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 .
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 a judicial exception (1e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Subject Matter Eligibility Standard
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.
Specifically, claims 10 and 19 are directed to a method. Claim 1 is directed to a system. Each of the claims falls under one of the four statutory classes of invention.
If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea).
Step 2A, Prong One: the claims recite the following limitations that are understood to recite an abstract idea absent the bolded limitations
Claim 1 recites:
A computer system for generating dynamic outputs using machine learning tools, the computer system comprising at least one processor coupled to a memory device and a communication interface, the at least one processor operable to execute an assessment module and a pricing module, the at least one processor configured to:
determine a plurality of transportation modes available for a trip to be taken by a user;
train one or more machine learning tools using real-time contextual data associated with the trip and a user profile;
generate, by executing the assessment module and based upon at least one of the real-time contextual data or the user profile, at least one risk score, each associated with use of one of the plurality of transportation modes;
generate, using the at least one risk score, a dynamic pricing model associated with the plurality of transportation modes;
apply, by executing the pricing module, the trained one or more machine learning tools to the dynamic pricing model to generate an offering output associated with the use of at least one of the plurality of transportation modes; and
transmit, using the communication interface, the offering output in real time to a user computing device associated with the user.
Claim 2 recites: wherein the at least one processor is further configured to generate the user profile of the user by processing telematics data received using the communication interface from the user computing device.
Claim 3 recites: wherein the at least one processor is further configured to receive, using the communication interface, the real-time contextual data associated with the trip and the user profile.
Claim 4 recites: wherein the at least one processor is further configured to generate, using the at least one risk score, at least one travel route to be taken by the user during the trip.
Claim 5 recites: wherein the offering output includes an insurance offering for purchase by the user.
Claim 6 recites: wherein the real-time contextual data includes at least one of weather data, age of the user, data associated with a start location of the trip, or data associated with an end location of the trip.
Claim 7 recites: wherein the at least one processor is further configured to determine, based upon the user profile, the plurality of transportation modes available for the trip to be taken by the user.
Claim 8 recites: wherein the at least one processor is further configured to generate, by executing the pricing module and based upon the application of the trained the one or more machine learning tools to the dynamic pricing model, the offering output.
Claim 9 recites: wherein the at least one processor is further configured to:
determine the plurality of transportation modes available for the trip by accessing transportation data associated with the trip;
generate a plurality of risk scores, each generated for each of the plurality of transportation modes;
compare the generated risk scores to one another;
rank, based upon the comparison, the plurality of transportation modes based upon the generated risk scores;
generate, based upon an associated rank, a plurality of offering outputs, each generated for each of the plurality of transportation modes, wherein each offering output includes a price corresponding to the associated rank; and
transmit the plurality of offering outputs to the user computing device for selection by the user.
Claim 10 recites: A computer-implemented method for generating dynamic outputs using machine learning tools, the method implemented by computer system including at least one processor coupled to a memory device and a communication interface, the at least one processor operable to execute an assessment module and a pricing module, the method comprising:
determining a plurality of transportation modes available for a trip to be taken by a user;
training one or more machine learning tools using real-time contextual data associated with the trip and a user profile;
generating, by executing the assessment module and based upon at least one of the real-time contextual data or the user profile, at least one risk score, each associated with use of one of the plurality of transportation modes;
generating, using the at least one risk score, a dynamic pricing model associated with the plurality of transportation modes;
applying, by executing the pricing module, the trained one or more machine learning tools to the dynamic pricing model to generate an offering output associated with the use of at least one of the plurality of transportation modes; and
transmitting, using the communication interface, the offering output in real time to a user computing device associated with the user.
Claim 11 recites: generating the user profile of the user by processing telematics data
received using the communication interface from the user computing device.
Claim 12 recites: receiving, using the communication interface, the real-time contextual data associated with the trip and the user profile.
Claim 13 recites: generating, using the at least one risk score, at least one travel route to be taken by the user during the trip.
Claim 14 recites: wherein the offering output includes an insurance offering for purchase by the user.
Claim 15 recites: wherein the real-time contextual data includes at least one of weather data, age of the user, data associated with a start location of the trip, or data associated with an end location of the trip.
Claim 16 recites: determining, based upon the user profile, the plurality of transportation modes available for the trip to be taken by the user.
Claim 17 recites: generating, by executing the pricing module and based upon the application of the trained the one or more machine learning tools to the dynamic pricing model, the offering output.
Claim 18 recites:
determining the plurality of transportation modes available for the trip by accessing transportation data associated with the trip;
generating a plurality of risk scores, each generated for each of the plurality of transportation modes;
comparing the generated risk scores to one another;
ranking, based upon the comparison, the plurality of transportation modes based upon the generated risk scores;
generating, based upon an associated rank, a plurality of offering outputs, each generated for each of the plurality of transportation modes, wherein each offering output includes a price corresponding to the associated rank; and
transmitting the plurality of offering outputs to the user computing device for selection by the user.
Claim 19 recites:
At least one non-transitory computer-readable storage medium having computer-executable instructions embodied thereon, wherein when executed by computer system including at least one processor coupled to a memory device and a communication interface, the at least one processor operable to execute an assessment module and a pricing module, the computer-executable instructions cause the at least one processor to:
determine a plurality of transportation modes available for a trip to be taken by a user;
train one or more machine learning tools using real-time contextual data associated with the trip and a user profile;
generate, by executing the assessment module and based upon at least one of the real-time contextual data or the user profile, at least one risk score, each associated with use of one of the plurality of transportation modes;
generate, using the at least one risk score, a dynamic pricing model associated with the plurality of transportation modes;
apply, by executing the pricing module, the trained one or more machine learning tools to the dynamic pricing model to generate an offering output associated with the use of at least one of the plurality of transportation modes; and
transmit, using the communication interface, the offering output in real time to a user computing device associated with the user.
Claim 20 recites: wherein the computer-executable instructions further cause the at least one processor to generate the user profile of the user by processing telematics data received using the communication interface from the user computing device.
In removing the bolded additional elements, it is noted that the remaining limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106. 04(a}(2), such as
managing commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations).
Step 2A, Prong two: This judicial exception is not integrated into a practical application, In particular, the clams recite the above noted bolded limitations understood to be additional limitations.
These limitations performing functions using a computer system comprising at least a processor coupled to a memory device and a communication device, am assessment module, a pricing module, and a machine learning tool to generate an offering output associated with the use of at least one of a plurality of transportation modes, merely amount to instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f), also see applicant's specification for guiding interpretation of these claim features, describing implementation with generic commercially available devices or any machine capable of executing a set of instructions. The computer system and machine learning model are similarly understood in light of applicant's specification as mere usage of any arrangement of generic computers and hardware intermediate components potentially using networks to communicate between systems.
Performance of a receiving step by a computer processor or machine learning tool amounts to performing steps which amount io insignificant extra-solution activity of data gathering - see MPEP 2106.05(g).
Performing steps by computer processor hardware with scores for insurance comparison purposes using a machine learning model limit the abstraction to computer field by execution by generic computers - see MPEP 2106.05(h).
As noted in MPEP 2106.04(d), limitations which amount to instructions to implement an abstract idea on a computer or merely using a computer as a tool, limitations which amount to insignificant extra-solution activity, and limitations which amount generally linking to a particular technological environment do not integrate a judicial exception into a practical application.
The breadth of the limitations reasonably includes generating at least one risk score, each associated with use of one of a plurality of transportation modes, and generating a dynamic pricing model associated with the plurality of transportation modes.
Reciting "a computer system”, a “computing device” and a “communication interface” is understood to be similar to Alappat, which as noted in MPEP 2106. 05(b)(I), is superseded, and the correct analysis is to look whether the added elements integrate the exception into a practical application or provide significantly more than the judicial exception.
The claims in the instant application are performed by one or computer system
comprising at least one processor coupled to a memory device and a communication device using a machine learning tool.
Consideration of these steps or functions as a combination does not change the analysis
as they do not add anything compared to when the steps are considered separately. The claims recite a particular sequence of determining scores for an insurance of at least one risk score of a plurality of transportation modes. Performance of these steps or functions technologically does present a meaningful limit to the scope of the claim which would reasonably integrate the abstraction into a practical application.
Step 2B: The elements discussed above with respect to the practical application in Step 2A, prong 2 are equally applicable to consideration of whether the claims amount to significantly more. Accordingly, the clams fail to recite additional elements which, when considered individually and in combination, amount to significantly more. Reconsideration of these elements identified as insignificant extra-solution activity as part of Step 2B does not change the analysis.
Independent claims 10 and 19: Independent claims 10 and 19 recite the same limitations as independent claim 1. The same reasons discussed above with respect to claim 1 are equally applicable to claims 10 and 19.
These claimed elements also as found in the dependent claims are also recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using a generic component.
In processing the claims, it is noted that the recitation of these additional elements do not impact the analysis of the claims because these elements in combination are noted only to be a general purpose computer for performing basic or routine computer functions. These claimed elements are noted to a be a generic computer for collecting data, storing data, training data, generating data and transmitting data, and performing routine and conventional functions.
These additional elements do not overcome the analysis as these elements are merely considered as additional elements which amount to instructions to be applied to the generic computer or processor.
Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claimed elements are also seen as generic computer components for receiving data, training data, generating data, determining data, and transmitting data, thus performing generic functions without an inventive concept as they do not amount to significantly more than the abstract idea. The claimed additional elements are interpreted as being recited at a high level of generality and even if the claims recited in the affirmative.
The type of data being manipulated does not impose meaningful limitations or renders the idea less abstract. Looking at the elements as a combination, the elements do not add anything more than the elements analyzed individually. Therefore, the claims do not amount to significantly more than the abstract idea itself.
Applicant is reminded that a statutory claim would recite an automated machine implemented method or system with specific structures for performing the claimed invention so as to provide an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. Each claim as a whole, does not amount to significantly more than the abstract idea itself. This is because the claims do not effect an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer itself; and the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment.
The reliance of a computer or processor to perform its routine tasks even more accurately is not sufficient to transform a claim into patent eligible subject matter as noted in Alice 134 S. Ct. at 2359. As indicated by the court "use of a computer to create electronic records, track multiple transactions and issue simultaneous instructions" was not an inventive concept. The claims or even the applicant's specification does not support or provide or claim any specifically inventive technology or algorithm for performing the claimed functions.
Therefore, the recited additional elements do not integrate the abstract idea into a
practical application when reading the claims. The independent claims 1, 10 and 19 and do not contain structures to provide a significantly more than the abstract idea.
The dependent claim(s) when analyzed and each taken as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea.
Accordingly, claims 1-20 are directed to an abstract idea.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the "right to exclude" granted by a patent and to prevent possible harassment by multiple assignees. See In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970);and, In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent is shown to be commonly owned with this application. See 37 CFR 1.130(b).
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1-20 are rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11,599,951. Although the conflicting claims are not identical, they are not patentably distinct from each other because claims 1-20 of the instant application are directed to a similar subject matter contained in claims 1-20 of the '951 patent. The only difference between the instant application and the '951 patent is merely a labeling difference. It is noted that all the features of claims 1-20 are contained in claims 1-20 of the '951 patent.
Claims 1-20 are rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,217,313. Although the conflicting claims are not identical, they are not patentably distinct from each other because claims 1-20 of the instant application are directed to a similar subject matter contained in claims 1-20 of the '313 patent. The only difference between the instant application and the '951 patent is merely a labeling difference. It is noted that all the features of claims 1-20 are contained in claims 1-20 of the '313 patent.
The prior art taken alone or in combination failed to teach or suggest:
“generate, using the at least one risk score, a dynamic pricing model associated with the plurality of transportation modes, apply, by executing the pricing module, the trained one or more machine learning tools to the dynamic pricing model to generate an offering output associated with the use of at least one of the plurality of transportation modes, and transmit, using the communication interface, the offering output in real time to a user computing device associated with the user”.
Kawamura ( US Pub. No. 20140052479 A1 ) discloses a method for estimating insurance risk is described. The method may include receiving, by a processor, real-time information related to a travel itinerary. The method may also include estimating, by the processor, the insurance risk by analyzing the real-time information based on a quantitative assessment of risks posed to a population by the at least one factor. The method may also include generating, by the processor, an insurance risk profile based on the estimated insurance risk.
Peak et al ( US Pub. No. 20110213628 A1 ) disclose an insurance system, method and device which include a data storage device for storing, updating and providing access to loss risk score data. A request for information associated with a user's location identified by user location data may be received over a communications network. A computer processing system may then be operated to generate a safety score associated with said use location data, the safety score being based on a plurality of loss risk factors associated with the user location data. At least one element of an insurance policy associated with the user may be modified in accordance with the safety score. A response, including the safety score, may then be transmitted to the user over the communications network.
Conclusion
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/fp/
/FRANTZY POINVIL/Primary Examiner, Art Unit 3693
March 19, 2026
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