DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This is a Non-Final Office Action on the merits. Claims 1-20 are currently pending and are addressed below.
Examiner notes that the fundamentals of the rejection are based on the broadest reasonable interpretation of the claim language. Applicant is kindly invited to consider the reference as a whole. References are to be interpreted as by one of ordinary skill in the art rather than as by a novice. See MPEP 2141. Therefore, the relevant inquiry when interpreting a reference is not what the reference expressly discloses on its face but what the reference would teach or suggest to one of ordinary skill in the art.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/15/2026 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/19/2026, 05/21/2026, 06/09/2025 and 03/17/2023 are being considered by the examiner.
Response to Arguments
Applicant’s amendments and/or arguments with respect to the rejection of claims 1-20 under 35 USC 103 as set forth in the office action of 12/16/2026 have been considered but are moot because the new ground(s) of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Objections
Claims 1 , 9 and 17 objected to because of the following informalities: claims 1, 9 and 17 recite “based on an execution of a machine learning model on the speed and the size” should be replaced with -- based on an execution of a machine learning model on the speed of the vehicle and the size of lead vehicle---
Appropriate correction is required.
Claim Rejections - 35 USC § 103
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, 7, 9, 15 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lindholm et al US2019/023296 in view of Haga et al US2022/0221550 further in view of Ghalami et al. US2023/0343145.
Regarding claims 1, 9 and 17, Lindholm discloses a method, computer-readable storage medium comprising instructions, and an apparatus (apparatus see at least [¶ 03]) comprising:
a processor that executes instructions stored in a memory (memory stores machine learning to be usable see at least [¶ 33 & 44-46]) to configure the processor to: determine a size of aand a speed of a vehicle following the lead vehicle based on sensor data (determine the vehicle speed (travelling speed of the vehicle [¶ 26-27]) and size (weight and shape) of the lead vehicle see at least [¶34]);
generate a recommended gap distance between the vehicle and the lead vehicle based on an execution of a machine learning model on the speed and the size (machine leaning [¶ 46], system determines the gap distance between vehicle based on the host vehicle speed and the size of the leading vehicle see at least [¶ 21, 24, 27 & 44-46] and Fig. 1-8A-C);
control the speed of the vehicle to maintain the recommended gap distance (the controller changes the speed to implement the gap distance, furthermore Lindholm discloses an adaptive cruise control system that determined if the speed need to be changed/adjusted to maintain recommended gap, that means the system control speed of the speed of the vehicle to maintain gap see at least [¶ 21, 27, 34, 42 & 46]);
Lindholm is silent on, however, in the same field of endeavor, Haga discloses generate a digital twin of the vehicle; and transfer the recommended gap distance to a different vehicle via the digital twin (the system determines distance between vehicles using front tags (succeeding vehicle) and back tags (preceding vehicle), additionally, Haga discloses the system generates digital twin of vehicles. Furthermore, Haga discloses the “vehicle-to-vehicle (V2V) communication protocols for high-performance and high-reliability platooning that may be used for inter-vehicle communication to optimize the distance between the vehicles, ….. automatic driving during platooning…. gap between vehicles”, that means the digital twin communicate/transfer with other vehicles different information including gap in order to optimize/recommended the gap between vehicle see at least [¶ 03-04, 026-027 & 043-044 & 046] and Fig. 3 &5). Therefore, from the teaching of Haga, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Lindholm to use the technique of generating a digital twin of the vehicle, and transferring the recommended gap distance to a different vehicle via the digital twin similar of that of the teaching of Haga in order to enhance motor vehicle safety.
Furthermore, Ghalami discloses the digital twin transfer data and information to other vehicles (see at least [¶ 021 & 027] and Fig. 1). Therefore, from the teaching of Ghalami, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Lindholm to use the technique of digital twin transferring information/ gap to other vehicle similar of that of the teaching of Ghalami in order to enhance motor vehicle safety.
5. Regarding claims 7 and 15, Lindholm, Haga and Ghalami in combination limitations of claim 1 as discussed above, Lindholm is silent on, however, in the same field of endeavor, Haga discloses wherein the recommended gap distance is transferred to the different vehicle via the digital twin in response to a request (the system determines distance between vehicles using front tags (succeeding vehicle) and back tags (preceding vehicle), additionally, Haga discloses the system generates digital twin of vehicles furthermore, Haga discloses the “vehicle-to-vehicle (V2V) communication protocols for high-performance and high-reliability platooning that may be used for inter-vehicle communication to optimize the distance between the vehicles, ….. automatic driving during platooning…. gap between vehicles”, that means the digital twin communicate/transfer with other vehicles different information including gap in order to optimize/recommend the gap between vehicle based on request see at least [¶ 03-04, 026-027 & 043-044 & 046] and Fig. 3 &5). Therefore, from the teaching of Haga, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Lindholm to use the technique of transferring the recommended gap distance to a different vehicle via the digital twin in response to a request similar of that of the teaching of Haga in order to enhance motor vehicle safety.
Claims 2 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Lindholm in view of Haga and Ghalami further in view of Wigard (US20190184993 A1).
7. Regarding claims 2 and 10, Lindholm, Haga and Ghalami in combination limitations of claims 1 and 9 as discussed above, furthermore, Lindholm discloses wherein the apparatus comprises a network interface configured to receive the sensor data from the vehicle while following the lead vehicle (see at least [¶ 27& 31] and Fig. 2),
Lindholm does not appear to explicitly disclose on, however, in the same field of endeavor, Wigard discloses receive environmental data comprising weather data associated with the vehicle; and receive traffic data associated with the vehicle (Wigard see at least [¶ 0002] discloses: “Vehicles may include vehicle-based radio systems in order to provide or obtain information, such as traffic information, weather information, and the like. This information may be provided to, or obtained from, other vehicles, roadside sensors, servers, and/or the like.”). furthermore, Wigard discloses “Moreover, the vehicle-based radios may establish communications with other devices or objects, such as sensors, internet of things devices” see at least [¶ 0036]). Therefore, from the teaching of Wigard, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Lindholm to use the technique of receiving sensor data, weather data and traffic data associated with the vehicle similar of that of the teaching of Wigard in order to enhance motor vehicle safety.
Claims 3-6, 8, 11-14, 16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lindholm in view of Haga and Ghalami further in view of Laws WO2020014090.
9. Regarding claims 3, 11 and 18, Lindholm, Haga and Ghalami in combination limitations of claim 1 as discussed above, furthermore, Lindholm discloses wherein the processor is configured to train the machine learning model to recommend different gap distances (see at least [¶ 44-46])
Lindholm does not appear to explicitly disclose on, however, in the same field of endeavor, Laws discloses different types of lead vehicles based on a plurality of different events sequences in which the vehicle is following a respective lead vehicle (“When determining what operations may need to be performed by a following vehicle (to actuate any and all embodiments described herein), a system may base its determination on attributes including, … path history, path projection, travel plans,… vehicle type”), additionally, (Laws, see at least [¶ 0058], discloses: “For example a neural network may be trained to accomplish operations described herein with respect to automated/semi-automated platooning”). Furthermore, Laws see at least [¶ 0058], discloses “Similarly, such systems be used to assist with .… vehicle controls, vehicle dynamics”). Platooning is a term of the art used to refer to groups of vehicles following each other, for example, vehicles that are following a lead vehicle such as a (“respective lead vehicle”) as described in the claim language supra. furthermore, Laws discloses that neural networks may be used by a following vehicle to assist with dynamics, that is, the motion of the vehicle, such as implementing a gap distance). Therefore, from the teaching of Laws, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Lindholm to use the technique different gap distances for different types of lead vehicle based on a plurality of different event sequences in which the vehicle is following a respective lead vehicle similar of that of the teaching of Laws in order to enhance motor vehicle safety.
Regarding claims 4, 12 and 19, Lindholm, Haga and Ghalami in combination disclose limitations of claim 1 as discussed above, furthermore, Lindholm discloses train the machine learning model to recommend different gap distances (see at least [¶ 38 & 46]. Lindholm does not appear to explicitly disclose on, however, in the same field of endeavor, Laws discloses wherein the processor is configured to train the machine learning model to recommend different gap distances for the vehicle based on different types of weather events (Laws, see at least [¶ 0097] teaches: “the control for path following is adjusted as a function of one or more dynamic conditions (e.g., the gap, vehicle speeds, weather, wind or other perturbations, potholes, traffic, etc.”). it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Lindholm to use the technique recommending different gap distances for the vehicle based on existence of different types of weather events similar of that of the teaching of Laws in order to enhance motor vehicle safety.
10. Regarding claims 5, 13 and 20, Lindholm, Haga and Ghalami in combination limitations of claim 1 as discussed above, discloses train the machine learning model to recommend different gap distances (see at least [¶ 38 & 46]).
Lindholm does not appear to explicitly disclose on, however, in the same field of endeavor, Laws discloses wherein the processor is configured to train the machine learning model to recommend different gap distances for the vehicle based on of different types of traffic patterns (“This gap may also be chosen to reduce the frequency of vehicles cutting in between the two vehicles, for example by reducing the gap in areas of heavy traffic.”, that means the system recommends different gap distance based on different types of traffic see at least [¶ 98]. it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Lindholm to use the technique recommending different gap distances for the vehicle based on existence of different types of traffic patterns similar of that of the teaching of Laws in order to enhance motor vehicle safety.
11. Regarding claims 6 and 14, Lindholm, Haga and Ghalami in combination limitations of claim 1 as discussed above, furthermore, Lindholm discloses iteratively update the machine learning model to recommend different gap distances (see at least [¶ 34 & 46]). Lindholm does not appear to explicitly disclose on, however, in the same field of endeavor, Laws discloses processor is configured to iteratively update the machine learning model to generate the recommend gap distance based on data captured by the vehicle over time (“the time delay between the lead vehicle encountering an environment and a following vehicle encountering the same environment may only be fractions of a second. if the system is oriented to allow automated following at a distance, around corners, or with some significant distance between the two vehicles, some time may elapse before the second vehicle encounters the same environment. In some embodiments, a time "limit" may be imposed, so only data from the lead truck within a certain time period (e.g., 10 seconds) may be considered "current", and useable by the following vehicle.” see at least [¶ 127] ). it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Lindholm to use the technique generating recommend gap distance based on captured data by the vehicle over time similar of that of the teaching of Laws in order to enhance motor vehicle safety.
12. Regarding claims 8 and 16, Lindholm, Haga and Ghalami in combination limitations of claim 1 as discussed above, Lindholm, furthermore, discloses model predictive controller (see at least [¶ 25-26 & 42]).
Lindholm does not appear to explicitly disclose on, however, in the same field of endeavor, Laws discloses wherein the processor is configured to update the machine learning model based on static gap distance preferences via reinforcement learning and on dynamic gap distance preferences via a model predictive controller (MPC). (“via reinforcement learning” as Laws, regarding a vehicle following another vehicle, see at least [¶ 0058]: “In some embodiments, a machine learning algorithm may be implemented such as …… reinforcement learning”). Furthermore, Laws discloses “the control for path following is adjusted as a function of one or more dynamic conditions (e.g., the gap, vehicle speeds, weather, wind or other perturbations, potholes, traffic, etc.” see at least [¶ 93 & 97]), furthermore, (Laws, see at least [¶ 93 & 98] discloses “This predetermined gap amount may vary, depending on the speed of the vehicles, and the environment of the vehicles”). it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Lindholm to use the technique of updating the machine learning model based on static gap distance preferences via reinforcement learning and on dynamic gap distance preferences via a model predictive controller similar of that of the teaching of Laws in order to enhance motor vehicle safety.
Conclusion
13. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Any inquiry concerning this communication or earlier communications from the examiner should be directed to RACHID BENDIDI whose telephone number is (571)272-4896. The examiner can normally be reached M-F 8AM-4PM.
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/RACHID BENDIDI/Supervisory Patent Examiner, Art Unit 3664
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