Detailed Office Action
Status of Claims
This Office Action is in response to the Applicant’s amendments and remarks filed 04/30/2026. The applicant has amended claims 1-10. Claims 1-10 are presently pending and are presented for examination.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 04/28/2026 is being considered by the examiner.
Response to Amendment
The amendment filed 04/30/2026 has been entered. Claims 1-10 remain pending in the application.
Reply to Applicant’s Remarks
Applicant’s remarks filed 04/30/2026 have been fully considered and are addressed as follows:
Claim Interpretation Under 35 U.S.C. 112(f):
Applicant’s amendments to the claims filed 04/30/2026 have overcome the 35 U.S.C. 112(f) interpretation previously set forth. Therefore, the claims are no longer interpreted under 112(f).
Claim Rejections Under 35 U.S.C. 101:
Applicant’s amendments to the claims filed 04/30/2026 have overcome the 35 U.S.C. 101 rejections previously set forth. Therefore, the rejection has been withdrawn.
Claim Rejections Under 35 U.S.C. 103:
Applicant’s arguments, see Arguments/Remarks, filed 04/30/2026, with regard to the rejections of Claims 1, 9, and 10 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of newly found prior art reference(s).
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.
Claims 1, 5-6, 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al (CN 115543583 A) in view of Park et al (US 20210247762 A1) and Marsillach et al (US 20240132104 A1). Hereafter referred to as Zhang, Park, and Marsillach respectively.
Regarding Claim 1, Zhang teaches a vehicle control device (see at least Zhang [English Translation pg.5 para.7] The task allocation method according to the embodiment of the present disclosure, the task processing method can be executed by the terminal device...the terminal device can be a...vehicle device)
comprising:
a first processor that generates an output signal using only a machine learning-trained model (see at least Zhang [English Translation pg.7 para.2, pg.6 para.7] the processing architecture 300 comprises…a special processor 322…the task to be processed comprises a general task and a special task, wherein the general task is the task of the general type, generally corresponding to the general program, the special task refers to the task of some special field (e.g., task in the field of neural network))
a second processor that has lower power consumption than the first processor and generates an output signal without using a machine learning-trained model (see at least Zhang [English Translation pg.7 para.2, pg.6 para.7] the processing architecture 300 comprises...a general processor 312…the task to be processed comprises a general task and a special task, wherein the general task is the task of the general type, generally corresponding to the general program)
a third processor configured to decide a processing ratio between a portion processed by the first processor and a portion processed by the second processor (see at least Zhang [English Translation Abstract and pg.6 para.6-7] it is capable of considering the computing power of the cost, selecting proper compiling mode and processing mode for each task to be processed, so as to more reasonably use processing resource of two different processing mode...for a plurality of tasks to be processed, determining each task to be processed by the first cost corresponding to the general processing mode and the second cost corresponding to the special processing mode; according to the first cost and the second cost corresponding to each task to be processed...Furthermore, in general, the special program can only be run in the special processor, so the special task can only be executed by the special processor) The disclosure in Zhang teaches a control device (processor) that can allocate tasks between two processors, a general processor and a special processor, wherein the special processor utilizes neural network technology (which is analogous to using a machine learning trained model) and can perform special tasks that the general processor, without the neural network technology, cannot. The system in Zhang will allocate the tasks between the processors depending on the task, which is analogous to deciding a processing ratio. Zhang further discloses such a system can be used for a vehicle.
However, while Zhang teaches a system that can allocate tasks (decide processing ratio) between a generic processor and a neural network processor for a vehicle based on information regarding a particular task, it does not explicitly teach wherein the information represents a state of a vehicle, environment information representing surrounding environment of the vehicle, and terrain information representing terrain including a current location of the vehicle.
Park, in the same field as the endeavor, teaches a third processor configured to decide a processing ratio between a portion processed by the first processor and a portion processed by the second processor, based on vehicle information representing a state of a vehicle, environment information representing surrounding environment of the vehicle, and terrain information representing terrain including a current location of the vehicle (see at least Park [¶ 29, 32, 36, 76] The term “system-on-chip” (SOC)…including one or more processors, a memory, and a communication interface. The SOC may include a variety of different types of processors and processor cores…With reference to FIGS. 1A-3, the processing device SOC 300 may include a variety of processing resources, including a number of heterogeneous processors…a resource and power management (RPM) processor 317. The processing device SOC 300 may also include one or more coprocessors 310 (e.g., vector co-processor) connected to one or more of the heterogeneous processors 303, 304, 306, 307, 308, 317…Various embodiments enable a vehicle processor to intelligently manage resource allocation and a processing quality of each of a plurality of concurrent automotive applications, processes, or other functions (referred to herein collectively as “applications”)…the processor may allocate finite processing resources to each of a plurality of vehicle applications based on a determination of a priority of each vehicle application to safe vehicle operations in a moment-to-moment, context-determined manner based on internal and external vehicle conditions… External vehicle conditions that may be considered in various embodiments when allocating processing resources to applications may include a driving speed and direction of the vehicle; road conditions, ambient temperature, ambient humidity, and weather conditions; proximity, speed, and direction of motion of other objects such as vehicles, pedestrians, etc) The disclosure in Park teaches a processor configured to allocate resources between a plurality of other processors on the vehicle to perform different tasks, the allocation is based on driving speed and direction of the vehicle (vehicle state), the motion of other objects and pedestrians (environment information), and the road and weather conditions (terrain information).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the system set forth in Zhang to contain a third processor configured to decide a processing ratio between a portion processed by the first processor and a portion processed by the second processor, based on vehicle information representing a state of a vehicle, environment information representing surrounding environment of the vehicle, and terrain information representing terrain including a current location of the vehicle with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of improving the safety of the driving of the vehicle by allocating resources to the most important tasks (see at least Park [¶ 44] Various embodiments improve the functionality of vehicle systems by providing at least some computing resources to a plurality of vehicle applications that are important to the safe operation of the vehicle even when overall computing resources are reduced. Various embodiments improve the safety of vehicles by allocating finite computing resources to the plurality of vehicle applications according to the importance of the vehicle application to safe vehicle operations).
Further, Zhang does not explicitly teach wherein the first processor controls the vehicle based on the state of the vehicle, the environment information, and the terrain information.
Marsillach, in the same field as the endeavor teaches wherein the first processor controls the vehicle based on the state of the vehicle, the environment information, and the terrain information (see at least Marsillach [¶ 5, 18] a trajectory planning system for an autonomous vehicle is disclosed and includes one or more controllers in electronic communication with one or more external vehicle networks that collect data with respect to one or more moving obstacles located in an environment surrounding the autonomous vehicle...The one or more controllers determine, based on the discrete-time relative vehicle state, a position avoidance set representing relative lateral positions and longitudinal positions that the autonomous vehicle avoids while bypassing the one or more moving obstacles when performing a maneuver. The one or more controllers determine a set of ego states for which the autonomous vehicle is unable to execute maneuvers without entering the position avoidance set for a set of given speed limits and road conditions….where the autonomous vehicle follows the trajectory while performing the maneuver…an autonomous vehicle including a trajectory planning system is disclosed and includes a plurality of sensors that determine a plurality of dynamic variables, one or more external vehicle networks that collect data with respect to one or more moving obstacles located in an environment surrounding the autonomous vehicle) The disclosure in Marsillach teaches a processor configured to control a vehicle based on vehicle state, the motion of moving obstacles (environment information), and the speed limits and road conditions (terrain information).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the system set forth in Zhang to contain a system for wherein the first processor controls the vehicle based on the state of the vehicle, the environment information, and the terrain information with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of improving the safety of the driving of the vehicle by ensuring an evasive maneuver is always possible as discussed in Marsillach (see at least Marsillach [¶ 59] the disclosure provides a methodology and architecture that ensures maneuvers for avoiding the moving obstacles always exist and are executable by the autonomous vehicle within an operating domain of interest).
Regarding Claim 5, Zhang in view of Park and Marsillach teaches all limitations of Claim 1 as set forth above. However, Zhang does not explicitly teach wherein the environment information includes complexity of the surrounding environment of the vehicle, and the third processor is further configured to decide the processing ratio so that the portion processed by the first processor device is greater than the portion processed by the second processor device when the degree of complexity of the surrounding environment of the vehicle is high, compared to when the degree of complexity of the surrounding environment of the vehicle is low.
Park, in the same field as the endeavor, teaches wherein the environment information includes complexity of the surrounding environment of the vehicle, and the third processor is further configured to decide the processing ratio so that the portion processed by the first processor device is greater than the portion processed by the second processor device when the degree of complexity of the surrounding environment of the vehicle is high, compared to when the degree of complexity of the surrounding environment of the vehicle is low (see at least Park [¶ 32, 36-37] External vehicle conditions that may be considered in various embodiments when allocating processing resources to applications may include...direction of motion of other objects such as vehicles, pedestrians, etc.; an anticipated or planned path of the vehicle; the presence or detection of road hazards, accidents, or other similar threatening conditions…the processor may allocate finite processing resources to each of a plurality of vehicle applications based on a determination of a priority of each vehicle application to safe vehicle operations in a moment-to-moment, context-determined manner based on internal and external vehicle conditions...Each of a plurality of vehicle applications may be assigned different safety-related priorities based on various vehicle conditions....External vehicle conditions that may be considered in various embodiments when allocating processing resources to applications may include...direction of motion of other objects such as vehicles, pedestrians, etc.; an anticipated or planned path of the vehicle; the presence or detection of road hazards, accidents, or other similar threatening conditions (For example, as driving speed increases, sensor polling rates, data analysis, safety decisions by the processor, information display adjustments, and the like may need to be performed or refreshed more frequently to maintain a threshold level of safety performance)) Per the example of increasing the allocation computing resources when vehicle speed increases, it would be obvious to anyone of ordinary skill in the art that the same is true when the complexity of the environment increases due to the disclosed road hazards and obstacles.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the system set forth in Zhang to contain a system for wherein the environment information includes complexity of the surrounding environment of the vehicle, and the third processor is further configured to decide the processing ratio so that the portion processed by the first processor device is greater than the portion processed by the second processor device when the degree of complexity of the surrounding environment of the vehicle is high, compared to when the degree of complexity of the surrounding environment of the vehicle is low with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of improving the safety of the vehicle when determining how to allocate computing resources as discussed in Park (see at least Park [¶ 44] Various embodiments improve the safety of vehicles by allocating finite computing resources to the plurality of vehicle applications according to the importance of the vehicle application to safe vehicle operations or impact on a driver's ability to perform one or more safety-related tasks in a particular situation or context).
Regarding Claim 6, Zhang in view of Park and Marsillach teaches all limitations of Claim 1 as set forth above. However, Zhang does not explicitly teach wherein the terrain information includes degree of complexity of the terrain including the current location of the vehicle, and the third processor is further configured to decide the processing ratio so that the portion processed by the first processor is greater than the portion processed by the second processor when the degree of complexity of the terrain including the current location of the vehicle is high, compared to when the degree of complexity of the terrain including the current location of the vehicle is low.
Park, in the same field as the endeavor, teaches wherein the terrain information includes degree of complexity of the terrain including the current location of the vehicle, and the third processor is further configured to decide the processing ratio so that the portion processed by the first processor is greater than the portion processed by the second processor when the degree of complexity of the terrain including the current location of the vehicle is high, compared to when the degree of complexity of the terrain including the current location of the vehicle is low (see at least Park [¶ 32, 36-37, 61] External vehicle conditions that may be considered in various embodiments when allocating processing resources to applications may include...road conditions, ambient temperature, ambient humidity, and weather conditions...the presence or detection of road hazards, accidents, or other similar threatening conditions…the map fusion and arbitration vehicle application 208 may convert latitude and longitude information from GPS into locations within a surface map of roads contained in the HD map database…the processor may allocate finite processing resources to each of a plurality of vehicle applications based on a determination of a priority of each vehicle application to safe vehicle operations in a moment-to-moment, context-determined manner based on internal and external vehicle conditions...Each of a plurality of vehicle applications may be assigned different safety-related priorities based on various vehicle conditions....External vehicle conditions that may be considered in various embodiments when allocating processing resources to applications may include...road conditions, ambient temperature, ambient humidity, and weather conditions...the presence or detection of road hazards, accidents, or other similar threatening conditions (For example, as driving speed increases, sensor polling rates, data analysis, safety decisions by the processor, information display adjustments, and the like may need to be performed or refreshed more frequently to maintain a threshold level of safety performance)) Per the example of increasing the allocation computing resources when vehicle speed increases, it would be obvious to anyone of ordinary skill in the art that the same is true when the complexity of the terrain increases due to the disclosed road conditions and weather conditions.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the system set forth in Zhang to contain a system for wherein the terrain information includes degree of complexity of the terrain including the current location of the vehicle, and the third processor is further configured to decide the processing ratio so that the portion processed by the first processor is greater than the portion processed by the second processor when the degree of complexity of the terrain including the current location of the vehicle is high, compared to when the degree of complexity of the terrain including the current location of the vehicle is low with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of improving the safety of the vehicle when determining how to allocate computing resources as discussed in Park (see at least Park [¶ 44] Various embodiments improve the safety of vehicles by allocating finite computing resources to the plurality of vehicle applications according to the importance of the vehicle application to safe vehicle operations or impact on a driver's ability to perform one or more safety-related tasks in a particular situation or context).
Regarding Claim 9, Zhang teaches a computer-readable, non-transitory storage medium storing a computer program for vehicle control, which causes a third processor to execute a process (see at least Zhang [English Translation pg.18 para.11] The embodiment of the invention further claims a computer program product, comprising a computer readable code, or a non-volatile computer readable storage medium bearing computer readable code, when the computer readable code in the processor of the electronic device, The processor in the electronic device executes the task allocation method or task processing method)
the process comprising:
deciding a processing ratio between a portion processed by a first processor that generates an output signal using only a machine learning-trained model, and a portion processed by a second processor that has lower power consumption than the first processor and generates an output signal without using a machine learning-trained model (see at least Zhang [English Translation Abstract and pg.7 para.2, pg.6 para.6-7] the processing architecture 300 comprises…a general processor 312…a special processor 322…the task to be processed comprises a general task and a special task, wherein the general task is the task of the general type, generally corresponding to the general program, the special task refers to the task of some special field (e.g., task in the field of neural network)… it is capable of considering the computing power of the cost, selecting proper compiling mode and processing mode for each task to be processed, so as to more reasonably use processing resource of two different processing mode...for a plurality of tasks to be processed, determining each task to be processed by the first cost corresponding to the general processing mode and the second cost corresponding to the special processing mode; according to the first cost and the second cost corresponding to each task to be processed...Furthermore, in general, the special program can only be run in the special processor, so the special task can only be executed by the special processor) The disclosure in Zhang teaches a control device (processor) that can allocate tasks between two processors, a general processor and a special processor, wherein the special processor utilizes neural network technology (which is analogous to using a machine learning trained model) and can perform special tasks that the general processor, without the neural network technology, cannot. The system in Zhang will allocate the tasks between the processors depending on the task, which is analogous to deciding a processing ratio. Zhang further discloses such a system can be used for a vehicle.
However, while Zhang teaches a system that can allocate tasks (decide processing ratio) between a generic processor and a neural network processor for a vehicle based on information regarding a particular task, it does not explicitly teach wherein the information represents a state of a vehicle, environment information representing surrounding environment of the vehicle, and terrain information representing terrain including a current location of the vehicle.
Park, in the same field as the endeavor, teaches deciding a processing ratio between a portion processed by a first processor that generates an output signal using only a machine learning-trained model, and a portion processed by a second processor that has lower power consumption than the first processor and generates an output signal without using a machine learning-trained model, based on vehicle information representing a state of a vehicle, environment information representing surrounding environment of the vehicle, and terrain information representing terrain including a current location of the vehicle (see at least Park [¶ 29, 32, 36, 76] The term “system-on-chip” (SOC)…including one or more processors, a memory, and a communication interface. The SOC may include a variety of different types of processors and processor cores…With reference to FIGS. 1A-3, the processing device SOC 300 may include a variety of processing resources, including a number of heterogeneous processors…a resource and power management (RPM) processor 317. The processing device SOC 300 may also include one or more coprocessors 310 (e.g., vector co-processor) connected to one or more of the heterogeneous processors 303, 304, 306, 307, 308, 317…Various embodiments enable a vehicle processor to intelligently manage resource allocation and a processing quality of each of a plurality of concurrent automotive applications, processes, or other functions (referred to herein collectively as “applications”)…the processor may allocate finite processing resources to each of a plurality of vehicle applications based on a determination of a priority of each vehicle application to safe vehicle operations in a moment-to-moment, context-determined manner based on internal and external vehicle conditions… External vehicle conditions that may be considered in various embodiments when allocating processing resources to applications may include a driving speed and direction of the vehicle; road conditions, ambient temperature, ambient humidity, and weather conditions; proximity, speed, and direction of motion of other objects such as vehicles, pedestrians, etc) The disclosure in Park teaches a processor configured to allocate resources between a plurality of other processors on the vehicle to perform different tasks, the allocation is based on driving speed and direction of the vehicle (vehicle state), the motion of other objects and pedestrians (environment information), and the road and weather conditions (terrain information).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the system set forth in Zhang to contain a system for deciding a processing ratio between a portion processed by a first processor that generates an output signal using only a machine learning-trained model, and a portion processed by a second processor that has lower power consumption than the first processor and generates an output signal without using a machine learning-trained model, based on vehicle information representing a state of a vehicle, environment information representing surrounding environment of the vehicle, and terrain information representing terrain including a current location of the vehicle with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of improving the safety of the driving of the vehicle by allocating resources to the most important tasks (see at least Park [¶ 44] Various embodiments improve the functionality of vehicle systems by providing at least some computing resources to a plurality of vehicle applications that are important to the safe operation of the vehicle even when overall computing resources are reduced. Various embodiments improve the safety of vehicles by allocating finite computing resources to the plurality of vehicle applications according to the importance of the vehicle application to safe vehicle operations).
Further, Zhang does not explicitly teach wherein the first processor controls the vehicle based on the state of the vehicle, the environment information, and the terrain information.
Marsillach, in the same field as the endeavor teaches wherein the first processor controls the vehicle based on the state of the vehicle, the environment information, and the terrain information (see at least Marsillach [¶ 5, 18] a trajectory planning system for an autonomous vehicle is disclosed and includes one or more controllers in electronic communication with one or more external vehicle networks that collect data with respect to one or more moving obstacles located in an environment surrounding the autonomous vehicle...The one or more controllers determine, based on the discrete-time relative vehicle state, a position avoidance set representing relative lateral positions and longitudinal positions that the autonomous vehicle avoids while bypassing the one or more moving obstacles when performing a maneuver. The one or more controllers determine a set of ego states for which the autonomous vehicle is unable to execute maneuvers without entering the position avoidance set for a set of given speed limits and road conditions….where the autonomous vehicle follows the trajectory while performing the maneuver…an autonomous vehicle including a trajectory planning system is disclosed and includes a plurality of sensors that determine a plurality of dynamic variables, one or more external vehicle networks that collect data with respect to one or more moving obstacles located in an environment surrounding the autonomous vehicle) The disclosure in Marsillach teaches a processor configured to control a vehicle based on vehicle state, the motion of moving obstacles (environment information), and the speed limits and road conditions (terrain information).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the system set forth in Zhang to contain a system for wherein the first processor controls the vehicle based on the state of the vehicle, the environment information, and the terrain information with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of improving the safety of the driving of the vehicle by ensuring an evasive maneuver is always possible as discussed in Marsillach (see at least Marsillach [¶ 59] the disclosure provides a methodology and architecture that ensures maneuvers for avoiding the moving obstacles always exist and are executable by the autonomous vehicle within an operating domain of interest).
Regarding Claim 10, Zhang a method for controlling a vehicle carried out by a vehicle control device (see at least Zhang [English Translation pg.5 para.7] The task allocation method according to the embodiment of the present disclosure, the task processing method can be executed by the terminal device...the terminal device can be a...vehicle device)
and the method comprising:
deciding a processing ratio between a portion processed by a first processor that generates an output signal using only a machine learning-trained model, and a portion processed by a second processor that has lower power consumption than the first processor and generates an output signal without using a machine learning-trained model (see at least Zhang [English Translation Abstract and pg.7 para.2, pg.6 para.6-7] the processing architecture 300 comprises…a general processor 312…a special processor 322…the task to be processed comprises a general task and a special task, wherein the general task is the task of the general type, generally corresponding to the general program, the special task refers to the task of some special field (e.g., task in the field of neural network)… it is capable of considering the computing power of the cost, selecting proper compiling mode and processing mode for each task to be processed, so as to more reasonably use processing resource of two different processing mode...for a plurality of tasks to be processed, determining each task to be processed by the first cost corresponding to the general processing mode and the second cost corresponding to the special processing mode; according to the first cost and the second cost corresponding to each task to be processed...Furthermore, in general, the special program can only be run in the special processor, so the special task can only be executed by the special processor) The disclosure in Zhang teaches a control device (processor) that can allocate tasks between two processors, a general processor and a special processor, wherein the special processor utilizes neural network technology (which is analogous to using a machine learning trained model) and can perform special tasks that the general processor, without the neural network technology, cannot. The system in Zhang will allocate the tasks between the processors depending on the task, which is analogous to deciding a processing ratio. Zhang further discloses such a system can be used for a vehicle.
However, while Zhang teaches a system that can allocate tasks (decide processing ratio) between a generic processor and a neural network processor for a vehicle based on information regarding a particular task, it does not explicitly teach wherein the information represents a state of a vehicle, environment information representing surrounding environment of the vehicle, and terrain information representing terrain including a current location of the vehicle.
Park, in the same field as the endeavor, teaches deciding a processing ratio between a portion processed by a first processor that generates an output signal using only a machine learning-trained model, and a portion processed by a second processor that has lower power consumption than the first processor and generates an output signal without using a machine learning-trained model, based on vehicle information representing a state of a vehicle, environment information representing surrounding environment of the vehicle, and terrain information representing terrain including a current location of the vehicle (see at least Park [¶ 29, 32, 36, 76] The term “system-on-chip” (SOC)…including one or more processors, a memory, and a communication interface. The SOC may include a variety of different types of processors and processor cores…With reference to FIGS. 1A-3, the processing device SOC 300 may include a variety of processing resources, including a number of heterogeneous processors…a resource and power management (RPM) processor 317. The processing device SOC 300 may also include one or more coprocessors 310 (e.g., vector co-processor) connected to one or more of the heterogeneous processors 303, 304, 306, 307, 308, 317…Various embodiments enable a vehicle processor to intelligently manage resource allocation and a processing quality of each of a plurality of concurrent automotive applications, processes, or other functions (referred to herein collectively as “applications”)…the processor may allocate finite processing resources to each of a plurality of vehicle applications based on a determination of a priority of each vehicle application to safe vehicle operations in a moment-to-moment, context-determined manner based on internal and external vehicle conditions… External vehicle conditions that may be considered in various embodiments when allocating processing resources to applications may include a driving speed and direction of the vehicle; road conditions, ambient temperature, ambient humidity, and weather conditions; proximity, speed, and direction of motion of other objects such as vehicles, pedestrians, etc) The disclosure in Park teaches a processor configured to allocate resources between a plurality of other processors on the vehicle to perform different tasks, the allocation is based on driving speed and direction of the vehicle (vehicle state), the motion of other objects and pedestrians (environment information), and the road and weather conditions (terrain information).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the system set forth in Zhang to contain a system for deciding a processing ratio between a portion processed by a first processor that generates an output signal using only a machine learning-trained model, and a portion processed by a second processor that has lower power consumption than the first processor and generates an output signal without using a machine learning-trained model, based on vehicle information representing a state of a vehicle, environment information representing surrounding environment of the vehicle, and terrain information representing terrain including a current location of the vehicle with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of improving the safety of the driving of the vehicle by allocating resources to the most important tasks (see at least Park [¶ 44] Various embodiments improve the functionality of vehicle systems by providing at least some computing resources to a plurality of vehicle applications that are important to the safe operation of the vehicle even when overall computing resources are reduced. Various embodiments improve the safety of vehicles by allocating finite computing resources to the plurality of vehicle applications according to the importance of the vehicle application to safe vehicle operations).
Further, Zhang does not explicitly teach wherein the first processor controls the vehicle based on the state of the vehicle, the environment information, and the terrain information.
Marsillach, in the same field as the endeavor teaches wherein the first processor controls the vehicle based on the state of the vehicle, the environment information, and the terrain information (see at least Marsillach [¶ 5, 18] a trajectory planning system for an autonomous vehicle is disclosed and includes one or more controllers in electronic communication with one or more external vehicle networks that collect data with respect to one or more moving obstacles located in an environment surrounding the autonomous vehicle...The one or more controllers determine, based on the discrete-time relative vehicle state, a position avoidance set representing relative lateral positions and longitudinal positions that the autonomous vehicle avoids while bypassing the one or more moving obstacles when performing a maneuver. The one or more controllers determine a set of ego states for which the autonomous vehicle is unable to execute maneuvers without entering the position avoidance set for a set of given speed limits and road conditions….where the autonomous vehicle follows the trajectory while performing the maneuver…an autonomous vehicle including a trajectory planning system is disclosed and includes a plurality of sensors that determine a plurality of dynamic variables, one or more external vehicle networks that collect data with respect to one or more moving obstacles located in an environment surrounding the autonomous vehicle) The disclosure in Marsillach teaches a processor configured to control a vehicle based on vehicle state, the motion of moving obstacles (environment information), and the speed limits and road conditions (terrain information).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the system set forth in Zhang to contain a system for wherein the first processor controls the vehicle based on the state of the vehicle, the environment information, and the terrain information with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of improving the safety of the driving of the vehicle by ensuring an evasive maneuver is always possible as discussed in Marsillach (see at least Marsillach [¶ 59] the disclosure provides a methodology and architecture that ensures maneuvers for avoiding the moving obstacles always exist and are executable by the autonomous vehicle within an operating domain of interest).
Claims 2-4 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al (CN 115543583 A) in view of Park et al (US 20210247762 A1), Marsillach et al (US 20240132104 A1), and Li (CN 115827244 A). Hereafter referred to as Zhang, Park, Marsillach and Li respectively.
Regarding Claim 2, Zhang in view of Park and Marsillach teaches all limitations of Claim 1 as set forth above. Zhang further teaches a plurality of the first processor and a plurality of the second processors (see at least Zhang [English Translation pg.19 para.1] one physical component may have a plurality of functions, or one function or step may be performed by a plurality of physical components. Some physical components or all physical components may be implemented as a processor)
However, Zhang does not explicitly teach a first electronic control unit that has one of the first processors and one of the processors devices; a second electronic control unit that has higher power consumption than the first electronic control unit and generates an output signal using only another of the first processors; a third electronic control unit that has lower power consumption than the first electronic control unit and generates an output signal using only another of the second processors
and wherein the third processor is further configured to select a selected electronic control unit for control of the vehicle from among the first electronic control unit, second electronic control unit and third electronic control unit based on the decided processing ratio.
Li, in the same field as the endeavor, teaches a first electronic control unit that has one of the first processors and one of the processors devices; a second electronic control unit that has higher power consumption than the first electronic control unit and generates an output signal using only another of the first processors; a third electronic control unit that has lower power consumption than the first electronic control unit and generates an output signal using only another of the second processors (see at least Li [English Translation pg.2 para.10-14] the processor resource pool further comprises a multi-path selection switch, the multi-path selection switch comprises a first port, a second port and a plurality of third ports, the first port is connected with the ADAS, the second port is connected with the IVI system, each of the third port is connected with one of the shared processor;...selecting the M shared processors to be allocated to the ADAS in the processor resource pool, and selecting the N shared processors to be allocated to the IVI system in the shared processor remaining in the processor resource pool, comprising: controlling the multi-path selection switch, conducting the first port and the M third ports in the plurality of third ports, and conducting the second port and the N third ports in the remaining third port) The disclosure in Li describes allocating processing resources to different combinations of different processing devices, in view of Zhang’s disclosed first and second processing devices, any combination of processing devices composed of the first and second processing devices would be obvious to anyone of ordinary skill in the art.
and wherein the third processor is further configured to select a selected electronic control unit for control of the vehicle from among the first electronic control unit, second electronic control unit and third electronic control unit based on the decided processing ratio (see at least Li [English Translation Abstract and pg.2 para.2, pg.3 para.3, pg.9 para.1-3] The application claims a processor resource distribution method and device of vehicle, relating to the technical field of intelligent automobile, the vehicle comprises a processor resource pool, the processor resource pool comprises a plurality of shared processors…The application can dynamically allocate different number of processor resources to the ADAS and IVI system according to the using state of the vehicle...The allocation module 403 is specifically configured to: controlling the multi-path selection switch, conducting the first port and the M third ports in the plurality of third ports, and conducting the second port and the N third ports in the remaining third port...according to the current use state of the vehicle, and a predetermined processor allocation scheme corresponding to each use state, determining the number of processors needed by the ADAS and the IVI system).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the system set forth in Zhang to contain teach a first electronic control unit that has one of the first processors and one of the processors devices; a second electronic control unit that has higher power consumption than the first electronic control unit and generates an output signal using only another of the first processors; a third electronic control unit that has lower power consumption than the first electronic control unit and generates an output signal using only another of the second processors and wherein the third processor is further configured to select a selected electronic control unit for control of the vehicle from among the first electronic control unit, second electronic control unit and third electronic control unit based on the decided processing ratio with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of reducing the waste of computing power and production cost as discussed in Li (see at least Li [English Translation Abstract] can avoid the waste of the processor computing power reduce the production cost of the vehicle).
Regarding Claim 3, Zhang in view of Park and Marsillach teaches all limitations of Claim 1 as set forth above. However, Zhang does not explicitly teach wherein the vehicle information includes degree of operation of the vehicle, and the third processor is further configured to decide the processing ratio according to the degree of operation of the vehicle.
Li, in the same field as the endeavor, teaches wherein the vehicle information includes degree of operation of the vehicle, and the third processor is further configured to decide the processing ratio according to the degree of operation of the vehicle (see at least Li [English Translation pg.9 para.2-3] The determining module 402 is specifically used for: according to the current use state of the vehicle, and a predetermined processor allocation scheme corresponding to each use state, determining the number of processors needed by the ADAS and the IVI system).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the system set forth in Zhang to contain a system for wherein the vehicle information includes degree of operation of the vehicle, and the processor is further configured to decide the processing ratio according to the degree of operation of the vehicle with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of reducing the waste of computing power and production cost as discussed in Li (see at least Li [English Translation Abstract] can avoid the waste of the processor computing power reduce the production cost of the vehicle).
Regarding Claim 4, Zhang in view of Park, Marsillach, and Li teaches all limitations of Claim 3 as set forth above. However, Zhang does not explicitly teach wherein the vehicle information includes speed of the vehicle, and the third processor is further configured to decide the processing ratio so that the portion processed by the first processor is greater than the portion processed by the second processor when the speed of the vehicle is slow compared to when the speed of the vehicle is fast.
Park, in the same field as the endeavor, teaches wherein the vehicle information includes speed of the vehicle, and the third processor is further configured to decide the processing ratio so that the portion processed by the first processor is greater than the portion processed by the second processor when the speed of the vehicle is slow compared to when the speed of the vehicle is fast (see at least Park [¶ 32, 36-37] External vehicle conditions that may be considered in various embodiments when allocating processing resources to applications may include a driving speed…the processor may allocate finite processing resources to each of a plurality of vehicle applications based on a determination of a priority of each vehicle application to safe vehicle operations in a moment-to-moment, context-determined manner based on internal and external vehicle conditions...Each of a plurality of vehicle applications may be assigned different safety-related priorities based on various vehicle conditions. For example, as driving speed increases, sensor polling rates, data analysis, safety decisions by the processor, information display adjustments, and the like may need to be performed or refreshed more frequently to maintain a threshold level of safety performance).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the system set forth in Zhang to contain a system for wherein the vehicle information includes speed of the vehicle, and the third processor is further configured to decide the processing ratio so that the portion processed by the first processor is greater than the portion processed by the second processor when the speed of the vehicle is slow compared to when the speed of the vehicle is fast with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of improving the safety of the vehicle when determining how to allocate computing resources as discussed in Park (see at least Park [¶ 44] Various embodiments improve the safety of vehicles by allocating finite computing resources to the plurality of vehicle applications according to the importance of the vehicle application to safe vehicle operations or impact on a driver's ability to perform one or more safety-related tasks in a particular situation or context).
Claims 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al (CN 115543583 A) in view of Park et al (US 20210247762 A1), Marsillach et al (US 20240132104 A1), Li (CN 115827244 A), and Noda et al (US 20220407821 A1). Hereafter referred to as Zhang, Park, Marsillach, Li, and Noda respectively.
Regarding Claim 7, Zhang in view of Park, Marsillach, and Li teaches all limitations of Claim 2 as set forth above. However, Zhang does not explicitly teach wherein the third processor is further configured to decide amount of information to be input to the selected electronic control unit that has been selected, based on at least one information from among the vehicle information, environment information and terrain information.
Noda, in the same field as the endeavor, teaches wherein the third processor is further configured to decide amount of information to be input to the selected electronic control unit that has been selected, based on at least one information from among the vehicle information, environment information and terrain information (see at least Noda [Abstract and ¶ 37, 50] The process includes, calculating, in a case where a notification that a resource is increased is received from a first processing device in a group of processing devices at a previous stage of the processing device among the plurality of processing devices, a ratio of an amount of data received from the first processing device to a total amount of data received from each of the group of processing devices...The processing device 101 receives an increase notification from a first processing device in the processing device group at the previous stage of the processing device 101. The increase notification is a notification indicating that a resource has been increased. Increasing the resource of a processing device tends to improve the processing capacity of the processing device and increase the amount of data flowing from the processing device to a processing device at the subsequent stage....in the case of a connected car, it is possible to analyze a large amount of data collected from a vehicle such as speed and position and to feed risk information or the like back to the driver of the 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 have modified the system set forth in Zhang to contain a system for wherein the third processor is further configured to decide amount of information to be input to the selected electronic control unit that has been selected, based on at least one information from among the vehicle information, environment information and terrain information with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of improving the processing capabilities by adjusting the amount of data input to a processor as discussed in Noda (see at least Noda [¶ 37] Increasing the resource of a processing device tends to improve the processing capacity of the processing device and increase the amount of data flowing from the processing device to a processing device at the subsequent stage).
Regarding Claim 8, Zhang in view of Park, Marsillach, Li, and Noda teaches all limitations of Claim 7 as set forth above. However, Zhang does not explicitly teach wherein the amount of information includes number of sensors with which detected information is input to the selected electronic control unit, the resolution of image input to the selected electronic control unit, and detection frequency of a sensor with which detected information is input to the selected electronic control unit.
Park, in the same field as the endeavor, teaches wherein the amount of information includes number of sensors with which detected information is input to the selected electronic control unit, the resolution of image input to the selected electronic control unit, and detection frequency of a sensor with which detected information is input to the selected electronic control unit (see at least Park [37, 45, 66] as driving speed increases, sensor polling rates, data analysis, safety decisions by the processor, information display adjustments, and the like may need to be performed or refreshed more frequently to maintain a threshold level of safety performance. As another example, if the vehicle is traveling along a long straightaway or in light traffic conditions, a polling rate of lane detection sensors and/or vehicle proximity sensors may be reduced with minimal impact on driving safety…a vehicle 100 may include a control unit 140 and a plurality of sensors 102-138, including…cameras 122, 136…The plurality of sensors 102-138, disposed in or on the vehicle, may be used for various purposes, such as autonomous and semi-autonomous navigation and control, crash avoidance, position determination, etc., as well to provide sensor data regarding objects and people in or on the vehicle 100…state information may include…on board sensor resolution).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified the system set forth in Zhang to contain a system for wherein the amount of information includes number of sensors with which detected information is input to the selected electronic control unit, the resolution of image input to the selected electronic control unit, and detection frequency of a sensor with which detected information is input to the selected electronic control unit with reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification for benefit of including sensor data that is commonly used in vehicle control operations.
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.
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/JOSEPH ANDERSON YANOSKA/Examiner, Art Unit 3664
/RACHID BENDIDI/Supervisory Patent Examiner, Art Unit 3664
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