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 reply to the Application Number 19/185,486 filed on 04/22/2025.
Claims 1 – 5 are currently pending and have been examined.
This action is made NON-FINAL.
Priority
Acknowledgement is made of applicant’s claim for foreign priority under 35 U.S.C. 119(a)-(d). Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements filed 04/22/2025 have been received and considered.
Claim Objections
Claims 2 and 3 are objected to because of the following informalities:
Claim 2, lines 1 – 2 state “the learning vehicle” however the claim contains no earlier recitation or limitation “learning vehicle”, therefore it lacks antecedent basis. Furthermore, the “learning vehicle” is not mentioned in claim 1 wherein a “host vehicle” is specified instead. Are these the same vehicles? The only other claims referring a “learning vehicle” is claim 4, which claim 2 is not dependent upon. Claim 3 is objected per its dependency on claim 2.
Appropriate correction is required.
Claim Rejections - 35 USC § 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.
Claims 1 – 5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The determination of whether a claim recites patent ineligible subject matter is a 2-step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), see MPEP 2106.03, or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: see MPEP 2106.04
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? see MPEP 2106.04(II)(A)(1)
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? see MPEP 2106.04(II)(A)(2)
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? see MPEP 2106.05
101 Analysis – Step 1
Claim 1 is directed to a vehicle control device (i.e., a system). Therefore, claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. see MPEP 2106(A)(II)(1) and MPEP 2106.04(a)-(c)
Independent claim 1 includes limitations that recite an abstract idea (emphasized below [with the category of abstract idea in brackets]) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A vehicle control device comprising a processor configured to:
infer a limit detection distance which is a maximum value of a distance from a surrounding situation sensor [mental process/step]
detectable by the surrounding situation sensor,
the surrounding situation sensor being mounted on a host vehicle,
wherein the processor is configured to
infer the limit detection distance of the surrounding situation sensor [mental process/step]
based on sensor data of the surrounding situation sensor by using a machine learning model obtained by performing learning
using teacher data which is a data set of sensor data of a learning surrounding situation sensor mounted on a learning vehicle and a label indicating the limit detection distance of the learning surrounding situation sensor when the sensor data of the learning surrounding situation sensor is obtained.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “infer…” in the context of this claim encompasses a person looking at data collected and forming a simple judgement (the limit detection distance of the surrounding situation sensor). Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. see MPEP 2106.04(II)(A)(2) and MPEP 2106.04(d)(2). It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” [with a description of the additional limitations in brackets], while the bolded portions continue to represent the “abstract idea”.):
A vehicle control device comprising a processor configured to:
infer a limit detection distance which is a maximum value of a distance from a surrounding situation sensor [mental process/step]
detectable by the surrounding situation sensor, [additional element used for data gathering]
the surrounding situation sensor being mounted on a host vehicle, [additional element used for data gathering]
wherein the processor is configured to [applying the abstract idea using generic computing module]
infer the limit detection distance of the surrounding situation sensor [mental process/step]
based on sensor data of the surrounding situation sensor [pre-solution activity (data gathering) using generic sensors]
by using a machine learning model obtained by performing learning [applying the abstract idea using generic computing module]
using teacher data which is a data set of sensor data of a learning surrounding situation sensor mounted on a learning vehicle [pre-solution activity (data gathering) using generic sensors]
and a label indicating the limit detection distance of the learning surrounding situation sensor when the sensor data of the learning surrounding situation sensor is obtained. [insignificant post-solution activity (displaying results of the mental process)]
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “a surrounding situation sensor” and a “host vehicle” are merely additional elements which are used for the data gathering step. The limitations of “based on sensor data” and “using teacher data” is merely reciting the insignificant activity of data gathering by the use of generic sensors. In particular, the receiving steps from the sensors and from the external source are recited at a high level of generality (i.e. as a general means of gathering vehicle and road condition data for use in the evaluating step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The additional limitation of a “processor” and the “machine learning model” are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of ranking information based on a determined amount of use) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Particularly the “machine learning model” is merely a model which acquires “teacher data” to determine the limit detection distance. A human is able to mentally acquire the same “teacher data” and is able to make the same determination, therefore the “machine learning mode” is merely performing the mental process on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to 8 perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Lastly, the “label…” step is also recited at a high level of generality (i.e. as a general means of displaying the weather evaluation result from the evaluating step), and amounts to mere post solution displaying, which is a form of insignificant extra-solution activity.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception. see MPEP § 2106.05. Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the Revised Guidance, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations of “a surrounding situation sensor” and a “host vehicle” are merely additional elements which are used for the data gathering step. The limitations of “based on sensor data” and “using teacher data” is merely reciting the insignificant activity of data gathering by the use of generic sensors. In particular, the receiving steps from the sensors and from the external source are recited at a high level of generality (i.e. as a general means of gathering vehicle and road condition data for use in the evaluating step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The additional limitation of a “processor” and the “machine learning model” are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of ranking information based on a determined amount of use) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Particularly the “machine learning model” is merely a model which acquires “teacher data” to determine the limit detection distance. A human is able to mentally acquire the same “teacher data” and is able to make the same determination, therefore the “machine learning mode” is merely performing the mental process on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to 8 perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Lastly, the “label…” step is also recited at a high level of generality (i.e. as a general means of displaying the weather evaluation result from the evaluating step), and amounts to mere post solution displaying, which is a form of insignificant extra-solution activity. In addition, these additional limitations (and the combination, thereof) amount to no more than what is well-understood, routine and conventional activity. Hence, the claim is not patent eligible.
Dependent claim(s) 2 and 3 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Claim 2 states: “wherein a distance between the learning vehicle and a preceding vehicle of the learning vehicle detected by a radar mounted on the learning vehicle when a state switches between a state in which the preceding vehicle can be detected based on the sensor data of the learning surrounding situation sensor and a state in which the preceding vehicle cannot be detected based on the sensor data of the learning surrounding situation sensor is used as the limit detection distance of the learning surrounding situation sensor.”, is directed towards the mental process of a person, based on the state of the learning surrounding situation sensor being able to detect a preceding vehicle, able to signal that the radar will be used for determining the distances instead. The state of the learning surrounding situation sensor is merely acquired data which is used for determining whether or not to use the radar instead. Therefore, dependent claims 2 and 3 are not patent eligible under the same rationale as provided for in the rejection of 1.
Please see below for the analysis of claims 4 and 5.
101 Analysis – Step 1
Claim 4 is directed to a vehicle control method (i.e., a process). Therefore, claim 4 is within at least one of the four statutory categories.
Claim 5 is directed to a non-transitory recoding medium (i.e., a system). Therefore, claim 4 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. see MPEP 2106(A)(II)(1) and MPEP 2106.04(a)-(c)
Independent claim 4 includes limitations that recite an abstract idea (emphasized below [with the category of abstract idea in brackets]) and will be used as a representative claim for the remainder of the 101 rejection. Claim 4 recites:
A vehicle control method comprising:
inferring a limit detection distance which is a maximum value of a distance from a surrounding situation sensor [mental process/step]
detectable by the surrounding situation sensor, the surrounding situation sensor being mounted on a host vehicle,
wherein the limit detection distance of the surrounding situation sensor is inferred [mental process/step]
based on sensor data of the surrounding situation sensor by using a machine learning model obtained by performing learning using teacher data which is a data set of sensor data of a learning surrounding situation sensor mounted on a learning vehicle and a label indicating the limit detection distance of the learning surrounding situation sensor when the sensor data of the learning surrounding situation sensor is obtained.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “infer…” in the context of this claim encompasses a person looking at data collected and forming a simple judgement (the limit detection distance of the surrounding situation sensor). Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. see MPEP 2106.04(II)(A)(2) and MPEP 2106.04(d)(2). It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” [with a description of the additional limitations in brackets], while the bolded portions continue to represent the “abstract idea”.):
A vehicle control method comprising:
inferring a limit detection distance which is a maximum value of a distance from a surrounding situation sensor [mental process/step]
detectable by the surrounding situation sensor, [additional element used for data gathering]
the surrounding situation sensor being mounted on a host vehicle, [additional element used for data gathering]
wherein the limit detection distance of the surrounding situation sensor is inferred [mental process/step]
based on sensor data of the surrounding situation sensor [pre-solution activity (data gathering) using generic sensors]
by using a machine learning model obtained by performing learning [applying the abstract idea using generic computing module]
using teacher data which is a data set of sensor data of a learning surrounding situation sensor mounted on a learning vehicle [pre-solution activity (data gathering) using generic sensors]
and a label indicating the limit detection distance of the learning surrounding situation sensor when the sensor data of the learning surrounding situation sensor is obtained. [insignificant post-solution activity (displaying results of the mental process)]
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “a surrounding situation sensor” and a “host vehicle” are merely additional elements which are used for the data gathering step. The limitations of “based on sensor data” and “using teacher data” is merely reciting the insignificant activity of data gathering by the use of generic sensors. In particular, the receiving steps from the sensors and from the external source are recited at a high level of generality (i.e. as a general means of gathering vehicle and road condition data for use in the evaluating step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The additional limitation the “machine learning model” are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of ranking information based on a determined amount of use) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Particularly the “machine learning model” is merely a model which acquires “teacher data” to determine the limit detection distance. A human is able to mentally acquire the same “teacher data” and is able to make the same determination, therefore the “machine learning mode” is merely performing the mental process on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to 8 perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Lastly, the “label…” step is also recited at a high level of generality (i.e. as a general means of displaying the weather evaluation result from the evaluating step), and amounts to mere post solution displaying, which is a form of insignificant extra-solution activity. Claim 5 only differs from claim 4 by including a “non-transitory recording medium having recorded thereon a computer program for causing a processor to perform a process”, which like for claim 1 is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of ranking information based on a determined amount of use) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception. see MPEP § 2106.05. Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the Revised Guidance, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations of “a surrounding situation sensor” and a “host vehicle” are merely additional elements which are used for the data gathering step. The limitations of “based on sensor data” and “using teacher data” is merely reciting the insignificant activity of data gathering by the use of generic sensors. In particular, the receiving steps from the sensors and from the external source are recited at a high level of generality (i.e. as a general means of gathering vehicle and road condition data for use in the evaluating step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The additional limitation of the “machine learning model” are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of ranking information based on a determined amount of use) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Particularly the “machine learning model” is merely a model which acquires “teacher data” to determine the limit detection distance. A human is able to mentally acquire the same “teacher data” and is able to make the same determination, therefore the “machine learning mode” is merely performing the mental process on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to 8 perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Lastly, the “label…” step is also recited at a high level of generality (i.e. as a general means of displaying the weather evaluation result from the evaluating step), and amounts to mere post solution displaying, which is a form of insignificant extra-solution activity. In addition, these additional limitations (and the combination, thereof) amount to no more than what is well-understood, routine and conventional activity. Claim 5 states a “non-transitory recording medium having recorded thereon a computer program for causing a processor to perform a process”, which is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of ranking information based on a determined amount of use) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Hence, the claim is not patent eligible.
Therefore, claim(s) 1 – 5 is/are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim 5 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Dai et al. (CN114545415A).
Regarding claim 5, Dai teaches a non-transitory recording medium having recorded thereon a computer program for causing a processor to perform a process comprising: (Dai: lines 139 – 143: “The present application also provides a road visibility monitoring device, the road visibility monitoring device is an entity node device, and the road visibility monitoring device includes: a memory, a processor, and a device stored on the memory and running on the processor. The program of the highway visibility monitoring method described above, when the program of the highway visibility monitoring method is executed by the processor, can realize the steps of the highway visibility monitoring method described above.”)
inferring a limit detection distance which is a maximum value of a distance from a surrounding situation sensor detectable by the surrounding situation sensor, (Dai: lines 43 – 54: “The embodiment of the present application provides a method for monitoring road visibility, the method comprising: determining whether a first static target within the field of view of the camera is detected by the camera, wherein the first static target corresponds to a first visibility; If the first static target is not detected by the camera, determine a second static target that can be detected by the camera within the field of view of the camera; wherein, the second static target corresponds to the second visibility, The second visibility is less than the first visibility, and is greater than the visibility corresponding to other static targets that can be detected by the camera within the camera's field of view; determining a visibility range based on the second visibility and other preset visibility;”)
the surrounding situation sensor being mounted on a host vehicle, (Dai: lines 248 – 250: “As an example, the camera is installed in the back, that is, the camera is installed facing the driving direction of the vehicle, and can detect the contour of the rear of the vehicle or the back of the vehicle when the vehicle is identified, as shown in FIG. 4 .”)
wherein the limit detection distance of the surrounding situation sensor is inferred based on sensor data of the surrounding situation sensor by using a machine learning model obtained by performing learning using teacher data which is a data set of sensor data of a learning surrounding situation sensor mounted on a learning vehicle and a label indicating the limit detection distance of the learning surrounding situation sensor when the sensor data of the learning surrounding situation sensor is obtained (Dai: lines 277 – 292 :“As an example, determining whether a first static target within the field of view of the camera is detected by the camera, where the first static target corresponds to the first visibility, may be: determining a lane at 500m within the field of view of the camera Whether the line (ie the first static target) is detected by the camera, the lane line at 500m corresponds to the visibility (500m). Among them, there are also lane lines less than 500m in the field of view of the camera, such as 200m, 100m, and 50m, which correspond to the visibility of 200m, the visibility of 100m, and the visibility of 50m, respectively. Step S20, if the first static target is not detected by the camera, determine a second static target that can be detected by the camera within the camera's field of view; wherein the second static target is the same as the second static target. Corresponding visibility, the second visibility is smaller than the first visibility, and greater than the visibility corresponding to other static targets that the camera can detect within the camera's field of view; continuing the above example, step S20 may be: if the camera The lane line at 200m within the field of view can be detected by the camera, then the lane line at 200m is determined as the second static target, and the second visibility is 200m at this time; If it is detected by the camera, but the lane line at 100m can be detected by the camera, then the lane line at 100m is determined as the second static target, and the second visibility is 100m; and so on.”,
Supplemental Note: the system is able to determine the distance the camera can detect by evaluating the static objects. This is interpreted as a learning model as it is able to continuously infer the camera detection. The visibility range of the camera based on the detected targets is interpreted as label indicating the detection distance limits).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1 – 4 are rejected under 35 U.S.C. 103 as being unpatentable over Dai et al. (CN114545415A), further in view of Ryo et al. (WO 2019159647 A1).
Regarding claim 1, Dai teaches comprising a processor configured to: (Dai: lines 139 – 143: “The present application also provides a road visibility monitoring device, the road visibility monitoring device is an entity node device, and the road visibility monitoring device includes: a memory, a processor, and a device stored on the memory and running on the processor. The program of the highway visibility monitoring method described above, when the program of the highway visibility monitoring method is executed by the processor, can realize the steps of the highway visibility monitoring method described above.”)
infer a limit detection distance which is a maximum value of a distance from a surrounding situation sensor detectable by the surrounding situation sensor, (Dai: lines 43 – 54: “The embodiment of the present application provides a method for monitoring road visibility, the method comprising: determining whether a first static target within the field of view of the camera is detected by the camera, wherein the first static target corresponds to a first visibility; If the first static target is not detected by the camera, determine a second static target that can be detected by the camera within the field of view of the camera; wherein, the second static target corresponds to the second visibility, The second visibility is less than the first visibility, and is greater than the visibility corresponding to other static targets that can be detected by the camera within the camera's field of view; determining a visibility range based on the second visibility and other preset visibility;”)
the surrounding situation sensor being mounted on a host vehicle, (Dai: lines 248 – 250: “As an example, the camera is installed in the back, that is, the camera is installed facing the driving direction of the vehicle, and can detect the contour of the rear of the vehicle or the back of the vehicle when the vehicle is identified, as shown in FIG. 4 .”)
wherein the processor is configured to infer the limit detection distance of the surrounding situation sensor based on sensor data of the surrounding situation sensor by using a machine learning model obtained by performing learning using teacher data which is a data set of sensor data of a learning surrounding situation sensor mounted on a learning vehicle and a label indicating the limit detection distance of the learning surrounding situation sensor when the sensor data of the learning surrounding situation sensor is obtained (Dai: lines 277 – 292 :“As an example, determining whether a first static target within the field of view of the camera is detected by the camera, where the first static target corresponds to the first visibility, may be: determining a lane at 500m within the field of view of the camera Whether the line (ie the first static target) is detected by the camera, the lane line at 500m corresponds to the visibility (500m). Among them, there are also lane lines less than 500m in the field of view of the camera, such as 200m, 100m, and 50m, which correspond to the visibility of 200m, the visibility of 100m, and the visibility of 50m, respectively. Step S20, if the first static target is not detected by the camera, determine a second static target that can be detected by the camera within the camera's field of view; wherein the second static target is the same as the second static target. Corresponding visibility, the second visibility is smaller than the first visibility, and greater than the visibility corresponding to other static targets that the camera can detect within the camera's field of view; continuing the above example, step S20 may be: if the camera The lane line at 200m within the field of view can be detected by the camera, then the lane line at 200m is determined as the second static target, and the second visibility is 200m at this time; If it is detected by the camera, but the lane line at 100m can be detected by the camera, then the lane line at 100m is determined as the second static target, and the second visibility is 100m; and so on.”,
Supplemental Note: the system is able to determine the distance the camera can detect by evaluating the static objects. This is interpreted as a learning model as it is able to continuously infer the camera detection. The visibility range of the camera based on the detected targets is interpreted as label indicating the detection distance limits).
In sum Dai teaches comprising a processor configured to: infer a limit detection distance which is a maximum value of a distance from a surrounding situation sensor detectable by the surrounding situation sensor, the surrounding situation sensor being mounted on a host vehicle, wherein the processor is configured to infer the limit detection distance of the surrounding situation sensor based on sensor data of the surrounding situation sensor by using a machine learning model obtained by performing learning using teacher data which is a data set of sensor data of a learning surrounding situation sensor mounted on a learning vehicle and a label indicating the limit detection distance of the learning surrounding situation sensor when the sensor data of the learning surrounding situation sensor is obtained. Dai however does not teach a vehicle control device.
Ryo teaches a vehicle control device (Ryo: lines 113 – 118: “The control device 100 maintains the distance between the preceding vehicle and the host vehicle at a constant distance when there is a preceding vehicle by the throttle valve driving device 31 and the braking assist device 32, and is set when there is no preceding vehicle. The host vehicle is driven at the vehicle speed. Such control is called constant speed traveling / inter-vehicle distance control, so-called adaptive cruise control (ACC). The constant speed travel / inter-vehicle distance control process may be abbreviated as “inter-vehicle distance control” in this specification.”).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Dai with the teachings of Ryo with a reasonable expectation of success. Dai teaches the ability of determining the visibility of a camera sensor in areas of reduced visibility which increases the chances of serious traffic incidents (Dai: lines 19 – 22). Ryo further teaches the ability of utilizing automatic cruise control (ACC) to control the speed of the vehicle when a preceding vehicle is identified using an inter-vehicle distance control (Ryo: lines 212 – 225). One of ordinary skill in the art would find it obvious to try to implement the ACC utilizing the inter-vehicle distance control as taught by Ryo with the vehicle system of Dai. For example, the vehicle of Dai is already able to detect the vehicle in areas of reduced visibility, with the addition of the ACC function the vehicle can now travel at a safe distance and speed behind a preceding vehicle.
Regarding claim 2, Dai, as modified, teaches wherein a distance between the learning vehicle and a preceding vehicle of the learning vehicle detected by a radar mounted on the learning vehicle when a state switches between a state in which the preceding vehicle can be detected based on the sensor data of the learning surrounding situation sensor and a state in which the preceding vehicle cannot be detected based on the sensor data of the learning surrounding situation sensor is used as the limit detection distance of the learning surrounding situation sensor (Dai: lines 56 – 58: “Within the visibility range, a passing vehicle is detected by the camera, and when the camera detects that the passing vehicle disappears, the measured distance between the passing vehicle and the camera is determined by radar;”,
Supplemental Note: when the vehicle is within the visibility range of the camera, the distance be measured based off it. When the vehicle crosses the visibility range of the camera, then the radar is used to measure distances).
Regarding claim 3, Dai, as modified, teaches when the limit detection distance of the surrounding situation sensor is less than or equal to a threshold value (Dai: lines 277 – 292 :“As an example, determining whether a first static target within the field of view of the camera is detected by the camera, where the first static target corresponds to the first visibility, may be: determining a lane at 500m within the field of view of the camera Whether the line (ie the first static target) is detected by the camera, the lane line at 500m corresponds to the visibility (500m). Among them, there are also lane lines less than 500m in the field of view of the camera, such as 200m, 100m, and 50m, which correspond to the visibility of 200m, the visibility of 100m, and the visibility of 50m, respectively. Step S20, if the first static target is not detected by the camera, determine a second static target that can be detected by the camera within the camera's field of view; wherein the second static target is the same as the second static target. Corresponding visibility, the second visibility is smaller than the first visibility, and greater than the visibility corresponding to other static targets that the camera can detect within the camera's field of view; continuing the above example, step S20 may be: if the camera The lane line at 200m within the field of view can be detected by the camera, then the lane line at 200m is determined as the second static target, and the second visibility is 200m at this time; If it is detected by the camera, but the lane line at 100m can be detected by the camera, then the lane line at 100m is determined as the second static target, and the second visibility is 100m; and so on.”,
Supplemental Note: the system is able to determine the distance the camera can detect by evaluating the static objects).
In sum, Dai teaches when the limit detection distance of the surrounding situation sensor is less than or equal to a threshold value. Dai however does not teach wherein the processor is configured to set maximum speed limit of the host vehicle while performing driving assistance of the host vehicle, the processor is configured to assume that the distance between the preceding vehicle of the host vehicle and the host vehicle is approximately equal to the limit detection distance of the surrounding situation sensor and set a speed at which the host vehicle can follow the preceding vehicle as the maximum speed limit.
Ryo teaches wherein the processor is configured to set maximum speed limit of the host vehicle while performing driving assistance of the host vehicle, (Ryo: lines 113 – 118: “The control device 100 maintains the distance between the preceding vehicle and the host vehicle at a constant distance when there is a preceding vehicle by the throttle valve driving device 31 and the braking assist device 32, and is set when there is no preceding vehicle. The host vehicle is driven at the vehicle speed. Such control is called constant speed traveling / inter-vehicle distance control, so-called adaptive cruise control (ACC). The constant speed travel / inter-vehicle distance control process may be abbreviated as “inter-vehicle distance control” in this specification.”)
the processor is configured to assume that the distance between the preceding vehicle of the host vehicle and the host vehicle is approximately equal to the limit detection distance of the surrounding situation sensor (Ryo: lines 129 – 132: “The control device 100 uses the output of the millimeter wave radar 211 and the output of the front camera 221 to control the vehicle 500 so that the distance between the preceding vehicle ahead of the vehicle 500 and the vehicle 500 is within a predetermined range. The inter-vehicle distance control to be executed is executed.”)
and set a speed at which the host vehicle can follow the preceding vehicle as the maximum speed limit (Ryo: lines 212 – 215: “After selecting the preceding vehicle, the control device 100 calculates the target acceleration of the vehicle 500 based on the distance between the vehicle 500 and the preceding vehicle and the relative speed between the vehicle 500 and the preceding vehicle. Thereafter, the control device 100 transmits information on the target acceleration to the throttle valve driving device 31 and the braking support device 32.”).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Dai with the teachings of Ryo with a reasonable expectation of success. As stated for claim 1, Dai teaches the ability of determining the visibility of a camera sensor in areas of reduced visibility which increases the chances of serious traffic incidents (Dai: lines 19 – 22). Ryo further teaches the ability of utilizing automatic cruise control (ACC) to control the speed of the vehicle when a preceding vehicle is identified using an inter-vehicle distance control (Ryo: lines 212 – 225). This process is done by using a radar device to measure the distance between the host vehicle the preceding vehicle in which the inter-vehicle distance control can be executed to maintain the relative speed and distance between the two. One of ordinary skill in the art would find it obvious to try to implement the ACC utilizing the inter-vehicle distance control as taught by Ryo with the vehicle system of Dai. For example, the vehicle of Dai is already able to detect the vehicle in areas of reduced visibility, with the addition of the ACC function the vehicle can now travel at a safe distance and speed behind a preceding vehicle.
Regarding claim 4, Dai teaches inferring a limit detection distance which is a maximum value of a distance from a surrounding situation sensor detectable by the surrounding situation sensor, (Dai: lines 43 – 54: “The embodiment of the present application provides a method for monitoring road visibility, the method comprising: determining whether a first static target within the field of view of the camera is detected by the camera, wherein the first static target corresponds to a first visibility; If the first static target is not detected by the camera, determine a second static target that can be detected by the camera within the field of view of the camera; wherein, the second static target corresponds to the second visibility, The second visibility is less than the first visibility, and is greater than the visibility corresponding to other static targets that can be detected by the camera within the camera's field of view; determining a visibility range based on the second visibility and other preset visibility;”)
the surrounding situation sensor being mounted on a host vehicle, (Dai: lines 248 – 250: “As an example, the camera is installed in the back, that is, the camera is installed facing the driving direction of the vehicle, and can detect the contour of the rear of the vehicle or the back of the vehicle when the vehicle is identified, as shown in FIG. 4 .”)
wherein the limit detection distance of the surrounding situation sensor is inferred based on sensor data of the surrounding situation sensor by using a machine learning model obtained by performing learning using teacher data which is a data set of sensor data of a learning surrounding situation sensor mounted on a learning vehicle and a label indicating the limit detection distance of the learning surrounding situation sensor when the sensor data of the learning surrounding situation sensor is obtained (Dai: lines 277 – 292 :“As an example, determining whether a first static target within the field of view of the camera is detected by the camera, where the first static target corresponds to the first visibility, may be: determining a lane at 500m within the field of view of the camera Whether the line (ie the first static target) is detected by the camera, the lane line at 500m corresponds to the visibility (500m). Among them, there are also lane lines less than 500m in the field of view of the camera, such as 200m, 100m, and 50m, which correspond to the visibility of 200m, the visibility of 100m, and the visibility of 50m, respectively. Step S20, if the first static target is not detected by the camera, determine a second static target that can be detected by the camera within the camera's field of view; wherein the second static target is the same as the second static target. Corresponding visibility, the second visibility is smaller than the first visibility, and greater than the visibility corresponding to other static targets that the camera can detect within the camera's field of view; continuing the above example, step S20 may be: if the camera The lane line at 200m within the field of view can be detected by the camera, then the lane line at 200m is determined as the second static target, and the second visibility is 200m at this time; If it is detected by the camera, but the lane line at 100m can be detected by the camera, then the lane line at 100m is determined as the second static target, and the second visibility is 100m; and so on.”,
Supplemental Note: the system is able to determine the distance the camera can detect by evaluating the static objects. This is interpreted as a learning model as it is able to continuously infer the camera detection. The visibility range of the camera based on the detected targets is interpreted as label indicating the detection distance limits).
In sum, Dai teaches inferring a limit detection distance which is a maximum value of a distance from a surrounding situation sensor detectable by the surrounding situation sensor, the surrounding situation sensor being mounted on a host vehicle, wherein the limit detection distance of the surrounding situation sensor is inferred based on sensor data of the surrounding situation sensor by using a machine learning model obtained by performing learning using teacher data which is a data set of sensor data of a learning surrounding situation sensor mounted on a learning vehicle and a label indicating the limit detection distance of the learning surrounding situation sensor when the sensor data of the learning surrounding situation sensor is obtained. Dai however does not teach a vehicle control method.
Ryo teaches a vehicle control method comprising: (Ryo: lines 113 – 118: “The control device 100 maintains the distance between the preceding vehicle and the host vehicle at a constant distance when there is a preceding vehicle by the throttle valve driving device 31 and the braking assist device 32, and is set when there is no preceding vehicle. The host vehicle is driven at the vehicle speed. Such control is called constant speed traveling / inter-vehicle distance control, so-called adaptive cruise control (ACC). The constant speed travel / inter-vehicle distance control process may be abbreviated as “inter-vehicle distance control” in this specification.”).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Dai with the teachings of Ryo with a reasonable expectation of success. Please refer to the rejection of claim 1 as both claim the same function and therefore rejected under the same pretenses.
Conclusion
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/SHIVAM SHARMA/Examiner, Art Unit 3665
/Erin D Bishop/Supervisory Patent Examiner, Art Unit 3665
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