DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-20 are pending in the instant patent application. This Non-Final Office Action is in response to the claims filed.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Regarding Claims 1-9, they are directed to a method, however the claims are directed to a judicial exception without significantly more. Claims 1-9 are directed to the abstract idea of managing a sales representative’s performance.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 1, claim 1 recites obtaining, by an analyzer, historical sales drivers (HSDs); generating, by the analyzer and using the HSDs, an analysis model that identifies a set of key sales drivers and target cut-off values associated with the set of key sales drivers; obtaining, by the analyzer and based on a target parameter, a trained analysis model, wherein the analysis model is trained using at least the HSDs; obtaining, historical key sales drivers (HKSDs), internal parameters (IPs), and external parameters (EPs); analyzing, the HKSDs, the IPs, and the EPs to generate an insights model that provides an insight for the SR; obtaining, and based on the target parameter, a trained insights model, wherein the insights model is trained using at least the HKSDs, the IPs, and the EPs; inferring, by the analyzer and the trained analysis model, a key sales driver for the SR and a target cut-off value associated with the key sales driver, wherein the set of key sales drivers comprises at least the using key sales driver, wherein the key sales driver is provided to the SR; generating, and using the trained insights model and the key sales driver, a second insight for the SR, wherein the second insight is provided to the SR; monitoring, by the analyzer, the SR’s performance with respect to the key sales driver and the second insight; in response to the monitoring, by the analyzer, making a determination that the SR’s performance is above the target cut-off value; identifying, based on the determination and by the analyzer, the SR as a high performing SR; and initiating, by the analyzer, displaying of a score to an administrator, wherein the score indicates the SR as the high performing SR.
These claim limitations fall within the Certain Methods of Organizing Human Activity grouping of abstract ideas due to the managing personal behavior or relationships and commercial interactions due to the marketing or sales activities or behaviors taking place. In addition, the claims fall within the Mental Processes grouping of abstract ideas for these are concepts that can be practically performed in the human mind and/or with pen/paper.
Accordingly, the claim recites an abstract idea and dependent claims 2-9 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of an engine. The engine is merely a generic computing device and does not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 1 includes various elements that are not directed to the abstract idea under 2A. These elements include an engine and the generic computing elements described in the Applicant's specification in at least Para 0199-0201. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claim 1 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
Regarding Claims 10-17, they are directed to a method, however the claims are directed to a judicial exception without significantly more. Claims 10-17 are directed to the abstract idea of managing a sales representative’s performance.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 10, claim 10 recites obtaining, by an analyzer, historical sales drivers (HSDs); generating, by the analyzer and using the HSDs, an analysis model that identifies a set of key sales drivers and target cut-off values associated with the set of key sales drivers; obtaining, by the analyzer and based on a target parameter, a trained analysis model, wherein the analysis model is trained using at least the HSDs; obtaining, historical key sales drivers (HKSDs), internal parameters (IPs), and external parameters (EPs); analyzing, the HKSDs, the IPs, and the EPs to generate an insights model that provides an insight for the SR; obtaining, and based on the target parameter, a trained insights model, wherein the insights model is trained using at least the HKSDs, the IPs, and the EPs; notifying, the analyzer about the trained insights model; and initiating, by the analyzer, notification of an administrator about the trained analysis model and the trained insights model.
These claim limitations fall within the Certain Methods of Organizing Human Activity grouping of abstract ideas due to the managing personal behavior or relationships and commercial interactions due to the marketing or sales activities or behaviors taking place. In addition, the claims fall within the Mental Processes grouping of abstract ideas for these are concepts that can be practically performed in the human mind and/or with pen/paper.
Accordingly, the claim recites an abstract idea and dependent claims 11-17 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of an engine. The engine is merely a generic computing device and does not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 10 and 11 include various elements that are not directed to the abstract idea under 2A. These elements include an engine and the generic computing elements described in the Applicant's specification in at least Para 0199-0201. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claims 10 and 11, alone or in combination, are not drawn to eligible subject matter as they are directed to abstract ideas without significantly more.
Regarding Claims 18-20, they are directed to a method, however the claims are directed to a judicial exception without significantly more. Claims 18-20 are directed to the abstract idea of managing a sales representative’s performance.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 18, claim 18 inferring, by the analyzer and using a trained analysis model, a key sales driver for the SR and a target cut-off value associated with the key sales driver, wherein the set of key sales drivers comprises at least the key sales driver, wherein the key sales driver is provided to the SR and to the engine; generating, by the engine and using the trained insights model and the key sales driver, a second insight for the SR, wherein the second insight is provided to the SR; monitoring, by the analyzer, the SR’s performance with respect to the key sales driver and the second insight; in response to the monitoring, by the analyzer, making a determination that the SR’s performance is above the target cut-off value; identifying, based on the determination and by the analyzer, the SR as a high performing SR; and initiating, by the analyzer, displaying of a score to an administrator, wherein the score indicates the SR as the high performing SR.
These claim limitations fall within the Certain Methods of Organizing Human Activity grouping of abstract ideas due to the managing personal behavior or relationships and commercial interactions due to the marketing or sales activities or behaviors taking place. In addition, the claims fall within the Mental Processes grouping of abstract ideas for these are concepts that can be practically performed in the human mind and/or with pen/paper.
Accordingly, the claim recites an abstract idea and dependent claims 19-20 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of an engine. The engine is merely a generic computing device and does not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 18 and 19 include various elements that are not directed to the abstract idea under 2A. These elements include an engine and the generic computing elements described in the Applicant's specification in at least Para 0199-0201. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claims 18 and 19, alone or in combination, are not drawn to eligible subject matter as they are directed to abstract ideas without significantly more.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 10 and 12 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Venkata et al. (US 2020/0097879 A1).
Regarding Claim 10, Venkata teaches the limitations of Claim 10 which state
obtaining, by an analyzer, historical sales drivers (HSDs) (Venkata: Para 0003, 0018 via Using historical information as well as machine learning algorithms, failing opportunities may be improved using recommendations generated automatically. Specifically, the new and currently in-pursuit opportunities may be classified using information from past opportunities to identify which of the new and in-pursuit opportunities might be at risk and, by estimating distances between opportunities 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 or an ordered list of actions or changes to actions needed for a successful disposition of the opportunity… The historical information may be obtained using automated data entry into a database from other data sources or by mining the information from other data sources);
generating, by the analyzer and using the HSDs, an analysis model that identifies a set of key sales drivers and target cut-off values associated with the set of key sales drivers (Venkata: Para 0056, 0062, Table 1 via A representative list of variables that may be used in the models described herein includes those listed in Table 1… the recommendation is generated for the opportunity classified as a Loss based on the shortest path identified in step 325. The behaviors identified in the Winning opportunities relevant to the locally important variables are identified and used to generate the recommendation. For example, 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 a target parameter, a trained analysis model, wherein the analysis model is trained using at least the HSDs (Venkata: Para 0003, 0032 via Using historical information as well as machine learning algorithms, failing opportunities may be improved using recommendations generated automatically. Specifically, the new and currently in-pursuit opportunities may be classified using information from past opportunities to identify which of the new and in-pursuit opportunities might be at risk and, by estimating distances between opportunities 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 or an ordered list of actions or changes to actions needed for a successful disposition of the opportunity… Email communications and other structured and unstructured historical data are captured from data sources 120. Data sources 120 can include historical information about opportunities including closing information and other activities that occurred during the various stages of an opportunity including, for example, email messages, calls, meetings, tasks, revenue forecast changes based on order changes (e.g., price changes and/or quantity changes), timeline changes (e.g., closing date changes), and so forth. 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. Specifically, smart search and navigation engine 135 can search for information relevant to improving the prospects of each opportunity, and provide it to activity manager 160 for use in evaluating open opportunities. Accordingly, data sources 120 can be sales tracking systems, email systems, and any other data source that stores data relevant to sales that can be mined);
obtaining, by an engine, historical key sales drivers (HKSDs), internal parameters (IPs), and external parameters (EPs) (Venkata: Para 0038, 0046, 0056, 0068, Table 1 via Based on the historical data and data about the opportunity, a score can be generated for pending opportunities in the sales pipeline. In some embodiments, once similar opportunities are identified, the outcomes of the similar opportunities can be used to identify metrics to be used to analyze the current opportunity. For example, the sentiment identified in the current deal can be a metric (i.e., variable). The timing of the deal can be a metric. The products involved in the deal can be a metric. The various activities of the deal can be metrics. 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. The score may have a temporal and variable driven weighting based on long range and short range order calculated through multi-variate nonstationary time series models with attention, including but not limited to long short term memory based models… If the opportunity is identified as at risk for not successfully closing (e.g., having a classification of lose or possible, or having a score of less than 7), the goal of the system 100 is to generate an ordered list of shortest paths each including a sequence of the next best actions as a recommendation to the user to help the salesperson 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… A representative list of variables that may be used in the models described herein includes those listed in Table 1… The completed activities box 625 may provide a listing of reported activities engaged in by the sales representative or sales team for this particular opportunity 605. The completed activities box 625 may help the sales representative remember key activities as well as provide reporting to the remainder of the team and management. Additionally, this information may be used to analyze this opportunity 605 as well as other opportunities);
analyzing, by the engine, the HKSDs, the IPs, and the EPs to generate an insights model that provides an insight for the SR (Venkata: Para 0030-0032, 0038, 0056, 0065, Table 1 via 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… Based on the historical data and data about the opportunity, a score can be generated for pending opportunities in the sales pipeline. In some embodiments, once similar opportunities are identified, the outcomes of the similar opportunities can be used to identify metrics to be used to analyze the current opportunity. For example, the sentiment identified in the current deal can be a metric (i.e., variable). The timing of the deal can be a metric. The products involved in the deal can be a metric. The various activities of the deal can be metrics. 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);
obtaining, by the engine and based on the target parameter, a trained insights model, wherein the insights model is trained using at least the HKSDs, the IPs, and the Eps (Venkata: Para 0030-0032, 0056, 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 (Venkata: Para 0017 via 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), and are influenced by several internal and external events at micro (e.g., the opportunity) and/or macro (e.g., the account) levels. Structured and unstructured data associated with past opportunities can provide a data source for building a model to leverage the information to assign a probability score to an opportunity instance. The probability score can indicate the probability that the opportunity will close successfully with a sale. Furthermore, a rationale can be provided to a user as to the specific reasons the probability score was assigned. Additionally, based on historical data and model simulations, prescriptive guidelines can be provided to the sales representative in terms of potential next-best actions that can be taken. For instance, the sales representative can be notified of what activities can be performed to move an at-risk opportunity to a better state or what activities can be performed to close an opportunity with low risk);
and initiating, by the analyzer, notification of an administrator about the trained analysis model and the trained insights model (Venkata: Para 0040 via Classifying opportunities into a losing or winning category is based on the score assigned to each opportunity. The score is calculated using input from multiple variables including but not limited to derived and compounded variables and indicators from emails, calls, meetings, tasks, revenue forecast changes, and the like. Similarity of opportunities and distance between opportunities are based on the size of the deal, the timing of the deal, one or more products involved in the deal, or similarity of activities in the deal. Variables are transformed non-linearly to forms relevant to the science of the problem of opportunity scoring with a numerical score of 0-10. For example, text and speech may be converted through natural language processing (“NLP”) to sentiment by a NLP engine including word embeddings based models to feed to the semantic analysis engine 140 using a normalized standard deviation-based score of 0-10 (or any other suitable scale including, for example 0-20, 1-100, or the like), where 0 is negative and 10 is positive. In some embodiments, the score may be directly displayed to the user for different variables, so that the user may know how much worse or better each case is with respect to other local opportunities using, for example, explanations based on locally interpretable model interpretations and Shapley values).
Regarding Claim 12, Venkata teaches the limitations of Claim 12 which state
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 (Venkata: Para 0017, 0032 via 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), and are influenced by several internal and external events at micro (e.g., the opportunity) and/or macro (e.g., the account) levels. Structured and unstructured data associated with past opportunities can provide a data source for building a model to leverage the information to assign a probability score to an opportunity instance. The probability score can indicate the probability that the opportunity will close successfully with a sale. Furthermore, a rationale can be provided to a user as to the specific reasons the probability score was assigned. Additionally, based on historical data and model simulations, prescriptive guidelines can be provided to the sales representative in terms of potential next-best actions that can be taken. For instance, the sales representative can be notified of what activities can be performed to move an at-risk opportunity to a better state or what activities can be performed to close an opportunity with low risk… Email communications and other structured and unstructured historical data are captured from data sources 120. Data sources 120 can include historical information about opportunities including closing information and other activities that occurred during the various stages of an opportunity including, for example, email messages, calls, meetings, tasks, revenue forecast changes based on order changes (e.g., price changes and/or quantity changes), timeline changes (e.g., closing date changes), and so forth. 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. Specifically, smart search and navigation engine 135 can search for information relevant to improving the prospects of each opportunity, and provide it to activity manager 160 for use in evaluating open opportunities. Accordingly, data sources 120 can be sales tracking systems, email systems, and any other data source that stores data relevant to sales that can be mined).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Venkata et al. (US 2020/0097879 A1) in view of Shi et al. (US 10,965,811 B1) further in view of Hicyilmaz et al. (US 2013/0179236 A1).
Regarding Claim 1, Venkata teaches the limitations of Claim 1 which state
obtaining, by an analyzer, historical sales drivers (HSDs) (Venkata: Para 0003, 0018 via Using historical information as well as machine learning algorithms, failing opportunities may be improved using recommendations generated automatically. Specifically, the new and currently in-pursuit opportunities may be classified using information from past opportunities to identify which of the new and in-pursuit opportunities might be at risk and, by estimating distances between opportunities 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 or an ordered list of actions or changes to actions needed for a successful disposition of the opportunity… The historical information may be obtained using automated data entry into a database from other data sources or by mining the information from other data sources);
generating, by the analyzer and using the HSDs, an analysis model that identifies a set of key sales drivers and target cut-off values associated with the set of key sales drivers (Venkata: Para 0056, 0062, Table 1 via A representative list of variables that may be used in the models described herein includes those listed in Table 1… the recommendation is generated for the opportunity classified as a Loss based on the shortest path identified in step 325. The behaviors identified in the Winning opportunities relevant to the locally important variables are identified and used to generate the recommendation. For example, 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 a target parameter, a trained analysis model, wherein the analysis model is trained using at least the HSDs (Venkata: Para 0003, 0032 via Using historical information as well as machine learning algorithms, failing opportunities may be improved using recommendations generated automatically. Specifically, the new and currently in-pursuit opportunities may be classified using information from past opportunities to identify which of the new and in-pursuit opportunities might be at risk and, by estimating distances between opportunities 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 or an ordered list of actions or changes to actions needed for a successful disposition of the opportunity… Email communications and other structured and unstructured historical data are captured from data sources 120. Data sources 120 can include historical information about opportunities including closing information and other activities that occurred during the various stages of an opportunity including, for example, email messages, calls, meetings, tasks, revenue forecast changes based on order changes (e.g., price changes and/or quantity changes), timeline changes (e.g., closing date changes), and so forth. 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. Specifically, smart search and navigation engine 135 can search for information relevant to improving the prospects of each opportunity, and provide it to activity manager 160 for use in evaluating open opportunities. Accordingly, data sources 120 can be sales tracking systems, email systems, and any other data source that stores data relevant to sales that can be mined);
obtaining, by an engine, historical key sales drivers (HKSDs), internal parameters (IPs), and external parameters (EPs) (Venkata: Para 0038, 0046, 0056, 0068, Table 1 via Based on the historical data and data about the opportunity, a score can be generated for pending opportunities in the sales pipeline. In some embodiments, once similar opportunities are identified, the outcomes of the similar opportunities can be used to identify metrics to be used to analyze the current opportunity. For example, the sentiment identified in the current deal can be a metric (i.e., variable). The timing of the deal can be a metric. The products involved in the deal can be a metric. The various activities of the deal can be metrics. 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. The score may have a temporal and variable driven weighting based on long range and short range order calculated through multi-variate nonstationary time series models with attention, including but not limited to long short term memory based models… If the opportunity is identified as at risk for not successfully closing (e.g., having a classification of lose or possible, or having a score of less than 7), the goal of the system 100 is to generate an ordered list of shortest paths each including a sequence of the next best actions as a recommendation to the user to help the salesperson 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… A representative list of variables that may be used in the models described herein includes those listed in Table 1… The completed activities box 625 may provide a listing of reported activities engaged in by the sales representative or sales team for this particular opportunity 605. The completed activities box 625 may help the sales representative remember key activities as well as provide reporting to the remainder of the team and management. Additionally, this information may be used to analyze this opportunity 605 as well as other opportunities);
analyzing, by the engine, the HKSDs, the IPs, and the EPs to generate an insights model that provides an insight for the SR (Venkata: Para 0030-0032, 0038, 0056, 0065, Table 1 via 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… Based on the historical data and data about the opportunity, a score can be generated for pending opportunities in the sales pipeline. In some embodiments, once similar opportunities are identified, the outcomes of the similar opportunities can be used to identify metrics to be used to analyze the current opportunity. For example, the sentiment identified in the current deal can be a metric (i.e., variable). The timing of the deal can be a metric. The products involved in the deal can be a metric. The various activities of the deal can be metrics. 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);
obtaining, by the engine and based on the target parameter, a trained insights model, wherein the insights model is trained using at least the HKSDs, the IPs, and the Eps (Venkata: Para 0030-0032, 0056, 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);
inferring, by the analyzer and using the trained analysis model, a key sales driver for the SR and a target cut-off value associated with the key sales driver, wherein the set of key sales drivers comprises at least the key sales driver, wherein the key sales driver is provided to the SR and to the engine (Venkata: Para 0056, Table 1, 0062 via A representative list of variables that may be used in the models described herein includes those listed in Table 1… the recommendation is generated for the opportunity classified as a Loss based on the shortest path identified in step 325. The behaviors identified in the Winning opportunities relevant to the locally important variables are identified and used to generate the recommendation. For example, 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);
generating, by the engine and using the trained insights model and the key sales driver, a second insight for the SR, wherein the second insight is provided to the SR (Venkata: Para 0030-0032, 0056, 0065, Table 1 via The activity manager 160 includes the action simulator 162, guidance engine 164, and action recognizer 166. The activity manager receives information on the current activities and context from the working memory 175, the smart search and navigation engine 135 to find next actions on the path, the semantic analysis engine 140, the inference engine 145, the ontology knowledge base 105, and the activity knowledge base 150. With these sources of information, including opportunity specific information about the activities associated with an opportunity, an opportunity can be evaluated based on its activities and known previous activities to determine a score and guidance for the sales representative. The opportunity may be scored in two ways: as a probability of winning or as a size of the business. Action simulator 162 can perform simulations (e.g., Monte Carlo simulations) to determine scores of the opportunities and potential shortest paths needed to be traversed to get a winning classification. These shortest paths are used by the guidance engine 164 to generate recommendations for the opportunity to help the sales representative make corrections to attempt to salvage the opportunity if the opportunity is at a risk of loss. Email communications and other structured and unstructured historical data are captured from data sources 120. Data sources 120 can include historical information about opportunities including closing information and other activities that occurred during the various stages of an opportunity including, for example, email messages, calls, meetings, tasks, revenue forecast changes based on order changes (e.g., price changes and/or quantity changes), timeline changes (e.g., closing date changes), and so forth. 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. A representative list of variables that may be used in the models described herein includes those listed in Table 1… FIG. 6 provides an example user interface 600 for providing next best action recommendations for deals/opportunities. A specific opportunity 605 may be identified, in this example Optical Communications—Long Haul Services. The opportunity 605 may have any suitable name given by the sales representative or company. The opportunity 605 may be selected from a drop-down menu of opportunities. The user interface 600 may provide information for the opportunity 605 including the persona type of the primary contact (e.g., a straight shooter), the type of industry the opportunity 605 falls into (e.g., manufacturing), the stage of the opportunity 605 (e.g., Discovery, which is the second stage of the deal flow process), the closing date that is currently set or targeted (e.g., Dec. 31, 2016), the projected revenue from the opportunity 605 (e.g., $825,000), and the load type (e.g., long haul). In this example, the opportunity may be a delivery of goods that is a long haul (e.g., cross-country) delivery).
However, Venkata does not explicitly disclose the limitations of Claim 1 which state monitoring, by the analyzer, the SR’s performance with respect to the key sales driver and the second insight; in response to the monitoring, by the analyzer, making a determination that the SR’s performance is above the target cut-off value.
Shi though, with the teachings of Venkata, teaches of
monitoring, by the analyzer, the SR’s performance with respect to the key sales driver and the second insight; in response to the monitoring, by the analyzer, making a determination that the SR’s performance is above the target cut-off value (Shi: Col 13 line 31 – Col 14 line 28 via FIG. 6A illustrates an example agent performance chart 600, in one embodiment. The performance chart 600 includes, in this example, various sales behavioral targets 604 arrayed around a circumference of a circular graph. Within the circular graph are rings indicating performance levels 608. An agent's actual performance with regard to each of the indicated sales behavioral targets is indicated by shaded region 612. Better performance is indicated by shading further from the origin (center) of the graph and closer to the outermost performance ring 608 of the graph 600. In this case, the agent performs well on setting expectations, overcoming objections and asking the right questions. In light of the above description, these behavioral targets may be provided less frequently to the agent given the high performance values. In this example, the agent has low performance regarding the promotion of products, getting customers into a chat flow, and assume the sale. Accordingly, behavioral targets may be provided more frequently and/or more prominently when corresponding behavioral opportunities are identified. FIG. 6B illustrates an example agent performance chart 616 that tracks agent performance per behavioral target as a function of time (in this case, week number). In this chart, darker shading indicates more severe non-compliance with behavioral targets. In this case, the agent's performance for “assume the sale” has improved from week 1 to week 5. The agent's performance for “pitch with value” declines from week 1 to week 2, but then improved thereafter. The agent's performance was mostly steady for “always be closing” from week 1 to week 5. It will be appreciated that performance metrics, whether in the forms depicted in FIGS. 6A and 6B or some other form (e.g., a table of ratios, a stoplight chart) may be presented for review (e.g., by supervisory staff). FIG. 7 illustrates an example 700 of operations for real time monitoring of a conversation to determine if a behavioral target has been met (operation 424). Based on the contemporaneous monitoring of a conversation, the system can determine whether an agent has acted in a way that indicates the behavioral target has been followed (operation 704). This may be accomplished using any of the machine learning model techniques indicated above (e.g., using a trained machine learning model and/or rule based monitoring). A performance profile for an agent may then be updated to reflect whether or not the behavioral target was met (operation 708). The performance profile can include both absolute numbers of followed behavioral targets as well as a ratio of followed behavioral targets to provided behavioral targets and/or a ratio of followed behavioral targets to behavioral opportunities. In some examples, a performance profile may be based on a ratio of a number of prompts followed during a conversation divided by a total number of prompts provided. Upon conclusion of a conversation, an outcome of the conversation can be compared with a predefined desired outcome (operation 712). If the outcome of the conversation is favorable (e.g., a sale is completed, a problem resolved), then the conversation can be analyzed, labeled, and added to the training dataset so as to improve the training of the machine learning model by expanding the data in the training dataset (operation 716). Similarly, if the outcome of the conversation is not favorable (e.g., no sale, no problem resolution), the conversation can be analyzed for behaviors that were not helpful, and similarly added to the training dataset of the machine learning model (operation 716)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Venkata with the teachings of Shi in order to have monitoring, by the analyzer, the SR’s performance with respect to the key sales driver and the second insight; in response to the monitoring, by the analyzer, making a determination that the SR’s performance is above the target cut-off value. The motivations behind this being to incorporate the teachings of utilizing machine learning models to analyze and identify behavioral targets. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Furthermore, Venkata does not explicitly disclose the limitations of Claim 1 which state identifying, based on the determination and by the analyzer, the SR as a high performing SR; and initiating, by the analyzer, displaying of a score to an administrator, wherein the score indicates the SR as the high performing SR.
Hicyilmaz though, with the teachings of Venkata/Shi, teaches of
identifying, based on the determination and by the analyzer, the SR as a high performing SR; and initiating, by the analyzer, displaying of a score to an administrator, wherein the score indicates the SR as the high performing SR (Hicyilmaz: Para 0039-0040 via As shown if FIG. 1B, the computer application will generally include or provide access to a computerized database or spreadsheet for storing a plurality of sales professional records. These records will generally comprise one or more dimensions (D1, D2, . . . , Dn) in which a score for that dimension is recorded. The dimensions are one of, as indicated, (i) objective measures (such as years of selling, sales or profit attainment numbers), (ii) subjective evaluations (the ability to work independently, problem-solving ingenuity), and (iii) dimensions derived from a combination of other dimensions; and preferably all three. Although only one or two of each dimension type are depicted in the simplified example database structure of FIG. 1B, those skilled in the art will appreciate that any number of individual fields or dimensions can be arranged or associated with a given dimension type (i.e., objective, subjective, combination). The dimensions will generally conform to a construct useful for assessing sales performance and for determining sales performance enhancement techniques applicable to selected assessed sales professionals. For this reason, the sales professional records will generally include, for each high-performing sales rep, one or more high-performance indicators. This can be, as depicted in FIG. 1B, as simple as a binary database field (e.g., "yes" they are high performers or "no" they are not high performers) to more typically, objective sales performance metrics being stored as dimension scores (i.e., meeting or exceeding an arbitrary number, or falling in the top portion of the company's sales performance distribution curve). Ideally, a combination of dimensions will be identified whose combination of dimension scores meet or exceed a predetermined value along the associated dimension indicative of a high-performing sales rep).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Venkata/Shi with the teachings of Hicyilmaz in order to have identifying, based on the determination and by the analyzer, the SR as a high performing SR; and initiating, by the analyzer, displaying of a score to an administrator, wherein the score indicates the SR as the high performing SR. The motivations behind this being to incorporate the teachings of computerized techniques for enhancing the sales performance of selected sales force professionals, and thereby, organizational sales performance, based on such assessments. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 2, the combination of Venkata/Shi/Hicyilmaz teaches the limitations of Claim 2 which states
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 (Venkata: Para 0017, 0032 via 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), and are influenced by several internal and external events at micro (e.g., the opportunity) and/or macro (e.g., the account) levels. Structured and unstructured data associated with past opportunities can provide a data source for building a model to leverage the information to assign a probability score to an opportunity instance. The probability score can indicate the probability that the opportunity will close successfully with a sale. Furthermore, a rationale can be provided to a user as to the specific reasons the probability score was assigned. Additionally, based on historical data and model simulations, prescriptive guidelines can be provided to the sales representative in terms of potential next-best actions that can be taken. For instance, the sales representative can be notified of what activities can be performed to move an at-risk opportunity to a better state or what activities can be performed to close an opportunity with low risk…Email communications and other structured and unstructured historical data are captured from data sources 120. Data sources 120 can include historical information about opportunities including closing information and other activities that occurred during the various stages of an opportunity including, for example, email messages, calls, meetings, tasks, revenue forecast changes based on order changes (e.g., price changes and/or quantity changes), timeline changes (e.g., closing date changes), and so forth. 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. Specifically, smart search and navigation engine 135 can search for information relevant to improving the prospects of each opportunity, and provide it to activity manager 160 for use in evaluating open opportunities. Accordingly, data sources 120 can be sales tracking systems, email systems, and any other data source that stores data relevant to sales that can be mined…).
Regarding Claim 18, Venkata teaches the limitations of Claim 18 which state
inferring, by the analyzer and using a trained analysis model, a key sales driver for the SR and a target cut-off value associated with the key sales driver, wherein the set of key sales drivers comprises at least the key sales driver, wherein the key sales driver is provided to the SR and to the engine (Venkata: Para 0056, Table 1, 0062 via A representative list of variables that may be used in the models described herein includes those listed in Table 1… the recommendation is generated for the opportunity classified as a Loss based on the shortest path identified in step 325. The behaviors identified in the Winning opportunities relevant to the locally important variables are identified and used to generate the recommendation. For example, 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);
generating, by the engine and using the trained insights model and the key sales driver, a second insight for the SR, wherein the second insight is provided to the SR (Venkata: Para 0030-0032, 0056, 0065, Table 1 via The activity manager 160 includes the action simulator 162, guidance engine 164, and action recognizer 166. The activity manager receives information on the current activities and context from the working memory 175, the smart search and navigation engine 135 to find next actions on the path, the semantic analysis engine 140, the inference engine 145, the ontology knowledge base 105, and the activity knowledge base 150. With these sources of information, including opportunity specific information about the activities associated with an opportunity, an opportunity can be evaluated based on its activities and known previous activities to determine a score and guidance for the sales representative. The opportunity may be scored in two ways: as a probability of winning or as a size of the business. Action simulator 162 can perform simulations (e.g., Monte Carlo simulations) to determine scores of the opportunities and potential shortest paths needed to be traversed to get a winning classification. These shortest paths are used by the guidance engine 164 to generate recommendations for the opportunity to help the sales representative make corrections to attempt to salvage the opportunity if the opportunity is at a risk of loss. Email communications and other structured and unstructured historical data are captured from data sources 120. Data sources 120 can include historical information about opportunities including closing information and other activities that occurred during the various stages of an opportunity including, for example, email messages, calls, meetings, tasks, revenue forecast changes based on order changes (e.g., price changes and/or quantity changes), timeline changes (e.g., closing date changes), and so forth. 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. A representative list of variables that may be used in the models described herein includes those listed in Table 1… FIG. 6 provides an example user interface 600 for providing next best action recommendations for deals/opportunities. A specific opportunity 605 may be identified, in this example Optical Communications—Long Haul Services. The opportunity 605 may have any suitable name given by the sales representative or company. The opportunity 605 may be selected from a drop-down menu of opportunities. The user interface 600 may provide information for the opportunity 605 including the persona type of the primary contact (e.g., a straight shooter), the type of industry the opportunity 605 falls into (e.g., manufacturing), the stage of the opportunity 605 (e.g., Discovery, which is the second stage of the deal flow process), the closing date that is currently set or targeted (e.g., Dec. 31, 2016), the projected revenue from the opportunity 605 (e.g., $825,000), and the load type (e.g., long haul). In this example, the opportunity may be a delivery of goods that is a long haul (e.g., cross-country) delivery).
However, Venkata does not explicitly disclose the limitations of Claim 18 which state monitoring, by the analyzer, the SR’s performance with respect to the key sales driver and the second insight; in response to the monitoring, by the analyzer, making a determination that the SR’s performance is above the target cut-off value.
Shi though, with the teachings of Venkata, teaches of
monitoring, by the analyzer, the SR’s performance with respect to the key sales driver and the second insight; in response to the monitoring, by the analyzer, making a determination that the SR’s performance is above the target cut-off value (Shi: Col 13 line 31 – Col 14 line 28 via FIG. 6A illustrates an example agent performance chart 600, in one embodiment. The performance chart 600 includes, in this example, various sales behavioral targets 604 arrayed around a circumference of a circular graph. Within the circular graph are rings indicating performance levels 608. An agent's actual performance with regard to each of the indicated sales behavioral targets is indicated by shaded region 612. Better performance is indicated by shading further from the origin (center) of the graph and closer to the outermost performance ring 608 of the graph 600. In this case, the agent performs well on setting expectations, overcoming objections and asking the right questions. In light of the above description, these behavioral targets may be provided less frequently to the agent given the high performance values. In this example, the agent has low performance regarding the promotion of products, getting customers into a chat flow, and assume the sale. Accordingly, behavioral targets may be provided more frequently and/or more prominently when corresponding behavioral opportunities are identified. FIG. 6B illustrates an example agent performance chart 616 that tracks agent performance per behavioral target as a function of time (in this case, week number). In this chart, darker shading indicates more severe non-compliance with behavioral targets. In this case, the agent's performance for “assume the sale” has improved from week 1 to week 5. The agent's performance for “pitch with value” declines from week 1 to week 2, but then improved thereafter. The agent's performance was mostly steady for “always be closing” from week 1 to week 5. It will be appreciated that performance metrics, whether in the forms depicted in FIGS. 6A and 6B or some other form (e.g., a table of ratios, a stoplight chart) may be presented for review (e.g., by supervisory staff). FIG. 7 illustrates an example 700 of operations for real time monitoring of a conversation to determine if a behavioral target has been met (operation 424). Based on the contemporaneous monitoring of a conversation, the system can determine whether an agent has acted in a way that indicates the behavioral target has been followed (operation 704). This may be accomplished using any of the machine learning model techniques indicated above (e.g., using a trained machine learning model and/or rule based monitoring). A performance profile for an agent may then be updated to reflect whether or not the behavioral target was met (operation 708). The performance profile can include both absolute numbers of followed behavioral targets as well as a ratio of followed behavioral targets to provided behavioral targets and/or a ratio of followed behavioral targets to behavioral opportunities. In some examples, a performance profile may be based on a ratio of a number of prompts followed during a conversation divided by a total number of prompts provided. Upon conclusion of a conversation, an outcome of the conversation can be compared with a predefined desired outcome (operation 712). If the outcome of the conversation is favorable (e.g., a sale is completed, a problem resolved), then the conversation can be analyzed, labeled, and added to the training dataset so as to improve the training of the machine learning model by expanding the data in the training dataset (operation 716). Similarly, if the outcome of the conversation is not favorable (e.g., no sale, no problem resolution), the conversation can be analyzed for behaviors that were not helpful, and similarly added to the training dataset of the machine learning model (operation 716)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Venkata with the teachings of Shi in order to have monitoring, by the analyzer, the SR’s performance with respect to the key sales driver and the second insight; in response to the monitoring, by the analyzer, making a determination that the SR’s performance is above the target cut-off value. The motivations behind this being to incorporate the teachings of utilizing machine learning models to analyze and identify behavioral targets. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Furthermore, Venkata does not explicitly disclose the limitations of Claim 18 which state identifying, based on the determination and by the analyzer, the SR as a high performing SR; and initiating, by the analyzer, displaying of a score to an administrator, wherein the score indicates the SR as the high performing SR.
Hicyilmaz though, with the teachings of Venkata/Shi, teaches of
identifying, based on the determination and by the analyzer, the SR as a high performing SR; and initiating, by the analyzer, displaying of a score to an administrator, wherein the score indicates the SR as the high performing SR (Hicyilmaz: Para 0039-0040 via As shown if FIG. 1B, the computer application will generally include or provide access to a computerized database or spreadsheet for storing a plurality of sales professional records. These records will generally comprise one or more dimensions (D1, D2, . . . , Dn) in which a score for that dimension is recorded. The dimensions are one of, as indicated, (i) objective measures (such as years of selling, sales or profit attainment numbers), (ii) subjective evaluations (the ability to work independently, problem-solving ingenuity), and (iii) dimensions derived from a combination of other dimensions; and preferably all three. Although only one or two of each dimension type are depicted in the simplified example database structure of FIG. 1B, those skilled in the art will appreciate that any number of individual fields or dimensions can be arranged or associated with a given dimension type (i.e., objective, subjective, combination). The dimensions will generally conform to a construct useful for assessing sales performance and for determining sales performance enhancement techniques applicable to selected assessed sales professionals. For this reason, the sales professional records will generally include, for each high-performing sales rep, one or more high-performance indicators. This can be, as depicted in FIG. 1B, as simple as a binary database field (e.g., "yes" they are high performers or "no" they are not high performers) to more typically, objective sales performance metrics being stored as dimension scores (i.e., meeting or exceeding an arbitrary number, or falling in the top portion of the company's sales performance distribution curve). Ideally, a combination of dimensions will be identified whose combination of dimension scores meet or exceed a predetermined value along the associated dimension indicative of a high-performing sales rep).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Venkata/Shi with the teachings of Hicyilmaz in order to have identifying, based on the determination and by the analyzer, the SR as a high performing SR; and initiating, by the analyzer, displaying of a score to an administrator, wherein the score indicates the SR as the high performing SR. The motivations behind this being to incorporate the teachings of computerized techniques for enhancing the sales performance of selected sales force professionals, and thereby, organizational sales performance, based on such assessments. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 19, Venkata/Shi/Hicyilmaz teaches the limitations of Claim 19 which states
prior to the inferring the key sales driver for the SR and the target cut-off value associated with the key sales driver:
obtaining, by an analyzer, historical sales drivers (HSDs) (Venkata: Para 0003, 0018 via Using historical information as well as machine learning algorithms, failing opportunities may be improved using recommendations generated automatically. Specifically, the new and currently in-pursuit opportunities may be classified using information from past opportunities to identify which of the new and in-pursuit opportunities might be at risk and, by estimating distances between opportunities 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 or an ordered list of actions or changes to actions needed for a successful disposition of the opportunity… The historical information may be obtained using automated data entry into a database from other data sources or by mining the information from other data sources);
generating, by the analyzer and using the HSDs, an analysis model that identifies a set of key sales drivers and target cut-off values associated with the set of key sales drivers (Venkata: Para 0056, 0062, Table 1 via A representative list of variables that may be used in the models described herein includes those listed in Table 1… the recommendation is generated for the opportunity classified as a Loss based on the shortest path identified in step 325. The behaviors identified in the Winning opportunities relevant to the locally important variables are identified and used to generate the recommendation. For example, 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 a target parameter, a trained analysis model, wherein the analysis model is trained using at least the HSDs (Venkata: Para 0003, 0032 via Using historical information as well as machine learning algorithms, failing opportunities may be improved using recommendations generated automatically. Specifically, the new and currently in-pursuit opportunities may be classified using information from past opportunities to identify which of the new and in-pursuit opportunities might be at risk and, by estimating distances between opportunities 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 or an ordered list of actions or changes to actions needed for a successful disposition of the opportunity… Email communications and other structured and unstructured historical data are captured from data sources 120. Data sources 120 can include historical information about opportunities including closing information and other activities that occurred during the various stages of an opportunity including, for example, email messages, calls, meetings, tasks, revenue forecast changes based on order changes (e.g., price changes and/or quantity changes), timeline changes (e.g., closing date changes), and so forth. 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. Specifically, smart search and navigation engine 135 can search for information relevant to improving the prospects of each opportunity, and provide it to activity manager 160 for use in evaluating open opportunities. Accordingly, data sources 120 can be sales tracking systems, email systems, and any other data source that stores data relevant to sales that can be mined);
obtaining, by an engine, historical key sales drivers (HKSDs), internal parameters (IPs), and external parameters (EPs) (Venkata: Para 0038, 0046, 0056, 0068, Table 1 via Based on the historical data and data about the opportunity, a score can be generated for pending opportunities in the sales pipeline. In some embodiments, once similar opportunities are identified, the outcomes of the similar opportunities can be used to identify metrics to be used to analyze the current opportunity. For example, the sentiment identified in the current deal can be a metric (i.e., variable). The timing of the deal can be a metric. The products involved in the deal can be a metric. The various activities of the deal can be metrics. 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. The score may have a temporal and variable driven weighting based on long range and short range order calculated through multi-variate nonstationary time series models with attention, including but not limited to long short term memory based models… If the opportunity is identified as at risk for not successfully closing (e.g., having a classification of lose or possible, or having a score of less than 7), the goal of the system 100 is to generate an ordered list of shortest paths each including a sequence of the next best actions as a recommendation to the user to help the salesperson 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… A representative list of variables that may be used in the models described herein includes those listed in Table 1… The completed activities box 625 may provide a listing of reported activities engaged in by the sales representative or sales team for this particular opportunity 605. The completed activities box 625 may help the sales representative remember key activities as well as provide reporting to the remainder of the team and management. Additionally, this information may be used to analyze this opportunity 605 as well as other opportunities);
analyzing, by the engine, the HKSDs, the IPs, and the EPs to generate an insights model that provides an insight for the SR (Venkata: Para 0030-0032, 0038, 0056, 0065, Table 1 via 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… Based on the historical data and data about the opportunity, a score can be generated for pending opportunities in the sales pipeline. In some embodiments, once similar opportunities are identified, the outcomes of the similar opportunities can be used to identify metrics to be used to analyze the current opportunity. For example, the sentiment identified in the current deal can be a metric (i.e., variable). The timing of the deal can be a metric. The products involved in the deal can be a metric. The various activities of the deal can be metrics. 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);
obtaining, by the engine and based on the target parameter, a trained insights model, wherein the insights model is trained using at least the HKSDs, the IPs, and the Eps (Venkata: Para 0030-0032, 0056, 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 (Venkata: Para 0017 via 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), and are influenced by several internal and external events at micro (e.g., the opportunity) and/or macro (e.g., the account) levels. Structured and unstructured data associated with past opportunities can provide a data source for building a model to leverage the information to assign a probability score to an opportunity instance. The probability score can indicate the probability that the opportunity will close successfully with a sale. Furthermore, a rationale can be provided to a user as to the specific reasons the probability score was assigned. Additionally, based on historical data and model simulations, prescriptive guidelines can be provided to the sales representative in terms of potential next-best actions that can be taken. For instance, the sales representative can be notified of what activities can be performed to move an at-risk opportunity to a better state or what activities can be performed to close an opportunity with low risk);
and initiating, by the analyzer, notification of an administrator about the trained analysis model and the trained insights model (Venkata: Para 0040 via Classifying opportunities into a losing or winning category is based on the score assigned to each opportunity. The score is calculated using input from multiple variables including but not limited to derived and compounded variables and indicators from emails, calls, meetings, tasks, revenue forecast changes, and the like. Similarity of opportunities and distance between opportunities are based on the size of the deal, the timing of the deal, one or more products involved in the deal, or similarity of activities in the deal. Variables are transformed non-linearly to forms relevant to the science of the problem of opportunity scoring with a numerical score of 0-10. For example, text and speech may be converted through natural language processing (“NLP”) to sentiment by a NLP engine including word embeddings based models to feed to the semantic analysis engine 140 using a normalized standard deviation-based score of 0-10 (or any other suitable scale including, for example 0-20, 1-100, or the like), where 0 is negative and 10 is positive. In some embodiments, the score may be directly displayed to the user for different variables, so that the user may know how much worse or better each case is with respect to other local opportunities using, for example, explanations based on locally interpretable model interpretations and Shapley values).
Regarding Claim 20, Venkata/Shi/Hicyilmaz teaches the limitations of Claim 20 which state
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 (Venkata: Para 0017, 0032 via 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), and are influenced by several internal and external events at micro (e.g., the opportunity) and/or macro (e.g., the account) levels. Structured and unstructured data associated with past opportunities can provide a data source for building a model to leverage the information to assign a probability score to an opportunity instance. The probability score can indicate the probability that the opportunity will close successfully with a sale. Furthermore, a rationale can be provided to a user as to the specific reasons the probability score was assigned. Additionally, based on historical data and model simulations, prescriptive guidelines can be provided to the sales representative in terms of potential next-best actions that can be taken. For instance, the sales representative can be notified of what activities can be performed to move an at-risk opportunity to a better state or what activities can be performed to close an opportunity with low risk… Email communications and other structured and unstructured historical data are captured from data sources 120. Data sources 120 can include historical information about opportunities including closing information and other activities that occurred during the various stages of an opportunity including, for example, email messages, calls, meetings, tasks, revenue forecast changes based on order changes (e.g., price changes and/or quantity changes), timeline changes (e.g., closing date changes), and so forth. 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. Specifically, smart search and navigation engine 135 can search for information relevant to improving the prospects of each opportunity, and provide it to activity manager 160 for use in evaluating open opportunities. Accordingly, data sources 120 can be sales tracking systems, email systems, and any other data source that stores data relevant to sales that can be mined).
Claim(s) 3 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Venkata et al. (US 2020/0097879 A1) in view of Shi et al. (US 10,965,811 B1) in view of Hicyilmaz et al. (US 2013/0179236 A1) further in view of Kuhn et al. (US 2023/0244837 A1).
Regarding Claim 3, while the combination of Venkata/Shi/Hicyilmaz teaches the limitations of Claim 1, it does not explicitly disclose the limitations of Claim 3 which state wherein the analysis model is a combination of a random forest regression model and a framework that explains the random forest regression model to the administrator.
Kuhn though, with the teachings of Venkata/Shi/Hicyilmaz teaches of
wherein the analysis model is a combination of a random forest regression model and a framework that explains the random forest regression model to the administrator (Kuhn: Para 0025 via the embodiments of the present disclosure may include Machine Learning (ML) modeling using for example, random forests to unravel the relationship of product attributes on product non promotional sales, combined with game theory framework using Shapley Additive exPlanations (SHAP) values to quantify the contribution each product attribute brings to the prediction made by the ML model).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Venkata/Shi/Hicyilmaz with the teachings of Kuhn in order to have wherein the analysis model is a combination of a random forest regression model and a framework that explains the random forest regression model to the administrator. The motivation behind this being to incorporate the teachings of attribute based modeling. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 13, while Venkata teaches the limitations of Claim 10, it does not explicitly disclose the limitations of Claim 13 which states wherein the analysis model is a combination of a random forest regression model and a framework that explains the random forest regression model to the administrator.
Kuhn though, with the teachings of Venkata teaches of
wherein the analysis model is a combination of a random forest regression model and a framework that explains the random forest regression model to the administrator (Kuhn: Para 0025 via the embodiments of the present disclosure may include Machine Learning (ML) modeling using for example, random forests to unravel the relationship of product attributes on product non promotional sales, combined with game theory framework using Shapley Additive exPlanations (SHAP) values to quantify the contribution each product attribute brings to the prediction made by the ML model).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Venkata with the teachings of Kuhn in order to have wherein the analysis model is a combination of a random forest regression model and a framework that explains the random forest regression model to the administrator. The motivation behind this being to incorporate the teachings of attribute based modeling. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Venkata et al. (US 2020/0097879 A1) in view of Shi et al. (US 10,965,811 B1) in view of Hicyilmaz et al. (US 2013/0179236 A1) in view of Kuhn et al. (US 2023/0244837 A1) further in view of Hameed et al. (US 2016/0063560 A1).
Regarding Claim 4, while the combination of Venkata/Shi/Hicyilmaz/Kuhn teaches Claim 3, and Venkata teaches of Shapley framework (Venkata: Para 0040 via explanations based on locally interpretable model interpretations and Shapley values) it does not explicitly disclose the limitations of Claim 4 which state wherein the framework is a Shapley framework, wherein the analysis model implements the Shapley framework at a role-region-segment level, 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.
Hameed though, with the teachings of Venkata/Shi/Hicyilmaz/Kuhn, teaches of
wherein the framework is a Shapley framework, wherein the analysis model implements the Shapley framework at a role-region-segment level, 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 (Hameed: Para 0056 via 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).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Venkata/Shi/Hicyilmaz/Kuhn with the teachings of Hameed in order to have wherein the framework is a Shapley framework, wherein the analysis model implements the Shapley framework at a role-region-segment level, 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. The motivations behind this being to incorporate the teachings of profiling user agents by factors including industry, region, role, and segment to model buyer engagement. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Venkata et al. (US 2020/0097879 A1) in view of Shi et al. (US 10,965,811 B1) in view of Hicyilmaz et al. (US 2013/0179236 A1) further in view of Mahalanobish (US 2025/0037183 A1).
Regarding Claim 5, while the combination of Venkata/Shi/Hicyilmaz teaches the limitations of Claim 1, it does not explicitly teach the limitations of Claim 5 which state 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.
Mahalanobish though, with the teachings of Venkata/Shi/Hicyilmaz, teaches of
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 (Mahalanobish: Para 0071 via 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).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Venkata/Shi/Hicyilmaz with the teachings of Mahalanobish in order to have 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. The motivations behind this being to incorporate the teachings of recommending based on increased sales and revenues compared with previous time periods as taught by Mahalanobish. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Venkata et al. (US 2020/0097879 A1) in view of Shi et al. (US 10,965,811 B1) in view of Hicyilmaz et al. (US 2013/0179236 A1) in view of Mahalanobish (US 2025/0037183 A1) further in view of Gershon et al. (US 2022/0138820 A1).
Regarding Claim 6, while Venkata/Shi/Hicyilmaz/Mahalanobish teaches the limitations of Claim 5, it does not explicitly disclose the limitations of Claim 6 which state 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 a customer.
Gershon though, with the teachings of Venkata/Shi/Hicyilmaz/Mahalanobish, teaches of
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 a customer (Gershon: Para 0010 via 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).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modifyVenkata/Shi/Hicyilmaz/Mahalanobish with the teachings of Gershon in order to have 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 a customer. The motivations behind this being to incorporate the teachings of a sales recommendation tool that includes modeling the probability that a quote will be converted into a sale, as taught by Gershon. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Venkata et al. (US 2020/0097879 A1) in view of Shi et al. (US 10,965,811 B1) in view of Hicyilmaz et al. (US 2013/0179236 A1) further in view of Sperling et al. (US 2016/0378932 A1).
Regarding Claim 7, while Venkata/Shi/Hicyilmaz teaches the limitations of Claim 1, it does not explicitly disclose the limitation of Claim 7 which states wherein the IPs comprise at least one selected from a group consisting of a historical revenue obtained for a product that is delivered to a customer, a historical quote associated with the product, and a technical specification of the product.
Sperling though, with the teachings of Venkata/Shi/Hicyilmaz teaches of
wherein the IPs comprise at least one selected from a group consisting of a historical revenue obtained for a product that is delivered to a customer, a historical quote associated with the product, and a technical specification of the product (Sperling: Para 0100 via opportunities with quote loss controls that include quote loss by month, compared to peers, sector, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Venkata/Shi/Hicyilmaz with the teachings of Sperling in order to have wherein the IPs comprise at least one selected from a group consisting of a historical revenue obtained for a product that is delivered to a customer, a historical quote associated with the product, and a technical specification of the product. The motivations behind this being to incorporate the teachings of quote loss control as taught by Sperling. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Venkata et al. (US 2020/0097879 A1) in view of Shi et al. (US 10,965,811 B1) further in view of Hicyilmaz et al. (US 2013/0179236 A1) further in view of Colliat et al. (US 2011/0196717 A1).
Regarding Claim 8, while Venkata/Shi/Hicyilmaz teaches the limitations of Claim 1, it does not explicitly disclose the limitation of Claim 8 which states wherein the EPs comprise at least one selected from a group consisting of an annual revenue of the customer during a last fiscal year, a business expansion plan of the customer for a next year, and a total number of employees hired by the customer during the last fiscal year.
Colliat though, with the teachings of Venkata/Shi/Hicyilmaz, teaches of
wherein the EPs comprise at least one selected from a group consisting of an annual revenue of the customer during a last fiscal year, a business expansion plan of the customer for a next year, and a total number of employees hired by the customer during the last fiscal year (Colliat: Para 0030 via The task of setting quotas appropriately has increased in complexity and difficulty as markets have increased in complexity. An accurate quota should reflect the sales opportunities that a sales force can reasonably be expected to close. If certain geographies previously owned by a sales representative are no longer assigned to the sales representative's territory, then the quota for the territory should reflect that reality and be appropriately adjusted to reflect the territory's potential. Sales administrators take various approaches in their efforts to set quotas appropriately. In one embodiment, a sales administrator can set a quota based on organizational goals. An organizational goal can be a goal, or target amount of sales, specified by a company's leadership. A quota specified by a company's leadership is referred to herein as a top-down quota. For example, 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. Quota planning involves establishing a quota the organizational level in concert with a company's leadership. The quota is then distributed through the sales force. Since the sales force and sales territories can be organized hierarchically, the quota is distributed "top-down" through this hierarchy).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Venkata/Shi/Hicyilmaz with the teachings of Colliat in order to have wherein the EPs comprise at least one selected from a group consisting of an annual revenue of the customer during a last fiscal year, a business expansion plan of the customer for a next year, and a total number of employees hired by the customer during the last fiscal year. The motivations behind this being to incorporate the teachings of including the goal of increase in sales from the previous year as taught by Colliat. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Venkata et al. (US 2020/0097879 A1) in view of Shi et al. (US 10,965,811 B1) in view of Hicyilmaz et al. (US 2013/0179236 A1) further in view of Calabrese et al. (US 2012/0095804 A1).
Regarding Claim 9, while Venkata/Shi/Hicyilmaz teaches the limitations of Claim 1, it does not explicitly disclose the limitation of Claim 9 which states wherein being above the target cut-off value indicates a positive impact on a year-over-year (YoY) revenue growth performance of the SR.
Calabrese though, with the teachings of Venkata/Shi/Hicyilmaz, teaches of
wherein being above the target cut-off value indicates a positive impact on a year-over-year (YoY) revenue growth performance of the SR (Calabrese: Para 0034-0037 via 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).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Venkata/Shi/Hicyilmaz with the teachings of Calabrese in order to have wherein being above the target cut-off value indicates a positive impact on a year-over-year (YoY) revenue growth performance of the SR. The motivations behind this being to incorporate the teachings of including actions that positively affect revenue as taught by Calabrese. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Venkata et al. (US 2020/0097879 A1) in view of Shi et al. (US 10,965,811 B1) further in view of Hicyilmaz et al. (US 2013/0179236 A1).
Regarding Claim 11, while Venkata teaches the limitations of Claim 10 and the limitations of Claim 11 which state
inferring, by the analyzer and using the trained analysis model, a key sales driver for the SR and a target cut-off value associated with the key sales driver, wherein the set of key sales drivers comprises at least the key sales driver, wherein the key sales driver is provided to the SR and to the engine (Venkata: Para 0056, Table 1, 0062 via A representative list of variables that may be used in the models described herein includes those listed in Table 1… the recommendation is generated for the opportunity classified as a Loss based on the shortest path identified in step 325. The behaviors identified in the Winning opportunities relevant to the locally important variables are identified and used to generate the recommendation. For example, 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);
generating, by the engine and using the trained insights model and the key sales driver, a second insight for the SR, wherein the second insight is provided to the SR (Venkata: Para 0030-0032, 0056, 0065, Table 1 via The activity manager 160 includes the action simulator 162, guidance engine 164, and action recognizer 166. The activity manager receives information on the current activities and context from the working memory 175, the smart search and navigation engine 135 to find next actions on the path, the semantic analysis engine 140, the inference engine 145, the ontology knowledge base 105, and the activity knowledge base 150. With these sources of information, including opportunity specific information about the activities associated with an opportunity, an opportunity can be evaluated based on its activities and known previous activities to determine a score and guidance for the sales representative. The opportunity may be scored in two ways: as a probability of winning or as a size of the business. Action simulator 162 can perform simulations (e.g., Monte Carlo simulations) to determine scores of the opportunities and potential shortest paths needed to be traversed to get a winning classification. These shortest paths are used by the guidance engine 164 to generate recommendations for the opportunity to help the sales representative make corrections to attempt to salvage the opportunity if the opportunity is at a risk of loss. Email communications and other structured and unstructured historical data are captured from data sources 120. Data sources 120 can include historical information about opportunities including closing information and other activities that occurred during the various stages of an opportunity including, for example, email messages, calls, meetings, tasks, revenue forecast changes based on order changes (e.g., price changes and/or quantity changes), timeline changes (e.g., closing date changes), and so forth. 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. A representative list of variables that may be used in the models described herein includes those listed in Table 1… FIG. 6 provides an example user interface 600 for providing next best action recommendations for deals/opportunities. A specific opportunity 605 may be identified, in this example Optical Communications—Long Haul Services. The opportunity 605 may have any suitable name given by the sales representative or company. The opportunity 605 may be selected from a drop-down menu of opportunities. The user interface 600 may provide information for the opportunity 605 including the persona type of the primary contact (e.g., a straight shooter), the type of industry the opportunity 605 falls into (e.g., manufacturing), the stage of the opportunity 605 (e.g., Discovery, which is the second stage of the deal flow process), the closing date that is currently set or targeted (e.g., Dec. 31, 2016), the projected revenue from the opportunity 605 (e.g., $825,000), and the load type (e.g., long haul). In this example, the opportunity may be a delivery of goods that is a long haul (e.g., cross-country) delivery).
It does not explicitly disclose the limitation of Claim 11 which states Venkata does not explicitly disclose the limitations of Claim 1 which state monitoring, by the analyzer, the SR’s performance with respect to the key sales driver and the second insight; in response to the monitoring, by the analyzer, making a determination that the SR’s performance is above the target cut-off value.
Shi though, with the teachings of Venkata, teaches of
monitoring, by the analyzer, the SR’s performance with respect to the key sales driver and the second insight; in response to the monitoring, by the analyzer, making a determination that the SR’s performance is above the target cut-off value (Shi: Col 13 line 31 – Col 14 line 28 via FIG. 6A illustrates an example agent performance chart 600, in one embodiment. The performance chart 600 includes, in this example, various sales behavioral targets 604 arrayed around a circumference of a circular graph. Within the circular graph are rings indicating performance levels 608. An agent's actual performance with regard to each of the indicated sales behavioral targets is indicated by shaded region 612. Better performance is indicated by shading further from the origin (center) of the graph and closer to the outermost performance ring 608 of the graph 600. In this case, the agent performs well on setting expectations, overcoming objections and asking the right questions. In light of the above description, these behavioral targets may be provided less frequently to the agent given the high performance values. In this example, the agent has low performance regarding the promotion of products, getting customers into a chat flow, and assume the sale. Accordingly, behavioral targets may be provided more frequently and/or more prominently when corresponding behavioral opportunities are identified. FIG. 6B illustrates an example agent performance chart 616 that tracks agent performance per behavioral target as a function of time (in this case, week number). In this chart, darker shading indicates more severe non-compliance with behavioral targets. In this case, the agent's performance for “assume the sale” has improved from week 1 to week 5. The agent's performance for “pitch with value” declines from week 1 to week 2, but then improved thereafter. The agent's performance was mostly steady for “always be closing” from week 1 to week 5. It will be appreciated that performance metrics, whether in the forms depicted in FIGS. 6A and 6B or some other form (e.g., a table of ratios, a stoplight chart) may be presented for review (e.g., by supervisory staff). FIG. 7 illustrates an example 700 of operations for real time monitoring of a conversation to determine if a behavioral target has been met (operation 424). Based on the contemporaneous monitoring of a conversation, the system can determine whether an agent has acted in a way that indicates the behavioral target has been followed (operation 704). This may be accomplished using any of the machine learning model techniques indicated above (e.g., using a trained machine learning model and/or rule based monitoring). A performance profile for an agent may then be updated to reflect whether or not the behavioral target was met (operation 708). The performance profile can include both absolute numbers of followed behavioral targets as well as a ratio of followed behavioral targets to provided behavioral targets and/or a ratio of followed behavioral targets to behavioral opportunities. In some examples, a performance profile may be based on a ratio of a number of prompts followed during a conversation divided by a total number of prompts provided. Upon conclusion of a conversation, an outcome of the conversation can be compared with a predefined desired outcome (operation 712). If the outcome of the conversation is favorable (e.g., a sale is completed, a problem resolved), then the conversation can be analyzed, labeled, and added to the training dataset so as to improve the training of the machine learning model by expanding the data in the training dataset (operation 716). Similarly, if the outcome of the conversation is not favorable (e.g., no sale, no problem resolution), the conversation can be analyzed for behaviors that were not helpful, and similarly added to the training dataset of the machine learning model (operation 716)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Venkata with the teachings of Shi in order to have monitoring, by the analyzer, the SR’s performance with respect to the key sales driver and the second insight; in response to the monitoring, by the analyzer, making a determination that the SR’s performance is above the target cut-off value. The motivations behind this being to incorporate the teachings of utilizing machine learning models to analyze and identify behavioral targets. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Furthermore, Venkata does not explicitly disclose the limitations of Claim 1 which state identifying, based on the determination and by the analyzer, the SR as a high performing SR; and initiating, by the analyzer, displaying of a score to an administrator, wherein the score indicates the SR as the high performing SR.
Hicyilmaz though, with the teachings of Venkata/Shi, teaches of
identifying, based on the determination and by the analyzer, the SR as a high performing SR; and initiating, by the analyzer, displaying of a score to an administrator, wherein the score indicates the SR as the high performing SR (Hicyilmaz: Para 0039-0040 via As shown if FIG. 1B, the computer application will generally include or provide access to a computerized database or spreadsheet for storing a plurality of sales professional records. These records will generally comprise one or more dimensions (D1, D2, . . . , Dn) in which a score for that dimension is recorded. The dimensions are one of, as indicated, (i) objective measures (such as years of selling, sales or profit attainment numbers), (ii) subjective evaluations (the ability to work independently, problem-solving ingenuity), and (iii) dimensions derived from a combination of other dimensions; and preferably all three. Although only one or two of each dimension type are depicted in the simplified example database structure of FIG. 1B, those skilled in the art will appreciate that any number of individual fields or dimensions can be arranged or associated with a given dimension type (i.e., objective, subjective, combination). The dimensions will generally conform to a construct useful for assessing sales performance and for determining sales performance enhancement techniques applicable to selected assessed sales professionals. For this reason, the sales professional records will generally include, for each high-performing sales rep, one or more high-performance indicators. This can be, as depicted in FIG. 1B, as simple as a binary database field (e.g., "yes" they are high performers or "no" they are not high performers) to more typically, objective sales performance metrics being stored as dimension scores (i.e., meeting or exceeding an arbitrary number, or falling in the top portion of the company's sales performance distribution curve). Ideally, a combination of dimensions will be identified whose combination of dimension scores meet or exceed a predetermined value along the associated dimension indicative of a high-performing sales rep).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Venkata/Shi with the teachings of Hicyilmaz in order to have identifying, based on the determination and by the analyzer, the SR as a high performing SR; and initiating, by the analyzer, displaying of a score to an administrator, wherein the score indicates the SR as the high performing SR. The motivations behind this being to incorporate the teachings of computerized techniques for enhancing the sales performance of selected sales force professionals, and thereby, organizational sales performance, based on such assessments. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Venkata et al. (US 2020/0097879 A1) further in view of Kuhn et al. (US 2023/0244837 A1).
Regarding Claim 13, while Venkata teaches the limitations of Claim 10, it does not explicitly disclose the limitations of Claim 13 which state wherein the analysis model is a combination of a random forest regression model and a framework that explains the random forest regression model to the administrator.
Kuhn though, with the teachings of Venkata teaches of
wherein the analysis model is a combination of a random forest regression model and a framework that explains the random forest regression model to the administrator (Kuhn: Para 0025 via the embodiments of the present disclosure may include Machine Learning (ML) modeling using for example, random forests to unravel the relationship of product attributes on product non promotional sales, combined with game theory framework using Shapley Additive exPlanations (SHAP) values to quantify the contribution each product attribute brings to the prediction made by the ML model).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Venkata with the teachings of Kuhn in order to have wherein the analysis model is a combination of a random forest regression model and a framework that explains the random forest regression model to the administrator. The motivation behind this being to incorporate the teachings of attribute based modeling. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Venkata et al. (US 2020/0097879 A1) in view of Kuhn et al. (US 2023/0244837 A1) further in view of Hameed et al. (US 2016/0063560 A1).
Regarding Claim 14, while the combination of Venkata/Kuhn teaches Claim 13, and Venkata teaches of Shapley framework (Venkata: Para 0040 via explanations based on locally interpretable model interpretations and Shapley values) it does not explicitly disclose the limitations of Claim 14 which state wherein the framework is a Shapley framework, wherein the analysis model implements the Shapley framework at a role-region-segment level, 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.
Hameed though, with the teachings of Venkata/Kuhn, teaches of
wherein the framework is a Shapley framework, wherein the analysis model implements the Shapley framework at a role-region-segment level, 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 (Hameed: Para 0056 via 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).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Venkata/Kuhn with the teachings of Hameed in order to have wherein the framework is a Shapley framework, wherein the analysis model implements the Shapley framework at a role-region-segment level, 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. The motivations behind this being to incorporate the teachings of profiling user agents by factors including industry, region, role, and segment to model buyer engagement. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Venkata et al. (US 2020/0097879 A1) in view of Mahalanobish (US 2025/0037183 A1).
Regarding Claim 15, while Venkata teaches the limitations of Claim 10, it does not explicitly teach the limitations of Claim 15 which state 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.
Mahalanobish though, with the teachings of Venkata, teaches of
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 (Mahalanobish: Para 0071 via 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).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Venkata with the teachings of Mahalanobish in order to have 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. The motivations behind this being to incorporate the teachings of recommending based on increased sales and revenues compared with previous time periods as taught by Mahalanobish. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Venkata et al. (US 2020/0097879 A1) in view of Mahalanobish (US 2025/0037183 A1) further in view of Gershon et al. (US 2022/0138820 A1).
Regarding Claim 16, while Venkata/Mahalanobish teaches the limitations of Claim 15, it does not explicitly disclose the limitations of Claim 16 which state 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 a customer.
Gershon though, with the teachings of Venkata/Mahalanobish, teaches of
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 a customer (Gershon: Para 0010 via 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).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Venkata/Mahalanobish with the teachings of Gershon in order to have 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 a customer. The motivations behind this being to incorporate the teachings of a sales recommendation tool that includes modeling the probability that a quote will be converted into a sale, as taught by Gershon. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Venkata et al. (US 2020/0097879 A1) further in view of Sperling et al. (US 2016/0378932 A1).
Regarding Claim 17, while Venkata teaches the limitations of Claim 10, it does not explicitly disclose the limitation of Claim 17 which states wherein the IPs comprise at least one selected from a group consisting of a historical revenue obtained for a product that is delivered to a customer, a historical quote associated with the product, and a technical specification of the product.
Sperling though, with the teachings of Venkata teaches of
wherein the IPs comprise at least one selected from a group consisting of a historical revenue obtained for a product that is delivered to a customer, a historical quote associated with the product, and a technical specification of the product (Sperling: Para 0100 via opportunities with quote loss controls that include quote loss by month, compared to peers, sector, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Venkata with the teachings of Sperling in order to have wherein the IPs comprise at least one selected from a group consisting of a historical revenue obtained for a product that is delivered to a customer, a historical quote associated with the product, and a technical specification of the product. The motivations behind this being to incorporate the teachings of quote loss control as taught by Sperling. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Richter (US 2008/0162487 A1)
Gilmore (US 2023/0162212 A1)
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/T.E.S./ Examiner, Art Unit 3625
/BETH V BOSWELL/ Supervisory Patent Examiner, Art Unit 3625