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 .
This is a Non-Final Office Action in response to application 19/103,516 entitled "SYSTEM FOR CALCULATING DRIVER DRIVING SCORE, AND DRIVING SCORE CALCULATION METHOD FOR SYSTEM" originally filed on February 12, 2025, with claims 1 to 18 pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on February 12, 2025, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Please see MPEP 2106 for additional information regarding Patent Subject Matter Eligibility Guidance.
Claims 1-18 are directed to a method/process, machine/apparatus, (article of) manufacture, or composition of matter, which are/is one of the statutory categories of invention, which are/is one of the statutory categories of invention. (Step 1: YES).
The claimed invention is directed to an abstract idea without significantly more.
Independent Claim 1 recites:
“A method of calculating, …a driver's driving score based on …data items detected …the method comprising:
receiving … data items detected …provided…;
determining at least one preset risk event based on the received … data items;
determining at least one context matching each of the at least one determined risk event based on context data items included in the received … data items;
rescoring an event score corresponding to each of the determined risk events based on the at least one determined context; and
calculating a driving score related to the driving … based on the rescored event scores of the respective risk events.”
These limitations clearly relate to managing transactions/interactions between drivers and/or insurance providers. These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. Specific instances include instructions for “determining at least one preset risk event” and “calculating a driving score” recite managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) and/or a fundamental economic principles or practice. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic, financial, or behavioral action, principle, or practice then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. (Step 2A-Prong 1: YES. The claims recite an abstract idea).
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of:
[by a network-based data warehouse system]:
merely applying computer processing, storage, and networking technology as tools to perform an abstract idea
[sensor] [from a vehicle][from a plurality of sensors][in the vehicle]:
merely applying automotive technology as a tool to perform an abstract idea
are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer components and/or electronic processes. For example, the Applicant’s Specification reads:
[0225] The disclosure may be implemented as computer-readable codes in a program-recorded medium. The computer readable medium includes all kinds of recording devices in which data readable by a computer system is stored. Examples of the computer-readable medium include a hard disk drive (HDD), a solid state disk (SSD), a silicon disk drive (SDD), a ROM, a RAM, a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device and the like, and may also be implemented in the form of a carrier wave (e.g., transmission over the Internet). Therefore, the detailed description should not be limitedly construed in all of the aspects... and all changes within the equivalent scope of the present disclosure are embraced by the appended claims.
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, Claim 1 is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). Accordingly, the additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. The claim further defines the abstract idea and hence is abstract for the reasons presented above. The claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the claim is directed to an abstract idea. Thus, the claim is not patent eligible. (Step 2B: NO. The claim does not provide significantly more)
Dependent Claims recite additional elements.
This judicial exception is not integrated into a practical application. In particular, the recited additional elements of
Claims 2 and 3:
“sensor”: merely applying automotive sensing technologies as a tool to perform an abstract idea
Claim 4:
“sensor”, “vehicle”: merely applying automotive sensing technologies as a tool to perform an abstract idea
Claims 5:
“sensor”: merely applying automotive sensing technologies as a tool to perform an abstract idea
Claims 6 and 7:
“vehicle”: merely applying automotive sensing technologies as a tool to perform an abstract idea
Claim 8:
“sensor”, “vehicle”, “advanced driver assistance systems (ADAS)”, “camera”: merely applying automotive sensing technologies as a tool to perform an abstract idea
Claim 9:
“vehicle”: merely applying automotive sensing technologies as a tool to perform an abstract idea
Claims 10-14: (none found: does not include additional elements and merely narrows the abstract idea)
are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer components and/or electronic processes. For support from the Applicant’s Specification, see the analysis as applied to Independent Claim 1 (Step 2A-Prong 2) earlier. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, the claim is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Dependent claims further define the abstract idea that is present in their respective independent claims and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea. Thus, the dependent claims are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Independent Claim 15 recites:
“A data collection …that collects, … data items detected …provided … so as to calculate a driver's driving score, …comprising:
…that performs wireless communication with the network-based data warehouse system;
a driving context …that collects driving context data items sensed …which is pre-designated to infer a situation related to the driving…;
a driving situation context … that collects driving situation context data items sensed …which is pre-designated to infer a background situation in which … is driven;
a driving action … that collects driving action data items sensed ….which is pre-designated to infer a driving action of a driver driving …and
…that controls the communication unit to …sensor data including the driving context data, the driving situation context data and the driving action data …“
These limitations clearly relate to managing transactions/interactions between drivers and/or insurance providers. These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. Specific instances include instructions that “collects driving context data” and “collects driving action data” recite managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) and/or a fundamental economic principles or practice. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic, financial, or behavioral action, principle, or practice then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. (Step 2A-Prong 1: YES. The claims recite an abstract idea).
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of:
[device] [by a network-based data warehouse system][the device] [a communication unit] [collection unit] [a processor]:
merely applying computer processing, storage, and networking technology as tools to perform an abstract idea
[sensor] [from a plurality of sensors] [in a vehicle][from at least one first device of the vehicle][of the vehicle] [from at least one second device of the vehicle][from at least one third device of the vehicle]:
merely applying automotive technology as a tool to perform an abstract idea
[transmit]:
insignificant extra-solution activity to the judicial exception of data gathering
are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer components and/or electronic processes. For example, the Applicant’s Specification reads:
[0225] The disclosure may be implemented as computer-readable codes in a program-recorded medium. The computer readable medium includes all kinds of recording devices in which data readable by a computer system is stored. Examples of the computer-readable medium include a hard disk drive (HDD), a solid state disk (SSD), a silicon disk drive (SDD), a ROM, a RAM, a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device and the like, and may also be implemented in the form of a carrier wave (e.g., transmission over the Internet). Therefore, the detailed description should not be limitedly construed in all of the aspects... and all changes within the equivalent scope of the present disclosure are embraced by the appended claims.
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, Claim 15 is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). Accordingly, the additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. The claim further defines the abstract idea and hence is abstract for the reasons presented above. The claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination.
For causing the transmission, MPEP 2106.05(d)(II) indicates that the courts have recognized receiving or transmitting data over a network as well-understood, routine and conventional functions when claimed in a merely generic manner: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Therefore, the claim is directed to an abstract idea. Thus, the claim is not patent eligible. (Step 2B: NO. The claim does not provide significantly more)
Dependent Claims recite additional elements.
This judicial exception is not integrated into a practical application. In particular, the recited additional elements of
Claim 16:
“device”, “network-based data warehouse system”: merely applying automotive sensing technologies as a tool to perform an abstract idea
Claims 17 and 18:
“device”, “processor”: merely applying automotive sensing technologies as a tool to perform an abstract idea
“sensor”, “vehicle”: merely applying automotive sensing technologies as a tool to perform an abstract idea
“transmit”: insignificant extra-solution activity to the judicial exception of data gathering
are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer components and/or electronic processes. For support from the Applicant’s Specification, see the analysis as applied to Independent Claim 1 (Step 2A-Prong 2) earlier. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, the claim is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Dependent claims further define the abstract idea that is present in their respective independent claims and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination.
For causing the transmission, MPEP 2106.05(d)(II) indicates that the courts have recognized receiving or transmitting data over a network as well-understood, routine and conventional functions when claimed in a merely generic manner: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Therefore, the dependent claims are directed to an abstract idea. Thus, the dependent claims are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Claim Rejections - 35 USC § 102
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 5-13, 15, and 16 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Genovese ("MOBILE DEVICE AND SYSTEM FOR IDENTIFYING AND/OR CLASSIFYING OCCUPANTS OF A VEHICLE AND CORRESPONDING METHOD THEREOF", U.S. Publication Number: US 20220080976 A1).
Regarding Claim 1,
Genovese teaches,
A method of calculating, by a network-based data warehouse system, a driver's driving score based on sensor data items detected from a vehicle, the method comprising: receiving sensor data items detected from a plurality of sensors provided in the vehicle;
(Genovese [0088] within a corresponding data transmission network
Genovese [0015] stored to the database
Genovese [0016] The DPD input sensors are here (1) Gyroscope...Accelerometer... Current values are obtained after a small grid exploration, and correspond to a F1 score for Driver detection)
determining at least one preset risk event based on the received sensor data items;
(Genovese [0004] radar, LIDAR (measuring device to measure distances by means of laser light), GPS (Global Positioning System), odometry (measuring device for measuring changings in position over time by means of using motion sensor data).... IMU-sensors (Inertial Measurement Unit) providing a sensor....measuring occupant-specific risk-exposure parameters.
Genovese [0005] elevated risk for major crashes)
determining at least one context matching each of the at least one determined risk event based on context data items included in the received sensor data items;
(Genovese [0018] A “Rotation Window” is a window related to the gyroscope signal. In the context of DPD, a “rotation” is a coherent movement in the xy-plane
Genovese [0005] real-time monitoring, accident identification or risk measurements etc.)
rescoring an event score corresponding to each of the determined risk events based on the at least one determined context;
(Genovese [0030] Find and separate Movements from Gaps using the rotated accelerometer variance along the z component; 4) Score every Movement found)
and calculating a driving score related to the driving of the vehicle based on the rescored event scores of the respective risk events.
(Genovese [0041] the final driver score D.)
Regarding Claim 2,
Genovese teaches the driving score calculation of Claim 1 as described earlier.
Genovese teaches,
wherein the calculating of the driving score comprises: detecting data related to a driver's driving action from the sensor data items; detecting the driver's driving characteristic from the detected driving action data;
(Genovese [0006] in-vehicle sensing systems ... to learn preferences, profiles and behaviors. Biunique driver and passenger identification
Genovese [0194] to score the driver behavior through the recording of GPS, Accelerometer, Gyroscope and other integrated sensors
Genovese [0006] adapting its systems to the driver “and” passenger(s) profiles and preferences)
classifying the driver's situational driving style based on the detected driving characteristic and the driver's driving situation; and
(Genovese [0060] Biunique driver and passenger identification is also relevant to insurance telematics, for instance the driving style can allow automated setting the driver's...risk-transfer premium
Genovese [0194] Different combination of driver and transport mode have different driving style, moreover each driver has a different driving style depending on external factors e.g. weather, road type, and on personal factors e.g. motivation of the trip, time constraints and trip familiarity. Given previous assumptions, the transport mode recognition 113 and driver passenger detection 112 can be improved based on an in depth recognition and/or analysis of a single person driving style...can contribute to technically define a driving style... allows to define and measure the driver's driving style.)
reflecting the rescored event scores corresponding to the respective determined risk events to a base driving score on a trip-by-trip basis calculated according to the classified driver's situational driving style to calculate a moving score on the trip-by-trip basis.
(Genovese [0133] These separate input features will be used by the DPD+F classifier (server-side) to re-score the session
Genovese [0079] diagram with a TMR baseline (given by the straight line) illustrating schematic an exemplary weighting of the parameters and evaluating the performance
Genovese [0078] measuring of a trip using an appropriate trip summary.
Genovese [0194] extracted from an historical set of trips of a single user allows to define and measure the driver's driving style)
Regarding Claim 5,
Genovese teaches the driving score calculation of Claim 2 as described earlier.
Genovese teaches,
detecting the driver's driving characteristic from the remaining driving action data items
(Genovese [0041] the final driver score D.
Genovese [0194] extracted from an historical set of trips of a single user allows to define and measure the driver's driving style)
excluding the driving action data corresponding to the determined risk event from among the sensor data items.
(Genovese [0052] the introduction of thresholds to ignore bad quality DPD results thus improving the accuracy of remaining ones.)
Regarding Claim 6,
Genovese teaches the driving score calculation of Claim 2 as described earlier.
Genovese teaches,
at least one of a speed characteristic according to an average speed of the vehicle, an area- specific driving characteristic according to an area-specific speed of a path on which the vehicle drives, and a driving stability characteristic according to a speed deviation.
(Genovese [0149] Regarding the GPS features, over the array of GPS speeds, the following features can e.g. be generated: (1) Average, (2) Standard deviation
Genovese [0004] to determine speed)
Regarding Claim 7,
Genovese teaches the driving score calculation of Claim 2 as described earlier.
Genovese teaches,
at least one of a driving history to a destination on a driving path, a driving time, whether there is a passenger, whether the driver is driving his or her own vehicle, and a distance to the destination.
(Genovese [0078] able to efficiently retrieve historical annotated trip data and define a trajectory similarity measure.
Genovese [0015] the end-trip trigger
Genovese [0096] time-dependent duration
Genovese [0005] correct identification of driver versus passenger)
Regarding Claim 8,
Genovese teaches the driving score calculation of Claim 1 as described earlier.
Genovese teaches,
wherein the context data is data collected from at least one sensor that detects a situation inside and outside the vehicle, the context data comprising at least one of detection values of advanced driver assistance systems (ADAS), an image of a camera sensing an image inside or outside the vehicle, and information on a location of another vehicle, a speed and a moving direction of the other vehicle sensed from a vehicle-to-vehicle (V2V) communication unit.
(Genovese [0089] a Global System for Mobile Communications (GSM) unit. The plurality of interfaces of the mobile telecommunications apparatuses 10 for connection with at least one of a motor vehicle's data transmission bus can for example comprise at least on interface for connection with a motor vehicle's Controller Area Network (CAN) bus, ... obtaining information access to on-board sensors or entertainment systems (such as Apple Carplay etc.) providing the necessary vehicle sensor information
Genovese [0209] 1025 Accelerometer
Genovese [0210] 1026 Gyroscope
Genovese [0211] 1027 Cameras
Genovese [0147] trip data is then enriched by the system 1 with e.g. external APIs (Application Programming Interface))
Regarding Claim 9,
Genovese teaches the driving score calculation of Claim 2 as described earlier.
Genovese teaches,
at least one of location information of the vehicle, speed information of the vehicle, and information on a driving path of the vehicle.
(Genovese [0205] 10242 Positioning system modules
Genovese [0209] 1025 Accelerometer
Genovese [0210] 1026 Gyroscope
Genovese [0211] 1027 Cameras
Genovese [0147] trip data is then enriched by the system 1 with e.g. external APIs (Application Programming Interface)
Genovese [0146] set of GPS locations… (i) Speed<=3 m/s, … over the array of speeds.)
Regarding Claim 10,
Genovese teaches the driving score calculation of Claim 2 as described earlier.
Genovese teaches,
wherein the classifying of the driver's situational driving style comprises: rescoring the rescored event scores again based on the classified driver's situational driving style.
(Genovese [0133] These separate input features will be used by the DPD+F classifier (server-side) to re-score the session
Genovese [0079] diagram with a TMR baseline (given by the straight line) illustrating schematic an exemplary weighting of the parameters and evaluating the performance
Genovese [0194] Different combination of driver and transport mode have different driving style, …on an in depth recognition and/or analysis of a single person driving style...can contribute to technically define a driving style... extracted from an historical set of trips of a single user allows to define and measure the driver's driving style)
Regarding Claim 11,
Genovese teaches the driving score calculation of Claim 10 as described earlier.
Genovese teaches,
wherein the rescoring of the rescored event scores again comprises: rescoring the event score by reflecting a context score corresponding to at least one context matching the risk event to an event base score according to the determined risk event;
(Genovese [0085] for risk scoring
Genovese [0109] The Acceleration Normalization and Gravity subtraction transformations allow to standardize signals
Genovese [0133] These separate input features will be used by the DPD+F classifier (server-side) to re-score the session
Genovese [0030] 1) Find all Rotations present in the gyroscope buffer; 2) Find all Discontinuities in the not-rotated accelerometer buffer; 3) Find and separate Movements from Gaps using the rotated accelerometer variance along the z component; 4) Score every Movement found
Genovese [0079] diagram with a TMR baseline (given by the straight line) illustrating schematic an exemplary weighting of the parameters and evaluating the performance
Genovese [0005] elevated risk for major crashes
Genovese [0005] real-time monitoring, accident identification or risk measurements etc.)
changing the event base score or the context score based on the classified driver's situational driving style; and rescoring the rescored event score again based on the changed base score or the context score.
(Genovese [0194] Different combination of driver and transport mode have different driving style, moreover each driver has a different driving style depending on external factors e.g. weather, road type, and on personal factors e.g. motivation of the trip, time constraints and trip familiarity. Given previous assumptions, the transport mode recognition 113 and driver passenger detection 112 can be improved based on an in depth recognition and/or analysis of a single person driving style...can contribute to technically define a driving style... extracted from an historical set of trips of a single user allows to define and measure the driver's driving style)
Regarding Claim 12,
Genovese teaches the driving score calculation of Claim 2 as described earlier.
Genovese teaches,
calculating at least one driving score on the trip-by-trip basis having a same classified driver's situational driving style as a single moving score.
(Genovese [0076] when the user goes once from point A to point B1 (session S1), and once from A to B2 (session S2)...If S1 and S2 have enough links in common (the user travels the same path but ends up in different places) the two trips are clustered together
Genovese [0194] to score the driver behavior)
Regarding Claim 13,
Genovese teaches the driving score calculation of Claim 2 as described earlier.
Genovese teaches,
collecting at least one driving score on the trip-by-trip basis calculated over a predetermined period of time to calculate a single moving score.
(Genovese [0076] when the user goes once from point A to point B1 (session S1), and once from A to B2 (session S2)...If S1 and S2 have enough links in common (the user travels the same path but ends up in different places) the two trips are clustered together
Genovese [0194] to score the driver behavior
Genovese [0131] final scores is distributed among all the sessions collected from an example time period as e.g. January 2019 to June 2019.)
Regarding Claim 15,
Genovese teaches,
A data collection device that collects, by a network-based data warehouse system, sensor data items detected from a plurality of sensors provided in a vehicle
(Genovese [0006] data storage-processing and wireless communications technologies in vehicles
Genovese [0004] odometry (measuring device for measuring changings in position over time by means of using motion sensor data))
so as to calculate a driver's driving score,
(Genovese [0131] the final driver score)
the device comprising: a communication unit that performs wireless communication with the network-based data warehouse system;
(Genovese [Claim 1] a wireless node within a cellular data transmission network by antenna connections of the mobile device to the cellular data transmission network)
a driving context collection unit that collects driving context data items sensed from at least one first device of the vehicle, which is pre-designated to infer a situation related to the driving of the vehicle; a driving situation context collection unit that collects driving situation context data items sensed from at least one second device of the vehicle, which is pre-designated to infer a background situation in which the vehicle is driven; a driving action collection unit that collects driving action data items sensed from at least one third device of the vehicle, which is pre-designated to infer a driving action of a driver driving the vehicle; and
(Genovese [Claim 1] based on sensory data measured by a plurality of sensors of a mobile device of the user, the plurality of sensors at least comprising an accelerometer and a gyroscope, the mobile device comprising at least one wireless connection
Genovese [0205] 10242 Positioning system modules
Genovese [0209] 1025 Accelerometer
Genovese [0210] 1026 Gyroscope
Genovese [0211] 1027 Cameras
Genovese [0147] trip data is then enriched by the system 1 with e.g. external APIs (Application Programming Interface
Genovese [0194] Frequency of maneuvers and phone distraction events per kilometers)
a processor that controls the communication unit to transmit sensor data including the driving context data, the driving situation context data and the driving action data to the network- based data warehouse system.
(Genovese [0086] The automated system 1 can e.g. include at least a processor and associated memory modules.
Genovese [0088] a cellular data transmission network 2 by means of antenna connections of the cellular mobile device to the cellular data transmission network)
Regarding Claim 16,
Genovese teaches the driving score calculation of Claim 15 as described earlier.
Genovese teaches,
wherein the at least one first device, the at least one second device and the at least one third device overlap one another at least partially.
(Genovese [0002] relates to telematics based devices
Genovese [0089] installed systems obtaining information access to on-board sensors
Genovese [0004] computer system of the vehicle can related to other data than voice transmission,... radar, LIDAR (measuring device to measure distances by means of laser light), GPS (Global Positioning System), odometry (measuring device for measuring changings in position over time by means of using motion sensor data), and computer vision.
Genovese [0085] Driver Passenger Detection (DPD) 112 and/or Transport Mode Recognition (TMR)
Genovese [Claim 5] triggered for rotations with an overlap with the sitting movement)
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 3, 4, 14, 17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Genovese ("MOBILE DEVICE AND SYSTEM FOR IDENTIFYING AND/OR CLASSIFYING OCCUPANTS OF A VEHICLE AND CORRESPONDING METHOD THEREOF", U.S. Publication Number: US 20220080976 A1),in view of Gutierrez (“MACHINE LEARNING MODEL FOR PREDICTING DRIVING EVENTS”, U.S. Publication Number: US 20250121818 A1).
Regarding Claim 3,
Genovese teaches the driving score calculation of Claim 1 as described earlier.
Genovese teaches,
wherein the determining of the at least one risk event comprises:
detecting sensor data items that satisfy any one of preset risk event occurrence conditions from among the sensor data items, determining a time section in which the sensor data items are detected
(Genovese [0098] Upon detecting movement patterns (M) best matching a sitting movement (S), the gyroscope movement sensory data corresponding to the acceleration movement sensory data are determined
Genovese [0027] collect data using elapsed seconds only, including the starting timestamps in the metadata of a session
Genovese [0016] The DPD input sensors are here (1) Gyroscope (timestamp,x,y,z), (2) Accelerometer(timestamp,x,y,z), (3) RotationVector(timestamp,x,y,z,w), and (4) (optional)StepDetector(timestamp).
Genovese [0144] based on the following conditions: (1) At least one minute long, (2) At least 30 GPS positions)
as an event zone
(Genovese [0017] with a Window being a simple multi-use class containing minimum info about a certain time slice, plus some additional info. Three scenarios are anticipated, in which windows are used)
Genovese does not teach determining a risk event corresponding to the event zone based on the sensor data items of the each determined event zone and a risk event occurrence condition that satisfies the sensor data items.
Gutierrez teaches,
determining a risk event corresponding to the event zone based on the sensor data items of the each determined event zone and a risk event occurrence condition that satisfies the sensor data items.
(Gutierrez [0002] to analyze data received from vehicle sensors.
Gutierrez [0003] to calculate the risk of collision
Gutierrez [0179] the analytics server may determine whether the driving session is taking place at a geographical location (e.g., neighborhood or zip code) that indicates a likelihood of collision or other events that is higher or lower than other geographical areas. For instance, the analytics server may determine that a driving session is associated with a neighborhood with a higher likelihood of theft or collision.)
It is prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the driving score calculation of Genovese to incorporate the event zone risk correlation teachings of Gutierrez to “determine whether the driving session is taking place at a geographical location.” (Gutierrez [0179]). The modification would have been obvious, because it is merely applying a known technique (i.e. event zone risk correlation) to a known concept (i.e. driving score calculation) ready for improvement to yield predictable result (i.e. “indicates a likelihood of collision or other events that is higher or lower than other geographical areas” Gutierrez [0179])
Regarding Claim 4,
Genovese and Gutierrez teaches the driving score calculation of Claim 3 as described earlier.
Genovese teaches,
wherein the determining of the at least one context comprises: extracting context data items related to a driving situation of the vehicle from respective time sections of the sensor data corresponding to each event zone and a time section including predetermined periods of time before and after the each event zone;
(Genovese [0079] diagram with a TMR baseline (given by the straight line) illustrating schematic an exemplary weighting of the parameters and evaluating the performance
Genovese [0027] collect data using elapsed seconds only, including the starting timestamps in the metadata of a session
Genovese [0016] The DPD input sensors are here (1) Gyroscope (timestamp,x,y,z), (2) Accelerometer(timestamp,x,y,z), (3) RotationVector(timestamp,x,y,z,w), and (4) (optional)StepDetector(timestamp).
Genovese [0131] final scores is distributed among all the sessions collected from an example time period as e.g. January 2019 to June 2019.
Genovese [0017] with a Window being a simple multi-use class containing minimum info about a certain time slice, plus some additional info. Three scenarios are anticipated, in which windows are used)
Genovese does not teach determining a context representing a driving situation of the vehicle matching the each event zone based on the extracted context data items.
Gutierrez teaches,
determining a context representing a driving situation of the vehicle matching the each event zone based on the extracted context data items.
(Gutierrez [0179] Using GPS data and/or camera data, the analytics server may determine whether the driving session is taking place at a geographical location (e.g., neighborhood or zip code) that indicates a likelihood of collision or other events that is higher or lower than other geographical areas. For instance, the analytics server may determine that a driving session is associated with a neighborhood with a higher likelihood of theft or collision.)
It is prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the driving score calculation of Genovese to incorporate the event zone risk correlation teachings of Gutierrez to “determine whether the driving session is taking place at a geographical location.” (Gutierrez [0179]). The modification would have been obvious, because it is merely applying a known technique (i.e. event zone risk correlation) to a known concept (i.e. driving score calculation) ready for improvement to yield predictable result (i.e. “indicates a likelihood of collision or other events that is higher or lower than other geographical areas” Gutierrez [0179])
Regarding Claim 14,
Genovese and Gutierrez teaches the driving score calculation of Claim 3 as described earlier.
Genovese teaches,
storing the moving score calculated over time; and
(Genovese [0194] to score the driver behavior
Genovese [0131] final scores is distributed among all the sessions collected from an example time period as e.g. January 2019 to June 2019.
Genovese [0133] results from each session will be stored in a table.
Genovese [0080] Collect user history)
providing a result of analyzing a history of moving scores stored for a preset period of time according to a risk event or context,
(Genovese [0080] Collect user history
Genovese [0122] Discontinuity Type Score: This transformation assigns a score... given parameters for the data processing and analysis
Genovese [0131] final scores is distributed among all the sessions collected from an example time period as e.g. January 2019 to June 2019.
Genovese [0085] for risk scoring)
Genovese does not teach analyzing the driver's driving action based on an increase or decrease in the driving score, as feedback information on the driver's driving score for the preset period of time.
Gutierrez teaches,
analyzing the driver's driving action based on an increase or decrease in the driving score,
(Gutierrez [Abstract] if the driver's actions has caused their score to increase/decrease
Gutierrez [0002] techniques to analyze data)
as feedback information on the driver's driving score for the preset period of time.
(Gutierrez [0145] providing user feedback (e.g., through user interface 140 a)
Gutierrez [0049] a timestamp of the data associated with the likelihood of the event.)
It is prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the driving score calculation of Genovese to incorporate the event zone risk correlation teachings of Gutierrez to “determine whether the driving session is taking place at a geographical location.” (Gutierrez [0179]). The modification would have been obvious, because it is merely applying a known technique (i.e. event zone risk correlation) to a known concept (i.e. driving score calculation) ready for improvement to yield predictable result (i.e. “indicates a likelihood of collision or other events that is higher or lower than other geographical areas” Gutierrez [0179])
Regarding Claim 17,
Genovese teaches the driving score calculation of Claim 15 as described earlier.
Genovese teaches,
detect sensor data items that satisfy any one of preset risk event occurrence conditions from among the sensor data items, determine a time section in which the sensor data items are detected as an event zone, and determine a risk event corresponding to the event zone based on the sensor data items of the each determined event zone and a risk event occurrence condition that satisfies the sensor data items; extract context data items related to a driving situation of the vehicle from each time section of the sensor data corresponding to each event zone and a time section including predetermined periods of time before and after the each event zone,
(Genovese [0020] a window related to the accelerometer signal. It has been built using a threshold
Genovese [0021] every time the acceleration variance crosses the threshold lines.
Genovese [0028] Acceleration Normalization is technically necessary if the same thresholds is to be used for both the signals.
Genovese [0030] windows detection and scoring transformation.
Genovese [0018] A “Rotation Window” is a window related to the gyroscope signal. In the context of DPD, a “rotation” is a coherent movement in the xy-plane
Genovese [0005] real-time monitoring, accident identification or risk measurements etc.)
and transmit the matching result to the network-based data warehouse system, and wherein the network-based data warehouse system
(Genovese [0030] 1) Find all Rotations present in the gyroscope buffer; 2) Find all Discontinuities in the not-rotated accelerometer buffer; 3) Find and separate Movements from Gaps using the rotated accelerometer variance along the z component; 4) Score every Movement found
Genovese [0079] diagram with a TMR baseline (given by the straight line) illustrating schematic an exemplary weighting of the parameters and evaluating the performance
Genovese [0086] The automated system 1 can e.g. include at least a processor and associated memory modules.
Genovese [0088] a cellular data transmission network 2 by means of antenna connections of the cellular mobile device to the cellular data transmission network
Genovese [0006] data storage-processing and wireless communications technologies in vehicles)
is configured to: rescore an event score of the each risk event based on at least one context matching the each risk event.
(Genovese [0133] These separate input features will be used by the DPD+F classifier (server-side) to re-score the session
Genovese [0030] 4) Score every Movement found )
Genovese does not teach determine a context representing a driving situation of the vehicle matching the each event zone based on the extracted context data items; and match each risk event with at least one context based on a risk event and a context determined from each event zone.
Gutierrez teaches,
determine a context representing a driving situation of the vehicle matching the each event zone based on the extracted context data items; and match each risk event with at least one context based on a risk event and a context determined from each event zone.
(Gutierrez [0002] to analyze data received from vehicle sensors.
Gutierrez [0003] to calculate the risk of collision
Gutierrez [0179] the analytics server may determine whether the driving session is taking place at a geographical location (e.g., neighborhood or zip code) that indicates a likelihood of collision or other events that is higher or lower than other geographical areas. For instance, the analytics server may determine that a driving session is associated with a neighborhood with a higher likelihood of theft or collision.)
It is prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the driving score calculation of Genovese to incorporate the event zone risk correlation teachings of Gutierrez to “determine whether the driving session is taking place at a geographical location.” (Gutierrez [0179]). The modification would have been obvious, because it is merely applying a known technique (i.e. event zone risk correlation) to a known concept (i.e. driving score calculation) ready for improvement to yield predictable result (i.e. “indicates a likelihood of collision or other events that is higher or lower than other geographical areas” Gutierrez [0179])
Regarding Claim 18,
Genovese and Gutierrez teaches the driving score calculation of Claim 17 as described earlier.
Genovese teaches,
classify a background situation in which the vehicle is driven into one of a plurality of preset driving situations based on the driving situation context data items; detect at least one driving characteristic of a driver driving the vehicle based on the driving action data items, and classify the driver's driving style into one of a plurality of preset driving styles based on the detected driving characteristic; and classify a driver's situational driving style corresponding to the sensor data based on the classified driving situation and driving style,
(Genovese [0194] Different combination of driver and transport mode have different driving style, moreover each driver has a different driving style depending on external factors e.g. weather, road type, and on personal factors e.g. motivation of the trip, time constraints and trip familiarity. Given previous assumptions, the transport mode recognition 113 and driver passenger detection 112 can be improved based on an in depth recognition and/or analysis of a single person driving style...can contribute to technically define a driving style... extracted from an historical set of trips of a single user allows to define and measure the driver's driving style)
and transmit the identification information of the classified driver's situational driving style the network-based data warehouse system, and
(Genovese [0086] The automated system 1 can e.g. include at least a processor and associated memory modules.
Genovese [0088] a cellular data transmission network 2 by means of antenna connections of the cellular mobile device to the cellular data transmission network
Genovese [0182] UserID: Identifier for the user,)
wherein the network-based data warehouse system is configured to: calculate a driving score related to the driving of the vehicle based on a base score determined according to the driver's situational driving style corresponding to the received identification information, and the rescored event scores of the respective risk events.
(Genovese [0085] for risk scoring
Genovese [0109] The Acceleration Normalization and Gravity subtraction transformations allow to standardize signals
Genovese [0133] These separate input features will be used by the DPD+F classifier (server-side) to re-score the session
Genovese [0182] UserID: Identifier for the user)
Prior Art Cited But Not Applied
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Carver (“DYNAMIC DRIVER AND VEHICLE ANALYTICS BASED ON VEHICLE TRACKING AND DRIVING STATISTICS”, U.S. Publication Number: US 20200286310 A1) proposes driver safety, vehicle safety, and environment safety may be scored based on a variety of input data concerning a driver, a vehicle, or an environment in which the vehicle drives. An overall safety score may be generated based on at least some of these three scores. These scores may be compared to thresholds to trigger or initiate actions such as providing notifications to drivers, raising or reducing vehicle insurance rates, providing coupons and promotions to drivers, or limiting vehicle speed in a manner that is personalized to the driver and/or vehicle and/or environment.
Galm (“DROWSINESS DETECTION”, U.S. Publication Number: US 20190223773 A1) provides to detect and display a mental state of a user such as drowsiness. The mobile electronic device includes a heartrate sensor, a processor, and a display. The heartrate sensor is operable to provide a heartbeat signal indicative of a heartbeat of the user. The processor is operable to: acquire a beat-to-beat interval based upon the heartbeat signal and determine a drowsiness level of the user based at least in part upon the beat-to-beat interval. The display is operable to display an indication of the drowsiness level.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHINEDU EKECHUKWU whose telephone number is (571)272-4493. The examiner can normally be reached on Mon-Fri 10am to 4pm ET.
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/C.E./Examiner, Art Unit 3695
/CHRISTINE M Tran/Supervisory Patent Examiner, Art Unit 3695
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