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 .
Status of Claims
Claims 1, 7-9 and 16-18 have been amended. Claims 19-20 have been cancelled. Claims 21-22 are newly added. Claims 1-18 and 21-22 are currently pending and are examined below.
Response to Arguments
Applicant's amendments filed March 15th, 2026 with respect to the Objections have been fully considered and are therefore withdrawn.
Applicant's amendments filed March 15th, 2026 with respect to the 35 U.S.C. 112(a) have been fully considered and are therefore withdrawn.
Applicant's amendments filed March 15th, 2026 with respect to the 35 U.S.C. 101 have been fully considered and are therefore withdrawn.
Applicant's amendments filed March 15th, 2026 with respect to the 35 U.S.C. 103 have been fully considered but they are not persuasive.
Examiners notes that the English Translation of the entire Nagy reference was provided in the Nonfinal Office Action mailed January 14th, 2026. The Abstract is on page 2 of the English Translation. As a matter of courtesy, the examiner will provide the English translation with this Final Office Action.
Applicant argues that Nagy fails to disclose the amended limitations, specifically, “train the machine learning model to learn gap distance preferences of an occupant of the vehicle, wherein the processor causes the machine learning model to learn a mapping between the contextual feature set and the observed gap distance; obtain live contextual data and speed data of the vehicle in travel on a road behind a lead vehicle and convert the live contextual data into a live feature set comprising live static and dynamic state data.” However, examiner respectfully disagrees. Examiner points the applicant’s specification paragraph 0064 which indicates that contextual static is gap distance and dynamic state data is e.g., weather scenarios, traffic scenarios, lead vehicle types, etc.. Nagy discloses, sensor 16 may include, for example, vehicle control sensors (e.g., detecting the accelerator pedal position, brake pedal position and the position of the steering wheel (steering angle) of the sensor), a wheel speed sensor, a vehicle speed sensor, a yaw sensor, a force sensor, a distance measurement sensor and a vehicle proximity sensor (e.g., a camera, a radar, ultrasonic). electronic controller 12 receives and interprets signals received from sensor 16 to determine the value of one or more vehicle attributes, the one or more vehicle attributes include, for example, vehicle speed, steering angle, vehicle position, pitch, yaw and roll (see Page 7). On page 14 Nagy further discloses, wherein determining the at least one external factor comprises: determining from at least one of the following from the group of: weather conditions, road conditions and driver activities. Therefore, Nagy discloses capturing gap distance and static and dynamic state data. Further, Nagy discloses on Page 12, the electronic controller 12 can be received using the approved and not approved (with or without other types of driver input) as the machine learning engine input along with time to determine preferences of the operating settings. Nagy discloses, learning engine can use data (i.e., training data) collected when the driver operating the autonomous vehicle 11 to develop the model, the model used to operate the autonomous vehicle 12 by the electronic controller 11. (Page 6). In other words, Nagy discloses using real-time data as the driver is driving to train the machine learning model. Examiner notes that Nagy’s disclosure of “collected when the driver operating the vehicle” is live data.
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 1-18 and 21-22 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. The applicant amended Claims 1, 9 and 17 to recite, “machine learning model to learn a mapping between the contextual feature set and the observed gap distance”. Examiner notes that the recitation fails to provide support for Mapping, feature sets let alone observed gap distance.
Further, the examiner notes that “convert the live contextual data into a live feature set comprising live static and dynamic state data”, lacks support.
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.
Claims 1, 3-7, 9, 11-15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Nagy et al., CN107249954B in view of Rebhan et al., Pub., No. 2016/0304092A1, hereinafter referred to As Nagy and Rebhan, respectively.
As per Claims 1, 6, 9, 14 and 17, Nagy discloses a method and system comprising:
a storage configured to store a machine learning model (see at least page 4 and 6): and
a processor configured to
capture an observed gap distance and a contextual feature set comprising static and dynamic state data of a vehicle from a manual vehicle-following event of the vehicle (see at least Abstract, page 3, 6-7, 13, “when the autonomous vehicle in a manual driving mode operation using the electronic processor receives data from at least one sensor”);
train the machine learning model to learn gap distance preferences of an occupant of the vehicle, wherein the processor causes the machine learning model to learn a mapping between the contextual feature set and the observed gap distance (see at least Pages 6, 7, 9 and 12, “training data”, “The plurality of setting can include driving such as acceleration of preference of the below items: stronger or weaker, faster or slower by curve, cutting or not cutting curve, braking and travelling distance sharp or relatively slow, the gap size between some other vehicle during operation. setting a driver profile 29 can include the security standard and law limit is suitable for almost any of the driving characteristics.”, “the electronic controller 12 may use received approved and not approved (with or without other types of driver input) as machine learning input of an engine to determines the preference of the operating set over time.” Page 12);
obtain live contextual data and speed data of the vehicle in travel on a road behind a lead vehicle and convert the live contextual data into a live feature set comprising live static and dynamic state data (see Pages 6-7 and 12, “vehicle control sensors (e.g., detecting the accelerator pedal position, brake pedal position and the position of the steering wheel (steering angle) of the sensor), a wheel speed sensor, a vehicle speed sensor, a yaw sensor, a force sensor, a distance measurement sensor and a vehicle proximity sensor (e.g., a camera, a radar, ultrasonic). electronic controller 12 receives and interprets signals received from sensor 16 to determine the value of one or more vehicle attributes, the one or more vehicle attributes include, for example, vehicle speed, steering angle, vehicle position, pitch, yaw and roll. the electronic controller 12 at least partially autonomously controlling the autonomous vehicle 11 based on information for controlling the vehicle control system 14 (e.g., received from the sensor 16 by generating a brake signal, an acceleration signal, a steering signal). some of the sensors 16 may be integrated into the vehicle control system 14, while the other sensor can be separately deployed with the vehicle control system 14 on the vehicle 11.”);
control a speed of the vehicle based on the predicted recommended gap distance and the speed data (see at least Page 11-12, “In another embodiment, in response to a specific operation performed by the autonomous vehicle 11, the autonomous vehicle control system 10 receiving feedback input driver. For example, as shown in FIG. 6, autonomously executes operation (e.g., greater than another vehicle, crossroad turning, lane changing, turning, the road after combining the transport medium), electronic controller 12 using feedback request screen 62 to prompt for driver feedback input. the electronic controller 12 via a graphical user interface for display on the touch screen, for example by the HMI 22 receives driver feedback input from a driver. electronic controller 12 using the driver feedback input changing each value, such as longitudinal movement, in the lane position, acceleration, the gap size (between autonomous vehicle 11 and other vehicles), and so on.).
Nagy fails to explicitly disclose a predict a recommended gap distance between the vehicle and the lead vehicle based on execution of the machine learning model on the live feature set. However, Rebhan teaches this above limitation (see at least 0081-0082, “Further a gap adaption indicator can be calculated by subtracting the current gap size from a reference/recommended gap size determined based on the environmental condition. For example, the recommended gap size can be determined based on the gap sizes between other vehicles (average gap size, minimum gap size, maximum gap, trend etc.) or based on the gap sizes between other vehicles and the velocity of the ego-vehicle E….”Additionally, the gap adaptation indicators might be weighted to account for e.g. reliability of the indicators or user preferences, etc., or might be combined in a more complex manner e.g. by using machine learning techniques.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Nagy and include predicting a recommended gap size as taught by Rebhan with a reasonable expectation of success because gap size can be adapted to different environmental conditions automatically therefore providing the driver a more comfortable drive (0014-0015).
As per Claims 3-4, 11-12 and 19-20, Nagy discloses wherein the processor is further configured to receive feedback about the gap distance from the vehicle and retrain the machine learning model based on a combination of the gap distance and the feedback about the gap distance (see at least Pages 6, 8-11, “The plurality of setting can include driving such as acceleration of preference of the below items: stronger or weaker, faster or slower by curve, cutting or not cutting curve, braking and travelling distance sharp or relatively slow, the gap size between some other vehicle during operation. setting a driver profile 29 can include the security standard and law limit is suitable for almost any of the driving characteristics.”, “Therefore, when the driver habit changes, the electronic controller 12 can gradually adjust the driver profile 29 over time. For example, a single exhibit aggressive driving, generally reflect the driver profile of the driven driving style 29 can only be adjusted to 10% more aggressive.” Page 10, “gap size value” Page 11).
Nagy fails to explicitly disclose a recommended gap distance. However, Rebhan teaches this above limitation (see at least 0081-0082, “Further a gap adaption indicator can be calculated by subtracting the current gap size from a reference/recommended gap size determined based on the environmental condition. For example, the recommended gap size can be determined based on the gap sizes between other vehicles (average gap size, minimum gap size, maximum gap, trend etc.) or based on the gap sizes between other vehicles and the velocity of the ego-vehicle E….”Additionally, the gap adaptation indicators might be weighted to account for e.g. reliability of the indicators or user preferences, etc., or might be combined in a more complex manner e.g. by
using machine learning techniques.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Nagy and include a recommended gap size as taught by Rebhan with a reasonable expectation of success because gap size can be adapted to different environmental conditions automatically therefore providing the driver a more comfortable drive (0014-0015).
As per Claims 5 and 13, Nagy discloses wherein the processor is further configured to receive traffic data of the road while the vehicle is travelling behind the lead vehicle, and predict the gap distance based on the received traffic data (see at least Page 13, “road conditions”).
Nagy fails to explicitly disclose a recommended gap distance. However, Rebhan teaches this above limitation (see at least 0081-0082, “Further a gap adaption indicator can be calculated by subtracting the current gap size from a reference/recommended gap size determined based on the environmental condition. For example, the recommended gap size can be determined based on the gap sizes between other vehicles (average gap size, minimum gap size, maximum gap, trend etc.) or based on the gap sizes between other vehicles and the velocity of the ego-vehicle E….”Additionally, the gap adaptation indicators might be weighted to account for e.g. reliability of the indicators or user preferences, etc., or might be combined in a more complex manner e.g. by
using machine learning techniques.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Nagy and include a recommended gap size as taught by Rebhan with a reasonable expectation of success because gap size can be adapted to different environmental conditions automatically therefore providing the driver a more comfortable drive (0014-0015).
As per Claims 7 and 15, Nagy discloses wherein the processor is further configured to obtain initial sensor data from the vehicle and determine an occupant of the vehicle does not align with any driver types from among a plurality of predefined driver types based on the initial sensor data (see at least Page 10, ” In one exemplary embodiment, the autonomous vehicle 11 prompting the driver to select his or her profile (i.e., the driver profile 29). As shown in FIG. 3, the driver can use such profile 29 displayed by the HMI 22 of the profile selection screen 42 the selected driver. For example, the driver can use the profile selector button 44 selecting one of his or her profile. Alternatively, no existing profile of the driver can be selected using the profile-management button 46 to create a new profile, or can be skipped by selecting button 48 is selected in the case of not using the driver profile 29 operation for autonomous vehicle 11.”).
Claims 2, 10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Nagy in view of Rebhan as applied to claims 1 and 9 above, and further in view of Kuramochi et al., Pub. No. US2001/0018631 A1, hereinafter referred to Kuramochi.
As per Claims 2, 10 and 18, Nagy fails to disclose wherein the apparatus further comprises a network interface configured to receive image data captured by the vehicle of the lead vehicle, a size of the lead vehicle based on the received image data, and predict the recommended gap distance based on the size. However, Kuramochi teaches the above limitation (see at least 0051, Figure 12, “a leading vehicle discriminating portion 13 processes an image picked-up by a camera or other image pick-up device for discriminating whether the leading vehicle is a large size vehicle or a standard size vehicle.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Nagy and include determining gap size based on preceding vehicle size taught by Kuramochi with a reasonable expectation of success because depending on the size of the preceding vehicle the stopping distance maybe greater thus requiring a different gap distance for safety.
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Nagy in view of Rebhan as applied to claims 1 and 9 above, and further in view of Graves et al., Pub. US2019/0329772A1, hereinafter referred to as Graves.
As per Claims 8 and 16, Nagy fails to discloses wherein the processor is configured to train a machine learning model to learn the gap distance preferences (see at least Pages 11,“…in the learning mode. learning mode display screen 50 also prompts the driver to use slider control 56 and control dial 58 for feedback. slider control 56 receives driver preference input of the following items: steering characteristic (e.g., comfortable, standard and motion), braking, acceleration, gap size, preference following distance of the common driving period, prior to exceeding the tolerable deviation of the marking speed and so on. dial control 58 receives driver preference input should generally to what motion or aggressive mode for operations relating to autonomous vehicle 11. slider control 56 and a dial control 58 allows the driver to each value (e.g., acceleration value, gap size value, etc.) to smooth and continuously adjusted, or can allow the driver a plurality of discrete value selected from a certain range.”).
Nagy fails to explicitly disclose reward function. However, Graves teaches this limitation (see at least 0073, 0154, “AS controller module 412 with the input needed for it to manipulate the vehicle 105 (e.g. throttle or brake) within its environment to minimize a defined cost function or maximize total reward.”, “In the examples represented in the above table, the safety predictor DCT system includes two types of DCT controllers, namely a “basic controller” that ignores all other vehicles and only aims to achieve a target speed and a “baseline controller” that aims to achieve a target speed and target headway (e.g. inter-vehicle spacing).”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Nagy and include a reward function as taught by Graves with a reasonable expectation of success because it is well known to utilize reward functions to provide better comfort for a driver (0170).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Nagy in view of Rebhan as applied to claims 1 above, and further in view of Official Notice hereinafter referred to as Official Notice.
As per Claim 21, Nagy discloses wherein the processor is configured to classify the occupant into a driver type (see at least Page 10, “driver to be classified as such as motion, relaxed, aggressive or passive). Nagy fails to disclose based on a divergence metric of a distribution of observed gap distances of manual following events and distributions associated with predefined driver types.
However, the examiner takes Official Notice that using divergence metric to classify is old and well known in the art of.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Nagy and include using divergence metrics to classify driver type as taught by Official Notice with a reasonable expectation of success because it would provide a standard to classify driver types based on how close to certain driver types.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Nagy in view of Rebhan as applied to claims 1 above, and further in view of Fu et al., U.S. Patent No. 11,741,400 B1 hereinafter referred to as Fu.
As per Claim 22, Nagy fails to disclose wherein the processor is configured to retrieve a driver-type-specific version of the machine learning model from a remote platform prior to prediction of the recommended gap distance. However, Fu discloses the above limitation (see Col. 28, lines 56-67). Examiner notes that the system is capable of retrieving machine learning models associated with individuals and therefore capable of that individual being a driver.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Nagy and include retrieving machine learning models for individuals as taught by Fu with a reasonable expectation of success because the system provides the ability to provide specific types of models for specific vehicles whether they are drivers or riders.
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 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 Fadey S Jabr whose telephone number is (571)272-1516. The examiner can normally be reached Monday-Friday 8:30am-4:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fadey S Jabr can be reached at 571-272-1516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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FADEY S. JABR
Supervisory Patent Examiner
Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668
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