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 .
Status of Claims
This action is in reply to the response filed on June 23, 2026.
Claims 1-20 are currently pending and have been examined.
This action is made FINAL.
The examiner would like to note that this application is being handled by examiner Christine Huynh.
Response to Arguments
Applicant's arguments filed June 23, 2026 have been fully considered but they are not persuasive. With respect to the 35 U.S.C. 101 rejection, applicants argue in pages 8 that the amended claim 1 and similar claims 9 and 16 are not directed towards an abstract idea because the claim elements are directed to a specific, computer-implemented technical architecture for processing telematics data that constitutes a concrete improvement to the technical field of vehicle telematics data processing, and are not directed to an abstract idea (page 8). However, the examiner respectfully disagrees, because even if the limitations use specific computer-implemented technical architecture and pipeline (see pages 8 and 10), does not mean that the limitations cannot be performed in the human mind. For example, the limitations of “determining driving attributes based on the telematics data…”, “combining and denormalizing the driving attributes by associating different types of the driving attributes from multiple data sources together…”, and “aggregating the denormalized attributes for each of a plurality of event dates for a time period…” can be done mentally by a person by observing the given telematics data and making a determination of the driving attributes, organize the given data based on the driving attributes into a table, and combining and summarizing the given data. Even when taken from multiple different data sources, a person is able to look at a plurality of given data and pull and organize the necessary data for corresponding timestamps and driving attributes. The applicant argues that a human cannot perform this in their mind because “These operations are performed on continuous, high-frequency, multi-source vehicle sensor data streams that cannot practically be observed, organized, or summarized by a human mind. A person cannot mentally extract speed, acceleration, braking, and location data from continuous vehicle sensor streams, compute phone handling attributes from that data, cross-reference different attribute types from multiple data sources by timestamp into a unified denormalized record structure per event date, and aggregate those structures across a plurality of event dates into pre-computed feature inputs for external model ingestion. The sheer volume, speed, and complexity of the data involved renders the mental process characterization untenable” (page 10). However, the examiner disagrees because the limitation does not state that the data is extracted from continuous, high-frequency, multi-source vehicle sensor data streams where a person would need to extract a large volume, speed, and complexity of the data in real-time. A person can still mentally, using pen and paper, extract data from a given sensor, by observing the given telematics data and making a determination of the driving attributes, organize the given data based on the driving attributes and timestamps into a table, and combining and summarizing the given data. Therefore, the claims are not directed towards a specific technical implementation that improves the functioning of a computer system or solves a specific technical problem in a technical way.
The applicant argues that the "pre-staged state" is not an intended use as it is a specific storage architecture in which the aggregated attributes are prepared and positioned prior to any request being received, specifically to enable on-demand retrieval without delay (page 11). However, just because the storage architecture is specified so that the contents of it are prepared for a request does not mean it is not intended use. The limitation states “storing the aggregated attributes in a pre-staged state for communication to facilitate substantially real-time driver score calculation upon request”, where aggregated attributes, which are mathematical calculations that can be done by a person, are stored in said specified storage architecture, which states that the aggregated attributes can be used for real-time driver score calculation, and does not actively state that any of the calculations are being done in real-time. The term “real-time” is therefore recited for intended use and is not recited actively in the limitation.
Additional elements do not integrate the abstract idea into a practical application. The limitations of “receiving telematics data, the telematics data generated by a telematics device disposed within a vehicle”, “storing the aggregated attributes for immediate communication to facilitate substantially real-time driver score calculation upon request”, and “after receiving a request from a provider computing device for the aggregated attributes, communicating the aggregated attributes to the provider computing device wherein the aggregated attributes are configured to be input into a provider model to provide processed driving insights that allow the provider computing device to calculate a driver score without requiring the provider computing device to process the telematics data over an extended data collection period”, are merely gathering, storing, and sending data. The receiving steps from the sensors and from the external source are recited at a high level of generality (i.e. as a general means of gathering vehicle and road condition data for use in the evaluating step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The communicating information to the provider computing device which is then input into a generic model is also recited at a high level of generality, and amounts to mere post solution sending data, which is a form of insignificant extra-solution activity. The provider computing device and provider model are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of sorting information) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Therefore, the claims as amended are still directed to a 101 abstract idea. Accordingly, the 35 U.S.C. 101 rejection is maintained. See detailed rejection below.
Applicant’s arguments, see pages 24-30, filed June 23, 2026, with respect to the 35 U.S.C. 103 rejection have been fully considered and are persuasive. The 35 U.S.C. 103 rejection of claims 1-20 has been withdrawn.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1-20 are rejected under 35 U.S.C. 101, because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Independent claim 1 is directed toward a method, claim 9 is directed toward a system, and claim 16 is directed toward a non-transitory computer readable medium. Therefore, each of the independent claims 1, 9, and 16 along with the corresponding dependent claims 1, 9, and 16 are directed to a statutory category of invention under step 1.
101 Analysis – Step 2A, Prong 1
Under Step 2A, Prong 1, the claims are analyzed to determine whether one or more of the claims recites subject matter that falls within one of the following groups of abstract ideas: (1) mental processes, (2) certain methods of organizing human activity, and/or (3) mathematical concepts. In this case, the independent claims 1, 9, and 16 are directed to an abstract idea without significantly more. Specifically, the claims, under their broadest reasonable interpretation cover certain mental processes. The language of independent claim 1 is used for illustration:
A computer implemented method comprising: receiving telematics data, the telematics data generated by a telematics device disposed within a vehicle; (Pre solution insufficient activity);
determining driving attributes based on the telematics data by extracting one or more of speed data, acceleration data, braking data, and location data from the telematics data and computing one of more of braking attributes; (A person could mentally observe and process data information);
combining and denormalizing the driving attributes by associating different types of the driving attributes from multiple data sources together based at least in part on matching corresponding timestamps to cross-reference the driving attributes into a unified denormalized record structure per event date to create denormalized attributes; (A person could mentally observe and process data information);
aggregating the denormalized attributes for each of a plurality of event dates for a time period to produce aggregated attributes structured as pre-computed feature inputs configured for direct ingestion by an external provider model to create aggregated attributes; (A person could mentally observe and process data information);
storing the aggregated attributes in a pre-staged state for communication to facilitate substantially real-time driver score calculation upon request and after receiving a request from a provider computing device for the aggregated attributes (Insignificant extra-solution activity);
and after receiving a request from a provider computing device for the aggregated attributes, communicating the aggregated attributes to the provider computing device wherein the aggregated attributes are configured to be input into a provider model to provide processed driving insights that allow the provider computing device to calculate a driver score without requiring the provider computing device to collect and process raw telematics data over an extended data collection period prior to calculating the driver score (Post solution insufficient activity);
As explained above, independent claim 1 recites at least one abstract idea. The other independent claims 9 and 16 which are of a similar scope to claim 1, likewise recite at least one abstract idea under Step 2A, Prong 1.
101 Analysis – Step 2A, Prong 2
Under Step 2A, Prong 2, the claims are analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements such as merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application”; see at least MPEP 2106.04(d).
In this case, the mental processes judicial exception is/are not integrated into a practical application. For example, independent claims 1, 9, and 16 recite the additional elements of non-
transitory processor readable medium, one or more processing devices, and a data storage medium. These limitations amount to implementing the abstract idea on a computer, add insignificant extra solution activity, and/or generally link use of the judicial exception to a particular technological environment or field of use; see at least MPEP 2106.04(d). More specifically,
non-transitory processor readable medium… This limitation amounts to generally linking the use of the abstract idea to a particular technological environment or field of use
one or more processing devices… This limitation amounts to implementing the abstract idea on a computer
data storage medium… This limitation amounts to implementing the abstract idea on a computer
database… This limitation amounts to implementing the abstract idea on a computer
receiving a plurality of measure values… This limitation amounts to implementing the abstract idea on a computer
computing device… This limitation amounts to implementing the abstract idea on a computer
at least one processor… This limitation amounts to implementing the abstract idea on a computer
The receiving steps from the sensors and from the external source are recited at a high level of generality (i.e. as a general means of gathering vehicle and road condition data for use in the evaluating step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The storing information for communication is also recited at a high level of generality (i.e. as a general means of storing the data result from the aggregating step), and amounts to mere data storing, which is a form of insignificant extra-solution activity. The communicating information to the provider computing device which is then input into a generic model is also recited at a high level of generality, and amounts to mere post solution sending data, which is a form of insignificant extra-solution activity. The provider computing device and provider model are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of sorting information) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Furthermore, looking at the additional limitation(s) as an ordered combination or as a whole, the limitations add nothing significant that is not already present when looking at the elements taken individually. Because the additional elements, do not integrate the abstract idea into a practical application by imposing meaningful limits on practicing the abstract idea, independent claims 1, 9, and 16 are directed to an abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a vehicle controller to perform the evaluating… amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of “non-transitory processor readable medium” “one or more processing devices” “data storage medium” “database” “receiving a plurality of measure values…,” the examiner submits that these limitations are insignificant extra-solution activities.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well understood, routine, conventional activity in the field. The additional limitations of “one or more processing devices” “data storage medium” “database” “receiving a plurality of measure values…,” are well-understood, routine, and conventional activities because the background recites that the sensors are all conventional sensors mounted on the vehicle, and the specification does not provide any indication that the vehicle controller is anything other than a conventional computer within a vehicle. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. The Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function. Hence, the claim is not patent eligible.
Dependent claims 2-8, 10-15, and 17-20 have been given the full two-part analysis, including analyzing the additional limitations, both individually and in combination. Dependent claims 2-8, 10-15, and 17-20 when analyzed both individually and in combination, are also patent ineligible under 35 U.S.C. 101 based on same analysis as above. The additional limitations recited in the dependent claims fail to establish that the dependent claims are not directed to an abstract idea. The additional limitations of the dependent claims, when considered individually and as an ordered combination, do not amount to significantly more than the abstract idea. Accordingly, claims 2-8, 10-15, and 17-20 are patent ineligible. Therefore, claims 1-20 are patent ineligible under 35 U.S.C. 101.
Allowable Subject Matter
Claims 1, 9, and 16 are rejected under 35 U.S.C. 101, but would be allowable if rewritten to
overcome the 35 U.S.C. 101 rejection.
The following is a statement of reasons for the indication of allowable subject matter:
Although the closest prior art, Fernandes et al (U.S. Pub. NO. 2015/0248731), teaches the limitations of claims 1, 9, and 16 including:
receiving telematics data, the telematics data generated by a telematics device disposed within a vehicle; (The reference discloses receiving telematics data from a devices and sensors within a vehicle.) (Fernandes, [005], [042])
determining driving attributes based on the telematics data by extracting one or more of speed data, acceleration data, braking data, and location data from the telematics data and computing one of more of braking attributes, time of day attributes, speeding attributes, and phone handling attributes from the extracted data; (The reference discloses including determining driving behaviors from telematics data) (Fernandes, [004], [022], [023], [042])
aggregating the denormalized attributes for each of a plurality of event dates for a time period to produce aggregated attributes structured as pre-computed feature inputs configured for direct ingestion by an external provider model to create aggregated attributes; (The reference discloses aggregation of driving data by calculating the aggregate risk rating based on the aggregate of the safety scores and calculated in substantially real time or be recalculated using new data when the driver's safety scores are more likely change which includes aggregating the denormalized attributes over a period of time.) (Fernandes, [024], [028], [036], [059])
storing the aggregated attributes in a pre-staged state for communication to facilitate substantially real-time driver score calculation upon request and after receiving a request from a provider computing device for the aggregated attributes; (“storing the aggregated attributes for immediate communication to facilitate substantially real-time driver score calculation upon request” have been considered but are moot. The examiner points to Fernandes (“The data storage device 214 may store, for example, (i) an operating system 216 for the computing device 200; (ii) one or more applications 218 (e.g., computer program code and/or a computer program product) adapted to direct the CPU 202 in accordance with the present invention, and particularly in accordance with the processes described in detail with regard to the CPU 202; and/or (iii) database(s) 220 adapted to store information that may be utilized to store information required by the program. The database(s) 220 may including all or a subset of data stored in insurance company database 116, described above with respect to FIG. 1, as well as additional data, such as formulas or manual adjustments, used in establishing the insurance risk for a vehicle.” [0037], “The storage device 1830 stores a program 1812 and/or scoring system 1814 for controlling the processor 1810. The processor 1810 performs instructions of the programs 1812, 1814, and thereby operates in accordance with any of the embodiments described herein. For example, the processor 1810 may receive telematics data associated with a vehicle. The processor 1810 may also analyze the telematics data and/or transmit discount information to a potential entity to be insured based at least in part on a computed risk score.” [0070]), and an example of data storage shown in (“In some embodiments (such as shown in FIG. 18), the storage device 1830 stores an underwriting database 1860 and/or a telematics database 1900. An example of a database that may be used in connection with the insurance platform 1800 will now be described in detail with respect to FIG. 19.” [0073]), which shows that the system stores the aggregated attributes, in which the collected data can be communicated for calculating driver scores.
in response to the received request, communicating the aggregated attributes to the provider computing device wherein the aggregated attributes are configured to be input into a provider model to provide processed driving insights…; (The reference discloses sending aggregated safety scores to insurance companies or other entities who use the data for risk modeling to calculate based on the risk variables to generate a driving score which is the same as communicating the aggregated attributes to the aggregated attributes configured to be input into a provider model to calculate a driver score.) (Fernandes, [024], [028], [036], [053]), (“With a sufficient amount of data, the insurance company can calculate a level of risk score for the driver based on, for example, the driver's driving habits. The insurance company can use the score for setting or adjusting a discount value to be applied to an insurance premium. In some implementations, a score or discount is determined by a third party data processing service” [0022], “Using a data processing service 106 is in some implementations also preferable to having the data collection device 104 process data to output a risk score because it reduces the processing power needed by data collection device 104 and because using a third party data processing service 106 may also make it more difficult for drivers to tamper with the data.” [0024]), which teaches enabling an external provider, or a third party, to receive aggregated attributes and perform its own scoring calculations using its own model.)
However, Fernandes does not teach, but MATHUR et al. (U.S. Pub. NO. 2017/0364821) teaches …that allow the provider computing device to calculate a driver score without requiring the provider computing device to collect and process raw telematics data over an extended data collection period prior to calculating the driver score; (The reference teaches a probability of a user driving a vehicle and generating a risk score to develop at least one driver profile based on the computed probability, an analytics platform in the system quantifies the riskiness factor of each driver in a manner of driving the vehicle. This quantification is implemented in analyzing these risk scores which are attributed to each driver at a particular trip level and a driver level. The risk scores are further aggregated at the daily, weekly and monthly levels and can then be customized at any required level. The dynamic driver risk score module 218 is configured for computing the risk scores of an individual driver at the trip level, driver level and also aggregated risk scores at the day, week and month levels) (MATHUR, [008], [020], [062])
None of the prior art of record references taken either together or in combination with the prior art of record disclose a method, or system including the further instructions:
Regarding claims 1, 9, and 16:
combining and denormalizing the driving attributes by associating different types of the driving attributes from multiple data sources together based at least in part on matching corresponding timestamps to cross-reference the driving attributes into a unified denormalized record structure per event date to create denormalized attributes;
in combination with the remaining elements and features of the claimed invention. It is for these reasons that the applicants’ invention defines over the prior art of record.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Christine N Huynh whose telephone number is (571)272-9980. The examiner can normally be reached Monday - Friday 8 am - 4 pm.
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/CHRISTINE NGUYEN HUYNH/Examiner, Art Unit 3662
/CHRISTOPHER GEORGE FEES/Primary Examiner, Art Unit 3662
