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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/16/2025 has been entered.
Status
This action is in response to the amendment filed on 10/16/2025. Claims 1, 3-5, 7-10, 12-14, 16, 18-20, are pending. Claims 1, 4, 10, 13, 16, 19, are amended. No claims have been added. Claims 6, 15, have been cancelled.
Response to Arguments
Applicant's arguments filed 10/16/2025 have been fully considered but they are not persuasive. The applicant has argued “On the contrary, the claims are directed to methods and systems that including a user interface to receive customer attributes from a customer that are used to train a machine learning (ML) model that predicts a feature importance score for customer attributes and system attributes that affect survey response rates (i.e., the percentage of people who complete a survey out of the total number who received it).” 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 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. 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. 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. 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 “These steps do not merely link the use of the alleged abstract idea to a computer environment. Instead, they describe how a customer interacts with a user interface to select customer attributes that are used to train a ML model. The ML model is then used to more accurately predict feature importance scores that are used to derive a feature importance matrix. A computer is used not merely to receive, store, and transmit data, but to receive specific input from a customer and to train the ML model to predict the best time window to send a survey to maximize the rate of response.” The examiner respectfully disagrees. 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 invention does not claim machine learning itself, but rather relies on use of generic machine learning technology in carrying out generic functions that can be performed mentally or as part of a certain method of organizing human activity. Id. at 1212 (finding the patents at issue “rely on the use of generic machine learning technology in carrying out the claimed methods for generating event schedules and network maps” and “[t]he machine learning technology described in the patents is conventional, as the patents’ specifications demonstrate”); see Spec. ¶¶ 45 (describing generic usage of a ML model to predict a feature importance score). Thus, claim 1’s recitation of using and training an ML model is insufficient to integrate the abstract limitations of claim 1 into a practical application.
The applicant has argued “The claims as a whole integrate the alleged abstract idea into a practical application because the recited system, steps, and non-transitory computer-readable media each improve the technical methodology and process of increasing survey response rates via a user interface and machine learning as explained above.” The examiner respectfully disagrees. 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 that the claim limitations “encompass the claimed improvement in maximizing survey response rates by generating, training, and using a ML model.” The examiner respectfully disagrees. 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 “for example, is not well-understood, routine, or conventional activity in the field of obtaining survey responses.” It is noted that the Examiner did not find any additional element or combination of elements in the rejected claims that are recognized as well-understood, routine, and conventional activities. However, Berkheimer does not require the Examiner make a factual finding that all claim elements are well-understood, routine or conventional. Rather, a Berkheimer factual is required for additional elements or a combination of additional elements outside of the identified abstract idea. See Berkheimer Memo. P. 2. Therefore the previous 101 is maintained and updated below in view of applicant’s 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 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 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 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.” 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.” 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.” 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.” 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.” 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.” 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.” 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.
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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMIE H AUSTIN whose telephone number is (571)272-7363. The examiner can normally be reached Monday, Tuesday, Thursday, Friday 7am-2pm.
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JAMIE H. AUSTIN
Examiner
Art Unit 3625
/JAMIE H AUSTIN/Primary Examiner, Art Unit 3625