DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the original application filed on 4/25/2023. Acknowledgment is made with respect to a claim of priority to Provisional Application 63/450,915 filed on 3/8/2023.
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.
Claims 1-20 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Claim 1
Step 1: The claim recites an apparatus; therefore, it is directed to the statutory category of a machine.
Step 2A Prong 1: The claim recites, inter alia:
generate, in real time, output data indicative of a likelihood of an occurrence of at least one targeted event associated with the data exchange during a future temporal interval: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating output data indicating a likelihood of am event during a future time interval, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally predict an event in the future based on received information.
Step 2A Prong 2: The claim does not recite any additional limitations which integrate the abstract idea into a practical application. Specifically, the additional elements consist of “a memory storing instructions; a communications interface; and at least one processor coupled to the memory and the communications interface, the at least one processor being configured to execute the instructions to”, “receive, from a device via the communications interface, application data characterizing an exchange of data”, “based on an application of an artificial-intelligence process to an input dataset that includes at least a portion of the application data”, “the artificial-intelligence process being trained using datasets associated with inferred ground-truth labels”, and “transmit at least a portion of the output data to the device via the communications interface, the device being configured to present a graphical representation of the portion of the output data within a digital interface”.
The additional elements of “a memory storing instructions; a communications interface; and at least one processor coupled to the memory and the communications interface, the at least one processor being configured to execute the instructions to” amount to generic computer components used as a tool to perform an existing process. The additional element of “based on an application of an artificial-intelligence process to an input dataset that includes at least a portion of the application data” amounts to reciting 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 because it is not clear how the generic AI model is broadly applied to input data to generate a likelihood of an occurrence of an event in real-time. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
The additional elements “receive, from a device via the communications interface, application data characterizing an exchange of data” and “transmit at least a portion of the output data to the device via the communications interface, the device being configured to present a graphical representation of the portion of the output data within a digital interface” are insignificant extra-solution activities required for any uses of the abstract ideas (see MPEP § 2106.05(g)).
The additional element of “the artificial-intelligence process being trained using datasets associated with inferred ground-truth labels” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h).
Thus, even when viewed individually and as an ordered combination, these additional elements do not integrate the abstract idea into a practical application and the claim is thus directed to the abstract idea.
Step 2B: Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea.
The additional elements of “a memory storing instructions; a communications interface; and at least one processor coupled to the memory and the communications interface, the at least one processor being configured to execute the instructions to” amount to generic computer components used as a tool to perform an existing process. The additional element of “based on an application of an artificial-intelligence process to an input dataset that includes at least a portion of the application data” amounts to reciting 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 because it is not clear how the generic AI model is broadly applied to input data to generate a likelihood of an occurrence of an event in real-time. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
The additional elements “receive, from a device via the communications interface, application data characterizing an exchange of data” and “transmit at least a portion of the output data to the device via the communications interface, the device being configured to present a graphical representation of the portion of the output data within a digital interface” are insignificant extra-solution activities required for any uses of the abstract ideas (see MPEP § 2106.05(g)), and are well-understood, routine, conventional activities (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network”, “Presenting offers and gathering statistics”; and see Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016) at pages 9-10: “The claims at issue do not require any nonconventional computer, network, or display components, or even a “non-conventional and non generic arrangement of known, conventional pieces,” but merely call for performance of the claimed information collection, analysis, and display functions “on a set of generic computer components” and display devices).
The additional element of “the artificial-intelligence process being trained using datasets associated with inferred ground-truth labels” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 2
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “the at least one processor is further configured to execute the instructions to generate the input dataset based on at least the portion of the application data and on interaction data characterizing the applicant” amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). The additional element of “the application data characterizes an application for the data exchange, the application involving an applicant” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 3
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites inter alia:
based on the data that characterizes the composition, perform operations that (i) extract a first feature value from at least one of the portion of the application data or a portion of the interaction data: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of extracting feature values from data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
(ii) compute a second feature value based on at least one of the portion of the application data or the portion of the interaction data: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of computing feature values from data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper or is a mathematical concept.
generate the input dataset based on at least one of the extracted first feature value or the computed second feature value: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating an input dataset from feature information, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The additional element of “obtain data that characterizes a composition of the input dataset” is insignificant extra-solution activity required for any uses of the abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network”). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 4
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites inter alia:
generate the input dataset based on the portion of the application data and on the portion of the interaction data: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating an input dataset from data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The additional element of “obtain at least a portion of the interaction data from the memory based on the identifier of the applicant” is insignificant extra-solution activity required for any uses of the abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Storing and retrieving information in memory”). The additional element of “the application data comprises an identifier of the applicant” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h).Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 5
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites inter alia:
based on the output data, generate decision data associated with a decision to approve the application for the data exchange: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating decision data based on an approval, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The additional elements of “transmit at least a portion of the decision data to the device via the communications interface, the portion of the decision data causing an application program executed by the device to generate, and present within the digital interface, elements of digital content that characterize the decision to approve the application” are insignificant extra-solution activities required for any uses of the abstract ideas (see MPEP § 2106.05(g)), and are well-understood, routine, conventional activities (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network”, “Presenting offers and gathering statistics”; and see Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016) at pages 9-10: “The claims at issue do not require any nonconventional computer, network, or display components, or even a “non-conventional and non-generic arrangement of known, conventional pieces,” but merely call for performance of the claimed information collection, analysis, and display functions “on a set of generic computer components” and display devices). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 6
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “the executed application program causes the device to present the graphical representation of the portion of the output data within the digital interface” is insignificant extra-solution activity required for any uses of the abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Presenting offers and gathering statistics”; and see Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016) at pages 9-10: “The claims at issue do not require any nonconventional computer, network, or display components, or even a “non-conventional and non-generic arrangement of known, conventional pieces,” but merely call for performance of the claimed information collection, analysis, and display functions “on a set of generic computer components” and display devices). The additional element of “the device is operable by an applicant associated with the application data, and the application data is generated by an application program executed by the device” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h).Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 7
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites inter alia:
generate the input dataset in accordance with the data that characterizes the composition: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating an input dataset based on composition data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The additional element of “obtain (i) data that characterizes a composition of the input dataset and (ii) one or more process parameters that characterize the trained artificial-intelligence process” is insignificant extra-solution activity required for any uses of the abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network”). The additional element of “apply the trained artificial-intelligence process to the input dataset in accordance with the one or more process parameters” amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 8
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the trained artificial-intelligence process comprises a trained, gradient-boosted, decision-tree process” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h).Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 9
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites inter alia:
determine that the elements of the additional application data are associated with corresponding ones of a first element population and a second element population: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining that application information corresponds to population information, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
generate a plurality of first datasets based on the elements of the additional application data associated with the first element population, and perform operations that assign a ground-truth label to each of the first datasets based on corresponding elements of interaction data: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating datasets based on application data and assigning labels to the data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
generate first composition data and explainability data, the first composition data identifying a plurality of first sequential features, and the explainability data characterizing an impact of each of the first sequential features on an output of the artificial-intelligence process: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating composition and explainability data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The additional element “perform operations that train the artificial-intelligence process based on the first datasets and corresponding ones of the assigned ground-truth labels” amounts to reciting 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 because it is not clear how the generic AI model is broadly trained on certain datasets and labels. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 10
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites inter alia:
generate a plurality of second datasets based on the elements of the additional application data associated with the second element population;: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating datasets based on application information, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
based on the explainability data and on interaction data associated with the second element population, perform operations that generate a corresponding one of the inferred ground-truth labels for each of the second datasets: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating labels for datasets, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
generate second composition data, the elements of second composition data identifying a plurality of second sequential features: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating composition data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The additional element “perform operations that train the artificial-intelligence process based on the second datasets and corresponding ones of the inferred ground-truth labels” amounts to reciting 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 because it is not clear how the generic AI model is broadly trained on certain datasets and labels. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 11
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites inter alia:
generate combined composition data based on the first and second composition data, the combined composition data identifying combined sequential features that include at least one of the first sequential features and at least one of the second sequential features: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating composition data based on other composition data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
generate a plurality of third input datasets based on elements of the additional application data associated with the first and second element populations, each of the third input datasets having a composition consistent with the combined composition data: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of input datasets based on application data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
generate final composition data and final process data, the elements of final composition data identifying a plurality of final sequential features, and the final process data comprising a final value of one or more process parameters: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating composition data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The additional element “perform operations that train the artificial-intelligence process based on the third datasets and corresponding ones of the assigned and inferred ground-truth labels” amounts to reciting 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 because it is not clear how the generic AI model is broadly trained on certain datasets and labels. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claims 12-19
Claims 12-19 recite a method (step 1: a process) to perform the steps of claims 1-4, 7, and 9-11, respectively, without any additional elements that integrate the abstract ideas into a practical application or provide significantly more than the abstract idea by itself, and are thus rejected for the same reasons set forth in the rejection of claims 1-4, 7, and 9-11, respectively.
Claim 20
Claim 20 recites a non-transitory computer-readable medium (step 1: a manufacture) using a processor to perform the steps of claim 1, which by MPEP 2106.05(f) (“apply it”) cannot integrate an abstract idea into a practical application or provide significantly more than the abstract idea by itself, and are thus rejected for the same reasons set forth in the rejection of claim 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. § 103 as being obvious over Even et al. (US 20230410208 A1, hereinafter “Even”) in view of Dhar et al. (US 20020040339 A1, hereinafter “Dhar”).
Regarding claim 1, Even discloses [a]n apparatus, comprising: a memory storing instructions; a communications interface; and at least one processor coupled to the memory and the communications interface, the at least one processor being configured to execute the instructions to: (Abstract; and [0018]; and [0069]; and [0016])
receive, from a device via the communications interface, application data characterizing an exchange of data; ([0011]; “A risk-transfer (insurance) application is represented it the present disclosure as an input vector X that contains a combination of discrete, continuous, and attribute variables and/or parameters. These variables represent the applicant's medical and demographic information/data that, in the prior art, typically has been identified by actuarial studies to be pertinent to the estimation of the applicant's claim risk measurand”, which discloses receiving application data characterizing an exchange of data such as an input vector of applicant variables; and [0019]; “The UW process characteristics parameters can e.g. comprise at least a sum value assured and/or a term value of the risk-transfer and/or health characteristics data and/or medical cover parameter and/or past and recent UW decision parameters and/or personal disclosure data”; and [0069]; “It should also be able to connect to the internal database, so data can be easily manipulated and extracted directly”)
based on an application of an artificial-intelligence process to an input dataset that includes at least a portion of the application data, generate, in real time, output data indicative of a likelihood of an occurrence of at least one targeted event associated with the data exchange during a future temporal interval, ([0065]; “The AI-module 13 generates in the application phase 1312 for each inputted dataset 13121 of an individual 2 a measured risk score value 13122 as the measured occurrence probability value for the occurrence of one or more predefined life, health and/or medical events 4 to the individual 2 assigned to the inputted dataset 13121.”, which discloses applying an AI process or AI module to an input dataset to generate a risk score or probability value for a targeted event such as a life or health event withing a future time window; and [0009]; “Risk as understood within this application is a physical measurand providing a measure for an occurrence probability or occurrence frequency of a physically and measurably impacting defined physical event to a defined real-world object or individual, the impact having an associated level of damage in a defined future time-window to the real-world object or individual”; and [0008]; “any external data source or real-world link, such as a database table or linked measuring or sensory devices, can be used to supply engine parameters, even by adapting them in real-time based on the real-world link”, which discloses that the output data can be generated in real-time)
the artificial-intelligence process being trained using datasets associated with inferred ground-truth labels; and ([0032]; “This result will be combined/cross-checked with label generated from the rules to get the final “Ground-truth” label of Eligible or Non-eligible risk as illustrated in FIG. 8 “; and [0058])
Even fails to explicitly disclose but Dhar discloses transmit at least a portion of the output data to the device via the communications interface, the device being configured to present a graphical representation of the portion of the output data within a digital interface ([0072]; “The instant loan implementation of the workflow engine 20 allows a consumer to apply for a loan on a single form and receive multiple instant loan offers over a secure connection on the Internet. From the convenience of home, consumers can apply for loans, receive and compare multiple offers, and accept a loan offer within a matter of seconds”, which discloses transmitting a portion of output data to a device through an interface or workflow engine to present the output data such as a loan offer; and [0041]; “The loan workflow e-loan director is an Internet-based, front end application software package with extensive features for application processing, as well as automated loan status reporting for the customer and third party providers such as real estate agents, insurance agents, appraisers, auto dealers, and the like”).
Even and Dhar are analogous art because both are concerned with using artificial intelligence to process loan information. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in AI and loan processing to combine the data transmission and GUI of Dhar and the apparatus of Even to yield to the predictable result of transmit at least a portion of the output data to the device via the communications interface, the device being configured to present a graphical representation of the portion of the output data within a digital interface. The motivation for doing so would be to instantly evaluate and authorize on-line loan applications (Dhar; [0002]).
Regarding claim 12, it is a method claim corresponding to the steps of claim 1, and is rejected for the same reasons as claim 1.
Regarding claim 20, it is a non-transitory computer-readable medium claim corresponding to the steps of claim 1, and is rejected for the same reasons as claim 1.
Regarding claims 2 and 13, the rejection of claims 1 and 12 are incorporated and Even further discloses the application data characterizes an application for the data exchange, the application involving an applicant; and ([0009]; “the application is classified into one of the risk categories available for the type of risk-transfer requested by the applicant … The accept/reject decision is also part of this risk classification since risks above a certain tolerance level will typically be rejected”)
the at least one processor is further configured to execute the instructions to generate the input dataset based on at least the portion of the application data and on interaction data characterizing the applicant ([0019]; “The bank transaction parameters can e.g. comprise at least credit card transactions parameters and/or standard industry code of merchant”; and [0042]; and [0052]).
Regarding claims 3 and 14, the rejection of claims 1, 2, 12, and 13 are incorporated and Even further discloses obtain data that characterizes a composition of the input dataset; and ([0051]; “Model 1: With full feature set of Demographic+UW+Health (Claim); Model 2: With feature set of only Demographic+Health (Claim); and Model 3: With feature set of only Demographic … The difference between the 3 models is then only on the feature set, i.e.the dataset 102, selected during the training process”)
based on the data that characterizes the composition, perform operations that (i) extract a first feature value from at least one of the portion of the application data or a portion of the interaction data and that (ii) compute a second feature value based on at least one of the portion of the application data or the portion of the interaction data; and ([0028]; “demographic features, underwriting features and claim features from health rider data can e.g. be from different databases and measuring facilities”; and [0046]; “ For example, the captured and/or measured time series data can be transformed into a new latent space using the hyperparameters of a Multi-Task Gaussian Process (MTGP) structure … For the present system 1, the performance of the ML-based structures can e.g. be measured on the binned vector values on various time intervals. This can e.g. comprise analysis of dynamics of centroids and standard deviations for cohorts 21 of individuals 2 (critical and normal) on the defined time intervals”)
generate the input dataset based on at least one of the extracted first feature value or the computed second feature value ([0058]; “After filtering with previous UW decision, claims severity and loss ratio, applicable data points are extracted to train the ML-based modeling structure”).
Regarding claims 4 and 15, the rejection of claims 1, 2, 12, and 13 are incorporated and Even further discloses the application data comprises an identifier of the applicant; and ([0050]; “In addition, historical UW and/or medical and/or health and/or medical data and dataset 102, inter alia because of legacy issues, are stored in different formats at different places”; and [0044]; “Each record can e.g. be in a certain format as an XML file that includes medical observations, test results and reports compiled and maintained by medical equipment operated by medical doctors and/or nurses”)
the at least one processor is further configured to execute the instructions to: obtain at least a portion of the interaction data from the memory based on the identifier of the applicant; and ([0019]; “The bank transaction parameters can e.g. comprise at least credit card transactions parameters and/or standard industry code of merchant”; and [0050])
generate the input dataset based on the portion of the application data and on the portion of the interaction data ([0050]; and [0044]; and [0019]).
Regarding claim 5, the rejection of claims 1 and 2 are incorporated and Even further discloses based on the output data, generate decision data associated with a decision to approve the application for the data exchange; and ([0067]; “ In the signal, those with high score are eligible risks and are eligible for Guaranteed Issued Offer (GIO) for risk-transfers 101/6 i 4”; and [0067]; “ the present system, inter alia, provides an automated predictive underwriting process for signaling and triggering the operation (underwriting and/or coverage/claim processing) of associated automated risk-transfer system”; and [0071]; “The selected threshold also results in 60+% of individuals 2 being offered GIO/SIO”)
Even fails to explicitly disclose but Dhar discloses transmit at least a portion of the decision data to the device via the communications interface, the portion of the decision data causing an application program executed by the device to generate, and present within the digital interface, elements of digital content that characterize the decision to approve the application ([0041]; “If a match takes place, the customer is informed within seconds about the instant conditional offer. In the preferred embodiment, the customers informed within 45 to 60 seconds or sooner”; and [0068]; “Thus, the workflow engine 20 as shown provides a first pass automatic loan offer system that allows a consumer to apply for a loan and receive a conditional loan offer within seconds. The loan offer is conditioned on the accuracy of the information provided by the applicant”; and [0048]).
The motivation to combine Even and Dhar is the same as discussed above with respect to claim 1.
Regarding claim 6, the rejection of claim 1 is incorporated and Even further discloses the device is operable by an applicant associated with the application data, and the application data is generated by an application program executed by the device ([0014]; such an electronic, fast efficient-to-implement risk-transfer UW channel would streamline the risk-transfer/insurance procurement process by integrating automated insurance technology into the fast-moving consumer goods sector, an area devoid of tangible financial service products”; and [0014])
Even fails to explicitly disclose but Dhar discloses the executed application program causes the device to present the graphical representation of the portion of the output data within the digital interface ([0072]; “The instant loan implementation of the workflow engine 20 allows a consumer to apply for a loan on a single form and receive multiple instant loan offers over a secure connection on the Internet. From the convenience of home, consumers can apply for loans, receive and compare multiple offers, and accept a loan offer within a matter of seconds”; and [0068]; and [0006]).
The motivation to combine Even and Dhar is the same as discussed above with respect to claim 1.
Regarding claims 7 and 16, the rejection of claims 1 and 12 are incorporated and Even further discloses obtain (i) data that characterizes a composition of the input dataset and (ii) one or more process parameters that characterize the trained artificial-intelligence process; ([0051]; “The difference between the 3 models is then only on the feature set, i.e. the dataset 102, selected during the training process”; and [0060]; “A grid search can be used to find the best hyperparameters”)
generate the input dataset in accordance with the data that characterizes the composition; and apply the trained artificial-intelligence process to the input dataset in accordance with the one or more process parameters ([0051]; “the same set of base training data can e.g. be used with labels for all the 3 modelling structures”; and [0059]; “the same hyper-parameters can e.g. be used to retrain the model structure on all the data set (100% training set) to gain a few percentages of performance improvement”; and [0060]; “AUC is used for evaluation of the model during the training process”).
Regarding claim 8, the rejection of claim 1 is incorporated and Even further discloses wherein the trained artificial-intelligence process comprises a trained, gradient-boosted, decision-tree process ([0060]; “The Gradient Boosted Tree can be used as classification structure, with the famous python package XGBoost”).
Regarding claims 9 and 17, the rejection of claims 1 and 12 are incorporated and Even further discloses obtain additional application data from the memory, the elements of additional application data being associated with one or more temporal intervals; ([0050]; “historical UW and/or medical and/or health and/or medical data and dataset 102, inter alia because of legacy issues, are stored in different formats at different places” and [0059]; “Again, there are also other ways of splitting data for training and testing purposes. One of them is to again split on the timeline, so that we train a model with the historical data as the training set and test it in the recent one year as the testing set to check its predictive power for the future”)
determine that the elements of the additional application data are associated with corresponding ones of a first element population and a second element population; ([0060]; “For handling imbalanced dataset 102, it is to be noted that the present system 1 is associated with a binary classification problem on eligible and non-eligible risks” and [0032]’ “each individual 2 in the cluster 1212 will have a cluster label as “Eligible” or “Non-eligible”, otherwise assigned as “excluded” and will not be used as training data”; and [0060])
generate a plurality of first datasets based on the elements of the additional application data associated with the first element population, and perform operations that assign a ground-truth label to each of the first datasets based on corresponding elements of interaction data; and ([0053]; “the rules for deciding on a non-eligible risk case can e.g. be realized the following (with respect to the split on time dimension which is discussed below later): (1) Previous underwriting decision on the customer is substandard; (2) Previous claimed disease from this customer is ranked as high severity level; and (3) Loss ratio (claim amount over premium) on the customer is bigger than a certain level”; and [0058]; and [0052]).
perform operations that train the artificial-intelligence process based on the first datasets and corresponding ones of the assigned ground-truth labels, and generate first composition data and explainability data, the first composition data identifying a plurality of first sequential features, and the explainability data characterizing an impact of each of the first sequential features on an output of the artificial-intelligence process ([0059]; “Training set is for training the model itself, validation set is used to monitor the training process to get the best hyperparameters (mostly to avoid over-fitting), and testing set is to finally evaluate the model performance”; and [0061]).
Regarding claims 10 and 18, the rejection of claims 1, 9, 12, and 17 are incorporated and Even further discloses generate a plurality of second datasets based on the elements of the additional application data associated with the second element population ([0060]; “the present system 1 is associated with a binary classification problem on eligible and non-eligible risks. It can be found that the label data is very imbalanced with way more eligible risks than non-eligible risks (ratio around 5:1). One possible way of tackling this is to use techniques like oversampling or undersampling to balance the dataset. However, undersampling will significantly decrease the data volume for eligible risks. Oversampling methods like SMOTE will create some new synthetic data points imitating the non-eligible risks”)
based on the explainability data and on interaction data associated with the second element population, perform operations that generate a corresponding one of the inferred ground-truth labels for each of the second datasets; and ([0032]; “This result will be combined/cross-checked with label generated from the rules to get the final “Ground-truth” label of Eligible or Non-eligible risk as illustrated in FIG. 8”; and [0058]; “Note that with this mechanism of generating the ground-truth label, there will be some individuals 2 who get inconsistent labels from the rules and the clustering result, and the system 1 will exclude them from the training datasets 102 by filtering”)
perform operations that train the artificial-intelligence process based on the second datasets and corresponding ones of the inferred ground-truth labels and generate second composition data, the elements of second composition data identifying a plurality of second sequential feature ([0059]; “the same hyper-parameters can e.g. be used to retrain the model structure on all the data set (100% training set) to gain a few percentages of performance improvement”; and [0073]).
Regarding claims 11 and 19, the rejection of claims 1, 9, 10, 12, 17, and 18 are incorporated and Even further discloses generate combined composition data based on the first and second composition data, the combined composition data identifying combined sequential features that include at least one of the first sequential features and at least one of the second sequential features; and ([0051]; “Model 1: With full feature set of Demographic+UW+Health (Claim); Model 2: With feature set of only Demographic+Health (Claim); and Model 3: With feature set of only Demographic. However, other combinations of data input parameters are also imaginable”; and [0073])
generate a plurality of third input datasets based on elements of the additional application data associated with the first and second element populations, each of the third input datasets having a composition consistent with the combined composition data; and ([0051; “the same set of base training data can e.g. be used with labels for all the 3 modelling structures”; and [0058]; “applicable data points are extracted to train the ML-based modeling structure”)
perform operations that train the artificial-intelligence process based on the third datasets and corresponding ones of the assigned and inferred ground-truth labels, and generate final composition data and final process data, the elements of final composition data identifying a plurality of final sequential features, and the final process data comprising a final value of one or more process parameters ([0059]; “the same hyper-parameters can e.g. be used to retrain the model structure on all the data set (100% training set) to gain a few percentages of performance improvement”; and [0060]; and [0061]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
McKenna et al. (US 20220318901 A1).
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/BRENT JOHNSTON HOOVER/ Primary Examiner, Art Unit 2127
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