DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on April 15, 2026, has been entered.
Claims 1, 11, 12 ,18, and 19 are amended.
Claims 21-26 are added.
Claims 1, 2, 4, 7-13, and 17-26 are pending.
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 pp. 12-13. The Examiner respectfully disagrees As indicated in the rejection, below, the independent claims recite the abstract idea of managing call to actions for sales representatives in the preamble.
The Applicant further submits that the present claims are subject matter eligible due to similarities with Example 42 from the Subject Matter Eligibility Examples. See Remarks pp. 13-14. In response, the Examiner points out that the claim from Example 42 recites a step for standardizing formats that is rooted in computer technology. In contrast, the present claims recite steps for managing call to actions for sales representatives, which is not rooted in computer technology. No apparent improvement to computer technology is recited in the present claims.
The Applicant further submits that the claims are subject matter eligible because the claims provide a technical solution to a technical problem. See Remarks pp. 14-15. The Examiner respectfully disagrees. The claims recite steps for providing a business solution to a business problem – prioritizing call to actions. Prioritizing business leads or transactions is not a technical problem; it is a business problem. The present claims recite steps that could be implemented mentally or on paper by a human being. Therefore, the claims attempt to manage personal behavior or interactions between people. Specifically, a call to action represents a business transaction (or a potential business transaction). At best, the machine learning elements amount to a technological environment for implementing the abstract idea. The abstract idea of managing call to actions for sales representatives is generally linked to an environment with machine learning for implementation. See MPEP §2106.05(h).
The Applicant further contends that the claims provide a practical application of any recited abstract idea. See Remarks p. 16. Again, the Examiner respectfully disagrees. For reasons, explained above, the claims are not similar to the claims from Example 42. The present claims report insights regarding a modeling process to an administrator, which is not a practical application.
The Applicant further contends that the claims recite elements that amount to significantly more than the recited abstract idea. For essentially the same reasons set forth, above, the Examiner respectfully disagrees. Lack of conventionality does not imply subject matter eligibility. Additional elements outside the scope of the abstract idea have been considered, but they have been found to amount to generic computer hardware operating in a machine learning environment. Every limitation of exemplary independent claim 1 has been considered individually and in combination in arriving at the conclusion of ineligibility.
The rejection for lack of subject matter eligibility is updated and maintained.
35 USC §103 Rejections
Amendments to the independent claims changed the scope of the claims, necessitating further search and consideration of the prior art. A new search returned the Luo reference, which is cited in the rejection of the independent claims, below. The Applicant’s arguments with respect to the previously cited Kuhn reference are moot in light of the updated rejections.
The rejection of the dependent claims stands or falls with the rejection of the independent claims.
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, 2, 4, 7-13, and 17-26 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, 2, 4, 7-13, and 17-26 are all directed to one of the four statutory categories of invention, the claims are directed to managing call to actions for sales representatives (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: “obtaining . . . historical [call to actions] and information about the [historical call to actions];” “analyzing . . . the [historical call to actions] and the information;” “obtaining . . . a trained insights model:” “obtaining . . . historical sales drivers;” “analyzing . . . the [historical sales drivers];” “obtaining . . . a trained analysis model;” “obtaining [call to actions] relevant to a customer and each of the [call to actions’ revenue conversion value];” “inferring . . . a first ranking of the [call to actions];” “obtaining . . . an operating plan priority information;” “inferring . . . a second ranking of the [call to actions];” “inferring . . . a key sales driver . . . for the [sales representative and a corresponding target cut-off value associated with the key sales driver;” “inferring . . . a third ranking of the [call to actions];” “assigning . . . associated coefficients to the first ranking, the second ranking, and the third ranking;” “obtaining . . . a final ranking of the [call to actions];” and “displaying of the final ranking of the [call to actions] to the sales representative.” The steps are all steps for managing personal behavior related to the abstract idea of managing call to actions for sales representatives that, when considered alone and in combination, are part of the abstract idea of managing call to actions for sales representatives. The dependent claims further recite steps for managing personal behavior that are part of the abstract idea of managing call to actions for sales representatives. 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 determining the best sales actions that lead to increased sales and revenue.
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 (engines in independent claims 1, 11, and 18). 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 require no more than a generic computer (engines in independent claims 1, 11, and 18) 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) 11-13, 23, and 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20200097879 A1 to Venkata et al. (hereinafter ‘VENKATA’) in view of US 20160378932 A1 to Sperling et al. (hereinafter ‘SPERLING’), US 20230103753 A1 to Luo et al. (hereinafter ‘LUO’), and US 20160063560 A1 to Hameed et al. (hereinafter ‘HAMEED’).
Claim 11 (Currently Amended)
VENKATA discloses a method for managing call to actions (CTAs) for a sales representative (SR) (see abstract; identify at-risk opportunities and generating a recommendation that can be used by the representatives to help salvage the opportunities), the method comprising:
obtaining, by an engine, historical CTAs (HCTAs) and information about the HCTAs (see abstract; historical information as well as machine learning algorithms are used to identify the failing opportunities by classifying new and currently in-pursuit opportunities using information from past opportunities to identify which of the new and in-pursuit opportunities might be at risk);
wherein the information about the HCTAs comprises: each of the HCTAs' revenue conversion value (RCV) (see ¶[0030]-[0032], [0056], and [0065] & Table 1; Example data sources may include quantitative and qualitative data pertaining to Sales Opportunity Stages, Age, Revenue and the like regarding sales opportunity. Predicted revenue from an opportunity. In some cases, the opportunity may be scored as an expected value of business based on a combination of the expected probability of winning and the size of business expected).
VENKATA does not specifically disclose, but SPERLING discloses, an equal weighted sum of a total order amount, historical pipeline loss amount, and historical quote loss amount against the HCTAs (see ¶[0100]; opportunities with quote loss controls that include quote loss by month, compared to peers, sector, etc.).
VENKATA discloses opportunity evaluation and action recommendation that includes modeling the probability of successful sales (see ¶[0017]). SPERLING discloses subscription management with opportunity evaluation that includes quote loss controls. It would have been obvious for one of ordinary skill in the art at the time of invention to include the quote loss controls as taught by SPERLING in the system executing the method of VENKATA with the motivation to evaluate opportunities.
VENKATA further discloses analyzing, by the engine, the HCTAs and the information to generate an insights model that ranks the HCTAs based on the information about the HCTAs (see ¶[0030]-[0032], [0056], and [0065] & Table 1; Example data sources may include quantitative and qualitative data pertaining to Sales Opportunity Stages, Age, Revenue, and the like regarding the sales opportunity. Predicted revenue from an opportunity. In some cases, the opportunity may be scored as an expected value of business based on a combination of the expected probability of winning and the size of business expected);
obtaining, by the engine and based on a target parameter, a trained insights model, wherein the insights model is trained using at least the HCTAs and the information (see again ¶[0030]-[0032], [0056], and [0065] & Table 1; the opportunity may be scored as an expected value of business based on a combination of the expected probability of winning and the size of business expected. See also ¶[0036] and [0043]; sentiment can be determined based on linguistic analysis of email messages from the customers using word embeddings from publicly available corpuses and training on local data. The system may partition the data into training, validation, and test sets using, for example, standard sampling techniques that oversample low frequency instances while adding stochastic noise components to the independent variables. The system may also train a non-linear model such as an AdaBoost or XGBoost for overall top-level classification by global or non-sequential KPIs/metrics and by taking several variables as the encoded output of the sequential steps in an opportunity using long short term memory. The result is an opportunity scoring model.); and
notifying, by the engine, an analyzer about the trained insights model (see ¶[0040]; display the score).
obtaining, by an analyzer, historical sales drivers (HSDs) (see ¶[0046] and [0068]; determine a best action to move the at-risk opportunity to a better state with a higher likelihood of success. The next best action information can be generated based on similar opportunities that closed successfully, for example, and may also be generated based on the model simulation to find the shortest path to a winning classification. Before generating the recommendation, distances between the losing opportunity and winning opportunities with similar characteristics are calculated);
analyzing, by the analyzer, the HSDs to generate an analysis model that identifies a set of key sales drivers and, for each key sales driver of the identified set of key sales drives, a corresponding target cut-off value, (see ¶[0056] and Table 1; max days in Stage. See also ¶[0062]; if one of the subset of variables (locally important variables) is Number of Calls in Agreement, and the representative in the identified closest Winning opportunities made a minimum of fifteen calls, but the representative in the Losing opportunity has only made two calls, the recommendation may be to call once per week to increase the number of calls. In some embodiments, the recommendation may include the type of call (e.g., status update, check-in, or the like).
VENKATA does not specifically disclose, but LUO discloses, wherein the analysis model comprises a combination of a random forest regression model and a Shapley framework that explains the random forest regression model to an administrator (see ¶[0019] and ¶[0088]; a value of the particular input feature were to exceed a determined, threshold feature value, then a Shapley value associated with the particular input feature value would be likely to exceed a corresponding threshold Shapley value, which may indicate that any increase in the value of the particular input feature would also drive an increase in the predicted output of the trained, gradient-boosted decision-tree process (e.g., a value indicative of a predicted likelihood of an occurrence of a default event involving a customer of the financial institution and a corresponding credit-card account during the future temporal interval, as described herein).
The combination of VENKATA and LUO does not specifically disclose, but HAMEED discloses, wherein the Shapley framework is implemented at a role-region-segment level associated with the SR, wherein the role-region- segment level specifies at least a role of the SR in an organization, a region associated with the organization, and a segment associated with the organization (see ¶[0056]; buyer variables of an employer of the user include an industry identifier, a region identifier, a department identifier, a current role of the user, a current title of the user, or a current decision making authority of the user).
VENKATA does not specifically disclose, but LUO discloses, wherein the corresponding target cut-off value for each key sales driver is determined by evaluating Shapley values across a plurality of value ranges of that key sales driver and selecting, as the corresponding target cut- off value, a minimum cut-off value beyond which a corresponding Shapley value exhibits a monotonic positive correlation with a revenue growth metric (see ¶[0089]-[0092]; a value of the particular input feature were to exceed a determined, threshold feature value, then a Shapley value associated with the particular input feature value would be likely to exceed a corresponding threshold Shapley value, which may indicate that any increase in the value of the particular input feature would also drive an increase in the predicted output of the trained, gradient-boosted decision-tree process (e.g., a value indicative of a predicted likelihood of an occurrence of a default event involving a customer of the financial institution and a corresponding credit-card account during the future temporal interval, as described herein).
VENKATA further discloses obtaining, by the analyzer and based on the target parameter, a trained analysis model, wherein the analysis model is trained using at least the HSDs (see ¶[0003] and [0032]; Using historical information as well as machine learning algorithms, failing opportunities may be improved using recommendations generated automatically. he data sources 120 can be mined by machine learning algorithms to identify types of opportunities, opportunities that were successful, the products involved in opportunities, the activities that occurred during the opportunities, and so forth. The information gleaned from the mining can be used to assess current opportunities using machine learning based models including but not limited to capsule-network based neural networks for short range order and long short term memory for long range order to find if there is novel information of interest to the end user); and
initiating, by the analyzer, notification of an administrator about the trained analysis model and the trained insights model (see ¶[0040]; display the score).
VENKATA does not specifically disclose, but LUO discloses, wherein the notification about the trained analysis model includes the identified set of key sales drivers and their corresponding target cut-off values (see ¶[0088]-[0089]; Shapley additive explanations decompose output data generated through an application of the trained gradient-boosted, decision-tree processes to a corresponding input dataset, and characterize an impact of a value of each of the numerical or categorical input features on the predicted output, e.g., based on a magnitude of a corresponding, input-feature-specific Shapley value).
VENKATA discloses explanations of models using Shapley values (see ¶[0040]). LUO discloses explaining output predicted by machine learning process that uses Shapley values to explain decision tree processes. It would have been obvious to include the Shapley explanations of decision tree processes as taught by KUHN in the system executing the method of VENKATA with the motivation to understand a decision tree machine learning process.
VENKATA discloses explanations of models using Shapley values (see ¶[0040]) to explain a model regarding opportunity evaluation. LUO discloses explaining output predicted by machine learning process that uses Shapley values to explain decision tree processes. HAMEED discloses profiling user agents by factors including industry, region, role, and segment to model buyer engagement. It would have been obvious to profile agents as taught by HAMEED in the system executing the method of VENKATA and LUO with the motivation to model opportunities. Furthermore, it would have been obvious to explain the model using Shapley values to provide the known benefit of explaining a model to a user.
Claim 12 (Currently Amended)
The combination of VENKATA, SPERLING, LUO, and HAMEED discloses the method as set forth in claim 11.
VENKATA additionally discloses further comprising: after the notification of the administrator: obtaining, by the engine, CTAs relevant to a customer and each of the CTAs’ RCV (see abstract and ¶[0016]; Determining whether the opportunity is likely to close or whether it may be at risk may be based on actions of sales persons, health of the existing relationship with the customer such as service quality and history, and external factors including, but not limited to, news and social media. Providing a series of next best actions (NBA)/recommendations for the opportunities that are not likely to close or that are at risk may include sales personnel actions and customer service quality improvements. Distances between opportunities are estimated based on local neighborhoods determined by relevant variables influencing those opportunities in the local neighborhood. The shortest distance between at risk opportunities and winning opportunities can be identified and utilized to generate the recommendation based on the relevant variables for the shortest path);
inferring, by the engine and using the trained insights model (see ¶[0019] and Fig. 1; an inference engine), a first ranking of the CTAs based on each of the CTAs’ RCV, wherein the first ranking is provided to the analyzer (see ¶[0021]; accurately classify, rank, and calculate the probability of winning the opportunity through activities and actions of sales representatives on open opportunities. See also ¶[0005]; in some embodiments, grouping subsets of the opportunities into local neighborhoods is based at least in part on at least one of a size of each opportunity);
obtaining, by the analyzer and from an administrator, an operating plan priority information (see ¶[0004]; classify opportunities with a score. Assign a negative score for at risk opportunities);
The combination of VENKATA, SPERLING, KUH, and HAMEED does not explicitly disclose, but GILMORE discloses, related to a computing device that is targeted for the customer (see ¶[0053]; identify an entity by email address).
VENKATA further discloses inferring, by the analyzer, a second ranking of the CTAs based on the operating plan priority information, wherein the analyzer has obtained the CTAs from a database (see ¶[0038]; each of the identified metrics can be weighted based on the historical data analysis. The weighted metrics can be used to calculate a score for the opportunity. The score can be an indicator of the probability of a successful closing of the opportunity. This score can be used to classify the opportunity as a winning or losing opportunity);
inferring, by the analyzer and using the trained analysis model, a key sales driver selected from the identified set of key sales drivers for the SR (see again ¶[0056] and Table 1; max days in Stage. See also ¶[0062]; if one of the subset of variables (locally important variables) is Number of Calls in Agreement, and the representative in the identified closest Winning opportunities made a minimum of fifteen calls, but the representative in the Losing opportunity has only made two calls, the recommendation may be to call once per week to increase the number of calls. In some embodiments, the recommendation may include the type of call (e.g., status update, check-in, or the like).
The combination of VENKATA, SPERLING, KUH, and HAMEED does not explicitly disclose, but GILMORE discloses, and a corresponding target cut-off value associated with the key sales driver (see ¶[0069]; price and price range that affect the probability of acceptance).
VENKATA further discloses inferring, by the analyzer and using the trained analysis model, a third ranking of the CTAs based on a comparison between a performance metric associated with the SR and the corresponding target cut-off value associated with the key sales driver (see again ¶[0038]; each of the identified metrics can be weighted based on the historical data analysis. The weighted metrics can be used to calculate a score for the opportunity. The score can be an indicator of the probability of a successful closing of the opportunity. This score can be used to classify the opportunity as a winning or losing opportunity);
assigning, by the analyzer, associated coefficients to the first ranking, the second ranking, and the third ranking (see again ¶[0038]; each of the identified metrics can be weighted);
obtaining, by the analyzer, a final ranking of the CTAs based on the associated coefficients, the first ranking, the second ranking, and the third ranking (see ¶[0040]; classifying opportunities into a losing or winning category is based on the score assigned to each opportunity); and
initiating, by the analyzer, displaying of the final ranking of the CTAs to the SR (see again ¶[0040]; display the score).
VENKATA discloses opportunity evaluation and action recommendation that includes modeling the probability of successful sales (see ¶[0017]). GILMORE discloses a probability of acceptance of a price in a range of vehicles for sale, where customers are identified by email address. It would have been obvious to include the range of prices with probabilities of acceptance for sale, and identification of customers, as taught by GILMORE in the system executing the method of VENKATA with the motivation to model the probability of a successful sale.
Claim 13 (Original)
The combination of The combination of VENKATA, SPERLING, LUO, and HAMEED discloses the method as set forth in claim 11.
VENKATA further discloses wherein the HSDs comprise at least one selected from a group consisting of a quoting activity performed by a second SR, online participation information of a customer, line of business (LOB) information shared with the customer, information with respect to retain-acquire-develop (RAD) approach followed by an organization that shares the LOB information with the customer, and a sales activity associated with a partner that is employed by the organization (see ¶[0017] and [0032]; opportunities within a sales group can go through multiple stages at varying velocities, can have associated activities (e.g., email, calls, meetings, tasks, revenue forecast changes, and so forth. Sales Activities such as Total Calls, Emails, Demos, Meetings, and the like).
Claim 23 (New)
The combination of VENKATA, SPERLING, LUO, and HAMEED discloses the method as set forth in claim 11.
VENKATA further discloses wherein the trained analysis model is stored in a model repository and periodically retrained using newly obtained HSDs (see ¶[0077]; data feeds and/or event updates may include, but are not limited to, Twitter® feeds, Facebook® updates or real-time updates received from one or more third party information sources and continuous data streams, which may include real-time events related to sensor data applications, financial tickers, network performance measuring tools (e.g., network monitoring and traffic management applications), clickstream analysis tools, automobile traffic monitoring, and the like. Server 712 may also include one or more applications to display the data feeds and/or real-time events via one or more display devices of client computing devices 702, 704, 706, and 708).
Claim 25 (New)
The combination of VENKATA, SPERLING, LUO, and HAMEED discloses the method as set forth in claim 11.
VENKATA further discloses wherein notifying the administrator comprises presenting the Shapley explanations and corresponding target cut-off values via a graphical user interface (see ¶[0024] and [0055]; present information on a user interface).
Claim(s) 1, 2, 18-20, and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20200097879 A1 to VENKATA et al. in view of US 20160378932 A1 to SPERLING et al., US 20230103753 A1 to LUO et al., and US 20160063560 A1 to HAMEED et al., and US 20230162212 A1 to Gilmore (hereinafter ‘GILMORE’).
Claim 1 (Currently Amended)
VENKATA discloses a method for managing call to actions (CTAs) for a sales representative (SR) (see abstract; identify at-risk opportunities and generating a recommendation that can be used by the representatives to help salvage the opportunities), the method comprising:
obtaining, by an engine, historical CTAs (HCTAs) and information about the HCTAs (see abstract; historical information as well as machine learning algorithms are used to identify the failing opportunities by classifying new and currently in-pursuit opportunities using information from past opportunities to identify which of the new and in-pursuit opportunities might be at risk);
wherein the information about the HCTAs comprises: each of the HCTAs' revenue conversion value (RCV) (see ¶[0030]-[0032], [0056], and [0065] & Table 1; Example data sources may include quantitative and qualitative data pertaining to Sales Opportunity Stages, Age, Revenue and the like regarding sales opportunity. Predicted revenue from an opportunity. In some cases, the opportunity may be scored as an expected value of business based on a combination of the expected probability of winning and the size of business expected).
VENKATA does not specifically disclose, but SPERLING discloses, an equal weighted sum of a total order amount, historical pipeline loss amount, and historical quote loss amount against the HCTAs (see ¶[0100]; opportunities with quote loss controls that include quote loss by month, compared to peers, sector, etc.).
VENKATA discloses opportunity evaluation and action recommendation that includes modeling the probability of successful sales (see ¶[0017]). SPERLING discloses subscription management with opportunity evaluation that includes quote loss controls. It would have been obvious for one of ordinary skill in the art at the time of invention to include the quote loss controls as taught by SPERLING in the system executing the method of VENKATA with the motivation to evaluate opportunities.
VENKATA further discloses, analyzing, by the engine, the HCTAs and the information to generate an insights model that ranks the HCTAs based on the information about the HCTAs (see ¶[0030]-[0032], [0056], and [0065] & Table 1; Example data sources may include quantitative and qualitative data pertaining to Sales Opportunity Stages, Age, Revenue, and the like regarding the sales opportunity. Predicted revenue from an opportunity. In some cases, the opportunity may be scored as an expected value of business based on a combination of the expected probability of winning and the size of business expected);
obtaining, by the engine and based on a target parameter, a trained insights model, wherein the insights model is trained using at least the HCTAs and the information (see again ¶[0030]-[0032], [0056], and [0065] & Table 1; the opportunity may be scored as an expected value of business based on a combination of the expected probability of winning and the size of business expected. See also ¶[0036] and [0043]; sentiment can be determined based on linguistic analysis of email messages from the customers using word embeddings from publicly available corpuses and training on local data. The system may partition the data into training, validation, and test sets using, for example, standard sampling techniques that oversample low frequency instances while adding stochastic noise components to the independent variables. The system may also train a non-linear model such as an AdaBoost or XGBoost for overall top-level classification by global or non-sequential KPIs/metrics and by taking several variables as the encoded output of the sequential steps in an opportunity using long short term memory. The result is an opportunity scoring model.);
obtaining, by an analyzer, historical sales drivers (HSDs) (see ¶[0046] and [0068]; determine a best action to move the at-risk opportunity to a better state with a higher likelihood of success. The next best action information can be generated based on similar opportunities that closed successfully, for example, and may also be generated based on the model simulation to find the shortest path to a winning classification. Before generating the recommendation, distances between the losing opportunity and winning opportunities with similar characteristics are calculated);
analyzing, by the analyzer, the HSDs to generate an analysis model that identifies a set of key sales drivers and, for each key sales driver of the identified set of key sales drivers, a corresponding target cut-off value (see ¶[0056] and Table 1; max days in Stage. See also ¶[0062]; if one of the subset of variables (locally important variables) is Number of Calls in Agreement, and the representative in the identified closest Winning opportunities made a minimum of fifteen calls, but the representative in the Losing opportunity has only made two calls, the recommendation may be to call once per week to increase the number of calls. In some embodiments, the recommendation may include the type of call (e.g., status update, check-in, or the like);
VENKATA does not specifically disclose, but LUO discloses, a combination of a random forest regression model and a Shapley framework that explains the random forest regression model to an administrator (see ¶[0019] and ¶[0088]; a value of the particular input feature were to exceed a determined, threshold feature value, then a Shapley value associated with the particular input feature value would be likely to exceed a corresponding threshold Shapley value, which may indicate that any increase in the value of the particular input feature would also drive an increase in the predicted output of the trained, gradient-boosted decision-tree process (e.g., a value indicative of a predicted likelihood of an occurrence of a default event involving a customer of the financial institution and a corresponding credit-card account during the future temporal interval, as described herein).
The combination of VENKATA and LUO does not specifically disclose, but HAMEED discloses, wherein the Shapley framework is implemented at a role-region-segment level associated with the SR, wherein the role-region- segment level specifies at least a role of the SR in an organization, a region associated with the organization, and a segment associated with the organization (see ¶[0056]; buyer variables of an employer of the user include an industry identifier, a region identifier, a department identifier, a current role of the user, a current title of the user, or a current decision making authority of the user).
VENKATA does not specifically disclose, but LUO discloses, wherein the corresponding target cut-off value for each key sales driver is determined by evaluating Shapley values across a plurality of value ranges of that key sales driver and selecting, as the corresponding target cut- off value, a minimum cut-off value beyond which a corresponding Shapley value exhibits a monotonic positive correlation with a revenue growth metric (see ¶[0089]-[0092]; a value of the particular input feature were to exceed a determined, threshold feature value, then a Shapley value associated with the particular input feature value would be likely to exceed a corresponding threshold Shapley value, which may indicate that any increase in the value of the particular input feature would also drive an increase in the predicted output of the trained, gradient-boosted decision-tree process (e.g., a value indicative of a predicted likelihood of an occurrence of a default event involving a customer of the financial institution and a corresponding credit-card account during the future temporal interval, as described herein).
VENKATA further discloses, obtaining, by the analyzer and based on the target parameter, a trained analysis model, wherein the analysis model is trained using at least the HSDs (see ¶[0003] and [0032]; Using historical information as well as machine learning algorithms, failing opportunities may be improved using recommendations generated automatically. The data sources 120 can be mined by machine learning algorithms to identify types of opportunities, opportunities that were successful, the products involved in opportunities, the activities that occurred during the opportunities, and so forth. The information gleaned from the mining can be used to assess current opportunities using machine learning based models including but not limited to capsule-network based neural networks for short range order and long short term memory for long range order to find if there is novel information of interest to the end user);
obtaining, by the engine, CTAs relevant to a customer and each of the CTAs’ RCV (see abstract and ¶[0016]; Determining whether the opportunity is likely to close or whether it may be at risk may be based on actions of sales persons, health of the existing relationship with the customer such as service quality and history, and external factors including, but not limited to, news and social media. Providing a series of next best actions (NBA)/recommendations for the opportunities that are not likely to close or that are at risk may include sales personnel actions and customer service quality improvements. Distances between opportunities are estimated based on local neighborhoods determined by relevant variables influencing those opportunities in the local neighborhood. The shortest distance between at risk opportunities and winning opportunities can be identified and utilized to generate the recommendation based on the relevant variables for the shortest path);
inferring, by the engine and using the trained insights model (see ¶[0019] and Fig. 1; an inference engine), a first ranking of the CTAs based on each of the CTAs’ RCV, wherein the first ranking is provided to the analyzer (see ¶[0021]; accurately classify, rank, and calculate the probability of winning the opportunity through activities and actions of sales representatives on open opportunities. See also ¶[0005]; in some embodiments, grouping subsets of the opportunities into local neighborhoods is based at least in part on at least one of a size of each opportunity); and
obtaining, by the analyzer and from an administrator, an operating plan priority information (see ¶[0004]; classify opportunities with a score. Assign a negative score for at risk opportunities).
VENKATA does not explicitly disclose, but GILMORE discloses, related to a computing device that is targeted for the customer (see ¶[0053]; identify an entity by email address).
VENKATA further discloses inferring, by the analyzer, a second ranking of the CTAs based on the operating plan priority information, wherein the analyzer has obtained the CTAs from a database (see ¶[0038]; each of the identified metrics can be weighted based on the historical data analysis. The weighted metrics can be used to calculate a score for the opportunity. The score can be an indicator of the probability of a successful closing of the opportunity. This score can be used to classify the opportunity as a winning or losing opportunity);
inferring, by the analyzer and using the trained analysis model, a key sales driver selected from the identified set of key sales drivers for the SR (see again ¶[0056] and Table 1; max days in Stage. See also ¶[0062]; if one of the subset of variables (locally important variables) is Number of Calls in Agreement, and the representative in the identified closest Winning opportunities made a minimum of fifteen calls, but the representative in the Losing opportunity has only made two calls, the recommendation may be to call once per week to increase the number of calls. In some embodiments, the recommendation may include the type of call (e.g., status update, check-in, or the like).
VENKATA does not explicitly disclose, but GILMORE discloses, and a corresponding target cut-off value associated with the key sales driver (see ¶[0069]; price and price range that affect the probability of acceptance).
VENKATA further discloses inferring, by the analyzer and using the trained analysis model, a third ranking of the CTAs based on a comparison between a performance metric associated with the SR and the corresponding target cut-off value associated with the key sales driver (see again ¶[0038]; each of the identified metrics can be weighted based on the historical data analysis. The weighted metrics can be used to calculate a score for the opportunity. The score can be an indicator of the probability of a successful closing of the opportunity. This score can be used to classify the opportunity as a winning or losing opportunity);
assigning, by the analyzer, associated coefficients to the first ranking, the second ranking, and the third ranking (see again ¶[0038]; each of the identified metrics can be weighted);
obtaining, by the analyzer, a final ranking of the CTAs based on the associated coefficients, the first ranking, the second ranking, and the third ranking (see ¶[0040]; classifying opportunities into a losing or winning category is based on the score assigned to each opportunity); and
initiating, by the analyzer, displaying of the final ranking of the CTAs to the SR (see again ¶[0040]; display the score).
VENKATA discloses opportunity evaluation and action recommendation that includes modeling the probability of successful sales (see ¶[0017]). GILMORE discloses a probability of acceptance of a price in a range of vehicles for sale, where customers are identified by email address. It would have been obvious to include the range of prices with probabilities of acceptance for sale, and identification of customers, as taught by GILMORE in the system executing the method of VENKATA with the motivation to model the probability of a successful sale.
VENKATA discloses explanations of models using Shapley values (see ¶[0040]). LUO discloses explaining output predicted by machine learning process that uses Shapley values to explain decision tree processes. It would have been obvious to include the Shapley explanations of decision tree processes as taught by LUO in the system executing the method of VENKATA with the motivation to understand a decision tree machine learning process.
VENKATA discloses explanations of models using Shapley values (see ¶[0040]) to explain a model regarding opportunity evaluation. LUO discloses explaining output predicted by machine learning process that uses Shapley values to explain decision tree processes. HAMEED discloses profiling user agents by factors including industry, region, role, and segment to model buyer engagement. It would have been obvious to profile agents as taught by HAMEED in the system executing the method of VENKATA and LUO with the motivation to model opportunities. Furthermore, it would have been obvious to explain the model using Shapley values to provide the known benefit of explaining a model to a user.
Claim 2 (Original)
The combination of VENKATA, SPERLING, LUO, HAMEED, and GILMORE discloses the method as set forth in claim 1.
VENKATA further discloses wherein the HSDs comprise at least one selected from a group consisting of a quoting activity performed by a second SR, online participation information of a customer, line of business (LOB) information shared with the customer, information with respect to retain-acquire-develop (RAD) approach followed by an organization that shares the LOB information with the customer, and a sales activity performed by a partner that is employed by the organization (see ¶[0017] and [0032]; opportunities within a sales group can go through multiple stages at varying velocities, can have associated activities (e.g., email, calls, meetings, tasks, revenue forecast changes, and so forth. Sales Activities such as Total Calls, Emails, Demos, Meetings, and the like).
Claim 18 (Currently Amended)
VENKATA discloses a method for managing call to actions (CTAs) for a sales representative (SR) (see abstract; identify at-risk opportunities and generating a recommendation that can be used by the representatives to help salvage the opportunities), the method comprising:
obtaining, by an engine, CTAs relevant to a customer (see abstract; historical information as well as machine learning algorithms are used to identify the failing opportunities by classifying new and currently in-pursuit opportunities using information from past opportunities to identify which of the new and in-pursuit opportunities might be at risk) and each of the CTAs’ revenue conversion value (RCV) (see ¶[0030]-[0032], [0056], and [0065] & Table 1; Example data sources may include quantitative and qualitative data pertaining to Sales Opportunity Stages, Age, Revenue, and the like regarding the sales opportunity. Predicted revenue from an opportunity. In some cases, the opportunity may be scored as an expected value of business based on a combination of the expected probability of winning and the size of business expected).
VENKATA does not specifically disclose, but SPERLING discloses, and equal weighted sum of a total order amount, historical pipeline loss amount, and historical quote loss amount against each of the CTAs (see ¶[0100]; opportunities with quote loss controls that include quote loss by month, compared to peers, sector, etc.).
VENKATA discloses opportunity evaluation and action recommendation that includes modeling the probability of successful sales (see ¶[0017]). SPERLING discloses subscription management with opportunity evaluation that includes quote loss controls. It would have been obvious for one of ordinary skill in the art at the time of invention to include the quote loss controls as taught by SPERLING in the system executing the method of VENKATA with the motivation to evaluate opportunities.
VENKATA further discloses, inferring, by the engine and using a trained insights model, a first ranking of the CTAs based on each of the CTAs’ RCV, wherein the first ranking is provided to an analyzer (see ¶[0021]; accurately classify, rank, and calculate the probability of winning the opportunity through activities and actions of sales representatives on open opportunities. See also ¶[0005]; in some embodiments, grouping subsets of the opportunities into local neighborhoods is based at least in part on at least one of a size of each opportunity);
obtaining, by the analyzer and from an administrator, an operating plan priority information (see ¶[0004]; classify opportunities with a score. Assign a negative score for at risk opportunities).
VENKATA does not explicitly disclose, but GILMORE discloses, related to a computing device that is targeted for the customer (see ¶[0053]; identify an entity by email address).
VENKATA further discloses, inferring, by the analyzer, a second ranking of the CTAs based on the operating plan priority information, wherein the analyzer has obtained the CTAs from a database (see ¶[0038]; each of the identified metrics can be weighted based on the historical data analysis. The weighted metrics can be used to calculate a score for the opportunity. The score can be an indicator of the probability of a successful closing of the opportunity. This score can be used to classify the opportunity as a winning or losing opportunity);
inferring, by the analyzer and using a trained analysis model, a key sales driver selected from a set of key sales drivers identified by the trained analysis model for the SR (see again ¶[0056] and Table 1; max days in Stage. See also ¶[0062]; if one of the subset of variables (locally important variables) is Number of Calls in Agreement, and the representative in the identified closest Winning opportunities made a minimum of fifteen calls, but the representative in the Losing opportunity has only made two calls, the recommendation may be to call once per week to increase the number of calls. In some embodiments, the recommendation may include the type of call (e.g., status update, check-in, or the like).
VENKATA does not explicitly disclose, but GILMORE discloses, and a corresponding target cut-off value associated with the key sales driver (see ¶[0069]; price and price range that affect the probability of acceptance).
VENKATA does not specifically disclose, but LUO discloses, wherein the trained analysis model is a combination of a random forest regression model and a Shapley framework that explains the random forest regression model to the administrator (see ¶[0019] and ¶[0088]; a value of the particular input feature were to exceed a determined, threshold feature value, then a Shapley value associated with the particular input feature value would be likely to exceed a corresponding threshold Shapley value, which may indicate that any increase in the value of the particular input feature would also drive an increase in the predicted output of the trained, gradient-boosted decision-tree process (e.g., a value indicative of a predicted likelihood of an occurrence of a default event involving a customer of the financial institution and a corresponding credit-card account during the future temporal interval, as described herein).
The combination of VENKATA and LUO does not specifically disclose, but HAMEED discloses, wherein the Shapley framework is implemented at a role-region-segment level associated with the SR, wherein the role-region-segment level specifies at least a role of the SR in an organization, a region associated with the organization, and a segment associated with the organization (see ¶[0056]; buyer variables of an employer of the user include an industry identifier, a region identifier, a department identifier, a current role of the user, a current title of the user, or a current decision making authority of the user).
VENKATA does not specifically disclose, but LUO discloses, wherein the corresponding target cut-off value associated with the key sales driver is determined by evaluating Shapley values across a plurality of value ranges of the key sales driver and selecting, as the corresponding target cut-off value, a minimum cut-off value beyond which a corresponding Shapley value exhibits a monotonic positive correlation with a revenue growth metric (see ¶[0089]-[0092]; a value of the particular input feature were to exceed a determined, threshold feature value, then a Shapley value associated with the particular input feature value would be likely to exceed a corresponding threshold Shapley value, which may indicate that any increase in the value of the particular input feature would also drive an increase in the predicted output of the trained, gradient-boosted decision-tree process (e.g., a value indicative of a predicted likelihood of an occurrence of a default event involving a customer of the financial institution and a corresponding credit-card account during the future temporal interval, as described herein).
VENKATA further discloses, inferring, by the analyzer and using the trained analysis model, a third ranking of the CTAs based on a comparison between a performance metric associated with the SR and the corresponding target cut-off values associated with the key sales driver (see again ¶[0038]; each of the identified metrics can be weighted based on the historical data analysis. The weighted metrics can be used to calculate a score for the opportunity. The score can be an indicator of the probability of a successful closing of the opportunity. This score can be used to classify the opportunity as a winning or losing opportunity);
assigning, by the analyzer, associated coefficients to the first ranking, the second ranking, and the third ranking (see again ¶[0038]; each of the identified metrics can be weighted);
obtaining, by the analyzer, a final ranking of the CTAs based on the associated coefficients, the first ranking, the second ranking, and the third ranking (see ¶[0040]; classifying opportunities into a losing or winning category is based on the score assigned to each opportunity); and
initiating, by the analyzer, displaying of the final ranking of the CTAs to the SR (see again ¶[0040]; display the score).
VENKATA discloses opportunity evaluation and action recommendation that includes modeling the probability of successful sales (see ¶[0017]). GILMORE discloses a probability of acceptance of a price in a range of vehicles for sale, where customers are identified by email address. It would have been obvious to include the range of prices with probabilities of acceptance for sale, and identification of customers, as taught by GILMORE in the system executing the method of VENKATA with the motivation to model the probability of a successful sale.
VENKATA discloses explanations of models using Shapley values (see ¶[0040]). LUO discloses explaining output predicted by machine learning process that uses Shapley values to explain decision tree processes. It would have been obvious to include the Shapley explanations of decision tree processes as taught by LUO in the system executing the method of VENKATA with the motivation to understand a decision tree machine learning process.
VENKATA discloses explanations of models using Shapley values (see ¶[0040]) to explain a model regarding opportunity evaluation. LUO discloses explaining output predicted by machine learning process that uses Shapley values to explain decision tree processes. HAMEED discloses profiling user agents by factors including industry, region, role, and segment to model buyer engagement. It would have been obvious to profile agents as taught by HAMEED in the system executing the method of VENKATA and LUO with the motivation to model opportunities. Furthermore, it would have been obvious to explain the model using Shapley values to provide the known benefit of explaining a model to a user.
Claim 19 (Currently Amended)
The combination of VENKATA, SPERLING, LUO, HAMEED, and GILMORE discloses the method as set forth in claim 18.
VENKATA additionally discloses further comprising: prior to the obtaining the CTAs relevant to the customer and each of the CTA’s RCV: obtaining, by the engine, historical CTAs (HCTAs) and information about the HCTAs (see abstract; historical information as well as machine learning algorithms are used to identify the failing opportunities by classifying new and currently in-pursuit opportunities using information from past opportunities to identify which of the new and in-pursuit opportunities might be at risk);
analyzing, by the engine, the HCTAs and the information to generate the insights model that ranks the HCTAs based on each of the HCTAs’ RCV (see ¶[0030]-[0032], [0056], and [0065] & Table 1; Example data sources may include quantitative and qualitative data pertaining to Sales Opportunity Stages, Age, Revenue, and the like regarding the sales opportunity. Predicted revenue from an opportunity. In some cases, the opportunity may be scored as an expected value of business based on a combination of the expected probability of winning and the size of business expected);
obtaining, by the engine and based on a target parameter, the trained insights model, wherein the insights model is trained using at least the HCTAs and the information (see again ¶[0030]-[0032], [0056], and [0065] & Table 1; the opportunity may be scored as an expected value of business based on a combination of the expected probability of winning and the size of business expected. See also ¶[0036] and [0043]; sentiment can be determined based on linguistic analysis of email messages from the customers using word embeddings from publicly available corpuses and training on local data. The system may partition the data into training, validation, and test sets using, for example, standard sampling techniques that oversample low frequency instances while adding stochastic noise components to the independent variables. The system may also train a non-linear model such as an AdaBoost or XGBoost for overall top-level classification by global or non-sequential KPIs/metrics and by taking several variables as the encoded output of the sequential steps in an opportunity using long short term memory. The result is an opportunity scoring model.);
notifying, by the engine, the analyzer about the trained insights model see ¶[0040]; display the score).
obtaining, by the analyzer, historical sales drivers (HSDs) (see ¶[0046] and [0068]; determine a best action to move the at-risk opportunity to a better state with a higher likelihood of success. The next best action information can be generated based on similar opportunities that closed successfully, for example, and may also be generated based on the model simulation to find the shortest path to a winning classification. Before generating the recommendation, distances between the losing opportunity and winning opportunities with similar characteristics are calculated);
analyzing, by the analyzer, the HSDs to generate the analysis model that identifies the set of key sales drivers and, for each key sales driver of the identified set of key sales drivers, a target cut-off value associated with each key sales driver of the identified set of key sales drivers (see ¶[0056] and Table 1; max days in Stage. See also ¶[0062]; if one of the subset of variables (locally important variables) is Number of Calls in Agreement, and the representative in the identified closest Winning opportunities made a minimum of fifteen calls, but the representative in the Losing opportunity has only made two calls, the recommendation may be to call once per week to increase the number of calls. In some embodiments, the recommendation may include the type of call (e.g., status update, check-in, or the like);
obtaining, by the analyzer and based on the target parameter, the trained analysis model, wherein the analysis model is trained using at least the HSDs (see ¶[0003] and [0032]; Using historical information as well as machine learning algorithms, failing opportunities may be improved using recommendations generated automatically. The data sources 120 can be mined by machine learning algorithms to identify types of opportunities, opportunities that were successful, the products involved in opportunities, the activities that occurred during the opportunities, and so forth. The information gleaned from the mining can be used to assess current opportunities using machine learning based models including but not limited to capsule-network based neural networks for short range order and long short term memory for long range order to find if there is novel information of interest to the end user); and
initiating, by the analyzer, notification of the administrator about the trained analysis model and the trained insights model (see ¶[0040]; display the score), wherein the notification about the trained analysis model includes the identified set of key sales drivers and their corresponding target cut-off values.
Claim 20 (Original)
The combination of VENKATA, SPERLING, LUO, HAMEED, and GILMORE discloses the method as set forth in claim 19.
VENKATA further discloses wherein the HSDs comprise at least one selected from a group consisting of a quoting activity performed by a second SR, online participation information of a customer, line of business (LOB) information shared with the customer, information with respect to retain-acquire-develop (RAD) approach followed by an organization that shares the LOB information with the customer, and a sales activity associated with a partner that is employed by the organization (see ¶[0017] and [0032]; opportunities within a sales group can go through multiple stages at varying velocities, can have associated activities (e.g., email, calls, meetings, tasks, revenue forecast changes, and so forth. Sales Activities such as Total Calls, Emails, Demos, Meetings, and the like).
Claim 24 (New)
The combination of VENKATA, SPERLING, LUO, HAMEED, and GILMORE discloses the method as set forth in claim 18.
VENKATA further discloses wherein the third ranking of CTAs prioritizes CTAs associated with increasing the performance metric toward exceeding the corresponding target cut-off value (see ¶[0038]; each of the identified metrics can be weighted based on the historical data analysis. The weighted metrics can be used to calculate a score for the opportunity. The score can be an indicator of the probability of a successful closing of the opportunity. This score can be used to classify the opportunity as a winning or losing opportunity).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20200097879 A1 to VENKATA et al. in view of US 20160378932 A1 to SPERLING et al., US 20230103753 A1 to LUO et al., and US 20160063560 A1 to HAMEED et al. as applied to claim 11 above, and further in view of US 20250037183 A1 to Mahalanobish (hereinafter ‘MAHALANOBISH’).
Claim 17 (Original)
The combination of VENKATA, SPERLING, LUO, and HAMEED discloses the method as set forth in claim 11.
The combination of VENKATA, SPERLING, LUO, and HAMEED does not specifically disclose, but MAHALANOBISH discloses, wherein the target parameter specifies increasing a year-over-year (YoY) revenue growth performance of the SR and increasing a sales productivity of the SR (see ¶[0071]; each recommended item determined based on the first stage model has a sale probability larger than a first threshold in the future time period. In various examples, the sale probability means a probability to have a sale volume larger than a certain threshold, a probability to have a sale revenue larger than a certain threshold, a probability to have a sale increase compared to previous time period, e.g. larger than 10% increase compared to last week, last month or the same month last year).
VENKATA discloses opportunity evaluation and action recommendation where opportunities can have forecast revenue changes (see ¶[0017]) and success leads to sales (see ¶[0018]). MAHALANOBISH discloses recommending campaigns where the campaign will increase sales and sales revenue when compared with earlier time periods. It would have been obvious to include the recommending based on increased sales and revenues compared with previous time periods as taught by MAHALANOBISH in the system executing the method of VENKATA with the motivation to recommend actions that increase revenue and profits.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20200097879 A1 to VENKATA et al. in view of US 20160378932 A1 to SPERLING et al., US 20230103753 A1 to LUO et al., and US 20160063560 A1 to HAMEED et al., and US 20230162212 A1 to GILMORE as applied to claim 1 above, and further in view of US 20220138820 A1 to Gershon et al. (hereinafter ‘GERSHON’).
Claim 4 (Original)
The combination of VENKATA, SPERLING, LUO, HAMEED, and GILMORE discloses the method as set forth in claim 1.
The combination of VENKATA, SPERLING, LUO, HAMEED, and GILMORE does not specifically disclose, but GERSHON discloses, wherein a HCTA’s RCV indicates how useful was the HCTA for the SR to convert a sales quote into an actual purchase made by the customer (see ¶[0010]; employ a data driven approach that applies a machine learning model on historical data and predicts the probability that a proposed agreement, or quote, generated by a sales entity will be accepted by a particular customer entity).
VENKATA discloses opportunity evaluation and action recommendation that includes modeling the probability of successful sales (see ¶[0017]). GERSHON discloses a sales recommendation tool that includes modeling the probability that a quote will be converted into a sale. It would have been obvious for one of ordinary skill in the art at the time of invention to include the probability of sale as taught by GERSHON in the system executing the method of VENKATA with the motivation to recommend providing a quote to a customer regarding an opportunity.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20200097879 A1 to VENKATA et al. in view of US 20160378932 A1 to SPERLING et al., US 20230103753 A1 to LUO et al., US 20160063560 A1 to HAMEED et al., and US 20230162212 A1 to GILMORE as applied to claim 1 above, and further in view of US 20250037183 A1 to MAHALANOBISH.
Claim 7 (Original)
The combination of VENKATA, SPERLING, LUO, HAMEED, and GILMORE discloses the method as set forth in claim 1.
The combination of VENKATA, SPERLING, LUO, HAMEED, and GILMORE does not specifically disclose, but MAHALANOBISH discloses, wherein the target parameter specifies increasing a year-over-year (YoY) revenue growth performance of the SR and increasing a sales productivity of the SR (see ¶[0071]; each recommended item determined based on the first stage model has a sale probability larger than a first threshold in the future time period. In various examples, the sale probability means a probability to have a sale volume larger than a certain threshold, a probability to have a sale revenue larger than a certain threshold, a probability to have a sale increase compared to previous time period, e.g. larger than 10% increase compared to last week, last month or the same month last year).
VENKATA discloses opportunity evaluation and action recommendation where opportunities can have forecast revenue changes (see ¶[0017]) and success leads to sales (see ¶[0018]). MAHALANOBISH discloses recommending campaigns where the campaign will increase sales and sales revenue when compared with earlier time periods. It would have been obvious to include the recommending based on increased sales and revenues compared with previous time periods as taught by MAHALANOBISH in the system executing the method of VENKATA with the motivation to recommend actions that increase revenue and profits.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20200097879 A1 to VENKATA et al. in view of US 20160378932 A1 to SPERLING et al., US 20230103753 A1 to LUO et al., US 20160063560 A1 to HAMEED et al., and US 20230162212 A1 to GILMORE as applied to claim 1 above, and further in view of US 20110196717 A1 to Colliat et al. (hereinafter ‘COLLIAT’).
Claim 9 (Original)
The combination of VENKATA, SPERLING, LUO, HAMEED, and GILMORE discloses the method as set forth in claim 1.
The combination of VENKATA, SPERLING, LUO, HAMEED, and GILMORE does not specifically disclose, but COLLIAT discloses, wherein the operating plan priority information comprises at least one selected from a group consisting of an annual revenue target of an organization with respect to the computing device, a business expansion plan with respect to the computing device, and a total number of employees hired by the organization to perform the business expansion plan (see ¶[0030]; a company's executive team might decide upon a goal of increasing sales revenue by 10 percent over the sales revenue for the previous year. The top-down quota (target) of a 10 percent increase in sales can be communicated from the executives to the sales force management team and individual sales representatives.).
VENKATA discloses opportunity evaluation and action recommendation where opportunities can have forecast revenue changes (see ¶[0017]). COLLIAT discloses sales performance management where a goal is an increase in sales from the previous year. It would have been obvious for one of ordinary skill in the art at the time of invention to include the goal of increase in sales from the previous year as taught by COLLIAT in the system executing the method of VENKATA with the motivation to recommend action with successful outcomes and positive revenue changes.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20200097879 A1 to VENKATA et al. in view of US 20160378932 A1 to SPERLING et al., US 20230103753 A1 to LUO et al., US 20160063560 A1 to HAMEED et al., and US 20230162212 A1 to GILMORE as applied to claim 1 above, and further in view of US 20120095804 A1 to Calabrese et al. (hereinafter ‘CALABRESE’).
Claim 10 (Original)
The combination of VENKATA, SPERLING, LUO, HAMEED, and GILMORE discloses the method as set forth in claim 1.
The combination of VENKATA, SPERLING, LUO, HAMEED, and GILMORE does not specifically disclose, but CALABRESE discloses, wherein being above the target cut-off value indicates a positive impact on a year-over-year (YoY) revenue growth performance of the SR (see ¶[0034]-[0037]; actions are identified that positively impacted the metrics. New actions may be recommended for certain situations if they are determined to have the greatest probability of positive impact for generating revenue or for achieving another objective.
VENKATA discloses opportunity evaluation and action recommendation that includes modeling the probability of successful sales (see ¶[0017]). CALABRESE discloses sales optimization, where actions are identified that positively impact metrics including revenue. It would have been obvious for one of ordinary skill in the art at the time of invention to include the actions that positively affect revenue as taught by CALABRESE in the system executing the method of VENKATA with the motivation to increase revenue and profits.
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20200097879 A1 to VENKATA et al. in view of US 20160378932 A1 to SPERLING et al., US 20230103753 A1 to LUO et al., US 20160063560 A1 to HAMEED et al., and US 20230162212 A1 to GILMORE as applied to claim 1 above, and further in view of US 20210383268 A1 to Miroshnikov et al. (hereinafter ‘MIROSHNIKOV’).
Claim 21 (New)
The combination of VENKATA, SPERLING, LUO, HAMEED, and GILMORE discloses the method as set forth in claim 1.
The combination of VENKATA, SPERLING, LUO, HAMEED, and GILMORE does not specifically disclose, but MIROSHNIKOV discloses, wherein evaluating Shapley values across the plurality of value ranges comprises partitioning the plurality of value ranges into quantiles or deciles (see ¶[0008]; in another embodiment, the quantile function is based on a Shapley Additive Explanation (SHAP) metric).
VENKATA discloses explanations of models using Shapley values (see ¶[0040]) to explain a model regarding opportunity evaluation. MIROSHNIKOV discloses classification scores using a quantile function based on a SHAP metric. It would have been obvious to include the quantile function as taught by MIROSHNIKOV in the system executing the method of VENKATA with the motivation to use Shapley values to explain a model.
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20200097879 A1 to VENKATA et al. in view of US 20160378932 A1 to SPERLING et al., US 20230103753 A1 to LUO et al., US 20160063560 A1 to HAMEED et al., and US 20230162212 A1 to GILMORE as applied to claim 1 above, and further in view of US 11651380 B1 to Chakraborty et al. (hereinafter ‘CHAKRABORTY’).
Claim 22 (New)
The combination of VENKATA, SPERLING, LUO, HAMEED, and GILMORE discloses the method as set forth in claim 1.
The combination of VENKATA, SPERLING, LUO, HAMEED, and GILMORE does not explicitly disclose, but CHAKRABORTY discloses, wherein the Shapley framework identifies the corresponding target cut-off value based on a change in sign or slope of the Shapley contribution (see col 6, ln 54-col 7, ln 17; the sign of the SHAP value being positive or negative denotes an increase or decrease in churn propensity).
VENKATA discloses explanations of models using Shapley values (see ¶[0040]) to explain a model regarding opportunity evaluation. CHAKRABORTY discloses propensity prediction using Shapley values that provide different explanations based on a positive or negative value. It would have been obvious for one of ordinary skill in the art at the time of invention to include the positive and negative indications of Shapley values as taught by CHAKRABORTY in the system executing the method of VENKATA with the motivation to provide explanations with Shapley values.
Claim(s) 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20200097879 A1 to VENKATA et al. in view of US 20160378932 A1 to SPERLING et al., US 20230103753 A1 to LUO et al., US 20160063560 A1 to HAMEED et al., and US 20230162212 A1 to GILMORE as applied to claim 1 above, and further in view of US 20220076164 A1 to Conort et al. (hereinafter ‘CONORT’).
Claim 26 (New)
The combination of VENKATA, SPERLING, LUO, HAMEED, and GILMORE discloses the method as set forth in claim 1.
The combination of VENKATA, SPERLING, LUO, HAMEED, and GILMORE does not explicitly disclose, but CONORT discloses, wherein the coefficients assigned to the first, second, and third rankings are adjusted based on historical accuracy of prior CTA outcomes (see ¶[0396]; thus, an additional variable inserted into the dataset may indicate the relative weight of each observation. The engine 610 may then use this weight when training models and calculating their accuracy, with the goal being to produce more accurate predictions under higher-weighted conditions).
VENKATA discloses explanations of models using Shapley values (see ¶[0040]) to explain a model regarding opportunity evaluation. CONORT discloses machine learning models trained with weights based on accuracy to create more accurate predictions. It would have been obvious for one of ordinary skill in the art at the time of invention to train a model as taught by CONORT in the system executing the method of VENKATA with the motivation to train a model regarding opportunity evaluation.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20200097879 A1 to VENKATA et al. in view of US 20160378932 A1 to SPERLING et al., US 20230103753 A1 to LUO et al., US 20160063560 A1 to HAMEED et al., US 20230162212 A1 to GILMORE and US 20250037183 A1 to MAHALANOBISH as applied to claims 1 and 7 above, and further in view of US 20220138820 A1 to GERSHON et al.
Claim 8 (Original)
The combination of VENKATA, SPERLING, LUO, HAMEED, GILMORE, and MAHALANOBISH discloses the method as set forth in claim 7.
The combination of VENKATA, SPERLING, LUO, HAMEED, GILMORE, and MAHALANOBISH does not specifically disclose, but GERSHON discloses, wherein the key sales driver specifies an activity that is expected to have a positive impact on increasing the YoY revenue growth performance of the SR, wherein the activity is a hot quote follow-up with the customer (see ¶[0010]; employ a data driven approach that applies a machine learning model on historical data and predicts the probability that a proposed agreement, or quote, generated by a sales entity will be accepted by a particular customer entity).
VENKATA discloses opportunity evaluation and action recommendation that includes modeling the probability of successful sales (see ¶[0017]). GERSHON discloses a sales recommendation tool that includes modeling the probability that a quote will be converted into a sale. It would have been obvious for one of ordinary skill in the art at the time of invention to include the probability of sale as taught by GERSHON in the system executing the method of VENKATA with the motivation to recommend providing a quote to a customer regarding an opportunity.
Conclusion
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/RICHARD N SCHEUNEMANN/Primary Examiner, Art Unit 3624
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