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
The Amendment filed on 06/11/2026 has been entered. Claims 1-5, 7-13 and 15-22 are pending in the instant patent application. Claims 1-3, 5, 7-8, 10-13 and 16-20 are amended. Claims 6 and 14 are cancelled. This Final Office Action is in response to the claims filed.
Response to Claim Amendments
Applicant’s amendments to the claims are insufficient to overcome the 35 U.S.C. §101 rejections. The rejections remain pending and are updated and addressed below in light of the amendments and per guidelines for 101 analysis (PEG 2019).
Applicant’s amendments to the claims are insufficient to overcome the 35 U.S.C. §103 rejections. The rejections remain pending and are updated and addressed below in light of the amendments and newly cited art.
Response to 35 U.S.C. §101 Arguments
Applicant’s arguments regarding 35 U.S.C. §101 rejection of the claims have been fully considered, but are not persuasive.
Regarding Applicant’s arguments that the claims are not directed towards an abstract idea, Examiner respectfully disagrees and maintains that the claim recites abstract ideas, specifically Mental Processes.
Regarding Step 2A Prong Two, Examiner maintains that the additional elements presented in the amended claim language as a whole, do not integrate the judicial exception into a practical application. Examiner notes that the additional elements, such as the machine learning model, are merely being used as tools to implement the abstract idea and generally links the use of the abstract idea to a particular technological environment/field of use. In addition, the claim language does not convey any improvements to the functioning of a computer, technology or technical field. Examiner will further note an important consideration to evaluate when determining whether the claim as a whole integrates a judicial exception into a practical application is whether the claimed invention improves the functioning of a computer or other technology. MPEP 2106.04(a) and 2106.05(a) provide a detailed explanation of how to perform this analysis. In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. In analyzing the specification, Examiner maintains that the specification sets forth an improvement, but in a conclusory manner and furthermore the claims do not reflect the disclosed improvement or effectively demonstrate an improvement to existing technology. In addition, (ref: 2106.04(d)(1)). Examiner will further note that the use and training of machine learning in the claim language is recited at a high level of generality and performing it’s functions as intended and there are no improvements present in the claim language.
Regarding Applicant’s arguments that the claims include an inventive concepts, Examiner respectfully disagrees. In analyzing the amended claim language, the additional elements, individually and in combination, presented do not amount to significantly more. Furthermore, as stated prior, there is no improvement to the technology, the elements do not amount to more than the abstract idea because it is a generic computer performing generic functions and the claim language as currently amended does not convey in any form, what would be considered a ‘close call’.
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 1-5, 7-10 and 21-22, they are directed to a method, however the claims are directed to a judicial exception without significantly more. Claims 1-5, 7-10 and 21-22 are directed to the abstract idea of identifying predictive data features for target zones.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 1, claim 1 recites generating and based on data retrieved from a plurality of disparate data sources, a training data set comprising: multiple types of process data associated with performance of first instances of a process; and outcome scores associated with the first instances of the process; determining, target zones associated with the predictive data features, wherein: the target zones indicate values of the predictive data features that are associated with a target range of the outcome scores; configuring, to evaluate second process data, associated with second instances of the process, based on the predictive data features and the target zones; identifying, instances of the predictive data features within the second process data; and generating, insight output indicating whether the instances of the predictive data features, within the second process data, are associated with the target zones.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be practically performed in the human mind and/or with pen/paper. Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind (see MPEP 2106.04(a)(2)(III)(C)).
Accordingly, the claim recites an abstract idea and dependent claims 2-5, 7-10 and 21-22 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of a computing system, training, by the computing system, and based on the training data set, a machine learning model to identify predictive data features, indicated by the multiple types of process data, that are predictive of the outcome scores and an insight engine. The computing system, training, by the computing system, and based on the training data set, a machine learning model to identify predictive data features, indicated by the multiple types of process data, that are predictive of the outcome scores and an insight engine are merely generic computing devices and do not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 1 and 21-22 includes various elements that are not directed to the abstract idea under 2A. These elements include a computing system, training, by the computing system, and based on the training data set, a machine learning model to identify predictive data features, indicated by the multiple types of process data, that are predictive of the outcome scores, a user interface and an insight engine and the generic computing elements described in the Applicant's specification in at least Para 0108-0120. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claims 1 and 21-22, alone or in combination, are not drawn to eligible subject matter as they are directed to abstract ideas without significantly more.
Regarding Claims 11-13 and 15, they are directed to a system, however the claims are directed to a judicial exception without significantly more. Claims 11-13 and 15 are directed to the abstract idea of identifying predictive data features for target zones.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 11, claim 11 recites generate a training data set that comprises: multiple types of process data associated with performance of first instances of a process; and outcome scores associated with the first instances of the process; determine, target zones associated with the predictive data features, wherein: the target zones indicate values of the predictive data features that are associated with a target range of the outcome scores; configure, to evaluate second process data, associated with second instances of the process, based on the predictive data features and the target zones; identify, instances of the predictive data features within the second process data; and generate, insight output indicating whether the instances of the predictive data features, within the second process data, are associated with the target zones.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be practically performed in the human mind and/or with pen/paper. Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind (see MPEP 2106.04(a)(2)(III)(C)).
Accordingly, the claim recites an abstract idea and dependent claims 12-13 and 15 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 one or more processors, memory, train a machine learning model, based on the training data set, to identify predictive data features, indicated by the multiple types of process data, that are predictive of the outcome scores and an insight engine. The one or more processors, memory, train a machine learning model, based on the training data set, to identify predictive data features, indicated by the multiple types of process data, that are predictive of the outcome scores and an insight engine are merely generic computing devices and do not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 11 includes various elements that are not directed to the abstract idea under 2A. These elements include one or more processors, memory, train a machine learning model, based on the training data set, to identify predictive data features, indicated by the multiple types of process data, that are predictive of the outcome scores and the generic computing elements described in the Applicant's specification in at least Para 0108-0120. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claim 11 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
Regarding Claims 16-20, they are directed to a system, however the claims are directed to a judicial exception without significantly more. Claims 16-20 are directed to the abstract idea of identifying predictive data features for target zones.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 16, claim 16 recites generate a training data set that comprises: multiple types of process data associated with performance of first instances of a process; and outcome scores associated with the first instances of the process; determine, target zones associated with the predictive data features, wherein: the target zones indicate values of the predictive data features that are associated with a target range of the outcome scores; configure, to evaluate second process data, associated with second instances of the process, based on the predictive data features and the target zones; identify, instances of the predictive data features within the second process data; and generate, insight output indicating whether the instances of the predictive data features, within the second process data, are associated with the target zones.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be practically performed in the human mind and/or with pen/paper. Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind (see MPEP 2106.04(a)(2)(III)(C)).
Accordingly, the claim recites an abstract idea and dependent claims 17-20 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 one or more processors, computing system, and train a machine learning model, based on the training data set, to identify predictive data features, indicated by the multiple types of process data, that are predictive of the outcome scores and an insight engine. The one or more processors, computing system, and train a machine learning model, based on the training data set, to identify predictive data features, indicated by the multiple types of process data, that are predictive of the outcome scores and an insight engine are merely generic computing devices and do not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 16 includes various elements that are not directed to the abstract idea under 2A. These elements include one or more processors, computing system, and train a machine learning model, based on the training data set, to identify predictive data features, indicated by the multiple types of process data, that are predictive of the outcome scores, an insight engine and the generic computing elements described in the Applicant's specification in at least Para 0108-0120. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions.
Therefore, Claim 16 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
Response to 35 U.S.C. §103 Arguments
Applicant’s amendments to the claims are insufficient to overcome the 35 U.S.C. §103 rejections. The rejections remain pending and are updated and addressed below in light of the amendments and newly cited art. Furthermore, Applicant’s arguments are moot in light of newly amended language.
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, 9, 11, 16 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou et al. (US 2021/0123343 A1) in view of Nourian et al. (US 2021/0049503 A1) further in view of Wu et al. (US 2020/0151746 A1).
Regarding Claim 1, Zhou teaches the limitations of Claim 1 which state
generating, by a computing system, and based on data retrieved from a plurality of disparate data sources, a training data set (Zhou: Para 0017-0023, 0028-0031 via reservoir data that comes from disparate sources…importing and aggregating data from disparate databases to prepare it for machine learning…);
a training data set comprising: multiple types of process data associated with performance of first instances of a process; and outcome scores associated with the first instances of the process (Zhou: Para 0019-0023, 0038-0039 via geology, completion, development and production data for wells is identified and well-performance metrics are identified…reliable well-performance metrics and long term forecast production are identified…);
training, by the computing system, and based on the training data set, a machine learning model to identify predictive data features, indicated by the multiple types of process data, that are predictive of the outcome scores (Zhou: Para 0030-0031, 0041-0042, 0048-0050 via using machine learning training algorithms and transformed data optimized for training and using the machine learning to extract relevant info and creating predictive analytics…identify completion and geology features that are the most important drivers for well performance and using a trained model and accumulated local effects to measure feature influence on EUR).
However, Zhou does not explicitly disclose the limitations of Claim 1 which state determining, by the computing system, and based on the training of the machine learning model on the training data set, target zones associated with the predictive data features, wherein: the target zones indicate values of the predictive data features that are associated with a target range of the outcome scores.
Nourian though, with the teachings of Zhou, teaches of
determining, by the computing system, and based on the training of the machine learning model on the training data set, target zones associated with the predictive data features, wherein: the target zones indicate values of the predictive data features that are associated with a target range of the outcome scores (Nourian: Para 0049-0052, 0060, 0065-0066, 0069, 0077 via analyzing relationships between feature values and desirable predicted outcomes…partitioning feature values into regions and estimates each region’s influence of prediction… identifying feature value changes or maintained values that place an outcome across a threshold or within acceptable ranges).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhou with the teachings of Nourian in order to have determining, by the computing system, and based on the training of the machine learning model on the training data set, target zones associated with the predictive data features, wherein: the target zones indicate values of the predictive data features that are associated with a target range of the outcome scores. The motivations behind this being to incorporate the teachings of identifying feature value regions that cross or keep desired outcome thresholds and providing explanations as taught by Nourian. 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, Zhou does not explicitly disclose the limitations of Claim 1 which state configuring, by the computing system, and based on the training of the machine learning model, an insight engine to evaluate second process data, associated with second instances of the process, based on the predictive data features and the target zones; identifying, by the computing system, and via the insight engine, instances of the predictive data features within the second process data; and generating, by the computing system, and via the insight engine, insight output indicating whether the instances of the predictive data features, within the second process data, are associated with the target zones.
Wu though, with the teachings of Zhou/Nourian, teaches of
configuring, by the computing system, and based on the training of the machine learning model, an insight engine to evaluate second process data, associated with second instances of the process, based on the predictive data features and the target zones (Wu: Para 0029, 0055 via As described herein, the KPI analytics system generates actionable KPI-driven customer segments. At a high-level, to generate KPI-driven customer segments, the KPI analytics system builds a propensity model for a KPI of interest to generate predicted outcomes for customers that reflect the likelihood that each customer will reach/perform a particular outcome related to the KPI of interest. The propensity model can be generated using historical user behavior data and/or user attributes correlated with known outcomes. Combining user-level behavior features (e.g., product use frequency, product use recency, product variety, etc.) and user attributes (e.g., age of subscription, country, skill level, etc.) along with known outcomes can be used to train the propensity model (e.g., using machine learning). When applied to existing customers, the propensity model generates a predicted outcome for each customer indicative of a likelihood of the outcome of interest for which the model was trained… The propensity model can be updated periodically based on accuracy. For instance, the trained propensity model can be used to determine the probability of an outcome for an existing customer. After the designated timeframe for the determined probability has passed, an actual outcome for the existing customer is determined and the predicted outcome is compared with the actual outcome. When the predicted outcome does not match the actual outcome, the model can be updated with additional training data (e.g., when a predefined threshold level of outcomes are not correct));
identifying, by the computing system, and via the insight engine, instances of the predictive data features within the second process data (Wu: Para 0029, 0055 via As described herein, the KPI analytics system generates actionable KPI-driven customer segments. At a high-level, to generate KPI-driven customer segments, the KPI analytics system builds a propensity model for a KPI of interest to generate predicted outcomes for customers that reflect the likelihood that each customer will reach/perform a particular outcome related to the KPI of interest. The propensity model can be generated using historical user behavior data and/or user attributes correlated with known outcomes. Combining user-level behavior features (e.g., product use frequency, product use recency, product variety, etc.) and user attributes (e.g., age of subscription, country, skill level, etc.) along with known outcomes can be used to train the propensity model (e.g., using machine learning). When applied to existing customers, the propensity model generates a predicted outcome for each customer indicative of a likelihood of the outcome of interest for which the model was trained… The propensity model can be updated periodically based on accuracy. For instance, the trained propensity model can be used to determine the probability of an outcome for an existing customer. After the designated timeframe for the determined probability has passed, an actual outcome for the existing customer is determined and the predicted outcome is compared with the actual outcome. When the predicted outcome does not match the actual outcome, the model can be updated with additional training data (e.g., when a predefined threshold level of outcomes are not correct));
and generating, by the computing system, and via the insight engine, insight output indicating whether the instances of the predictive data features, within the second process data, are associated with the target zones (Wu: Para 0029, 0055 via As described herein, the KPI analytics system generates actionable KPI-driven customer segments. At a high-level, to generate KPI-driven customer segments, the KPI analytics system builds a propensity model for a KPI of interest to generate predicted outcomes for customers that reflect the likelihood that each customer will reach/perform a particular outcome related to the KPI of interest. The propensity model can be generated using historical user behavior data and/or user attributes correlated with known outcomes. Combining user-level behavior features (e.g., product use frequency, product use recency, product variety, etc.) and user attributes (e.g., age of subscription, country, skill level, etc.) along with known outcomes can be used to train the propensity model (e.g., using machine learning). When applied to existing customers, the propensity model generates a predicted outcome for each customer indicative of a likelihood of the outcome of interest for which the model was trained… The propensity model can be updated periodically based on accuracy. For instance, the trained propensity model can be used to determine the probability of an outcome for an existing customer. After the designated timeframe for the determined probability has passed, an actual outcome for the existing customer is determined and the predicted outcome is compared with the actual outcome. When the predicted outcome does not match the actual outcome, the model can be updated with additional training data (e.g., when a predefined threshold level of outcomes are not correct)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhou/Nourian with the teachings of Wu in order to have configuring, by the computing system, and based on the training of the machine learning model, an insight engine to evaluate second process data, associated with second instances of the process, based on the predictive data features and the target zones; identifying, by the computing system, and via the insight engine, instances of the predictive data features within the second process data; and generating, by the computing system, and via the insight engine, insight output indicating whether the instances of the predictive data features, within the second process data, are associated with the target zones. The motivations behind this being to incorporate the teachings of collecting and analyzing data and converting data of various formats into a format that a model can process. 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 9, the combination of Zhou/Nourian/Wu, teaches the limitations of Claim 9 which state
the training of the machine learning model identifies a combination of the predictive data features that is predictive of the outcome scores (Zhou: Para 0030-0031, 0041-0042, 0048-0050 via using machine learning training algorithms and transformed data optimized for training and using the machine learning to extract relevant info and creating predictive analytics…identify completion and geology features that are the most important drivers for well performance and using a trained model and accumulated local effects to measure feature influence on EUR); and
the target zones are associated with combinations of values, associated with the combination of the predictive data features, that are associated with the target range of the outcome scores (Nourian: Para 0049-0052, 0060, 0065-0066, 0069, 0077 via analyzing relationships between feature values and desirable predicted outcomes…partitioning feature values into regions and estimates each region’s influence of prediction… identifying feature value changes or maintained values that place an outcome across a threshold or within acceptable ranges).
Regarding Claims 11 and 16, they are substantially analogous to Claim 1 and are rejected for the same reasons (Zhou: Para 0059-0061).
Regarding Claim 21, the combination of Zhou/Nourian/Wu, teaches the limitations of Claim 21 which state
further comprising displaying, by the computing system, the insight output generated via the insight engine via a user interface (Wu: Para 0043 via the user device 114 may store and execute software/instructions to facilitate interactions between a user and the KPI analytics system 116 via the user interface 118 of the user device).
Regarding Claim 22, the combination of Zhou/Nourian/Wu, teaches the limitations of Claim 22 which state wherein determining the target zones comprises:
generating, by the computing system, an accumulated local effects plot, associated with at least one of the predictive data features, that indicates the outcome scores associated with different values of the at least one of the predictive data features; and identifying, by the computing system, and based on the accumulated local effects plot, the values of the at least one of the predictive data features that are associated with the outcome scores within the target range (Zhou: Para 0022 via FIG. 7 shows charts 700-702 illustrating example completion analysis of the performance analytics 106. The chart 700 focuses on proppant per ft only and shows its impact on EUR in terms of accumulated local effects (ALE), which describes how a feature influences the prediction of a machine learning model on average. The chart 702 shows the impact of PPG on EUR in terms of ALE. The charts 700-702 demonstrate that proppant per ft is more significant than PPG. Moreover, the uplift effect of proppant loading intensity slows down around 2000 lbs./ft. Development of the unconventional reservoir may be optimized based on such completion analysis. For example, completion design may be optimized for the unconventional reservoir using the charts 700-702).
Claim(s) 2-3, 10, 12, 15 and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou et al. (US 2021/0123343 A1) in view of Nourian et al. (US 2021/0049503 A1) in view of Wu et al. (US 2020/0151746 A1) further in view of Gvildys et al. (US 2021/0174288 A1).
Regarding Claim 2, while the combination of Zhou/Nourian/Wu teaches the limitations of Claim 1, it does not explicitly disclose the limitations of Claim 2 which state wherein the multiple types of process data comprise two or more of: operational data associated with the first instances of the process, customer data associated with customers associated with the first instances of the process, or worker data associated with workers that performed the first instances of the process.
Gvildys though, with the teachings of Zhou/Nourian/Wu, teaches of
wherein the multiple types of process data comprise two or more of: operational data associated with the first instances of the process, customer data associated with customers associated with the first instances of the process, or worker data associated with workers that performed the first instances of the process (Gvildys: Para 0032, 0035, 0073 via the data for training the model is provided by a performance monitoring module 116. Functionality of the performance monitoring module is described in detail in U.S. Pat. No. 8,589,215, the content of which is incorporated herein by reference. In general terms, the performance monitoring module 116 monitors agent performance in meeting certain contact center metrics, and determines objective performance measurements based on the monitoring. Such objective performance measurements may include, for example, a number of interactions that have been transferred to another agent per month, customer survey scores, number of repeat calls per month, and the like. In addition to objective performance measurements, the contact center may also consider certain subjective factors that may be important to the contact center, such as for example, enthusiasm, selling skills, teamwork, and the like. Scores for the subjective factors may be given, for example, by a supervisor who may evaluate the subjective factors after analyzing one or more interactions of the agent…The model may be, for example, a statistical model that is trained based on training data provided to the model. In one embodiment, the training data includes input features taking the form of attributes 202a-202c (collectively referenced as 202) of agents when handling, for example, a simulated call. Such attributes may include, without limitation, emotional feature scores 202a, adherence scores 202b, and clarity scores 202c. The input features are mapped/correlated to particular target values. In one embodiment, the target values are agent performance scores 204 provided by the performance monitoring module 116…the mass storage device(s) 1126 may store one or more databases relating to agent data (e.g. agent profiles, schedules, etc.), customer data (e.g. customer profiles and loyalty information), interaction data (e.g. details of each interaction with a customer, including reason for the interaction, disposition data, time on hold, handle time, etc.), and the like).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhou/Nourian/Wu with the teachings of Gvildys in order to have wherein the multiple types of process data comprise two or more of: operational data associated with the first instances of the process, customer data associated with customers associated with the first instances of the process, or worker data associated with workers that performed the first instances of the process. The motivations behind this being to incorporate the teachings of predicting performance of agents. 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 3, the combination of Zhou/Nourian/Wu/Gvildys, teaches the limitations of Claim 3 which state
obtaining the operational data, the customer data, the worker data, and the outcome scores from the plurality of disparate data sources (Gvildys: Para 0032, 0035, 0073 via the data for training the model is provided by a performance monitoring module 116. Functionality of the performance monitoring module is described in detail in U.S. Pat. No. 8,589,215, the content of which is incorporated herein by reference. In general terms, the performance monitoring module 116 monitors agent performance in meeting certain contact center metrics, and determines objective performance measurements based on the monitoring. Such objective performance measurements may include, for example, a number of interactions that have been transferred to another agent per month, customer survey scores, number of repeat calls per month, and the like. In addition to objective performance measurements, the contact center may also consider certain subjective factors that may be important to the contact center, such as for example, enthusiasm, selling skills, teamwork, and the like. Scores for the subjective factors may be given, for example, by a supervisor who may evaluate the subjective factors after analyzing one or more interactions of the agent…The model may be, for example, a statistical model that is trained based on training data provided to the model. In one embodiment, the training data includes input features taking the form of attributes 202a-202c (collectively referenced as 202) of agents when handling, for example, a simulated call. Such attributes may include, without limitation, emotional feature scores 202a, adherence scores 202b, and clarity scores 202c. The input features are mapped/correlated to particular target values. In one embodiment, the target values are agent performance scores 204 provided by the performance monitoring module 116…the mass storage device(s) 1126 may store one or more databases relating to agent data (e.g. agent profiles, schedules, etc.), customer data (e.g. customer profiles and loyalty information), interaction data (e.g. details of each interaction with a customer, including reason for the interaction, disposition data, time on hold, handle time, etc.), and the like);
converting the operational data, the customer data, the worker data, and the outcome scores to a common data format (Wu: Para 0051 via A pre-processing pipeline process can be utilized to generate features from raw data. Specifically, feature engineering can be performed on raw user event data. Raw user event data can be obtained from data store 202 (e.g., raw user event data stored in data store 202). Engineered features can generally be defined as user features that are processed such that the features can be input into the model. For instance, engineered features are the features that are used to train the model. While numeric variables can often be directly input into a model as a feature, categorical variables typically need to be converted in a way that they can input into the model. In this way, categorical variables such as user behavior and attributes can be standardized such that the model can process them accordingly)
identifying data elements of the operational data, the customer data, the worker data, and the outcome scores that are associated with same instances of the process (Gvildys: Para 0035 via The model may be, for example, a statistical model that is trained based on training data provided to the model. In one embodiment, the training data includes input features taking the form of attributes 202a-202c (collectively referenced as 202) of agents when handling, for example, a simulated call. Such attributes may include, without limitation, emotional feature scores 202a, adherence scores 202b, and clarity scores 202c. The input features are mapped/correlated to particular target values. In one embodiment, the target values are agent performance scores 204 provided by the performance monitoring module 116); and
linking the data elements, in the training data set, that are associated with the same instances (Gvildys: Para 0035 via The model may be, for example, a statistical model that is trained based on training data provided to the model. In one embodiment, the training data includes input features taking the form of attributes 202a-202c (collectively referenced as 202) of agents when handling, for example, a simulated call. Such attributes may include, without limitation, emotional feature scores 202a, adherence scores 202b, and clarity scores 202c. The input features are mapped/correlated to particular target values. In one embodiment, the target values are agent performance scores 204 provided by the performance monitoring module 116).
Regarding Claim 10, while the combination of Zhou/Nourian/Wu teaches the limitations of Claim 9, it does not explicitly disclose the limitations of Claim 10 which state the process data includes worker data associated with workers that performed the instances of the process, and the combination of the predictive data features includes at least one predictive data feature associated with the worker data.
Gvildys though, with the teachings of Zhou/Nourian/Wu, teaches of
the process data includes worker data associated with workers that performed the instances of the process (Gvildys: Para 0040 via the scorer module 114 gathers performance scores of the selected agents from the data storage device), and
the combination of the predictive data features includes at least one predictive data feature associated with the worker data (Gvildys: Para 0050 via the hiring model 200 takes the scores of the various attributes 202 detected for the candidate agent, and generates a predicted performance score 204 for the candidate agent 102. In one embodiment, the predicted performance score is for predicting performance of the candidate agent in meeting particular metrics of the contact center. Such metrics may relate to, for example, call transfers, repeat calls, number of interactions handled, enthusiasm, teamwork, and the like).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhou/Nourian/Wu with the teachings of Gvildys in order to have the process data includes worker data associated with workers that performed the instances of the process, and the combination of the predictive data features includes at least one predictive data feature associated with the worker data. The motivations behind this being to incorporate the teachings of predicting performance of agents. 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 12, it is analogous to Claim 2 and is rejected for the same reasons.
Regarding Claim 15, while the combination of Zhou/Nourian/Wu teaches the limitations of Claim 11, it does not explicitly disclose the limitations of Claim 15 which state wherein the training data set is generated by: obtaining the multiple types of process data and the outcome scores from a plurality of disparate data sources; identifying data elements, within the multiple types of process data and the outcome scores, that are associated with same instances of the process; and linking the data elements, in the training data set, that are associated with the same instances of the process.
Gvildys though, with the teachings of Zhou/Nourian/Wu teaches of
obtaining the multiple types of process data and the outcome scores from a plurality of disparate data sources (Gvildys: Para 0032, 0035, 0073 via the data for training the model is provided by a performance monitoring module 116. Functionality of the performance monitoring module is described in detail in U.S. Pat. No. 8,589,215, the content of which is incorporated herein by reference. In general terms, the performance monitoring module 116 monitors agent performance in meeting certain contact center metrics, and determines objective performance measurements based on the monitoring. Such objective performance measurements may include, for example, a number of interactions that have been transferred to another agent per month, customer survey scores, number of repeat calls per month, and the like. In addition to objective performance measurements, the contact center may also consider certain subjective factors that may be important to the contact center, such as for example, enthusiasm, selling skills, teamwork, and the like. Scores for the subjective factors may be given, for example, by a supervisor who may evaluate the subjective factors after analyzing one or more interactions of the agent…The model may be, for example, a statistical model that is trained based on training data provided to the model. In one embodiment, the training data includes input features taking the form of attributes 202a-202c (collectively referenced as 202) of agents when handling, for example, a simulated call. Such attributes may include, without limitation, emotional feature scores 202a, adherence scores 202b, and clarity scores 202c. The input features are mapped/correlated to particular target values. In one embodiment, the target values are agent performance scores 204 provided by the performance monitoring module 116…the mass storage device(s) 1126 may store one or more databases relating to agent data (e.g. agent profiles, schedules, etc.), customer data (e.g. customer profiles and loyalty information), interaction data (e.g. details of each interaction with a customer, including reason for the interaction, disposition data, time on hold, handle time, etc.), and the like);
identifying data elements, within the multiple types of process data and the outcome scores, that are associated with same instances of the process (Gvildys: Para 0035 via The model may be, for example, a statistical model that is trained based on training data provided to the model. In one embodiment, the training data includes input features taking the form of attributes 202a-202c (collectively referenced as 202) of agents when handling, for example, a simulated call. Such attributes may include, without limitation, emotional feature scores 202a, adherence scores 202b, and clarity scores 202c. The input features are mapped/correlated to particular target values. In one embodiment, the target values are agent performance scores 204 provided by the performance monitoring module 116); and
linking the data elements, in the training data set, that are associated with the same instances of the process (Gvildys: Para 0035 via The model may be, for example, a statistical model that is trained based on training data provided to the model. In one embodiment, the training data includes input features taking the form of attributes 202a-202c (collectively referenced as 202) of agents when handling, for example, a simulated call. Such attributes may include, without limitation, emotional feature scores 202a, adherence scores 202b, and clarity scores 202c. The input features are mapped/correlated to particular target values. In one embodiment, the target values are agent performance scores 204 provided by the performance monitoring module 116).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhou/Nourian/Wu with the teachings of Gvildys in order to have wherein the training data set is generated by: obtaining the multiple types of process data and the outcome scores from a plurality of disparate data sources; identifying data elements, within the multiple types of process data and the outcome scores, that are associated with same instances of the process; and linking the data elements, in the training data set, that are associated with the same instances of the process. The motivations behind this being to incorporate the teachings of predicting performance of agents. 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 17, while the combination of Zhou/Nourian/Wu teaches the limitations of Claim 16, it does not explicitly disclose the limitations of Claim 17 which state wherein the multiple types of process data includes worker data associated with workers that performed the first instances of the process.
Gvildys though, with the teachings of Zhou/Nourian/Wu, teaches of
wherein the multiple types of process data includes worker data associated with workers that performed the first instances of the process (Gvildys: Para 0032, 0035, 0073 via the data for training the model is provided by a performance monitoring module 116. Functionality of the performance monitoring module is described in detail in U.S. Pat. No. 8,589,215, the content of which is incorporated herein by reference. In general terms, the performance monitoring module 116 monitors agent performance in meeting certain contact center metrics, and determines objective performance measurements based on the monitoring. Such objective performance measurements may include, for example, a number of interactions that have been transferred to another agent per month, customer survey scores, number of repeat calls per month, and the like. In addition to objective performance measurements, the contact center may also consider certain subjective factors that may be important to the contact center, such as for example, enthusiasm, selling skills, teamwork, and the like. Scores for the subjective factors may be given, for example, by a supervisor who may evaluate the subjective factors after analyzing one or more interactions of the agent…The model may be, for example, a statistical model that is trained based on training data provided to the model. In one embodiment, the training data includes input features taking the form of attributes 202a-202c (collectively referenced as 202) of agents when handling, for example, a simulated call. Such attributes may include, without limitation, emotional feature scores 202a, adherence scores 202b, and clarity scores 202c. The input features are mapped/correlated to particular target values. In one embodiment, the target values are agent performance scores 204 provided by the performance monitoring module 116…the mass storage device(s) 1126 may store one or more databases relating to agent data (e.g. agent profiles, schedules, etc.), customer data (e.g. customer profiles and loyalty information), interaction data (e.g. details of each interaction with a customer, including reason for the interaction, disposition data, time on hold, handle time, etc.), and the like).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhou/Nourian/Wu with the teachings of Gvildys in order to have wherein the multiple types of process data includes worker data associated with workers that performed the first instances of the process. The motivations behind this being to incorporate the teachings of predicting performance of agents. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 18, the combination of Zhou/Nourian/Wu/Gvildys, teaches the limitations of Claim 18 which state
wherein the multiple types of first process data further includes at least one of: operational data associated with the performance of the first instances of the process, or customer data associated with customers associated with the first instances of the process (Gvildys: Para 0032, 0035, 0073 via the data for training the model is provided by a performance monitoring module 116. Functionality of the performance monitoring module is described in detail in U.S. Pat. No. 8,589,215, the content of which is incorporated herein by reference. In general terms, the performance monitoring module 116 monitors agent performance in meeting certain contact center metrics, and determines objective performance measurements based on the monitoring. Such objective performance measurements may include, for example, a number of interactions that have been transferred to another agent per month, customer survey scores, number of repeat calls per month, and the like. In addition to objective performance measurements, the contact center may also consider certain subjective factors that may be important to the contact center, such as for example, enthusiasm, selling skills, teamwork, and the like. Scores for the subjective factors may be given, for example, by a supervisor who may evaluate the subjective factors after analyzing one or more interactions of the agent…The model may be, for example, a statistical model that is trained based on training data provided to the model. In one embodiment, the training data includes input features taking the form of attributes 202a-202c (collectively referenced as 202) of agents when handling, for example, a simulated call. Such attributes may include, without limitation, emotional feature scores 202a, adherence scores 202b, and clarity scores 202c. The input features are mapped/correlated to particular target values. In one embodiment, the target values are agent performance scores 204 provided by the performance monitoring module 116…the mass storage device(s) 1126 may store one or more databases relating to agent data (e.g. agent profiles, schedules, etc.), customer data (e.g. customer profiles and loyalty information), interaction data (e.g. details of each interaction with a customer, including reason for the interaction, disposition data, time on hold, handle time, etc.), and the like).
Regarding Claim 19, while the combination of Zhou/Nourian/Wu teaches the limitations of Claim 16, it does not explicitly disclose the limitations of Claim 19 which state wherein the training data set is generated by: obtaining the multiple types of process data and the outcome scores from a plurality of disparate data sources; identifying data elements, within the multiple types of process data and the outcome scores, that are associated with same instances of the process; and linking the data elements, in the training data set, that are associated with the same instances of the process.
Gvildys though, with the teachings of Zhou/Nourian/Wu teaches of
obtaining the multiple types of process data and the outcome scores from a plurality of disparate data sources (Gvildys: Para 0032, 0035, 0073 via the data for training the model is provided by a performance monitoring module 116. Functionality of the performance monitoring module is described in detail in U.S. Pat. No. 8,589,215, the content of which is incorporated herein by reference. In general terms, the performance monitoring module 116 monitors agent performance in meeting certain contact center metrics, and determines objective performance measurements based on the monitoring. Such objective performance measurements may include, for example, a number of interactions that have been transferred to another agent per month, customer survey scores, number of repeat calls per month, and the like. In addition to objective performance measurements, the contact center may also consider certain subjective factors that may be important to the contact center, such as for example, enthusiasm, selling skills, teamwork, and the like. Scores for the subjective factors may be given, for example, by a supervisor who may evaluate the subjective factors after analyzing one or more interactions of the agent…The model may be, for example, a statistical model that is trained based on training data provided to the model. In one embodiment, the training data includes input features taking the form of attributes 202a-202c (collectively referenced as 202) of agents when handling, for example, a simulated call. Such attributes may include, without limitation, emotional feature scores 202a, adherence scores 202b, and clarity scores 202c. The input features are mapped/correlated to particular target values. In one embodiment, the target values are agent performance scores 204 provided by the performance monitoring module 116…the mass storage device(s) 1126 may store one or more databases relating to agent data (e.g. agent profiles, schedules, etc.), customer data (e.g. customer profiles and loyalty information), interaction data (e.g. details of each interaction with a customer, including reason for the interaction, disposition data, time on hold, handle time, etc.), and the like);
identifying data elements, within the multiple types of process data and the outcome scores, that are associated with same instances of the process (Gvildys: Para 0035 via The model may be, for example, a statistical model that is trained based on training data provided to the model. In one embodiment, the training data includes input features taking the form of attributes 202a-202c (collectively referenced as 202) of agents when handling, for example, a simulated call. Such attributes may include, without limitation, emotional feature scores 202a, adherence scores 202b, and clarity scores 202c. The input features are mapped/correlated to particular target values. In one embodiment, the target values are agent performance scores 204 provided by the performance monitoring module 116); and
linking the data elements, in the training data set, that are associated with the same instances of the process (Gvildys: Para 0035 via The model may be, for example, a statistical model that is trained based on training data provided to the model. In one embodiment, the training data includes input features taking the form of attributes 202a-202c (collectively referenced as 202) of agents when handling, for example, a simulated call. Such attributes may include, without limitation, emotional feature scores 202a, adherence scores 202b, and clarity scores 202c. The input features are mapped/correlated to particular target values. In one embodiment, the target values are agent performance scores 204 provided by the performance monitoring module 116).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhou/Nourian/Wu with the teachings of Gvildys in order to have wherein the training data set is generated by: obtaining the multiple types of process data and the outcome scores from a plurality of disparate data sources; identifying data elements, within the multiple types of process data and the outcome scores, that are associated with same instances of the process; and linking the data elements, in the training data set, that are associated with the same instances of the process. The motivations behind this being to incorporate the teachings of predicting performance of agents. 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 Zhou et al. (US 2021/0123343 A1) in view of Nourian et al. (US 2021/0049503 A1) in view of Wu et al. (US 2020/0151746 A1) in view of Gvildys et al. (US 2021/0174288 A1) further in view of Kannan (US 8,396,741 B2).
Regarding Claim 4, while the combination of Zhou/Nourian/Wu/Gvildys teaches the limitations of Claim 2, it does not explicitly disclose the limitation of Claim 4 which states wherein the worker data comprises worker satisfaction scores based on answers to worker surveys provided by the workers.
Kannan though, with the teachings of Zhou/Nourian/Wu, teaches of
wherein the worker data comprises worker satisfaction scores based on answers to worker surveys provided by the workers (Kannan: Col 9 lines 40-45, Col 15 lines 1-5 via In concert with the steps of mining a transcription 314 and assigning a score, the process 310 also gives follow-up surveys to customers, agents, or both to extract additional information about the interaction. Types of surveys include voice surveys, email surveys, text message surveys, browser-based online surveys, etc. The surveys ask for both structured data and instructed data… FIG. 10 illustrates a workflow for integrating customer surveys and agent surveys to increase customer satisfaction. The results of the agent surveys and customer surveys identify the attributes that drive customer dissatisfaction, and which attributes that have the highest affinity to each other).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhou/Nourian/Wu with the teachings of Kannan in order to have wherein the worker data comprises worker satisfaction scores based on answers to worker surveys provided by the workers. The motivations behind this being to incorporate the teachings of mining customer-agent interactions. 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 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou et al. (US 2021/0123343 A1) in view of Nourian et al. (US 2021/0049503 A1) in view of Wu et al. (US 2020/0151746 A1) further in view of Kannan (US 8,396,741 B2).
Regarding Claim 5, while the combination of Zhou/Nourian/Wu teaches the limitations of Claim 1, it does not explicitly disclose the limitation of Claim 5 which states wherein the outcome scores comprise customer satisfaction scores indicating subjective satisfaction levels of customers associated with the first instances of the process.
Kannan though, with the teachings of Zhou/Nourian/Wu, teaches of
wherein the outcome scores comprise customer satisfaction scores indicating subjective satisfaction levels of customers associated with the first instances of the process (Kannan: Col 5 lines 14-25, Col 9 lines 40-46 via Likewise, the data fusion engine 100 gathers information from one or more survey modules 23. The survey module 23 stores survey results 24, net experience scores, customer satisfaction scores and ratings, agent performance scores, etc., and verbatim survey data 29. In some embodiments of the invention, surveys are given to both customers and agents. According to these embodiments, a comparison between the customer survey and the agent survey reveals useful insights. For example, a customer may report a negative interaction experience because the agent was unable to give the customer a particular requested service. However, the company employing the agent may restrict the agent from giving customers the requested service. Therefore, the agent can self-report that they performed well in light of a customer asking for a service that they were unauthorized to provide… In concert with the steps of mining a transcription 314 and assigning a score, the process 310 also gives follow-up surveys to customers, agents, or both to extract additional information about the interaction. Types of surveys include voice surveys, email surveys, text message surveys, browser-based online surveys, 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 Zhou/Nourian/Wu with the teachings of Kannan in order to have wherein the outcome scores comprise customer satisfaction scores indicating subjective satisfaction levels of customers associated with the first instances of the process. The motivations behind this being to incorporate the teachings of mining customer-agent interactions. 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 the combination of Zhou/Nourian/Wu teaches the limitations of Claim 11, it does not explicitly disclose the limitation of Claim 13 which states wherein the outcome scores comprise one or more of: customer satisfaction scores associated with customers associated with the first instances of the process, customer retention scores associated with the customers, worker satisfaction scores associated with workers that performed the first instances of the process; worker retention scores associated with the workers, or third party satisfaction scores associated with third parties associated with the first instances of the process.
Kannan though, with the teachings of Zhou/Nourian/Wu, teaches of
wherein the outcome scores comprise one or more of: customer satisfaction scores associated with customers associated with the first instances of the process, customer retention scores associated with the customers, worker satisfaction scores associated with workers that performed the first instances of the process; worker retention scores associated with the workers, or third party satisfaction scores associated with third parties associated with the first instances of the process (Kannan: Col 5 lines 14-25, Col 9 lines 40-46 via Likewise, the data fusion engine 100 gathers information from one or more survey modules 23. The survey module 23 stores survey results 24, net experience scores, customer satisfaction scores and ratings, agent performance scores, etc., and verbatim survey data 29. In some embodiments of the invention, surveys are given to both customers and agents. According to these embodiments, a comparison between the customer survey and the agent survey reveals useful insights. For example, a customer may report a negative interaction experience because the agent was unable to give the customer a particular requested service. However, the company employing the agent may restrict the agent from giving customers the requested service. Therefore, the agent can self-report that they performed well in light of a customer asking for a service that they were unauthorized to provide… In concert with the steps of mining a transcription 314 and assigning a score, the process 310 also gives follow-up surveys to customers, agents, or both to extract additional information about the interaction. Types of surveys include voice surveys, email surveys, text message surveys, browser-based online surveys, 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 Zhou/Nourian/Wu with the teachings of Kannan in order to have wherein the outcome scores comprise one or more of: customer satisfaction scores associated with customers associated with the first instances of the process, customer retention scores associated with the customers, worker satisfaction scores associated with workers that performed the first instances of the process; worker retention scores associated with the workers, or third party satisfaction scores associated with third parties associated with the first instances of the process. The motivations behind this being to incorporate the teachings of mining customer-agent interactions. 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 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhou et al. (US 2021/0123343 A1) in view of Nourian et al. (US 2021/0049503 A1) in view of Wu et al. (US 2020/0151746 A1) further in view of Lee (US 2016/0171414 A1).
Regarding Claim 7, while the combination of Zhou/Nourian/Wu teaches the limitations of Claim 1, it does not explicitly disclose the limitations of Claim 7 which state wherein the insight output identifies one or more particular instances of the process, of the second instances of the process, that are associated with second values of one or more of the predictive data features that are outside the target zones.
Lee though, with the teachings of Zhou/Nourian/Wu, teaches of
wherein the insight output identifies one or more particular instances of the process, of the second instances of the process, that are associated with second values of one or more of the predictive data features that are outside the target zones (Lee: Para 0026, 0048 via At a high level, this disclosure is drawn to a method for generating an intelligent energy KPI system based on modular engineering. The disclosure provides an example of a systematic way to structure a large industrial complex hierarchically, a method to monitor overall energy performance using a few KPIs from the highest level, to transform raw process data to operational intelligence at the lowest level, and to integrate the interconnected information throughout all hierarchical levels. For example, operational intelligence can be obtained through analysis of all relevant historical and current process data. The intelligent energy KPI system can determine proper KPI targets to reflect current plant operations and monitor/detect any energy KPI violations…the system can include a representation of a hierarchical structure or portion of a hierarchical structure to assist a user. For example, any equipment KPI violation can be highlighted by orange or red status color, whereas good energy performance can be indicated by green or yellow status color).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhou/Nourian/Wu with the teachings of Lee in order to have wherein the insight output identifies one or more particular instances of the process, of the second instances of the process, that are associated with second values of one or more of the predictive data features that are outside the target zones. The motivations behind this being to incorporate the teachings of detecting and analyzing KPI violations. 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 20, while the combination of Zhou/Nourian/Wu teaches the limitations of Claim 16, it does not explicitly disclose the limitations of Claim 20 which state wherein the second instances of the process comprise current instances of the process that are evaluated via the insight engine as the current instances are being performed.
Lee though, with the teachings of Zhou/Nourian/Wu, teaches of
wherein the second instances of the process comprise current instances of the process that are evaluated via the insight engine as the current instances are being performed (Lee: Para 0026, 0048 via At a high level, this disclosure is drawn to a method for generating an intelligent energy KPI system based on modular engineering. The disclosure provides an example of a systematic way to structure a large industrial complex hierarchically, a method to monitor overall energy performance using a few KPIs from the highest level, to transform raw process data to operational intelligence at the lowest level, and to integrate the interconnected information throughout all hierarchical levels. For example, operational intelligence can be obtained through analysis of all relevant historical and current process data. The intelligent energy KPI system can determine proper KPI targets to reflect current plant operations and monitor/detect any energy KPI violations…the system can include a representation of a hierarchical structure or portion of a hierarchical structure to assist a user. For example, any equipment KPI violation can be highlighted by orange or red status color, whereas good energy performance can be indicated by green or yellow status color).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhou/Nourian/Wu with the teachings of Lee in order to have wherein the second instances of the process comprise current instances of the process that are evaluated via the insight engine as the current instances are being performed. The motivations behind this being to incorporate the teachings of detecting and analyzing KPI violations. 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 Zhou et al. (US 2021/0123343 A1) in view of Nourian et al. (US 2021/0049503 A1) in view of Wu et al. (US 2020/0151746 A1) in view of Lee (US 2016/0171414 A1) further in view of Balakrishnan et al. (US 2018/0032939 A1).
Regarding Claim 8, while the combination of Zhou/Nourian/Wu teaches the limitations of Claim 1, it does not explicitly disclose the limitations of Claim 8 which state wherein: the second instances of the process are current instances of the process that are evaluated via the insight engine as the current instances are being performed, and the insight output identifies one or more particular instances of the process, from among the current instances of the process, that are associated with one or more of the predictive data features: that currently have the values inside the target zones.
Lee though, with the teachings of Zhou/Nourian/Wu, teaches of
the second instances of the process are current instances of the process that are evaluated via the insight engine as the current instances are being performed (Lee: Para 0026, 0048 via At a high level, this disclosure is drawn to a method for generating an intelligent energy KPI system based on modular engineering. The disclosure provides an example of a systematic way to structure a large industrial complex hierarchically, a method to monitor overall energy performance using a few KPIs from the highest level, to transform raw process data to operational intelligence at the lowest level, and to integrate the interconnected information throughout all hierarchical levels. For example, operational intelligence can be obtained through analysis of all relevant historical and current process data. The intelligent energy KPI system can determine proper KPI targets to reflect current plant operations and monitor/detect any energy KPI violations…the system can include a representation of a hierarchical structure or portion of a hierarchical structure to assist a user. For example, any equipment KPI violation can be highlighted by orange or red status color, whereas good energy performance can be indicated by green or yellow status color), and
the insight output identifies one or more particular instances of the process, from among the current instances of the process, that are associated with one or more of the predictive data features: that currently have the values inside the target zones (Lee: Para 0026, 0048 via At a high level, this disclosure is drawn to a method for generating an intelligent energy KPI system based on modular engineering. The disclosure provides an example of a systematic way to structure a large industrial complex hierarchically, a method to monitor overall energy performance using a few KPIs from the highest level, to transform raw process data to operational intelligence at the lowest level, and to integrate the interconnected information throughout all hierarchical levels. For example, operational intelligence can be obtained through analysis of all relevant historical and current process data. The intelligent energy KPI system can determine proper KPI targets to reflect current plant operations and monitor/detect any energy KPI violations…the system can include a representation of a hierarchical structure or portion of a hierarchical structure to assist a user. For example, any equipment KPI violation can be highlighted by orange or red status color, whereas good energy performance can be indicated by green or yellow status color).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhou/Nourian/Wu with the teachings of Lee in order to have wherein: the second instances of the process are current instances of the process that are evaluated via the insight engine as the current instances are being performed, and the insight output identifies one or more particular instances of the process, from among the current instances of the process, that are associated with one or more of the predictive data features: that currently have the values inside the target zones. The motivations behind this being to incorporate the teachings of detecting and analyzing KPI violations. 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, Zhou/Nourian/Wu does not explicitly disclose the limitation of Claim 8 which states insight output identifies one or more particular instances of the process, from among the current instances of the process, that are associated with one or more of the predictive data features: are projected to move to second values outside the target zones within a future period of time.
Balakrishnan though, with the teachings of Zhou/Nourian/Wu/Lee, teaches of
insight output identifies one or more particular instances of the process, from among the current instances of the process, that are associated with one or more of the predictive data features: are projected to move to second values outside the target zones within a future period of time (Balakrishnan: Para 0041 via data analytics are used to determine customer satisfaction by using measurable metrics. Metrics include, but are not limited to, meal rate, meal item consumption, amount of leftover meal, amount of leftover meal item, customer reaction upon initial consumption, and customer reaction upon consumption of one or more meal items. Metrics may also involve a time taken to eat or consume each course, time taken to flag a server, customer looking for a server, time taken to find the server, tips received by each server, and various mood evolution analytics for assessing human emotional behavior. The human emotional behavior may include heart rate and skin temperature of the customers, in addition to facial expressions, gestures, mood, etc. The metrics allow for the prediction of variations indicating different satisfaction levels for customers/individuals visiting a food or eating facility, such as a restaurant. Each of the metrics may be associated with a threshold set by the restaurant. If the variations exceed one or more thresholds, then the restaurant may dynamically refine one or more variables/parameters in real-time).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhou/Nourian/Wu/Lee with the teachings of Balakrishnan in order to have insight output identifies one or more particular instances of the process, from among the current instances of the process, that are associated with one or more of the predictive data features: are projected to move to second values outside the target zones within a future period of time. The motivations behind this being to incorporate the teachings of predicting variations in measurable metrics. 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
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TYRONE E SINGLETARY whose telephone number is (571)272-1684. The examiner can normally be reached 9 - 5:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached at 571-272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/T.E.S./ Examiner, Art Unit 3625
/BETH V BOSWELL/ Supervisory Patent Examiner, Art Unit 3625
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