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 .
Response to Arguments
Applicant's arguments filed 11/5/2025 have been fully considered but they are not persuasive.
Applicant’s representative argues that each additional element contributes to the specific process
that automatically distinguishes between different types of driving activities based upon telematics data generated during driving, and none of the additional elements describes a mathematical concept.
In response, the claimed concept as amended still falls into the category of functions of organizing human activities 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) because it amounts to the functions of “each of the plurality of trip segments as being a gig-related trip segment or a non-gig-related trip segment based upon the respective likelihood score; and identifying, one or more gig-related trips comprising a plurality of gig-related trip segments. The functions of processing, accessing, generating, and identifying and outputting also are still being viewed as mental processes.
Applicant’s representative then argues:
“Applicant respectfully contends amended claim 1 thus integrates any recited abstract idea
into a practical application by reciting elements that reflect an improvement to the technical field
of automated driving activity monitoring and classification through a process of automatically
identifying and classifying trip segments driven by a vehicle operator, which improvement is
described with sufficient detail in the Specification that one of ordinary skill in the art would
recognize the improvement.
Accordingly, Applicant respectfully submits that claim 1 overcomes the rejection under
35 U.S.C. § 101 under at least Step 2A of the Alice/Mayo test for the reasons set forth above.
Independent claims 10 and 15 stand similarly rejected under 35 U.S.C. § 101 and have been
similarly amended”.
In response, Step 2A and Step 2B of the Alice/Mayo test have been previously argued. The Examiner’s response is repeated below.
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 (i.e., 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 1 and 15 are directed to a method. Claim 10 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).
Claim 1 recites :
by accessing, by one or more processors from a database telematics data associated with operation of one or more vehicles by the gig- economy worker, wherein the telematics data comprises at least a plurality of entries speed and location data for the one or more vehicles traversing each of a plurality of trip segments;
generating, by the one or more processors, an input vector for each of the plurality of trip segments, each input vector comprising a plurality of data elements representing aspects of the telematics data associated with the respective trip segment;
providing, by the one or more processors, each input vector as input data to a classifier executed by the one or more processors;
processing by the classifier executed by the one or more processors each respective input vector by:
generating a likelihood score from the corresponding input vector, using a trained machine learning model of the classifier, wherein the likelihood score indicates a probability or probability range of the respective trip segment being a gig-related trip segment associated with gig driving activities by the gig-economy worker;
identifying the classification of the respective trip segment, as being a gig-related trip segment or a non-gig-related trip segment based upon the respective likelihood score; and
outputting the classification of the respective trip segment; and
identifying, by the one or more processors, one or more gig-related trips comprising a plurality of gig-related trip segments.
Claim 2 recites wherein controlling by the one or more processors a plurality of sensors disposed within the one or more vehicles by transmitting instructions to the plurality of sensors to record periodic measurements of the telematics data.
Claim 3 recites obtaining, by the one or more processors, environmental data regarding an operating environment of the respective vehicle for each of the plurality of trip segments,
wherein the likelihood score for each of the plurality of trip segments is determined based upon the telematics data and the environmental data associated with such trip segment.
Claim 4 recites wherein the likelihood score for each of the plurality of trip segments is based at least in part upon comparison of the telematics data associated with the respective trip segment with a baseline profile of non-gig-related driving by the gig-economy worker.
Claim 5 recites wherein the trained machine learning model has been previously trained on additional telematics data associated with a plurality of additional trip segments including both gig-related driving and non-gig-related driving by the gig-economy worker.
Claim 6 recites wherein the trained machine learning model previously validated using log data from a gig-economy platform indicating a definitive classification of at least some of the additional trip segments.
Claim 7 recites wherein identifying the one or more gig-related trips comprises identifying a current trip is gig-related.
Claim 8 recites wherein identifying the one or more gig-related trips comprises identifying one or more sets of multiple gig-related trip segments separated by one or more sets of non-gig-related trip segments.
Claim 9 recites determining, by the one or more processors an aspect of an insurance policy for the gig- economy worker based upon the telematics data associated with the gig-related trip segments of the one or more gig-related trips, wherein the aspect of the insurance policy associated with the gig-economy worker includes at least one of type of insurance, an insurance premium, a deductible, an insured limit, or a condition.
Claim 10 recites: A system for automatically classifying driving activity by a gig-economy worker, comprising:
one or more processors; and
a non-transitory computer-readable storage medium storing computer-readable instructions that, when executed by the one or more processors, cause the system to:
access from a database, telematics data associated with operation of
one or more vehicles by the gig-economy worker, wherein the telematics data comprises
at least a plurality of entries of speed and location data for the one or more vehicles traversing each of a plurality of trip segments;
generate, an input vector for each of the plurality of trip segments, each input vector comprising a plurality of data elements representing aspects of the telematics data associated with the respective trip segment;
provide each input vector as input data to a classifier executed by the one or more processors;
process by the classifier, each respective input vector by:
generating a likelihood score from the corresponding input vector, using a trained
machine learning model of the classifier wherein the likelihood score indicates a probability or probability range of the respective trip segment being a gig- related trip segment associated with gig driving activities by the gig-economy worker;
identifying the classification of the respective trip segment as being a gig-related trip segment or a non- gig-related trip segment based upon the respective likelihood score; and
outputting the classification of the respective trip segment; and
identify by the one or more gig-related trips comprising a plurality of gig-related trip segments.
Claim 11 recites: wherein the computer-readable instructions, when executed by the one or more processors, further cause the system to control a plurality of sensors disposed within the one or more vehicles by transmitting instructions to the plurality of sensors to record periodic measurements of the telematics data.
Claim 12 recites wherein the likelihood score for each of the plurality of trip segments is based at least in part upon comparison of the telematics data associated with the respective trip segment with a baseline profile of non-gig-related driving by the gig-economy worker.
Claim 13 recites: the trained machine learning model has been previously on additional telematics data associated with a plurality of additional trip segments including both gig-related driving and non-gig-related driving by the gig-economy worker.
Claim 14 recites:
to: determine an aspect of an insurance policy for the gig-economy worker based upon the telematics data associated with the gig-related trip segments of the one or more gig-related trips, wherein the aspect of the insurance policy associated with the gig-economy worker includes at least one of type of insurance, an insurance premium, a deductible, an insured limit, or a condition.
Claim 15 recites:
access from the database telematics data associated with operation of one or more vehicles by the gig-economy worker, wherein the telematics data comprises at least a plurality of entries of speed and location data for the one or more vehicles traversing each of a plurality of trip segments;
generate, an input vector for each of the plurality of trip segments, each input vector comprising a plurality of data elements representing aspects of the telematics data associated with the respective trip segment;
provide each input vector as input data to a classifier executed by the one or more processors;
process by the classifier, each respective input sector by:
generating a likelihood score from the corresponding input vector using a trained machine learning model of the classifier, wherein the likelihood score indicates a probability or probability range of the respective trip segment being a gig-related trip segment associated with gig driving activities by the gig-economy worker;
identifying the classification of the respective trip segment as being a gig-related trip segment or a non- gig-related trip segment based upon the respective likelihood score; and
outputting the classification of the respective trip segment; and
identify one or more gig-related trips comprising a plurality of gig-related trip segments.
Claim 16 recites: wherein the computer-readable instructions, when executed by the one or more processors, further cause the system to control a plurality of sensors disposed within the one or more vehicles by instructions to the plurality of sensors to record periodic measurements of the telematics data.
Claim 17 recites:
The computer-readable instructions, when executed by the one or more processors, further cause the system to obtain environmental data regarding an operating environment of the respective vehicle for each of the plurality of trip segments; and
the likelihood score for each of the plurality of trip segments is determined based upon the telematics data and the environmental data associated with such trip segment.
Claim 18 recites: wherein the likelihood score for each of the plurality of trip segments is based at least in part upon comparison of the telematics data associated with the respective trip segment with a baseline profile of non-gig-related driving by the gig-economy worker.
Claim 19 recites: wherein the trained machine learning model has been previously trained to on additional telematics data associated with a plurality of additional trip segments including both gig-related driving and non-gig-related driving by the gig-economy worker.
Claim 20 recites to: determine an aspect of an insurance policy for the gig-economy worker based upon the telematics data associated with the gig-related trip segments of the one or more gig-related trips, wherein the aspect of the insurance policy associated with the gig-
economy worker includes at least one of type of insurance, an insurance premium, a deductible, an insured limit, or a condition.
As per claims 1, 10 and 15, applicant is to be noted that the steps or functions of “access” or “accessing” are considered as data gathering functions. The functions of “generate” or “generating”, and “identify” or “identifying” involve mental processes and/or generic computer functions.
Here, the claimed concept still falls into the category of functions of organizing human activities 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) because it amounts to the functions of “each of the plurality of trip segments as being a gig-related trip segment or a non-gig-related trip segment based upon the respective likelihood score; and identifying, the classification of the respective trip segment as being a gig-related trip segment or a non-gig-related trip segment based upon the respective likelihood score and identifying one or more gig-related trips comprising a plurality of gig-related trip segments.
The BRI of the claimed limitations describes functions of ”generating a likelihood score from the corresponding input vector, wherein the likelihood score indicates a probability or probability range of the respective trip segment being a gig-related trip segment associated with gig driving activities by the gig-economy worker, and identifying the classification of the respective trip segment as being a gig-related trip segment or a non-gig-related trip segment based upon the respective likelihood score”.
Step 2A, Prong Two: The judicial exception is not integrated into a practical application, In particular, the clams recite the bolded limitations found above as understood to be the additional limitations.
These limitations performing steps or functions of generating a likelihood score from the corresponding input vector, wherein the likelihood score indicates a probability or probability range of the respective trip segment being a gig-related trip segment associated with gig driving activities by the gig-economy worker, and identifying the classification of the respective trip segment as being a gig-related trip segment or a non-gig-related trip segment based upon the respective likelihood score” 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(1)). 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, similarly describing usage of general and special purpose computer and “any kind of digital computer” including generic commercially available devices.
The claimed “processors”, “database”, “processors”, and “trained machine learning” are similarly understood in light of applicant's specification as mere usage of any arrangement of computer software or hardware intermediate components potentially using networks to communicate with instructions are properly understood to be mere instructions to apply the abstraction using a computer or device or computer system.
The claims recite obtaining data, generating data, classifying data and identifying data.
Performing steps by a generic machine, a classifier, computing device or one or more processors with memories merely limit the abstraction to a 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 to generally linking to a particular technological environment do not integrate a practical exception into a practical application.
Generating, providing and identifying data are similar to Alappat, which as noted in MPEP 2106. 05(b)(1) 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 more processors or computing device which generate, classify and identify data.
Consideration of these steps 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 functions of “generating a likelihood score from the corresponding input vector , wherein the likelihood score indicates a probability or probability range of the respective trip segment being a gig-related trip segment associated with gig driving activities by the gig-economy worker, and identifying the classification of the respective trip segment as being a gig-related trip segment or a non-gig-related trip segment based upon the respective likelihood score”.
Performance of these steps or functions technologically may present a meaningful limit to the scope of the claim does not 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 claims 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.
Accessing data and outputting data by electronic means or hardware amounts to receiving data over a network has been recognized by the courts as routine, and conventional (See MPEP 2106.05(d)UD, citing Symantec, 835 F.3d at 1321, 120 OSPQ2d at 1362 (Utilizing an intermediary computer to forward information); TL Communications LEC v. AV Auto. LLC, 823 F.3d 607, G10, L18 USPO2d 1744, 1748 (ed. Cir. 2016) Casing a telephone for image transmission); OFF Techs., fac. v. Amazon.com, fic., 788 B.Ad 1359, 1363, Lis USPO2d 1090, 1093 (ed, Cir. 2015) (sending messages over a network}, buySAFE, fic. v. Google, Inc.. 768 F.3d 1350, 1355, 112 USPQ2d 1093, 1996 (Pod, Cyr. 2014) (computer receives and sends information over a network).
Positively reciting a “processor”, or a “classifier” or a “machine learning” does not change the analysis as these aspects are properly considered as additional elements which amount to instructions to apply it with a computer.
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.
The claims also call for using a “sensor” to capture telematics data.
The function of a sensor is to gather data of a specifically assigned environment or structure. The
functions of receiving data using sensors are merely viewed as basic data gathering which is a form of insignificant extra-solution activity. Also such functions of obtaining telematics data can also be done through observation. Such functions are considered as data gathering and observation and fundamental judgment using mental capabilities.
Applicant is being reminded that gathering data, and classifying data have been determined to be
directed to an abstract idea. See, e.g., Alice, 573 U.S. at 225 (using a computer-implemented system “to obtain data, adjust account balances, and issue automated instructions” did not result in claim being non-abstract), In re Killian, 45 F 4th 1373, 1380 (Fed. Cir. 2022) (claims “directed to collection of information, comprehending the meaning of that collected information, and indication of the results, all on a generic computer network operating in its normal, expected manner,” fail step one of the Alice framework), Univ. of Fla. Rsch. Found., Inc. v. Gen. Elec. Co., 916 F.3d 1363, 1368 (Fed. Cir. 2019) (claims “directed to the abstract idea of ‘collecting, analyzing, manipulating, and displaying data’”’), Cyberfone Sys., LLC v. CNN Interactive Grp., Inc., 558 F. App’x 988, 992 (Fed. Cir. 2014) (claims directed to organizing, storing, and transmitting information determined to be directed to an abstract idea).
The claims also make use of a database to gather data such as capturing image data which is a data gathering function. The claims also recite a broad “processor” which is regarded as a generic computing device.
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 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.
The judicial exception is not integrated into a practical application. In particular, the claimed “processors”, “database” and “trained machine learning” are recited at a high level of generality such they amount to no more than mere instructions to apply the exception using generic components. 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.
Accordingly, claims 1, 10 and 15 are is directed to an 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.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANTZY POINVIL whose telephone number is (571)272-6797. The examiner can normally be reached M-Th 7:00AM to 5:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael Anderson can be reached at 571-270-0508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/FRANTZY POINVIL/Primary Examiner, Art Unit 3693
January 28, 2026