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 the Claims
This Office Action is in response to the Applicants’ filing on 03/26/2026. Claims 1-10, and 21-30 were previously pending, of which claims 3-5 have been withdrawn in response to a restriction requirement, no claims have been amended, cancelled, or newly added. Accordingly, 1-10, and 21-30 claims are currently pending and claims 1-2, 6-10, and 21-30 are being examined below.
Response to Arguments
With respect to Applicant's remarks, see pages 1-4, filed 03/26/2026; Applicant’s “Amendment and Remarks” have been fully considered. Applicant’s remarks will be addressed in sequential order as they were presented.
With respect to the claim rejections of claims 1-2, 6-10, and 21-30 under 35 U.S.C. § 103, applicant’s “Amendment and Remarks” have been fully considered, but they are not persuasive. The argument that the Watanabe reference doesn’t use sensor data is not persuasive for at least the understanding that traveling at a previously set speed requires the sensed data that ensures that the speed is consistent with the setting, this would be accomplished using the vehicle own speed sensor 12. Further, the preceding vehicle is detected using at least the processed image of an object detected by a radar sensor. Watanabe paragraph [0053] does disclose that a driver turns on a switch, but not a mode switch, rather an ACC switch to turn on the automatic control which when active switches between the modes based on the detected preceding vehicle, in accordance with preset specific values that were input by the driver before activating the ACC switch.
With respect to Applicant’s argument that the prior art does not teach the mode of vehicle changed based on the output of the machine learning model used to control the first mode is not considered persuasive. Due to the broad language of the independent claim it is understood that the output of the machine learning model should be a consideration of the decision to change. This could include the control output from the machine learning model, used to control lateral movement and the distance between vehicles, that is fed back into the inputs as shown in Fig. A1 of Chen. The Chen reference further discloses the use of the machine learning model being used to determine the progress of the lane change; that progress output is used to determine when to change to the lane keeping mode. This means the second mode is initiated, at least in part, by the output of the machine learning model used to control the first mode. The combination of that determination method of when to establish a change in mode could reasonably be used in the same way to further automate the distance keeping modes of Watanabe. Therefore, the rejections under 35 U.S.C. § 103 are maintained.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
In claim 1, the “processing device” in the limitation “at least one processing device configured to… operate a vehicle in a first mode of cruise control… and change to a second mode of cruise control” invokes 112(f) as device is a term that does not have definite structure which enables the mode of cruise control to be changed.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification to these claim limitations:
“[00139] Examples of the processing device can include a microcontroller, a central processing unit (CPU), special purpose logic circuitry (e.g., a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), etc.), a system on a chip (SoC), or another suitable processor.”
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f).
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.
Claims 1-2, 6-10, and 21-30 are rejected under 35 U.S.C. 103 as being unpatentable over Watanabe (US 2018/0198955 A1), hereinafter Watanabe, in view of Chen et al. (US 2019/0212749 A1), hereinafter Chen.
With respect to claim 1, 21, and 27, Watanabe discloses a system comprising: at least one sensor; (see at least [0042] “an own vehicle speed sensor 12”)
and at least one processing device configured to: (see at least [0040] “The ACC system 3 includes an ACC controller 7 configured of a micro-computer including a CPU, a ROM, and a RAM for performing constant-speed and inter-vehicular-distance control.”)
operate a vehicle in a first mode of cruise control based on an output (see at least [0040] “The constant-speed travel control unit 72 executes a constant speed travel mode in which the own vehicle is caused to travel at a previously-set speed.”)
and change to a second mode of cruise control (see at least [0053] “The constant-speed and inter-vehicular-distance control is performed as being switched between the tailing travel mode when a preceding vehicle is detected and the constant speed travel mode when a preceding vehicle is not detected.”)
Watanabe discloses the changing of a constant speed mode to a tailing mode based on sensor data, but does not explicitly disclose using sensor data processed through machine learning.
However, Chen teaches ( see at least [0098] “Sensor data from one or more sensors of the vehicle… input into a machine learning model(s)” [0241] “The ACC systems may include longitudinal ACC and/or lateral ACC. Longitudinal ACC monitors and controls the distance to the vehicle immediately ahead of the vehicle 140 and automatically adjust the vehicle speed to maintain a safe distance from vehicles ahead.”)
As both pertain to changing modes when navigating in relation to other vehicles, 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 mode change of Watanabe to include the machine learning disclosed in Chen, with reasonable expectation of success. The motivation for doing so would have been to reduce computational expense from the conventional approaches, see Chen [0004].
With respect to claims 2 and 25, Watanabe discloses the changing of a constant speed mode to a tailing mode based on sensor data, but does not explicitly disclose using sensor data processed through machine learning.
However, Chen teaches the output from the machine learning model is used to evaluate a characteristic of the data from the sensor. (see at least [0028] “the machine learning model(s) may continue to receive the sensor data and may calculate the status of the lane change… output by the machine learning model(s)” [0032] “the noisy outputs of the machine learning model… may be filtered such that the resultant vehicle trajectories do not suffer from the noise of the outputs”)
As both pertain to changing modes when navigating in relation to other vehicles, 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 mode change of Watanabe to include the machine learning disclosed in Chen, with reasonable expectation of success. The motivation for doing so would have been to reduce computational expense from the conventional approaches, see Chen [0004].
With respect to claim 6, Watanabe discloses the processing device is further configured to determine a context of the vehicle, and generate a set point for the cruise control based on the context. (see at least [0059] “That is, when a current actual inter-vehicular distance against the target preceding vehicle is longer than the set inter-vehicular distance, the accelerating system 8 is controlled to shorten the inter-vehicular distance against the target preceding vehicle as increasing the speed of the own vehicle.”)
With respect to claim 7, Watanabe discloses at least one memory configured to store first data regarding operating characteristics of the vehicle, wherein the context includes the first data. (see at least [0054] “The preceding vehicle detecting portion 11 detects inter-vehicular distances and relative speeds with respect to all the detected preceding vehicles, orientations thereof with respect to the own vehicle, and the like in a memory.”)
With respect to claim 8, Watanabe discloses the first data is used to select a distance for following another vehicle. (see at least [0053] “The speed for constant speed travelling and an inter-vehicular distance against a preceding vehicle may be set by inputting specific values… a set value of the last time stored in a memory of the ACC system 3 as it is.”)
With respect to claims 9 and 26, Watanabe discloses the changing of a constant speed mode to a tailing mode based on sensor data, but does not explicitly disclose using sensor data processed through machine learning.
However, Chen teaches output from the machine learning model is used to determine whether the data from the sensor is sufficient for control of the vehicle when in the first mode. (see at least [0249] “The backup computer rationality monitor may run a redundant diverse software on hardware components to detect faults in perception and dynamic driving tasks.”)
As both pertain to changing modes when navigating in relation to other vehicles, 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 mode change of Watanabe to include the machine learning disclosed in Chen, with reasonable expectation of success. The motivation for doing so would have been to reduce computational expense from the conventional approaches, see Chen [0004].
With respect to claim 10 and 28-29, Watanabe discloses the processing device is further configured to, in response to changing to the second mode, operate the cruise control using data from a sensor other than the at least one sensor. (see at least [0042] “The preceding vehicle detecting portion 11 detects, using a later-mentioned radar sensor arranged at the front side, an inter-vehicular distance, a relative speed, a direction with respect to an orientation of the own vehicle”)
With respect to claim 22, Watanabe discloses the sensor data is received by the vehicle via a communication interface configured for vehicle-to-everything (V2X) communication. (see at least [0058] “For performing such vehicle-to-vehicle communication and/or road-to-vehicle communication, it is preferable that the ACC system 3 or the driving support system 1 includes a communication device for communicating with the outside.”)
With respect to claim 23, Watanabe discloses the sensor data is received by the vehicle via a communication interface, the communication interface is configured to wirelessly communicate with a server, and the sensor data is image data provided by the server. (see at least [0058] “road-to-vehicle communication in which radio communication is performed, directly or through servers”)
With respect to claim 24, Watanabe discloses the sensor data is received from another vehicle. (see at least [0091] “Some vehicles can also be capable of communication among them, sharing information, altering the peer vehicle of hazards or changes in the vehicles' surroundings, etc.”)
With respect to claim 30, Watanabe discloses using map data to select a following distance behind another vehicle. (see at least [0101] “The travel environment recognition controller 43 includes an object data processing unit 48, an object determining unit 49… determining objects existing in front of and around the own vehicle.” [0102] “the travel environment recognition controller 43 is capable of obtaining… a map around a currently located position”)
Conclusion
THIS ACTION IS MADE FINAL. 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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/S.M.O./Examiner, Art Unit 3669
/NAVID Z. MEHDIZADEH/Supervisory Patent Examiner, Art Unit 3669
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