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 . This office action is in response to applicant's communication of January 10, 2025. The rejections are stated below. Claims 1-20 are pending and have been examined.
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.
Claim 1 is directed to the abstract idea of “data processing and analysis” which is grouped under “mathematical concepts) in prong one of step 2A (See 2019 Revised Patent Subject Matter Eligibility Guidance).
Claim 1 recites “a …-implemented method comprising:
obtaining … data for a plurality of trips for one or more drivers for a policy;
…, respective latent
representation embeddings each of the plurality of trips for the one or more drivers for the policy;
combining the respective latent representation embeddings for the plurality of trips to generate a combined embedding representation for the policy; and
generating an output for the policy, using a …, based on inputs to the … comprising the combined embedding representation”.
These limitations describe an abstract idea data processing and analysis and corresponds to mathematical concepts.
Claim 1 also recites as additional elements such as “computer, telematics, generating, using an automatic feature extraction encoder, supervised machine-learning model” which do no more than implement the abstract idea and/or provide a particular technological environment. Therefore, claim 1 is directed to an abstract idea without a practical application (Step 2A - Prong 2: NO).
Further, as the additional elements of claim 1 do no more than serve as a tool to implement the abstract idea and/or provide a particular technological environment, they do not improve computer functionality or improve another technology or technical field. Thus, claim 1 is not patent eligible (Step 2B: NO).
Claims 9 and 15 also recite the abstract idea of data processing and analysis and corresponds to mathematical concepts. Claim 9 includes the additional elements of “a system comprising one or more processors and one or more non-transitory computer readable media storing comprising instructions that, when executed on the one or more processors, causes the one or more processors, telematics, generating, using an automatic feature extraction encoder, supervised machine-learning model”. Claim 15 includes the additional elements of “one or more non-transitory computer-readable media storing computing instructions that, when executed on or more processors, causes the one or more processors, telematics, generating, using an automatic feature extraction encoder, supervised machine-learning model”. The additional elements of claims no more than serve as a tool to implement the abstract idea and/or provide a particular technological environment. There is no improvement to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a).
Claims 2, 10, and 16 each recite “wherein the output comprises a risk metric for the policy” which further describe the abstract idea.
Claims 3, 11, and 17 each recite “wherein the output comprises a classification for the policy performed by the …” which further describe the abstract idea. The claim includes “supervised machine-learning model” as an additional element. The additional element does no more than serve as a tool to implement the abstract idea and/or provide a particular technological environment. And, as the additional element does no more than serve as a tool to implement the abstract idea and/or provide a particular technological environment, it does not improve the functioning of a computer or improve any other technology or technical field (MPEP 2106.05(a).
Claims 4, 12, and 18 each recite “wherein the … based on labels assigned to clusters generated by an …” which further describe the abstract idea. The claim includes “supervised machine-learning model is trained, unsupervised clustering algorithm” as additional elements. The additional elements do no more than serve as a tool to implement the abstract idea and/or provide a particular technological environment. And, as the additional elements do no more than serve as a tool to implement the abstract idea and/or provide a particular technological environment, they do not improve the functioning of a computer or improve any other technology or technical field (MPEP 2106.05(a).
Claim 5 recites “wherein the classification represents a driving behavior type” which further describes the abstract idea.
Claims 6, 13, and 19 each recite “wherein the automatic feature extraction encoder comprises a self-supervised learning model” which further defines the abstract as additional elements. The additional elements do no more than serve as a tool to implement the abstract idea and/or provide a particular technological environment. And, as the additional elements do no more than serve as a tool to implement the abstract idea and/or provide a particular technological environment, they do not improve the functioning of a computer or improve any other technology or technical field (MPEP 2106.05(a).
Claim 7 recites “wherein the automatic feature extraction encoder comprises a temporal autoencoder” as an additional element. The additional element does no more than serve as a tool to implement the abstract idea and/or provide a particular technological environment. And, as the additional element does no more than serve as a tool to implement the abstract idea and/or provide a particular technological environment, it does not improve the functioning of a computer or improve any other technology or technical field (MPEP 2106.05(a).
Claims 8, 14, and 20 each recite “wherein the temporal autoencoder uses attention mechanisms for each time-based data set of the telematics data” which further defines the abstract idea. The claim includes “temporal autoencoder, telematics” as additional elements. The additional elements do no more than serve as a tool to implement the abstract idea and/or provide a particular technological environment. And, as the additional elements do no more than serve as a tool to implement the abstract idea and/or provide a particular technological environment, they do not improve the functioning of a computer or improve any other technology or technical field (MPEP 2106.05(a).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The closest reference is T-MAE by Wei et al. presented at ECCV 2024. This reference teaches an automatic feature extraction encoder in the form of a SiamWCA backbone containing a Siamese encoder and a windowed cross-attention module, a temporal autoencoder architecture, and self-supervised learning for point cloud data . T-MAE also teaches combining representations through the windowed cross-attention module that incorporates historical information through interaction of two frames . However, T-MAE does not teach telematics data for vehicle insurance policies as it uses LiDAR point cloud data from autonomous driving datasets like Waymo and ONCE, and does not teach obtaining data for a plurality of trips in the context of driver insurance policies. T-MAE also does not teach generating embeddings specifically for individual trips and combining them into a policy-level combined embedding representation, nor does it teach generating an output for an insurance policy such as risk metrics or classifications for insurance underwriting.
The second closest reference is Ti-MAE by Li et al. published in January 2023. This reference teaches an autoencoder that maps time series signals to latent representations, self-supervised learning using mask modeling for time series data, and an encoder that generates latent representations from multivariate time series data . Ti-MAE teaches training an autoencoder to reconstruct masked time series data at the point-level and is applicable to time series forecasting and classification tasks. However, Ti-MAE does not teach telematics data specifically from vehicle trips for insurance policies, obtaining data for a plurality of trips and generating per-trip embeddings, combining embeddings from multiple trips to generate a combined embedding representation for a policy, generating risk metrics or classifications specifically for vehicle insurance policies, or processing driving behavior data as Ti-MAE is directed to general multivariate time series data.
The third closest reference is US 2021/0166322 A1. This reference teaches receiving telematics data generated by vehicle or mobile device sensors, inputting sensor data and telematics data into a machine learning program to generate a resident profile that includes an indicator of an overall level of risk and recommended changes to insurance coverage, generating an updated dynamically reconfigurable insurance product based upon the indicator of risk, and using a trained machine learning program to determine customer activity or risk type. However, US 2021/0166322 A1 does not teach an automatic feature extraction encoder generating latent representation embeddings, generating respective latent representation embeddings for each of a plurality of trips, combining embeddings from multiple trips to generate a combined embedding representation, a supervised machine-learning model that takes a combined embedding representation as input, or any encoder architecture for generating embeddings as the reference uses conventional feature engineering.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KEVIN T POE whose telephone number is (571)272-9789. The examiner can normally be reached on Monday-Friday 9:30 am through 6pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan Donlon can be reached on 571-270-3602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/K.T.P/Examiner, Art Unit 3692 /KEVIN T POE/
/RYAN D DONLON/Supervisory Patent Examiner, Art Unit 3692 July 24, 2026
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