DETAILED ACTION
Notice to Applicant
The following is a NON-FINAL Office action upon examination of application number 18/439,720 filed on 02/12/2024. Claims 1-20 are pending in this application, and have been examined on the merits discussed below.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) filed on 06/06/2025 has been acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
4. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
5. Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
6. Claims 1, 8, and 15 recite the limitation “perform a corrective action related to the selected customer in response to a determination that the metric has a negative impact on the selected customer.” The phrase “the metric” lacks antecedent basis and therefore renders the claims indefinite. While claims 1/8/15 recite “a predefined set of metrics” and “each metric”, claims 1/8/15 do not introduce “a metric.” Appropriate correction is required.
All claims dependent from above rejected claims are also rejected due to dependency.
Claim Rejections - 35 USC § 101
7. 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.
8. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The eligibility analysis in support of these findings is provided below, in accordance with MPEP 2106.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the system (claims 1-7), method (claims 8-14), and computer-readable medium (claims 15-20) are directed to at least one potentially eligible category of subject matter (i.e., machine, process, and article of manufacture, respectively). Thus, Step 1 of the Subject Matter Eligibility test for claims 1-14 is satisfied. Claim 15, however, recites “a computer-readable medium storing computer-readable instructions…” which could reasonably comprise a transitory propagating signal per se. The computer program product of claims 15-20 can be interpreted as covering a signal per se. The United States Patent and Trademark Office (USPTO) is obliged to give claims their broadest reasonable interpretation consistent with the specification during proceedings before the USPTO. See In re Zletz, 893 F.2d 319 (Fed. Cir. 1989). The broadest reasonable interpretation of a claim drawn to a computer readable medium typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent. See MPEP 2111.01. Here, Applicant has claimed a computer-readable medium, and the claims fail to explicitly exclude a transitory signal bearing medium from its inclusive definition of computer-readable media. Therefore, given the broadest reasonable interpretation of the claims, the recited computer readable medium could be interpreted as a transitory propagating signal per se. Thus, Step 1 of the Subject Matter Eligibility test for claims 15-20 is not satisfied.
With respect to Step 2A Prong One, it is next noted that the claims recite an abstract idea that falls into the “Certain Methods of Organizing Human Activity” abstract idea set forth in MPEP 2106 because the claims recite steps for managing customer satisfaction, which encompasses activity for managing personal behavior or relationships or interactions (e.g., following rules or instructions), and steps that can be performed in the human mind (including observation, evaluation, judgment, opinion), and therefore fall under the “Mental Processes” abstract idea grouping. With respect to independent claim 1, the limitations reciting the abstract idea are indicated in bold below: a processor; a display unit coupled to the processor; and a storage unit accessible by the processor, the storage unit storing an application thereon that, when executed by the processor, causes the system to: obtain performance data for a predefined set of metrics for a plurality of customers, the performance data including historical performance data for the predefined set of metrics and current performance data for the predefined set of metrics; perform an impact analysis using the historical performance data for the predefined set of metrics for the plurality of customers and current performance data for the predefined set of metrics for a selected customer, the impact analysis determining whether each metric in the predefined set of metrics has a negative impact, a positive impact, or a neutral impact on the selected customer; and perform a corrective action related to the selected customer in response to a determination that the metric has a negative impact on the selected customer. These steps are organizing human activity by managing interactions between people by following rules, or instructions, and may also be accomplished mentally such as via human observation and perhaps with the aid of pen and paper.
Therefore, because the limitations above set forth activities falling within the “Certain methods of organizing human activity” and “Mental Processes” abstract idea grouping described in MPEP 2106, the additional elements recited in the claims are further evaluated, individually and in combination, under Step 2A Prong Two and Step 2B below. Claims 8 and 15 recite similar limitations as those recited in claim 1 and therefore are found to recite the same abstract idea as claim 1.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. With respect to independent claim 8, it is noted that the claim does not recite additional elements (i.e., claim 8 is a method that recites several disembodied steps). With respect to independent claims 1/15, the additional elements are: a processor, display unit, a storage unit, an application, and the system (claim 1), computer-readable instructions and a processor (claim 15). These additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or computer-executable instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and merely serve to link the use of the judicial exception to a particular technological environment. See MPEP 2106.05(f) and 2106.05(h). Even if the step for obtaining is not deemed part of the abstract idea, this step is at most directed to insignificant extra-solution data gathering activity, which is not sufficient to amount to a practical application. See MPEP 2106.05(g). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As set forth above, claim 8 does not recite additional elements (i.e., claim
8 is a method that recites several disembodied steps). With respect to independent claims 1/15, the additional elements are: a processor, display unit, a storage unit, an application, and the system (claim 1), computer-readable instructions and a processor (claim 15). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), and merely serve to link the use of the judicial exception to a particular technological environment and does not amount to significantly more than the abstract idea itself. Notably, Applicant’s Specification suggests that virtually any type of computing device under the sun can be used to implement the claimed invention (Specification at paragraph [0007]). Accordingly, the generic computer involvement in performing the claim steps merely serves to generally link the use of the judicial exception to a particular technological environment, which does not add significantly more to the claim. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976.). Even if the step for obtaining is not deemed part of the abstract idea, this step is at most directed to insignificant extra-solution data gathering activity, which has been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent claims 2-7, 9-14, and 16-20 recite the same abstract idea as recited in the independent claims, and when evaluated under Step 2A Prong One are found to merely recite details that serve to narrow the same abstract idea recited in the independent claims accompanied by the same generic computing elements or software as those addressed above in the discussion of the independent claims, which is not sufficient to amount to a practical application or add significantly more, or other additional elements that fail to amount to a practical application or add significantly more, as noted above. In particular, dependent claims 2-7 recite “wherein the plurality of customers constitutes a cluster of customers, the cluster of customers being selected from several clusters of customers, each cluster of customers being defined by the application causing the system to perform a cluster analysis based on the historical performance data for the predefined set of metrics,” “obtain historical customer net satisfaction scores for the plurality of customers, each net satisfaction score having a value within a predefined range of values; and perform a correlational analysis using the historical performance data for the predefined set of metrics and the customer net satisfaction scores, the correlational analysis providing an importance value of each metric in the predefined set of metrics,” “generate a metric score for each metric in the predefined set of metrics based on the importance value for the metric and the impact of the metric on the selected customer,” “generate a customer score for the selected customer based on a sum of metric scores for the predefined set of metrics and a sum of importance values for the predefined set of metrics, the customer score providing an indication of a likelihood that the selected customer will increase or decrease its net satisfaction score based on the current performance data for the predefined set of metrics,” “display a result of the cluster analysis, the correlational analysis, the impact analysis, and/or the customer scores,” “displays the impact analysis using boxes having different colors and/or different sizes”, however these limitations cover activity for managing personal behavior or relationships or interactions (e.g., following rules or instructions), which is part of the same abstract idea as addressed in the independent claims that falls within the “Certain Methods of Organizing Human Activity” abstract idea grouping and also recite steps that may also be accomplished mentally such as via human observation and perhaps with the aid of pen and paper. Dependent claims 9-14 and 16-20 have been evaluated as well, but are subject to similar findings as claims 2-7. Dependent claims 6-7, 13-14, and 20 recite additional elements of: one or more interactive display screens. However, when evaluated under Step 2A Prong Two and Step 2B, these additional elements do not amount to a practical application or significantly more since they merely require generic computing devices (or computer-implemented instructions/code) which as noted in the discussion of the independent claims above is not enough to render the claims as eligible.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself.
For more information, see MPEP 2106.
Claim Rejections - 35 USC § 102
9. 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 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.
10. 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)(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.
11. Claims 1, 8, and 15 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Bhattacharya et al., Pub. No.: US 2023/0245022 A1, [hereinafter Bhattacharya].
As per claim 1, Bhattacharya teaches a system for improving customer satisfaction (paragraphs 0033, 0097), comprising:
a processor (paragraph 0097, discussing that the computer system includes a processor, memory, storage, an input/output (I/O) interface, a communication interface, and a bus…; paragraphs 0025, 0028);
a display unit coupled to the processor (paragraph 0025, 0028, 0097); and
a storage unit accessible by the processor, the storage unit storing an application thereon that, when executed by the processor, causes the system (paragraphs 0025, 0028, 0097, 0098) to:
obtain performance data for a predefined set of metrics for a plurality of customers, the performance data including historical performance data for the predefined set of metrics and current performance data for the predefined set of metrics (paragraph 0040, discussing an example customer-intelligence dashboard. The customer-intelligence dashboard includes information for the following customers, which may be customers of the business entity that the user is associated with: WALMART, PHILLIPS 66, CVS HEALTH, CHEVRON, COSTCO WHOLESALE, GENERAL MOTORS, and AMAZON. For each customer, the following categories are presented: (1) customer name; (2) customer owner; (3) product(s); (4) revenue; and (5) renewal date. The following KPIs are also presented: (1) product usage; (2) interaction frequency; (3) NPS/CSAT; (4) number of support tickets; (5) severity of support tickets; (6) customer sentiment; and (7) customer-owner pulse. Additional KPIs that could be presented…; paragraph 0041, discussing that particular embodiments set a threshold value (e.g. high, medium, or low) for customer health. In particular embodiments, these values are benchmarked across multiple businesses per product per ARR range per quarter. This metric may indicate to a user when and what action should be taken. For each KPI for each customer, customer-intelligence dashboard 302 presents a color-coded score of low, medium, or high. With some KPIs, a low score is considered positive and a high score is considered negative. With other KPIs, a high score is considered positive and a low score is considered negative. A KPI score of medium may be considered neutral. A positive KPI score may be color coded green, a negative KPI score may be color coded red, and a neutral KPI score may be color coded yellow; paragraph 0088, discussing that history window provides access to all interaction information associated with the customer. This may include chronological information of updates to the KPIs week over week or month over month, depending on how often the dashboard is refreshed. It may also include e-mail content and e-mail sentiment. This scoring may be based on sentiment analysis of each e-mail and particular pre-determined keywords. This may help the user access historical data and, using AI, assess that data to determine next steps);
perform an impact analysis using the historical performance data for the predefined set of metrics for the plurality of customers and current performance data for the predefined set of metrics for a selected customer, the impact analysis determining whether each metric in the predefined set of metrics has a negative impact, a positive impact, or a neutral impact on the selected customer (paragraph 0044, discussing that to generate a customer-health score, the customer-intelligence system may use clustering module to segment customer data into component parts by analyzing similarities among the feature space….reference to a feature may encompass a KPI, and vice versa, where appropriate…; paragraph 0045, discussing that FIG. 5 illustrates example KPI weighting in example customer-health score generation. Particular embodiments combine feature existence, weightage analysis, clustering, and benchmarking in a comprehensive UI for customer-health-score understanding and validation. In the example of FIG. 5, four KPIs are used to generate a customer-health score (product usage, NPS/CSAT, interaction frequency, and customer-owner pulse), with other KPIs being unavailable and weighted at zero…; paragraph 0046, discussing that particular embodiments facilitate better understanding of the relevance of each KPI and provide information describing the relative weightage of each KPI based on SHAP. The KPIs may be color coded and are represented in a pie chart. The outcome is a visual representation of the relative weightages of each feature component of the health-score algorithm executed by health-score module; paragraph 0051, discussing that features represent the key elements that contribute to the customer's health score. Particular embodiments show the customer, the presence, and the contribution made by each KPI. Customer-intelligence system 102 checks the existence of these KPIs for a business and helps the user understand those KPIs better. In particular embodiments, the values are represented in Boolean. This may help the business to understand what features exist in the database. As an example, the following KPIs may be used, in any suitable combination: (1) product usage, (2) interaction frequency, (3) NPS/CSAT, (4) number of support tickets, (5) severity of support tickets, (6) customer sentiment, (7) customer-owner pulse, (8) up-sells/down-sells, and (9) customer maturity. FIG. 6 illustrates example feature enablement in an example customer-health score, which may be presented to a user to help the user better understand the customer-health score being provided; paragraph 0060, discussing that after running the K-means clustering algorithm on the client's customer data and assigning the cluster values to each row, the customer data may then be separate and saved separately for further analysis. Particular embodiments iterate through each cluster and describe the key features for the client in each cluster, like customer count in each cluster, average revenue for each cluster, active versus churned users in each cluster, etc. The benchmarks for the KPIs are also calculated by evaluating the minimum and the maximum value for each category present. For example, a KPI will have “Low,” “Medium,” and “High” categories and the user will be able to see the minimum and maximum value in each category, which will help the user get an idea of how each KPI is contributing to the overall performance (or customer-health score). If a business has significantly lower values for any KPI on any cluster, then the business can focus on the customers that are present inside that cluster to find the similarities among that business's customers and strategize better; paragraph 0087, discussing that FIGS. 9A-9B illustrates example presentation of example key indicators for an example customer on an example customer-intelligence dashboard 302. In particular embodiments, key indicators provide an extension of high-level customer information, including KPIs measured as high, medium, or low and associated trends with each KPI; paragraphs 0047-0051, 0086); and
perform a corrective action related to the selected customer in response to a determination that the metric has a negative impact on the selected customer (paragraph 0035, discussing that the dashboard module generates for presentation to a client a customer-intelligence dashboard that unifies customer data and generates predictions about the client's customer churn, customer renewal, or customer upsell; the clustering module segments customer data into component parts by analyzing similarities among the feature space; the health-score module generates customer-health scores for presentation to users in the customer intelligence dashboard; the recommendation engine uses information from the dashboard to generate actionable recommendations based on data changes on dashboard…; paragraph 0086, discussing that general information for a customer may include high-level customer information, customer-health trend, and customer insights. The high-level customer information may include the following: customer name, customer-owner (or CSM) name, product(s), revenue (e.g. ARR), renewal date for the customer, and customer status (e.g. renewed or churned). The customer-health trend may show how customer health for that customer (high, medium, or low) has been trending since the launch of the product. The customer insights may include recommendations that help the user to dive deeper into the account with actionable insights on how to proceed. The customer insights may provide insights about customers that require immediate attention while managing a business's top 10 accounts, along with account details, churn/expansion profiles, and recommendations on next steps. These insights may be the results of analysis using trained AI models that analyze KPIs and best practices for customer success to give the user more accurate and intuitive recommendations to prevent churn and accelerate upsells; paragraph 0087, discussing that FIGS. 9A-9B illustrates example presentation of example key indicators for an example customer on an example customer-intelligence dashboard. In particular embodiments, key indicators provide an extension of high-level customer information, including KPIs measured as high, medium, or low and associated trends with each KPI).
Claims 8 and 15 recite substantially similar limitations that stand rejected via the art citations and rationale applied to claim 1, as discussed above. As per claim 8, Bhattacharya teaches a method for improving customer satisfaction (paragraph 0023: “an example method for providing a customer-intelligence dashboard”; paragraph 0093). As per claim 15, Bhattacharya teaches a computer-readable medium storing computer-readable instructions for causing a processor (paragraph 0105, discussing a computer-readable non-transitory storage medium or media may include one or more semiconductor-based or other integrated circuits (ICs) (such, as for example, field-programmable gate arrays (FPGAs) or application-specific ICs (ASICs)), hard disk drives (HDDs), hybrid hard drives (HHDs), optical discs, optical disc drives (ODDs), magneto-optical discs, magneto-optical drives, floppy diskettes, floppy disk drives (FDDs), magnetic tapes, solid-state drives (SSDs), RAM-drives, SECURE DIGITAL cards or drives, any other suitable computer-readable non-transitory storage media, or any suitable combination of two or more of these, where appropriate. A computer-readable non-transitory storage medium may be volatile, non-volatile, or a combination of volatile and non-volatile, where appropriate).
Claim Rejections - 35 USC § 103
12. 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 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.
13. 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 of this title, 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.
14. 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.
15. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
16. Claims 2-6, 9-13, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bhattacharya in view of Wood et al., Patent No.: US 10,607,242 B1, [hereinafter Wood].
As per claim 2, Bhattacharya teaches the system of claim 1. Bhattacharya further teaches wherein the plurality of customers constitutes a cluster of customers, the cluster of customers being selected from several clusters of customers, each cluster of customers being defined by the application causing the system to perform a cluster analysis (paragraph 0035, discussing that the clustering module segments customer data into component parts by analyzing similarities among the feature space; paragraph 0044, discussing that to generate a customer-health score, the customer-intelligence system may use clustering module to segment customer data into component parts by analyzing similarities among the feature space….reference to a feature may encompass a KPI, and vice versa, where appropriate. This may provide users with a deeper understanding of the data-normalization-and-inference process without requiring users to be well versed in machine learning (ML) or statistics. Particular embodiments provide a renewal-probability score for each customer based on a seven-feature or nine-feature model. In particular embodiments, clustering module 204 uses SHapley Additive exPlanations (SHAP) via Shapley values to represent feature weightages. Clustering module 204 may also incorporate segmentation using a K-Means Clustering Algorithm with Elbow Method to determine K. Clustering module 204 may also review which of all possible features are available for a particular business. In particular embodiments, use of the clustering module in the customer-intelligence system facilitates scaling the clustering and benchmarking process and the clustering module provides deep context on the feature space to help users contextualize the renewal probability outputs and understand customer segmentation in a comprehensive user interface; paragraph 0060, discussing that after running the K-means clustering algorithm on the client's customer data and assigning the cluster values to each row, the customer data may then be separate and saved separately for further analysis. Particular embodiments iterate through each cluster and describe the key features for the client in each cluster, like customer count in each cluster, average revenue for each cluster, active versus churned users in each cluster, etc. The benchmarks for the KPIs are also calculated by evaluating the minimum and the maximum value for each category present. For example, a KPI will have “Low,” “Medium,” and “High” categories and the user will be able to see the minimum and maximum value in each category, which will help the user get an idea of how each KPI is contributing to the overall performance (or customer-health score). If a business has significantly lower values for any KPI on any cluster, then the business can focus on the customers that are present inside that cluster to find the similarities among that business's customers and strategize better; paragraph 0088, discussing that FIG. 10 illustrates example presentation of example customer history for an example customer on an example customer-intelligence dashboard. In particular embodiments, a history window provides access to all interaction information associated with the customer. This may include chronological information of updates to the KPIs week over week or month over month, depending on how often the dashboard is refreshed. It may also include e-mail content and e-mail sentiment (e.g. whether the e-mail is positive (high), negative (low), or neutral (medium)). This scoring may be based on sentiment analysis of each e-mail and particular pre-determined keywords. This may help the user access historical data and, using AI, assess that data to determine next steps; paragraphs 0034, 0045, 0052).
While Bhattacharya suggests perform a cluster analysis based on the historical performance data for the predefined set of metrics, it does not explicitly teach perform a cluster analysis based on the historical performance data for the predefined set of metrics. However, Wood in the analogous art of performance analysis systems teaches this concept (col. 13, lines 46-54, discussing that the subscription performance and prediction engine may compute the aforementioned impacts, make the aforementioned comparisons, and/or determinations through the use of one or more statistical methods. Such statistical methods may include, but are not limited to: correlation analysis; regression analysis; clustering; decision tree analysis; and Chi-squared Automatic Interaction Detection (CHAID), a specific type of decision tree analysis; or a combination of one or more such methods; col. 13, lines 55-67, discussing that the subscription performance and prediction engine 20a may generate subscription recommendations based upon determinations made regarding the performance of a particular content creator, and may utilize computed values indicative of the impact of various factors on performance. For example, the subscription performance and prediction engine may apply impact values (learned via analysis of historical performance data and its impact on subscriptions) to current performance data of a particular content creator to predict that content creator's performance. From that prediction, the subscription performance and prediction engine may generate subscription recommendations determined to increase revenue and/or prominence).
Bhattacharya is directed to a system and method for customer intelligence. Wood is directed to a system for providing customer recommendations. Therefore, they are deemed to be analogous as they both are directed towards solutions for customer analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Bhattacharya with Wood because the references are analogous art because they are both directed to solutions for customer management, which falls within applicant’s field of endeavor (customer satisfaction analysis), and because modifying Bhattacharya to include Wood’s feature for including performing a cluster analysis based on the historical performance data for the predefined set of metrics, in the manner claimed, would serve the motivation of allowing the content creator to fine-tune offerings (Wood at col. 1, lines 12-13); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 3, the Bhattacharya-Wood combination teaches the system of claim 2. Bhattacharya further teaches wherein the application further causes the system to: obtain historical customer net satisfaction scores for the plurality of customers, each net satisfaction score having a value within a predefined range of values (paragraph 0035, discussing that the dashboard module generates for presentation to a client a customer-intelligence dashboard that unifies customer data and generates predictions about the client's customer churn, customer renewal, or customer upsell; the clustering module segments customer data into component parts by analyzing similarities among the feature space; the health-score module generates customer-health scores for presentation to users in the customer intelligence dashboard; the recommendation engine uses information from the dashboard to generate actionable recommendations based on data changes on dashboard; paragraph 0044, discussing that to generate a customer-health score, the customer-intelligence system may use clustering module to segment customer data into component parts by analyzing similarities among the feature space…; paragraph 0045, discussing that FIG. 5 illustrates example KPI weighting in example customer-health score generation. Particular embodiments combine feature existence, weightage analysis, clustering, and benchmarking in a comprehensive UI for customer-health-score understanding and validation. In the example of FIG. 5, four KPIs are used to generate a customer-health score (product usage, NPS/CSAT, interaction frequency, and customer-owner pulse), with other KPIs being unavailable and weighted at zero…; paragraph 0051, discussing that features represent the key elements that contribute to the customer's health score. Particular embodiments show the customer, the presence, and the contribution made by each KPI. Customer-intelligence system 102 checks the existence of these KPIs for a business and helps the user understand those KPIs better. In particular embodiments, the values are represented in Boolean. This may help the business to understand what features exist in the database. As an example, the following KPIs may be used, in any suitable combination: (1) product usage, (2) interaction frequency, (3) NPS/CSAT, (4) number of support tickets, (5) severity of support tickets, (6) customer sentiment, (7) customer-owner pulse, (8) up-sells/down-sells, and (9) customer maturity. FIG. 6 illustrates example feature enablement in an example customer-health score, which may be presented to a user to help the user better understand the customer-health score being provided; paragraph 0088, discussing that FIG. 10 illustrates example presentation of example customer history for an example customer on an example customer-intelligence dashboard. In particular embodiments, a history window provides access to all interaction information associated with the customer. This may include chronological information of updates to the KPIs week over week or month over month, depending on how often the dashboard is refreshed. It may also include e-mail content and e-mail sentiment...This may help the user access historical data and, using AI, assess that data to determine next steps.; claim 7: “a user selectable customer-history window for each customer and corresponding product”).
While Bhattacharya suggests perform a correlational analysis using the historical performance data for the predefined set of metrics and the customer net satisfaction scores, the correlational analysis providing an importance value of each metric in the predefined set of metrics (paragraph 0035, discussing that the dashboard module generates for presentation to a client a customer-intelligence dashboard that unifies customer data and generates predictions about the client's customer churn, customer renewal, or customer upsell; the clustering module segments customer data into component parts by analyzing similarities among the feature space; the health-score module generates customer-health scores for presentation to users in the customer intelligence dashboard; the recommendation engine uses information from the dashboard to generate actionable recommendations based on data changes on the dashboard; the reports module uses the dashboard to generate high-level reporting data summarizing customer revenue and churns and upsells, along with trends around the KPIs; paragraph 0041, discussing that particular embodiments set a threshold value (e.g. high, medium, or low) for customer health. In particular embodiments, these values are benchmarked across multiple businesses per product per ARR range per quarter. This metric may indicate to a user when and what action should be taken (e.g. whether to prevent churn for a customer with a low health score or capture an upsell opportunity for a customer with a high health score). For each KPI for each customer, the customer-intelligence dashboard presents a color-coded score of low, medium, or high. With some KPIs, a low score is considered positive and a high score is considered negative. With other KPIs, a high score is considered positive and a low score is considered negative. A KPI score of medium may be considered neutral. A positive KPI score may be color coded green, a negative KPI score may be color coded red, and a neutral KPI score may be color coded yellow. In the example of FIG. 3, high product usage is considered positive, medium product usage is considered neutral, low product usage is considered negative, high interaction frequency is considered positive, medium interaction frequency is considered neutral, low interaction frequency is considered negative, high NPS/CSAT is considered positive, medium NPS/CSAT is considered neutral, low NPS/CSAT is considered negative, high customer sentiment is considered positive, medium customer sentiment is considered neutral, low customer sentiment is considered negative, high customer-owner pulse is considered positive, medium customer-owner pulse is considered neutral, low customer-owner pulse is considered negative, low number of support tickets is considered positive, medium number of support tickets is considered neutral, high number of support tickets is considered negative, low severity of support tickets is considered positive, medium severity of support tickets is considered neutral, and high severity of support tickets is considered negative. These KPI scores may be color coded accordingly in the customer-intelligence dashboard . This color coding may facilitate a user more quickly understanding which customer relationships are strong (and thus present up-sell opportunities), which are weak (and thus at risk for churn), and, more particularly, in each group, what aspects of those relationships are contributing positively or negatively to their overall status; paragraph 0051, discussing that features represent the key elements that contribute to the customer's health score. Particular embodiments show the customer, the presence, and the contribution made by each KPI. The customer-intelligence system checks the existence of these KPIs for a business and helps the user understand those KPIs better; paragraph 0052, discussing that K-means is an unsupervised ML algorithm that groups similar data points together and discovers underlying patterns; paragraph 0060, discussing that after running the K-means clustering algorithm on the client's customer data and assigning the cluster values to each row, the customer data may then be separate and saved separately for further analysis. Particular embodiments iterate through each cluster and describe the key features for the client in each cluster, like customer count in each cluster, average revenue for each cluster, active versus churned users in each cluster, etc. The benchmarks for the KPIs are also calculated by evaluating the minimum and the maximum value for each category present. For example, a KPI will have “Low,” “Medium,” and “High” categories and the user will be able to see the minimum and maximum value in each category, which will help the user get an idea of how each KPI is contributing to the overall performance (or customer-health score). If a business has significantly lower values for any KPI on any cluster, then the business can focus on the customers that are present inside that cluster to find the similarities among that business's customers and strategize better; paragraphs 0044-0050), Bhattacharya does not explicitly teach perform a correlational analysis using the historical performance data for the predefined set of metrics and the customer net satisfaction scores. However, Wood in the analogous art of performance analysis systems teaches this concept. Wood teaches:
perform a correlational analysis using the historical performance data for the predefined set of metrics and the customer net satisfaction scores (col. 11, lines 56-67 & col. 12, lines 1-19, discussing that performance data can refer to data obtained regarding a content creator, the content creator's content, subscriber data associated with subscribers to content creator 12, and subscription level to preferential access correspondence that has been parsed and/or analyzed to determine revenue received from subscription and/or subscriber exposure. Subscription performance and prediction engine 20a can compare current performance data of a content creator (e.g., performance data from subscription level database 24e) with historical performance data of that content creator (e.g., performance data from historical subscription level database 24f) to determine whether the performance of that content creator is trending positively or negatively. Such trends can be determined from an overall performance perspective or a more granular perspective, e.g., from an individual subscriber perspective, i.e., whether or not a particular subscriber has shown a history of increasing or decreasing pledged donations, or altogether unsubscribing (which can be reflected as a churn rate). For example, historical content information can be correlated to subscription level information. That is, subscription performance and prediction engine 20a may determine whether or not the creation of content impacts subscriptions. For example, it can be determined whether the creation of content results in an increase in subscriptions from never-before subscribed consumers. It can be determined whether the creation of content results in the maintenance of existing subscriptions. It can be determined whether the creation of content results in a loss of subscription (e.g., suggesting that the type, amount, and/or frequency of content creation was not enough to satisfy a consumer); col. 13, lines 55-67, discussing that the subscription performance and prediction engine may generate subscription recommendations based upon determinations made regarding the performance of a particular content creator, and may utilize computed values indicative of the impact of various factors on performance. For example, the subscription performance and prediction engine may apply impact values (learned via analysis of historical performance data and its impact on subscriptions) to current performance data of a particular content creator to predict that content creator's performance. From that prediction, subscription performance and prediction engine 20a may generate subscription recommendations determined to increase revenue and/or prominence; col. 15, lines 40-57, discussing that it should be noted that the subscription performance and prediction engine may, through one or more of the aforementioned analytic methods, or one or more other analytic methods, determine predicted results of implementing selected subscription recommendations. Predicted results can comprise purely forecasted statistics and/or comparative statistics relative to the past. Chart 70 may reflect one or both types of predicted results. For example, as illustrated in chart 70, selecting to update the five dollar recurring subscription level to correspond to two live webcasts/month can be predicted to have resulted in greater returns amount to at least $200 per month. In predicting such results, the subscription performance and prediction engine may consider various factors, such as seasonality. For example, chart 70 indicates that during the months of November and December, earnings per month may be even greater as historical performance data may suggest consumers tend to be more generous during the holiday season).
Bhattacharya is directed to a system and method for customer intelligence. Wood is directed to a system for providing customer recommendations. Therefore, they are deemed to be analogous as they both are directed towards solutions for customer analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Bhattacharya with Wood because the references are analogous art because they are both directed to solutions for customer management, which falls within applicant’s field of endeavor (customer satisfaction analysis), and because modifying Bhattacharya to include Wood’s feature for including performing a correlational analysis using the historical performance data for the predefined set of metrics and the customer net satisfaction scores, in the manner claimed, would serve the motivation of allowing the content creator to fine-tune offerings (Wood at col. 1, lines 12-13); an