DETAILED ACTION
Acknowledgement
This final office action is in response to the amendment filed on 02/23/2026.
Status of Claims
Claims 1, 3-7, 9, 11-13, 15, 17 and 18 have been amended.
Claims 1-18 are now pending.
Response to Arguments
Applicant's arguments filed on 02/23/2026 regarding the 35 U.S.C. 101 and 102 rejections of claims 1-18 have been fully considered. The Applicant argues the following:
(1) As per the 101 rejection, the Applicant argues that the claims integrate any such concept into a practical application by reciting a concrete, sequential, model-driven workflow that improves computerized ticket routing by constraining processing to a process that include specific sequence of steps for determining a group and subsequent use of a group-appropriate agent mapping model trained on group-specific historical performance data, rather than merely presenting results of human decision-making. The claims also provide an improvement for automated routing of technical-issue work tickets, including reduction in overhead while improving performance.
The Examiner respectfully disagrees. The Examiner submits that the amended claims are still directed to the abstract groupings of Mental Processes and Certain Methods of Organizing Human Activity because the claims describe a process of receiving and analyzing work ticket data via trained models to determine issues to be resolved (i.e. mental processes) and to identify and assign an agent group/agent to resolve the issue (i.e. certain methods of organizing human activity). Per MPEP 2106.04(a), a claim recites a judicial exception when the judicial exception is “set forth” or “described” in the claim.
The Examiner also submits that the additional elements recited in the claims and listed in Steps 2A(2) and 2B do not integrate the abstract idea into a practical application because the additional elements do not improve the functioning of a computer or improve another technology. The Applicant argues that the improvement is in computerized ticket routing due to the use of the agent mapping models to process tickets. However, ticket routing and/or assigning is abstract and does not represent a specific technology. Performing an abstract process on a computer does not does not integrate a judicial exception into a practical application (MPEP 2106.05(f)). Also the use of agent mapping models to determine which agent to route a ticket to is also abstract. An improvement in an abstract idea is not an improvement in technology (MPEP 2106.05(a)). Therefore, the 35 U.S.C. 101 rejection is maintained.
(2) As per the 102 rejection, the Applicant argues that Rath does not disclose identifying an agent group, and responsive thereto (i) identifying a determined agent mapping model..., (ii) determining agent data indicative of current characteristics..., and (iii) applying the agent data and agent mapping model to determine an agent within the determined agent group as recited in amended claims 1, 7, and 13.
The Examiner respectfully disagrees. The Examiner submits that based on the broadest reasonable interpretation of the claims that Rath teaches all of the limitations of amended claims 1, 7, and 13 as shown in the updated claim mappings below. In summary, Rath teaches multiple agent mapping models 320-330 and 336-344 as shown in Fig. 3 and [0015] that map support tickets to agent groups (L1, L2, or L3) [0025] and/or pods [0070] and also to specific agents in the agent groups based on characteristics of the ticket and agent [0098]. Therefore, the 35 U.S.C. 103 rejection is maintained.
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 .
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention, “Ticket Engine System and Method”, is directed to an abstract idea, specifically Mental Processes and Certain Methods of Organizing Human Activity, without significantly more. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements individually or in combination provide mere instructions to implement the abstract idea on a computer.
Step 1: Claims 1-18 are directed to a statutory category, namely a machine (claims 1-6), a process (claims 7-12), and a manufacture (claims 13-18).
Step 2A (1): Independent claims 1, 7, and 13 are directed to an abstract idea of Mental Processes and Certain Methods of Organizing Human Activity, based on the following claim limitations: “a group mapping model trained to determine an agent group corresponding to a technical issue based on an issue description; and one or more agent mapping models trained to determine an agent of an agent group for resolving the technical issue based on identification of the agent group and current characteristics of agents in the agent group; receiving,…, a work ticket comprising a textual description of a technical issue to be resolved; determining,… based on the textual description, ticket data comprising an issue description indicative of the technical issue to be resolved; in response to determining the determined agent group: identifying, from the one or more agent mapping models, a determined agent mapping model for use in processing the work ticket for the determined agent group, the determined agent mapping model trained, using historical performance data for agents in the determined agent group, to identify an agent of the determined agent group for resolving the technical issue based on identification of the determined agent group and current characteristics of agents in the determined agent group; determining agent data that is indicative of current characteristics of agents in the determined agent group; and determining,… based on application of the issue description to the group mapping model, an agent group corresponding to the technical issue; in response to determining the determined agent group: identifying, from the one or more agent mapping models, a determined agent mapping model for use in processing the work ticket for the determined agent group, the determined agent mapping model trained, using historical performance data for agents in the determined agent group, to identify an agent of the determined agent group for resolving the technical issue based on identification of the determined agent group and current characteristics of agents in the determined agent group; determining agent data that is indicative of current characteristics of agents in the determined agent group; and determining,… based on application of the determined agent group and the agent data to the determined agent mapping model, an agent of the determined agent group for resolving the technical issue; and providing,… in response to determining the agent of the determined agent group for resolving the technical issue, the work ticket to the agent.”. These claims describe a process of receiving and analyzing work ticket data via trained models to determine issues to be resolved and to identify and assign an agent group/agent to resolve the issue. Dependent claims 2-6, 8-12, and 14-18 further describe the process of determining and assigning the work ticket to an agent and the process of training models to identify agent groups/agent to resolve issues. Determining ticket data from the textual descriptions of technical issues and applying the ticket data to trained models to determine agent groups and agents to resolve the technical issue are steps that can practically be performed mentally with pen and paper via observation, evaluation, and the use of mathematical algorithms. Training a model could entail the process of fitting a particular model/algorithm to a dataset by adjusting coefficients or weights in the model/algorithm to provide a specific output. Determining agent groups and agents to resolve technical issues reflect actions that manages the personal behavior of agents groups and agents. Therefore, these limitations, under the broadest reasonable interpretation, fall within the abstract groupings of Mental Processes which include concepts performed in the human mind such as observations, evaluations, judgments, and opinions and Certain Methods of Organizing Human Activity which encompasses managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions. Mental Processes include claims directed to collecting information, analyzing it, and displaying certain results of the collection and analysis even if they are claimed as being performed on a computer. The courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind. Certain Methods of Organizing Human Activity can encompass the activity of a single person (e.g. a person following a set of instructions), activity that involve multiple people (e.g. a commercial interaction), and certain activity between a person and a computer (e.g. a method of anonymous loan shopping). Therefore, claims 1-18 are directed to an abstract idea and are not patent eligible.
Step 2A (2): The claims as a whole do not integrate this abstract idea into a practical application. In particular, claims 1, 3-7, 9-13, and 15-18 recite additional elements of “a work ticket management system comprising a ticketing database storing ticketing data; non-transitory computer readable storage medium comprising program instructions stored thereon that are executable by a processor; and a work ticket engine ”. These additional elements do not integrate the abstract idea into a practical application because the claims do not recite (a) an improvement to another technology or technical field and (b) an improvement to the functioning of the computer itself and (c) implementing the abstract idea with or by use of a particular machine, (d) effecting a particular transformation or reduction of an article, or (e) applying the judicial exception in some other meaningful way beyond generally linking the use of an abstract idea to a particular technological environment. These additional elements evaluated individually and in combination are viewed as computing components that are used to perform the abstract process of determining and assigning work tickets to agents and training models to identify agent groups/agents to resolve issues. Limitations that recite mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea are not indicative of integration into a practical application (see MPEP 2106.05(f)). Therefore, claims 1-18 as a whole do not include individual or a combination of additional elements that integrate the judicial exception into a practical application and thus are not patent eligible.
Step 2B: The claims as a whole do not include additional elements that are sufficient to amount to significantly more than the abstract idea. Claims 1, 3-7, 9-13, and 15-18 recite additional elements of “a work ticket management system comprising a ticketing database storing ticketing data; non-transitory computer readable storage medium comprising program instructions stored thereon that are executable by a processor; and a work ticket engine ”. These additional elements evaluated individually and in combination are viewed as mere instructions to apply or implement the abstract idea on a computer. Applying an abstract idea on a computer does not integrate a judicial exception into a practical application or provide an inventive concept (see MPEP 2106.05(f)). Therefore, claims 1-18 as a whole do not include individual or a combination of additional elements that are sufficient to amount to significantly more than the judicial exception and thus are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Rath (US 2021/0014136 A1).
As per claims 1, 7, and 13 (Currently Amended), Rath teaches a work ticket management system comprising: a ticketing database storing ticketing data comprising (Rath e.g. FIG. 3 illustrates a block diagram of an example system 300 for assigning support tickets to support agents, under an embodiment [0014]. The system also has a production server that receives support tickets, which includes the support ticket 200 that contains the subsequent communication 202 and the support ticket's metadata 204, as depicted by FIG. 2 [0013].): Rath teaches a group mapping model trained to determine an agent group corresponding to a technical issue based on an issue description (Rath e.g. The training server 312 may include… a support ticket complexities machine-learning model 324,…(Fig. 3 and [0015]). The support ticket complexities machine-learning models 324 and/or 338 may also predict a more straightforward categorization of complexities, such as L1, L2 and L3, where L1 represents low complexity support tickets and L3 represents high complexity support tickets [0036]. In some support environments, level 1 (L1) support agents may provide initial support ticket triage, and work with a customer to find additional information about the support ticket's problem [0025]. The L1 support agents may resolve a problem themselves if the problem may be easily resolved [0025]. Otherwise, the support tickets assignment systems 318 and 332 can reassign support tickets that have higher complexities or require more skills to level 2 (L2) support agents or level 3 (L3) support agents [0025].); and one or more agent mapping models trained to determine an agent of an agent group for resolving the technical issue based on identification of the agent group and current characteristics of agents in the agent group; (Rath e.g. The training server 312 may include a training support tickets assignment system 318, which may include a natural language processor machine-learning model 320, a support ticket topics machine-learning model 322, a support ticket complexities machine-learning model 324, a support agent topical skills machine-learning model 326, a support agent complexity experiences machine-learning model 328, and a support agent projected workloads machine-learning model 330 (Fig. 3 and [0015]). Before assigning a support ticket to a support agent, the support tickets assignment system 318 and/or 332 may determine the support ticket's complexity by applying the support ticket complexities machine-learning models 324 and/or 338 to the analysis of the received support ticket by the natural language processor machine-learning models 320 and/or 334 [0033]. The servers 312 and/or 314 can extract information about support agents that qualify for the given support ticket in one or more aspects. Such information can include the support agent's skills in handling support tickets with topics relevant to the current support ticket and support tickets of similar complexity, the support agent's experience with the specific customer who submitted the current support ticket, the support agent's availability in terms of time zone, work hours and paid time off, and the support agent's current workload [0040]. The training server 312 may weigh the complexities of the support tickets, and then aggregates all support tickets for each support agent, thereby creating one summary row for each support agent, which can capture each support agent's overall experience, both in terms of volume or quantity and in terms of support quality, in resolving support tickets that have certain complexities, which are the columns in the matrix [0046]. In another example, after estimating the high complexity of the remote mount problem in the open support ticket 200, the production server 314 applied the support agent complexity experiences machine learning model 340 to the summary rows for the support agents in the support agent-complexity experiences matrix to identify only Dana as a support agent who has experiences resolving support tickets that have a high complexity [0047].); and non-transitory computer readable storage medium comprising program instructions stored thereon that are executable by a processor to perform the following operations for managing work tickets (Rath e.g. A system for assigning support tickets to support agents, the system comprising: one or more processors; and a non-transitory computer readable medium storing a plurality of instructions, which when executed, cause the one or more processors to (claim 1).):; and a method of managing work tickets, the method comprising; and a non-transitory computer readable storage medium comprising program instructions stored thereon that are executable by a processor to perform the following operations for managing work tickets (Rath e.g. FIG. 4 is a flowchart that illustrates a computer-implemented method for assigning support tickets to support agents, under an embodiment. Flowchart 400 depicts method acts illustrated as flowchart blocks for certain actions involved in and/or between the system elements 302-344 of FIG. 3 [0085].):
Rath teaches receiving, by a work ticket engine, a work ticket comprising a textual description of a technical issue to be resolved; (Rath e.g. After being trained, a support ticket is received, block 414. The system receives a support ticket for determining the support ticket's topic(s) and complexity. This can include the production support tickets assignment system 332 receiving support tickets, which includes the support ticket 200 that contains the subsequent communication 202 and the support ticket's metadata 204, as depicted by FIG. 2 (Fig. 4 and [0094]). A support ticket can be a request logged on a work tracking system detailing a problem that needs to be addressed [0019]. When submitting support tickets, customers may often express the complexity of the problem they are facing in order to convey that information to the support organization [0037]. This analysis may leverage features derived before the assignment of a support ticket, such as text-based features extracted from the support ticket body, the initial priority of the support ticket, and the identity and the history of the customer who submitted the support ticket [0033].)
Rath teaches determining, by the work ticket engine based on the textual description, ticket data comprising an issue description indicative of the technical issue to be resolved; (Rath e.g. When a customer opens a support ticket and uses natural language to describe the issue they are having with a product, product feature, or product use case, the servers 312 and/or 314 can receive the support ticket and use the natural language processor machine-learning models 320 and/or 334 to analyze the received support ticket [0026]. Following the receipt of a support ticket, a topic of the support ticket is determined, block 416. The system identifies a support ticket's topic(s). By way of example and without limitation, this can include the production server 314 applying the support ticket topics machine-learning model 336 to the analysis of the support ticket 200 by the natural language processor machine-learning model 334, thereby identifying a remote mount problem as the topic of the support ticket 200 (Fig. 4 and [0095]).)
Rath teaches determining, by the work ticket engine based on application of the issue description to the group mapping model, determined agent group corresponding to the technical issue; (Rath e.g. Having received a support ticket, a complexity of the support ticket is estimated, block 418. The system estimates a support ticket's complexity. In embodiments, this can include the production server 314 applying the support ticket complexities machine-learning model 338 to the natural language analysis of the support ticket 200 by the natural language processor machine-learning model 334, thereby estimating a high complexity from the customer's description of their remote mount problem because the support ticket 200 includes multiple machine language error messages (Fig. 4 and [0096]). The support ticket complexities machine-learning models 324 and/or 338 may also predict a more straightforward categorization of complexities, such as L1, L2 and L3, where L1 represents low complexity support tickets and L3 represents high complexity support tickets [0036].)
Rath teaches in response to determining the determined agent group: identifying, from the one or more agent mapping models, a determined agent mapping model for use in processing the work ticket for the determined agent group, the determined agent mapping model trained, using historical performance data for agents in the determined agent group, to identify an agent of the determined agent group for resolving the technical issue based on identification of the determined agent group and current characteristics of agents in the determined agent group; (Rath e.g. The support tickets assignment systems 318 and 332 may extract information about a support ticket from a variety of sources, such as support ticket metadata, support ticket comments, prior support ticket histories for each customer, and support ticket histories for each support agent, and then determine support agent compatibility for the support ticket based on the support agents' prior performances on similar support tickets, where similarity is determined on multiple distinct factors [0022]. The support ticket complexities machine-learning model 338 and the support ticket topics machine-learning model 340 may use the natural language analysis of the natural language processor machine-learning model 334 to determine that a support agent is required to have few skills to handle the simple topic of a remote mount problem, but must have experience resolving support tickets that have a high complexity because the support ticket includes multiple machine language error messages [0039]. The servers 312 and/or 314 can extract information about support agents that qualify for the given support ticket in one or more aspects. Such information can include the support agent's skills in handling support tickets with topics relevant to the current support ticket and support tickets of similar complexity, the support agent's experience with the specific customer who submitted the current support ticket, the support agent's availability in terms of time zone, work hours and paid time off, and the support agent's current workload [0040]. The training server 312 may weigh the complexities of the support tickets, and then aggregates all support tickets for each support agent, thereby creating one summary row for each support agent, which can capture each support agent's overall experience, both in terms of volume or quantity and in terms of support quality, in resolving support tickets that have certain complexities, which are the columns in the matrix [0046]. A support agent-complexity experience vector may contain the distribution of complex support tickets that a support agent has resolved in the past [0046].
Rath teaches determining agent data that is indicative of current characteristics of agents in the determined agent group; and (Rath e.g. After identifying the topic of the open support ticket 200 is a remote mount problem, the production server 314 applies the support agent topical skills machine-learning model 340 to the summary rows for the support agents in the support agent-topical skills matrix to identify Bob and Dana as support agents who have skills handling a remote mount problem [0044]. Similarly, the training server 312 can create a support agent-complexity experiences matrix. In addition to capturing the complexities estimated for each closed support ticket, the support ticket-complexity experiences matrix can capture information about the corresponding support agent [0045]. The training server 312 may weigh the complexities of the support tickets, and then aggregates all support tickets for each support agent, thereby creating one summary row for each support agent, which can capture each support agent's overall experience, both in terms of volume or quantity and in terms of support quality, in resolving support tickets that have certain complexities, which are the columns in the matrix [0046]. In another example, after estimating the high complexity of the remote mount problem in the open support ticket 200, the production server 314 applied the support agent complexity experiences machine learning model 340 to the summary rows for the support agents in the support agent-complexity experiences matrix to identify only Dana as a support agent who has experiences resolving support tickets that have a high complexity [0047].)
Rath teaches determining, by the work ticket engine based on application of the determined agent group and the agent data to the determined agent mapping model identified, an agent of the determined agent group for resolving the technical issue; and (Rath e.g. After estimating a support ticket's complexity, a machine-learning model identifies a second set of support agents who have experiences resolving support tickets of the estimated complexity, block 422. The system identifies support agents who have experiences resolving support ticket of the estimated complexity. By way of example and without limitation, this can include the production server 314 applying the support agent complexity experiences machine-learning model 340 to the summary rows for the support agents in the support agent-complexity experiences matrix to identify only Dana as a support agent who has experiences resolving support tickets that have the high complexity of the remote mount problem described in the open support ticket 200 (Fig. 4 and [0098]).)
Rath teaches providing, by the work ticket engine in response to determining the agent of the determined agent group for resolving the technical issue, the work ticket to the agent. (Rath e.g. Subsequent to scoring support agents, the support agent scores are used to assign a support ticket to a support agent from at least one of the sets of support agents, block 428. The system optimally assigns a support ticket to a support agent. By way of example and without limitation, this can include the production support tickets assignment system 332 assigning the open support ticket 200 to Dana because Dana's score of 90 is higher than Bob's score of 45, which reflects that Dana is the only support agent who is sufficiently qualified to resolve the open support ticket 200 since Dana has the experiences which Bob lacks resolving the high complexity support tickets such as the open support ticket 200 (Fig. 4 and [0101]).)
As per claims 2, 8, and 14 (Original), Rath teaches the system of claim 1, the method of claim 7, and the medium of claim 13, Rath also teaches the operations further comprising the agent, in response to the assignment of the work ticket to the agent, attending to resolving the work ticket. (Rath e.g. The system assigns, based on the support agent scores, the support ticket to an identified support agent (Abstract and [0011]). The machine learning system assigns support tickets to appropriate support agents suited to the task at hand. After support tickets are assigned to support agents, the support ticket progresses, and the support agents' workloads change, the frequently executing machine learning system can update its assignments by reassigning some support tickets to other support agents and/or recommend that subject matter experts from the support organization collaborate on various support tickets, thereby troubleshooting effectively in a context-specific manner [0013]. A support agent can be a person who is responsible for providing an act of assistance [0019].)
As per claims 3, 9, and 15 (Currently Amended), Rath teaches the system of claim 1, the method of claim 7, and the medium of claim 13, Rath also teaches the operations further comprising: determining, by the work ticket engine, ticket performance data indicative of assignment of the work ticket to the agent and resolution of the work ticket by the agent (Rath e.g. The training support tickets assignment system 318 can train the natural language processor machine-learning model 320 to provide analysis of a training set of closed support tickets, and may use this analysis to assist in determining which of these closed support tickets may have been assigned to specific support agents [0019]. The training server 312 can weigh each support ticket's topic(s) for every support agent by the factors specific to each support ticket, as well as the context in which each support ticket was handled, so as to provide an estimate of every support agent's performances handling their support tickets [0040].).; training, by the work ticket engine based on ticket performance data indicative of the assignment of the work ticket to the agent, the group mapping model ; and training, by the work ticket engine based on ticket performance data indicative of the resolution of the work ticket by the agent, the determined agent mapping model (Rath e.g. FIG. 4 is a flowchart that illustrates a computer-implemented method for assigning support tickets to support agents, under an embodiment. Flowchart 400 depicts method acts illustrated as flowchart blocks for certain actions involved in and/or between the system elements 302-344 of FIG. 3 [0085]. Closed support tickets are received, block 402. The system receives a training set of closed support tickets for training machine learning models. For example, and without limitation, this can include the training support tickets assignment system 318 receiving a training set of support ticket communications, which includes the support ticket 100 that contains all subsequent communications 102 and 104 and the support ticket's metadata 106, as depicted by FIG. 1. [0086]. Following receipt of closed support tickets, a machine learning model is optionally trained to estimate the complexities of the closed support tickets, block 406. The system can train to estimate support tickets' complexities. In embodiments, this can include the training server 312 training the support ticket complexities machine-learning model 324 to use the natural language analysis of the support ticket 100 by the natural language processor machine-learning model 320 to estimate a low complexity from the customer's description of their remote mount problem [0088]. Once closed support tickets are received, a machine-learning model is trained to identify support agents who have experiences resolving closed support tickets' multiple complexities, block 410. The system trains to identify support agents who have experiences resolving support tickets' complexities [0090].)
As per claims 4, 10, and 16 (Original), Rath teaches the system of claim 1, the method of claim 7, and the medium of claim 13, the operations further comprising:
Rath teaches obtaining, by the work ticket engine, a ticketing model training dataset comprising a ticket assignment log comprising an indication of historical assignments of work tickets to agent groups; and (Rath e.g. A system has a training server that receives a training set of closed support tickets, which includes the support ticket 100 that contains subsequent communications 102 and 104 and the support ticket's metadata 106, as depicted by FIG. 1 [0012]. A support ticket can be a request logged on a work tracking system detailing a problem that needs to be addressed. A closed support ticket can be a previous request that had been logged on a work tracking system detailing a problem that needed to be addressed [0019].)
Rath teaches training, by the work ticket engine based on the ticketing model training dataset, the group mapping model to identify an agent group for assignment of a work ticket based on an issue description indicative of a technical issue corresponding to the work ticket. (Rath e.g. Following receipt of closed support tickets, a machine learning model is optionally trained to estimate the complexities of the closed support tickets, block 406. The system can train to estimate support tickets' complexities. In embodiments, this can include the training server 312 training the support ticket complexities machine-learning model 324 to use the natural language analysis of the support ticket 100 by the natural language processor machine-learning model 320 to estimate a low complexity from the customer's description of their remote mount problem (Fig. 4 and [0088]). The support ticket complexities machine-learning models 324 and/or 338 may also predict a more straightforward categorization of complexities, such as L1, L2 and L3, where L1 represents low complexity support tickets and L3 represents high complexity support tickets [0036]. The L1 support agents may resolve a problem themselves if the problem may be easily resolved. Otherwise, the support tickets assignment systems 318 and 332 can reassign support tickets that have higher complexities or require more skills to level 2 (L2) support agents or level 3 (L3) support agents [0025].)
As per claims 5, 11, and 17 (Currently Amended), Rath teaches the system of claim 1, the method of claim 7, and the medium of claim 13, the operations further comprising:
Rath teaches obtaining, by the work ticket engine, a ticketing model training dataset comprising a ticket performance log comprising an indication of characteristics of historical resolution of work tickets by agents; and (Rath e.g. A system has a training server that receives a training set of closed support tickets, which includes the support ticket 100 that contains subsequent communications 102 and 104 and the support ticket's metadata 106, as depicted by FIG. 1 [0012]. The training server 312 can create a support agent-complexity experiences matrix. In addition to capturing the complexities estimated for each closed support ticket, the support ticket-complexity experiences matrix can capture information about the corresponding support agent. The training server 312 may weigh each support ticket's complexity for every support agent by the factors specific to each support ticket, as well as the context in which each support ticket was resolved, so as to provide an estimate of every support agent's performances on their support tickets [0045]. Standalone support ticket-specific factors that capture the support agent's performance include, but are not limited to, the support ticket resolution time, the sentiment score, and the escalation status of the support ticket [0045]. The training server 312 may weigh the complexities of the support tickets, and then aggregates all support tickets for each support agent, thereby creating one summary row for each support agent, which can capture each support agent's overall experience, both in terms of volume or quantity and in terms of support quality, in resolving support tickets that have certain complexities, which are the columns in the matrix [0046].)
Rath teaches training, by the work ticket engine based on the ticketing model training dataset, the determined agent mapping model to identify an agent for assignment of a work ticket based on an identified agent group for the work ticket. (Rath e.g. Once closed support tickets are received, a machine-learning model is trained to identify support agents who have experiences resolving closed support tickets' multiple complexities, block 410. The system trains to identify support agents who have experiences resolving support tickets' complexities [0090].)
As per claims 6, 12, and 18 (Currently Amended), Rath teaches the system of claim 1, the method of claim 7, and the medium of claim 13, wherein determining the agent of the determined agent group for resolving the technical issue comprises: determining, by the work ticket engine based on application of the determined agent group and agent data comprising a forecast of agent availability to the determined agent mapping model, the agent of the determined agent group for resolving the technical issue. (Rath e.g. The system projects workload availabilities, of identified support agents, for the support ticket (Abstract and [0011]). The servers 312 and/or 314 can extract information about support agents that qualify for the given support ticket in one or more aspects. Such information can include the support agent's skills in handling support tickets with topics relevant to the current support ticket and support tickets of similar complexity, the support agent's experience with the specific customer who submitted the current support ticket, the support agent's availability in terms of time zone, work hours and paid time off, and the support agent's current workload [0040]. The production server 314 can create and maintain a support agent-availability data structure to identify support agents who have projected availabilities for being assigned a support ticket. A projected availability can be the future condition of a person being able to provide assistance [0053]. The production server 314 can create and maintain the support agent-availability data structure which incorporates the support agent time zone, active work hours, and time off schedule [0054]. Following identification of support agents who can handle a support ticket's topic and resolve support tickets of the estimated complexity, workload availabilities of some of the identified support agents are projected for the support ticket, block 424. The system projects the workload availabilities of identified support agents. In embodiments, this can include the production server 312 applying the support ticket projected workloads machine-learning model 342 to the support agent-workload data structure to project the workloads of Bob and Dana, who were identified as support agents who had skills to handle remote mount problems [0099].)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/A.M./Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
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