information (claims 1 and 13), is recited at a high-level of generality (See Para. 68 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).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 6-11, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2018/0336485 to Bikumala et al. (hereinafter “Bikumala”) in view of U.S. Patent Application Publication No. 2016/0048514 to Allen (hereinafter “Allen”) and further in view of U.S. Patent No. 8,429,097 to Sivasubramanian et al. (hereinafter “Sivasubramanian”), and even further in view of U.S. Patent Application Publication No. 2021/0182606 to Maroo et al. (hereinafter “Maroo”).
In regard to claim 1, Bikumala discloses receiving, by a computing device, an information technology (IT) operations request (Para. 43) (A problem may occur during execution of the user application and a user may request help from customer support to resolve the problem. Typically, the user submits the problem to customer support using a ticket submission system… the ticket submission system may send the problem ticket including the problem and the information associated with the problem to an information handling system, such as information handling system (i.e., receiving, by a computing device, an information technology (IT) operations request).)
Bikumala discloses collecting, by the computing device, information associated with the IT operation request from a knowledge bank (Para. 49, 51-56, and 58-59; and Fig. 6; Paras. 69-70) (Ticketing information handling system 100-2 may be coupled to a learned a learned problem profile database 272, a learned analyst profile database 274, a knowledge database 276, an operational database 278, and a correlation database 280 (i.e., collectively from a knowledge bank)…. Ticketing processor subsystem 120-2 may categorize the first problem profile pattern by using categorization algorithm 255 to determine whether the first problem profile pattern has a known learned problem profile pattern that may be associated with a respective known learned problem profile of known learned problem profiles of learned problem profile database 272 (i.e., collecting, by the computing device, information associated with the IT operation request)…. Ticketing processor subsystem 120-2 may search knowledge database 276 by using knowledge matching algorithm 259 to identify one or more matching knowledge articles for resolution of the problem associated with problem ticket 212 (i.e., collecting, by the computing device, information associated with the IT operation request)…
Bikumala discloses identifying, by the computing device, patterns of IT operation requests using information from the knowledge bank including historical IT operation request, respective solutions from resolution resources, and information associated with the resolution resources (Para. 49, 51-56, and 58-59; and Fig. 6; Paras. 69-70) (Ticketing processor subsystem 120-2 may categorize the first problem profile pattern by using categorization algorithm 255 to determine whether the first problem profile pattern has a known learned problem profile pattern that may be associated with a respective known learned problem profile of known learned problem profiles of learned problem profile database 272 (i.e., identifying, by the computing device, patterns of IT operation request using information from the knowledge bank)…Knowledge database (KDB) 276 may include KDB entries, each KDB entry may include a KDB problem ticket, a KDB problem associated with the KDB problem ticket, KDB problem information associated with the KDB problem (i.e., knowledge bank including historical IT operation requests)…each of one or more completed problem tickets in operational database 278 (i.e., knowledge bank including historical IT operation request) retrieve an assigned analyst associated with a completed problem ticket, analyst information that may be associated with the assigned analyst and performance metrics that may be associated with the assigned analyst and the completed problem ticket from operational database 278 (i.e., respective solutions from resolution resources, and information associated with the resolution resources).)
Bikumala discloses determining, by the computing device, a resolution resource for completing (resolving) the IT operation request (Paras. 4,15, 53-54, 59 and 62) (Ticketing processor subsystem 120-2 may search knowledge database 276 by using knowledge matching algorithm 259 to identify one or more matching knowledge articles for resolution of the problem (i.e., a resolution resource) associated with problem ticket 212 that may be based on problem ticket 212, the problem associated with problem ticket 212, and the problem information associated with the problem… the ticketing processor subsystem may also be configured to assign one of the analysts of the rank ordered learned analyst profiles to the problem ticket by (i.e., determining, by the computing device, a resolution resource for completing/resolving the IT operation request).)
Bikumala discloses assigning, by the computing device, the resolution resource to resolve the IT operation request (Paras. 44, 55, 58 and 62-64) (The matching knowledge article includes instructions and information to enable an analyst to resolve the associated problem. By adding the matching knowledge articles to problem ticket 212, all of the information is already available when an analyst starts working on problem ticket 212 in order to resolve the problem more efficiently… method may also include assigning, by the ticketing processor subsystem, one of the analysts of the rank ordered learned analyst profiles to the problem ticket (i.e., assigning, by the computing device, the resolution resource to resolve the IT operation request).)
Bikumala discloses scheduling, by the computing device, the resolution resource to resolve the IT operation request based on….whether the resolution resource can take on additional work (Paras. 13, 56-57 and 62 and 64; Claim 10) (The analyst information may include one or more of … information, a time zone, a holiday zone, a start date, and a next available time. The analyst information may also include a current work load associated with an analyst, which may be added to the associated learned analyst profile of learned analyst profile database 274 during further processing of the learned analyst profile… Assignment algorithm 253 may select a learned analyst profile …with an analyst that may have availability for problem ticket 212 based on analyst information associated with the analyst…. Assigning problem tickets in this manner ensures that an available analyst (i.e. based on….whether the resolution resource can take on additional work), with the most expertise in resolving these particular types of problems may be assigned to the problem tickets.).
Bikumala discloses updating, by the computing device, an IT operations management database with data on the assigned resolution resource (Para. 58) (Ticketing processor subsystem 120-2 may (i.e., by the computing device), for each of one or more completed problem tickets in operational database 278 (i.e., updating an IT operations management database), add the analyst information, a cross-reference to the known learned problem profile, and the performance metrics to a learned analyst profile in learned analyst profile database 274 that may be associated with the assigned analyst (i.e., with data on the assigned resolution resource).)
As discussed above, Bikumala discloses a resolution resource for completing/resolving the IT operation request. Bikumala does not explicitly disclose or teach, however, Allen teaches determining, by the computing device, a success rate and a confidence score associated with each resolution…of a plurality of resolutions…for resolving the…request; and that determining the resolution…for completing the request…is based on the success rate and confidence score (Abst., Paras. 65-66) (A question-answering (QA) system first receives input questions (i.e., request). 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 (i.e., each resolution…of a plurality of resolutions) using a core information source. A set of confidence scores, including a confidence score for each candidate answer, is then calculated (i.e., determining, by the computing device…a confidence score associated with each resolution…of a plurality of resolutions)…. confidence scores and accuracy rates (…., percentages of answers that are correct) (i.e., determining, by the computing device, a success rate and a confidence score associated with each resolution…of a plurality of resolutions) for the new training answers may be calculated…the calculated confidence scores and accuracy rates may be compared to a set of training criteria…if all training confidence criteria are satisfied, then, per block 609, the updated information source may be used to identify new answers to one or more input questions previously answered inaccurately (as indicated by user feedback)….The input questions selected for new answers may include the input questions that ultimately triggered the ingestion of the updated information source. Per block 610, the QA system may confirm that the new answers to the input questions are accurate…..the updated information source, having been fully vetted, may then be added to the core information source and used (along with the core information source) in identifying sets of candidate answers to new input questions (i.e., determining the resolution…for completing the request…is based on the success rate and confidence score) as they are received by the QA system.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the confidence score and accuracy rates of the candidate answers of Allen with the system of Bikumala in order to provide a more accurate resolution resource.
As discussed above, Bikumala discloses updating, by the computing device, an IT operations management database with data on the assigned resolution resource. Bikumala in view of Allen does not explicitly disclose or teach, however, Sivasubramanian teaches that the updating the….database is by using a reinforcement learning model (Claim 15; Col. 2, lines 54-Col.3, line 5) ( ….the database service is configured to apply a reinforcement learning technique…to automatically update an execution parameter of at least one of the plurality of queries…and wherein the update is performed during execution of the plurality of queries…Reinforcement learning is a machine learning technique that may in some embodiments be used to automatically control the rate of queries made by the application. Evaluations of various embodiments described herein have shown that such a technique can be very effective in controlling the I/O utilization of applications for different kinds of workloads in a shared database environment).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the reinforcement learning technique of Sivasubramanian with the system of Bikumala in view of Allen in order to control the I/O utilization of applications for different kinds of workloads in a shared database environment (See Col. 2, lines 55- Col. 3, line 5 of Sivasubramanian).
As discussed above, Bikumala discloses identifying patterns of IT operation requests using information from the knowledge bank. Bikumala in view of Allen and further in view of Sivasubramanian does not explicitly disclose or teach, however, Maroo teaches that the identifying patterns includes using a multivariate classification model trained on information (Paras. 27-28) (…each of the set of trained classifier models 182-188 is specialized for…detecting a different pattern type…Additionally, the classifier module 18 may further include a model specialized for detecting…multivariate classifications).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the multivariate classification trained model of Maroo with the system of Bikumala in view of Allen and further in view of Sivasubramanian in order to provide a more efficient system for pattern analysis.
In regard to claim 2, Bikumala discloses determining that the resolution resource is available (Paras. 56-57; Claim 10) (The analyst information may include one or more of … information, a time zone, a holiday zone, a start date, and a next available time (i.e., the resolution resource is available). The analyst information may also include a current work load associated with an analyst, which may be added to the associated learned analyst profile of learned analyst profile database 274 during further processing of the learned analyst profile).
In regard to claim 3, Bikumala discloses wherein the assigning of the resolution resource is also based on a prediction analysis model (Paras. 51-56, and 58) (Ticketing processor subsystem 120-2 may create a first problem profile pattern for the problem of problem ticket 212 from the problem information of problem ticket 212 by identifying various keywords in the problem information of problem ticket 212. In one or more embodiments, the various keywords may be in the problem description of problem ticket 212. ….Categorization algorithm 255 may be an anytime optimal search-based algorithm that finds high-quality problem profile pattern matches and continuously learns and improves the relations between the first problem profile pattern, e.g. the various keywords in the problem information of problem ticket 212, and the known problem profile patterns of the known learned problem profiles, e.g. various keywords in the knowledge articles of knowledge database 276 associated with the known problem profile patterns of the known learned problem profiles).
In regard to claim 6, Bikumala discloses wherein the information from the knowledge bank includes at least one of: an issue type (Para. 51) (Ticketing processor subsystem 120-2 may categorize the first problem profile pattern by using categorization algorithm 255 to determine whether the first problem profile pattern has a known learned problem profile pattern that may be associated with a respective known learned problem profile of known learned problem profiles of learned problem profile database 272)
In regard to claim 7, Bikumala discloses wherein the information associated with the resolution resource includes at least one of: historical ticket assignments of the resolution resource (Paras. 60-62) (For example, the rank order value may be associated with the analyst and the learned problem profile and may be a value associated with a customer satisfaction score, a problem ticket closure rate, a SLA metric (e.g. meeting the SLA, exceeding the SLA, or missing the SLA), a reoccurrence metric (e.g. how many times it takes for an analyst to resolve a problem associated with the known learned problem profile), a problem acknowledgment time, a problem resolution time, or a value associated with a combination of one or more of these values….(i.e., historical ticket assignments)…. Assignment algorithm 253 may assign problem ticket 212 to the analyst associated with the selected learned analyst profile. Assigning problem tickets in this manner ensures that an available analyst with the most expertise in resolving these particular types of problems may be assigned to the problem tickets (i.e., information associated with the resolution resource).)
In regard to claim 8, Bikumala discloses generating a current state of environment of the IT operation request based on the information in the knowledge bank (Para. 56) (In one or more embodiments, ticketing processor subsystem 120-2 may create a first set of problem categories for the problems in learned problem profile database 272 based on the most repeated keywords in the problem description for each of the product names of the learned problem profile of learned problem profile database 272 (i.e., based on the information inf the knowledge bank). Next, the knowledge articles used to resolve the problems in learned problem profile database 272 is mapped to the first set of problem categories. The most used knowledge articles applied to resolving the problems in the particular problem categories are then grouped into a second set of problem categories. Ticketing processor subsystem 120-2 may continuously sort the sets of problem categories based on problem descriptions and associated knowledge articles of learned problem profile database 272 and rearrange the problem groupings and problem categories based on the learning. In one or more embodiments, ticketing processor subsystem 120-2 may combine problem categories to create new problem categories, where a new problem category may have a broader spread of keywords and knowledge articles (i.e., generating a current state of environment of the IT operation request).)
In regard to claim 9, Bikumala discloses wherein the determining of the resolution resource further comprises determining a pattern associated with the resolution resource and the IT operation request (Paras. 51-56, and 58) (Ticketing processor subsystem 120-2 may create a first problem profile pattern for the problem of problem ticket 212 from the problem information of problem ticket 212 by identifying various keywords in the problem information of problem ticket 212. In one or more embodiments, the various keywords may be in the problem description of problem ticket 212. ….Categorization algorithm 255 may be an anytime optimal search-based algorithm that finds high-quality problem profile pattern matches and continuously learns and improves the relations between the first problem profile pattern (i.e., determining a pattern associated with the resolution resource and the IT operation request), e.g. the various keywords in the problem information of problem ticket 212, and the known problem profile patterns of the known learned problem profiles, e.g. various keywords in the knowledge articles of knowledge database 276 associated with the known problem profile patterns of the known learned problem profiles).
In regard to claim 10, Bikumala discloses wherein the determining of the pattern further comprises mapping information associated with the resolution resource to the IT operation request (Paras. 60-64) (Ticketing processor subsystem 120-2 may… order the learned analyst profiles that may be associated with the known learned problem profile associated with the completed problem ticket by using rank ordering algorithm 257 that may be based on the performance metrics of one of the learned analyst profiles associated with the known learned problem profile, and the performance metrics of each of the other learned analyst profiles associated with the known learned problem profile… At step 304, the ticketing processor subsystem may identify a learned problem profile of the learned problem profile database that may be associated with the problem of the problem ticket. At step 306, the ticketing processor subsystem may add the learned problem profile and rank ordered learned analyst profiles of the learned analyst profile database that may include analysts that may have resolved learned problems that may be associated with the learned problem profile to a correlation database entry of the correlation database. At step 308, the ticketing processor subsystem may assign one of the analysts of the rank ordered learned analyst profiles to the problem ticket that may be based on analyst information of each of the analysts of the rank ordered learned analyst profiles and an assignment algorithm (i.e., mapping information associated with the resolution resource to the IT operation request).)
Bikumala discloses that the information associated with the resolution resource including at least one of: historical issues, areas of expertise, usage statistics, historical resolution resource skills utilized, resolution time…, interaction information…(Paras. 58-64) (…performance metrics that may be associated with the assigned analyst and the completed problem ticket from operational database 278. The performance metrics may include one or more of a customer satisfaction score (i.e., historical issues, usage statistics, interaction information), a service level agreement metric, a reoccurrence metric, a problem acknowledgment time (i.e., historical issues, usage statistics, historical resolution resource skills utilized, interaction information), and a problem resolution time (i.e., historical issues, usage statistics, historical resolution resource skills utilized, interaction information, resolution time)… Building and maintaining correlation database 280 in this manner allows for the efficient identification of analysts that are effective at resolving problems associated with the known learned problem profile (i.e., areas of expertise), for example, identifying the analyst with the highest customer satisfaction score for resolving these types of problems..
In regard to claim 11, Bikumala discloses prioritizing the IT operation request based on an indicated severity and business impact (Paras. 11, 48 and 55) (…the problem information may include one or more of an … a problem severity…a selected problem priority, an accepted problem priority, an adjusted SLA, an accepted SLA (i.e., an indicated severity and business impact).)… Client processor subsystem 120-1 may create a problem ticket 112 that may include the problem and the problem information …Ticketing processor subsystem 120-2 may also add one or more of a knowledge article identification associated with the matching knowledge article, a cross-reference for the matching knowledge article, the KDB problem associated with the KDB problem ticket, the KDB problem information associated with the KDB problem, a KDB problem complexity estimate, e.g. amount of effort required to resolve the KDB problem, and a KDB time estimate to resolve the problem to one or more of the learned problem profile associated with problem ticket 212 and problem ticket 212. The matching knowledge article includes instructions and information to enable an analyst to resolve the associated problem. By adding the matching knowledge articles to problem ticket 212, all of the information is already available when an analyst starts working on problem ticket 212 in order to resolve the problem more efficiently (i.e., prioritizing the IT operation request).)
In regard to claim 13, Bikumala discloses to schedule a solution to the IT operation request with the resolution resource (Paras. 56-57, 62; Claim 10) (The analyst information may include one or more of … information, a time zone, a holiday zone, a start date, and a next available time. The analyst information may also include a current work load associated with an analyst, which may be added to the associated learned analyst profile of learned analyst profile database 274 during further processing of the learned analyst profile… Assigning problem tickets in this manner ensures that an available analyst with the most expertise in resolving these particular types of problems may be assigned to the problem tickets.).
Examiner notes Bikumala in view of Allen and further in view of Sivasubramanian, and even further in view of Maroo teach the limitations of the claim, as discussed above in claim 1.
Claims 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Bikumala in view of Allen and further in view of Sivasubramanian, and even further in view of Maroo, as applied to claim 1, in view of U.S. Patent Application Publication No. 2016/0350595 to Solomin et al. (hereinafter “Solomin”).
In regard to claim 4, as discussed above in regard to claim 1, Bikumala discloses the IT operation request. Bikumala in view of Allen and further in view of Sivasubramanian and even further in view of Maroo does not explicitly disclose or teach, however, Solomin teaches receiving a rejection by the resolution resource; and assigning another resolution resource based on the information and the (IT operation) request (Para. 271) (Reassigning a ticket—If the person (expert) that is currently assigned to the ticket is not currently able to provide support (i.e., receiving a rejection by the resolution resource), the ticket can be reassigned to someone else (i.e., assigning another resolution resource based on the information and the (IT operation) request).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the person (expert) reassigning of Solomin with the system of Bikumala in view of Allen and further in view of Sivasubramanian and even further in view of Maroo in order to provide customer satisfaction.
In regard to claim 12, Bikumala discloses the computing device. Bikumala in view of Allen and further in view of Sivasubramanian and even further in view of Maroo does not explicitly disclose or teach, however, Solomin teaches that the computing device includes software provided as a service in a cloud environment (Paras. 13, 46, 48-49, and 200-201) (The intermediate computerized system may be a cloud environment server….cloud server 20).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the cloud environment of Solomin with the system of Bikumala in view of Allen and further in view of Sivasubramanian and even further in view of Maroo in order to provide a more efficient system with cloud based computing.
Claims 5, and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Bikumala in view of Allen and further in view of Sivasubramanian and even further in view of Maroo, as applied to claims 1 and 13 above, in view of Solomin and further in view of U.S. Patent Application Publication No. 2021/0004706 to Riddle et al. (hereinafter “Riddle”).
In regard to claims 5, and 14, as discussed above in regard to claims 1 and 13, Bikumala discloses the IT operation request. Bikumala in view of Allen and further in view of Sivasubramanian and further in view of Maroo does not explicitly disclose or teach, however, Solomin teaches verifying incompletion of the IT operation request; determining, based on the incompletion and an escalation template, another resolution resource and another resolution resource availability for reassignment and a party for notification of the incompletion; and reassigning the IT operation request to the another resolution resource (Para. 204. 213, and 271) (Field technicians and experts can re-assign or escalate an open ticket to another expert (i.e. based on an escalation template)… c. Communication is via the Fieldbit cloud server. This enables the field technician to select individual experts or a particular group from a list. If not available, ticket can be reassigned to another expert (i.e., another resolution resource availability for reassignment )…. Reassigning a ticket (i.e., operation request)—If the person (expert) that is currently assigned to the ticket is not currently able to provide support (i.e., verifying incompletion of the operation request), the ticket can be reassigned to someone else (i.e., a party for notification of the incompletion….determining, based on the incompletion, another resolution resource; and reassigning the request to the another resolution resource).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the person (expert) reassigning of Solomin with the system of Bikumala in view of Allen and further in view of Sivasubramanian even further in view of Maroo in order to provide customer satisfaction.
Bikumala in view of Allen and further in view of Sivasubramanian and even further in view of Maroo and further in view of Solomin does not explicitly disclose or teach, however, Riddle teaches to generate an artificial intrinsic pressure for completion of the IT operation request (Para. 42-45) (The system 300 can generate different types of training sets for training the change-based training machine-learning model 322 to predict escalations of service tickets. In one type of training set, a single observation either represents the “state” of a service ticket immediately prior to escalation or the state of an un-escalated service ticket prior to its successful resolution. In another type of training set, each of multiple observations are separated in time and capture the state of a service ticket at different points during the service ticket's lifespan. For example, the system 300 can periodically observe the state of a service ticket, with the first observation at the moment that the service ticket is opened, the second observation when the service ticket has been open for 1 hour, the third observation when the service ticket has been open for 2 hours, and so on. An observation is considered to be associated with an escalation if the system 300 determines that a service ticket was found to escalate within a certain time period following the time of the observation, such as if the escalation occurred within 72 hours of the observation (i.e., generate an artificial intrinsic pressure for completion of the IT operation request).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the escalation predictions of Riddle with the system of Bikumala in view of Allen and further in view of Sivasubramanian, and even further in view of Maroo and even further in view of Solomin in order to provide better user satisfaction in resolving the service ticket.
In regard to claim 15, Bikumala discloses wherein the scheduling of the solution is based on scheduling information including at least one of: customer working hours. (Abst.; Para. 48) (Problem ticket 112 may also include user information associated with the user. The user information may include one or more of … a next available time (i.e., customer working hours), and a SLA associated with the user. Client processor subsystem 120-1 may send problem ticket 212 to ticketing information handling system 100-2…[for assignment]).
In regard to claim 16, Bikumala discloses identifying a time window for the solution based on the scheduling information (Para. 55) (…ticketing processor subsystem 120-2 may also add…a KDB time estimate (i.e., a time window) to resolve the problem to one or more of the learned problem profile associated with problem ticket 212 and problem ticket 212).
Claims 17 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Bikumala in view of Allen and further in view of Sivasubramanian.
In regard to claim 17, Bikumala discloses 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, the program instructions executable to perform functions (Paras. 37, 39 and 41-42) (information handling system 100 may include… processor subsystem 120… Processor subsystem 120…may interpret and/or execute program instructions and/or process data stored locally (e.g., in memory subsystem 130 and/or another component of information handling system)…1memory subsystem 130 may comprise a system, device, or apparatus operable to retain and/or retrieve program instructions and/or data for a period of time (e.g., computer-readable media)….)
Bikumala discloses to receiving an information technology (IT) operations request (Para. 43) (A problem may occur during execution of the user application and a user may request help from customer support to resolve the problem. Typically, the user submits the problem to customer support using a ticket submission system… the ticket submission system may send the problem ticket including the problem and the information associated with the problem to an information handling system, such as information handling system (i.e., receive an information technology (IT) operations request).)
Bikumala discloses to collect information associated with the IT operation request from a knowledge bank (Para. 49, 51-56) (Ticketing information handling system 100-2 may be coupled to a learned problem profile database 272, a learned analyst profile database 274, a knowledge database 276, an operational database 278, and a correlation database 280 (i.e., collectively from a knowledge bank)…. Ticketing processor subsystem 120-2 may categorize the first problem profile pattern by using categorization algorithm 255 to determine whether the first problem profile pattern has a known learned problem profile pattern that may be associated with a respective known learned problem profile of known learned problem profiles of learned problem profile database 272 (i.e., collect information associated with the IT operation request)…. Ticketing processor subsystem 120-2 may search knowledge database 276 by using knowledge matching algorithm 259 to identify one or more matching knowledge articles for resolution of the problem associated with problem ticket 212 (i.e., collect information associated with the IT operation request)…)
Bikumala discloses to generate a current state of environment of the IT operation request based on the information in the knowledge bank (Para. 56) (In one or more embodiments, ticketing processor subsystem 120-2 may create a first set of problem categories for the problems in learned problem profile database 272 based on the most repeated keywords in the problem description for each of the product names of the learned problem profile of learned problem profile database 272 (i.e., based on the information inf the knowledge bank). Next, the knowledge articles used to resolve the problems in learned problem profile database 272 is mapped to the first set of problem categories. The most used knowledge articles applied to resolving the problems in the particular problem categories are then grouped into a second set of problem categories. Ticketing processor subsystem 120-2 may continuously sort the sets of problem categories based on problem descriptions and associated knowledge articles of learned problem profile database 272 and rearrange the problem groupings and problem categories based on the learning. In one or more embodiments, ticketing processor subsystem 120-2 may combine problem categories to create new problem categories, where a new problem category may have a broader spread of keywords and knowledge articles (i.e. current state of environment of the IT operation request).)
Bikumala discloses to determine a resolution resource for completing the IT operation request based on at least availability of the resolution resource and the current state (Paras. 4, 15, 53-57, 59, and 62) (Ticketing processor subsystem 120-2 may continuously sort the sets of problem categories based on problem descriptions and associated knowledge articles of learned problem profile database 272 and rearrange the problem groupings and problem categories based on the learning (i.e., based on the current state)….Ticketing processor subsystem 120-2 may search knowledge database 276 by using knowledge matching algorithm 259 to identify one or more matching knowledge articles for resolution of the problem (i.e., a resolution resource) associated with problem ticket 212 that may be based on problem ticket 212, the problem associated with problem ticket 212, and the problem information associated with the problem… the ticketing processor subsystem may also be configured to assign one of the analysts of the rank ordered learned analyst profiles to the problem ticket by (i.e., determine a resolution resource for completing the IT operation request)…. The analyst information may include one or more of … information, a time zone, a holiday zone, a start date, and a next available time. The analyst information may also include a current work load associated with an analyst, which may be added to the associated learned analyst profile of learned analyst profile database 274 during further processing of the learned analyst profile (i.e., based on at least availability of the resolution resource).)
Bikumala discloses to assign the resolution resource to resolve the IT operation request (Paras. 44, 55, 58 and 62-64) (The matching knowledge article includes instructions and information to enable an analyst to resolve the associated problem. By adding the matching knowledge articles to problem ticket 212, all of the information is already available when an analyst starts working on problem ticket 212 in order to resolve the problem more efficiently…(i.e., to resolve the operation request) method may also include assigning, by the ticketing processor subsystem, one of the analysts of the rank ordered learned analyst profiles to the problem ticket (i.e., assign the resolution resource).)
Bikumala discloses to schedule the resolution resource to resolve the IT operation request based on….whether the resolution resource can take on additional work (Paras. 13, 56-57 and 62 and 64; Claim 10) (The analyst information may include one or more of … information, a time zone, a holiday zone, a start date, and a next available time. The analyst information may also include a current work load associated with an analyst, which may be added to the associated learned analyst profile of learned analyst profile database 274 during further processing of the learned analyst profile… Assignment algorithm 253 may select a learned analyst profile …with an analyst that may have availability for problem ticket 212 based on analyst information associated with the analyst…. Assigning problem tickets in this manner ensures that an available analyst (i.e. based on….whether the resolution resource can take on additional work), with the most expertise in resolving these particular types of problems may be assigned to the problem tickets.).
Examiner notes Bikumala in view of Allen and further in view of Sivasubramanian teach the remaining limitations of the claim, as discussed above in claim 1.
Claims 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bikumala in view of Allen and further in view of Sivasubramanian, as applied to claim 17 above, in view of Solomin and further in view of Riddle.
In regard to claim 18, as discussed above in regard to claim 17, Bikumala discloses the IT operation request. Bikumala in view of Allen and further in view of Sivasubramanian does not explicitly disclose or teach, however, Solomin teaches verifying incompletion of the IT operation request; determining, based on the incompletion and an escalation template, another resolution resource and another resolution resource availability for reassignment and a party for notification of the incompletion; and reassigning the IT operation request to the another resolution resource (Para. 204. 213, and 271) (Field technicians and experts can re-assign or escalate an open ticket to another expert (i.e. based on an escalation template)… c. Communication is via the Fieldbit cloud server. This enables the field technician to select individual experts or a particular group from a list. If not available, ticket can be reassigned to another expert (i.e., another resolution resource availability for reassignment )…. Reassigning a ticket (i.e., operation request)—If the person (expert) that is currently assigned to the ticket is not currently able to provide support (i.e., verifying incompletion of the operation request), the ticket can be reassigned to someone else (i.e., a party for notification of the incompletion….determining, based on the incompletion, another resolution resource; and reassigning the request to the another resolution resource).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the person (expert) reassigning of Solomin with the system of Bikumala in view of Allen and further in view of Sivasubramanian in order to provide customer satisfaction.
Bikumala in view of Allen and further in view of Sivasubramanian and further in view of Solomin does not explicitly disclose or teach, however, Riddle teaches to generate an artificial intrinsic pressure for completion of the IT operation request (Para. 42-45) (The system 300 can generate different types of training sets for training the change-based training machine-learning model 322 to predict escalations of service tickets. In one type of training set, a single observation either represents the “state” of a service ticket immediately prior to escalation or the state of an un-escalated service ticket prior to its successful resolution. In another type of training set, each of multiple observations are separated in time and capture the state of a service ticket at different points during the service ticket's lifespan. For example, the system 300 can periodically observe the state of a service ticket, with the first observation at the moment that the service ticket is opened, the second observation when the service ticket has been open for 1 hour, the third observation when the service ticket has been open for 2 hours, and so on. An observation is considered to be associated with an escalation if the system 300 determines that a service ticket was found to escalate within a certain time period following the time of the observation, such as if the escalation occurred within 72 hours of the observation (i.e., generate an artificial intrinsic pressure for completion of the IT operation request).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the escalation predictions of Riddle with the system of Bikumala in view of Allen and further in view of Sivasubramanian, and even further in view of Solomin in order to provide better user satisfaction in resolving the service ticket.
In regard to claim 19, Bikumala discloses the limitations as discussed above in regard to claim 6.
In regard to claim 20, Bikumala discloses the limitations as discussed above in regard to claim 9.
Prior Art
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
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/RUPANGINI SINGH/
Examiner, Art Unit 3628