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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/29/2025 has been entered.
Status of Claims
This communication is a Non-Final Office Action in response to Applicant’s RCE for application number 18/533,688 received on 02/26/2026.
In accordance with Applicant’s amendment, claims 1-20 are amended, currently pending and have been examined.
Response to Amendment
Applicant’s amendment necessitated the new ground(s) of rejection set forth in this Office Action.
Regarding the §101 rejections previously applied to the original claims, upon review of the amended claims, the rejections are maintained and have been updated to address the amended claims.
Regarding the §103 rejections previously applied to the original claims, upon review of the amended claims, the rejections are maintained and have been updated to address the amended claims.
Response to Arguments
Response to §101 arguments – Applicant’s arguments with respect to the §101 rejections previously applied to the claims have been considered and are unpersuasive.
Applicant argues (Remarks at pgs. 8-9): ““In particular, training a LOTJ model comprises a machine learning (ML) and using the trained LOTJ model to dynamically change a pre-allocated weightage, cannot be performed in the human mind or with pen and paper. The following extrinsic evidence demonstrates that training and using a machine learning model cannot reasonably be performed in the human mind or with pen and paper.2
Machine learning is the subset of artificial intelligence (AI) focused on algorithms that can "learn" the patterns of training data and, subsequently, make accurate inferences about new data. This pattern recognition ability enables machine learning models to make decisions or predictions without explicit, hard-coded instructions.3
In machine learning, the computational analysis of machine learning algorithms and their performance is a branch of computational learning theory via the probably approximately correct learning model....by training data sets...4
As demonstrated by the above-noted extrinsic evidence, a machine learning model is necessarily trained on and run using a computer and not in the human mind. Since the claimed LOTJ model is a machine learning model, it then follows that the steps of "training, by the computing device, a learn on the job (LOTJ) model based on previously captured ticket data" and "dynamically changing, by the computing device, a pre-allocated weightage using the trained LOTJ model based on a ticket resolution to perform based on the plurality of data..." cannot be performed in the human mind.”. In response, Examiner respectfully disagrees and notes that the machine learning recited in the claims fail to integrate the abstract idea into a practical application, add significantly more, and otherwise represent an improvement to technology because the limitations are nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer. See §101 rejections below for further details.
Applicant argues (Remarks at pg. 9): “Applicant further points out the August 2025 USPTO Memorandum which explains that "[t]he mental process grouping is not without limits" and "[e]xaminers are reminded not to expand the mental process grouping in a manner that encompasses claim limitations that cannot practically be performed in the human mind." This memo explains that "a claim does not recite a mental process when it contains limitation(s) that cannot practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitation(s)" and "[c]laim limitations that encompass AI in a way that cannot be practically performed in the human mind do not fall within this grouping." In the current rejection of claim 1, the Examiner is doing exactly what is proscribed in the August 2025 USPTO Memorandum, i.e., expanding the mental process grouping in a manner that encompasses claim limitations that cannot practically be performed in the human mind. Applicant thus submits, for this additional reason, that the claims should not be interpreted as reciting a mental process under Step 2A Prong 1.”. In response, Examiner respectfully disagrees and notes that as stated in the memorandum titled Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101, the following is also communicated: “This memorandum is not intended to announce any new USPTO practice or procedure and is meant to be consistent with existing USPTO guidance.”. Therefore, the analysis and considerations in the office action dated 12/10/2025, as well as the instant office action, are consistent with the August 4, 2025 memorandum.
Applicant argues (Remarks at pgs. 9-10): “Applicant even further points out that Example 39 of the 2019 Revised Patent Subject Matter Eligibility Guidance (PEG) explicitly states that a claim that recites "training the neural network..." "does not recite a mental process because the steps are not practically performed in the human mind." Applicant's claims, which recite "training, by the computing device, a learn on the job (LOTJ) model based on previously captured ticket data" should be treated the same as the claim in Example 39 under the mental process analysis.”. In response, Examiner respectfully disagrees and notes that as Applicant correctly points out, training the neural network is not abstract. Examiner would also point out that as documented in previous office actions, as well as the instant office action, the steps for training the models are identified as additional elements. Therefore, said steps are analyzed under Step 2A, Prong 2, and Step 2B. Examiner also reminds Applicant that contrary to Example 39, Applicants claims recite abstract steps. For example, one of ordinary skill in the art would be able to “calculate a plurality of sentiment scores” with the help of pen and paper, or “eliminate a sentiment score” via judgment or opinion, or “dynamically re-rank a plurality of tickets with a same priority” via evaluation, judgement, opinion or with the help of pen and paper. Therefore, Examiner’s determination is that the claims recite abstract steps that fall under the “Mental Processes” abstract idea grouping. See §101 rejections below for further details.
Applicant argues (Remarks at pgs. 10-11): “Applicant submits that the claims pass muster under Step 2A Prong 2 because the claims apply, rely on, or use the alleged judicial exception in a manner that imposes a meaningful limit on the alleged judicial exception. The Examiner asserts that the judicial exception comprises the steps of "calculating...a plurality of sentiment scores...", "dynamically...re-ranking a plurality of tickets...", "dynamically...changing a pre-allocated weightage...", "adjusting.... the weight reallocation" (Final Office Action pages 5-7.) Taking these as the alleged judicial exception, Applicant points out that claim 1 recites limitations that impose a meaningful limit on the alleged judicial exception. In particular, claim 1 recites a specific manner of calculating a plurality of sentiment scores, e.g., based on authentication credentials of a user within the plurality of data and a corresponding plurality of penalty scores which corresponds with a number of times the user has previously abused the dynamic ranking priority system. Moreover, claim 1 recites a specific type of dynamically re-ranking a plurality of tickets, e.g., based on authentication credentials of a user within the plurality of data and a corresponding plurality of penalty scores which corresponds with a number of times the user has previously abused the dynamic ranking priority system. Claim 1 also recites a specific type of training, e.g., a learn on the job (LOTJ) model based on previously captured ticket data, and a specific type of dynamically changing a pre-allocated weightage, e.g., using the trained LOTJ model based on a ticket resolution to perform based on the plurality of data. Claim 1 further recites a specific type of adjusting the weight reallocation, e.g., based on the dynamically changed pre-allocated weightage. Therefore, the alleged abstract idea (i.e., dynamically changing...a pre-allocated weightage using the trained LOTJ model...) cannot be performed with simply any model, but rather is narrowly confined to a specific type of machine learning model (i.e., a LOTJ model) that is trained using a specific type of data (i.e., previously captured ticket data). Stated another way, the alleged abstract idea is recited generally with a model, but the additional limitations confine the claim to a specific type of LOTJ model that is trained using a specific type of data. These are meaningful limitations that prevent monopolizing the judicial exception, and the 101 rejection should be withdrawn for this reason.”. In response, Examiner respectfully disagrees and notes that the limitations fail to integrate the abstract idea into a practical application, add significantly more, and otherwise represent an improvement to technology because the use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes) does not integrate a judicial exception into a practical application, does not add significantly more to the anstract idea, and does not represent an improvement to technology. See MPEP 2106.05(f). Similarly, the limitations for using or training machine learning fail to integrate the abstract idea into a practical application, add significantly more, and otherwise represent an improvement to technology because these limitations are nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. See §101 rejections below for further details.
Applicant argues (Remarks at pgs. 10-11): “Applicant further submits that the claims pass muster under Step 2A Prong 2 because the claims provide an improvement to a technical field (as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a)). The claims provide an improvement to the technical field of ticket allocations because the claims provide ticket selections based on sentiments and business priorities to help a service agent to address the ticket and avoid ticket selection based on time bound priorities within a same rank and category. (Applicant's specification paragraph 0015.) Implementations of the invention prioritize tickets by avoiding human bias in ticket selection and provides a dynamic ranking priority system which ranks a sentiment in priority order.. (Id.) This represents an improvement in the technical field of distributed computing, and the claims should be deemed patent eligible under Step 2A Prong 2 for this reason.”. In response, Examiner respectfully disagrees and notes that the claim limitations, as currently recited, do not add significantly more for the reasons discussed above (documented in the §101 rejections section below). See §101 rejections below for further details.
Applicant argues (Remarks at pgs. 11-12): “On pages 8 and 9 of the Final Office Action, the Examiner asserts that the alleged abstract idea is not integrated into a practical application. Notwithstanding the Examiner's assertions, Applicant respectfully submits that the above recited features are integrated into a practical application which is an improvement to technology and the technical field. See 2019 Revised Patent Subject Matter Eligibility Guidance, January 7, 2019. Further, under Example 39 of the Patent Subject Matter Eligibility Guidance (PEG), the features of "training of a neural network" did not recite a judicial exception. Similar to Example 39 of the PEG, the claimed invention recites training, by the computing device, a learn on the job (LOTJ) model based on previously captured ticket data, and dynamically changing, by the computing device, a pre- allocated weightage using the trained LOTJ model based on a ticket resolution to perform based on the plurality of data. In fact, the claimed features of training a LOTJ model confines the alleged exception to a practical application which satisfies step 2A, prong 2. In other words, these features of claim 1 are a practical application of continuously learning and improving the dynamic ranking priority system through a learning on the job (LOTJ) model (see at least paragraph [0016] of the specification). These features are also more than a drafting effort designed to monopolize the exception. In this scenario, the implementation of the claimed invention results in a dynamic re-rank a priority of tickets based on business priorities and the plurality of sentiments and provides tangible value to the sentiments blended with impact and urgency. Implementations of the claimed invention also nullify artificial sentiments based on a correlation between historical data and conditions of data segments and categorization, which is a real world practical application of providing automation of ticket allocation using the trained LOTJ model. In contrast to the invention, prior art systems do not include the features of claim 1. Thus, Applicant respectfully submits that the features of claim 1 yield an improvement which renders the claim patent eligible.”. In response, Examiner respectfully disagrees and notes that as discussed above, Applicant assertion regarding training a neural network is correct. Because training a machine learning is an additional element, the steps for “training, by the computing device, a learn on the job (LOTJ) model”, and “training, by the computing device, the ML model” are analyzed under Step 2A, Prong 2, and Step 2B. Examiner also reminds Applicant that contrary to Example 39, Applicants claims recite abstract steps. For example, one of ordinary skill in the art would be able to “calculate a plurality of sentiment scores” with the help of pen and paper, or “eliminate a sentiment score” via judgment or opinion, or “dynamically re-rank a plurality of tickets with a same priority” via evaluation, judgement, opinion or with the help of pen and paper. Therefore, Examiner’s determination is that the claims recite abstract steps that fall under the “Mental Processes” abstract idea grouping. See §101 rejections below for further details.
Applicant argues (Remarks at pg. 12): “Moreover, as stated in Ex Parte Desjardins (Appeals Review Panel, September 26, 2025), the Appeals Review Panel stated that "Examiners and panels should not evaluate claims at such a high level of generality". Further, the Appeals Review Panel in Ex Parte Desjardins stated that Examiners should not conclude that "many AI innovations are potentially unpatentable - even if they are adequately described and nonobvious" and should not "equate any machine learning with an unpatentable 'algorithm' and the remaining additional elements as 'generic computer components,' without adequate explanation". In the present rejection, similar to the circumstances of Ex Parte Desjardins, Applicant submits that the Examiner has improperly evaluated the claimed invention at too high a level of generality. Applicant also respectfully submits that the Examiner had concluded that the machine learning innovations in the claimed invention are an unpatentable algorithm and that the remaining additional elements are merely generic computer components. Therefore, Applicant respectfully submits that the Examiner should reconsider patent eligibility of the claimed invention in view of Ex Parte Desjardins.”. In response, Examiner respectfully disagrees and notes that the improvement of Desjardins is related to training a machine learning model to mitigate catastrophic forgetting, whereas Applicant’s claims merely use machine learning as a tool to perform an abstract idea (e.g., ranking tickets) and the “training, by the computing device” is merely instructions to use generic computing components in it’s ordinary capacity to perform tasks.
Response to §103 arguments – Applicant’s arguments with respect to the §103 rejections previously applied to the claims are primarily raised in support of the amendments. The amendments and supporting arguments are believed to be fully addressed in the updated §103 rejections below.
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 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1-11 are directed to a method, claims 12-19 are directed to a computer program product comprising one or more computer readable storage media (see [0023] of the original disclosure), and claim 20 is directed to a system. Therefore, the claims are directed to patent eligible categories of invention. See MPEP 2160.03.
Step 2A, Prong 1: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether they recite a judicial exception. See MPEP 2106.04
Independent claims 1, 12, and 20 are related to allocating tickets, constituting an abstract idea based on “Mental Processes” related to concepts performed in the human mind including observation, evaluation, judgment, and opinion. Independent claim 12 recite abstract limitations, similarly recited in claims 1 and 20, including: “calculate a plurality of sentiment scores based on authentication credentials of a user within the plurality of data and a corresponding plurality of penalty scores which corresponds with a number of times the user has previously abused the dynamic ranking priority system; eliminate a sentiment score of the corresponding plurality of sentiment scores in response to a corresponding penalty score of the corresponding plurality of penalty scores being greater than a predetermined threshold; dynamically re-rank a plurality of tickets with a same priority based on a type of the plurality of data which is most similar to historical data and a plurality of remaining sentiment scores which are equal to or below the predetermined threshold; dynamically change a pre-allocated weightage based on a ticket resolution to perform based on the plurality of data; and adjust the weight reallocation based on the dynamically changed pre-allocated weightage, wherein the previously captured ticket data comprises at least one of an incident, problem, and change (IPC) ticket or a service request (SR) ticket.” These limitations, as drafted, but for the recitation of “by the computing device” and/or “by the trained LOTJ model,” is a process that covers performance of the limitations in the mind but for the recitation of generic computer components. That is, but for the “by the computing device” and/or “by the trained LOTJ model,” language, nothing in the claim elements preclude the steps from practically being performed in the human mind. For example, with the exception of the “by the computing device” and/or “by the trained LOTJ model,” language, the claim steps in the context of the claim encompass a user mentally or manually performing the steps of the claim.
Dependent claims 2-4, 7-11, and 16-19 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration.
Dependent claims 5-6 and 13-15 will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: An evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the judicial exception into a practical application of the exception. See MPEP 2106.04(d).
Independent claims 1, 12, and 20 do not integrate the judicial exception into a practical application. Independent claim 1 is directed to a method with limitations performed “by the computing device.” Independent claim 12 recites “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, the program instructions executable to” within the preamble of the claim. Independent claim 20 recites “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, the program instructions executable to.” Independent claims 1, 12, and 20 recite the additional element of “input a plurality of data and a weightage reallocation to a dynamic ranking priority system.” Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Independent claims 1, 12, and 20 recite the additional elements of “...using a machine learning (ML) model …,” “train a learn on the job (LOTJ) model based on previously captured ticket data,” and “…using the trained LOTJ model….” These limitations are nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 2-4, 7-11, and 16-19 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Dependent claim 5 introduces the additional element of “further comprising training, by the computing device, the ML model based on historical tickets to provide an accurate prediction for the dynamic re-ranking, wherein the ML model is a random forest model which combines multiple decision trees to provide dynamic re-ranking of the plurality of tickets with the same priority.” Dependent claim 13 introduces the additional element of “wherein the ML model is a random forest model which combines multiple decision trees for dynamic re-ranking of the plurality of tickets with the same priority, and wherein the LOTJ model is further trained based on previously captured feedback data, previously captured user data, and previously captured business data.” Dependent claim 14 introduces the additional element of “further comprising training, by the computing device, the ML model based on historical tickets to provide an accurate prediction for the dynamic re-ranking, wherein the ML model is a linear regression model which computes a linear relationship between a dependent variable and one or more independent features to output a dynamic re-ranking of the plurality of tickets with the same priority.” These limitations are nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
Dependent claim 6 introduces the additional element of “further comprising performing live monitoring, by the computing device, to generate live logs, live events, live video, live audio, and live text, wherein the ML model is a linear regression model which computes a linear relationship between a dependent variable and one or more independent features to output a dynamic re-ranking of the plurality of tickets with the same priority.” Dependent claim 15 introduces the additional element of “further comprising; performing live monitoring to generate live logs, live events, live video, live audio, and live text; and performing the ticket resolution based on the type of the plurality of data.” Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the claims above, are not sufficient to prove integration into a practical application.
Step 2B: The claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for "inventive concept." See MPEP 2106.05.
Independent claims 1, 12, and 20 do not comprise anything significantly more. Independent claim 1 is directed to a method with limitations performed “by the computing device.” Independent claim 12 recites “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, the program instructions executable to” within the preamble of the claim. Independent claim 20 recites “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, the program instructions executable to.” Independent claims 1, 12, and 20 recite the additional element of “input a plurality of data and a weightage reallocation to a dynamic ranking priority system.” Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes) is not anything significantly more. See MPEP 2106.05(f).
Independent claims 1, 12, and 20 recite the additional elements of “...using a machine learning (ML) model …,” “train a learn on the job (LOTJ) model based on previously captured ticket data,” and “…using the trained LOTJ model….” These limitations are nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes) is not anything significantly more. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not anything significantly more.
Dependent claims 2-4, 7-11, and 16-19 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which is not anything significantly more.
Dependent claim 5 introduces the additional element of “further comprising training, by the computing device, the ML model based on historical tickets to provide an accurate prediction for the dynamic re-ranking, wherein the ML model is a random forest model which combines multiple decision trees to provide dynamic re-ranking of the plurality of tickets with the same priority.” Dependent claim 13 introduces the additional element of “wherein the ML model is a random forest model which combines multiple decision trees for dynamic re-ranking of the plurality of tickets with the same priority, and wherein the LOTJ model is further trained based on previously captured feedback data, previously captured user data, and previously captured business data.” Dependent claim 14 introduces the additional element of “further comprising training, by the computing device, the ML model based on historical tickets to provide an accurate prediction for the dynamic re-ranking, wherein the ML model is a linear regression model which computes a linear relationship between a dependent variable and one or more independent features to output a dynamic re-ranking of the plurality of tickets with the same priority.” These limitations are nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes) is not anything significantly more. See MPEP 2106.05(f).
Dependent claim 6 introduces the additional element of “further comprising performing live monitoring, by the computing device, to generate live logs, live events, live video, live audio, and live text, wherein the ML model is a linear regression model which computes a linear relationship between a dependent variable and one or more independent features to output a dynamic re-ranking of the plurality of tickets with the same priority.” Dependent claim 15 introduces the additional element of “further comprising; performing live monitoring to generate live logs, live events, live video, live audio, and live text; and performing the ticket resolution based on the type of the plurality of data.” Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental processes) is not anything significantly more. See MPEP 2106.05(f).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the claims above, are not anything significantly more.
Accordingly, claims 1-20 are rejected under 35 USC 101.
Claim Rejections - 35 USC § 103
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.
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.
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.
Claims 1, 2, 6-11, and 14-19 are rejected under 35 U.S.C. 103 as being unpatentable over Matsuoka et al. US (20230037392 A1, hereinafter “Matsuoka”), in view of Scott-Green et al. (US 20200364727 A1, hereinafter “Scott-Green”), in further view of Bhan et al. (US 20230267502 A1, hereinafter “Bhan”).
Regarding claims 1/12: Matsuoka teaches a method ([Abstract] Systems and methods for predicting the likelihood that a member of a task facilitation service will delegate a given task for completion by the task facilitation service), and a computer program product ([0013] cause the computer system to perform the processes described) comprising one or more computer readable storage media ([0013] computer-readable storage medium stores thereon executable instructions) for intelligent sentiment-based ticket allocation/processing ([0036] a member may be assigned with a representative that, over time, may learn about the member's preferences and behavior, which can be used to recommend tasks that can be performed) with limitations for:
inputting, by a computing device, a plurality of data and a weightage reallocation to a dynamic ranking priority system; ([0040] teaches a system that uses the member's identifying information, any information related to the member's level of comfort or interest in delegating tasks to others, and any other information obtained during the onboarding process as input to a classification or clustering algorithm configured to identify representatives that may be well-suited to interact and communicate with the member 118; [0041] based on historical data corresponding to member interactions with representatives, the representative assignment system 104 may identify correlations between different factors and the polarities of these interactions (e.g., positive, negative, etc.). Based on these correlations (or lack thereof), the representative assignment system 104 may apply a weight to each factor.);
calculating, by the computing device, a plurality of sentiment scores based on authentication credentials of a user within the plurality of data ([0042] The scores determined for the various factors may be aggregated to obtain a composite score for each representative of the set of representatives 106. These composite scores may be used to create the ranking of the set of representatives 106.; [0038] During the onboarding process, the task facilitation service 102 may collect identifying information of the member 118, which may be used by a representative assignment system 104 to identify and assign a representative 106 to the member 118. Examiner notes that one of ordinary skill in the art would reasonably consider the identifying information disclosed by Matsuoka to be equivalent to authentication credentials of a user from Applicant’s claim.);
dynamically re-ranking, by the computing device, a plurality of tickets with a same priority using a machine learning (ML) model ([0023] FIG. 4 shows an illustrative example of an environment in which a task recommendation system generates and ranks recommendations for tasks to be performed for the benefit of a member; [0127] the task recommendation system 112 may use the priority of each of the projects created for the member as another factor in ranking; [0138] the task facilitation service 102 utilizes a machine learning algorithm or artificial intelligence.) based on a type of the plurality of data which is most similar to historical data ([0138] the task facilitation service 102 may use, as input to the machine learning algorithm or artificial intelligence, a member profile or model associated with the member, historical task data for the member (e.g., previously completed tasks, tasks for which proposals have been provided, etc.), and information corresponding to the task for which a proposal is being generated (e.g., a task type or category, etc.).) and a plurality of remaining sentiment scores which are equal to or below the predetermined threshold; ([0110] if the member-representative pairing sub-system 206 determines, based on the relationship score for a particular member-representative pairing (e.g., the relationship score is below a threshold value, etc.), that the member is to be assigned a new representative, the member-representative pairing sub-system 206 may select a new representative that may be assigned to the member).
training, by the computing device, a learn on the job (LOTJ) model based on previously captured ticket data; ([0039] In an embodiment, the task facilitation service 102 can prompt the member 118 to indicate a level or other measure of trust in delegating tasks to others, such as a representative and/or third-party. For instance, the task facilitation service 102 may utilize the identifying information submitted by the member 118 during the onboarding process to identify initial categories of tasks that may be relevant to the member's day-to-day life. In some instances, the task facilitation service 102 can utilize a machine learning algorithm or artificial intelligence to identify the categories of tasks that may be of relevance to the member 118. For instance, the task facilitation service 102 may implement a clustering algorithm to identify similarly situated members based on one or more vectors (e.g., geographic location, demographic information, likelihood to delegate tasks to others, family composition, home composition, etc.). In some instances, a dataset of input member characteristics corresponding to responses to prompts provided by the task facilitation service 102 provided by sample members (e.g., testers, etc.) may be analyzed using a clustering algorithm to identify different types of members that may interact with the task facilitation service 102. Example clustering algorithms that may trained using sample member datasets (e.g., historical member data, hypothetical member data, etc.) to classify a member in order to identify categories of tasks that may be of relevance to the member may include a k-means clustering algorithms, fuzzy c-means (FCM) algorithms, expectation-maximization (EM) algorithms, hierarchical clustering algorithms, density-based spatial clustering of applications with noise (DBSCAN) algorithms, and the like. Based on the output of the machine learning algorithm generated using the member's identifying information, the task facilitation service 102 may prompt the member 118 to provide responses as to a comfort level in delegating tasks corresponding to the categories of tasks provided by the machine learning algorithm. This may reduce the number of prompts provided to the member 118 and better tailor the prompts to the member's needs.; [0066] the task recommendation system 112 may utilize historical task data and corresponding messages from the task datastore 110 to train the NLP or other artificial intelligence to identify possible tasks.; [0090] various operations performed by the representative 106 may be additionally, or alternatively, performed using one or more machine learning algorithms or artificial intelligence. For example, as the representative 106 performs or otherwise coordinates performance of tasks on behalf of a member 118 over time, the task facilitation service 102 may continuously and automatically update the member profile according to member feedback related to the performance of these tasks by the representative 106 and/or third-party services 116. Examiner notes that one of ordinary skill in the art would interpret the continuous learning from feedback taught by Matsuoka to be equivalent to an LOTJ model as disclosed in Applicant’s claims.);
dynamically changing, by the computing device, a pre-allocated weightage ([0302] machine learning or artificial intelligence algorithms include, but are not limited to, genetic algorithms, backpropagation, reinforcement learning, decision trees, liner classification, artificial neural networks, anomaly detection, and such.) using the trained LOTJ model based on a ticket resolution to perform based on the plurality of data; ([0090] various operations performed by the representative 106 may be additionally, or alternatively, performed using one or more machine learning algorithms or artificial intelligence. For example, as the representative 106 performs or otherwise coordinates performance of tasks on behalf of a member 118 over time, the task facilitation service 102 may continuously and automatically update the member profile according to member feedback related to the performance of these tasks by the representative 106 and/or third-party services 116. Examiner notes that one of ordinary skill in the art would interpret the continuous learning from feedback taught by Matsuoka to be equivalent to an LOTJ model as disclosed in Applicant’s claims.);
and adjusting, by the computing device, the weight reallocation based on the dynamically changed pre-allocated weightage ([0302] Other examples of machine learning or artificial intelligence algorithms include, but are not limited to, genetic algorithms, backpropagation, reinforcement learning, decision trees, liner classification, artificial neural networks, anomaly detection, and such. More generally, machine learning or artificial intelligence methods may include regression analysis, dimensionality reduction, meta-learning, reinforcement learning, deep learning, and other such algorithms and/or methods.), wherein the previously captured ticket data comprises at least one of an incident, problem, and change (IPC) ticket or a service request (SR) ticket. ([0037] a member 118, via a computing device 120 (e.g., laptop computer, smartphone, etc.), may submit a request to the task facilitation service 102 to initiate an onboarding process for assignment of a representative 106 to the member 120 and to initiate identification of tasks that are performable for the benefit of the member 118.; [0066] In an embodiment, the data collected from a member 118 over a chat session with the representative may be evaluated by the task recommendation system 112 to identify one or more tasks that may be presented to the member 118 for completion. For instance, the task recommendation system 112 may utilize natural language processing (NLP) or other artificial intelligence to evaluate received messages or other communications from the member 118 to identify an intent. An intent may correspond to an issue that a member 118 wishes to have resolved. Examples of intents can include (for example) topic, sentiment, complexity, and urgency. A topic can include, but is not limited to, a subject, a product, a service, a technical issue, a use question, a complaint, a purchase request, etc. An intent can be determined, for example, based on a semantic analysis of a message (e.g., by identifying keywords, sentence structures, repeated words, punctuation characters and/or non-article words); user input (e.g., having selected one or more categories); and/or message-associated statistics (e.g., typing speed and/or response latency).; [0076] In an embodiment, the task recommendation system 112, periodically (e.g., monthly, bi-monthly, etc.) or in response to a triggering event (e.g., a set number of tasks are performed, member request, etc.), selects a set of experiences that may be recommended to the member 118.; [0092] FIG. 2 shows an illustrative example of an environment 200 in which a representative assignment system 104 performs an onboarding process for a member 118 and assigns a representative 106 to the member 118 based on member and representative attributes in accordance with at least one embodiment. In the environment 200, in response to a request from a member 118 to initiate an onboarding process to create an account with the task facilitation service, the representative assignment system 104 of the task facilitation service may transmit one or more onboarding prompts to the member 118 to gather information about the member 118 that may be used to create a member profile and to identify possible tasks that may be presented to the member 118 based on the member profile. For instance, as illustrated in FIG. 2, the member 118 may submit its request to a member onboarding sub-system 202 of the representative assignment system 104.; [0046] As described in greater detail herein, an entry in the user datastore 108 may further include historical data corresponding to communications between the member 118 and the assigned representative made over time. For instance, as a member 118 interacts with a representative 106 over a chat session or stream, messages exchanged over the chat session or stream may be recorded in the user datastore 108.).
Matsuoka doesn’t explicitly teach:
calculating, by the computing device, a plurality of sentiment scores based on a corresponding plurality of penalty scores which corresponds with a number of times the user has previously abused the dynamic ranking priority system based on the plurality of data;
eliminating, by the computing device, a sentiment score of the corresponding plurality of sentiment scores in response to a corresponding penalty score of the corresponding plurality of penalty scores being greater than a predetermined threshold;
Scott-Green teaches:
calculating, by the computing device, a plurality of sentiment scores based on a corresponding plurality of penalty scores which corresponds with a number of times the user has previously abused the dynamic ranking priority system based on the plurality of data; ([0011] calculating a risk score that indicates a risk associated with the user account of the user; [0021] the penalty to be administered to the user account corresponding to the user is based on a number of previous policy violations; [0079] process 200 can determine whether the content creator is eligible to use the feature based on a combination of a risk score associated with the content creator, information indicating a size of an audience of the content creator, information indicating previous violations of policies by the content creator, and/or a manual review of content item(s).).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine Matsuoka with Scott-Green feature(s) listed above. One would’ve been motivated to do so in order to identify abusive content (Scott-Green; [Abstract]), and assign a score to each item of information and can determine an aggregate score associated with the content creator based on the score for each item of information (Scott-Green; [0079]). By incorporating the teachings of Scott-Green, one would’ve been able to calculate sentiment score based on penalties from previous abuse.
Scott-Green doesn’t teach:
eliminating, by the computing device, a sentiment score of the corresponding plurality of sentiment scores in response to a corresponding penalty score of the corresponding plurality of penalty scores being greater than a predetermined threshold;
Bhan teaches:
eliminating, by the computing device, a sentiment score of the corresponding plurality of sentiment scores in response to a corresponding penalty score of the corresponding plurality of penalty scores being greater than a predetermined threshold; ([0016] In accordance with a first example embodiment, a method of generating a transitory sentiment community. The method comprises receiving data, in a database memory associated with a server computing device, the data extracted from a plurality of data sources, pre-processing the data, in a processor of the server computing device based on at least one of text character removal and text character replacement, to provide pre-processed data that includes a set of keywords used in a descriptive manner, performing a sentiment analysis on the set of keywords based at least in part upon a training model, the sentiment analysis identifying: (i) a conformance to at least one sentiment classification of a set of sentiment classifications recognized by the training model, and (ii) a sentiment intensity rating associated with the conformance, modifying the sentiment intensity rating associated with the at least one sentiment classification upon detecting a sarcasm sentiment that is above a sarcasm sentiment likelihood threshold, and generating the transitory sentiment community based at least in part on the at least one sentiment classification and the modified sentiment intensity rating.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Matsuoka with Bhan’s feature(s) listed above. One would’ve been motivated to do so in order to generate and capture a transitory sentiment community at a server computing device (Bhan; [0015]). By incorporating the teachings of Bhan, one would’ve been able to eliminate sentiment scores to when the penalty is greater than a threshold.
Regarding Claim 2: The combination of Matsuoka, Scott-Green and Bhan teaches the limitations of claim 1. Matsuoka further teaches:
further comprising inputting a list of highest priority types which have a high priority based on an impact on an organization which corresponds with the plurality of data ([0127] teaches the task recommendation system 112 assigns a priority to the project and the associated tasks based on input from the member (e.g., deadlines, desired priority, etc.). Examiner notes that one of ordinary skill in the art would reasonably interpret desired priority as being inclusive of high priority based on an impact on an organization.);
wherein the LOTJ model is further trained based on previously captured feedback data, previously captured user data, and previously captured business data. ([0039] In an embodiment, the task facilitation service 102 can prompt the member 118 to indicate a level or other measure of trust in delegating tasks to others, such as a representative and/or third-party. For instance, the task facilitation service 102 may utilize the identifying information submitted by the member 118 during the onboarding process to identify initial categories of tasks that may be relevant to the member's day-to-day life. In some instances, the task facilitation service 102 can utilize a machine learning algorithm or artificial intelligence to identify the categories of tasks that may be of relevance to the member 118. For instance, the task facilitation service 102 may implement a clustering algorithm to identify similarly situated members based on one or more vectors (e.g., geographic location, demographic information, likelihood to delegate tasks to others, family composition, home composition, etc.). In some instances, a dataset of input member characteristics corresponding to responses to prompts provided by the task facilitation service 102 provided by sample members (e.g., testers, etc.) may be analyzed using a clustering algorithm to identify different types of members that may interact with the task facilitation service 102. Example clustering algorithms that may trained using sample member datasets (e.g., historical member data, hypothetical member data, etc.) to classify a member in order to identify categories of tasks that may be of relevance to the member may include a k-means clustering algorithms, fuzzy c-means (FCM) algorithms, expectation-maximization (EM) algorithms, hierarchical clustering algorithms, density-based spatial clustering of applications with noise (DBSCAN) algorithms, and the like. Based on the output of the machine learning algorithm generated using the member's identifying information, the task facilitation service 102 may prompt the member 118 to provide responses as to a comfort level in delegating tasks corresponding to the categories of tasks provided by the machine learning algorithm. This may reduce the number of prompts provided to the member 118 and better tailor the prompts to the member's needs.; [0066] the task recommendation system 112 may utilize historical task data and corresponding messages from the task datastore 110 to train the NLP or other artificial intelligence to identify possible tasks.; [0090] various operations performed by the representative 106 may be additionally, or alternatively, performed using one or more machine learning algorithms or artificial intelligence. For example, as the representative 106 performs or otherwise coordinates performance of tasks on behalf of a member 118 over time, the task facilitation service 102 may continuously and automatically update the member profile according to member feedback related to the performance of these tasks by the representative 106 and/or third-party services 116.; [0071] In an embodiment, the task recommendation system 112 can automatically select one or more of the tasks for presentation to the member 118 via a task-specific interface without representative interaction. For instance, the task recommendation system 112 may utilize a machine learning algorithm or artificial intelligence to select which tasks from the listing of the set of tasks previously ranked by the task recommendation system 112 may be presented to the member 118 through task-specific interfaces. As an illustrative example, the task recommendation system 112 may use the member profile corresponding to the member 118 (which can include historical data corresponding to member-representative communications, member feedback corresponding to representative performance and presented tasks/proposals, etc.), from the user datastore 108, tasks currently in progress for the member 118, and the listing of the set of tasks as input to the machine learning algorithm or artificial intelligence. The output generated by the machine learning algorithm or artificial intelligence may indicate which tasks of the listing of the set of tasks are to be presented automatically to the member 118 via task-specific interfaces corresponding to these tasks. As the member 118 interacts with these newly presented tasks, the task recommendation system 112 may record these interactions and use these interactions to further train the machine learning algorithm or artificial intelligence to better determine which tasks to present to member 118 and other similarly situated members.; [0102] The machine learning algorithm or artificial intelligence may be trained using unsupervised training techniques. For instance, a dataset of input member attributes and representative attributes may be analyzed using a clustering algorithm to identify correlations between different types of members and representatives. Conversely, the dataset of input member attributes and representative attributes may also be analyzed using a clustering algorithm to identify the types of members and types of representatives that are not well-suited for each other. [0140] The resource library may include entries corresponding to businesses and/or products previously used by representatives for proposals related to particular tasks or task types or that are otherwise associated with particular tasks or task types. Examiner notes that one of ordinary skill in the art would reasonably consider representative attributes, as disclosed in Matsuoka, as equivalent to business data.).
Regarding claims 6: The combination of Matsuoka, Scott-Green and Bhan teaches the limitations of claim 1. Matsuoka further teaches:
further comprising performing live monitoring, by the computing device, to generate live logs, live events, live video, live audio, and live text, ([0278] This disclosure contemplates the computer system taking any suitable physical form. As example and not by way of limitation, the computer system can be an embedded computer system, a system-on-chip (SOC), a single-board computer system (SBC) (such as, for example, a computer-on-module (COM) or system-on-module (SOM)), a desktop computer system, a laptop or notebook computer system, a tablet computer system, a wearable computer system or interface, an interactive kiosk, a mainframe, a mesh of computer systems, a mobile telephone, a personal digital assistant (PDA), a server, or a combination of two or more of these. Where appropriate, the computer system may include one or more computer systems; be unitary or distributed; span multiple locations; span multiple machines; and/or reside in a cloud computing system which may include one or more cloud components in one or more networks as described herein in association with the computing resources provider 1428. Where appropriate, one or more computer systems may perform without substantial spatial or temporal limitation one or more steps of one or more methods described or illustrated herein. As an example, and not by way of limitation, one or more computer systems may perform in real time or in batch mode one or more steps of one or more methods described or illustrated herein. One or more computer systems may perform at different times or at different locations one or more steps of one or more methods described or illustrated herein, where appropriate.; [0067] For instance, based on an evaluation of data collected from different member sources (e.g., personal fitness or biometric devices, video and audio recordings, etc.); [0236] More generally, however, user-specific data 1002 may include any data collected by task facilitation service 102 regarding the member 118. Such data may include general information regarding member 118, including any characteristics, preferences, and the like. User-specific data 1002 may also include any historic interactions between the member 118 and the task facilitation service 102. Such historic information may include, without limitation, messages send to/from the member 118, chat logs for chats between the member 118 and the representative 106, logs of interactions of the member 118 with the task facilitation service 102, and any other similar historic data that may be used to determine tendencies, preferences, and characteristics of the member 118.);
wherein the ML model is a linear regression model which computes a linear relationship between a dependent variable and one or more independent features ([0302] machine learning or artificial intelligence methods may include regression analysis, dimensionality reduction, meta-learning, reinforcement learning, deep learning, and other such algorithms and/or methods. Examiner notes that one of ordinary skill in the art would reasonably interpret regression analysis to be inclusive of the linear regression which computes a linear relationship between a dependent variable and one or more independent features.);
to output a dynamic re-ranking of the plurality of tickets with the same priority ([0024] FIG. 5 shows an illustrative example of a process for generating new tasks and a ranking of tasks that can be used to determine what tasks are to be presented to a member.).
Regarding claim 7: The combination of Matsuoka, Scott-Green and Bhan teaches the limitations of claim 1. Matsuoka further teaches:
further comprising performing the ticket resolution based on the type of the plurality of data ([0172] teach the representative can utilize the task coordination system to generate one or more proposals for resolution of the task; [0173] the task coordination system may maintain proposal templates for different task types, whereby a proposal template for a particular task type may include various data fields associated with the task type.).
Regarding Claim 8: The combination of Matsuoka, Scott-Green and Bhan teaches the limitations of claim 7. Matsuoka further teaches:
wherein the type of the plurality of data comprises a plurality of IPC tickets ([0066] teaches the task recommendation system 112 may utilize natural language processing (NLP) or other artificial intelligence to evaluate received messages or other communications from the member 118 to identify an intent. An intent may correspond to an issue that a member 118 wishes to have resolved. Examples of intents can include (for example) topic, sentiment, complexity, and urgency. A topic can include, but is not limited to, a subject, a product, a service, a technical issue (incident), a use question, a complaint (problem), a purchase request, etc.; [0159] teaches the task creation sub-system 402 may prompt the member 118 to verify that the proposed change to the member profile (change) is accurate.).
Regarding claims 9/17: The combination of Matsuoka, Scott-Green and Bhan teaches the limitations of claims 1/12. Matsuoka further teaches:
further comprising receiving feedback from at least one of a support staff or a subject matter expert (SME) regarding the plurality of data ([0049] teaches the representative may modify the member profile to provide notes about the member 118 (e.g., the member's idiosyncrasies, any feedback regarding the member, etc.).).
Regarding claims 10/18: The combination of Matsuoka, Scott-Green and Bhan teaches the limitations of claims 9/17. Matsuoka further teaches:
wherein the dynamically changing the pre-allocated weightage using the LOTJ model ([0090] various operations performed by the representative 106 may be additionally, or alternatively, performed using one or more machine learning algorithms or artificial intelligence. For example, as the representative 106 performs or otherwise coordinates performance of tasks on behalf of a member 118 over time, the task facilitation service 102 may continuously and automatically update the member profile according to member feedback related to the performance of these tasks by the representative 106 and/or third-party services 116. Examiner notes that one of ordinary skill in the art would interpret the continuous learning from feedback taught by Matsuoka to be equivalent to an LOTJ model as disclosed in Applicant’s claims.).);
is further based on the feedback from at least one of the support staff or the SME ([0141] The machine learning model may further be dynamically trained by soliciting feedback from representatives and members of the task facilitation service with regard to the identification of resources from the resource library and to the proposals automatically generated by the task facilitation service 102 using these resources. For instance, if the task facilitation service 102 generates, based on the member profile associated with the member 118 and the selected resources from the resource library, a proposal that is not appealing to the member 118, the task facilitation service 102 may update the machine learning algorithm or artificial intelligence based on this feedback to reduce the likelihood of similar resources and proposals being generated for similarly-situated members.);
Regarding claims 11/19: The combination of Matsuoka, Scott-Green and Bhan teaches the limitations of claims 1/12. Matsuoka doesn’t explicitly teach:
further comprising determining a magnitude of the corresponding plurality of penalty scores score based on the number of times the user has previously abused the dynamic ranking priority system
Scott-Green teaches:
further comprising determining a magnitude of the corresponding plurality of penalty scores score based on the number of times the user has previously abused the dynamic ranking priority system ([0011] calculating a risk score that indicates a risk associated with the user account of the user; [0021] the penalty to be administered to the user account corresponding to the user is based on a number of previous policy violations; [0079] process 200 can determine whether the content creator is eligible to use the feature based on a combination of a risk score associated with the content creator, information indicating a size of an audience of the content creator, information indicating previous violations of policies by the content creator, and/or a manual review of content item(s).; [0022] In some embodiments, the penalty to be administered to the user account corresponding to the user based on a severity of the at least one policy associated with the media content platform violated by the user-generated content.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Matsuoka with Scott-Green feature(s) listed above. One would’ve been motivated to do so in order to inhibit the user-generated content from being made available on the media content platform (Scott-Green; [0018]). By incorporating the teachings of Scott-Green, one would’ve been able to calculate sentiment score based on penalties from previous abuse with a corresponding magnitude.
Regarding claims 14: The combination of Matsuoka, Scott-Green and Bhan teaches the limitations of claim 12. Matsuoka further teaches:
further comprising training, by the computing device, the ML model based on historical tickets to provide an accurate prediction for the dynamic re-ranking, ([0039] In an embodiment, the task facilitation service 102 can prompt the member 118 to indicate a level or other measure of trust in delegating tasks to others, such as a representative and/or third-party. For instance, the task facilitation service 102 may utilize the identifying information submitted by the member 118 during the onboarding process to identify initial categories of tasks that may be relevant to the member's day-to-day life. In some instances, the task facilitation service 102 can utilize a machine learning algorithm or artificial intelligence to identify the categories of tasks that may be of relevance to the member 118. For instance, the task facilitation service 102 may implement a clustering algorithm to identify similarly situated members based on one or more vectors (e.g., geographic location, demographic information, likelihood to delegate tasks to others, family composition, home composition, etc.). In some instances, a dataset of input member characteristics corresponding to responses to prompts provided by the task facilitation service 102 provided by sample members (e.g., testers, etc.) may be analyzed using a clustering algorithm to identify different types of members that may interact with the task facilitation service 102. Example clustering algorithms that may trained using sample member datasets (e.g., historical member data, hypothetical member data, etc.) to classify a member in order to identify categories of tasks that may be of relevance to the member may include a k-means clustering algorithms, fuzzy c-means (FCM) algorithms, expectation-maximization (EM) algorithms, hierarchical clustering algorithms, density-based spatial clustering of applications with noise (DBSCAN) algorithms, and the like. Based on the output of the machine learning algorithm generated using the member's identifying information, the task facilitation service 102 may prompt the member 118 to provide responses as to a comfort level in delegating tasks corresponding to the categories of tasks provided by the machine learning algorithm. This may reduce the number of prompts provided to the member 118 and better tailor the prompts to the member's needs.; [0066] the task recommendation system 112 may utilize historical task data and corresponding messages from the task datastore 110 to train the NLP or other artificial intelligence to identify possible tasks.; [0090] various operations performed by the representative 106 may be additionally, or alternatively, performed using one or more machine learning algorithms or artificial intelligence. For example, as the representative 106 performs or otherwise coordinates performance of tasks on behalf of a member 118 over time, the task facilitation service 102 may continuously and automatically update the member profile according to member feedback related to the performance of these tasks by the representative 106 and/or third-party services 116.; [0023] FIG. 4 shows an illustrative example of an environment in which a task recommendation system generates and ranks recommendations for tasks to be performed for the benefit of a member in accordance with at least one embodiment; [0164] the task selection sub-system 404 can use these responses to tasks recommended to the member 118 to further train or reinforce the machine learning algorithm or artificial intelligence utilized by the task ranking sub-system 406 to generate task recommendations that can be presented to the member 118 and other similarly situated members of the task facilitation service.);
wherein the ML model is a linear regression model which computes a linear relationship between a dependent variable and one or more independent features ([0302] machine learning or artificial intelligence methods may include regression analysis, dimensionality reduction, meta-learning, reinforcement learning, deep learning, and other such algorithms and/or methods. Examiner notes that one of ordinary skill in the art would reasonably interpret regression analysis to be inclusive of the linear regression which computes a linear relationship between a dependent variable and one or more independent features.);
to output a dynamic re-ranking of the plurality of tickets with the same priority ([0024] FIG. 5 shows an illustrative example of a process for generating new tasks and a ranking of tasks that can be used to determine what tasks are to be presented to a member.).
Regarding claims 15: The combination of Matsuoka, Scott-Green and Bhan teaches the limitations of claim 12. Matsuoka further teaches:
further comprising; performing live monitoring to generate live logs, live events, live video, live audio, and live text; ([0278] This disclosure contemplates the computer system taking any suitable physical form. As example and not by way of limitation, the computer system can be an embedded computer system, a system-on-chip (SOC), a single-board computer system (SBC) (such as, for example, a computer-on-module (COM) or system-on-module (SOM)), a desktop computer system, a laptop or notebook computer system, a tablet computer system, a wearable computer system or interface, an interactive kiosk, a mainframe, a mesh of computer systems, a mobile telephone, a personal digital assistant (PDA), a server, or a combination of two or more of these. Where appropriate, the computer system may include one or more computer systems; be unitary or distributed; span multiple locations; span multiple machines; and/or reside in a cloud computing system which may include one or more cloud components in one or more networks as described herein in association with the computing resources provider 1428. Where appropriate, one or more computer systems may perform without substantial spatial or temporal limitation one or more steps of one or more methods described or illustrated herein. As an example, and not by way of limitation, one or more computer systems may perform in real time or in batch mode one or more steps of one or more methods described or illustrated herein. One or more computer systems may perform at different times or at different locations one or more steps of one or more methods described or illustrated herein, where appropriate.; [0067] For instance, based on an evaluation of data collected from different member sources (e.g., personal fitness or biometric devices, video and audio recordings, etc.); [0236] More generally, however, user-specific data 1002 may include any data collected by task facilitation service 102 regarding the member 118. Such data may include general information regarding member 118, including any characteristics, preferences, and the like. User-specific data 1002 may also include any historic interactions between the member 118 and the task facilitation service 102. Such historic information may include, without limitation, messages send to/from the member 118, chat logs for chats between the member 118 and the representative 106, logs of interactions of the member 118 with the task facilitation service 102, and any other similar historic data that may be used to determine tendencies, preferences, and characteristics of the member 118.);
and performing the ticket resolution based on the type of the plurality of data. ([0172] teach the representative can utilize the task coordination system to generate one or more proposals for resolution of the task; [0173] the task coordination system may maintain proposal templates for different task types, whereby a proposal template for a particular task type may include various data fields associated with the task type.).
Regarding claims 16: The combination of Matsuoka, Scott-Green and Bhan teaches the limitations of claim 15. Matsuoka further teaches:
wherein the type of the plurality of data comprises a plurality of SR tickets ([0066] A topic can include, but is not limited to, a subject, a product, a service, a technical issue, a use question, a complaint, a purchase request, etc. Examiner notes that one of ordinary skill in the art would reasonably interpret a purchase request, as disclosed in Matsuoka, as equivalent to a service request.).
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Matsuoka et al. US (20230037392 A1, hereinafter “Matsuoka”), in view of Scott-Green et al. (US 20200364727 A1, hereinafter “Scott-Green”), in further view of Bhan et al. (US 20230267502 A1, hereinafter “Bhan”) as applied to claim 2 above, in further view of Wolfson et al. (US 20200019455 A1, hereinafter “Wolfson”).
Regarding Claim 3: The combination of Matsuoka, Scott-Green and Bhan teach the method of claim 2. Matsuoka doesn’t teach:
wherein the list of highest priority types comprises a power outage within a region of the dynamic ranking priority system.
Wolfson teaches:
wherein the list of highest priority types comprises a power outage within a region of the dynamic ranking priority system: ([0005] Threats such as those noted can pose great liability and other legal issues to the company owning or operating the datacenter. The cost, legal and public relation penalties could be disastrous to the company.; [0047] Examples of such threats include, but are not limited to, fire, flood, power outages, and gas or chemical leaks. Examiner notes that one of ordinary skill in the art would reasonably interpret “great liability” and “could be disastrous to the company” as high priority situations.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Matsuoka with Wolfson’s feature(s) listed above. One would’ve been motivated to do so in order to implement, and/or cause the implementation of, those preemptive actions (Wolfson; [0047]). By incorporating the teachings of Wolfson, one would’ve been able to assign high priority to power outages.
Regarding Claim 4: The combination of Matsuoka, Scott-Green and Bhan teach the method of claim 2. Matsuoka doesn’t teach:
wherein the list of highest priority types comprises a data center being on fire within a region of the dynamic ranking priority system.
Wolfson further teaches:
wherein the list of highest priority types comprises a data center being on fire within a region of the dynamic ranking priority system ([0005] Threats such as those noted can pose great liability and other legal issues to the company owning or operating the datacenter. The cost, legal and public relation penalties could be disastrous to the company.; [0056] Potential threats to a datacenter include natural and human-caused disasters. In the cases of natural disasters, fires. Examiner notes that one of ordinary skill in the art would reasonably interpret “great liability” and “could be disastrous to the company” as high priority situations.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Matsuoka with Wolfson’s additional feature(s) listed above. One would’ve been motivated to do so in order to implement, and/or cause the implementation of, those preemptive actions (Wolfson; [0047]). By incorporating the teachings of Wolfson, one would’ve been able to assign high priority to fires in a datacenter facility.
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Matsuoka et al. US (20230037392 A1, hereinafter “Matsuoka”), in view of Scott-Green et al. (US 20200364727 A1, hereinafter “Scott-Green”), in further view of Bhan et al. (US 20230267502 A1, hereinafter “Bhan”), as applied to claims 1/12 above, in further view of Cella et al. (US 20230222531 A1, hereinafter “Cella”).
Regarding claims 5: The combination of Matsuoka, Scott-Green and Bhan teach the method of claim 1. Matsuoka further teaches:
further comprising training, by the computing device, the ML model based on historical tickets to provide an accurate prediction for the dynamic re-ranking, ([0039] In an embodiment, the task facilitation service 102 can prompt the member 118 to indicate a level or other measure of trust in delegating tasks to others, such as a representative and/or third-party. For instance, the task facilitation service 102 may utilize the identifying information submitted by the member 118 during the onboarding process to identify initial categories of tasks that may be relevant to the member's day-to-day life. In some instances, the task facilitation service 102 can utilize a machine learning algorithm or artificial intelligence to identify the categories of tasks that may be of relevance to the member 118. For instance, the task facilitation service 102 may implement a clustering algorithm to identify similarly situated members based on one or more vectors (e.g., geographic location, demographic information, likelihood to delegate tasks to others, family composition, home composition, etc.). In some instances, a dataset of input member characteristics corresponding to responses to prompts provided by the task facilitation service 102 provided by sample members (e.g., testers, etc.) may be analyzed using a clustering algorithm to identify different types of members that may interact with the task facilitation service 102. Example clustering algorithms that may trained using sample member datasets (e.g., historical member data, hypothetical member data, etc.) to classify a member in order to identify categories of tasks that may be of relevance to the member may include a k-means clustering algorithms, fuzzy c-means (FCM) algorithms, expectation-maximization (EM) algorithms, hierarchical clustering algorithms, density-based spatial clustering of applications with noise (DBSCAN) algorithms, and the like. Based on the output of the machine learning algorithm generated using the member's identifying information, the task facilitation service 102 may prompt the member 118 to provide responses as to a comfort level in delegating tasks corresponding to the categories of tasks provided by the machine learning algorithm. This may reduce the number of prompts provided to the member 118 and better tailor the prompts to the member's needs.; [0066] the task recommendation system 112 may utilize historical task data and corresponding messages from the task datastore 110 to train the NLP or other artificial intelligence to identify possible tasks.; [0090] various operations performed by the representative 106 may be additionally, or alternatively, performed using one or more machine learning algorithms or artificial intelligence. For example, as the representative 106 performs or otherwise coordinates performance of tasks on behalf of a member 118 over time, the task facilitation service 102 may continuously and automatically update the member profile according to member feedback related to the performance of these tasks by the representative 106 and/or third-party services 116.; [0023] FIG. 4 shows an illustrative example of an environment in which a task recommendation system generates and ranks recommendations for tasks to be performed for the benefit of a member in accordance with at least one embodiment; [0164] the task selection sub-system 404 can use these responses to tasks recommended to the member 118 to further train or reinforce the machine learning algorithm or artificial intelligence utilized by the task ranking sub-system 406 to generate task recommendations that can be presented to the member 118 and other similarly situated members of the task facilitation service.);
to provide dynamic re-ranking of the plurality of tickets with the same priority ([0069] teaches the task recommendation system 112 may rank the listing of the set of tasks based on the level of urgency for completion of each task.).
Matusoka doesn’t teach:
wherein the ML model is a random forest model which combines multiple decision trees.
Cella teaches:
wherein the ML model is a random forest model which combines multiple decision trees ([0930] teaches an expert agent may include one or more machine-learned models (e.g., neural networks, prediction models, classification models, Bayesian models, Gaussian models, decision trees, random forests, and the like, including any of the artificial intelligence systems, expert systems, or the like described throughout this disclosure and/or the documents incorporated herein by reference)).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Matsuoka with Cella’s additional feature(s) listed above. One would’ve been motivated to do so in order to perform machine-learning tasks, including robotic process automation, in connection with a defined role (Cella; [0930]). By incorporating the teachings of Cella, one would’ve been able to use a machine learning model with a random forest architecture to assign tasks/tickets.
Regarding claims 13: The combination of Matsuoka, Scott-Green and Bhan teach the computer program product of claim 12. Matsuoka further teaches:
for dynamic re-ranking of the plurality of tickets with the same priority, ([0069] teaches the task recommendation system 112 may rank the listing of the set of tasks based on the level of urgency for completion of each task.);
and wherein the LOTJ model is further trained based on previously captured feedback data, previously captured user data, and previously captured business data. ([0039] In an embodiment, the task facilitation service 102 can prompt the member 118 to indicate a level or other measure of trust in delegating tasks to others, such as a representative and/or third-party. For instance, the task facilitation service 102 may utilize the identifying information submitted by the member 118 during the onboarding process to identify initial categories of tasks that may be relevant to the member's day-to-day life. In some instances, the task facilitation service 102 can utilize a machine learning algorithm or artificial intelligence to identify the categories of tasks that may be of relevance to the member 118. For instance, the task facilitation service 102 may implement a clustering algorithm to identify similarly situated members based on one or more vectors (e.g., geographic location, demographic information, likelihood to delegate tasks to others, family composition, home composition, etc.). In some instances, a dataset of input member characteristics corresponding to responses to prompts provided by the task facilitation service 102 provided by sample members (e.g., testers, etc.) may be analyzed using a clustering algorithm to identify different types of members that may interact with the task facilitation service 102. Example clustering algorithms that may trained using sample member datasets (e.g., historical member data, hypothetical member data, etc.) to classify a member in order to identify categories of tasks that may be of relevance to the member may include a k-means clustering algorithms, fuzzy c-means (FCM) algorithms, expectation-maximization (EM) algorithms, hierarchical clustering algorithms, density-based spatial clustering of applications with noise (DBSCAN) algorithms, and the like. Based on the output of the machine learning algorithm generated using the member's identifying information, the task facilitation service 102 may prompt the member 118 to provide responses as to a comfort level in delegating tasks corresponding to the categories of tasks provided by the machine learning algorithm. This may reduce the number of prompts provided to the member 118 and better tailor the prompts to the member's needs.; [0066] the task recommendation system 112 may utilize historical task data and corresponding messages from the task datastore 110 to train the NLP or other artificial intelligence to identify possible tasks.; [0090] various operations performed by the representative 106 may be additionally, or alternatively, performed using one or more machine learning algorithms or artificial intelligence. For example, as the representative 106 performs or otherwise coordinates performance of tasks on behalf of a member 118 over time, the task facilitation service 102 may continuously and automatically update the member profile according to member feedback related to the performance of these tasks by the representative 106 and/or third-party services 116.; [0071] In an embodiment, the task recommendation system 112 can automatically select one or more of the tasks for presentation to the member 118 via a task-specific interface without representative interaction. For instance, the task recommendation system 112 may utilize a machine learning algorithm or artificial intelligence to select which tasks from the listing of the set of tasks previously ranked by the task recommendation system 112 may be presented to the member 118 through task-specific interfaces. As an illustrative example, the task recommendation system 112 may use the member profile corresponding to the member 118 (which can include historical data corresponding to member-representative communications, member feedback corresponding to representative performance and presented tasks/proposals, etc.), from the user datastore 108, tasks currently in progress for the member 118, and the listing of the set of tasks as input to the machine learning algorithm or artificial intelligence. The output generated by the machine learning algorithm or artificial intelligence may indicate which tasks of the listing of the set of tasks are to be presented automatically to the member 118 via task-specific interfaces corresponding to these tasks. As the member 118 interacts with these newly presented tasks, the task recommendation system 112 may record these interactions and use these interactions to further train the machine learning algorithm or artificial intelligence to better determine which tasks to present to member 118 and other similarly situated members.; [0102] The machine learning algorithm or artificial intelligence may be trained using unsupervised training techniques. For instance, a dataset of input member attributes and representative attributes may be analyzed using a clustering algorithm to identify correlations between different types of members and representatives. Conversely, the dataset of input member attributes and representative attributes may also be analyzed using a clustering algorithm to identify the types of members and types of representatives that are not well-suited for each other. [0140] The resource library may include entries corresponding to businesses and/or products previously used by representatives for proposals related to particular tasks or task types or that are otherwise associated with particular tasks or task types. Examiner notes that one of ordinary skill in the art would reasonably consider representative attributes, as disclosed in Matsuoka, as equivalent to business data.).
Matsuoka doesn’t teach:
wherein the ML model is a random forest model which combines multiple decision trees
Cella teaches:
wherein the ML model is a random forest model which combines multiple decision trees ([0930] teaches an expert agent may include one or more machine-learned models (e.g., neural networks, prediction models, classification models, Bayesian models, Gaussian models, decision trees, random forests, and the like, including any of the artificial intelligence systems, expert systems, or the like described throughout this disclosure and/or the documents incorporated herein by reference)).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Matsuoka with Cella’s additional feature(s) listed above. One would’ve been motivated to do so in order to perform machine-learning tasks, including robotic process automation, in connection with a defined role (Cella; [0930]). By incorporating the teachings of Cella, one would’ve been able to use a machine learning model with a random forest architecture to assign tasks/tickets.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Matsuoka et al. US (20230037392 A1, hereinafter “Matsuoka”), in view of Scott-Green et al. (US 20200364727 A1, hereinafter “Scott-Green”), in further view of Bhan et al. (US 20230267502 A1, hereinafter “Bhan”), in further view of Carr (US 20220058561 A1, hereinafter “Carr”).
Regarding claim 20: Matsuoka teaches 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 ([0013] a system includes one or more processors and memory including instructions that, as a result of being executed by the one or more processors, cause the system to perform the processes described herein. In another aspect, a non-transitory computer-readable storage medium) with limitations for:
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: ([0013] In another aspect of this disclosure, a system includes one or more processors and memory including instructions that, as a result of being executed by the one or more processors, cause the system to perform the processes described herein.) input a plurality of data and a weightage reallocation to a dynamic ranking priority system; ([0040] teaches a system that uses the member's identifying information, any information related to the member's level of comfort or interest in delegating tasks to others, and any other information obtained during the onboarding process as input to a classification or clustering algorithm configured to identify representatives that may be well-suited to interact and communicate with the member 118; [0041] based on historical data corresponding to member interactions with representatives, the representative assignment system 104 may identify correlations between different factors and the polarities of these interactions (e.g., positive, negative, etc.). Based on these correlations (or lack thereof), the representative assignment system 104 may apply a weight to each factor.);
calculate a plurality of sentiment scores based on authentication credentials of a user, ([0042] The scores determined for the various factors may be aggregated to obtain a composite score for each representative of the set of representatives 106. These composite scores may be used to create the ranking of the set of representatives 106.; [0038] During the onboarding process, the task facilitation service 102 may collect identifying information of the member 118, which may be used by a representative assignment system 104 to identify and assign a representative 106 to the member 118. Examiner notes that one of ordinary skill in the art would reasonably consider the identifying information disclosed by Matsuoka to be equivalent to authentication credentials of a user from Applicant’s claim.) biometric identification of the user, and natural language of the user within the plurality of data ([0110] the member-representative pairing sub-system 206 may process the obtained feedback using a machine learning algorithm or artificial intelligence to determine a relationship score for the relationship between the member 118 and the assigned representative)
dynamically re-rank a plurality of tickets with a same priority using a machine learning (ML) model ([0023] FIG. 4 shows an illustrative example of an environment in which a task recommendation system generates and ranks recommendations for tasks to be performed for the benefit of a member; [0127] the task recommendation system 112 may use the priority of each of the projects created for the member as another factor in ranking; [0138] the task facilitation service 102 utilizes a machine learning algorithm or artificial intelligence.) based on a type of the plurality of data which is most similar to historical data ([0138] the task facilitation service 102 may use, as input to the machine learning algorithm or artificial intelligence, a member profile or model associated with the member, historical task data for the member (e.g., previously completed tasks, tasks for which proposals have been provided, etc.), and information corresponding to the task for which a proposal is being generated (e.g., a task type or category, etc.).) and a plurality of remaining sentiment scores which are equal to or below the predetermined threshold; ([0110] if the member-representative pairing sub-system 206 determines, based on the relationship score for a particular member-representative pairing (e.g., the relationship score is below a threshold value, etc.), that the member is to be assigned a new representative, the member-representative pairing sub-system 206 may select a new representative that may be assigned to the member).
receive feedback from at least one of a support staff and a subject matter expert (SME) regarding the plurality of data; ([0049] teaches the representative may modify the member profile to provide notes about the member 118 (e.g., the member's idiosyncrasies, any feedback regarding the member, etc.).);
train a learn on the job (LOTJ) model based on previously captured ticket data; ([0039] In an embodiment, the task facilitation service 102 can prompt the member 118 to indicate a level or other measure of trust in delegating tasks to others, such as a representative and/or third-party. For instance, the task facilitation service 102 may utilize the identifying information submitted by the member 118 during the onboarding process to identify initial categories of tasks that may be relevant to the member's day-to-day life. In some instances, the task facilitation service 102 can utilize a machine learning algorithm or artificial intelligence to identify the categories of tasks that may be of relevance to the member 118. For instance, the task facilitation service 102 may implement a clustering algorithm to identify similarly situated members based on one or more vectors (e.g., geographic location, demographic information, likelihood to delegate tasks to others, family composition, home composition, etc.). In some instances, a dataset of input member characteristics corresponding to responses to prompts provided by the task facilitation service 102 provided by sample members (e.g., testers, etc.) may be analyzed using a clustering algorithm to identify different types of members that may interact with the task facilitation service 102. Example clustering algorithms that may trained using sample member datasets (e.g., historical member data, hypothetical member data, etc.) to classify a member in order to identify categories of tasks that may be of relevance to the member may include a k-means clustering algorithms, fuzzy c-means (FCM) algorithms, expectation-maximization (EM) algorithms, hierarchical clustering algorithms, density-based spatial clustering of applications with noise (DBSCAN) algorithms, and the like. Based on the output of the machine learning algorithm generated using the member's identifying information, the task facilitation service 102 may prompt the member 118 to provide responses as to a comfort level in delegating tasks corresponding to the categories of tasks provided by the machine learning algorithm. This may reduce the number of prompts provided to the member 118 and better tailor the prompts to the member's needs.; [0066] the task recommendation system 112 may utilize historical task data and corresponding messages from the task datastore 110 to train the NLP or other artificial intelligence to identify possible tasks.; [0090] various operations performed by the representative 106 may be additionally, or alternatively, performed using one or more machine learning algorithms or artificial intelligence. For example, as the representative 106 performs or otherwise coordinates performance of tasks on behalf of a member 118 over time, the task facilitation service 102 may continuously and automatically update the member profile according to member feedback related to the performance of these tasks by the representative 106 and/or third-party services 116. Examiner notes that one of ordinary skill in the art would interpret the continuous learning from feedback taught by Matsuoka to be equivalent to an LOTJ model as disclosed in Applicant’s claims.);
dynamically change a pre-allocated weightage ([0302] machine learning or artificial intelligence algorithms include, but are not limited to, genetic algorithms, backpropagation, reinforcement learning, decision trees, liner classification, artificial neural networks, anomaly detection, and such.) using a learn on the job (LOTJ) model based on a ticket resolution to perform based on the plurality of data and the feedback from the at least one of the support staff and the SME ([0090] various operations performed by the representative 106 may be additionally, or alternatively, performed using one or more machine learning algorithms or artificial intelligence. For example, as the representative 106 performs or otherwise coordinates performance of tasks on behalf of a member 118 over time, the task facilitation service 102 may continuously and automatically update the member profile according to member feedback related to the performance of these tasks by the representative 106 and/or third-party services 116. Examiner notes that one of ordinary skill in the art would interpret the continuous learning from feedback taught by Matsuoka to be equivalent to an LOTJ model as disclosed in Applicant’s claims.).
and adjust the weight reallocation based on the dynamically changed pre-allocated weightage, ([0302] Other examples of machine learning or artificial intelligence algorithms include, but are not limited to, genetic algorithms, backpropagation, reinforcement learning, decision trees, liner classification, artificial neural networks, anomaly detection, and such. More generally, machine learning or artificial intelligence methods may include regression analysis, dimensionality reduction, meta-learning, reinforcement learning, deep learning, and other such algorithms and/or methods.), wherein the previously captured ticket data comprises at least one of an incident, problem, and change (IPC) ticket or a service request (SR) ticket. ([0037] a member 118, via a computing device 120 (e.g., laptop computer, smartphone, etc.), may submit a request to the task facilitation service 102 to initiate an onboarding process for assignment of a representative 106 to the member 120 and to initiate identification of tasks that are performable for the benefit of the member 118.; [0066] In an embodiment, the data collected from a member 118 over a chat session with the representative may be evaluated by the task recommendation system 112 to identify one or more tasks that may be presented to the member 118 for completion. For instance, the task recommendation system 112 may utilize natural language processing (NLP) or other artificial intelligence to evaluate received messages or other communications from the member 118 to identify an intent. An intent may correspond to an issue that a member 118 wishes to have resolved. Examples of intents can include (for example) topic, sentiment, complexity, and urgency. A topic can include, but is not limited to, a subject, a product, a service, a technical issue, a use question, a complaint, a purchase request, etc. An intent can be determined, for example, based on a semantic analysis of a message (e.g., by identifying keywords, sentence structures, repeated words, punctuation characters and/or non-article words); user input (e.g., having selected one or more categories); and/or message-associated statistics (e.g., typing speed and/or response latency).; [0076] In an embodiment, the task recommendation system 112, periodically (e.g., monthly, bi-monthly, etc.) or in response to a triggering event (e.g., a set number of tasks are performed, member request, etc.), selects a set of experiences that may be recommended to the member 118.; [0092] FIG. 2 shows an illustrative example of an environment 200 in which a representative assignment system 104 performs an onboarding process for a member 118 and assigns a representative 106 to the member 118 based on member and representative attributes in accordance with at least one embodiment. In the environment 200, in response to a request from a member 118 to initiate an onboarding process to create an account with the task facilitation service, the representative assignment system 104 of the task facilitation service may transmit one or more onboarding prompts to the member 118 to gather information about the member 118 that may be used to create a member profile and to identify possible tasks that may be presented to the member 118 based on the member profile. For instance, as illustrated in FIG. 2, the member 118 may submit its request to a member onboarding sub-system 202 of the representative assignment system 104.; [0046] As described in greater detail herein, an entry in the user datastore 108 may further include historical data corresponding to communications between the member 118 and the assigned representative made over time. For instance, as a member 118 interacts with a representative 106 over a chat session or stream, messages exchanged over the chat session or stream may be recorded in the user datastore 108.).
Matsuoka doesn’t explicitly teach the following limitations:
calculate a plurality of sentiment scores based on biometric identification of the user, and a corresponding plurality of penalty scores which corresponds with a number of times the user has previously abused the dynamic ranking priority system based on the plurality of data;
eliminate a sentiment score of the corresponding plurality of sentiment scores in response to a corresponding penalty score of the corresponding plurality of penalty scores being greater than a predetermined threshold;
Scott-Green teaches:
calculate a plurality of sentiment scores based on biometric identification of the user, and a corresponding plurality of penalty scores which corresponds with a number of times the user has previously abused the dynamic ranking priority system based on the plurality of data; ([0011] calculating a risk score that indicates a risk associated with the user account of the user; [0021] the penalty to be administered to the user account corresponding to the user is based on a number of previous policy violations);
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine Matsuoka with Scott-Green feature(s) listed above. One would’ve been motivated to do so in order to identify abusive content (Scott-Green; [Abstract]). By incorporating the teachings of Scott-Green, one would’ve been able to calculate sentiment score based on penalties from previous abuse.
Scott-Green doesn’t teach:
calculate a plurality of sentiment scores based on biometric identification of the user,
eliminating, by the computing device, a sentiment score of the corresponding plurality of sentiment scores in response to a corresponding penalty score of the corresponding plurality of penalty scores being greater than a predetermined threshold;
Bhan teaches:
eliminating, by the computing device, a sentiment score of the corresponding plurality of sentiment scores in response to a corresponding penalty score of the corresponding plurality of penalty scores being greater than a predetermined threshold; ([0016] In accordance with a first example embodiment, a method of generating a transitory sentiment community. The method comprises receiving data, in a database memory associated with a server computing device, the data extracted from a plurality of data sources, pre-processing the data, in a processor of the server computing device based on at least one of text character removal and text character replacement, to provide pre-processed data that includes a set of keywords used in a descriptive manner, performing a sentiment analysis on the set of keywords based at least in part upon a training model, the sentiment analysis identifying: (i) a conformance to at least one sentiment classification of a set of sentiment classifications recognized by the training model, and (ii) a sentiment intensity rating associated with the conformance, modifying the sentiment intensity rating associated with the at least one sentiment classification upon detecting a sarcasm sentiment that is above a sarcasm sentiment likelihood threshold, and generating the transitory sentiment community based at least in part on the at least one sentiment classification and the modified sentiment intensity rating.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Matsuoka with Bhan’s feature(s) listed above. One would’ve been motivated to do so in order to generate and capture a transitory sentiment community at a server computing device (Bhan; [0015]). By incorporating the teachings of Bhan, one would’ve been able to eliminate sentiment scores to when the penalty is greater than a threshold.
Bhan doesn’t teach:
calculate a plurality of sentiment scores based on biometric identification of the user,
Carr teaches:
calculate a plurality of sentiment scores based on biometric identification of the user, ([0134] Sentiment analysis may generally include the use of computational natural language processing, text analysis, computational linguistics, and/or biometrics to systematically identify, extract, quantify, and study affective states and subjective information. In some implementations, change management process 10 may perform sentiment analysis on one or more portions of the communications (e.g., communications 124) from the plurality of recipients (e.g., plurality of recipients 120) to define the one or more sentiment metrics using a machine learning model).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Matsuoka with Carr’s feature(s) listed above. One would’ve been motivated to do so in order to determine that the one or more portions include a sentiment metric of “positive”, “negative”, and/or “neutral” (Carr; [0135]). By incorporating the teachings of Carr, one would’ve been able to calculate sentiment scores based on biometrics.
Accordingly, claims 1-20 are rejected under 35 USC 103.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Biswas et al. (WO 2021165430 A1), which discloses methods and apparatuses for interactive communication between a user device and a server, including steps for determining a final sentiment score based on comparing a plurality of sentiment scores.
C. C. A. Blaz and K. Becker, "Sentiment Analysis in Tickets for IT Support," 2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR), Austin, TX, USA, 2016, pp. 235-246, which discloses a method to evaluate the sentiment contained in tickets for IT (Information Technology) support.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GABRIEL J TORRES CHANZA whose telephone number is (571)272-3701. The examiner can normally be reached Monday thru Friday 8am - 5pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached on (571)270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/G.J.T./Examiner, Art Unit 3625
/SARA GRACE BROWN/Primary Examiner, Art Unit 3625
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