DETAILED ACTION
Introduction
This Final Office Action is in response to amendments and remarks filed on December 15, 2025, for the application with serial number 18/643,384.
Claims 1, 8-10, and 17-19 are amended.
Claims 1-20 are pending.
Interview
The Examiner acknowledges the interview conducted on November 19, 2025, in which proposed amendments were discussed with respect to the outstanding rejections.
Response to Remarks/Amendments
35 USC §101 Rejections
The Applicant traverses the rejection of the claims as being directed to an ineligible abstract idea, contending that the claims do not recite an abstract idea. See Remarks p. 14. In response, the Examiner points to the preamble of exemplary independent claim 1, which recites: “a method for proactive third party risk monitoring and management.” The claims are directed to an ineligible abstract idea, as described in the preamble. The claims recite a business process for risk management that could be implemented mentally or on paper by contracting business parties, but a general purpose computer employing machine learning is recited for implementation. No apparent improvement to machine learning is recited in the claims. Contrary to the Applicant’s assertions, an human being could do steps (i)-(iv) listed by the Applicant in the Remarks p. 14. A human being could (i) identify threats, (ii), match triggers to scenarios, (iii), apply thresholds to reduce false positives, and (iv) update a model. The rejection for lack of subject matter eligibility is updated and maintained.
35 USC §103 Rejections
Amendments to the claims changed the scope of the claims, necessitating further search and consideration of the prior art. A new search returned the Tarler reference, which is cited in the rejection of the independent claims, below. The Applicant’s arguments with respect to the outstanding prior art rejections are moot in light of the newly cited reference.
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.
The Manual of Patent Examining Procedure (MPEP) provides detailed rules for determining subject matter eligibility for claims in §2106. Those rules provide a basis for the analysis and finding of ineligibility that follows.
Claims 1-20 are rejected under 35 U.S.C. 101. The claimed invention is directed to non-statutory subject matter because the claimed invention recites a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Although claims(s) 1-20 are all directed to one of the four statutory categories of invention, the claims are directed to risk monitoring and management (as evidenced by the preamble of exemplary independent claim 1), an abstract idea. Certain methods of organizing human activity are ineligible abstract ideas, including managing personal behavior or relationships or interactions between people. See MPEP §2106.04(a). The limitations of exemplary claim 1 include: “identifying base criteria;” “identify[ing] suppliers that meet a certain set of the base criteria;” “identifying or mapping triggers . . . to curate triggers data;” “automatically matching . . . the triggers data with [sic] corresponding scenario;” “defining risk thresholds;” “applying . . . risk-threshold definitions;” “implementing a validation process to identify critical threat associated with the particular supplier;” “automatically assigning . . . a priority to the particular supplier;” and “updating . . . parameters of the predictive analytics model.” The steps are all steps for managing personal behavior related to the abstract idea of risk monitoring and management that, when considered alone and in combination, are part of the abstract idea of risk monitoring and management. The dependent claims further recite steps for managing personal behavior that are part of the abstract idea of risk monitoring and management. These claim elements, when considered alone and in combination, are considered to be abstract ideas because they are directed to a method of organizing human activity which includes applying thresholds to risk criteria to mitigate risk associated with a business partner.
Under step 2A of the subject matter eligibility analysis, a claim that recites a judicial exception must be evaluated to determine whether the claim provides a practical application of the judicial exception. Additional elements of the independent claims amount to generic computer hardware that does not provide a practical application (processors and memory in independent claim 1; a system with a processor and memory independent claim 10; and a computer readable medium in independent claim 19). See MPEP §2106.04(d)[I]. The claims do not recite an improvement to another technology or technical field, nor do they recite an improvement to the functioning of the computer itself. See MPEP §2106.05(a). The claims do recite the use of machine learning, but the abstract idea of risk monitoring and management is generally linked to a machine learning environment for implementation. Therefore, the machine learning merely amounts to a technological environment for implementing the abstract idea. See MPEP §2106.05(h). The claims require no more than a generic computer (processors and memory in independent claim 1; a system with a processor and memory independent claim 10; and a computer readable medium in independent claim 19) to implement the abstract idea, which does not amount to significantly more than an abstract idea. See MPEP §2106.05(f). Because the claims only recite use of a generic computer, they do not apply the judicial exception with a particular machine. See MPEP §2106.05(b). For these reasons, the claims do not provide a practical application of the abstract idea, nor do they amount to significantly more than an abstract idea under step 2B of the subject matter eligibility analysis. Using a generic computer to implement an abstract idea does not provide an inventive concept. Therefore, the claims recite ineligible subject matter under 35 USC §101.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1, 10, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20060167706 A1 to Kenyon et al. (hereinafter ‘KENYON’) in view of US 20140379387 A1 to Au Li (hereinafter ‘AU LI’), US 20150073929 A1 to Psota et al. (hereinafter ‘PSOTA’), and US 20210042824 A1 to Tarler et al. (hereinafter ‘TARLER’).
Claim 1 (Currently Amended)
KENYON discloses a method for proactive third party risk monitoring and management (see ¶[0063]; model thresholds as an indicator function on a value at risk (VaR) type criteria. Whilst VaR has various difficulties as a risk measure (e.g. lack of coherence (Artzner, et al. 1999, Riedl 2003)) it is applicable here in describing the attitude of many finance departments that wish to control "worst case" scenarios on an individual contract basis and do not combine risks from different contracts) by utilizing one or more processors along with allocated memory (see ¶[0140]; the present invention may be embodied as a computer program product for use with a computer system), the method comprising: identifying base criteria that is already known data about third party suppliers who provide services to an organization (see abstract and ¶[0030]; provide a utility function that models objectives of the provider and the client.. Use contractual attributes and offeror and offeree preferences).
KENYON does not specifically disclose, but AU LI discloses, running the base criteria continuously according to a configurable time window to identify suppliers that meet a certain set of the base criteria (see ¶[0212] and [0216]; form a new supply chain consisting of the buyer and the top level supplier for the supply chain business transaction. Calculate the score for the estimate of the productivity of the contract, and move to next steps if the score passes a preset threshold. See also ¶[0057]-[0059]; a contract period);
KENYON does not specifically disclose, but PSOTA discloses, identifying or mapping triggers from curated sources to curate triggers data, that are not yet validated, received from external sources in addition to the base criteria (see ¶[0207] and [0425]; filtering, classification, and clustering are important and facilitate merging of external data source records. Techniques for obtaining, weighting, and using information from third-party sources are described for supplier and buyer ratings throughout this document.).
KENYON does not explicitly disclose, but TARLER discloses, automatically matching, by a machine-learning-based predictive analytics model (see ¶[0034]-[0035] and [0055]; user inputs may include a risk tolerance threshold. The system inputs may include an option for the rule engine 116 to consider performance levels of existing optimized functions (e.g., fraud rules) and a threshold to indicate whether a new rule (e.g., an optimized function) may be needed. The threshold may be based on the user inputs, such as the one or more fraud scores and/or fraud rules, the optimization metric, and/or the risk tolerance threshold. The machine learning module 208 may include any number of machine learning models to perform the techniques herein, such as for cognitive analytics, predictive analysis, and/or trending analytics as known in the art),
KENYON further discloses, the triggers data with corresponding scenario which is a predefined or preconfigured combination of the base criteria and trigger criteria (see ¶[0058]-[0063]; there are a set of financial thresholds that a contract must meet in order to be acceptable under the rules of the finance department. [0060] 2. Once thresholds are met a contract is judged on its expected return);
KENYON does not specifically disclose, but AU LI discloses, defining risk thresholds for each of the base criteria and the trigger criteria (see ¶[0099]-[0100] and [0136] and [0216] and Table 2; each day item weight in score risk for Monitoring as agreed receipt date. Determine if a score passes a threshold), wherein breaching this combined combination is treated as a positive match for displaying to an analyst for prioritization (see ¶[0094] and [0138]; detect breach of contract. Notify relevant parties of the occurrence of a certain event, such as default that may breach a supply chain business transaction contract).
KENYON does not explicitly disclose, but TARLER discloses, applying, by the predictive analytics model, risk-threshold definitions stored in a configuration file to determine whether a combined threshold associated with the scenario is breached (see again ¶[0034]-[0035] and [0055]; the system inputs may include an option for the rule engine 116 to consider performance levels of existing optimized functions (e.g., fraud rules) and a threshold to indicate whether a new rule (e.g., an optimized function) may be needed. The threshold may be based on the user inputs, such as the one or more fraud scores and/or fraud rules, the optimization metric, and/or the risk tolerance threshold. The machine learning module 208 may include any number of machine learning models to perform the techniques herein, such as for cognitive analytics, predictive analysis, and/or trending analytics as known in the art),
implementing a validation process to identify critical threat associated with the particular supplier by eliminating false positives from the match (see ¶[0025, [0034], and [0062]; performance of the machine learning model 410 may be evaluated in a number of ways based on a number of true positives, false positives, true negatives, and/or false negatives classifications of the plurality of data points indicated by the machine learning model 410. For example, the false positives of the machine learning model 410 may refer to a number of times the model incorrectly classified one or more variable calculations as not meeting or exceeding the prediction threshold. An optimization metric may include a false positive rate. See also abstract; detect fraud. A fraud score may represent likelihood of an event being true).
KENYON does not specifically disclose, but AU LI discloses, automatically assigning, in response to a positive validation result (see ¶[0223] and [0282]; verify the information for loan and insurance approvals), a priority to the particular supplier for continuous risk monitoring and management to eliminate or remediate the identified critical threat (see ¶[0279]-[0283]; send compensation agreement for dispute resolution).
KENYON does not explicitly disclose, but TARLER discloses, updating, via a feedback loop, parameters of the predictive analytics model based on the positive validation result to improve future matching and threshold-breach prediction (see ¶[0062]-[0063] and Fig. 6; train a machine learning model. Performance of the machine learning model 410 may be evaluated in a number of ways based on a number of true positives, false positives, true negatives, and/or false negatives classifications of the plurality of data points indicated by the machine learning model 410. See also ¶[0046]-[0048] and [0052]-[0052]; train the classifier to output improved predictions. Prevent systemic errors).
KENYON discloses outsourcing contracts that includes using thresholds to determine when a contract is acceptable (see ¶[0058]-[0059]. AU LI discloses supply chain business transactions that includes matching suppliers for supply chain transactions to form contracts based on a determination that a risk score passes a threshold. It would have been obvious to include the evaluation as taught by AU LI in the system executing the method of KENYON with the motivation to match supplying contracting entities to entities in need of supplies.
KENYON discloses outsourcing contracts that includes using thresholds to determine when a contract is acceptable (see ¶[0058]-[0059]. PSOTA discloses facilitating contracts with suppliers using external data sources. It would have been obvious to include external data sources as taught by PSOTA in the system executing the method of KENYON with the motivation to evaluate acceptability of a contract.
KENYON discloses outsourcing contracts that includes using thresholds to determine when a contract is acceptable (see ¶[0058]-[0059]. AU LI discloses supply chain business transactions that includes matching suppliers for supply chain transactions to form contracts based on a determination that a risk score passes a threshold that may indicate fraud (see ¶[0004]). TARLER discloses fraud detection using machine learning that is improved by reducing errors, including false positives. It would have been obvious to include the machine learning as taught by TARLER in the system executing the method of KENYON and AU LI to reduce the risk of fraud in contractual relationships.
Claim 10 (Currently Amended)
KENYON discloses a system for proactive third party risk monitoring and management, the system comprising: a processor (see ¶[0140]; the present invention may be embodied as a computer program product for use with a computer system); and
a memory operatively connected to the processor via a communication interface, the memory storing computer readable instructions (see ¶[0140]; software and computer readable instructions), when executed, causes the processor to:
identify base criteria that is already known data about third party suppliers who provide services to an organization (see abstract and ¶[0030]; provide a utility function that models objectives of the provider and the client.. Use contractual attributes and offeror and offeree preferences).
KENYON does not specifically disclose, but AU LI discloses, run the base criteria continuously according to a configurable time window to identify suppliers that meet a certain set of the base criteria (see ¶[0212] and [0216]; form a new supply chain consisting of the buyer and the top level supplier for the supply chain business transaction. Calculate the score for the estimate of the productivity of the contract, and move to next steps if the score passes a preset threshold. See also ¶[0057]-[0059]; a contract period);
KENYON does not specifically disclose, but PSOTA discloses, implement a control assessment process to curate triggers data, that are not yet validated, received from external sources in addition to the base criteria (see ¶[0207] and [0425]; filtering, classification, and clustering are important and facilitate merging of external data source records. Techniques for obtaining, weighting, and using information from third-party sources are described for supplier and buyer ratings throughout this document.).
KENYON further discloses, automatically match by a machine-learning-based predictive analytics model (see ¶[0034]-[0035] and [0055]; user inputs may include a risk tolerance threshold. The system inputs may include an option for the rule engine 116 to consider performance levels of existing optimized functions (e.g., fraud rules) and a threshold to indicate whether a new rule (e.g., an optimized function) may be needed. The threshold may be based on the user inputs, such as the one or more fraud scores and/or fraud rules, the optimization metric, and/or the risk tolerance threshold. The machine learning module 208 may include any number of machine learning models to perform the techniques herein, such as for cognitive analytics, predictive analysis, and/or trending analytics as known in the art),
KENYON further discloses, the triggers data with corresponding scenario which is a predefined or preconfigured combination of the base criteria and trigger criteria (see ¶[0058]-[0063]; there are a set of financial thresholds that a contract must meet in order to be acceptable under the rules of the finance department. [0060] 2. Once thresholds are met a contract is judged on its expected return);
KENYON does not specifically disclose, but AU LI discloses, defining risk thresholds for each of the base criteria and the trigger criteria (see ¶[0099]-[0100] and [0136] and [0216] and Table 2; each day item weight in score risk for Monitoring as agreed receipt date. Determine if a score passes a threshold), wherein breaching this combined combination is treated as a positive match for displaying to an analyst for prioritization (see ¶[0094] and [0138]; detect breach of contract. Notify relevant parties of the occurrence of a certain event, such as default that may breach a supply chain business transaction contract).
KENYON does not explicitly disclose, but TARLER discloses, apply, by the predictive analytics model, risk-threshold definitions stored in a configuration file to determine whether a combined threshold associated with the scenario is breached (see again ¶[0034]-[0035] and [0055]; the system inputs may include an option for the rule engine 116 to consider performance levels of existing optimized functions (e.g., fraud rules) and a threshold to indicate whether a new rule (e.g., an optimized function) may be needed. The threshold may be based on the user inputs, such as the one or more fraud scores and/or fraud rules, the optimization metric, and/or the risk tolerance threshold. The machine learning module 208 may include any number of machine learning models to perform the techniques herein, such as for cognitive analytics, predictive analysis, and/or trending analytics as known in the art),
implement a validation process to identify critical threat associated with the particular supplier by eliminating false positives from the match (see ¶[0025, [0034], and [0062]; performance of the machine learning model 410 may be evaluated in a number of ways based on a number of true positives, false positives, true negatives, and/or false negatives classifications of the plurality of data points indicated by the machine learning model 410. For example, the false positives of the machine learning model 410 may refer to a number of times the model incorrectly classified one or more variable calculations as not meeting or exceeding the prediction threshold. An optimization metric may include a false positive rate. See also abstract; detect fraud. A fraud score may represent likelihood of an event being true).
KENYON does not specifically disclose, but AU LI discloses, automatically assign, in response to a positive validation result (see ¶[0223] and [0282]; verify the information for loan and insurance approvals), a priority to the particular supplier for continuous risk monitoring and management to eliminate or remediate the identified critical threat (see ¶[0279]-[0283]; send compensation agreement for dispute resolution).
KENYON does not explicitly disclose, but TARLER discloses, update, via a feedback loop, parameters of the predictive analytics model based on the positive validation result to improve future matching and threshold-breach prediction (see ¶[0062]-[0063] and Fig. 6; train a machine learning model. Performance of the machine learning model 410 may be evaluated in a number of ways based on a number of true positives, false positives, true negatives, and/or false negatives classifications of the plurality of data points indicated by the machine learning model 410. See also ¶[0046]-[0048] and [0052]-[0052]; train the classifier to output improved predictions. Prevent systemic errors).
KENYON discloses outsourcing contracts that includes using thresholds to determine when a contract is acceptable (see ¶[0058]-[0059]. AU LI discloses supply chain business transactions that includes matching suppliers for supply chain transactions to form contracts based on a determination that a risk score passes a threshold. It would have been obvious to include the evaluation as taught by AU LI in the system executing the method of KENYON with the motivation to match supplying contracting entities to entities in need of supplies.
KENYON discloses outsourcing contracts that includes using thresholds to determine when a contract is acceptable (see ¶[0058]-[0059]. PSOTA discloses facilitating contracts with suppliers using external data sources. It would have been obvious to include external data sources as taught by PSOTA in the system executing the method of KENYON with the motivation to evaluate acceptability of a contract.
KENYON discloses outsourcing contracts that includes using thresholds to determine when a contract is acceptable (see ¶[0058]-[0059]. AU LI discloses supply chain business transactions that includes matching suppliers for supply chain transactions to form contracts based on a determination that a risk score passes a threshold that may indicate fraud (see ¶[0004]). TARLER discloses fraud detection using machine learning that is improved by reducing errors, including false positives. It would have been obvious to include the machine learning as taught by TARLER in the system executing the method of KENYON and AU LI to reduce the risk of fraud in contractual relationships.
Claim 19 (Currently Amended)
KENYON discloses a non-transitory computer readable medium configured to store instructions for proactive third party risk monitoring and management (see ¶[0140]; the present invention may be embodied as a computer program product for use with a computer system. Software and computer readable instructions), the instructions, when executed, cause a processor to perform the following:
identifying base criteria that is already known data about third party suppliers who provide services to an organization (see abstract and ¶[0030]; provide a utility function that models objectives of the provider and the client.. Use contractual attributes and offeror and offeree preferences).
KENYON does not specifically disclose, but AU LI discloses, running the base criteria continuously according to a configurable time window to identify suppliers that meet a certain set of the base criteria see ¶[0212] and [0216]; form a new supply chain consisting of the buyer and the top level supplier for the supply chain business transaction. Calculate the score for the estimate of the productivity of the contract, and move to next steps if the score passes a preset threshold. See also ¶[0057]-[0059]; a contract period);
KENYON does not specifically disclose, but PSOTA discloses, identifying or mapping triggers from curated sources to curate triggers data, that are not yet validated, received from external sources in addition to the base criteria (see ¶[0207] and [0425]; filtering, classification, and clustering are important and facilitate merging of external data source records. Techniques for obtaining, weighting, and using information from third-party sources are described for supplier and buyer ratings throughout this document.).
KENYON does not explicitly disclose, but TARLER discloses, automatically matching, by a machine-learning-based predictive analytics model (see ¶[0034]-[0035] and [0055]; user inputs may include a risk tolerance threshold. The system inputs may include an option for the rule engine 116 to consider performance levels of existing optimized functions (e.g., fraud rules) and a threshold to indicate whether a new rule (e.g., an optimized function) may be needed. The threshold may be based on the user inputs, such as the one or more fraud scores and/or fraud rules, the optimization metric, and/or the risk tolerance threshold. The machine learning module 208 may include any number of machine learning models to perform the techniques herein, such as for cognitive analytics, predictive analysis, and/or trending analytics as known in the art),
KENYON further discloses, the triggers data with corresponding scenario which is a predefined or preconfigured combination of the base criteria and trigger criteria (see ¶[0058]-[0063]; there are a set of financial thresholds that a contract must meet in order to be acceptable under the rules of the finance department. [0060] 2. Once thresholds are met a contract is judged on its expected return);
KENYON does not specifically disclose, but AU LI discloses, defining risk thresholds for each of the base criteria and the trigger criteria (see ¶[0099]-[0100] and [0136] and [0216] and Table 2; each day item weight in score risk for Monitoring as agreed receipt date. Determine if a score passes a threshold), wherein breaching this combined combination is treated as a positive match for displaying to an analyst for prioritization (see ¶[0094] and [0138]; detect breach of contract. Notify relevant parties of the occurrence of a certain event, such as default that may breach a supply chain business transaction contract).
KENYON does not explicitly disclose, but TARLER discloses, applying, by the predictive analytics model, risk-threshold definitions stored in a configuration file to determine whether a combined threshold associated with the scenario is breached (see again ¶[0034]-[0035] and [0055]; the system inputs may include an option for the rule engine 116 to consider performance levels of existing optimized functions (e.g., fraud rules) and a threshold to indicate whether a new rule (e.g., an optimized function) may be needed. The threshold may be based on the user inputs, such as the one or more fraud scores and/or fraud rules, the optimization metric, and/or the risk tolerance threshold. The machine learning module 208 may include any number of machine learning models to perform the techniques herein, such as for cognitive analytics, predictive analysis, and/or trending analytics as known in the art),
implementing a validation process to identify critical threat associated with the particular supplier by eliminating false positives from the match (see ¶[0025, [0034], and [0062]; performance of the machine learning model 410 may be evaluated in a number of ways based on a number of true positives, false positives, true negatives, and/or false negatives classifications of the plurality of data points indicated by the machine learning model 410. For example, the false positives of the machine learning model 410 may refer to a number of times the model incorrectly classified one or more variable calculations as not meeting or exceeding the prediction threshold. An optimization metric may include a false positive rate. See also abstract; detect fraud. A fraud score may represent likelihood of an event being true).
KENYON does not specifically disclose, but AU LI discloses, automatically assigning, in response to a positive validation result (see ¶[0223] and [0282]; verify the information for loan and insurance approvals), a priority to the particular supplier for continuous risk monitoring and management to eliminate or remediate the identified critical threat (see ¶[0279]-[0283]; send compensation agreement for dispute resolution).
KENYON does not explicitly disclose, but TARLER discloses, updating, via a feedback loop, parameters of the predictive analytics model based on the positive validation result to improve future matching and threshold-breach prediction (see ¶[0062]-[0063] and Fig. 6; train a machine learning model. Performance of the machine learning model 410 may be evaluated in a number of ways based on a number of true positives, false positives, true negatives, and/or false negatives classifications of the plurality of data points indicated by the machine learning model 410. See also ¶[0046]-[0048] and [0052]-[0052]; train the classifier to output improved predictions. Prevent systemic errors).
KENYON discloses outsourcing contracts that includes using thresholds to determine when a contract is acceptable (see ¶[0058]-[0059]. AU LI discloses supply chain business transactions that includes matching suppliers for supply chain transactions to form contracts based on a determination that a risk score passes a threshold. It would have been obvious to include the evaluation as taught by AU LI in the system executing the method of KENYON with the motivation to match supplying contracting entities to entities in need of supplies.
KENYON discloses outsourcing contracts that includes using thresholds to determine when a contract is acceptable (see ¶[0058]-[0059]. PSOTA discloses facilitating contracts with suppliers using external data sources. It would have been obvious to include external data sources as taught by PSOTA in the system executing the method of KENYON with the motivation to evaluate acceptability of a contract.
KENYON discloses outsourcing contracts that includes using thresholds to determine when a contract is acceptable (see ¶[0058]-[0059]. AU LI discloses supply chain business transactions that includes matching suppliers for supply chain transactions to form contracts based on a determination that a risk score passes a threshold that may indicate fraud (see ¶[0004]). TARLER discloses fraud detection using machine learning that is improved by reducing errors, including false positives. It would have been obvious to include the machine learning as taught by TARLER in the system executing the method of KENYON and AU LI to reduce the risk of fraud in contractual relationships.
Claim(s) 2, 11, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20060167706 A1 to KENYON et al. in view of US 20140379387 A1 to AU LI, US 20150073929 A1 to PSOTA et al., and US 20210042824 A1 to TARLER et al. as applied to claim 1 above, and further in view of US 20220343433 A1 to Yan (hereinafter ‘YAN’).
Claim 2 (Original)
The combination of KENYON, AU LI, PSOTA, and TARLER discloses the method according to claim 1.
The combination of KENYON, AU LI, PSOTA, and TARLER does not specifically disclose, but YAN discloses further comprising: automatically assigning a priority to the particular supplier for risk monitoring and management based on a corresponding degree of match (see abstract; rank businesses based on environmental, social and governance objectives. See also ¶[0050]; the techniques disclosed herein strive to ensure that a company's ESG ranking would be of use to its customers, particularly with regard to third-party risk and financial risk management.),
wherein multiple triggers match elevates the risk accordingly in a manner such that triggers match for medium priority is greater than the triggers match for low priority, triggers match for high priority is greater than the triggers match for medium priority, and triggers match for critical priority is greater than the triggers match for high priority (see ¶[0192]; prioritizing monitoring or engaging with highest-risk or lowest-risk suppliers; evaluating hotspots of ESG risk among suppliers and throughout tiers; identifying suppliers to assist with corporate-led sustainability goals; identifying low-risk suppliers with which to build relationships by increasing spending or awarding long-term contracts or preferred contract terms).
KENYON discloses outsourcing contracts that includes using thresholds to determine when a contract is acceptable (see ¶[0058]-[0059]. YAN discloses ESG rankings to manage risk associated with businesses. It would have been obvious to include the ESG rankings as taught by YAN in the system executing the method of KENYON with the motivation to monitor and engage with suppliers based on levels of identified risk.
Claim 11 (Original)
The combination of KENYON, AU LI, PSOTA, and TARLER discloses the system according to claim 10.
The combination of KENYON, AU LI, PSOTA, and TARLER does not specifically disclose, but YAN discloses wherein the processor is further configured to: automatically assign a priority to the particular supplier for risk monitoring and management based on a corresponding degree of match (see abstract; rank businesses based on environmental, social and governance objectives. See also ¶[0050]; the techniques disclosed herein strive to ensure that a company's ESG ranking would be of use to its customers, particularly with regard to third-party risk and financial risk management.),
wherein multiple triggers match elevates the risk accordingly in a manner such that triggers match for medium priority is greater than the triggers match for low priority, triggers match for high priority is greater than the triggers match for medium priority, and triggers match for critical priority is greater than the triggers match for high priority (see ¶[0192]; prioritizing monitoring or engaging with highest-risk or lowest-risk suppliers; evaluating hotspots of ESG risk among suppliers and throughout tiers; identifying suppliers to assist with corporate-led sustainability goals; identifying low-risk suppliers with which to build relationships by increasing spending or awarding long-term contracts or preferred contract terms).
KENYON discloses outsourcing contracts that includes using thresholds to determine when a contract is acceptable (see ¶[0058]-[0059]. YAN discloses ESG rankings to manage risk associated with businesses. It would have been obvious to include the ESG rankings as taught by YAN in the system executing the method of KENYON with the motivation to monitor and engage with suppliers based on levels of identified risk.
Claim 20 (Original)
The combination of KENYON, AU LI, PSOTA, and TARLER discloses the non-transitory computer readable medium according to claim 19.
The combination of KENYON, AU LI, PSOTA, and TARLER does not specifically disclose, but YAN discloses wherein the instructions, when executed, cause the processor to perform the following: automatically assign a priority to the particular supplier for risk monitoring and management based on a corresponding degree of match (see abstract; rank businesses based on environmental, social and governance objectives. See also ¶[0050]; the techniques disclosed herein strive to ensure that a company's ESG ranking would be of use to its customers, particularly with regard to third-party risk and financial risk management.),
wherein multiple triggers match elevates the risk accordingly in a manner such that triggers match for medium priority is greater than the triggers match for low priority, triggers match for high priority is greater than the triggers match for medium priority, and triggers match for critical priority is greater than the triggers match for high priority (see ¶[0192]; prioritizing monitoring or engaging with highest-risk or lowest-risk suppliers; evaluating hotspots of ESG risk among suppliers and throughout tiers; identifying suppliers to assist with corporate-led sustainability goals; identifying low-risk suppliers with which to build relationships by increasing spending or awarding long-term contracts or preferred contract terms).
KENYON discloses outsourcing contracts that includes using thresholds to determine when a contract is acceptable (see ¶[0058]-[0059]. YAN discloses ESG rankings to manage risk associated with businesses. It would have been obvious to include the ESG rankings as taught by YAN in the system executing the method of KENYON with the motivation to monitor and engage with suppliers based on levels of identified risk.
Claim(s) 3, 4, 12, and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20060167706 A1 to KENYON et al. in view of US 20140379387 A1 to AU LI, US 20150073929 A1 to PSOTA et al., and US 20110016109 A1 to VASSILVITSKII et al. as applied to claim 1 above, and further in view of US 20200019932 A1 to Harris et al. (hereinafter ‘HARRIS’).
Claim 3 (Original)
The combination of KENYON, AU LI, PSOTA, and TARLER discloses the method according to claim 1.
The combination of KENYON, AU LI, PSOTA, and TARLER does not explicitly disclose, but HARRIS discloses, further comprising: collecting the known data about third party suppliers and storing internally during onboarding of the suppliers to the organization in outsourcing such services (see ¶[0047]; one form can be a (1) “new supplier request form” used by a buyer computer 122 for initially requesting onboarding of a new supplier into a corresponding CRM system).
KENYON discloses outsourcing contracts that includes using thresholds to determine when a contract is acceptable (see ¶[0058]-[0059]. HARRIS discloses supplier information management that includes new supplier onboarding into a customer relationship management system for contract renewal and creation. It would have been obvious to include the onboarding of new suppliers to create contracts as taught by HARRIS in the system executing the method of KENYON with the motivation to create new supply contracts and onboard suppliers.
Claim 4 (Original)
The combination of KENYON, AU LI, PSOTA, TARLER, and HARRIS discloses the method according to claim 3.
KENYON does not specifically disclose, but AU LI discloses, further comprising: continuously monitoring the identified suppliers for risk management and mitigation (see ¶[0137]; the Alert System 6300 also direct with the Risk Management System 6320 to provide risk management strategies to relevant parties to prevent dispute and mitigate damages).
KENYON discloses outsourcing contracts that includes using thresholds to determine when a contract is acceptable (see ¶[0058]-[0059]). AU LI discloses supply chain business transactions that includes matching suppliers for supply chain transactions to form contracts based on a determination that a risk score passes a threshold, where risk management includes mitigating damages. It would have been obvious to include the mitigation of damages as taught by AU LI in the system executing the method of KENYON with the motivation to reduce damage and risk.
Claim 12 (Original)
The combination of KENYON, AU LI, PSOTA, and TARLER discloses the system according to claim 10.
The combination of KENYON, AU LI, PSOTA, and TARLER does not explicitly disclose, but HARRIS discloses, wherein the processor is further configured to: collect the known data about third party suppliers and store internally during onboarding of the suppliers to the organization in outsourcing such services (see ¶[0047]; one form can be a (1) “new supplier request form” used by a buyer computer 122 for initially requesting onboarding of a new supplier into a corresponding CRM system).
KENYON discloses outsourcing contracts that includes using thresholds to determine when a contract is acceptable (see ¶[0058]-[0059]. HARRIS discloses supplier information management that includes new supplier onboarding into a customer relationship management system for contract renewal and creation. It would have been obvious to include the onboarding of new suppliers to create contracts as taught by HARRIS in the system executing the method of KENYON with the motivation to create new supply contracts and onboard suppliers.
Claim 13 (Original)
The combination of KENYON, AU LI, PSOTA, TARLER, and HARRIS discloses the system according to claim 12.
KENYON does not specifically disclose, but AU LI discloses, wherein the processor is further configured to: continuously monitor the identified suppliers for risk management and mitigation (see ¶[0137]; the Alert System 6300 also direct with the Risk Management System 6320 to provide risk management strategies to relevant parties to prevent dispute and mitigate damages).
KENYON discloses outsourcing contracts that includes using thresholds to determine when a contract is acceptable (see ¶[0058]-[0059]). AU LI discloses supply chain business transactions that includes matching suppliers for supply chain transactions to form contracts based on a determination that a risk score passes a threshold, where risk management includes mitigating damages. It would have been obvious to include the mitigation of damages as taught by AU LI in the system executing the method of KENYON with the motivation to reduce damage and risk.
Claim(s) 5 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20060167706 A1 to KENYON et al. in view of US 20140379387 A1 to AU LI, US 20150073929 A1 to PSOTA et al., and US 20210042824 A1 to TARLER et al.as applied to claim 1 above, and further in view of US 20160055202 A1 to Rosenberg et al. (hereinafter ‘ROSENBERG’).
Claim 5 (Original)
The combination of KENYON, AU LI, PSOTA, and TARLER discloses the method according to claim 1.
The combination of KENYON, AU LI, PSOTA, and TARLER does not specifically disclose, but ROSENBERG discloses further comprising: displaying onto a monitor, utilized by an analyst, preset matches for each risk category (see claim 35; query data based on user-defined thresholds in a risk category);
overlaying the base criteria with predetermined additional triggers or selecting new triggers based on current industry risk factors (see claim 35; query data based on user-defined thresholds in a risk category).
KENYON discloses outsourcing contracts that includes using thresholds to determine when a contract is acceptable (see ¶[0058]-[0059]). ROSENBERG discloses analytics in a corpus of data that includes matching queries for entity profiles based on risk categories and thresholds. It would have been obvious to include the risk categories and thresholds as taught by ROSENBERG in the system executing the method of KENYON with the motivation to determine whether a contract associated with an entity is acceptable.
Claim 14 (Original)
The combination of KENYON, AU LI, PSOTA, and TARLER discloses the system according to claim 10.
The combination of KENYON, AU LI, PSOTA, and TARLER does not specifically disclose, but ROSENBERG discloses wherein the processor is further configured to: display onto a monitor, utilized by an analyst, preset matches for each risk category (see claim 35; query data based on user-defined thresholds in a risk category);
overlay the base criteria with predetermined additional triggers or selecting new triggers based on current industry risk factors (see claim 35; query data based on user-defined thresholds in a risk category).
KENYON discloses outsourcing contracts that includes using thresholds to determine when a contract is acceptable (see ¶[0058]-[0059]). ROSENBERG discloses analytics in a corpus of data that includes matching queries for entity profiles based on risk categories and thresholds. It would have been obvious to include the risk categories and thresholds as taught by ROSENBERG in the system executing the method of KENYON with the motivation to determine whether a contract associated with an entity is acceptable.
Claim(s) 6 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20060167706 A1 to KENYON et al. in view of US 20140379387 A1 to AU LI, US 20150073929 A1 to PSOTA et al., US 20210042824 A1 to TARLER et al., and US 20160055202 A1 to ROSENBERG et al. as applied to claims 1 and 5 above, and further in view of US 20040059588 A1 to Burritt et al. (hereinafter ‘BURRITT’).
Claim 6 (Original)
The combination of KENYON, AU LI, PSOTA, TARLER, and ROSENBERG discloses the method according to claim 5.
The combination of KENYON, AU LI, PSOTA, TARLER, and ROSENBERG does not specifically disclose, but BURRITT discloses wherein in implementing the validation process, the method further comprising: assigning a corresponding subject matter expert, based on loading indicators of risk and matched attributes, for manually validating the identified critical threat and subsequent case creation (see ¶[0022]; the subject matter expert may become part of the project team or part of a review team. The activities of the subject matter expert may include a further assessment of the risk to the effected process, to verify the initial risk assessment if there was one).
KENYON discloses outsourcing contracts that includes using thresholds to determine when a contract is acceptable (see ¶[0058]-[0059]). BURRITT discloses using a subject matter expert to verify a risk assessment. It would have been obvious for one of ordinary skill in the art at the time of invention to include the verification of the risk assessment as taught by BURRIT in the system executing the method of KENYON with the motivation to determine if a contract is acceptable.
Claim 15 (Original)
The combination of KENYON, AU LI, PSOTA, TARLER, and ROSENBERG discloses the system according to claim 14.
The combination of KENYON, AU LI, PSOTA, TARLER, and ROSENBERG does not specifically disclose, but BURRITT discloses wherein in implementing the validation process, the processor is further configured to: assign a corresponding subject matter expert, based on loading indicators of risk and matched attributes, for manually validating the identified critical threat and subsequent case creation (see ¶[0022]; the subject matter expert may become part of the project team or part of a review team. The activities of the subject matter expert may include a further assessment of the risk to the effected process, to verify the initial risk assessment if there was one).
KENYON discloses outsourcing contracts that includes using thresholds to determine when a contract is acceptable (see ¶[0058]-[0059]). BURRITT discloses using a subject matter expert to verify a risk assessment. It would have been obvious for one of ordinary skill in the art at the time of invention to include the verification of the risk assessment as taught by BURRIT in the system executing the method of KENYON with the motivation to determine if a contract is acceptable.
Claim(s) 7 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20060167706 A1 to KENYON et al. in view of US 20140379387 A1 to AU LI, US 20150073929 A1 to PSOTA et al., US 20110016109 A1 to VASSILVITSKII et al., and US 20210042824 A1 to TARLER et al. as applied to claims 1 and 5 above, and further in view of US 20200265357 A1 to Vashistha (hereinafter ‘VASHISTHA’).
Claim 7 (Original)
The combination of KENYON, AU LI, PSOTA, TARLER, and ROSENBERG discloses the method according to claim 5.
The combination of KENYON, AU LI, PSOTA, TARLER, and ROSENBERG does not specifically disclose, but VASHISTHA discloses, wherein in implementing the validation process, the method further comprising: implementing an artificial intelligence or rules engine to automatically validate the identified critical threat and subsequent case creation (see ¶[0012]; a change in any metric is captured by a machine-based learning system that has built-in validation algorithms to ensure data authenticity and accuracy by verifying and triangulating information from all available open sources. [0015] Once the gathered information is verified and validated, the machine runs the updated data sets through intelligent algorithms that make changes to the quantified risk profile of the location or third party entity).
KENYON discloses outsourcing contracts that includes using thresholds to determine when a contract is acceptable (see ¶[0058]-[0059]. VASHISTHA discloses risk associated with supplier profiles that includes validating data with machine learning. It would have been obvious to include validation with machine learning as taught by VASHISHTA in the system executing the method of KENYON with the motivation to determine risk associated with a contracting supplier.
Claim 16 (Original)
The combination of KENYON, AU LI, PSOTA, TARLER, and ROSENBERG discloses the system according to claim 14.
The combination of KENYON, AU LI, PSOTA, TARLER, and ROSENBERG does not specifically disclose, but VASHISTHA discloses, wherein in implementing the validation process, the processor is further configured to: implement an artificial intelligence or rules engine to automatically validate the identified critical threat and subsequent case creation (see ¶[0012]; a change in any metric is captured by a machine-based learning system that has built-in validation algorithms to ensure data authenticity and accuracy by verifying and triangulating information from all available open sources. [0015] Once the gathered information is verified and validated, the machine runs the updated data sets through intelligent algorithms that make changes to the quantified risk profile of the location or third party entity).
KENYON discloses outsourcing contracts that includes using thresholds to determine when a contract is acceptable (see ¶[0058]-[0059]. VASHISTHA discloses risk associated with supplier profiles that includes validating data with machine learning. It would have been obvious to include validation with machine learning as taught by VASHISHTA in the system executing the method of KENYON with the motivation to determine risk associated with a contracting supplier.
Claim(s) 8 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20060167706 A1 to KENYON et al. in view of US 20140379387 A1 to AU LI, US 20150073929 A1 to PSOTA et al., US 20210042824 A1 to TARLER et al., US 20160055202 A1 to ROSENBERG et al., and US 20200265357 A1 to VASISTHA as applied to claims 1 and 7 above, and further in view of US 20220374797 A1 to Kalinski (hereinafter ‘KALINSKI’).
Claim 8 (Currently Amended)
The combination of KENYON, AU LI, PSOTA, TARLER, ROSENBERG, and VASHISTHA discloses the method according to claim 7.
The combination of KENYON, AU LI, PSOTA, TARLER, ROSENBERG, and VASHISTHA does not specifically disclose, but KALINSKI discloses, further comprising: implementing, by the artificial intelligence or rules engine, a machine learning based predictive analytics modeling technique where the predictive analytics identifies, matches patterns, and prioritizes suppliers with higher degree or likelihood of risk threshold breach over a future timeline (see abstract and ¶[0112] & [0195]; per shipment cargo insurance determined using risk probability values determined through machine learning based on patterns. Sensors may create alerts).
KENYON discloses outsourcing contracts that includes using thresholds to determine when a contract is acceptable (see ¶[0058]-[0059]. KALINSKI discloses alerts based on risk determined for shipments of goods. It would have been obvious to include the alerts as taught by KALINSKI in the system executing the method of KENYON with the motivation to identify unacceptable contracts based on risk.
Claim 17 (Currently Amended)
The combination of KENYON, AU LI, PSOTA, TARLER, ROSENBERG, and VASHISTHA discloses the system according to claim 16.
The combination of KENYON, AU LI, PSOTA, TARLER, ROSENBERG, and VASHISTHA does not specifically disclose, but KALINSKI discloses, wherein the processor is further configured to: implement, by the artificial intelligence or rules engine, a machine learning based predictive analytics modeling technique where the predictive analytics model identifies, matches patterns, and prioritizes suppliers with higher degree or likelihood of risk threshold breach over a future timeline (see abstract and ¶[0112] & [0195]; per shipment cargo insurance determined using risk probability values determined through machine learning based on patterns. Sensors may create alerts).
KENYON discloses outsourcing contracts that includes using thresholds to determine when a contract is acceptable (see ¶[0058]-[0059]. KALINSKI discloses alerts based on risk determined for shipments of goods. It would have been obvious to include the alerts as taught by KALINSKI in the system executing the method of KENYON with the motivation to identify unacceptable contracts based on risk.
Claim(s) 9 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20060167706 A1 to KENYON et al. in view of US 20140379387 A1 to AU LI, US 20150073929 A1 to PSOTA et al., US 20210042824 A1 to TARLER et al., US 20160055202 A1 to ROSENBERG et al., US 20200265357 A1 to VASISTHA, and US 20220374797 A1 to KALINSKI as applied to claims 1 and 8 above, and further in view of US 11429927 B1 to Melancon et al. (hereinafter ‘MELANCON’).
Claim 9 (Currently Amended)
The combination of KENYON, AU LI, PSOTA, TARLER, ROSENBERG, VASHISTHA, and KALINKSI discloses the method according to claim 8.
The combination of KENYON, AU LI, PSOTA, TARLER, ROSENBERG, VASHISTHA, and KALINKSI does not explicitly disclose, but MELANCON discloses, further comprising: generating, by the predictive analytics model, potential future scenario combinations based on outputs of rules engine (see col 2, ln 42-59; apply machine learning techniques to archived supply chain data to predict supply chain failures before they occur, generate alerts and contextual visualizations that identify the underlying causes of the predicted failures, and provide situational awareness of the past and predicted state of the supply chain affected by the predicted failure).
KENYON discloses outsourcing contracts that includes using thresholds to determine when a contract is acceptable (see ¶[0058]-[0059]) that includes potential client failure (see ¶[0048]). MELANCON discloses predicting supply chain failures before they occur to prevent the failures. It would have been obvious to predict supply chain failures before they occur as taught by MELANCON in the system executing the method of KENYON with the motivation to evaluate contracts and predict client failure.
Claim 18 (Currently Amended)
The combination of KENYON, AU LI, PSOTA, TARLER, ROSENBERG, VASHISTHA, and KALINKSI discloses the system according to claim 17.
The combination of KENYON, AU LI, PSOTA, TARLER, ROSENBERG, VASHISTHA, and KALINKSI does not explicitly disclose, but MELANCON discloses, wherein the processor is further configured to: generate, by the predictive analytics model, potential future scenario combinations based on outputs of rules engine (see col 2, ln 42-59; apply machine learning techniques to archived supply chain data to predict supply chain failures before they occur, generate alerts and contextual visualizations that identify the underlying causes of the predicted failures, and provide situational awareness of the past and predicted state of the supply chain affected by the predicted failure).
KENYON discloses outsourcing contracts that includes using thresholds to determine when a contract is acceptable (see ¶[0058]-[0059]) that includes potential client failure (see ¶[0048]). MELANCON discloses predicting supply chain failures before they occur to prevent the failures. It would have been obvious to predict supply chain failures before they occur as taught by MELANCON in the system executing the method of KENYON with the motivation to evaluate contracts and predict client failure.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/RICHARD N SCHEUNEMANN/ Primary Examiner, Art Unit 3624