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 .
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 02/17/2026 and 07/31/2024 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS(s) has/have been considered by the examiner.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 2, and 5 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Motomura et al. (US 20190347879 A1, hereinafter Motomura).
Regarding claim 1, Motomura teaches:
A control device (Fig. 1, vehicle controller 2) comprising:
a storage that stores a plurality of trained control models (at least as in paragraph 0032, “Returning to FIG. 1, storage 7 may be a storage device such as a read only memory (ROM), a random access memory (RAM), a hard disk device, a solid state drive (SSD), or the like. Storage 7 stores various information such as detection results of operation system detector 3, input information to input unit 4, detection results of state detector 6, knowledge for behavior estimation in automatic driving control system 10 (also called machine learning data), and a neural network used for machine learning, which will be described below”), and
a controller (at least as in paragraph 0022, “As illustrated in FIG. 1, vehicle 1 includes vehicle controller 2, operation system detector 3, input unit 4, information notification unit 5, state detector 6, storage 7, automatic driving control system 10”),
wherein each of the plurality of trained control models is generated corresponding to a movement mode of a mobile body (at least as in paragraph 0034, “Learning unit 11 constructs a neural network of specific driver x of vehicle 1 from a driving history… running history of driver x”; at least as in paragraph 0035-0036, wherein the driving history and running history is constructed based on feature quantities, such as running state, and environmental parameters such as peripheral conditions; at least as in paragraph 0048, “The running scene includes elements such as running environment, weather during running, and a traffic condition of a vehicle”; see also 0048), and
wherein the controller is configured to perform:
acquiring movement condition data related to a condition of movement of the mobile body (at least as in paragraph 0049, “In step S101, during automatic driving of vehicle 1, learning unit 11 of automatic driving control system 10 acquires, from state detector 6 via storage 7, information about vehicle 1 and conditions around vehicle 1, such as position information on vehicle 1, map information on a position of vehicle 1, traffic jam information, and weather information”; see also 0025-0027 for state detector detecting the running state and peripheral condition),
determining the movement mode according to the acquired movement condition data (at least as in paragraph 0049, “Learning unit 11 specifies a running scene of vehicle 1 corresponding to this information from the acquired information”; at least as in paragraph 0048, “The running scene includes elements such as running environment, weather during running, and a traffic condition of a vehicle”),
selecting one of the plurality of trained control models according to a result of determining the movement mode (at least as in paragraph 0050, “in step S102, learning unit 11 selects a dedicated behavior estimation NN corresponding to specific driver x of vehicle 1 and corresponding to the running scene of vehicle 1 from among various dedicated behavior estimation NN stored in storage 7”),
deriving a control command for the mobile body by using the selected trained control model (at least as in paragraph 0051, “in step S103, by using the acquired dedicated behavior estimation NN, behavior estimation unit 12 performs behavior estimation, that is, prediction of a driving action, of vehicle 1 after a first predetermined time passes from a present time”; at least as in paragraph 0056, “If the behavior estimation result is valid (Yes in step S107), in step S108, automatic driving evaluation unit 13 outputs the behavior estimation result to vehicle controller 2 and notification device 5b of information notification unit 5”; at least as in paragraph 0062, “In step S201, learning unit 11 and behavior estimation unit 12 execute processing in steps S101 to S103 in FIG. 5, specifies a running scene of vehicle 1 from information about vehicle 1 and a condition about vehicle 1, and outputs a behavior estimation result of vehicle 1 to image display system 100 by using a dedicated behavior estimation NN corresponding to the specified running scene”), and
controlling an operation of the mobile body according to the derived control command (at least as in paragraph 0066, “If the input instructing the behavior by the driver of vehicle 1 is not executed (No in step S203), automatic driving evaluation unit 13 outputs the estimated behavior “acceleration” to vehicle controller 2, and vehicle controller 2 controls vehicle 1 according to the acquired behavior “acceleration” (step S206)”).
Regarding claim 2, Motomura further teaches:
The control device according to claim 1, wherein
determining the movement mode includes calculating an intensity (at least as in paragraph 0051, “behavior estimation unit 12 inputs parameters about a present running state and a peripheral condition of vehicle 1 acquired from state detector 6 to the dedicated behavior estimation NN, and obtains a behavior estimation result of vehicle 1”; at least as in paragraph 0055, “in step S107, automatic driving evaluation unit 13 calculates a change amount between present behavior of vehicle 1 and the behavior estimation result output from behavior estimation unit 12, and determines validity of the behavior estimation result based on this change amount”), and
the deriving the control command comprises deriving the control command in accordance with the calculated intensity (at least as in paragraph 0056, “If the behavior estimation result is valid (Yes in step S107), in step S108, automatic driving evaluation unit 13 outputs the behavior estimation result to vehicle controller 2 and notification device 5b of information notification unit 5”; Examiner notes wherein, as currently claimed, “intensity” may be reasonably construed to include the validity calculation).
Regarding claim 5, Motomura further teaches:
The control device according to claim 1, wherein
an input of each of the plurality of trained control models is optimized according to the corresponding movement mode (at least as in paragraph 0038, “learning unit 11 optimizes weighting between the nodes of the neural network such that an output from the neural network corresponds to behavior which is supervised-learning data associated with the input parameters. As a result of the weighting adjustment, learning unit 11 makes the neural network learn a relation between the input parameters and the supervised-learning data, and constructs a general-purpose behavior estimation NN corresponding to the arbitrary drivers”).
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) 3 and 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Motomura in view of Hatano (US 20190061745 A1).
Regarding claim 3, Motomura further teaches:
The control device according to claim 1, wherein
the mobile body is a vehicle (at least as in paragraph 0021, “image display system 100 is mounted on vehicle 1 capable of running on a road, such as an automobile, a truck, or a bus… may be mounted to any moving body, such as an airplane, a ship, or an unmanned conveyer”),
the plurality of trained control models includes a first trained control model(at least as in paragraph 0034, “Learning unit 11 constructs a neural network of specific driver x of vehicle 1 from a driving history… running history of driver x”; at least as in paragraph 0035-0036, wherein the driving history and running history is constructed based on feature quantities, such as running state, and environmental parameters such as peripheral conditions; at least as in paragraph 0048, “The running scene includes elements such as running environment, weather during running, and a traffic condition of a vehicle”; see also 0048), and
the determining the movement mode includes selecting one of the modes of the lane change, the lane keeping, and the emergency stop (at least as in paragraph 0065-0067, wherein the behavior estimation results may include “acceleration” or “lane change”).
But Motomura does not explicitly teach:
for lane change… for lane keeping… for emergency stop.
However, Hatano, in the same field of endeavor of a vehicle control device configured to conduct an automatic driving control, specifically teaches:
for lane change… for lane keeping… for emergency stop (at least as in paragraph 0053, “The automatic driving control unit 110, for example, performs control by performing switching between a drive mode A, a drive mode B, a drive mode C, and a drive mode D in accordance with a direction from the switching control unit 140”; at least as in paragraph 0064, “The action plan, for example, is configured of a plurality of events that are sequentially executed. The events, for example, include a deceleration event of decelerating the subject vehicle M, an acceleration event of accelerating the subject vehicle M, a lane keeping event of causing the subject vehicle M to run without deviating from a running lane, a lane changing event of changing a running lane, an overtaking event of causing the subject vehicle M to outrun a vehicle running ahead, a branching event of changing a lane to a desired lane at a branching point or causing the subject vehicle M to run without deviating from a current running lane, a merging event of accelerating/decelerating the subject vehicle M and changing a running lane in a merging lane for merging into a main lane, and the like”; at least as in paragraph 0051, “The pre-operation alarm device 96 receives a control direction from the unexpected event avoiding control device 200 and performs a visible, audible, or tactile notification operation for an occupant of the subject vehicle M before execution of an emergency avoidance operation. The emergency avoidance operation includes execution of automatic drive stopping control for stopping automatic driving control when an unexpected event occurs in accordance with detection of a target or execution of avoidance control of automatically controlling one or both of acceleration/deceleration and steering of the subject vehicle M").
Therefore, it would have been obvious to one of the ordinary skill in the art at the effective filing date of the instant invention to modify the teachings of Motomura, to include Hatano's teaching of vehicle control device utilizing different action planes for various events, since Hatano teaches wherein the control device improves safety for occupants and other personnel by avoiding unexpected events appropriately.
Regarding claim 4, in view of the above combination of Motomura and Hatano, Motomura further teaches:
The control device according to claim 3, wherein
the acquired movement condition data includes reaction data indicating a reaction of an occupant riding the vehicle (at least as in paragraph 0024, “Operation system detector 3 detects information operated by a driver of vehicle 1 and information operated by automatic driving control system 10”; at least as in paragraph 0029, “Information acquisition unit 5a may also acquire output information from operation system detector 3, output information from input unit 4, output information from state detector 6, etc. and display the acquired output information on notification device 5b”; at least as in paragraph 0035, “The driving history is constructed by associating each behavior performed by a vehicle in the past with a plurality of feature quantities… The feature quantity is, for example, a running state of the vehicle to be detected by operation system detector 3 and state detector 6”; at least as in paragraph 0040, “Note that behavior estimation unit 12 is configured to acquire detection results from operation system detector 3 and state detector 6”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RICARDO ICHIKAWA VISCARRA whose telephone number is (571)270-0154. The examiner can normally be reached M-F 9-12 & 2-4 PST.
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/RICARDO I VISCARRA/Examiner, Art Unit 3657
/ADAM R MOTT/Supervisory Patent Examiner, Art Unit 3657
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