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
• Claims 1-20 are currently pending and have been examined.
• This action is made Non-FINAL.
• The Examiner would like to note that this application is now being handled by Examiner Raven Yono.
Information Disclosure Statement
The information disclosure Statement(s) filed on 08/01/2025 and 01/14/2026 have been considered. Initialed copies of the Form 1449 are enclosed herewith.
Double Patenting
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 5-12, 14-15, and 18 of U.S. Patent No. 12,159,294. Although the claims at issue are not identical, they are not patentably distinct from each other because both claims speak to obtaining, via one or more sensors related to a vehicle operator, a first set of trip data associated with a first set of vehicular trips operated by the vehicle operator during a first time period, the first set of trip data including a first set of telematics data and a first set of travel conditions for the first set of vehicular trips, wherein at least one of one or more respective dates or one or more respective days of one or more respective weeks are assigned to the first set of telematics data; determining a second set of travel conditions associated with a target vehicular trip and occurring during a second time period different from the first time period and during at least one of a particular date or a particular day of a particular week; predicting a second set of telematics data associated with the target vehicular trip based at least in part on the second set of travel conditions, comprising at least one of: (a) weighing the first set of telematics data to generate a first weighted set of telematics data based at least in part on a respective amount of respective travel conditions of the first set of travel conditions matching a respective amount of travel conditions of the second set of travel conditions; and (b) weighing the first set of telematics data to generate a second weighted set of telematics data based at least in part on a respective secondary weight associated with the one or more respective dates or the one or more respective days of the one or more respective weeks; and determining a set of vehicle operation behaviors based at least in part on the second set of telematics data, as predicted. Therefore, since the claims of U.S. Patent No. 12,159,294 anticipates every limitation of the instant application’s independent claims, the claims are being rejected as being double-patenting (see MPEP 804(II)(B)(2)).
The dependent claims of the instant application recite substantially similar limitations to those of claims 1, 5-12, 14-15, and 18 of U.S. Patent No. 12,159,294, and therefore are anticipated by U.S. Patent No. 12,159,294.
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 recites an abstract idea without significantly more. Independent claims 1, 8, and 15 are directed to a method (claim 1), a system (claim 8), and an apparatus (claim 15). Therefore, on its face, each independent claim 1, 8, and 15 are directed to a statutory category of invention under Step 1 of the Patent Subject Matter Eligibility analysis (see MPEP 2106.03).
Under Step 2A, Prong One of the Patent Subject Matter Eligibility analysis (see MPEP 2106.04), claims 1, 8, and 15 recite, in part, a method, a system, and an apparatus of organizing human activity. Using the limitations in claim 1 to illustrate, the claim recites a method for predicting trip data, the method comprising: obtaining, related to a vehicle operator, a first set of trip data associated with a first set of vehicular trips operated by the vehicle operator during a first time period, the first set of trip data including a first set of data and a first set of travel conditions for the first set of vehicular trips, wherein at least one of one or more respective dates or one or more respective days of one or more respective weeks are assigned to the first set of data; determining a second set of travel conditions associated with a target vehicular trip and occurring during a second time period different from the first time period and during at least one of a particular date or a particular day of a particular week; predicting a second set of data associated with the target vehicular trip based at least in part on the second set of travel conditions, comprising at least one of: (a) weighing the first set of data to generate a first weighted set of data based at least in part on a respective amount of respective travel conditions of the first set of travel conditions matching a respective amount of travel conditions of the second set of travel conditions; and (b) weighing the first set of data to generate a second weighted set of data based at least in part on a respective secondary weight associated with the one or more respective dates or the one or more respective days of the one or more respective weeks; and determining a set of vehicle operation behaviors based at least in part on the second set of data, as predicted. The Specification at [0008] states: “Generally, a driver’s driving behavior during a vehicle trip may be monitored for insurance related purposes.” The limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers fundamental economic principles or practices (certain methods of organizing human activity), but for the recitation of generic computer components. The claims as a whole recite a method of organizing human activity. The claimed inventions allows for predicting driving behavior for purpose of insurance including determining an insurance policy premium, which is a fundamental economic principle or practice of insurance. The mere nominal recitation of a ***merchant device, a computer, a database, a communication device, a stored value product processor in communication with memory to process instructions stored in memory*** do not take the claim out of the methods of organizing human activity grouping. Thus, the claims recite an abstract idea.
Under Step 2A, Prong Two of the Patent Subject Matter Eligibility analysis (see MPEP 2106.04), the judicial exception is not integrated into a practical application. In particular, the additional elements of a computer-implemented method; a system for predicting trip data, the system comprising: one or more processors; and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations; a non-transitory computer readable storage medium storing one or more computing instructions that, when run on one or more processors, cause the one or more processors to perform operations; one or more sensors; and telematics data are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of obtaining sensor data; predicting vehicle trip data; and determining vehicle operations) such that they amount to no more than mere instructions to apply the exception using a generic computer components (see MPEP 2106.05(f)).
Accordingly, the combination of 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 claims are directed to an abstract idea.
Under Step 2B of the Patent Subject Matter Eligibility analysis (see MPEP 2106.05), the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements in the claims amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
The claims are not patent eligible.
The dependent claims have been given the full two part analysis including analyzing the additional limitations both individually and in combination. The dependent claim(s) when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. 101 because for the same reasoning as above and the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. Dependent claims 3, 10, and 16 simply further describes the technological environment. Dependent claims 2, 4-7, 9, 11-14, and 16-20 simply help to define the abstract idea. The additional limitations of the dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Viewing the claim limitations as an ordered combination does not add anything further than looking at the claim limitations individually. When viewed either individually, or as an ordered combination, the additional limitations do not amount to a claim as a whole that is significantly more than the abstract idea. Accordingly, claims 1-20 are ineligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-6, 8-13, and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over US 20170124660 A1 (“Srivastava”) in view of US 20200070679 A1 (“Wang”).
Regarding claim 1, Srivastava discloses a computer-implemented method for predicting trip data, the method comprising (see at least FIG. 13.):
obtaining, via one or more sensors related to a vehicle operator, a first set of trip data associated with a first set of vehicular trips operated by the vehicle operator during a first time period, the first set of trip data including a first set of telematics data (A driver may have a recurring pattern of driving behavior in which the driver drives vehicle to and from work on Mondays, Wednesdays, and Fridays and another recurring behavior in which the driver works from home on Tuesdays and Thursdays. Telematics computing system may analyze aggregate telematics data to determine a first discrete segment of driving behavior that includes the driver driving vehicle to and from work a workplace on Mondays, Wednesdays, and Fridays and a second, separate discrete segment of driving behavior that includes the driver working from home on Tuesdays and Thursdays. See at least [0100]. See also FIG. 13, step 1302-1308. See also FIG. 12.) and
a first set of travel conditions for the first set of vehicular trips (a telematics device may monitor actual miles driven, types of roads traveled (e.g., low risk roads, high risk roads, etc.), safe or unsafe operation of the vehicle by monitoring, for example, speeds driven, safety equipment used (e.g., seat belts, turn signals, etc.), time of day driven, rate of acceleration, rate of braking (i.e., deceleration), observation of traffic signs (e.g., stop lights, stop signs, etc.), operator behavior during traffic conditions (e.g., during low traffic conditions, high traffic conditions, etc.), road conditions (e.g., bumpy conditions, wet conditions, snowy conditions, icy conditions, etc.), acceleration events, deceleration events, lateral acceleration or any other characteristic indicative of a hard turning maneuver, driver identification, and/or any other suitable information. See at least [0053].),
wherein at least one of one or more respective dates or one or more respective days of one or more respective weeks are assigned to the first set of telematics data (Telematics computing system may analyze aggregate telematics data to determine a first discrete segment of driving behavior that includes the driver driving vehicle to and from work a workplace on Mondays, Wednesdays, and Fridays. See at least [0100]. See also FIG. 13, step 1302-1308. See also FIG. 12.);
determining a second set of travel conditions associated with a target vehicular trip (a telematics device may monitor actual miles driven, types of roads traveled (e.g., low risk roads, high risk roads, etc.), safe or unsafe operation of the vehicle by monitoring, for example, speeds driven, safety equipment used (e.g., seat belts, turn signals, etc.), time of day driven, rate of acceleration, rate of braking (i.e., deceleration), observation of traffic signs (e.g., stop lights, stop signs, etc.), operator behavior during traffic conditions (e.g., during low traffic conditions, high traffic conditions, etc.), road conditions (e.g., bumpy conditions, wet conditions, snowy conditions, icy conditions, etc.), acceleration events, deceleration events, lateral acceleration or any other characteristic indicative of a hard turning maneuver, driver identification, and/or any other suitable information. See at least [0053].) and
occurring during a second time period different from the first time period and during at least one of a particular date or a particular day of a particular week (Telematics computing system may analyze aggregate telematics data to determine a second, separate discrete segment of driving behavior that includes the driver working from home on Tuesdays and Thursdays. See at least [0100]. See at least [0100]. See also FIG. 13, step 1302-1308. See also FIG. 12.);
determining data associated with the target vehicular trip based at least in part on the second set of travel conditions, comprising at least one of (generating a representation of driving behavior based on the classification of the discrete segments of driving behavior. See at least [0116]. See also FIG. 13, step 1310.):
(a) weighing the first set of telematics data to generate a first weighted set of telematics data based at least in part on a respective amount of respective travel conditions of the first set of travel conditions matching a respective amount of travel conditions of the second set of travel conditions; and
(b) weighing the first set of telematics data to generate a second weighted set of telematics data based at least in part on a respective secondary weight associated with the one or more respective dates or the one or more respective days of the one or more respective weeks (Telematics computing system 1106 has assigned weighting factor values that represent ratios of specific patterns of driving behavior to total driving days. Specifically, classification B is assigned a 0.4 weighting factor value to represent that three out of seven days of a week are associated with the discrete segment 1202-1 classified as classification B, classification H is assigned a 0.3 weighting factor value to represent that two out of seven days of a week are associated with the discrete segment 1202-2 classified as classification H, and classification F is assigned a 0.3 weighting factor value to represent that two out of seven days of a week are associated with the discrete segment 1202-3 classified as classification F. In certain examples, a sum total of the set of weighting factors for classifications of driving behavior for a vehicle may be equal to a value of one. See at least [0110] and see also FIG. 12.); and
determining a set of vehicle operation behaviors (generating a representation of driving behavior based on the classification of the discrete segments of driving behavior. See at least [0116]. See also FIG. 13, step 1310.).
While Srivastava discloses determining data associated with the target vehicular trip, Srivastava does not expressly disclose predicting a second set of telematics data. Furthermore, while Srivastava discloses determining a set of vehicle operation behaviors, Srivastava does not expressly disclose the determining is based at least in part on the second set of telematics data, as predicted.
However, Wang discloses predicting a second set of telematics data; determining is based at least in part on the second set of telematics data, as predicted (Continuously collecting vehicle sensor data and telematic data, see at least [0029]-[0030]. Deep structured hierarchical learning (“deep learning”) is employed to predict driver behavior, which in turn is used to derive a drive cycle profile that is defined, at least in part, by a preview route and available traffic information, geopositional and geospatial data, roadway map information, etc. Key sensor-generated propulsion data, along with route and traffic information, are collected by a cloud computing resource service or similarly suitable high-speed, high power processing device. The drive cycle profile may include, in some non-limiting examples, a predicted vehicle speed V.sub.p(t), a predicted propulsion torque T.sub.p(t), a predicted steering angle S.sub.p(t), and/or predicted accelerator/brake pedal positions A.sub.p(t) and B.sub.p(t) for a given preview route of a host vehicle. See at least [0047]. The Examiner is interpreting predicting the vehicle speed, steering angle, accelerator/brake data as predicting a second set of telematics data. And the Examiner interprets determining a drive cycle profile as determining based on the second set of telematics data.).
From the teaching of Wang, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify Srivastava to predict a second set of telematics data, as taught by Wang, and to modify the determining of Srivastava to determine based at least in part on the second set of telematics data as predicted, as taught by Wang, in order to improve prediction of driver behavior and how a vehicle will operate (see Wang at least at Abstract, [0008]). Since the claimed invention is merely a combination of old elements, and in the combination, each element merely would have performed the same function it performed separately, one having ordinary skill in the art at the time of the invention would have recognized that the results of the combination were predictable.
Regarding claim 2, the combination of Srivastava and Wang disclose the limitations of claim 1, as discussed above, and Srivastava further discloses determining a policy premium based at least in part on the set of vehicle operation behaviors (Adjusting insurance to lower a premium for a less risky driver, See at least [0026]-[0027]. Telematics to collect data for insurance, see at least [0012].).
Regarding claim 3, the combination of Srivastava and Wang disclose the limitations of claim 2, as discussed above, and Srivastava further discloses transmitting the policy premium, as determined, for display on a user interface of an electronic device (provide, for display in a graphical user interface on a display screen of a computing device, a price quote associated with the first insurance premium together with a graphical object configured to be selected by the insurance policy holder to invoke a one-click switch of insurance coverage from being provided to the insurance policy holder by the second insurance carrier to being provided to the insurance policy holder by the first insurance carrier. See at least [0015].).
Regarding claim 4, the combination of Srivastava and Wang disclose the limitations of claim 2, as discussed above, and Srivastava further discloses applying at least one of the set of vehicle operation behaviors or the policy premium to an operator profile for the vehicle operator (Generating a driving behavior profile based on telematics, see at least [0117].).
Regarding claim 5, the combination of Srivastava and Wang disclose the limitations of claim 1, as discussed above, and Srivastava further discloses the set of vehicle operation behaviors comprises at least one of acceleration characteristics, braking characteristics, steering characteristics, and focus characteristics (A telematics device may monitor and/or store (e.g. in a storage device of the telematics device) any suitable insurance parameter data to be used by management facility to determine a level of risk associated with the vehicle and/or operation of the vehicle. For example, a telematics device may monitor actual miles driven, types of roads traveled (e.g., low risk roads, high risk roads, etc.), safe or unsafe operation of the vehicle by monitoring, for example, speeds driven, safety equipment used (e.g., seat belts, turn signals, etc.), time of day driven, rate of acceleration, rate of braking (i.e., deceleration), observation of traffic signs (e.g., stop lights, stop signs, etc.), operator behavior during traffic conditions (e.g., during low traffic conditions, high traffic conditions, etc.), road conditions (e.g., bumpy conditions, wet conditions, snowy conditions, icy conditions, etc.), acceleration events, deceleration events, lateral acceleration or any other characteristic indicative of a hard turning maneuver, driver identification, and/or any other suitable information that may be used by management facility as insurance parameter data See at least [0053].).
Regarding claim 6, the combination of Srivastava and Wang disclose the limitations of claim 1, as discussed above, and Srivastava further discloses the first set of travel conditions comprises at least one of weather conditions, route conditions, route difficulties, lighting conditions, visibility conditions, or focus conditions (A telematics device may monitor and/or store (e.g. in a storage device of the telematics device) any suitable insurance parameter data to be used by management facility to determine a level of risk associated with the vehicle and/or operation of the vehicle. For example, a telematics device may monitor actual miles driven, types of roads traveled (e.g., low risk roads, high risk roads, etc.), safe or unsafe operation of the vehicle by monitoring, for example, speeds driven, safety equipment used (e.g., seat belts, turn signals, etc.), time of day driven, rate of acceleration, rate of braking (i.e., deceleration), observation of traffic signs (e.g., stop lights, stop signs, etc.), operator behavior during traffic conditions (e.g., during low traffic conditions, high traffic conditions, etc.), road conditions (e.g., bumpy conditions, wet conditions, snowy conditions, icy conditions, etc.), acceleration events, deceleration events, lateral acceleration or any other characteristic indicative of a hard turning maneuver, driver identification, and/or any other suitable information that may be used by management facility as insurance parameter data See at least [0053]. The insurance parameter data may include telematics in-drive data (car make/model, miles driven, location, driver behavior, etc.), customer relationship management (“CRM”) in-drive data (data plans, contract information, credit class information, demographic information), weather telematics data, mobility data, navigator data. See at least [0075].).
Claim 8 has similar limitations found in claim 1 above, and therefore is rejected by the same art and rationale. And Srivastava further discloses a system for predicting trip data, the system comprising: one or more processors; and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations (see at least [0120]-[0122]. See also [0123]-[0129].).
Claim 9 has similar limitations found in claim 2 above, and therefore is rejected by the same art and rationale.
Claim 10 has similar limitations found in claim 3 above, and therefore is rejected by the same art and rationale.
Claim 11 has similar limitations found in claim 4 above, and therefore is rejected by the same art and rationale.
Claim 12 has similar limitations found in claim 5 above, and therefore is rejected by the same art and rationale.
Claim 13 has similar limitations found in claim 6 above, and therefore is rejected by the same art and rationale.
Claim 15 has similar limitations found in claim 1 above, and therefore is rejected by the same art and rationale. And Srivastava further discloses a non-transitory computer readable storage medium storing one or more computing instructions that, when run on one or more processors, cause the one or more processors to perform operations (see at least [0120]-[0122]. See also [0123]-[0129].).
Claim 16 has similar limitations found in claims 2-3 above, and therefore is rejected by the same art and rationale.
Claim 17 has similar limitations found in claim 4 above, and therefore is rejected by the same art and rationale.
Claim 18 has similar limitations found in claim 5 above, and therefore is rejected by the same art and rationale.
Claim 19 has similar limitations found in claim 6 above, and therefore is rejected by the same art and rationale.
Claims 7, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Srivastava in view of Wang, and in further view of US 11223543 B1 (“Fauber”).
Regarding claim 7, the combination of Srivastava and Wang disclose the limitations of claim 1, as discussed above, and Srivastava further discloses when the target vehicular trip is an occurred trip, further comprising at least one of: identifying data for the target vehicular trip based at least in part upon the first set of trip data, as obtained (A driver may have a recurring pattern of driving behavior in which the driver drives vehicle to and from work on Mondays, Wednesdays, and Fridays and another recurring behavior in which the driver works from home on Tuesdays and Thursdays. Telematics computing system may analyze aggregate telematics data to determine a first discrete segment of driving behavior that includes the driver driving vehicle to and from work a workplace on Mondays, Wednesdays, and Fridays and a second, separate discrete segment of driving behavior that includes the driver working from home on Tuesdays and Thursdays. See at least [0100]. See also FIG. 13, step 1302-1308. See also FIG. 12.); or
identifying the second time period and the target vehicular trip based at least in part upon the data gap.
While Srivastava discloses identifying data for the target vehicular, and while Srivastava discloses the first set of trip data, Srivastava does not expressly disclose identifying a data gap based at least in part upon a gap in a continuity of the data.
However, Fauber discloses identifying a data gap based at least in part upon a gap in a continuity of the data (The telemetry data parsing module is configured to obtain a time series dataset and to determine that the obtained time series dataset has one or more missing values. The machine learning-based missing value imputation module is configured to generate, utilizing a machine learning algorithm, a reconstructed time series dataset having one or more imputed values for the one or more missing values in the obtained time series dataset, the machine learning algorithm comprising a generative network implementing inverse network parameter determination for network parameters of the generative network. See at least col. 4, line 40 to col. 5, line 2.).
From the teaching of Fauber, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the identifying of Srivastava to identify identifying a data gap based at least in part upon a gap in a continuity of the data, as taught by Fauber, in order to address the need for imputing missing values with reasonable values to enable various analysis to be performed on the telemetry data (see Fauber at least at col. 1, lines 12-23.)
Claim 14 has similar limitations found in claim 7 above, and therefore is rejected by the same art and rationale.
Claim 20 has similar limitations found in claim 7 above, and therefore is rejected by the same art and rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20220306125 A1 (“Javeri”) discloses a device may receive historical telematics data associated with a vehicle. The device may determine a set of base line driving parameters based on the historical telematics data. The device may receive current telematics data associated with the vehicle. The device may determine a set of current driving parameters based on the current telematics data. The device may determine, based on processing the set of base line driving parameters and the set of current driving parameters with a machine learning model, a type of driving behavior of a driver of the vehicle. The device may determine, based on processing the set of base line driving parameters and the set of current driving parameters with another machine learning model, a severity score associated with the type of driving behavior. The device may perform an action based on the type of driving behavior and the severity score.
US 20210125076 A1 (“Zhang”) discloses a system for predicting aggressive driving behavior for a driver of a vehicle includes a first edge computing device that can acquire spatial-temporal data for the vehicle from one or more sensors that are part of traffic infrastructure. The first edge computing device includes a processor and instructions executable by the processor that execute deep learning methods on the data from the sensors to cluster the data as a driving score. A trained model is applied to the driving score to determine an aggressive driving behavior risk level, and the first edge computing device is configured to predict the aggressive driving behavior based on the aggressive driving behavior risk level.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAVEN E YONO whose telephone number is (313)446-6606. The examiner can normally be reached Monday - Friday 8-5PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bennett M Sigmond can be reached at (303) 297-4411. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RAVEN E YONO/Primary Examiner, Art Unit 3694
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