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 .
Response to Amendment
The amendment filed February 27, 2026 has been entered. Claims 1-4, 6-14, and 16-20 remain pending in the application. Claims 1, 3-4, 6-9, 11, 13, 16, and 18-20 are noted as amended and claims 5 and 15 are noted as cancelled. Applicant’s amendments to the specification and claims have overcome all previous objections and 112(b) rejections set forth in the Non-Final Office Action mailed September 29, 2025 and all objections and rejections therein have been withdrawn. However, new objections are noted below.
Claim Objections
Claim 1 is objected to because of the following informalities:
In claim 1, line 3, “of a vehicle having” should read “of the vehicle having” as the limitation has antecedence in line 2.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-4, 6-14, and 16-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 1 and 11 recite “a vehicle computer device coupled to each of the plurality of vehicle sensors” while also reciting “a control circuit” as a separate device/processor as evidenced by Applicant’s Remarks, filed February 27, 2026. While the specification provides support for a “driving assessment system”, the control circuit is part of the driving assessment system as shown in Fig 2A and not a separate device or system. Therefore, the “vehicle computer device” is new matter as it is not sufficiently described or support in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention.
Claims 2-4, 6-10, 12-14, and 16-20 are rejected by virtue of their dependency from claims 1 and 11.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, 6, 8-13, 16, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuehnle (US PGPub 20220379900) in view of Verma et al. (US PGPub 20240208522), hereinafter referred to as Verma.
With regard to claims 1 and 11, Kuehnle teaches a vehicle safety system [claim 1] (Abstract; Paragraph 0019; “vehicle-based safety intervention system”) and a method [claim 11] (Paragraphs 0003, 0066; “method” ) for assessing a driver's operation of a vehicle over a selected time period and automatically providing a driver performance rating of the driver's operation (Paragraphs 0022-0024, 0041 teach the system can assess driver’s patterns of behavior and/or performance over time including designated time intervals) of a vehicle having a plurality of vehicle sensors disposed in the vehicle (Paragraphs 0020, 0024-0025 teach the system can include one or more sensors as part of the vehicle), comprising:
a vehicle computer device coupled to each of the plurality of vehicle sensors, and adapted to receive vehicle operational signals from each of the vehicle sensors (Fig 2A; Paragraphs 0024-0025 teach the system includes an event detection and reporting system collecting data from the one or more sensors), wherein the vehicle computer device executes a first routine to:
analyze the operational signals and to detect one or more driving events based on the vehicle operational signals (Paragraphs 0019-0020, 0024 teach the system can analyze system and sensor signals/data to detect vehicle events);
analyze if each of the detected driving events is associated with non-compliant operation of the vehicle (Paragraphs 0020, 0024, 0033, 0041 teach the event data/detected events can be representative of various driving events and sensor data including occurrences of “deviant or irregular driving behavior” and thereby determining if the driver’s performance is unsatisfactory (non-compliant));
store an indication of each driving event determined to be associated with non-complaint operation of the vehicle in a first memory as a non-compliant driving event (Paragraphs 0024, 0029, 0038 teach the detection system includes a memory portion and the event detection and event data associated with an event detection may be stored);
a control circuit (Paragraphs 0037-0038; teach the process may be executed on one or more computing systems such as a server) comprising:
a second memory (Paragraph 0038 teaches the server can include a non-transitory computer readable medium/memory for storing instructions and the data/records);
control logic stored in the second memory (Paragraph 0038; “executing instructions stored in a non-transitory computer readable medium”); and
a processor operatively coupled with the second memory, the processor being configured to execute the control logic (Paragraph 0038; “processors capable of executing instructions stored in a non-transitory computer readable medium”) to:
receive a set of event data representative of the non-compliant driving events comprising occurrences of operation of the vehicle during the selected time period that have been determined to be associated with non-compliant operation of the vehicle from the first memory (Paragraphs 0020, 0024, 0033, 0038, 0041 teach the system gathers/receives event data representative of various driving events and sensor data including occurrences of “deviant or irregular driving behavior” wherein the data and events may be transmitted to the server for processing);
analyze the data based on a trend detection model to generate a trend detection result (Paragraphs 0041-0042, 0046 teach the data may be analyzed using a trend-over-time model to determine how the behavior of the driver is changing with time);
determine a driver performance rating of the driver's operation based on the trend detection result (Paragraphs 0041-0042 teach the system may evaluate the driver’s performance based on the trend-over-time model including determining if the performance is unsatisfactory (rating)); and
generate based on the determined driver performance rating a driver performance rating control signal for use in controlling one or more functional aspects of the vehicle (Paragraphs 0028, 0047, 0049 teach the system can intervene based on the evaluation including sending control signals to warn the driver or take a corrective action such as controlling the brakes or throttle).
Kuehnle may not explicitly teach determine, from the set of event data, a rate of non-compliant driving events for each of a plurality of different time periods within the selected time period, wherein each of the plurality of different time periods has a duration less than the selected time period; analyze the determined rates of non-compliant driving events of the plurality of different time periods by applying a trend detection model to the rates of non-compliant driving events to generate a trend detection result. However, Verma teaches a system and method for selectively transmitting alerts based on monitored behavior of a driver including determining frequencies of IDMS alert types for the detected unsafe driving events wherein the system can determine a driver score based on components including frequency of the events and trends based on those frequencies wherein scores and values can be determined on a periodic basis including weekly (different time periods) and wherein scores can be compared over a past number of weeks (selected period) to determine changes in driver behavior and improvements (Paragraphs 0013, 0067, 0077, 0082, 0085, 0123).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kuehnle to incorporate the teachings of Verma by analyzing the event/driving data of Kuehnle to determine frequencies of the event types that result in alerts due to “deviant” driving behavior as taught by Verma, as both references and the claimed invention are directed to driver evaluation and feedback systems using vehicle sensors and gathering vehicle event/driving data. One of ordinary skill in the art would modify Kuehnle by coding the system to determine frequencies of alerts on a periodic basis such as weekly over a selected period of time and analyzing the frequencies to determine driver scores and changes over time to determine trends in driver behavior and alerts. Upon such modification, the method and system of Kuehnle would include determine, from the set of event data, a rate of non-compliant driving events for each of a plurality of different time periods within the selected time period, wherein each of the plurality of different time periods has a duration less than the selected time period; analyze the determined rates of non-compliant driving events of the plurality of different time periods by applying a trend detection model to the rates of non-compliant driving events to generate a trend detection result. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Verma with Kuehnle’s system and method in order to detect trends in driver behavior and provide necessary feedback and alerts based on driving behaviors.
With regard to claims 2 and 12, Kuehnle further teaches wherein the control circuit operates to deliver the driver performance rating control signal to an electronic control unit (ECU) of the vehicle to thereby control one or more functional aspects of the vehicle based on the determined driver performance rating (Paragraphs 0028, 0047, 0049 teach the process may generate and send the control signal to an engine electronic control unit to control aspects of the vehicle based on the evaluated driver performance).
With regard to claims 3 and 13, Kuehnle further teaches wherein the processor is configured to execute the control logic to: generate event rate data representative of rates of occurrences of the driving events determined to be associated with the non-compliant operation during each of a plurality of separate driving trips spanning the selected time period (Paragraphs 0020, 0024 teach the event data can include statistical event data including the rate of occurrences and/or how frequently occurrences happen in a given area or time period).
With regard to claims 4 and 14, Kuehnle further teaches wherein the processor is configured to execute the control logic to: receive the set of event data (Paragraphs 0020, 0024 teach the event data can include statistical event data in a given area or time period); analyze the event data based on the trend detection model to generate the trend detection result (Paragraphs 0041-0042, 0046 teach the data may be analyzed using a trend-over-time model to determine how the behavior of the driver is changing with time); and determine the driver performance rating of the driver's operation based on the trend detection result (Paragraphs 0041-0042 teach the system may evaluate the driver’s performance based on the trend-over-time model including determining if the performance is unsatisfactory (rating)). Kuehnle may not explicitly teach the set of event data comprising event type data representative of type of occurrences of the driving events during the selected time period being determined to be non-compliant operation. However, as discussed above, Verma teaches a system and method for selectively transmitting alerts based on monitored behavior of a driver including determining frequencies of IDMS alert types for the detected unsafe driving events wherein the types correspond to detectable driving scenarios and event types which can be grouped based on the type (Paragraphs 0013, 0067-0071).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kuehnle to incorporate the teachings of Verma by classifying the event/driving data of Kuehnle into alert and event types as taught by Verma, as both references and the claimed invention are directed to driver evaluation and feedback systems using vehicle sensors and gathering vehicle event/driving data. One of ordinary skill in the art would modify Kuehnle by coding the system to classify event/driving data into alert and event types and then performing the trend analysis/applying the trend-over-time model to event type data in order to detect trends and determine driver performance based on event types as Verma also teaches trend analysis per Verma Paragraphs 0085, 0129. Upon such modification, the method and system of Kuehnle would include the set of event data comprising event type data representative of type of occurrences of the driving events during the selected time period being determined to be non-compliant operation and analyzing the event type data. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Verma with Kuehnle’s system and method in order to detect driver trends and improve driver performance across various event types by grouping types and providing corresponding coaching and alerts.
With regard to claims 6 and 16, Kuehnle further teaches wherein the processor is configured to execute the control logic to: analyze the set of event data based on the trend detection model comprising one or more of a linear fit model (Paragraphs 0041-0042, 0044 teach the trend-over-time model can determine a best fit line (linear fit) for the data) and/or a polynomial fit model (Paragraphs 0045-0047 teach the fitting of the line/model may include multi-dimensional/multi-variable analysis resulting in a curve fitting (polynomial fit)) to generate the trend detection result (Paragraphs 0041-0042, 0046); and determine the driver performance rating of the driver's operation based on the trend detection result (Paragraphs 0041-0042 teach the system may evaluate the driver’s performance based on the trend-over-time model including determining if the performance is unsatisfactory (rating)).
With regard to claims 7 and 17, Kuehnle further teaches wherein the processor is configured to execute the control logic to analyze the set of event data using the one or more of the linear fit model and/or the polynomial fit model comprising the occurrences of operation of the vehicle determined to be non-compliant operation (see prior art rejection of claims 6 and 16 above). Kuehnle may not explicitly teach applied to predetermined event types of the driving events. However, as discussed above, Verma teaches a system and method for selectively transmitting alerts based on monitored behavior of a driver including determining frequencies of IDMS alert types for the detected unsafe driving events wherein the types correspond to detectable driving scenarios and event types which can be grouped based on the type (Paragraphs 0013, 0067-0071).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kuehnle to incorporate the teachings of Verma by classifying the event/driving data of Kuehnle into alert and event types as taught by Verma, as both references and the claimed invention are directed to driver evaluation and feedback systems using vehicle sensors and gathering vehicle event/driving data. One of ordinary skill in the art would modify Kuehnle by coding the system to classify event/driving data into alert and event types and then performing the trend analysis/applying the trend-over-time model to event type data of a selected/predefined/predetermined event type and/or grouping of types in order to detect trends and determine driver performance based on event types and provide corresponding coaching and alerts. Upon such modification, the method and system of Kuehnle would include applied to predetermined event types of the driving events. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Verma with Kuehnle’s system and method in order to detect driver trends and improve driver performance across various event types by grouping types and providing corresponding coaching and alerts.
With regard to claims 8 and 18, Kuehnle further teaches further comprising: driver coaching logic stored in the memory device (Paragraphs 0028-0029; “system control logic” which includes corrective actions and warnings), wherein the processor is configured to execute the driver coaching logic to: generate a driver coaching signal representative of a driving instruction (Paragraph 0028 teaches the system may generate a control signal to provide warnings and/or intervene in the operation of the vehicle) based on one or more of the determined driver performance rating (Paragraphs 0041, 0047 teach the system will intervene based on a determination of current poor performance/unsatisfactory performance) and/or a degree of agreement between the one or more of the linear fit model and/or the polynomial fit model and the set of event data (Paragraphs 0042, 0047-0048 teach the system can extrapolate, based on current behavior/event data in view of the trend-over-time model including a best fit line or curve fitting, poor performance or behavior and intervene when the parameters exceed a threshold (degree of agreement)), wherein the driving instruction of the driver coaching signal informs the driver recommended control of the operation of the vehicle based on the determined driver performance rating (Paragraphs 0028, 0047, 0049 teach the system can intervene based on the evaluation including sending control signals to warn the driver or take a corrective action such as controlling the brakes or throttle) and/or a degree of agreement between the one or more of the linear fit model and/or the polynomial fit model and the set of event data (Paragraphs 0042, 0047-0048 teach the system can extrapolate, based on current behavior/event data in view of the trend-over-time model including a best fit line or curve fitting, poor performance or behavior and intervene when the parameters exceed a threshold (degree of agreement)).
With regard to claims 9 and 19, Kuehnle further teaches further comprising: an annunciator operatively coupled with the processor (Fig. 2A, Ref 266; Paragraph 0026 teaches the system may include instrument panel lights), wherein the processor is configured to execute the driver coaching logic to annunciate the driving instruction to the driver via the annunciator (Paragraphs 0026, 0028, 0049, 0052 teach the system including the instrument panel lights can provide warnings (driving instruction)).
With regard to claims 10 and 20, Kuehnle further teaches further comprising: incident prediction logic stored in the memory device (Paragraphs 0042, 0051, 0073 teach the system can include various prediction models including neural networks for predicting future performance or incidents); and incident threshold data stored in the memory device (Paragraphs 0027, 0041, 0048 teach the system includes stored/predetermined thresholds), wherein the processor is configured to execute the incident prediction logic to determine a driving incident prediction by determining an imminent intersection of a trajectory or event rate level resulting from fitting the trend detection model to the set of event data with a predetermined threshold setting represented by the incident threshold data stored in the memory device (Paragraphs 0041-0042, 0049, 0058-0059 teach the system can predict future performance and driver behavior including incidents and unsatisfactory performance based on future extrapolated data generated based on the best fit line/trend-over-time model exceeding a threshold value/predetermined threshold).
Response to Arguments
Applicant’s arguments, see Remarks, filed February 27, 2026, with respect to the rejection(s) of claim(s) 1-4, 6-14, and 16-20 under 35 U.S.C. 101 have been fully considered, but they are not persuasive. Specifically, Applicant’s arguments are primarily directed to arguing the claims cannot be performed as a mental step which is determine under Step 2A Prong One. This is not persuasive as the limitations are interpreted under their broadest reasonable interpretation and whether or not the limitations “recite” limitations that include judicial exceptions under their broadest reasonable interpretation. In this case, the claims still recite limitations that, under their broadest reasonable interpretation, can be interpreted as mental processes except for the recitation of application by computing technology including analyzing the data to detect driving events, analyzing if the events are non-compliant, determining a rate of non-compliant driving, analyzing the determined rates by applying a trend detection model, and determining a driver performance rating. However, Applicant’s amendments to the claims are sufficient in overcoming the rejection on different grounds. Specifically, under Step 2A Prong Two, the amended limitations integrate the judicial exceptions into a practical application by amounting to a particular machine and configuration as the implementation in a vehicle with a plurality of sensors and the “two different computer devices” as Applicant argues amounts to a particular and specific implementation of the judicial exceptions. Thereby, the judicial exceptions are integrated into a practical application and significantly more than the judicial exceptions. The rejection(s) of claim(s) 1-4, 6-14, and 16-20 under 35 U.S.C. 101 has been withdrawn.
Applicant’s arguments, see Remarks, filed February 27, 2026, with respect to the rejection(s) of claim(s) 1-4, 6-14, and 16-20 under 35 U.S.C. 102/103 have been fully considered and are persuasive by virtue of Applicant’s amendments to the claims. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of 35 U.S.C. 103 in view of the newly cited combination of prior art discussed above. It is further noted that not all of Applicant’s arguments are persuasive. Specifically, Applicant argues and implies the statistical data including event rates of Kuehnle only applies to determining if the vehicle has problems and the trend analysis is not applied to the vehicle events. These arguments are not persuasive because the cited paragraphs include teaching that the statistical event data can be used to determine if a driver incurs different numbers of vehicle events (thereby determining driver behavior and performance as discussed in other teachings) and the parameters are used to determine driving events such that the parameter data is associated with events and the trend analysis is performed on the “event data” in the form of the parameter data to determine driver trends, behavior, and performance. Regardless, the new ground of rejection is discussed above.
Conclusion
Accordingly, claims 1-4, 6-14, and 16-20 are rejected.
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CORRELL T FRENCH whose telephone number is (571)272-8162. The examiner can normally be reached M-Th 7:30am-5pm; Alt Fri 7:30am-4pm EST.
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/CORRELL T FRENCH/Examiner, Art Unit 3715
/KANG HU/Supervisory Patent Examiner, Art Unit 3715
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