DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-20 are pending in the instant patent application.
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.
Regarding Claims 1-7, they are directed to a method, however the claims are directed to a judicial exception without significantly more. Claims 1-7 are directed to the abstract idea of generating advice plans for a company.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 1, claim 1 recites receiving, an advice state of a user; generating, a first health score of the advice state; determining, an action by optimizing a likelihood of improving the first health score, wherein the action is one of a plurality of actions in an advice library, the determining comprising: applying a policy to the plurality of actions to map at least one of the plurality of actions to the advice state; determining, by an estimator, the likelihood of improving the first health score by the at least one of the plurality of actions; generating a ranking of the likelihood of improving the first health score by an amount of improvement; and selecting the action from the at least one of the plurality of actions with a highest ranked likelihood of improving the first health score; generating, an action plan including the advice state and the action; presenting, the action plan including a graphical icon representing the action; receiving, a selection of the graphical icon representing the action; generating, a second health score responsive to the selection.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be practically performed in the human mind (including an observation, evaluation, judgment, opinion) and/or with pen/paper. Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind (see MPEP 2106.04(a)(2)(III)(C)).
Accordingly, the claim recites an abstract idea and dependent claims 2-7 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of a computing device, a user interface and training the computer device based on the second health score. The computing device, a user interface and training the computer device based on the second health score are merely generic computing devices and do not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 1 and 7 include various elements that are not directed to the abstract idea under 2A. These elements include computing device, a user interface and training the computer device based on the second health score, training the computing device based on the third health score and the generic computing elements described in the Applicant's specification in at least Para 0055-0063. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions. In addition, Claim 1 recites computer functions that the courts have recognized as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (See MPEP 2106.05(d)(ii)…at least, Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network))).
Therefore, Claims 1 and 7 alone or in combination, are not drawn to eligible subject matter as they are directed to abstract ideas without significantly more.
Regarding Claims 8-13, they are directed to a method, however the claims are directed to a judicial exception without significantly more. Claims 8-13 are directed to the abstract idea of generating advice plans for a company.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 8, claim 8 recites receiving, a profile of a user; receiving, an advice state of the user; generating, a first health score of the user, the first health score incorporating the profile of the user and the advice state; calculating, by a per-item estimator a likelihood of a plurality of actions to change the advice state; applying, a policy to the plurality of actions to determine a first action, the policy comprising an Epsilon-Greedy algorithm; generating, an action plan incorporating the first action and the advice state; and updating, the advice state responsive to the first action.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be practically performed in the human mind (including an observation, evaluation, judgment, opinion) and/or with pen/paper. Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind (see MPEP 2106.04(a)(2)(III)(C)).
Accordingly, the claim recites an abstract idea and dependent claims 9-13 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of a computing device. The computing device is merely a generic computing device and does not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 8, 12 and 13 include various elements that are not directed to the abstract idea under 2A. These elements include computing device, a user interface, training the computer device based on the updated advice state and the generic computing elements described in the Applicant's specification in at least Para 0055-0063. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions. In addition, Claim 8 recites computer functions that the courts have recognized as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (See MPEP 2106.05(d)(ii)…at least, Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network))).
Therefore, Claims 8 and 12-13 alone or in combination, are not drawn to eligible subject matter as they are directed to abstract ideas without significantly more.
Regarding Claims 14-20, they are directed to a non-transitory storage medium, however the claims are directed to a judicial exception without significantly more. Claims 14-20 are directed to the abstract idea of generating advice plans for a company.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 14, claim 14 recites receiving, an advice state of a user; generating, a first health score of the advice state; determining, an action by optimizing a likelihood of improving the first health score, wherein the action is one of a plurality of actions in an advice library, the determining comprising: applying a policy to the plurality of actions to map at least one of the plurality of actions to the advice state; determining, by an estimator, the likelihood of improving the first health score by the at least one of the plurality of actions; generating a ranking of the likelihood of improving the first health score by an amount of improvement; and selecting the action from the at least one of the plurality of actions with a highest ranked likelihood of improving the first health score; generating, an action plan including the advice state and the action; presenting, the action plan including a graphical icon representing the action; receiving, a selection of the graphical icon representing the action; generating, a second health score responsive to the selection.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be practically performed in the human mind (including an observation, evaluation, judgment, opinion) and/or with pen/paper. Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind (see MPEP 2106.04(a)(2)(III)(C)).
Accordingly, the claim recites an abstract idea and dependent claims 15-20 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of a computing device, a user interface and training the computer device based on the second health score. The computing device, a user interface and training the computer device based on the second health score are merely generic computing devices and do not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 14 and 20 include various elements that are not directed to the abstract idea under 2A. These elements include computing device, a user interface, training the computer device based on the second health score, training the computing device based on the third health score and the generic computing elements described in the Applicant's specification in at least Para 0055-0063. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions. In addition, Claim 14 recites computer functions that the courts have recognized as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (See MPEP 2106.05(d)(ii)…at least, Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network))).
Therefore, Claims 14 and 20 alone or in combination, are not drawn to eligible subject matter as they are directed to abstract ideas without significantly more.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-4 and 14-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stroh (US 12,333,599 B1) in view of Ridlington et al. (US 2020/0410596 A1) further in view of Evans et al. (US 2022/0028003 A1).
Regarding Claim 1, Stroh teaches the limitations of Claim 1 which state
receiving, by a computing device, an advice state of a user (Stroh: Col 4 lines 46-60 via the computing system 100 includes an action planning engine 135 that can initially provide a customized and detailed financial action plan for each client 190 based on a global view of that client's 190 current financial situation. The initial individualized action plan can be generated by the action planning engine 135 based on the financial records of the client 190, which the planning engine 135 can access or receive from the client's device 185, or any accounts from financial entities associated with the client 190. In one example, the client device 185 can execute an application that enables the computing system 100 to link with the various accounts associated with the client 190, such that the action planning engine 135 can create and store a client profile 146 for the client 190);
generating, by the computing device, a first health score of the advice state (Stroh: Col 6 lines 17-30 via the computing system 100 can include a scoring engine 125 that executing financial scoring logic to generate a financial health score for the client 190. The financial scoring logic can receive, as input, current financial data pertaining to the client 190 (e.g., as stored in the client's profile 146 in the database 140), the current individualized action plan of the client 190, and any recent updates in the client's 190 profile 146 that indicates recent financial changes described above. In various examples, the scoring engine 125 can be triggered each instance an update is detected in the client's 190 financial situation, and can include such granular aspects as making a goods purchase, receiving a paycheck, making a debt payment, paying off a debt account, and the like);
determining, by the computing device, an action by optimizing a likelihood of improving the first health score (Stroh: Col 5 lines 9-14, 25-37 via the action planning engine 135 can receive the financial records, described herein, of the client 190 to generate an individualized financial action plan for the client 190 to follow when steadying, improving upon, or maintaining financial health (e.g., repaying debt, managing payments, building credit, saving, budgeting, etc.)…the action planning engine 135 provides a granular financial action plan for the client 190 that can be updated dynamically as financial updates of the client 190 are received. In doing so, the action planning engine 135 can execute optimization logic using the client's financial records and updates to dynamically generate a real-time individualized action plan for the client 190. As described in further detail below, the individualized action plan can facilitate the client 190 in generating an optimal budget based on the financial information of the client 190, facilitate in managing and/or paying down debt, assist the client 190 with monthly spending, improving the client's credit, aid the client in savings, and the like);
However, Stroh does not explicitly disclose the limitation of Claim 1 which states wherein the action is one of a plurality of actions in an advice library and applying a policy of the computing device to the plurality of actions to map at least one of the plurality of actions to the advice state.
Ridlington though, with the teachings of Stroh, teaches of
wherein the action is one of a plurality of actions in an advice library (Ridlington: Para 0056 via receive a rule for generating a recommendation using the plurality of factors, and the categories, and using the respective weightings of the plurality of factors and the categories, the rule including a set of possible recommendations (e.g. transfer pension plan, don't transfer pension plan), such that a generated recommendation is one of the set of possible recommendations).
applying a policy of the computing device to the plurality of actions to map at least one of the plurality of actions to the advice state (Ridlington: Para 0056 via receive a plurality of factors (e.g. capacity for loss, state of health) for use in providing automated advice (e.g. financial advice), each factor including a defined respective set of categories, and each factor and each category including a respective initial weighting; receive a rule for generating a recommendation using the plurality of factors, and the categories, and using the respective weightings of the plurality of factors and the categories, the rule including a set of possible recommendations (e.g. transfer pension plan, don't transfer pension plan), such that a generated recommendation is one of the set of possible recommendations; receive a plurality of training cases, each training case including inputs relating to each factor of the plurality of factors, and to the categories, and each training case including a respective validated recommendation which is one of the set of possible recommendations; process the plurality of training cases, to derive a respective optimized weighting for each factor of the plurality of factors, and for each category, to satisfy or to match the respective validated recommendations optimally, using the rule. Thus teaching a policy mapping advice state (factors) to one of multiple recommendations).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Stroh with the teachings of Ridlington in order to have wherein the action is one of a plurality of actions in an advice library and applying a policy of the computing device to the plurality of actions to map at least one of the plurality of actions to the advice state. The motivations behind this being to incorporate the teachings of utilizing factors to provide automated financial advice. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
In addition, Stroh does not explicitly disclose the limitations of Claim 1 which state determining, by an estimator of the computing device, the likelihood of improving the first health score by the at least one of the plurality of actions; generating a ranking of the likelihood of improving the first health score by an amount of improvement; and selecting the action from the at least one of the plurality of actions with a highest ranked likelihood of improving the first health score.
Evans though, with the teachings of Stroh/Ridlington, teaches of
determining, by an estimator of the computing device, the likelihood of improving the first health score by the at least one of the plurality of actions (Evans: Para 0145 via Each respective one financial strategy model of the set of financial strategy models is then ranked and prioritized and evaluated based the efficacy of the strategy through ranking criteria based on constraints and objectives, as discussed herein, in addition to weighing and calculating the impact on the overall or holistic financial plan, thereby providing a “cost and benefit” score that allows all the applicable strategies to be ranked such that the top ranked strategy represents the next best financial decision for the client);
generating a ranking of the likelihood of improving the first health score by an amount of improvement (Evans: Para 0145 via Each respective one financial strategy model of the set of financial strategy models is then ranked and prioritized and evaluated based the efficacy of the strategy through ranking criteria based on constraints and objectives, as discussed herein, in addition to weighing and calculating the impact on the overall or holistic financial plan, thereby providing a “cost and benefit” score that allows all the applicable strategies to be ranked such that the top ranked strategy represents the next best financial decision for the client); and
selecting the action from the at least one of the plurality of actions with a highest ranked likelihood of improving the first health score (Evans: Para 0031-0033 via ranking each respective one of the set of modified financial plans relative to each other based on said respective score; and h) selecting a highest ranked modified financial plan of the set of modified financial plans; and i) modifying the display to show the highest ranked modified financial plan).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Stroh/Ridlington with the teachings of Evans in order to have determining, by an estimator of the computing device, the likelihood of improving the first health score by the at least one of the plurality of actions; generating a ranking of the likelihood of improving the first health score by an amount of improvement; and selecting the action from the at least one of the plurality of actions with a highest ranked likelihood of improving the first health score. The motivations behind this being to incorporate the teachings of a financial planning system that incorporates artificial intelligence techniques to analyze client inputs and select appropriate financial strategies based on the analysis of the inputs. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
The combination of Stroh/Ridlington/Evans further teaches the limtiations of Claim 1 which state
generating, by the computing device, an action plan including the advice state and the action (Stroh: Col 5 lines 9-24 via the action planning engine 135 can receive the financial records, described herein, of the client 190 to generate an individualized financial action plan for the client 190 to follow when steadying, improving upon, or maintaining financial health (e.g., repaying debt, managing payments, building credit, saving, budgeting, etc.). As described below, the individualized action plan can be accessed by the client 190 via an executing financial service application provided by the computing system 100. In certain implementations, the client 190 can input a set of personal goals, such as purchasing a home or paying off debt, which the action planning engine 135 can utilize to construct the customized action plan for the client 190. Accordingly, based on the current financial records of the client 190, the action planning engine 135 can generate an optimal budget, debt payment strategy, debt refinancing strategy).
presenting, via a user interface, the action plan including a graphical icon representing the action (Stroh: Col 10 lines 20-41 via Additional features of the individualized action plan 350 are also contemplated. For example, if the client 190 owns a home and is struggling to pay down high interest debt (e.g., credit card debt), the optimization logic executed by the action planning engine 135 described in FIG. 1 can determine an optimal manner in which to refinance the high-interest debt, such as taking out a HELOC at a significantly lower interest rate to pay off the credit card debt balance outright. Upon determining such optimal actions for the client 190, the interactive user interface 242 displaying the individualized action plan 350 can further provide selectable links to third-party or internal services (e.g., causing a corresponding application on the client's computing device 185 to be launched) that enable the client 190 to readily undertake the process of completing the optimal action. In the example of a HELOC, the individualized action plan 350 can provide a selectable icon that, when selected, links the client 190 to one or multiple HELOC providers, showing offered rates based on the client's 190 current financial situation, which can be provided to the HELOC provider(s) automatically by the computing system 100);
receiving, via the user interface, a selection of the graphical icon representing the action (Stroh: Col 10 lines 20-41 via Additional features of the individualized action plan 350 are also contemplated. For example, if the client 190 owns a home and is struggling to pay down high interest debt (e.g., credit card debt), the optimization logic executed by the action planning engine 135 described in FIG. 1 can determine an optimal manner in which to refinance the high-interest debt, such as taking out a HELOC at a significantly lower interest rate to pay off the credit card debt balance outright. Upon determining such optimal actions for the client 190, the interactive user interface 242 displaying the individualized action plan 350 can further provide selectable links to third-party or internal services (e.g., causing a corresponding application on the client's computing device 185 to be launched) that enable the client 190 to readily undertake the process of completing the optimal action. In the example of a HELOC, the individualized action plan 350 can provide a selectable icon that, when selected, links the client 190 to one or multiple HELOC providers, showing offered rates based on the client's 190 current financial situation, which can be provided to the HELOC provider(s) automatically by the computing system 100);
generating, by the computing device, a second health score responsive to the selection (Stroh: Col 11 lines 4-15 via FIG. 4B is a flow chart describing an example method of dynamically updating a financial health score of a client 190. In various examples, the computing system 100 can generate and dynamically update a financial health score for a client 190 based on financial updates and a current individualized action plan 350 (425). Based on a transaction between the client 190 and a transaction entity 180, the computing system 100 can receive a financial score request (430). In one example, the request can be received from the service application 232 executing on the client's 190 computing device 185. In variations, the request can be received from the computing system of the transaction entity 180 (434)).
training the computing device based on the second health score (Ridlington: Para 0065 via According to an eighth aspect of the invention, there is provided a computer implemented method of providing automated advice, such as financial advice, in which weightings of a plurality of factors (e.g. capacity for loss, state of health) for use in providing automated advice, each factor optionally including a defined respective set of categories, and optionally including weightings of the categories, are trained using a plurality of validated training cases, to provide a trained plurality of weightings for the factors, and optionally, trained weightings for the categories, for providing automated advice).
Regarding Claim 2, the combination of Stroh/Ridlington/Evans teaches the limitations of Claim 2 which state
generating profile characteristics of the user, wherein the determining the likelihood of improving the first health score is based on the advice state and the profile characteristics (Stroh: Col 4 line 46 – Col 5 line 8 via According to examples described herein, the computing system 100 includes an action planning engine 135 that can initially provide a customized and detailed financial action plan for each client 190 based on a global view of that client's 190 current financial situation. The initial individualized action plan can be generated by the action planning engine 135 based on the financial records of the client 190, which the planning engine 135 can access or receive from the client's device 185, or any accounts from financial entities associated with the client 190. In one example, the client device 185 can execute an application that enables the computing system 100 to link with the various accounts associated with the client 190, such that the action planning engine 135 can create and store a client profile 146 for the client 190. In various examples, the client profile 146 can comprise identifying information of the client, such as a name, address, contact information, as well as continuously updated financial information, such as current balances of the client's accounts, current assets and liabilities, monthly expenditures, and the like. For example, the financial records of the client 190 can comprise any personal property (e.g., jewelry, gold, memorabilia, vehicles, art assets, etc.), debt obligations (e.g., personal loans or debts to individuals, monthly financed service payments, mortgage payments, etc.), personal income (e.g., wages, received rents, and other income sources), rent and utility payments, monthly bills, and the like. Such information is used by the action planning engine 135 to generate the customized financial action plan for the client 190).
Regarding Claim 3, the combination of Stroh/Ridlington/Evans teaches the limitations of Claim 3 which state
wherein the advice library comprises the plurality of actions and a plurality of advice states related to the plurality of actions (Ridlington: Para 0056 via the processor configured to: (i) receive a plurality of factors (e.g. capacity for loss, state of health) for use in providing automated advice (e.g. financial advice), each factor including a defined respective set of categories, and each factor and each category including a respective initial weighting; (ii) receive a rule for generating a recommendation using the plurality of factors, and the categories, and using the respective weightings of the plurality of factors and the categories, the rule including a set of possible recommendations (e.g. transfer pension plan, don't transfer pension plan), such that a generated recommendation is one of the set of possible recommendations; (iii) receive a plurality of training cases, each training case including inputs relating to each factor of the plurality of factors, and to the categories, and each training case including a respective validated recommendation which is one of the set of possible recommendations; (iv) process the plurality of training cases, to derive a respective optimized weighting for each factor of the plurality of factors, and for each category, to satisfy or to match the respective validated recommendations optimally, using the rule, and (v) store the derived respective optimized weightings for the plurality of factors and for each category).
Regarding Claim 4, the combination of Stroh/Ridlington/Evans teaches the limitations of Claim 4 which state
wherein the plurality of advice states have at least one related key performance indicator, wherein the generating the first health score of the advice state incorporates the at least one key performance indicator (Ridlington: Para 0056 via receive a plurality of factors (e.g. capacity for loss, state of health) for use in providing automated advice (e.g. financial advice), each factor including a defined respective set of categories, and each factor and each category including a respective initial weighting. These are used to produce the overall score and recommendations).
Regarding Claims 14-17, they are analogous to Claims 1-4 and are rejected for the same reasons (Stroh: Col 4 lines 6-16).
Claim(s) 5 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stroh (US 12,333,599 B1) in view of Ridlington et al. (US 2020/0410596 A1) in view of Evans et al. (US 2022/0028003 A1) further in view of DiMaggio et al. (US 2018/0018602 A1).
Regarding Claim 5, while the combination of Stroh/Ridlington/Evans teaches the limitations of Claim 4, it does not explicitly disclose the limitations of Claim 5 which state wherein the determining the likelihood of improving the first health score comprises predicting a change in the at least one key performance indicator.
DiMaggio though, with the teachings of Stroh/Ridlington/Evans, teaches of
wherein the determining the likelihood of improving the first health score comprises predicting a change in the at least one key performance indicator (DiMaggio: Para 0119 via business intelligence system 117 can further include an artificial intelligence component 610 that predicts a growth in one or more future maturity level based on a set of forecast data or historical data corresponding to the maturity level. In an aspect, artificial intelligence component 610 can employ various artificial intelligence-based schemes for carrying out various aspects of the system operations. For instance, processor 112 can execute artificial intelligence component 610 to perform a prediction process that predicts one or more achievable maturity levels based on an active learning algorithm that utilizes historical maturity level data inputs to predict future maturity level data. In an aspect, the artificial intelligence component 610 can employ directed or undirected model classification approaches (e.g., naive Bayes, Bayesian networks, decision trees, neural networks, fuzzy logic models, and probabilistic classification models) to provide different predictive patterns of future maturity levels. Furthermore, processor 112 can execute artificial intelligence component 610 to predict risk scores based on projected implementation of compliance activities (e.g., represented by potential compliance data) and remediation activities (e.g., represented by remediation data)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Stroh/Ridlington/Evans with the teachings of DiMaggio in order to have wherein the determining the likelihood of improving the first health score comprises predicting a change in the at least one key performance indicator. The motivation behind this being to incorporate the teachings of determining maturity levels and risk scores associated with compliance activities and remediation activities of covered entities as taught by DiMaggio. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 18, it is analogous to Claim 5 and is rejected for the same reasons.
Claim(s) 6-7 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stroh (US 12,333,599 B1) in view of Ridlington et al. (US 2020/0410596 A1) in view of Evans et al. (US 2022/0028003 A1) further in view of Olson et al. (US 2022/0075704 A1).
Regarding Claim 6, while the combination of Stroh/Ridlington/Evans teaches the limitations of Claim 1, it does not explicitly disclose the limitation of Claim 6 which states determining a loss of the second health score in response to the selected action; comparing the determined loss to an optimal loss; and updating the policy of the computing device according to the comparing.
Olson though, with the teachings of Stroh/Ridlington/Evans, teaches of
determining a loss of the second health score in response to the selected action; comparing the determined loss to an optimal loss; and updating the policy of the computing device according to the comparing (Olson: Para 0042, 0058 via In forward propagation 216, a set of weights are applied to the input data 218, 220 to calculate an output 224. For the first forward propagation, the set of weights are selected randomly. In back propagation 222 a measurement is made the margin of error of the output 224 and the weights are adjusted to decrease the error. Back propagation 222 compares the output that the neural network 202 produces with the output that the neural network 202 was meant to produce, and uses the difference between them to modify the weights of the connections between the nodes of the neural network 202, starting from the output layer 214 through the hidden layers 212 to the input layer 210, i.e., going backward in the neural network. 202. In time, back propagation 222 causes the neural network 202 to learn, reducing the difference between actual and intended output to the point where the two exactly coincide. Thus, the neural network 202 is configured to repeat both forward and back propagation until the weights (and potentially the biases) of the neural network 202 are calibrated to accurately predict an output…Control starts at block 602 in which an expected risk score is computed. A determination is made of input values for an impacted device (at block 604) and a risk score calculated using forward propagation in the machine learning module 106 (at block 606). Then the calculated risk scored is compared to the expected risk score, and the margin of error that is determined is used to adjust the weights and biases of the machine learning module 106 via back propagation (at block 608). As a result, the machine learning module 106 learns to improve its operation and calculate superior outputs in the future. Margin of error aligns with a loss and the expected risk score functions as a target/optimal score; adjusting weights based on the comparison maps to updating the policy based on loss).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Stroh/Ridlington/Evans with teachings of Olson in order to have determining a loss of the second health score in response to the selected action; comparing the determined loss to an optimal loss; and updating the policy of the computing device according to the comparing. The motivations behind this being to incorporate the teachings of performing preemptive identification and reduction of risk of failure in computational systems as taught by Olson. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 7, while the combination of Stroh/Ridlington/Evans teaches the limitations of Claim 7 which state
determining, by the computing device, a second action(Stroh: Col 5 lines 9-14, 25-37 via the action planning engine 135 can receive the financial records, described herein, of the client 190 to generate an individualized financial action plan for the client 190 to follow when steadying, improving upon, or maintaining financial health (e.g., repaying debt, managing payments, building credit, saving, budgeting, etc.)…the action planning engine 135 provides a granular financial action plan for the client 190 that can be updated dynamically as financial updates of the client 190 are received. In doing so, the action planning engine 135 can execute optimization logic using the client's financial records and updates to dynamically generate a real-time individualized action plan for the client 190. As described in further detail below, the individualized action plan can facilitate the client 190 in generating an optimal budget based on the financial information of the client 190, facilitate in managing and/or paying down debt, assist the client 190 with monthly spending, improving the client's credit, aid the client in savings, and the like)
a second action by randomly selecting an action from the advice library (Ridlington: Para 0056 via receive a rule for generating a recommendation using the plurality of factors, and the categories, and using the respective weightings of the plurality of factors and the categories, the rule including a set of possible recommendations (e.g. transfer pension plan, don't transfer pension plan), such that a generated recommendation is one of the set of possible recommendations);
presenting, via the user interface, a second graphical icon representing the second action (Stroh: Col 10 lines 20-41 via Additional features of the individualized action plan 350 are also contemplated. For example, if the client 190 owns a home and is struggling to pay down high interest debt (e.g., credit card debt), the optimization logic executed by the action planning engine 135 described in FIG. 1 can determine an optimal manner in which to refinance the high-interest debt, such as taking out a HELOC at a significantly lower interest rate to pay off the credit card debt balance outright. Upon determining such optimal actions for the client 190, the interactive user interface 242 displaying the individualized action plan 350 can further provide selectable links to third-party or internal services (e.g., causing a corresponding application on the client's computing device 185 to be launched) that enable the client 190 to readily undertake the process of completing the optimal action. In the example of a HELOC, the individualized action plan 350 can provide a selectable icon that, when selected, links the client 190 to one or multiple HELOC providers, showing offered rates based on the client's 190 current financial situation, which can be provided to the HELOC provider(s) automatically by the computing system 100);
receiving, via the user interface, a selection of the second graphical icon representing the second action (Stroh: Col 10 lines 20-41 via Additional features of the individualized action plan 350 are also contemplated. For example, if the client 190 owns a home and is struggling to pay down high interest debt (e.g., credit card debt), the optimization logic executed by the action planning engine 135 described in FIG. 1 can determine an optimal manner in which to refinance the high-interest debt, such as taking out a HELOC at a significantly lower interest rate to pay off the credit card debt balance outright. Upon determining such optimal actions for the client 190, the interactive user interface 242 displaying the individualized action plan 350 can further provide selectable links to third-party or internal services (e.g., causing a corresponding application on the client's computing device 185 to be launched) that enable the client 190 to readily undertake the process of completing the optimal action. In the example of a HELOC, the individualized action plan 350 can provide a selectable icon that, when selected, links the client 190 to one or multiple HELOC providers, showing offered rates based on the client's 190 current financial situation, which can be provided to the HELOC provider(s) automatically by the computing system 100);
updating, via the computing device, the advice state responsive to the second action (Stroh: Col 11 lines 4-15 via FIG. 4B is a flow chart describing an example method of dynamically updating a financial health score of a client 190. In various examples, the computing system 100 can generate and dynamically update a financial health score for a client 190 based on financial updates and a current individualized action plan 350 (425). Based on a transaction between the client 190 and a transaction entity 180, the computing system 100 can receive a financial score request (430). In one example, the request can be received from the service application 232 executing on the client's 190 computing device 185. In variations, the request can be received from the computing system of the transaction entity 180 (434));
generating, by the computing device, a third health score of the updated advice state (Stroh: Col 11 lines 4-15 via FIG. 4B is a flow chart describing an example method of dynamically updating a financial health score of a client 190. In various examples, the computing system 100 can generate and dynamically update a financial health score for a client 190 based on financial updates and a current individualized action plan 350 (425). Based on a transaction between the client 190 and a transaction entity 180, the computing system 100 can receive a financial score request (430). In one example, the request can be received from the service application 232 executing on the client's 190 computing device 185. In variations, the request can be received from the computing system of the transaction entity 180 (434));
training the computing device based on the third health score (Ridlington: Para 0065 via According to an eighth aspect of the invention, there is provided a computer implemented method of providing automated advice, such as financial advice, in which weightings of a plurality of factors (e.g. capacity for loss, state of health) for use in providing automated advice, each factor optionally including a defined respective set of categories, and optionally including weightings of the categories, are trained using a plurality of validated training cases, to provide a trained plurality of weightings for the factors, and optionally, trained weightings for the categories, for providing automated advice).
However, Stroh/Ridlington/Evans does not explicitly disclose the limitation of Claim 7 which states wherein the training comprises: determining a loss of the third health score in response to the selected action; comparing the determined loss to an optimal loss; and updating the policy of the computing device according to the comparing.
Olson though, with the teachings of Stroh/Ridlington/Evans, teaches of
determining a loss of the third health score in response to the selected action; comparing the determined loss to an optimal loss; and updating the policy of the computing device according to the comparing (Olson: Para 0042, 0058 via In forward propagation 216, a set of weights are applied to the input data 218, 220 to calculate an output 224. For the first forward propagation, the set of weights are selected randomly. In back propagation 222 a measurement is made the margin of error of the output 224 and the weights are adjusted to decrease the error. Back propagation 222 compares the output that the neural network 202 produces with the output that the neural network 202 was meant to produce, and uses the difference between them to modify the weights of the connections between the nodes of the neural network 202, starting from the output layer 214 through the hidden layers 212 to the input layer 210, i.e., going backward in the neural network. 202. In time, back propagation 222 causes the neural network 202 to learn, reducing the difference between actual and intended output to the point where the two exactly coincide. Thus, the neural network 202 is configured to repeat both forward and back propagation until the weights (and potentially the biases) of the neural network 202 are calibrated to accurately predict an output…Control starts at block 602 in which an expected risk score is computed. A determination is made of input values for an impacted device (at block 604) and a risk score calculated using forward propagation in the machine learning module 106 (at block 606). Then the calculated risk scored is compared to the expected risk score, and the margin of error that is determined is used to adjust the weights and biases of the machine learning module 106 via back propagation (at block 608). As a result, the machine learning module 106 learns to improve its operation and calculate superior outputs in the future. Margin of error aligns with a loss and the expected risk score functions as a target/optimal score; adjusting weights based on the comparison maps to updating the policy based on loss).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Stroh/Ridlington/Evans with teachings of Olson in order to have determining a loss of the third health score in response to the selected action; comparing the determined loss to an optimal loss; and updating the policy of the computing device according to the comparing. The motivations behind this being to incorporate the teachings of performing preemptive identification and reduction of risk of failure in computational systems as taught by Olson. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claims 19-20, they are analogous to Claims 6-7 respectively and are rejected for the same reasons.
Claim(s) 8-9 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stroh (US 12,333,599 B1) in view of Nalam et al. (US 2025/0138887 A1) in view of Evans et al. (US 2022/0028003 A1) further in view of Hong et al. (US 2023/0103426 A1).
Regarding Claim 8, Stroh teaches the limitations of Claim 8 which state
receiving, by a computing device, a profile of a user (Stroh: Col 4 line 46 – Col 5 line 8 via According to examples described herein, the computing system 100 includes an action planning engine 135 that can initially provide a customized and detailed financial action plan for each client 190 based on a global view of that client's 190 current financial situation. The initial individualized action plan can be generated by the action planning engine 135 based on the financial records of the client 190, which the planning engine 135 can access or receive from the client's device 185, or any accounts from financial entities associated with the client 190. In one example, the client device 185 can execute an application that enables the computing system 100 to link with the various accounts associated with the client 190, such that the action planning engine 135 can create and store a client profile 146 for the client 190. In various examples, the client profile 146 can comprise identifying information of the client, such as a name, address, contact information, as well as continuously updated financial information, such as current balances of the client's accounts, current assets and liabilities, monthly expenditures, and the like. For example, the financial records of the client 190 can comprise any personal property (e.g., jewelry, gold, memorabilia, vehicles, art assets, etc.), debt obligations (e.g., personal loans or debts to individuals, monthly financed service payments, mortgage payments, etc.), personal income (e.g., wages, received rents, and other income sources), rent and utility payments, monthly bills, and the like. Such information is used by the action planning engine 135 to generate the customized financial action plan for the client 190);
receiving, by the computing device, an advice state of the user (Stroh: Col 4 lines 46-60 via the computing system 100 includes an action planning engine 135 that can initially provide a customized and detailed financial action plan for each client 190 based on a global view of that client's 190 current financial situation. The initial individualized action plan can be generated by the action planning engine 135 based on the financial records of the client 190, which the planning engine 135 can access or receive from the client's device 185, or any accounts from financial entities associated with the client 190. In one example, the client device 185 can execute an application that enables the computing system 100 to link with the various accounts associated with the client 190, such that the action planning engine 135 can create and store a client profile 146 for the client 190);
generating, by the computing device, a first health score of the user, the first health score incorporating the profile of the user and the advice state (Stroh: Col 6 lines 17-30 via the computing system 100 can include a scoring engine 125 that executing financial scoring logic to generate a financial health score for the client 190. The financial scoring logic can receive, as input, current financial data pertaining to the client 190 (e.g., as stored in the client's profile 146 in the database 140), the current individualized action plan of the client 190, and any recent updates in the client's 190 profile 146 that indicates recent financial changes described above. In various examples, the scoring engine 125 can be triggered each instance an update is detected in the client's 190 financial situation, and can include such granular aspects as making a goods purchase, receiving a paycheck, making a debt payment, paying off a debt account, and the like);
generating, by the computing device, an action plan incorporating the first action and the advice state (Stroh: Col 5 lines 9-24 via the action planning engine 135 can receive the financial records, described herein, of the client 190 to generate an individualized financial action plan for the client 190 to follow when steadying, improving upon, or maintaining financial health (e.g., repaying debt, managing payments, building credit, saving, budgeting, etc.). As described below, the individualized action plan can be accessed by the client 190 via an executing financial service application provided by the computing system 100. In certain implementations, the client 190 can input a set of personal goals, such as purchasing a home or paying off debt, which the action planning engine 135 can utilize to construct the customized action plan for the client 190. Accordingly, based on the current financial records of the client 190, the action planning engine 135 can generate an optimal budget, debt payment strategy, debt refinancing strategy); and
updating, by the computing device, the advice state responsive to the first action (Stroh: Col 5 lines 25-47 via In various examples, the action planning engine 135 provides a granular financial action plan for the client 190 that can be updated dynamically as financial updates of the client 190 are received. In doing so, the action planning engine 135 can execute optimization logic using the client's financial records and updates to dynamically generate a real-time individualized action plan for the client 190. As described in further detail below, the individualized action plan can facilitate the client 190 in generating an optimal budget based on the financial information of the client 190, facilitate in managing and/or paying down debt, assist the client 190 with monthly spending, improving the client's credit, aid the client in savings, and the like. Furthermore, the action planning engine 135 can update the individualized action plan based on update triggers that indicate any changes in the client's 190 finances, such as receiving wages, making purchases, making debt payments, making rental payments, and the like. For example, the action planning engine 135 can receive an update trigger indicating that the client 190 has received a paycheck. In response, the action planning engine 135 can perform a lookup of a client profile of 146 of the client 190, which can indicate a current action plan specific to the client 190).
However, Stroh does not explicitly teach the limitations of Claim 1 which states calculating, by a per-item estimator of the computing device.
Nalam though with the teachings of Stroh, teaches of
calculating, by a per-item estimator of the computing device (Nalam: Para 0051 via FIG. 3 illustrates a device provisioning management methodology 300 according to an illustrative embodiment. Step 302 utilizes a machine learning algorithm comprising one or more first decision trees generated based on device data representing a set of one or more physical devices in an information processing system to determine a recommendation for adding one or more additional physical devices to the information processing system based on a given system goal. Step 304, in response to a recommendation for adding one or more additional physical devices, utilizes the machine learning algorithm comprising one or more second decision trees generated based on information processing system data to determine one or more hardware profiles for the one or more additional physical devices in accordance with the given system goal. Step 306 deploys the one or more hardware profiles to the one or more additional physical devices to enable the one or more additional physical devices to operate in the information processing system with the set of one or more physical devices).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Stroh with the teachings of Nalam in order to have calculating, by a per-item estimator of the computing device. The motivations behind this being to incorporate the teachings of utilizing decision trees where each decision tree evaluates candidate device additions (items) relative to achieving a goal, i.e. a per-item estimator, as taught by Nalam. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Furthermore, Stroh does not explicitly teach the limitation of Claim 8 which states a likelihood of a plurality of actions to change the advice state.
Evans though, with the teachings of Stroh/Nalam, teaches of
a likelihood of a plurality of actions to change the advice state (Evans: Para 0145 via Each respective one financial strategy model of the set of financial strategy models is then ranked and prioritized and evaluated based the efficacy of the strategy through ranking criteria based on constraints and objectives, as discussed herein, in addition to weighing and calculating the impact on the overall or holistic financial plan, thereby providing a “cost and benefit” score that allows all the applicable strategies to be ranked such that the top ranked strategy represents the next best financial decision for the client).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Stroh/Nalam, with the teachings of Evans in order to have a likelihood of a plurality of actions to change the advice state. The motivations behind this being to incorporate the teachings of a financial planning system that incorporates artificial intelligence techniques to analyze client inputs and select appropriate financial strategies based on the analysis of the inputs. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
In addition, Stroh does not explicitly disclose the limitation of Claim 8 which states applying, by the computing device, a policy to the plurality of actions to determine a first action, the policy comprising an Epsilon-Greedy algorithm.
Hong though, with the teachings of Stroh/Nalam/Evans, teaches of
applying, by the computing device, a policy to the plurality of actions to determine a first action, the policy comprising an Epsilon-Greedy algorithm (Hong: Para 0085-0088, 0098 via The determining unit 130 may select an energy consumption action at time h of the energy consuming device based on, e.g., an epsilon-greedy (ε-greedy) policy. The epsilon-greedy policy is a variant of the greedy policy that performs the best choice at each step. The epsilon-greedy policy means a policy that randomly selects an energy consumption action with a probability of ε, a value between 0 and 1 and selects an action with the best outcome with a probability of 1−ε, that is, an energy consumption action to maximize the Q value. The operation of selecting the energy consumption action at time h of the energy consuming device according to the epsilon-greedy policy may vary depending on the type of the energy consuming device. For example, if the type of the energy consuming device is the non-shiftable load type, the energy consuming device may execute only one energy consumption action action #0. Accordingly, the determining unit 130 always selects the energy consumption action action #0 even when the epsilon-greedy policy is used. For example, in the determining step S930, when performing the update step for each energy consuming device, the energy consumption action at time h of each energy consuming device may be selected based on the epsilon-greedy policy).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Stroh/Nalam/Evans with the teachings of Hong in order to have applying, by the computing device, a policy to the plurality of actions to determine a first action, the policy comprising an Epsilon-Greedy algorithm. The motivations behind this being to incorporate the teachings of utilizing the best choices for actions as taught by Hong. Furthermore, combining prior art elements according to known methods will yield predictable results.
Regarding Claim 9, the combination of Stroh/Nalam/Evans/Hong teaches the limitations of Claim 9 which state
wherein the per-item estimator is a tree-based estimator (Nalam: Para 0051 via FIG. 3 illustrates a device provisioning management methodology 300 according to an illustrative embodiment. Step 302 utilizes a machine learning algorithm comprising one or more first decision trees generated based on device data representing a set of one or more physical devices in an information processing system to determine a recommendation for adding one or more additional physical devices to the information processing system based on a given system goal. Step 304, in response to a recommendation for adding one or more additional physical devices, utilizes the machine learning algorithm comprising one or more second decision trees generated based on information processing system data to determine one or more hardware profiles for the one or more additional physical devices in accordance with the given system goal).
Regarding Claim 11, the combination of Stroh/Nalam/Evans/Hong teaches the limitations of Claim 11 which state
wherein the plurality of advice states have at least one related key performance indicator, wherein the generating the first health score of the advice state incorporates the at least one key performance indicator (Stroh: Col 6 lines 17-30 via the computing system 100 can include a scoring engine 125 that executing financial scoring logic to generate a financial health score for the client 190. The financial scoring logic can receive, as input, current financial data pertaining to the client 190 (e.g., as stored in the client's profile 146 in the database 140), the current individualized action plan of the client 190, and any recent updates in the client's 190 profile 146 that indicates recent financial changes described above. In various examples, the scoring engine 125 can be triggered each instance an update is detected in the client's 190 financial situation, and can include such granular aspects as making a goods purchase, receiving a paycheck, making a debt payment, paying off a debt account, and the like).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stroh (US 12,333,599 B1) in view of Nalam et al. (US 2025/0138887 A1) in view of Evans et al. (US 2022/0028003 A1) in view of Hong et al. (US 2023/0103426 A1) further in view of Ridlington et al. (US 2020/0410596 A1).
Regarding Claim 10, while the combination of Stroh/Nalam/Evans/Hong teaches the limitations of Claim 8, it does not explicitly disclose the limitations of Claim 10 which state wherein the plurality of actions are stored in an advice library comprising the plurality of actions and a plurality of advice states related to the plurality of actions.
Ridlington though, with the teachings of Stroh/Nalam/Evans/Hong, teaches of
wherein the plurality of actions are stored in an advice library comprising the plurality of actions and a plurality of advice states related to the plurality of actions (Ridlington: Para 0056 via the processor configured to: (i) receive a plurality of factors (e.g. capacity for loss, state of health) for use in providing automated advice (e.g. financial advice), each factor including a defined respective set of categories, and each factor and each category including a respective initial weighting; (ii) receive a rule for generating a recommendation using the plurality of factors, and the categories, and using the respective weightings of the plurality of factors and the categories, the rule including a set of possible recommendations (e.g. transfer pension plan, don't transfer pension plan), such that a generated recommendation is one of the set of possible recommendations; (iii) receive a plurality of training cases, each training case including inputs relating to each factor of the plurality of factors, and to the categories, and each training case including a respective validated recommendation which is one of the set of possible recommendations; (iv) process the plurality of training cases, to derive a respective optimized weighting for each factor of the plurality of factors, and for each category, to satisfy or to match the respective validated recommendations optimally, using the rule, and (v) store the derived respective optimized weightings for the plurality of factors and for each category).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Stroh/Nalam/Evans/Hong with the teachings of Ridlington in order to have wherein the plurality of actions are stored in an advice library comprising the plurality of actions and a plurality of advice states related to the plurality of actions. The motivations behind this being to incorporate the teachings of utilizing factors to provide automated financial advice. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stroh (US 12,333,599 B1) in view of Nalam et al. (US 2025/0138887 A1) in view of Evans et al. (US 2022/0028003 A1) in view of Hong et al. (US 2023/0103426 A1) further in view of Olson et al. (US 2022/0075704 A1).
Regarding Claim 12, while the combination of Stroh/Nalam/Evans/Hong teaches the limitations of Claim 12 which state
generating, by the computing device, a second health score of the updated advice state (Stroh: Col 11 lines 4-15 via FIG. 4B is a flow chart describing an example method of dynamically updating a financial health score of a client 190. In various examples, the computing system 100 can generate and dynamically update a financial health score for a client 190 based on financial updates and a current individualized action plan 350 (425). Based on a transaction between the client 190 and a transaction entity 180, the computing system 100 can receive a financial score request (430). In one example, the request can be received from the service application 232 executing on the client's 190 computing device 185. In variations, the request can be received from the computing system of the transaction entity 180 (434)).
It does not explicitly disclose the limitations of Claim 12 which state training the computing device based on the updated advice state, wherein the training comprises: determining a loss of the second health score in response to the first action; comparing the determined loss to an optimal loss; and updating the policy of the computing device according to the comparing.
Olson though, with the teachings of Stroh/Nalam/Evans/Hong, teaches of
training the computing device based on the updated advice state, wherein the training comprises: determining a loss of the second health score in response to the first action; comparing the determined loss to an optimal loss; and updating the policy of the computing device according to the comparing (Olson: Para 0042, 0058 via In forward propagation 216, a set of weights are applied to the input data 218, 220 to calculate an output 224. For the first forward propagation, the set of weights are selected randomly. In back propagation 222 a measurement is made the margin of error of the output 224 and the weights are adjusted to decrease the error. Back propagation 222 compares the output that the neural network 202 produces with the output that the neural network 202 was meant to produce, and uses the difference between them to modify the weights of the connections between the nodes of the neural network 202, starting from the output layer 214 through the hidden layers 212 to the input layer 210, i.e., going backward in the neural network. 202. In time, back propagation 222 causes the neural network 202 to learn, reducing the difference between actual and intended output to the point where the two exactly coincide. Thus, the neural network 202 is configured to repeat both forward and back propagation until the weights (and potentially the biases) of the neural network 202 are calibrated to accurately predict an output…Control starts at block 602 in which an expected risk score is computed. A determination is made of input values for an impacted device (at block 604) and a risk score calculated using forward propagation in the machine learning module 106 (at block 606). Then the calculated risk scored is compared to the expected risk score, and the margin of error that is determined is used to adjust the weights and biases of the machine learning module 106 via back propagation (at block 608). As a result, the machine learning module 106 learns to improve its operation and calculate superior outputs in the future. Margin of error aligns with a loss and the expected risk score functions as a target/optimal score; adjusting weights based on the comparison maps to updating the policy based on loss).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Stroh/Nalam/Evans/Hong with the teachings of Olson in order to have training the computing device based on the updated advice state, wherein the training comprises: determining a loss of the second health score in response to the first action; comparing the determined loss to an optimal loss; and updating the policy of the computing device according to the comparing. The motivations behind this being to incorporate the teachings of performing preemptive identification and reduction of risk of failure in computational systems as taught by Olson. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Stroh (US 12,333,599 B1) in view of Nalam et al. (US 2025/0138887 A1) in view of Evans et al. (US 2022/0028003 A1) in view of Hong et al. (US 2023/0103426 A1) further in view of Popelka et al. (US 2020/0401978 A1).
Regarding Claim 13, while the combination of Stroh/Nalam/Evans/Hong teaches the limitations of Claim 8, it does not explicitly disclose the limitations of Claim 13 which state receiving, via a user interface, a goal from the user; wherein the calculating the likelihood of the plurality of actions to change the advice state incorporates the goal received from the user.
Popelka though, with the teachings of Stroh/Nalam/Evans/Hong, teaches of
receiving, via a user interface, a goal from the user; wherein the calculating the likelihood of the plurality of actions to change the advice state incorporates the goal received from the user (Popelka: Para 0026, 0034, 0056, 0058-0059 via a goal recommendation may be provided within a goal recommendation component of a graphical user interface (GUI). The goal recommendation may be provided based upon an initial goal selected by a user. More particularly, where the initial goal is determined to be unrealistic or unlikely to be achieved, a goal recommendation that is more realistic and likely to be achieved is presented. The user may confirm a selection of the initial goal or, alternatively, may choose to pursue the recommended goal…By way of illustration, Aaron is a marketing employee at an organization, Pyramid Construction, Inc. Aaron logs in to access a Console, which enables employees of the organization to access information maintained in data records. Aaron accesses a goal configuration component via the Console by selecting a corresponding option from a GUI. Upon accessing the goal configuration component, Aaron submits input indicating a set of goal configuration parameters that includes an identifier of a goal defined by a goal definition, a desired target improvement including a numerical value indicating a desired percentage increase in relation to the goal, and a target date. More particularly, Aaron indicates that the goal is a click-through-rate (CTR) increase, a desired target improvement of a 30 percent increase, and a target date of Dec. 25, 2019… FIG. 2C shows an example of a user interface 250 in the form of a GUI presenting a goal recommendation responsive to user input submitted via a goal configuration component, in accordance with some implementations. As shown in this example, the user has accessed goal configuration component 252 and has submitted a numerical value 254 that indicates a desired target percentage, 18.2%, by which the goal amount pertaining to goal 224, CTR, is to be increased over time from a current goal amount corresponding to goal 224. Responsive to user input indicating goal parameter values including numerical value 254 or indicating a request to save the goal at 242, the system may provide one or more goal recommendations, as appropriate…In accordance with various implementations, a goal recommendation 256 may include a recommended target improvement including or otherwise indicating one or more of: a) a second numerical value that is different from the value of the user-configured goal target (shown as target percentage 254), b) a recommended modification to the goal definition, or c) a recommended modification to the target date… goal recommendation 256 further indicates a level of confidence associated with the goal recommendation. The level of confidence can indicate a likelihood that an improvement pertaining to goal 224 according to goal recommendation 256 can be achieved. More particularly, the level of confidence can include a numerical value that indicates a probability that an improvement, as indicated by goal recommendation 256, can be achieved).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Stroh/Nalam/Evans/Hong with the teachings of Popelka in order to have receiving, via a user interface, a goal from the user; wherein the calculating the likelihood of the plurality of actions to change the advice state incorporates the goal received from the user. The motivations behind this being to incorporate the teachings of recommending and simulating goals based upon a user-selected goal configuration. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Carbajales et al. (US 2023/0376827 A1)
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/T.E.S./Examiner, Art Unit 3625
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625
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