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 Final Office Action in response to application 19/103,516 entitled "SYSTEM FOR CALCULATING DRIVER DRIVING SCORE, AND DRIVING SCORE CALCULATION METHOD FOR SYSTEM" filed on August 4, 2026, with claims 1, 3-9 and 11-17 pending.
Status of Claims
Claims 1 and 11-15 have been amended and are hereby entered.
Claims 1, 3-9 and 11-17 are pending and have been examined.
Response to Amendment
The amendment filed August 4, 2026 has been entered. Claims 1, 3-9 and 11-17 remain pending in the application. Applicant’s amendments to the Specification, Drawings, and/or Claims have been noted in response to the Non-Final Office Action mailed March 27, 2026.
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, 3-9 and 11-17 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, 3-9 and 11-17 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…in chronological order as a continuous data stream during operation …;
determining, using a …model, at least one preset risk event based on the received … data items;
determining, using a … model, 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, wherein the at least one context is determined only from among a plurality of preset context types corresponding to a type of the each determined risk event …for the determining of the at least one context;
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
wherein the calculating of the driving score comprises:
detecting data related to a driver's driving action from the … data items;
detecting the driver's driving characteristic from the detected driving action data;
classifying the driver's situational driving style based on the detected driving characteristic and the driver's driving situation, and rescoring the rescored event scores again based on the classified driver's situational 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.”
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
[machine learning]:
merely applying machine learning as a tool to perform an abstract idea
[to reduce a computational load]:
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 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).
The act to “reduce a computational load” is a result of “wherein the at least one context is determined only from among a plurality of preset context types corresponding to a type of the each determined risk event” that is an abstract idea related to Selecting A Particular Data Source or Type Of Data To Be Manipulated [Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)]
One abstract idea cannot integrate another abstract idea into a practical application. The invention is merely the abstract idea performed on a processor. An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself." Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016). See also Alice Corp., 573 U.S. at 21-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 78, 101 USPQ2d at 1968 (after determining that a claim is directed to a judicial exception, "we then ask, ‘[w]hat else is there in the claims before us?") (emphasis added)); RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract").
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
Claim 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 11-14: (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 …
wherein …further configured to:
determine a risk event corresponding to an event zone, which is a time section in which … data is detected, based on the …. data items of the event zone and a preset risk event occurrence condition that satisfies the sensor data items;
classify a background situation …is driven based on the driving situation context data into one of a plurality of preset driving situations;
detect at least one driving characteristic of a driver …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, …the identification information of the classified driver's situational driving style the …system, and wherein the …system is configured to:
rescore an event score of the each risk event based on at least one context matching the each risk event; and
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. “
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] [the processor] [network-based data warehouse]:
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] [in which the vehicle] [driving 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
Claim 17:
“network-based data warehouse”: merely applying computer storage 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 § 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 1, 3, 6-9, and 11-16 are rejected under 35 U.S.C. 103 as being unpatentable over Carver (“VEHICLE OPERATION ANALYTICS, FEEDBACK, AND ENHANCEMENT”, U.S. Publication Number: US 20200334762 A1), in view of Vij (“ANALYTICS PLATFORM USING TELEMATICS DATA”, U.S. Publication Number: US 20170140293 A1),in view of Cella (“ROBOT FLEET MANAGEMENT FOR VALUE CHAIN NETWORKS”, U.S. Publication Number: US 20220187847 A1).
Regarding Claim 1,
Carver 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 in chronological order as a continuous data stream during operation of the vehicle;
(Carver [0027] A connected vehicle may collect and share...data over a telemetry network in real-time.
Carver [0037] gathers data for use by a Driver Scoring Processor/Database
Carver [0036] Using these various sensors
Carver [0039] identifying and coding events recorded on audio or video records ... by going back in the data in reverse chronological order)
determining, using a machine learning model, at least one preset risk event based on the received sensor data items;
(Carver [0116] personalized or customized thresholds or limits can be generated for acceleration, deceleration, turning radius, brake strength, duration of horn usage, frequency of horn usage, duration of high-beam usage, frequency of high-beam usage, or combinations thereof
Carver [0036] Driving data 105 may include driver identity, position, velocity, yaw/pitch/roll, lateral, vertical and longitudinal acceleration/deceleration, heading and/or time. Additionally driving data can include driver inputs such as throttle position, rate of change of steering commands and use of automation such as ADAS
Carver [0042] captured data 105 may additionally or alternatively include vehicle data, such as speed, velocity, yaw, pitch, roll, linear acceleration or deceleration, non-linear acceleration or deceleration, lateral acceleration or deceleration, longitudinal acceleration or deceleration, lift, vertical acceleration or deceleration, heading.
Carver [0081] The machine learning algorithm performs the function to mine the incoming data stream for this data in an on-demand process.
Examiner notes the Specification defines [0004] "preset risk event (rapid acceleration, rapid deceleration, speeding, impact, etc.)" )
determining, using a machine learning model, 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, wherein the at least one context is determined only from among a plurality of preset context types corresponding to a type of the each determined risk event
(Carver [0022] The safety indices may be used to generate a personalized speed threshold or acceleration threshold for a vehicle and/or driver
Carver [0123] that may identify a speed limit along a roadway or a replacement maximum speed, such as the personalized/customized speed thresholds/limits
Carver [Claim 1] identifying that the first speed of the vehicle exceeds a speed threshold
Examiner notes the "speed threshold" only considers inputs related to velocity, ignoring elements such as "duration of horn usage, frequency of horn usage, duration of high-beam usage, frequency of high-beam usage")
wherein the calculating of the driving score comprises: detecting data related to a driver's driving action from the sensor data items;
(Carver [0042] capturing data from which safety scores may be generated. Data collected ... may include driver information, vehicle identifying information, camera image data, and infrastructure data...captured data 105 may additionally or alternatively include sensor data from sensors onboard the vehicle in question
Carver [0039] frequency of corrective actions)
detecting the driver's driving characteristic from the detected driving action data;
(Carver [0094] for specific driver characteristics)
classifying the driver's situational driving style based on the detected driving characteristic and the driver's driving situation,
(Carver [0007] generate accurate heuristics for vehicle and driver behavior.
Carver [0062] trip to trip variation with respect to quantifying risk. FIG. 4 includes a vertical axis of percent variation and a horizontal axis of risk (0-10)
Carver [0116] an extremely aggressive driver with a low driver safety index
Carver [0027] evaluate driver behavior (e.g., as low-risk, versus high-risk))
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.
(Carver [0014] percentages of trip to trip variation with respect to quantifying risk.
Carver [0038] indices are then summed for a common group of trips, or period of time using a time weighted average)
Carver does not teach to reduce a computational load for the determining of the at least one context; 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 of the vehicle based on the rescored event scores of the respective risk events; and rescoring the rescored event scores again based on the classified driver's situational driving style; reflecting the rescored event scores.
Vij teaches,
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 of the vehicle based on the rescored event scores of the respective risk events,
(Vij [Claim 4] applying a second weight factor to the acceleration/trip score to create weighted second score... generating the trip risk score based on a sum of the weighted first score, the weighted second score)
and rescoring the rescored event scores again based on the classified driver's situational driving style;
(Vij [0022] trip risk score may include a numerical value (e.g., from 1 to 10), a string value (e.g., high risk or low risk), or the like, that indicates a level of risk associated with the trip
Vij [0004] where the driver risk score may include a second metric that indicates a level of risk associated with the driver;
Vij [0018] generating a basic trip risk score and/or an adjusted trip risk score
Vij [Claim 5] determining the trip risk score based on the first score, the second score, or the third score.
Vij [Claim 6] determining the trip risk score based on the first score, the second score, and the third score comprises: determining the trip risk score based on a sum of the weighted first score, the weighted second score, and the weight third score.)
reflecting the rescored event scores
(Vij [0004] where the driver risk score may include a second metric that indicates a level of risk associated with the driver;
Vij [Claim 6] determining the trip risk score based on the first score, the second score, and the third score comprises: determining the trip risk score based on a sum of the weighted first score, the weighted second score, and the weight third score.)
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 of Carver to incorporate the re-scoring teachings of Vij such that “determining the … score based on the first score, the second score, and the third score.” (Vij [Claim 6]). The modification would have been obvious, because it is merely applying a known technique (i.e. re-scoring) to a known concept (i.e. driving score) ready for improvement to yield predictable result (i.e. “analytics platform may generate a driver risk score that more accurately assesses the driving risk associated with the driver and/or vehicle.” Vij [0017])
Vij does not teach to reduce a computational load for the determining of the at least one context;
Cella teaches,
to reduce a computational load for the determining of the at least one context;
(Cella [2200] selective sensor data filtering for reduced impact on communication bandwidth (e.g., reducing the demand for wireless network utilization))
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 of Carver to incorporate the computational load reduction teachings of Cella for “bandwidth allocation.” (Cella [0469]). The modification would have been obvious, because it is merely applying a known technique (i.e. computational load reduction) to a known concept (i.e. driving score) ready for improvement to yield predictable result (i.e. “reduce bandwidth on a communication network, and/or reduce the computational resources required at a backend system.” Cella [1231])
Regarding Claim 3,
Carver, Vij, and Cella teach the driving score of Claim 1 as described earlier.
Carver teaches,
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 as an event zone,
(Carver [0063] collection of vehicle data that includes VIN number, odometer readings, dates, times,
Carver [0113] Time of day is important, as drivers driving late at night generally have lower visibility of their surroundings and are generally more fatigued and therefore drive less safely. At the same time, drivers driving at or near local sunset or local sunrise times are likely to experience sunlight shining in their eyes, which may similarly reduce visibility… Road direction can be important in combination with other factors such as slope, to determine if the road travels uphill or downhill, or wind direction, to determine whether the wind is pushing/pulling vehicles)
and 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.
(Carver [0036] Driving data 105 may include driver identity, position, velocity, yaw/pitch/roll, lateral, vertical and longitudinal acceleration/deceleration, heading and/or time.
Carver [0042] captured data 105 may additionally or alternatively include environmental data such a data describing weather, road conditions, time zone, time of day, exact time
Carver [0113] to determine whether the wind is pushing/pulling vehicles toward an unsafe direction, such as toward a cliff. Whether the road is a one-way road or a two-way road may also impact safety, as driving along one-way roads carries a lower risk of head-on collisions between vehicles.)
Regarding Claim 6,
Carver, Vij, and Cella teach the driving score of Claim 1 as described earlier.
Carver 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,
(Carver [0036] Driving data 105 may include driver identity, position, velocity
Carver [0008] identifying a speed of the vehicle from the location data and timing information....identifying that the speed of the vehicle exceeds a speed threshold, the speed threshold based on the safety index.
Carver [0037] speed limit of the road)
and a driving stability characteristic according to a speed deviation.
(Carver [0036] Driving data 105 may include driver identity, position, velocity, yaw/pitch/roll, lateral, vertical and longitudinal
Carver [0098] driver safety indices to be used to predict collisions based on the trip to trip standard deviation of the results.)
Regarding Claim 7,
Carver, Vij, and Cella teach the driving score of Claim 1 as described earlier.
Carver 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.
(Carver [0115] how likely the driver of the vehicle is to get into am accident/collision based on the driver's past history of accidents/collisions
Carver [0042] time of day
Carver [0077] Predicting number of passengers in the vehicle, based on input location, time, music selection, prior behavior)
Regarding Claim 8,
Carver, Vij, and Cella teach the driving score of Claim 1 as described earlier.
Carver 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,
(Carver [0036] driving data can include driver inputs such as throttle position, rate of change of steering commands and use of automation such as ADAS (advanced driver assistance systems).
Carver [0042] include driver information, vehicle identifying information, camera image data
Carver [0067] external systems monitoring the general environment of the vehicle, such as ...onboard vehicle cameras and roadside safety cameras.)
a speed and a moving direction of the other vehicle sensed from a vehicle-to-vehicle (V2V) communication unit.
(Carver [0037] vehicle to infrastructure (“V2I”) information, and vehicle to vehicle (“V2V”) data exchanges.
Carver [0138] FIG. 9 illustrates intelligent vehicle control based on nearby vehicle behavior....may receive data from telemetric devices of all three of the vehicles
Carver [0042] vehicle data, such as speed, velocity, yaw, pitch, roll, linear acceleration or deceleration... heading....proximity sensors (e.g., laser rangefinders, radar transceivers, sonar transceivers, LIDAR transceivers) identifying distance from the vehicle in question to nearby vehicles/obstacles... may additionally or alternatively include sensor data from sensors such as any of those discussed above but onboard other nearby vehicles)
Regarding Claim 9,
Carver, Vij, and Cella teach the driving score of Claim 1 as described earlier.
Carver teaches,
at least one of location information of the vehicle, speed information of the vehicle, and information on a driving path of the vehicle.
(Carver [0042] vehicle data, such as speed, velocity, yaw, pitch, roll, linear acceleration or deceleration... heading
Carver [0121] GPS module 740 may provide information (such as longitude and latitude data) that identifies a current location of the vehicle)
Regarding Claim 11,
Carver, Vij, and Cella teach the driving score of Claim 1 as described earlier.
Carver does not teach 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; 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.
Vij 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;
(Vij [0022] trip risk score may include a numerical value (e.g., from 1 to 10), a string value (e.g., high risk or low risk), or the like, that indicates a level of risk associated with the trip
Vij [0004] where the driver risk score may include a second metric that indicates a level of risk associated with the driver;
Vij [0021] driving style variables may include one or more variables associated with a manner in which the driver drives the vehicle during a particular trip, (e.g., as compared to a threshold, as compared to other drivers, or the like) such as one or more speed related driving style variables, one or more acceleration related driving style variables
Vij [0018] generating a basic trip risk score and/or an adjusted trip risk score)
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.
(Vij [Claim 14] derive driving style variables, associated with a manner in which the vehicle is driven during the driving trip
Vij [0115] The risk adjustment may indicate an amount by which the basic trip risk score may be modified (e.g., increased or decreased) due to the occurrence (or non-occurrence) of high risk or low risk events during the trip
Vij [0021] manner in which the driver drives the vehicle during a particular trip, (e.g., as compared to a threshold, as compared to other drivers, or the like))
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 of Carver to incorporate the re-scoring teachings of Vij such that “determining the … score based on the first score, the second score, and the third score.” (Vij [Claim 6]). The modification would have been obvious, because it is merely applying a known technique (i.e. re-scoring) to a known concept (i.e. driving score) ready for improvement to yield predictable result (i.e. “analytics platform may generate a driver risk score that more accurately assesses the driving risk associated with the driver and/or vehicle.” Vij [0017])
Regarding Claim 12,
Carver, Vij, and Cella teach the driving score of Claim 1 as described earlier.
Carver does not teach 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.
Vij 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.
(Vij [0067] derive a turning related location-based variable based on comparing a first direction of movement (e.g., from 0 degrees to 360 degrees), determined from a first latitude/longitude pair and a second latitude/longitude pair, ...and a third latitude/longitude pair.
Vij [0026] basic trip risk scores or adjusted trip risk scores (e.g., determined in the manner described above) for a set of vehicle trips associated with the driver.)
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 of Carver to incorporate the trip scoring teachings of Vij such that “determine a set of trip attributes.” (Vij [Abstract]). The modification would have been obvious, because it is merely applying a known technique (i.e. trip scoring) to a known concept (i.e. driving score) ready for improvement to yield predictable result (i.e. “generate a trip risk score, associated with the driving trip and based on the set of trip attributes” Vij [Abstract])
Regarding Claim 13,
Carver, Vij, and Cella teach the driving score of Claim 1 as described earlier.
Carver does not teach 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.
Vij 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.
(Vij [0026] basic trip risk scores or adjusted trip risk scores (e.g., determined in the manner described above) for a set of vehicle trips associated with the driver.
Vij [0069] location-based variable (e.g., mid-night, early morning, daytime, afternoon, evening, or night).
Vij [0065] may derive a speed of vehicle 205 based on a distance travelled (as determined by a difference in distance from a first latitude/longitude pair to a second latitude/longitude pair) and based on an amount of time elapsed (as determined by a difference between a first timestamp, associated with the first latitude/longitude pair, and a second timestamp
Vij [0067] analytics platform 235 may associate a timestamp with each turning related location-based variable (e.g., a time of day, a date).)
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 of Carver to incorporate the trip scoring teachings of Vij such that “determine a set of trip attributes.” (Vij [Abstract]). The modification would have been obvious, because it is merely applying a known technique (i.e. trip scoring) to a known concept (i.e. driving score) ready for improvement to yield predictable result (i.e. “generate a trip risk score, associated with the driving trip and based on the set of trip attributes” Vij [Abstract])
Regarding Claim 14,
Carver, Vij, and Cella teach the driving score of Claim 1 as described earlier.
Carver teaches,
providing a result of analyzing a history of moving scores stored for a preset period of time according to a risk event or context, or analyzing the driver's driving action
(Carver [0092] trip scores are associated with specific time points....Mining temporal patterns backward in time, starting from trip or behavior patterns
Carver [0031] data collection or similarity of data sources, are demonstrable predictive results which correlate very well with loss history over time.
Carver [0091] the historical sum of events)
as feedback information on the driver's driving score for the preset period of time.
(Carver [0029] capable of time stamping spatiotemporal data....two-way exchange with server side generated risk variables, can provide contextually relevant and peer-based driver feedback)
Carver does not teach storing the moving score calculated over time; based on an increase or decrease in the driving score.
Vij teaches,
storing the moving score calculated over time;
(Vij [[0018] the location data and/or the acceleration data is stored by the telematics data server
Vij [0065] may derive a speed of vehicle 205 based on a distance travelled ...and based on an amount of time elapsed (as determined by a difference between a first timestamp, associated with the first latitude/longitude pair, and a second timestamp)
based on an increase or decrease in the driving score.
(Vij [0004] risk scores, corresponding to a driver, for a set of driving trips
Vij [0115] The risk adjustment may indicate an amount by which the basic trip risk score may be modified (e.g., increased or decreased) due to the occurrence (or non-occurrence) of high risk or low risk events during the trip.)
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 of Carver to incorporate the score adjustment teachings of Vij such that “analytics platform may adjust and/or provide the trip risk score for use.” (Vij [0023]). The modification would have been obvious, because it is merely applying a known technique (i.e. score adjustment) to a known concept (i.e. driving score) ready for improvement to yield predictable result (i.e. “score may be modified (e.g., increased or decreased) due to the occurrence (or non-occurrence) of high risk or low risk events during the trip” Vij [0115])
Claim 15 is rejected on the same basis as Claims 1 and Claims 3, combined with specific mention of multiple data collection devices.
(Carver [0004] Some vehicles include navigation devices with integrated GNSS receivers
Carver [0036] onboard hardware may include cameras, sensors, factory installed telematics equipment, an after-market telematics device, a mobile device (such as a phone or tablet), and the like. Using these various sensors)
Regarding Claim 16,
Carver, Vij, and Cella teach the driving score of Claim 15 as described earlier.
Carver 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.
(Carver [0003] a GNSS receiver device receives signals broadcast by multiple GNSS satellites orbiting the Earth, and, based on the signals from these satellites, is able to determine its own location. Common GNSS systems include the US-based Global Positioning System (GPS), the Russia-based Global Navigation Satellite System (GLONASS), the China-based BeiDou Navigation Satellite System (BDS), and the Europe-based Galileo GNSS.
Carver [0029] one or more Global Navigation Satellite System (GNSS) receiver (using, for example, the GPS, GLONASS, Galileo or Beidou systems)
Carver [0158] Alternative embodiments may perform the operations in a different order, combine certain operations, overlap certain operations)
Claims 4 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Carver, Vij, and Cella in view of Hansen (“CENTRALLY MANAGED WAYPOINTS ESTABLISHED, COMMUNICATED AND PRESENTED VIA VEHICLE TELEMATICS/INFOTAINMENT INFRASTRUCTURE”, U.S. Publication Number: US 20170108348 A1).
Regarding Claim 4,
Carver teaches,
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
(Carver [0036] Driving data 105 may include..., heading and/or time.
Carver [0042] approach to capturing data from which safety scores may be generated...captured data 105 may additionally or alternatively include ... time zone, time of day, exact time)
determining a context representing a driving situation of the vehicle matching the each event zone based on the extracted context data items.
(Carver [0064] subject matter and index approach is the clear delineation of risk associated with the collision level index 110 for the driving situation...from that of a driver (e.g., aggressiveness, impaired vision, fatigue and other debilitating conditions) in a driver safety index 108.)
Carver does not teach a time section including predetermined periods of time before and after the each event zone;
Hansen teaches,
a time section including predetermined periods of time before and after the each event zone;
(Hansen [0032] a changed configuration parameter value and/or a changed status parameter value—including the passage of a period of time after a current subset has been designated
Hansen [0037] previously visited waypoints
Hansen [0091] multiple position-linked maximum delay values are maintained in the delay factors 420 to account for timeliness requirements of waypoints prior to the trip final destination.
Hansen [0095] including another waypoint at a point along the trip route before the waypoint described in the waypoint record.
Hansen [0003] a specified location (current or intended future))
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 of Carver to incorporate the zoned time section teachings of Hansen for “an estimated time of arrival (ETA) at the destination, a desired time of arrival at the destination, and a delay factor indicating a degree to which the ETA can exceed the desired time of arrival.” (Hansen [Claim 1]). The modification would have been obvious, because it is merely applying a known technique (i.e. zoned time sections) to a known concept (i.e. driving score) ready for improvement to yield predictable result (i.e. “configuring,…a trip definition.” Hansen [Claim 1])
Claim 17 is rejected on the same basis as Claim 4 with the additional limitation of “match each risk event with at least one context based on a risk event and a context determined from each event zone, and transmit the matching result to the network-based data warehouse system.”
(Genovese [0194] (i) Correlation between accelerometer and GPS speed, (ii) Frequency of maneuvers and phone distraction events per kilometers, (iii) In depth analysis of speed distribution while turning taking into consideration curvature degrees.....taking in consideration road sinuosity, speed limit and road class, and (v) Analysis and feature extraction.... as a function of road class, sinuosity and shape.....from an historical set of trips of a single user allows to define and measure the driver's driving style.
Genovese [Claim 1] a wireless node within a cellular data transmission network .... captures usage-based and/or user-based telematics data of the mobile device and/or the user of the mobile device
Genovese [0015] is sent and stored to the database as “DPD events”.)
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Carver, Vij, and Cella in view of Chun (“ON-VEHICLE SITUATION DETECTION APPARATUS AND METHOD”, U.S. Publication Number: US 20160009295 A1).
Regarding Claim 5,
Carver, Vij, and Cella teach the driving score of Claim 1 as described earlier.
Carver teaches,
wherein the classifying of the driver's situational driving style comprises:
detecting the driver's driving characteristic from the remaining driving action data items
(Carver [0091] The majority of existing temporal classification methods assume that each input trip score (normally represented by a single series, but can be represented by multiple time series) is associated with a uni-variant classification, or profile, representing behavior for the entire trip.
Carver [0092] Our approach to mining the indices proposes a novel temporal pattern mining approach for event detection within the clustered trip scores,...Mining temporal patterns backward in time...results in a trip classification more valuable than the simple summary of events by classification within a trip....pattern mining produces results more predictive of actual loss experience (as measured by collisions) than simply treating all trips as time independent (both from an aging and a duration perspective).
Carver [0023] indexing methods allows for and anticipates the exclusion of blocks of private information for individual drivers.)
Carver does not teach excluding the driving action data corresponding to the determined risk event from among the sensor data items.
Chun teaches,
excluding the driving action data corresponding to the determined risk event from among the sensor data items.
(Chun [0016] performing noise removal (S440) of excluding a current driving pattern from a subject to be learned)
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 of Carver to incorporate the data exclusion teachings of Chun for “excluding a current driving pattern.” (Chun [0016]). The modification would have been obvious, because it is merely applying a known technique (i.e. data exclusion) to a known concept (i.e. driving score) ready for improvement to yield predictable result (i.e. “performing noise removal.” Chun [0016])
Response to Remarks
Applicant's arguments filed on August 4, 2026, have been fully considered and Examiner’s remarks to Applicant’s amendments follow.
Response Remarks on Receipt of Priority Documents
Examiner acknowledge receipt of all certified copies of the priority documents.
Response Remarks on Claim Rejections - 35 USC § 112
Applicant's amendments rectify the previous rejections under 35 USC § 112.
The rejection under 35 USC § 112 is lifted.
Response Remarks on Claim Rejections - 35 USC § 101
The Applicant states:
“Firstly, Applicant respectfully submits that the previous rejections under 101 are improper as the Office Action identifies the alleged abstract idea by quoting only the isolated phrases "determining at least one preset risk event" and "calculating a driving score." It is submitted that the claimed features must be evaluated as a whole."
Examiner responds:
Lines 26-41 and lines 151-167 of the previous office action detail in totality the extent of the abstract idea of the independent claims.
The Applicant states:
“As for the amended claim 1, the claim recites a specific technique in which a machine learning model, applied to a continuous, chronologically-ordered stream of sensor data from a plurality of heterogeneous vehicle sensors, detects risk events and determines context after which event scores are rescored at successive levels of granularity and aggregated into a moving score. The determination of the context is limited only to context types relevant to the detected risk-event type to reduce computational overhead."
Examiner responds:
A “specific technique in which a … model, applied to a continuous, chronologically-ordered stream of … data …detects risk events and determines context after which event scores are rescored at successive levels of granularity and aggregated into a moving score” expresses an abstract idea.
The gathering, sharing, and manipulation of data expresses an Abstract Idea [Intellectual Ventures I v. Capital One Fin. Corp., 850 F.3d 1332, 121 USPQ2d 1940 (Fed. Cir. 2017) “collecting, displaying, and manipulating data” was considered part of the abstract idea]
The “machine learning” and “vehicle sensors” 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.
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).
The focus of the claims is not on an improvement in computers, machine learning, and sensors as tools, but on certain independently abstract ideas that use computers, machine learning, and sensors as tools.
The act “to reduce computational overhead” is a result of “wherein the at least one context is determined only from among a plurality of preset context types corresponding to a type of the each determined risk event” that is an abstract idea related to Selecting A Particular Data Source or Type Of Data To Be Manipulated [Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)]
One abstract idea cannot integrate another abstract idea into a practical application. The invention is merely the abstract idea performed on a processor. An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself." Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016). See also Alice Corp., 573 U.S. at 21-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 78, 101 USPQ2d at 1968 (after determining that a claim is directed to a judicial exception, "we then ask, ‘[w]hat else is there in the claims before us?") (emphasis added)); RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract").
The Applicant states:
“With respect to claim 15, the claim recites a distributed processing architecture in which the device's processor itself determines the risk event, classifies the driving situation, classifies the driver's driving style, and classifies the driver's situational driving style, and transmits to the network-based data warehouse system only the identification information of the classified situational driving style--not raw sensor data or context data. ….reduces the amount of data transmitted over the network relative to transmitting raw sensor data for centralized processing."
Examiner responds:
The act that “determines the risk event, classifies the driving situation, classifies the driver's driving style, and classifies the driver's situational driving style, and …. to the network-based data warehouse system only the identification information of the classified situational driving style--not raw sensor data or context data” amounts to gathering, sharing, and manipulation of data that expresses an Abstract Idea [Intellectual Ventures I v. Capital One Fin. Corp., 850 F.3d 1332, 121 USPQ2d 1940 (Fed. Cir. 2017) “collecting, displaying, and manipulating data” was considered part of the abstract idea]
The Applicant states:
“This is not the type of generic "receiving or transmitting data over a network" activity addressed in the cases cited for well-understood, routine, conventional activity. Instead, the claimed features are analogous to the distributed architecture the Federal Circuit found to supply an inventive concept in Amdocs (Israel) Ltd. v. Openet Telecom, Inc., 841 F.3d 1288 (Fed. Cir. 2016), where processing network usage information close to its source, rather than centrally, reduced the burden on network resources."
Examiner responds:
In the case Amdocs (Israel) Ltd. v. Openet Telecom, Inc, the suit involved four related patents—U.S. Patents Nos. 7,631,065 (the '065 patent), 7,412,510 (the '510 patent), 6,947,984 (the '984 patent), and 6,836,797 (the '797 patent). Three of whom had provisional applications filed as early as November 20, 1997 (priority to US6689897P) describing servers, databases, and client terminals:
“arrayed in a distributed architecture that minimizes the impact on network and system resources. Through this distributed architecture, the system minimizes network impact by collecting and processing data close to its source. The system includes distributed data gathering, filtering, and enhancements that enable load distribution. This allows data to reside close to the information sources, thereby reducing congestion in network bottlenecks, while still allowing data to be accessible from a central location. Each patent [specification] explains that this is an advantage over prior art systems that stored information in one location, which made it difficult to keep up with massive record flows from the network devices and which required huge databases”
It required a non-conventional network and “non-conventional and non-generic arrangement of known, conventional pieces,” (see MPEP 2106.05(d) in overcoming “well-understood, routine, and conventional”) yielding improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a).
Moreover, in this Instant Application, neither the Claims nor Specification mention distributed networks/computing or edge architecture.
Therefore, the rejection under 35 USC § 101 remains.
Response Remarks on Claim Rejections - 35 USC § 102/103
Applicant's amendments required the application of new/additional prior art.
Applicant’s remarks regarding the rejection made under 35 USC § 102/103 are rendered moot by the introduction of new prior art.
Therefore, a rejection under 35 USC § 103 remains.
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
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/C.E./Examiner, Art Unit 3695
/CHRISTINE M Tran/Supervisory Patent Examiner, Art Unit 3695
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