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
This action is in response to the amendment filed on 5/29/2026. Claims 1, 3-5, 7-10, 12-14, 16, 18-20, are pending. Claims 1, 10, 16,are amended. No claims have been added. No claims are currently cancelled.
Response to Arguments
Applicant's arguments filed 5/29/2026 have been fully considered but they are not persuasive. The applicant has argued “…the recited features of the amended claims improve machine learning model efficiency by precomputing and aggregating feature importance across ensemble decision trees using weighted impurity metrics, normalizing the results to enable direct comparison, and generating a compact feature-level representation that eliminates redundant runtime computations, reduces dimensionality, and enables low-complexity inference operations. This results in reduced computational overhead, lower latency, and improved scalability of the ML system. These improvements are rooted in the internal functioning of the machine learning model itself (i.e., how feature contributions are computed, aggregated, and utilized), and therefore constitute an improvement to computer-implemented predictive systems rather than merely an abstract mathematical calculation.” Claim 1 recites a system that uses data to train a generically recited machine learning model to determine the best possible time window to send surveys to maximize survey response rates. Claim 1 recites training a ML model using attribute and time data. The machine learning model is generically training two data points. The applicant did not invent or improve upon machine learning models or point to anything in the Specification disclosing, e.g., a new machine learning algorithm. The Federal Circuit has informed us that “patents may be directed to abstract ideas when they disclose the use of an ‘already available [technology], with [its] already available basic functions, to use as [a] tool[ ] in executing the claimed process.” Id. at 1214 (alterations in original) (quoting SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1169–70 (Fed. Cir. 2018)).” The steps of the invention do not create an efficiency improvement but recite the pre-existing default output of a conventional random forest classifier. The efficiency improvement the applicant is arguing appears to just be an automatic output of a standard off the shelf random forest tool. This specific feature does not require any additional computation. Normalizing the numbers and averaging them is basic math. This is not a technical improvement that makes the computer run faster it is merely arithmetic applied to numbers that the computer has already produced. There is also no disclosure of the efficiency gain. No efficiency metric benchmark, or comparison to any prior or alternative approach is recited in the claims or disclosed in the Specification. A claim is not rendered eligible because it describes how a component of a computer system operates internally. The assessment is directed to whether the claim reflects a change to the internal functioning that improves it. The Applicant has not identified, and the specification does not disclose anyway in which the claimed model computes, aggregates, or utilizes feature contributions differently than a conventional random forest model already does. Using a tool in a manner of how it was created is not the same as improving an operation. Further, even though the underlying tool happens to be a machine learning model does not transform an abstract mathematical calculation into a technical improvement merely because the calculation occurs inside that model.
The applicant has argued “Therefore, the present claims do not merely cover abstract methods of a certain methods of organizing human activity, a mental process or mathematical concepts, and do not fit into any of the abstract idea groupings required to reject the claims under Step 2A, Prong One as discussed in the 2019 Guidance and as discussed in the Office's August 2025 Memo. The steps of receiving a selection of customer attributes on a user interface, moving the selected customer attributes to a grid on the user interface, displaying a save button, receiving a click on the save button, saving the customer attributes, generating a ML model, and training the ML model cannot be performed in the human mind or with pen and paper.” The examiner respectfully disagrees. Applicant’s invention is directed to a mental process. The limitations in claim 1 amount to applying an abstract idea with a computer used as a tool to perform the steps of the invention, which does not confer patent eligibility to the abstract idea. Alice, 573 U.S. at 223; Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1216 (Fed. Cir. 2025) (“[P]atents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.”). The applicant is describing a generic, conventional computer learning model. Humans can develop programming for such models in their minds. The claim does not recite or specify how such generation occurs. As the components described are conventional and generic, such generation may be accomplished by generating the parameters from such programming that are entered into a conventional generic machine learning apparatus. Simply placing a generic, conventional machine learning apparatus in a new context as recited is insufficient to confer eligibility.
The applicant has argued “Further, the currently amended claims recite numerous steps that cannot practically be performed in the human mind or with pen and paper. For example, as recited by claim 1, the claims require "receiving, from a customer of a contact center via a user interface, a selection of one or more customer attributes; in response to the selection, moving the selected one or more customer attributes to a grid on the user interface; displaying, on the user interface, a save button; receiving, from the customer via the user interface, a click on the save button; in response to the click on the save button, saving the selected one or more customer attributes." These steps involve specific interactions with a graphical user interface receiving user selections, dynamically moving selected attributes to a grid, displaying a save button, and receiving a click on that button none of which can be performed mentally or with pen and paper. These are not mere data-gathering steps; they define a specific, interactive computer-implemented process through which a customer configures the attributes that will be used for ML model training.” The examiner respectfully disagrees. The test is not literal replication with pen and paper it is whether the underlying human activity is a mental process a human could perform. A person reviewing a list of attributes, choose one that is relevant, and recording the choice is the same underlying activity as selecting attributes and saving them to a list. Selecting and recording user input is data gathering and is not a technical solution. There is no unconventional technical mechanism disclosed either in the claims or in the specification.
The applicant has argued “Furthermore, the claims require "training the ML model using the selected one or more customer attributes and a plurality of time windows to output a feature importance score on a survey response rate for each of a plurality of time windows and each of the selected one or more customer attributes" and "predicting, by the ML model, a feature importance score on a survey response rate for each of the plurality of time windows and each of the selected one or more customer attributes." Training a machine learning model and using it to predict feature importance scores are operations that inherently require a computer and cannot be performed in the human mind. Accordingly, Applicant respectfully requests that the rejection under 35 U.S.C. § 101 of the pending claims as amended be reconsidered and withdrawn.” The examiner respectfully disagrees. Requiring a computer to carry out a step does not remove the invention from the mental processing grouping. Applicants own specification discloses the use of a standard random forest algorithm (see paragraph 50-67). Nothing in the specification discloses a modified training algorithm,, an altered model architecture, or any departure from how a random forest classifier is conventionally trained to generate predictions. Requiring a processor or a ML model to perform steps that are otherwise directed to an abstract idea is a generic computer implementation and not a technical improvement. Applicants argument rests entirely on the premise that a computer is required but does not identify any technical feature of the claimed training or prediction steps that depart from a standard generic ML model that is conventionally trained. The applicant is using a computer as a tool to perform a mental process. Using a computer to process the data is not a technological improvement. Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures “can be carried out in existing computers long in use, no new machinery being necessary.” 409 U.S at 67, 175 USPQ at 675. An example of a case in which a computer was used as a tool to perform a mental process is Mortgage Grader, 811 F.3d. at 1324, 117 USPQ2d at 1699. The patentee in Mortgage Grader claimed a computer-implemented system for enabling borrowers to anonymously shop for loan packages offered by a plurality of lenders, comprising a database that stores loan package data from the lenders, and a computer system providing an interface and a grading module. The interface prompts a borrower to enter personal information, which the grading module uses to calculate the borrower’s credit grading, and allows the borrower to identify and compare loan packages in the database using the credit grading. 811 F.3d. at 1318, 117 USPQ2d at 1695. The Federal Circuit determined that these claims were directed to the concept of “anonymous loan shopping”, which was a concept that could be “performed by humans without a computer.” 811 F.3d. at 1324, 117 USPQ2d at 1699. Another example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes. 881 F.3d at 1366, 125 USPQ2d at 1652-53.
The applicant has argued “The amended claims recite a specific technical implementation for calculating feature importance scores that goes beyond merely applying a generic machine learning algorithm. The node importance is determined using Gini importance (or mean decrease impurity), which is computed from the random forest structure based on the weighted number of samples reaching the node and the impurity value of the node. The importance of each feature on a decision tree is calculated by summing node importances for nodes that split on the feature divided by the sum of all node importances. This is not a generic mathematical operation but rather a specific technical approach to evaluating feature contributions within decision tree structures. The feature importance values are normalized to a value between 0 and 1 by dividing by the sum of all feature importance values, and the final feature importance at the random forest level is calculated as an average over all the trees. These features specify a particular technical implementation of how the ML model processes data through decision tree structures to determine which features are most predictive. This is not merely using a computer as a tool to perform an abstract idea, but rather recites specific technical operations performed within the ML model architecture.” The examiner respectfully disagrees. The recited calculation is not a technical implementation created by the applicant, it is a known Gini importance formula. The Gini index is a typical splitting criterion, commonly used variable importance measure available in standard random forest software. As claimed every mathematical operation is performed on some underlying data structure the operations themselves are merely arithmetic. Reciting a known mathematical formula in greater detail does not integrate an abstract idea into a practical application. A claim does not become eligible merely because a mathematical concept is recited with precision, the additional elements must reflect a technological improvement, not merely a more detailed abstract idea. The applicant argues the steps of the Gini importance calculation but does not identify any modification to the calculation.
The applicant has argued “The claims as amended recite details of how a solution to a problem is accomplished specifically, how feature importance is computed through a multi-step process involving node importance determination using Gini importance, feature importance calculation across decision trees, normalization, and averaging across all trees. This addresses the Office Action's concern under MPEP 2106.05(f) that claims should recite details of how a solution is accomplished rather than merely the idea of a solution. Further, Applicant submits that the claims integrate any alleged abstract idea into a practical application because they are directed to an improvement in technology. The specification describes at least a technical problem: in contact centers, surveys are sent using predefined outcast windows without regard to patterns in customer response behavior, which leads to lower survey response rates and degradation of system and product effectiveness. The specification further explains that typical systems "do not include identification of patterns when customers are responding to surveys, and do not include continuous identification of the sample populations that should receive surveys," and that "low response rates can lead to a loss of revenue (or missed opportunities for revenue) for system providers, which bill customers based on survey response rates." The claims provide a specific technological solution to this technical problem by reciting a particular ordered combination of steps: enabling a customer to select specific attributes via a user interface, training an ML model using those customer-selected attributes together with a plurality of time windows to predict feature importance scores, deriving a feature importance matrix that ranks customer attributes and associates each with a recommended time window, and then using that matrix to evaluate incoming survey requests and transmit surveys during the optimal time window. This is not merely applying generic machine learning to a new data environment; it is a specific, structured process that produces a concrete, technical result a feature importance matrix that maps customer attributes to recommended time windows and uses that result to control when surveys are transmitted.” The examiner respectfully disagrees. The identified problem in the specification is a business problem (see paragraph 3). The problem that is identified involves surveys went without regard to customer response rates, which is a business problem, not a technical one. Claims that recite only the idea of a solution or outcome without specifying how the solution is accomplished are considered equivalent to the words "apply it" and do not integrate the exception into a practical application (Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44; Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366). The claims do not recite a specific means or method that solves a problem merely how Gini importance is calculated. Details about a conventional mechanism is merely the idea of using existing technology to reach a result not a disclosed technical means of achieving the result. A result being concrete does not by itself establish that the result is a technical one or that generating it involves anything beyond applying an abstract idea using conventional computer components. Using a matrix to control when surveys are transmitted is the automation of a business decision not a technical application. Automating a scheduling or transmission decision based on a ranked list of factors is itself part of the abstract idea.
The applicant has argued “The Examiner has cited Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025), for the proposition that "patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101." In this regard, Applicant submits that the present claims are distinguishable from Recentive. In Recentive, the court found the claims ineligible because the patentee conceded it was not claiming machine learning itself, the specifications disclosed the use of "any suitable machine learning technique," and the claims did not delineate steps through which the machine learning technology achieved an improvement. Here, by contrast, the recited features of claims do not merely invoke generic machine learning in a new field. Rather, the claims recite a specific implementation: the ML model is trained using particular inputs customer-selected attributes and a plurality of time windows to output specific results feature importance scores on a survey response rate for each time window and each customer attribute. The claims further recite deriving a feature importance matrix from those predicted scores, and then using that matrix in a specific matching and evaluation process to determine the optimal time window for survey transmission. This ordered combination of steps constitutes a "specific implementation of a solution to a problem," as contemplated by Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339 (Fed. Cir. 2016).” The examiner respectfully disagrees. The case involved a random forest, and the Federal Circuit still found the claims ineligible. IT is not whether a claim names a specific algorithm but whether the specification discloses a modification to operation. Applicants disclosure does not include a modification to the algorithm’s operation. The specification recites only the unmodified published output of a conventional random forest classified. Reciting specific inputs and outputs to generate a generic ML model does not constitute a specific implementation under Enfish. The specific matching and evaluation process is the abstract idea itself, not a technical implementation of it. Nothing distinguishes the ordered combination here from the ordered combination rejected in Recentive. The ordered combination the applicant identifies here has each step reflecting the conventional operation of a generic technology.
The applicant has argued “Moreover, the precedential decision in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision), reinforces that claims involving machine learning can be patent eligible when they reflect improvements to how the ML model itself operates, as disclosed in the specification. The Desjardins decision instructs that examiners "should not evaluate claims at such a high level of generality" that potentially meaningful technical limitations are dismissed without adequate explanation, and that examiners should not dismiss additional elements as mere "generic computer components" without considering whether such elements confer a technological improvement. Here, the specification describes in detail how the ML model is trained with customer-selected attributes and time windows to predict feature importance scores, how those scores are used to derive a feature importance matrix (as illustrated in Tables 1-3 of the specification), and how that matrix is applied to incoming survey requests to determine optimal transmission windows. The claims reflect this disclosed improvement by reciting the specific steps of this process, including the user interface4918-4467-5212 v.1 interaction for attribute selection, the training and prediction steps, the derivation of the feature importance matrix, and the matching and evaluation process.” The examiner respectfully disagrees. Desjardins involved a discloses, specific improvement to the ML model’s internal training behavior, not a recitation of an algorithm conventional output. In Desjardins, the claims and the claims and specification disclosed that eh claimed training method allowed a model to learn new tasks while protecting previously learned tasks. No comparable disclosure exists here. Applicants user interface interaction, the training and prediction steps, the specific mathematics of the feature importance calculation, the matching, and the evaluating process have each been considered individually and in combination. The disclosures (Tables 1-3) that the applicant relies on describe a business logic and conventional ML mechanic, not a technical improvement analogous to Desjardins.
The applicant has argued “The claims as a whole recite a particular technological solution not merely the idea of optimizing survey timing, but a specific, computer-implemented process for doing so through a defined sequence of user interface interactions, ML model training with particular inputs, feature importance matrix derivation, and attribute matching. Applicant submits that this ordered combination of limitations meaningfully recitations to the claims beyond generally linking the use of any alleged judicial exception to a particular technological environment, and integrates any such exception into a practical application.” The examiner respectfully disagrees. A defined sequence of conventional steps is not analogous with a technological solution. The claims recite the idea of optimizing survey timing implemented with generic technology. The applicant is receiving a request for a survey, generating a model, deriving a matrix, evaluating an attribute, matching an attribute, determining a matched feature, selecting a recommended time window, and transmitting the survey in the time window. Though the claims state that a ML learning model is generated there are not details about testing of an algorithm/software there is no using of historical data for accuracy/training. The applicant is not setting up the parameters to monitor the output to see how it performs. Claim 3 has limitations related to testing an accuracy of each ML model however it is not clear how the accuracy is tested or use of feedback. Under the USPTO Guidance, under Step 2A, Prong Two, the claims do not recite additional elements that integrate the judicial exception into a practical application (see MPEP §§ 2106.05(a)–(c), (e)–(h)). To integrate the exception into a practical application, the additional claim elements must, for example, improve the functioning of a computer or any other technology or technical field (see MPEP § 2106.05(a)), apply the judicial exception with a particular machine (see MPEP § 2106.05(b)), or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment (see MPEP § 2106.05(e)). At best, claim 1 recites an improvement to certain methods of organizing human activity, which is still an abstract idea. The additional elements of claim 1 do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Merely adding generic computer components to perform abstract ideas does not integrate those ideas into a practical application. See MPEP §§ 2106.04(a), (d) (citing 2019 Revised Guidance, 84 Fed. Reg. at 55 (identifying “merely includ[ing] instructions to implement an abstract idea on a computer” as an \example of when an abstract idea has not been integrated into a practical application)). Because the present claims recite an abstract idea that is not integrated into a practical application, the claims are directed to an abstract idea.
The applicant has argued “In the present case, and as further explained below, this combination of features is not well-understood, routine, or conventional activity in the field of contact center performance: "... training the ML model using the selected one or more customer attributes and a plurality of time windows to output a feature importance score on a survey response rate for each of a plurality of time windows and each of the selected one or more customer attributes; predicting, by the ML model, a feature importance score on a survey response rate for each of the plurality of time windows and each of the selected one or more customer attributes, wherein the feature importance score is calculated by: determining, for each decision tree in the ML model, a node importance for each node using Gini importance based on a weighted number of samples reaching the node and an impurity value of the node; calculating a feature importance for each feature on each decision tree by summing node importances for nodes that split on the feature divided by a sum of all node importances; normalizing the feature importance for each feature to a value between 0 and 1 by dividing by a sum of all feature importance values; and averaging the normalized feature importance for each feature across all decision trees in the ML model; deriving a feature importance matrix from the predicted feature importance scores, wherein the feature importance matrix comprises a ranking of importance of each of the selected one or more customer attributes and a recommended time window associated with each of the selected one or more customer attributes; ...selecting a recommended time window associated with the matched customer attribute having the highest ranked importance, wherein selecting the recommended time window triggers automated scheduling and transmission; and automatically transmitting the customer survey to the customer in the recommended time window associated with the matched customer attribute having the highest ranked importance to maximize the survey response rate".” The examiner respectfully disagrees. In step 2B the claims are evaluated under whether the additional elements are well-understood, routine, or conventional in the field, not whether the specific application is novel. The applicant argument of the inquiry by refence to the field of contact center performance does not alter this conclusion. An abstract idea or conventional technical element is not rendered inventive merely by application to a new field of use. Although the claim may encompass an improvement to data (the abstract idea) this would not be an improvement to the functioning of computers or an improvement to other technology or technical field. To show that the computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. See MPEP § 2106.05(f) for more information about mere instructions to apply an exception.
The applicant has argued “Further, the amended claims explicitly recite how feature importance is calculated, including: " Gini-based node importance weighted by sample distribution," aggregation across decision-tree nodes," normalization across features, and" averaging across ensemble. This is not merely "applying" machine learning, but instead defines a specific internal mechanism for improving ML model operation. The amended claims recite significantly more than any alleged abstract idea. They define a specific, non-conventional machine learning architecture that computes feature importance using a structured, multi-step process, generates a normalized and reusable feature-importance matrix, improves computational efficiency and scalability, and directly controls real-world system behavior by automatically scheduling and transmitting surveys. This ordered combination of elements is not well-understood, routine, or conventional, and provides a concrete technological improvement to both machine learning systems and communication infrastructure.” The examiner respectfully disagrees. The four listed steps are not an internal mechanism for improving ML model operations, it appears to be a conventional Gini importance, provided automatically according to applications’ specification. Applicants’ argument that the claims improve computation efficiency and scalability is unsupported by the disclosed benchmark, comparison, or technical mechanisms. Automatically scheduling and transmitting surveys based on the output of a conventional ranking calculation does not constitute a concrete technological improvement to a communication infrastructure. The ordered combination, considered individually and as a whole, does not integrate the identified abstract idea into a practical application under Step 2A, Prong 2, does not amount ot significantly more than that abstract idea under Step 2B. The rejection under 101 is therefore maintained and updated below in view of applications amendments.
Applicant’s arguments, see pg. 18-31, filed 10/16/2025, with respect to the previous 103, prior art rejection, have been fully considered and are persuasive. The previous 103 rejections of the claims have been withdrawn. The closest prior art of record Zhou et al. (US 20210287111 A1) teaches a machine learning model outputting one or more predicting features having influence in predicting the response value for each of the datasets and determining an important feature based on the one or more predicting features. Huang et al. (US 20170169448 A1) teaches displaying derived explicit and non-explicit group information are configured to be displayed in the form of a two-dimensional grid. Meyer et al. (US 20190180299 A1) teaches receiving a survey result information from the survey recipient in response to the automated survey. The closest prior art of record does not specifically teach the combination of deriving a feature importance matrix from the predicted feature importance scores, wherein the feature importance matrix comprises a ranking of importance of each of the selected one or more customer attributes and a recommended time window associated with each of the selected one or more customer attributes; receiving, from the customer, a request for a customer survey, wherein the request comprises one or more customer attributes; evaluating each customer attribute in the request against each of the selected one or more customer attributes on the feature importance matrix; matching a customer attribute in the request to a selected customer attribute on the feature importance matrix. The previous 103 rejection is withdrawn not based on one claim limitation but a combination of claim limitations.
The previous 103 rejections of claims 1, 3-5, 7-10, 12-14, 16, 18-20, have been withdrawn in view of applicant’s arguments and amendments.
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.
Claim 1, 3-5, 7-10, 12-14, 16, 18-20, are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1, 3-5, 7-9 are directed to a system, claims 10, 12-14 are directed to a method, and claims 16, 18-20 are directed to a non-transitory computer-readable medium. Therefore, claims 1, 3-10, 12-16, 18-20, are directed to patent eligible categories of invention.
Step 2A, Prong 1: Claims 1, 10, 16, recite determine the best possible time window to send surveys to maximize survey response rates which is an abstract idea based on “Certain Methods of Organizing Human Activity” related to managing personal behavior or interactions between individuals including social activities. Claim 1 recites abstract limitations including “receiving, …, a selection of one or more customer attributes; in response to the selection, moving the selected one or more customer attributes…; displaying, …, a save button; receiving, …, a click on the save button; in response to the click on the save button, saving the selected one or more customer attributes; … using the selected one or more customer attributes and a plurality of time windows to output a feature importance score on a survey response rate for each of a plurality of time windows and each of the selected one or more customer attributes; predicting, …, a feature importance score on a survey response rate for each of the plurality of time windows and each of the selected one or more customer attributes wherein the feature importance score is calculated by; … calculating a feature importance for each feature on each decision tree by summing node importances for nodes that split on the feature divided by a sum of all node importances; normalizing the feature importance for each feature to a value between 0 and 1 by dividing by a sum of all feature importance values; and averaging the normalized feature importance for each feature across all decision trees wherein the feature importance matrix comprises a ranking of importance of each of the selected one or more customer attributes and a recommended time window associated with each of the selected one or more customer attributes; receiving, from the customer, a request for a customer survey, wherein the request comprises one or more customer attributes; … selecting a recommended time window associated with the matched customer attribute having the highest ranked importance; and transmitting the customer survey to the customer in the recommended time window associated with the matched customer attribute having the highest ranked importance to maximize the survey response rate.” Claim 10 recites abstract limitations including “A method for improving a survey response rate, which comprises; receiving, from a customer of a contact center…, a selection of one or more customer attributes; in response to the selection, moving the selected one or more customer attributes…; displaying, …, a save button; receiving, …, a click on the save button; in response to the click on the save button, saving the selected one or more customer attributes;…training …using the selected one or more customer attributes and a plurality of time windows to output a feature importance score on a survey response rate for each of a plurality of time windows and each of the selected one or more customer attributes; predicting, …, a feature importance score on a survey response rate for each of the plurality of time windows and each of the selected one or more customer attributes wherein the feature importance score is calculated by; … calculating a feature importance for each feature on each decision tree by summing node importances for nodes that split on the feature divided by a sum of all node importances; normalizing the feature importance for each feature to a value between 0 and 1 by dividing by a sum of all feature importance values; and averaging the normalized feature importance for each feature across all decision trees…, wherein the feature importance matrix comprises a ranking of importance of each of the selected one or more customer attributes and a recommended time window associated with each of the selected one or more customer attributes; receiving, from the customer, a request for a customer survey, wherein the request comprises one or more customer attributes;… selecting a recommended time window associated with the matched customer attribute having the highest ranked importance; and transmitting the customer survey to the customer in the recommended time window associated with the matched customer attribute having the highest ranked importance to maximize the survey response rate. Claim 16 recites abstract limitations including “receiving, …, a selection of one or more customer attributes; in response to the selection, moving the selected one or more customer attributes to a grid…; displaying, …, a save button; receiving, …, a click on the save button; in response to the click on the save button, saving the selected one or more customer attributes;…; training … using the selected one or more customer attributes and a plurality of time windows to output a feature importance score on a survey response rate for each of a plurality of time windows and each of the selected one or more customer attributes; predicting, …, a feature importance score on a survey response rate for each of the plurality of time windows and each of the selected one or more customer attributes wherein the feature importance score is calculated by; … calculating a feature importance for each feature on each decision tree by summing node importances for nodes that split on the feature divided by a sum of all node importances; normalizing the feature importance for each feature to a value between 0 and 1 by dividing by a sum of all feature importance values; and averaging the normalized feature importance for each feature across all decision trees;… wherein the feature importance matrix comprises a ranking of importance of each of the selected one or more customer attributes and a recommended time window associated with each of the selected one or more customer attributes; receiving, from the customer, a request for a customer survey, wherein the request comprises one or more customer attributes; selecting a recommended time window associated with the matched customer attribute having the highest ranked importance; and transmitting the customer survey to the customer in the recommended time window associated with the matched customer attribute having the highest ranked importance to maximize the survey response rate.”
The claims also recites a mathematical concept (which can include a mathematical relationships, mathematical formulas or equations, and mathematical calculations), and in this case using a matrix to determine a matched features. Claim 1 recites abstract limitations including deriving a feature importance matrix from the predicted feature importance scores, evaluating each customer attribute in the request against each of the selected one or more customer attributes on the feature importance matrix; matching a customer attribute in the request to a selected customer attribute on the feature importance matrix; determining a matched customer attribute having the highest ranked importance on the feature importance matrix. Claim 10 recites abstract limitations including “deriving a feature importance matrix from the predicted feature importance scores, evaluating each customer attribute in the request against each of the selected one or more customer attributes on the feature importance matrix; matching a customer attribute in the request to a selected customer attribute on the feature importance matrix; determining a matched customer attribute having the highest ranked importance on the feature importance matrix.” Claim 16 recites abstract limitations including “deriving a feature importance matrix from the predicted feature importance scores, evaluating each customer attribute in the request against each of the selected one or more customer attributes on the feature importance matrix; matching a customer attribute in the request to a selected customer attribute on the feature importance matrix; determining a matched customer attribute having the highest ranked importance on the feature importance matrix.” The claims were also amended to include language of a Gini importance. Thus, the claim recites a mathematical concept. Note that, in this example, the “encoding” step is determined to recite a mathematical concept because the claim explicitly recites a mathematical calculation. “Mathematical Calculations” A claim that recites a mathematical calculation will be considered as falling within the “mathematical concepts” grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word “calculating” in order to be considered a mathematical calculation. For example, a step of “determining” a variable or number using mathematical methods or “performing” a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation.
These claimed limitations are also directed to a “mental process.” The limitations, as drafted, is a process that, under its broadest reasonable interpretation, but for the language of “by a processor,” covers an abstract idea but for the recitation of generic computer components. That is, other than reciting “using the at least one processor,” nothing in the claim elements preclude the steps from being interpreted as an abstract idea. For example, with the exception of the “using the at least one processor” language, the claim steps in the context of the claim encompass an abstract idea directed to “Certain Methods of Organizing Human Activity”, “Mathematical concept”, and a “mental process.”
Dependent claims 4-5, 9, 13-14, 19, 20, further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration.
Dependent claims 2, 3, 7, 8, 11, 12, 17, 18 will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Independent claims 1, 10, and 16 do not integrate the judicial exception into a practical application. Claim 1 is a system comprising “a processor and a non-transitory computer readable medium operably coupled thereto, the non-transitory computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform operations which comprise: from a customer of a contact center via a user interface, grid on the user interface, on the user interface, from the customer via the user interface, a machine learning (ML) model... determining, for each decision tree in the ML model, a node importance for each node using Gini importance based on a weighted number of samples reaching the node and an impurity value of the node;… in the ML model…wherein selecting the recommended time window triggers automated scheduling and transmission.” Claim 10 is a method that recites limitations performed “via a user interface, to a grid on the user interface, on the user interface, from the customer via the user interface, a machine learning (ML) model…determining, for each decision tree in the ML model, a node importance for each node using Gini importance based on a weighted number of samples reaching the node and an impurity value of the node; in the ML model.” Claim 16 is a medium specifically “A non-transitory computer-readable medium having stored thereon computer-readable instructions executable by a processor to perform operations which comprise: from a customer of a contact center via a user interface, on the user interface, on the user interface, from the customer via the user interface, a machine learning (ML) model…determining, for each decision tree in the ML model, a node importance for each node using Gini importance based on a weighted number of samples reaching the node and an impurity value of the node; in the ML model...” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 4, 5, 9, 13, 14, 19, 20, further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Dependent claim 3, 12, 18, introduces the additional element of “generating the ML model comprises: retrieving historical data comprising customer attributes and system attributes; inputting the historical data into a random forest algorithm to create a plurality of ML models, wherein each ML model predicts a feature importance score for each of the customer attributes and each of the system attributes affecting the survey response rate; testing an accuracy of each ML model to create an ML model accuracy ranking; selecting the ML model with the highest ranked accuracy; and storing the ML model with the highest ranked accuracy.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, this limitation is not sufficient to prove integration into a practical application. This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claim 7 introduces the additional element of “wherein inputting the historical data into a random forest algorithm to create a plurality of ML models comprises using hyperparameters of the random forest algorithm, the hyperparameters comprising a number of trees the random forest algorithm builds before averaging predictions, a maximum number of features considered by the random forest algorithm before splitting a node, and a minimum number of leaves required to split an internal node.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, this limitation is not sufficient to prove integration into a practical application. This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claim 8 introduces the additional element of “wherein the operations further comprise: updating the generated ML model using new data comprising customer attributes and system attributes; and storing the updated generated ML model.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, this limitation is not sufficient to prove integration into a practical application. This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not sufficient to prove integration into a practical application.
Step 2B: Independent claims 1, 10, and 16 do not comprise anything significantly more than the judicial exception. As can be seen above with respect to Step 2A, Prong 2, Claim 1 is a system comprising “a processor and a non-transitory computer readable medium operably coupled thereto, the non-transitory computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform operations which comprise: from a customer of a contact center via a user interface, grid on the user interface, on the user interface, from the customer via the user interface, a machine learning (ML) model... determining, for each decision tree in the ML model, a node importance for each node using Gini importance based on a weighted number of samples reaching the node and an impurity value of the node;… in the ML model…wherein selecting the recommended time window triggers automated scheduling and transmission.” Claim 10 is a method that recites limitations performed “via a user interface, to a grid on the user interface, on the user interface, from the customer via the user interface, a machine learning (ML) model…determining, for each decision tree in the ML model, a node importance for each node using Gini importance based on a weighted number of samples reaching the node and an impurity value of the node; in the ML model.” Claim 16 is a medium specifically “A non-transitory computer-readable medium having stored thereon computer-readable instructions executable by a processor to perform operations which comprise: from a customer of a contact center via a user interface, on the user interface, on the user interface, from the customer via the user interface, a machine learning (ML) model…determining, for each decision tree in the ML model, a node importance for each node using Gini importance based on a weighted number of samples reaching the node and an impurity value of the node; in the ML model...” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
The additional elements of the independent claims, when considered both individually and in combination, do not comprise anything significantly more than the judicial exception.
Dependent claims 4, 5, 9, 13, 14, 19, 20 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which is not anything significantly more than the judicial exception.
Dependent claim 3, 12, 18, introduces the additional element of “generating the ML model comprises: retrieving historical data comprising customer attributes and system attributes; inputting the historical data into a random forest algorithm to create a plurality of ML models, wherein each ML model predicts a feature importance score for each of the customer attributes and each of the system attributes affecting the survey response rate; testing an accuracy of each ML model to create an ML model accuracy ranking; selecting the ML model with the highest ranked accuracy; and storing the ML model with the highest ranked accuracy.” This limitation is not anything significantly more than the judicial exception because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h). This limitation provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, this limitation is not anything significantly more than the judicial exception.
Dependent claim 7 introduces the additional element of “wherein inputting the historical data into a random forest algorithm to create a plurality of ML models comprises using hyperparameters of the random forest algorithm, the hyperparameters comprising a number of trees the random forest algorithm builds before averaging predictions, a maximum number of features considered by the random forest algorithm before splitting a node, and a minimum number of leaves required to split an internal node.” This limitation is not anything significantly more than the judicial exception because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h). This limitation provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, this limitation is not anything significantly more than the judicial exception.
Dependent claim 8 introduces the additional element of “wherein the operations further comprise: updating the generated ML model using new data comprising customer attributes and system attributes; and storing the updated generated ML model.” This limitation is not anything significantly more than the judicial exception because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h). This limitation provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, this limitation is not anything significantly more than the judicial exception.
The additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not anything significantly more than the judicial exception.
Accordingly, claims 1, 3-5, 7-10, 12-14, 16, 18-20 are rejected under 35 USC 101.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 1, 3-5, 7-10, 12-14, 16, 18-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The applicant has newly amended in the limitation of “wherein selecting the recommended time window triggers automated scheduling and transmission” into the independent claims. The newly added claim language attributes a specific causal/triggering relationship to the selection step that is not described in the originally filed disclosure.
The dependent claims inherit the rejections of the claims they depend.
Other pertinent prior art includes Srinivasan (US 20200134637 A1) discloses the automatic execution of processes to analyze data points in the course of a service request for a targeted survey to pre-populate the survey with predictive response data thereby increasing the response rate and effectivity of the survey results. Kannan et al. (US 20140143017 A1) discloses enhancing the customer experience by providing surveys to customers based on customer information. Jayarajan et al. (US 20220398611 A1) discloses dynamically determine various types of actions that should be taken in response to survey response content received by those user accounts that are actually currently participating in an online webinar event.
Conclusion
Applicants’ amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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JAMIE H. AUSTIN
Examiner
Art Unit 3625
/JAMIE H AUSTIN/Primary Examiner, Art Unit 3625
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