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
This action is in response to the Applicant’s filing on 12/17/2024. Claims 1-12 are pending and are examined below.
PRIORITY
Acknowledgement is made of Applicant’s claim of foreign priority to JP2023-23923, filed on 12/19/2023.
CLAIM OBJECTIONS
Claim(s) 10 is/are objected to because of claim informalities.
As to claim 10, claim element “increases the processing volume for collection of information” lacks antecedent basis. Note that claim 1’s element of “the processing volume processing volume for detection processing” appears to pertain to a different type of processing volume and therefore does not establish antecedent basis.
Appropriate correction is required.
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.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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 limitations are: “a collecting device that collects” and “a detecting device that carries out” in claims 1 (with dependent claims 2-10); and “a first detecting device that can carry out detecting processing independently” and “a second detecting device that can carry out detection processing independently” in claim 9.
The corresponding structure described in the specification as performing the claimed function at least includes: processor (See PGPUB ¶ 35.)
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
Because these claim limitation(s) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (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) or pre-AIA 35 U.S.C. 112, sixth paragraph.
CLAIM REJECTIONS—35 U.S.C. § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 1-12 is/are rejected under 35 U.S.C. § 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. § 112, the applicant), regards as the invention.
As to claims 1, 11 and 12, the recitation “when it has been estimated that processing volume … is low, compared to when it has been estimated that processing volume … is high” is vague and indefinite. Namely, it is unclear what criteria denotes a processing volume as low as opposed to being high, and vice versa. Applicant’s specification does not clarify this matter. In light of the above, it is unclear what is being claimed in light of Applicant’s original disclosure.
As to claim 2, the recitation “estimate that the processing volume … is low when operation of the vehicle is slow, compared to when operation of the vehicle is fast” is vague and indefinite. Namely, it is unclear what criteria denotes a processing volume as slow as opposed to fast, and vice versa. Applicant’s specification does not clarify this matter. In light of the above, it is unclear what is being claimed in light of Applicant’s original disclosure.
As to claim 3, the recitation “estimate that the processing volume … is low when the surrounding environment of the vehicle is not complex, compared to when the surrounding environment of the vehicle is complex” is vague and indefinite. Namely, it is unclear what criteria denotes a surrounding environment as complex vs. non-complex, and vice versa. At best, Applicant’s specification ties a degree of complexity to the following: “the environment information includes the number of moving objects around the vehicle 10, the number of road features around the vehicle 10, and the presence or absence of a construction zone around the vehicle 10.” (PGPUB ¶ 85.) However, this disclosure does not provide guidance on what delineates a complex environment from a non-complex one. In light of the above, it is unclear what is being claimed in light of Applicant’s original disclosure.
As to claim 4, the recitation “estimate that the processing volume … is low when the terrain including the current location of the vehicle is not complex, compared to when the terrain including the current location of the vehicle is complex” is vague and indefinite. Namely, it is unclear what criteria denotes a terrain as complex vs. non-complex, and vice versa. At best, Applicant’s specification ties terrain complexity to “the number of lane links around the vehicle 10, the curvature of the road and the type of road.” (PGPUB ¶ 90.) However, this disclosure does not provide guidance on what delineates a complex terrain from a non-complex one. In light of the above, it is unclear what is being claimed in light of Applicant’s original disclosure.
As to claim 9, the recitation “decide to use the second detecting device when the processing volume … has been estimated to be low, and to use the first detecting device when the processing volume … has been estimated to be high” is vague and indefinite. Namely, it is unclear what criteria denotes a processing volume as low as opposed to being high, and vice versa. Applicant’s specification does not clarify this matter. In light of the above, it is unclear what is being claimed in light of Applicant’s original disclosure.
Claims 5-8 and 10 depend from claim 1.
Therefore, claims 1-12 are rejected under 35 U.S.C. § 112(b) or 35 U.S.C. § 112 (pre-AIA ), second paragraph.
Appropriate correction is required.
CLAIM REJECTIONS—35 U.S.C. § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-12 is/are rejected under 35 U.S.C. § 101 because the claims fail to pass the Alice/Mayo test for determining patent eligibility.
The patent eligibility test is performed below for independent claims 1, 11 and 12.
Step 1—Does the claim fall within a statutory category?
Claim 1: Yes, the claim recites a machine or manufacture.
Claim 11: Yes, the claim recites a machine or manufacture.
Claim 12: Yes, the claim recites a process.
Step 2A, Prong One—Is a judicial exception recited?
Claim 1 is provided below with the abstract idea indicated in bold and additional elements without bold. Examiner notes that claims 11 and 12 recite similar subject matter but for minor differences; hence, the analysis of claim 1 will pertain to claims 11 and 12 as well.
1. An information processing device comprising:
a collecting device that collects information relating to control of a vehicle for machine learning, wherein processing is carried out using hardware common to a detecting device that carries out detection processing for control of the vehicle; and
a processor configured to
estimate a degree of processing volume for detection processing by the detecting device, based on at least one type of information from among vehicle information representing a state of the vehicle, environment information representing surrounding environment of the vehicle, and terrain information representing terrain including a current location of the vehicle, and
decide to lower detection accuracy by the detecting device when it has been estimated that processing volume for detection processing by the detecting device is low, compared to when it has been estimated that processing volume for detection processing by the detecting device is high.
The above shows: yes, a judicial exception is recited. But for the additional elements, the claim limitations pertaining to estimating a degree of processing value based on acquired information and deciding to lower detection accuracy based on the result of the estimation are processes which can practically be performed in the human mind with or without the use of a physical aid. Specifically, the broadest reasonable interpretation (BRI) of the claim encompasses performing evaluations over obtained data and performing judgments over detection accuracy. The courts have held such forms of observation, evaluation, judgment, or opinion to represent the abstract idea of a mental process. As a result, the bolded limitations represent a mental process. Hence, the claim recites an abstract idea. (See MPEP § 2106.04(a)(2)(C)(III).)
Step 2A, Prong Two—Is the abstract idea integrated into a practical application?
No. The claims as a whole merely use generic computer components — i.e., hardware common to a detecting device and a processor — that are recited at a high level of generality such that they cannot be considered more than mere instructions to apply the judicial exception using generic computer components. Therefore, the abstract idea is not integrated into a practical application.
In regards to the elements of “for machine learning” and “for control of the vehicle,” these elements are insufficient to integrate the abstract idea into a practical application because they are mere general links of the use of the judicial exception to a particular technological environment or field of use. These are mere general links because the claim does not positively recite that these elements are performed; rather, the claim merely states that information is collected for an intended use of machine learning, and that detection processing is performed for an intended use of vehicle control — the claim does not necessarily require carry out either machine learning or vehicle control. A mere general link of the use of the judicial exception to a particular technological environment or field of use is insufficient to integrate an abstract idea into a practical application. (See MPEP 2106.05(h).)
Step 2B—Does the claim provide an inventive concept?
No. The additional elements of the claims amount to:
Insignificant pre-solution activity in the form of mere data gathering, wherein the data gathering is performed through a generic computing component in a routine and conventional manner:
a collecting device that collects information relating to control of a vehicle for machine learning
Claims 2-10 depend from claim 1 but do not render the claimed invention patent eligible because they are directed to additional mental steps:
estimate that the processing volume … is low when operation of the vehicle is slow, compared to when operation of the vehicle is fast,
estimate that the processing volume for detection processing … is low when the surrounding environment of the vehicle is not complex, compared to when the surrounding environment of the vehicle is complex
decide to lower detection accuracy … by lengthening a cycle at which detection processing is carried out,
decide to lower detection accuracy … by reducing a number of sensors by which detected information is input,
decide to lower detection accuracy … by reducing a region in an image detected by the detecting device,
decide to lower detection accuracy … by reducing a distance of a trajectory of a moving object that is estimated by the detecting device, and
decide to use the second detecting device when the processing volume … has been estimated to be low, and to use the first detecting device when the processing volume … has been estimated to be high;
or insignificant extra-solution activity (e.g., gathering data):
increases the processing volume for collection of information.
Claims 1-12 do not pass the patent eligibility test. Accordingly, claims 1-12 are rejected under § 101.
Eligibility Note
Amending the independent claims to positively recite a form of vehicle control may render the claims as patent eligible pending further consideration. Applicant’s PGPUB at [0031] appears to provide support for such an amendment: “When the vehicle 10 is self-driven, the control device 12 … outputs an automatic steering signal that controls the steering device 13, an automatic driving signal that controls the drive unit 14 such as an engine or motor, and an automatic braking signal that controls the braking device 15.”
CLAIM REJECTIONS—35 U.S.C. § 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 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.
Claim(s) 1-5 and 11-12 is/are rejected under § 103 as being unpatentable over Takemura et al. (US20140300731A1; “Takemura”) in view of Hu (US20210001880A1; “Hu”).
As to independent claim 1, Takemura discloses an information processing device comprising:
a processor (CPU - ¶ 23 and FIG. 1.) configured to
estimate a degree of processing volume for detection processing by the detecting device, based on at least environment information representing surrounding environment of the vehicle (“A lateral-position detection process A for detecting the lateral-position of the white line, on the basis of the image taken by the imaging unit 110, is executed by the lateral-position detection unit 211. The lateral-position detection unit 211 can enhance the detection accuracy of the lateral-position by enhancing resolution for finding the lateral-position of the white line. However, because this will entail a rise in a process-load as well, it is necessary to decide a CPU process-load to be allocated to the respective detection units, while grasping process-time as a whole for app-control, thereby adjusting the detection accuracy.” ¶ 26. “With the curvature detection unit 213, there is available a method for enhancing the detection accuracy of the curvature by making use of an image in higher resolution. Otherwise, there is also available a method whereby the detection accuracy is lowered on the assumption that a road curvature will not undergo a[n] abrupt change, and a process cycle is changed such that one curvature-detection is executed for every two lane-recognitions to thereby allocate part of the CPU process-load to other processes.” ¶ 28; see also ¶ 73. Note: That is, based on environment information (i.e., the presence of curvature), it is estimated that a degree of processing volume will constitute a need for lowered detection accuracy.), and
decide to lower detection accuracy by the detecting device when it has been estimated that processing volume for detection processing by the detecting device is low, compared to when it has been estimated that processing volume for detection processing by the detecting device is high (“With the curvature detection unit 213, there is available a method for enhancing the detection accuracy of the curvature by making use of an image in higher resolution. Otherwise, there is also available a method whereby the detection accuracy is lowered on the assumption that a road curvature will not undergo a[n] abrupt change, and a process cycle is changed such that one curvature-detection is executed for every two lane-recognitions to thereby allocate part of the CPU process-load to other processes.” ¶ 28; see also ¶¶ 26, 73.).
Takemura fails to explicitly disclose: a collecting device that collects information relating to control of a vehicle for machine learning, wherein processing is carried out using hardware common to a detecting device that carries out detection processing for control of the vehicle.
Nevertheless, Hu teaches: a collecting device that collects information relating to control of a vehicle for machine learning, wherein processing is carried out using hardware common to a detecting device that carries out detection processing for control of the vehicle (“The vehicle-mounted control unit … includes a MCU and a first SoC [system on chip] implemented by being integrated with an ARM through the FPGA, and the vehicle-mounted control unit is set on an automatic driving vehicle. During the process of the automatic driving, the FPGA of the first SoC receives video data sent by a vehicle-mounted camera; perform[s] visual perception on the video data by using a first neural network algorithm to obtain first perception information; and sends the first perception information to the ARM of the first SoC.” ¶ 50 and FIG. 2. See also ¶ 48. Note: A control device obtains video data (information relating to control of a vehicle) for a neural network (machine learning). The control device also performs visual perception (detecting processing). Hence, the claimed processes are performed on common hardware.).
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 Takemura to include the feature of: a collecting device that collects information relating to control of a vehicle for machine learning, wherein processing is carried out using hardware common to a detecting device that carries out detection processing for control of the vehicle, as taught by Hu, with a reasonable expectation of success because this feature is useful “to reduce the burden of the MCU” in the context of carrying out detection processing for control of a vehicle and performing machine learning. (See Hu, ¶ 50.) The foregoing aligns with Takemura’s similar goal of reducing CPU burden in the context of performing detection processing.
Independent claims 11 and 12 are rejected for at least the same reasons as claim 1 as the claims recite similar subject matter but for minor differences.
As to claim 2, Takemura discloses:
the vehicle information includes information representing degree of operation of the vehicle (“Information on the behavior of the vehicle,” including “vehicle speed,” may be obtained - ¶ 36. See also ¶ 47 and FIG. 5.), and
the processor is further configured to estimate that the processing volume for detection processing by the detecting device is low when operation of the vehicle is slow, compared to when operation of the vehicle is fast (“In the case of lane-deviation at the low-speed lateral-velocity lane-deviation time, it can be presumed that the inclination of the vehicle, against the lane, is small, and there is not so high necessity for the detection accuracy of the yaw angle indicating the inclination of the vehicle, against the lane.” Emphases added; ¶ 83. “The detection accuracy and the stability of the yaw angle, serving as an important criterion for the alarm and vehicle-control at the high-speed lateral-velocity lane-deviation time, are enhanced, and the process-load of the curvature is checked, thereby enabling the accuracy as well as the stability of the alarm and vehicle-control, imparted to a driver, to be enhanced.” Emphasis added; ¶ 86.).
As to claim 3, Takemura discloses:
the environment information includes information representing degree of complexity of the surrounding environment of the vehicle (“With the curvature detection unit 213, there is available a method for enhancing the detection accuracy of the curvature by making use of an image in higher resolution. Otherwise, there is also available a method whereby the detection accuracy is lowered on the assumption that a road curvature will not undergo a[n] abrupt change, and a process cycle is changed such that one curvature-detection is executed for every two lane-recognitions to thereby allocate part of the CPU process-load to other processes.” ¶ 28; see also ¶¶ 26, 73. Note: The presence of road curvature meets the BRI of a degree of environmental complexity as curvature introduces complexity to vehicle processing), and
the processor is further configured to estimate that the processing volume for detection processing by the detecting device is low when the surrounding environment of the vehicle is not complex, compared to when the surrounding environment of the vehicle is complex (“With the curvature detection unit 213, there is available a method for enhancing the detection accuracy of the curvature by making use of an image in higher resolution. Otherwise, there is also available a method whereby the detection accuracy is lowered on the assumption that a road curvature will not undergo a[n] abrupt change, and a process cycle is changed such that one curvature-detection is executed for every two lane-recognitions to thereby allocate part of the CPU process-load to other processes.” ¶ 28; see also ¶ 73.).
As to claim 4, Takemura discloses:
the terrain information includes information representing degree of complexity of the terrain including the current location of the vehicle (“With the curvature detection unit 213, there is available a method for enhancing the detection accuracy of the curvature by making use of an image in higher resolution. Otherwise, there is also available a method whereby the detection accuracy is lowered on the assumption that a road curvature will not undergo a[n] abrupt change, and a process cycle is changed such that one curvature-detection is executed for every two lane-recognitions to thereby allocate part of the CPU process-load to other processes.” ¶ 28; see also ¶¶ 26, 73. Note: The presence of road curvature meets the BRI of a degree of terrain complexity as curvature introduces complexity to vehicle processing), and
the processor is further configured to estimate that the processing volume for detection processing by the detecting device is low when the terrain including the current location of the vehicle is not complex, compared to when the terrain including the current location of the vehicle is complex (“With the curvature detection unit 213, there is available a method for enhancing the detection accuracy of the curvature by making use of an image in higher resolution. Otherwise, there is also available a method whereby the detection accuracy is lowered on the assumption that a road curvature will not undergo a[n] abrupt change, and a process cycle is changed such that one curvature-detection is executed for every two lane-recognitions to thereby allocate part of the CPU process-load to other processes.” ¶ 28; see also ¶¶ 26, 73.).
As to claim 5, Takemura discloses: wherein the processor is further configured to decide to lower detection accuracy by the detecting device by lengthening a cycle at which detection processing is carried out by the detecting device ((“With the curvature detection unit 213, there is available a method for enhancing the detection accuracy of the curvature by making use of an image in higher resolution. Otherwise, there is also available a method whereby the detection accuracy is lowered on the assumption that a road curvature will not undergo a[n] abrupt change, and a process cycle is changed such that one curvature-detection is executed for every two lane-recognitions to thereby allocate part of the CPU process-load to other processes.” Emphasis added; ¶ 28; see also ¶¶ 26, 73.).
Claim(s) 6 is/are rejected under § 103 as being unpatentable over Takemura in view of Hu as applied to claim 1 — further in view of Ewert (US20220324440A1; “Ewert”).
As to claim 6, the combination of Takemura and Hu fails to explicitly disclose: wherein the processor is further configured to decide to lower detection accuracy by the detecting device by reducing a number of sensors by which detected information is input to the detecting device.
Nevertheless, Ewert teaches: decide to lower detection accuracy by the detecting device by reducing a number of sensors by which detected information is input to the detecting device (“As a result of the selection of sensors and/or adaptation of the measuring rate of at least one sensor provided according to the present invention, the processing load of the computer unit for the provision of the autonomous driving function is dynamically adapted.” ¶ 42. “By shutting off sensors which are not selected, or transferring them into a stand-by mode, and reducing the processing load of the computer unit, additionally an energy demand for the provision of the autonomous driving function is decreased.” ¶ 55.).
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 combination of Takemura and Hu to include the feature of: decide to lower detection accuracy by the detecting device by reducing a number of sensors by which detected information is input to the detecting device, as taught by Ewert, with a reasonable expectation of success because this feature is useful for reducing processing load and also achieving the desirable effect of reducing energy consumption. (See Ewert, ¶ 55.)
Claim(s) 7 is/are rejected under § 103 as being unpatentable over Takemura in view of Hu as applied to claim 1 — further in view of Nakamura (US20120221207A1; “Nakamura”)
As to claim 7, the combination of Takemura and Hu fails to explicitly disclose: wherein the processor is further configured to decide to lower detection accuracy by the detecting device by reducing a region in an image detected by the detecting device.
Nevertheless, Nakamura teaches: decide to lower detection accuracy by a detecting device by reducing a region in an image detected by the detecting device (“[I]t is conceived that a processing area is reduced with regard to the information detected by the sensors, so as to lower an amount of the computational throughput, thereby decreasing the processing load.” ¶ 5.).
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 combination of Takemura and Hu to include the feature of: decide to lower detection accuracy by a detecting device by reducing a region in an image detected by the detecting device, as taught by Nakamura, with a reasonable expectation of success because this feature is useful to decrease processing load in the context of detection processing. (See Nakamura, ¶ 5.)
Claim(s) 8 is/are rejected under § 103 as being unpatentable over Takemura in view of Hu as applied to claim 1 — further in view of NPL “Obstacle Avoidance in Real Time With Nonlinear Model Predictive Control of Autonomous Vehicles”1 (“Abbas”)
As to claim 8, the combination of Takemura and Hu fails to explicitly disclose: wherein the processor is further configured to decide to lower detection accuracy by the detecting device by reducing a distance of a trajectory of a moving object that is estimated by the detecting device.
Nevertheless, Abbas teaches: decide to lower detection accuracy by a detecting device by reducing a distance of a trajectory of a moving object that is estimated by the detecting device (“A Nonlinear model predictive control (NMPC) for trajectory tracking with the obstacle avoidance of autonomous road vehicles traveling at realistic speeds is presented in this paper, with a focus on the performance of those controllers with respect to the look-ahead horizon of the NMPC. …. The CPU time is also analyzed to evaluate these schemes for real-time applications. …. [I]t is shown that the longer prediction horizons allow for better responses of the controllers, which reduce the deviations while avoiding the obstacles, as compared with shorter horizons.” Abstract. “The location of each obstacle was assumed to be accurately known … once they were within the look-ahead horizon. We should note that, in reality, this horizon would be dependent on the type of sensor available and the look-ahead horizon of the controller would be adapted to this particular sensor. However, for this paper, it is reasonably assumed that a suitable sensor is available that provides an accurate estimate of the obstacle locations at the desired range.” Section III. Simulation Results, B. Advanced Obstacle Avoidance at pp. 17-18. “The second set of tests compare shorte[r] and longer look-ahead horizons at different road speeds. It was shown that longer horizons provided better performance in avoiding obstacles, particularly in terms of how the NMPC was able to perform an anticipated move which reduced the amount of excursion into the oncoming lane. This did come at a computational cost, of course.” Section IV. Discussion at p. 20. Note: Summarizing, Abbas teaches that there is a correlation between lowering computational cost and lowering detection accuracy via reducing a distance of an estimated trajectory of a moving object (i.e., a horizon).).
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 combination of Takemura and Hu to include the feature of: decide to lower detection accuracy by a detecting device by reducing a distance of a trajectory of a moving object that is estimated by the detecting device, as taught by Abbas, with a reasonable expectation of success, because this feature is useful for balancing computational cost with detection accuracy. (See Abbas, p. 20.)
Claim(s) 9 and 10 is/are rejected under § 103 as being unpatentable over Takemura in view of Hu as applied to claim 1 — further in view of Kundu et al. (US11546503B1; “Kundu”)
As to claim 9, Takemura disclose: decide to lower detection accuracy by the detecting device when it has been estimated that processing volume for detection processing by the detecting device is low, compared to when it has been estimated that processing volume for detection processing by the detecting device is high (“With the curvature detection unit 213, there is available a method for enhancing the detection accuracy of the curvature by making use of an image in higher resolution. Otherwise, there is also available a method whereby the detection accuracy is lowered on the assumption that a road curvature will not undergo a[n] abrupt change, and a process cycle is changed such that one curvature-detection is executed for every two lane-recognitions to thereby allocate part of the CPU process-load to other processes.” ¶ 28; see also ¶¶ 26, 73.).
The combination of Takemura and Hu fails to explicitly disclose:
the detecting device has a first detecting device that can carry out detection processing independently, and a second detecting device that can carry out detection processing independently and has lower detection accuracy and lower load on hardware during operation than the first detecting device; and
the processor is further configured to decide to use the second detecting device when the processing volume for detection processing by the detecting device has been estimated to be low, and to use the first detecting device when the processing volume for detection processing by the detecting device has been estimated to be high.
Nevertheless, Kundu teaches: a detecting device has a first detecting device that can carry out detection processing independently, and a second detecting device that can carry out detection processing independently and has lower detection accuracy and lower load on hardware during operation than the first detecting device (“[E]ach camera 120 may be assigned to a dedicated core that compresses the image stream from that camera 120. The image compression technique that is assigned to each camera 120 may be scaled up or down based on the load on the core dedicated to that camera 120. …. [A] camera 120 may be assigned a high-quality (e.g., level 80 or higher) image compression technique, such that the core, dedicated to that camera 120, reaches its processing limit while new images are waiting to be processed …. [T]he ICT configuration may be adjusted by scaling down the image compression technique assigned to that camera 120 to a lower-quality image compression technique (e.g., level 50), such that the load on the core, dedicated to that camera 120, decreases so as to prevent or reduce frame drop. In other words, if the computational load on a processor core is too high (e.g., exceeds a threshold), the priority of the camera 120, to which that processor core is dedicated, may be decreased, such that a lower-quality image compression technique is assigned to that camera 120.” Col. 19, ll. 4-26. Note: That is, each camera has its own independent detecting device. A detecting device may be configured to operate at a lower detection accuracy which has a lower load on hardware, thereby yielding the claimed second detecting device.).
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 combination of Takemura and Hu to include the feature of: a detecting device has a first detecting device that can carry out detection processing independently, and a second detecting device that can carry out detection processing independently and has lower detection accuracy and lower load on hardware during operation than the first detecting device, as taught by Kundu, with a reasonable expectation of success because this feature is useful for lowering computational load in the context of detection processing. (See Kundu, col. 19, ll. 4-26.)
Moreover, the combination of Takemura and Kundu would have rendered obvious the claim limitation of: the processor is further configured to decide to use the second detecting device when the processing volume for detection processing by the detecting device has been estimated to be low, and to use the first detecting device when the processing volume for detection processing by the detecting device has been estimated to be high. That is, Takemura establishes that a processor may lower detection accuracy when it is estimated that processing volume for detection processing is low, compared to when it has been estimated that processing volume for detection processing is high. (See Takemura, ¶ 28.) In the combination of Takemura-Kundu, one of ordinary skill in the art would have recognized that it would be a natural and obvious choice to use Kundu’s second detecting device to lower detection accuracy as opposed to Kundu’s first detecting device configured for higher detection accuracy, as otherwise would go against the principles of operation of Takemura’s disclosure. Therefore, the claim limitation would have been obvious in view of Takemura and Kundu.
As to claim 10, Takemura fails to explicitly disclose: wherein the collecting device obtains a processing volume for collection of information relating to vehicle control for machine learning.
Nevertheless, Hu teaches: wherein the collecting device obtains a processing volume for collection of information relating to vehicle control for machine learning (“The vehicle-mounted control unit … includes a MCU and a first SoC [system on chip] implemented by being integrated with an ARM through the FPGA, and the vehicle-mounted control unit is set on an automatic driving vehicle. During the process of the automatic driving, the FPGA of the first SoC receives video data sent by a vehicle-mounted camera; perform[s] visual perception on the video data by using a first neural network algorithm to obtain first perception information; and sends the first perception information to the ARM of the first SoC.” ¶ 50 and FIG. 2. See also ¶ 48. Note: A control device obtains video data (information relating to control of a vehicle) for a neural network (machine learning). The control device also performs visual perception (detecting processing). Hence, the claimed processes are performed on common hardware.).
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 Takemura to include the feature of: wherein the collecting device obtains a processing volume for collection of information relating to vehicle control for machine learning, as taught by Hu, with a reasonable expectation of success because this feature is useful “to reduce the burden of the MCU” in the context of carrying out detection processing for control of a vehicle and performing machine learning. (See Hu, ¶ 50.) The foregoing aligns with Takemura’s similar goal of reducing CPU burden in the context of performing detection processing.
The combination of Takemura and Hu fails to explicitly disclose: wherein the collecting device increases the processing volume for collection of information relating to vehicle control for machine learning, when it has been decided to lower detection accuracy by the detecting device.
Nevertheless, Kundu teaches: increasing the processing volume for collection of information relating to vehicle control, when it has been decided to lower detection accuracy by the detecting device (“[E]ach camera 120 may be assigned to a dedicated core that compresses the image stream from that camera 120. The image compression technique that is assigned to each camera 120 may be scaled up or down based on the load on the core dedicated to that camera 120. …. [A] camera 120 may be assigned a high-quality (e.g., level 80 or higher) image compression technique, such that the core, dedicated to that camera 120, reaches its processing limit while new images are waiting to be processed …. [T]he ICT configuration may be adjusted by scaling down the image compression technique assigned to that camera 120 to a lower-quality image compression technique (e.g., level 50), such that the load on the core, dedicated to that camera 120, decreases so as to prevent or reduce frame drop. In other words, if the computational load on a processor core is too high (e.g., exceeds a threshold), the priority of the camera 120, to which that processor core is dedicated, may be decreased, such that a lower-quality image compression technique is assigned to that camera 120.” Col. 19, ll. 4-26. Note: That is, each camera has its own independent detecting device. A detecting device may be configured to operate at a lower detection accuracy which prevents frame drop; thereby increasing processing volume.).
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 Takemura and Hu to include the feature of: increasing the processing volume for collection of information relating to vehicle control, when it has been decided to lower detection accuracy by the detecting device, as taught by Kundu, to yield the claim limitation at issue with a reasonable expectation of success because this feature is useful for taking advantage of a lessened computational load to increase processing volume, thereby obtaining more accurate detection data. (See Kundu, col. 19, ll. 4-26.) One of ordinary skill in the art would have found the foregoing as especially useful for Hu’s information from machine learning as it is well-known in the art that it is desirable to provide accurate and complete data to a machine learning model as compared to incomplete data (e.g., data with frame drops).
CONCLUSION
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/MARIO C GONZALEZ/Examiner, Art Unit 3668
1 M. A. Abbas, R. Milman and J. M. Eklund, "Obstacle Avoidance in Real Time With Nonlinear Model Predictive Control of Autonomous Vehicles," in Canadian Journal of Electrical and Computer Engineering, vol. 40, no. 1, pp. 12-22, winter 2017, doi: 10.1109/CJECE.2016.2609803.
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