DETAILED ACTION
The following is a Final Office action. In response to Non-Final communications received 2/11/2026, Applicant, on 7/13/2026, amended Claims 1, 8, and 15, cancelled Claims 4, 11, and 18, and added Claims 25-33. Claims 1, 5-8, 12-15, 19-21, and 25-33 are pending in this action and have been considered in full, and are rejected below.
Response to Arguments
Arguments regarding 35 USC §101 Alice – Applicant asserts that the claims as a whole are not directed towards an abstract idea by stating some of the limitations and amended limitations of the claims, such as use of machine learning module using agent feedback, and re-training the model, stating that no human supervisor can mentally execute a machine learning clustering algorithm, and that the claims are similar to that of Enfish and McRO as there is a closed loop self improving machine learning system for ticket, and thus the claims are eligible as there is no abstract idea. Examiner disagrees as first Enfish and McRO decisions have nothing to do with the case at hand, as the fact patterns are different in the cases, and the limitations of the current invention are utilize current technologies such as a machine learning module and clustering algorithm, to perform the abstract limitations of the Claims. There are two identified abstract ideas, as per the rejection below. That of both a “Mental Process” and a “Certain Method of Organizing Human Activity”, as the claims recite limitations for the purposes of managing agents through identified skills of the agents, which is Managing Human Activity. As per the assertion that no supervisor could do this, the Specification in [0067] states:
“[0067] Humans can provide feedback on how the agents are performing so that the algorithm can improve over time. As shown in table 1000 of FIG. 10, when agents are scored by a human and ranked on who did better than other agents (let’s say on the scale of 0 to 1), we can represent it as a “Ground truth score”. This ground truth score can then be used to learn the weight embeddings (w1, w2, w3...) by training a machine learning module 144 with L2 loss (regression, Neural Network, support vector machine (SVM) learning, etc.). These weight embeddings when learned in a supervised manner with human feedback scores as the ground truth score, will provide accurate skill scores for every agent. Re-training of weight embeddings networks also reveals the importance of different skill score categories and their changing significance over periods of time.”
Which shows that this is supervised learning, requiring a user or supervisor to help the machine learning, and thus these are abstract concepts, utilization of current technologies, with two identified abstractions ideas and no improvement to any technology.
Applicant asserts the claims are integrated into a practical application as there is a specific technical problem which is solved by the amended claims as in [0106] where there is a retraining in weight embeddings, that there is a continuous feedback loop which constitutes how the machine learning model itself operates by citing the amended limitations of the independent claims and stating that the claims are similar to that of Desjardins because there is an improvement to the machine learning model itself and that the Specification support this similar to Desjardins, also stating that the routing step is the closing of the feedback loop, and that the claim recites “without user intervention” and again that this is a closed loop, and therefore because of this the claims are eligible as they are practically integrated. Examiner disagrees as the claims are not practically integrated, as the claim limitations merely utilize current technologies such as a machine learning to perform the abstract limitations of the claims, similar to that of Alice, essentially “Applying It” for identifying agent skills to manage agents which is completely abstract. The claims are not directed at a specific technical problem as the claims are directed at identifying skills, which is completely abstract, and any purported improvement would be entirely within the abstraction and there is no closed loop, as the feedback is provided by humans as in [0067] and this new data/information is used to then update and train the machine learning which is not a closed loop. Further, Desjardins again has no parallel here as this is not directed at improving a machine learning model, but rather utilizing a machine learning model to identify skills of agents, and thus any purported improvement would be entirely within the abstraction. Automatically routing a new ticket without user intervention to an agent is not an improvement as for instance a supervisor could say automatically route, notify, alert a particular agent always when a new ticket comes in, and using a computer to send a notification or new ticket here is not an improvement but rather utilization of current technology. There is no improvement to a technology or any technological process, as any inventive concept as well as the amendments would be contained wholly within the abstraction. There is no improvement to the system, processor, medium, machine learning, or any other additional element, alone or in combination, as per Applicant’s Specification shown in the rejection below. This is “Applying It”, similar to Alice, on a generic computing system.
Applicant asserts the claims are significantly more, by stating that there is no Berkheimer analysis performed, and that examiner has produced no evidence that the limitations of the claims are not well-understood, routine, and conventional, which Applicant states the amended limitations of the claims to support, and thus is eligible. Examiner disagrees as the claims are not significantly more, the amended limitations of the claims are part of the abstraction other than the use of a machine learning model, and a Berkheimer analysis is only required if examiner asserts the claims are well-understood, routine, and conventional. Examiner has only stated that if they were considered well-understood, routine, and conventional, which again they are not, then Applicant’s own specification has been used to prove this assertion as in the rejection below. Further this analysis only has to do with the additional elements themselves, alone and in combination, and there is no improvement to any of these additional elements alone or in combination as per above and the rejection below. The claim limitations merely utilize current technologies to perform the abstract limitations of the claims, similar to that of Alice, essentially “Applying It” for identifying skills of agents. There is no improvement to the computer, medium, processor, machine learning, or any other additional element, as per Applicant’s Specification shown in the rejection below, nor is there any improvement to the machine learning as there is no loop and no improvement to it, as it is merely utilized as intended, nor is there any improvement to any other additional element within the claim alone or in combination. This is “Applying It”, similar to Alice, on a generic computing system, a server with a database.
Applicant asserts the new Claims are provide specific, technical structures and computational techniques that are not directed to an abstract idea. Examiner disagrees as there are two identified abstract ideas identified in these claims, which are further narrowed such as by generating skill nodes and embeddings and computing a distance of a new ticket, with no new additional elements to be considered as part of a practical application or under prong 2 of the Alice Analysis of the MPEP, and thus not integrated into a practical application, nor are they significantly more for the same reasons and rationale as above.
Therefore, the arguments are non-persuasive, the Claims are ineligible as there is no inventive concept, and the rejection of the Claims and their dependents are maintained under 35 USC 101.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1, 5-8, 12-15, 19-21, and 25-33 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 8, and 15 are directed to “creating a continuous feedback loop to the machine learning module by inputting the new agent feedback to the machine learning module”. This continuous feedback loop is not supported in the specification as there is no mention of a continuous feedback loop. For instance, the Specification states:
“[0067] Humans can provide feedback on how the agents are performing so that the algorithm can improve over time. As shown in table 1000 of FIG. 10, when agents are scored by a human and ranked on who did better than other agents (let’s say on the scale of 0 to 1), we can represent it as a “Ground truth score”. This ground truth score can then be used to learn the weight embeddings (w1, w2, w3...) by training a machine learning module 144 with L2 loss (regression, Neural Network, support vector machine (SVM) learning, etc.). These weight embeddings when learned in a supervised manner with human feedback scores as the ground truth score, will provide accurate skill scores for every agent. Re-training of weight embeddings networks also reveals the importance of different skill score categories and their changing significance over periods of time.”
Which states that human feedback is utilized to update the model and learn the embeddings, and that there is a retraining of weight embeddings, but not that there is continuous human feedback. The Specification makes no mention of this. To satisfy the written description requirement, a patent specification must describe the claimed invention in sufficient detail that a patent must describe the technology; the requirement serves both to satisfy the inventor’s obligation to disclose the technologic knowledge upon which the patent is based, and to demonstrate that the patentee was in possession of the invention that is claimed." Capon v. Eshhar, 418 F.3d 1349, 1357, 76 USPQ2d 1078, 1084 (Fed. Cir. 2005). The dependent Claims inherit the deficiencies of the independent claims and thus are similarly rejected.
Therefore, the claims and their dependent claims are rejected under 35 U.S.C. 112(a), written description, as being directed to non-statutory subject matter.
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.
Alice – Claims 1, 5-8, 12-15, 19-21, and 25-33 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 8, and 15 are directed to the limitations for receiving a plurality of tickets, wherein each ticket in the plurality of tickets includes a plurality of fields and at least one agent that resolved the ticket (Collecting Information, an observation, a Mental Process; Managing Human Behavior, i.e. Identifying Skills of Agents; a Certain Method of Organizing Human Activity); determining skills from the plurality of tickets by inputting the plurality of tickets and one or more of the plurality of fields into a clustering algorithm and outputting the skills (Analyzing and Transmitting the Information, an evaluation and judgment, a Mental Process; Managing Human Behavior, i.e. Identifying Skills of Agents; a Certain Method of Organizing Human Activity); generating a taxonomy of the skills (Analyzing the Information, an evaluation, a Mental Process; Managing Human Behavior, i.e. Identifying Skills of Agents; a Certain Method of Organizing Human Activity); creating and outputting a skills knowledge graph using the taxonomy of the skills with agents connected to the skills (Transmitting the Information, a judgment, a Mental Process; Managing Human Behavior, i.e. Identifying Skills of Agents; a Certain Method of Organizing Human Activity) computing a skills score for each agent (Analyzing and Transmitting the Information, an evaluation, a Mental Process; Managing Human Behavior, i.e. Identifying Skills of Agents; a Certain Method of Organizing Human Activity); updating the skills using the machine learning clustering algorithm to output updated skills (Analyzing and Transmitting the Information, an evaluation and judgment, a Mental Process; Managing Human Behavior, i.e. Identifying Skills of Agents; a Certain Method of Organizing Human Activity); inputting agent feedback to a machine learning module and improve/utilize the machine learning module using the agent feedback as a ground truth score to update weight embeddings using an L2 loss function in a supervised manner (Collecting and Analyzing the Information, an observation and evaluation, a Mental Process; Managing Human Behavior, i.e. Identifying Skills of Agents; a Certain Method of Organizing Human Activity); update the skill using the machine learning clustering algorithm to output updated skills (Analyzing and Transmitting the Information, an evaluation and judgment, a Mental Process; Managing Human Behavior, i.e. Identifying Skills of Agents; a Certain Method of Organizing Human Activity); updating the skills knowledge graph with the updated skills score and the updated skills to generate an updated skills knowledge graph; (Analyzing and Transmitting the Information, an evaluation and judgment, a Mental Process; Managing Human Behavior, i.e. Identifying Skills of Agents; a Certain Method of Organizing Human Activity); receiving a new ticket (Collecting Information, an observation, a Mental Process; Managing Human Behavior, i.e. Identifying Skills of Agents; a Certain Method of Organizing Human Activity); determining the skills needed to resolve the new ticket (Analyzing the Information, an evaluation, a Mental Process; Managing Human Behavior, i.e. Identifying Skills of Agents; a Certain Method of Organizing Human Activity); using a search engine to search for the determined skills in the updated skills knowledge graph and an agent with a high skills score for the determined skills (Analyzing the Information, an evaluation, a Mental Process; Managing Human Behavior, i.e. Identifying Skills of Agents; a Certain Method of Organizing Human Activity); automatically routing the new ticket to the agent without user intervention with the high skills score for the determined skills from the updated skills knowledge graph (Transmitting the Information, a judgment, a Mental Process; Managing Human Behavior, i.e. Identifying Skills of Agents; a Certain Method of Organizing Human Activity), in response to the agent resolving the routed new ticket, receiving new agent feedback (Receiving new Information, an Observation, a Mental Process; Managing Human Behavior, i.e. Identifying Skills of Agents; a Certain Method of Organizing Human Activity), creating a continuous feedback loop to the machine learning module by inputting the new agent feedback to the machine learning module, re-training the weight embeddings of the machine learning module using the new agent feedback to dynamically adjust for changing significance of skill categories over periods of time, re-computing the skills score for the agent and the determined skills using the re-trained machine learning module (Analyzing the Received new Information, an Evaluation, a Mental Process; Managing Human Behavior, i.e. Identifying Skills of Agents; a Certain Method of Organizing Human Activity), and updating the skills knowledge graph with the re-computed skills score (Analyzing and Transmitting the Analyzed Information, an Evaluation and Judgment, a Mental Process; Managing Human Behavior, i.e. Identifying Skills of Agents; a Certain Method of Organizing Human Activity), which under their broadest reasonable interpretation, covers performance of the limitation in the mind for the purposes of Managing Human Behavior, but for the recitation of generic computer components. That is, other than reciting a system, at least one processor, machine learning module, and a non-transitory computer-readable medium, nothing in the claim element precludes the step from practically being performed or read into the mind for the purposes of Managing Human Behavior, i.e. Tracking Skills of Agents. For example, determining skills from the plurality of tickets encompasses what a supervisor or manager would do while observing and evaluating how agents do on calls, and assigning skills to them or ranking those skills, an observation, evaluation, and judgment. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas, an observation and evaluation. Further, as described above, the claims recite limitations for Managing Human Behavior, a “Certain Method of Organizing Human Activity”. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the above stated additional elements to perform the abstract limitations as above. The system, processors, and computer readable medium are recited at a high-level of generality (i.e., as a generic software/module performing a generic computer function of storing, retrieving, sending, and processing data) such that they amount to no more than mere instructions to apply the exception using generic computer components. Even if taken as an additional element, the receiving and transmission steps above are insignificant extra-solution activity as these are receiving, storing, and transmitting data as per the MPEP 2106.05(d). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered both individually and as an ordered combination. As discussed above with respect to integration of the abstract idea into a practical application, the additional element being used to perform the abstract limitations stated above amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible. Applicant’s Specification states:
“[0029] The system 100 may be implemented on a computing device 101. The computing device 101 includes at least one memory 154, at least one processor 156, and at least one application 158. The computing device 101 may communicate with one or more other computing devices over a network (not shown). The computing device 101 may be implemented as a server (e.g., an application server), a desktop computer, a laptop computer, a mobile device such as a tablet device or mobile phone device, a mainframe, as well as other types of computing devices.”
Which states that any computing device or server can be used, such as any personal computer, smart phone, tablet, etc., to perform the abstract limitations, and from this interpretation, one would reasonably deduce the aforementioned steps are all functions that can be done on generic components, and thus application of an abstract idea on a generic computer, as per the Alice decision and not requiring further analysis under Berkheimer, but for edification the Applicant’s specification has been used as above satisfying any such requirement. This is “Applying It” by utilizing current technologies. For the receiving and transmitting steps that were considered extra-solution activity in Step 2A above, if they were to be considered additional elements, they have been re-evaluated in Step 2B and determined to be well-understood, routine, conventional, activity in the field. The background does not provide any indication that the additional elements, such as the processor, system, etc., nor the receiving and transmitting steps as above, are anything other than a generic, and the MPEP Section 2106.05(d) indicates that mere collection or receipt, storing, or transmission of data is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For these reasons, there is no inventive concept. The claim is not patent eligible.
Claims 5-7, 12-14, and 19-21 contain the identified abstract ideas, further narrowing them, with no additional elements to be considered as part of a practical application or under prong 2 of the Alice Analysis of the MPEP, thus not integrated into a practical application, nor are they significantly more for the same reasons and rationale as above.
New Claims 25-33 contain the identified abstract ideas, further narrowing them such as by generating skill nodes and embeddings and computing a distance of a new ticket, with no new additional elements to be considered as part of a practical application or under prong 2 of the Alice Analysis of the MPEP, thus not integrated into a practical application, nor are they significantly more for the same reasons and rationale as above.
After considering all claim elements, both individually and in combination, Examiner has determined that the claims are directed to the above abstract ideas and do not amount to significantly more. Therefore, the claims and dependent claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank International, No. 13–298.
Allowable Subject Matter
Claims 1, 5-8, 12-15, 19-21, and 25-33 are objected to as being dependent upon a rejected base claim, but would be allowable if the independent claims were amended in such a way as to overcome the 35 USC 101 rejection and any other rejections.
The closest prior art of record are Rath (U.S. Publication No. 2021/001,4136), Dialani (U.S. Publication No. 2018/023,2421), and Skomoroch (U.S. Publication No. 2014/008,1928). Rath, a system and method for assigning support tickets to support agents, teaches at least one processor, receiving a plurality of tickets, wherein each ticket in the plurality of tickets includes a plurality of fields and at least one agent that resolved the ticket, computing a skills score for each agent and a related skill, inputting agent feedback to a machine learning module which is used to update the scores for agents based on feedback collected, receiving a new ticket, determining skills needed to resolve the new ticket with use of a machine learning model, automatically routing the new ticket to the agent with the high skills score for the determined skills, determining skills from the plurality of tickets using a clustering algorithm on one or more of the plurality of fields, outputting clusters of experts in pods (groups) based on the scoring, and to search for the determined skills in the skills matrix or the skills knowledge graph and an agent with a high skills score for the determined skills, but it does not teach this using a search engine, nor it does not explicitly state using a taxonomy of skills, and does not explicitly state a skills matrix or knowledge graph in use with the skills score or using a ground truth score. Dialani, a system and method for query intent clustering for automated sourcing teaches generating a taxonomy of the skills using an algorithm, creating and outputting a skills matrix or a skills knowledge graph using the taxonomy of the skills with agents connected to the skills, updating the skills matrix or the skills knowledge graph with the skills score where the expertise/skill scores are updated in the profile for the taxonomy, and using a search engine to search for skills and professional expertise, but does not teach use of a ground truth score for improving a machine learning model. Skomoroch, a skill extraction system and method, teaches weighting from agent feedback to come up with an initial score for nodes, and use of machine learning, but does not explicitly state a ground truth score. None of the above prior art explicitly teaches this particular manner as to which a ground truth score is utilized along with a machine learning model, along with the other limitations of the claims in combination, as Applicant points out on pgs. 2-3 of the Remarks of 11/12/2024, and these are the reasons which adequately reflect the Examiner's opinion as to why Claims 1, 5-8, 12-15, 19-21, and 25-33 are allowable over the prior art of record, and are objected to as provided above.
Conclusion
The prior art made of record is considered pertinent to applicant's disclosure.
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Rath; Poonam
ASSIGNING SUPPORT TICKETS TO SUPPORT AGENTS
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CLUSTERING AND TAGGING ENGINE FOR USE IN PRODUCT SUPPORT SYSTEMS
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US 20190042988 A1
Brown; Stephen et al.
OMNICHANNEL, INTELLIGENT, PROACTIVE VIRTUAL AGENT
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HOLTZ; Manuel et al.
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US 20160048579 A1
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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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH M WAESCO whose telephone number is (571)272-9913. The examiner can normally be reached on 8 AM - 5 PM M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, BETH BOSWELL can be reached on (571) 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-1348.
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/JOSEPH M WAESCO/Primary Examiner, Art Unit 3625B 9/29/2026
