Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1-20 is 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. Claim(s) 1, 8 and 15 “recites” abstract ideas under the 2019 PEG:
Step 2A, Prong One – Judicial Exception
Claims are directed to an abstract idea, namely collecting, comparing and evaluating telematics data to score vehicle operation and determine insurance pricing. The claims recite obtaining telematics data from different sources, comparing the data to determine driving attributes, calculating scoring factors, and generating an insurance policy and rate structure based on those factors. These limitations amount to data collection, data analysis, and risk assessment for insurance underwriting, which are fundamental economic practices and methods of organizing human activity.
Step 2A, Prong Two
The claims do not integrate the abstract idea into a practical application. The additional elements merely recite:
Generic data sources (vehicle connections and mobile devices); and
Generic computing functions (determining, comparing, calculating and generating)
The claims do not improve computer functionality, telematics technology, or data processing techniques, and do not apply the abstract idea in a non-conventional manner. Identifying a matching location at a matching time is a basic data correlation operation.
Step 2B- Inventive Concept
The processor, connections, telematics data, and computer readable media are generic and conventional, and the claimed steps are routine and well-understood. Considering individually and as an ordered combination, the claims merely apply the abstract idea using generic computing technology.
Therefore, claims 1-20 are not drawn to eligible subject matter as they directed to an abstract idea without significantly more.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huls (US 2017/0365007) in view of Silver et al (US 2015/0140991).
Per claim 1, Huls teaches a method of driving behavior assessment using telematics data, the method comprising (abstract teaches driving behavior assessment for travel-based insurance rating system comprising a telematics device and a mobile devices associated with a driver):
determining vehicle behavior telematics data generated using a first type of connection with a vehicle (0019 teaches collect data from one or more vehicle sensors and/or by the vehicles OBD system. 0058 teaches a telematic device…plugged into the vehicle’s OBD system);
determining personal mobility telematics data generated using a second type of connection with the personal mobility telematics data being associated with one or more vehicle operators (0017 teaches collect data from a travel’s mobile device…using one or more sensors of the mobile device. 0018 teaches movement data representing changes in position, velocity, and/or acceleration of a mobile device. Also see paragraph 0030 wherein teaches travelers includes drivers and passengers);
determining one or more driving attributes for each of the one or more vehicle operators based [on a comparison of the vehicle behavior telematics data and the personal mobility telematics data] (0073 teaches determine one or more driving behaviors of the driver based on the driving data. 0074 teaches driving behavior includes hard braking, sharp turns, inconsistent acceleration and speeding);
calculating one or more scoring factors based on the one or more vehicle operators (0075 teaches calculating an insurance rating….based on the determined driving behavior. 0076 teaches compute an overall driving score based on varying weights of different driving behavior factors);
generating a policy level rate structure for an insurance policy based on the one or more scoring factors (0001 teaches determine an appropriate insurance cost and premium. 0062 teaches insurance rating stored with premium level, deductible level and amounts); and
generating the insurance policy using the policy level rate structure (0027 teaches assign insurance premiums accordingly and 0028 teaches provide travel-based insurance ratings and premiums).
But, Huls does not explicitly teach comparison of the vehicle behavior telematics data and the personal mobility telematics data.
However, in an analogous art, Silver teaches motor vehicle identification based on driving behavior of a driver (abstract). Silver further teaches comparison of the vehicle behavior telematics data and the personal mobility telematics data (0045 teaches location information of their mobile devices can be used and compared with the location information of the driver and/or the vehicle itself. 0046 teaches comparing vehicle speed/location to mobile device GPS/speed). Therefore, before the effective filling date of the invention, it would have been obvious to one of ordinary skill in the art to incorporate Silver’s vehicle to mobile data comparison techniques into Huls in order to improve attribution accuracy of telematics data to the correct vehicle operator, particularity where multiple travelers or mobile devices are present, thereby improving the accuracy and reliability of insurance risk assessment and policy pricing.
Per claim 2, 9 and 16, Huls teaches wherein each of the one or more scoring factors correspond to one of the one or more vehicle operators (0006, 0017-0019 and 0062 teaches calculating an insurance rating/score for a particular traveler/person which corresponds to a specific user/operator).
Per claim 3, 10 and 17, Huls teaches wherein the insurance policy applies the policy level rate structure to a plurality of vehicle associated with each of the one or more vehicle operators (0062-0063, 0017, 0075-0077 and 0042-0043 teaches applying insurance ratings and premiums at the policy/customer level and associating those ratings with records for passengers, drivers, or customers, rather than limiting application to a single vehicle).
Per claim 4, 11 and 18, Huls teaches wherein the first type of connection is to an on-board diagnostics (OBD)-II device installed at the vehicle (0019 and 0052).
Per claim 5, 12 and 19, Huls teaches wherein the second type of connection is to a mobile device associated with one of the one or more vehicle operators (0017-0019 and 0040-0041 teaches collecting personal mobility/traveling data using a mobile device associated with a driver/passenger).
Per claim 6, 13 and 20, Huls does not explicitly teach wherein determining one or more driving attributes for each of the one or more vehicle operators based on a comparison of the vehicle behavior telematics data and the personal mobility telematics data comprises identifying a matching location at a matching time between the vehicle behavior telematics data and the personal mobility telematics data.
However, Silver teaches wherein determining one or more driving attributes for each of the one or more vehicle operators based on a comparison of the vehicle behavior telematics data and the personal mobility telematics data comprises identifying a matching location at a matching time between the vehicle behavior telematics data and the personal mobility telematics data (0045 teaches location information of mobile devices can be used and compared with the location information of the driver and/or the vehicle itself. 0045 further teaches the location of a person’s mobile phone continues to be near the location of the vehicle as the vehicle continues its motion. 0046 teaches matching vehicle GPS/speed with mobile-device GPS/speed to associate the mobile device with the moving vehicle, which necessarily aligns the data by location). Therefore, before the effective filling date of the invention, it would have been obvious to one of ordinary skill in the art to incorporate Silver’s location based matching technique into the system of Huls to improve accuracy in associating mobile-device data with vehicle telematic data, thereby enabling more reliable determination of driving attributes for individual vehicle operators and reducing misattribution of the telematics events.
Per claim 7 and 14, Huls teaches wherein: the vehicle behavior telematics data generated using the first type of connection includes: first acceleration data; first braking data; and first location data (052-0053 and 0056 teaches vehicle driving data includes rates of acceleration or braking…vehicle location collected via vehicle sensors and telematics devices); and the personal mobility telematics data generated using the second type of connection includes: second acceleration data; second braking data; and second location data (0019, 0048-0049 teaches collecting movement data from sensors of a mobile device includes changes in position, velocity, and/or acceleration and GPS location data).
Per claim 8 and 15, see rejection of claim 1 and further paragraph 0007 of Huls that teaches memory storing computer readable instruction when executed by at least one processor)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Warren et al (US 2006/0253307) abstract teaches calculation of driver score based on vehicle operation
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/OMEED ALIZADA/Primary Examiner, Art Unit 2686