DETAILED ACTION
Response to Amendment
This office action regarding application number 18/628,499, filed April 5, 2024, is in response to the applicants arguments and amendments filed December 17, 2025. Claim 2 has been cancelled. Claims 1 and 3-13 have been amended. Claims 1-13 are currently pending and are addressed below.
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 .
Response to Arguments
The applicants arguments and amendments to the application have overcome some of the objections and rejections previously set forth in the Non-Final action mailed September 18, 2025. Claim 2 has been cancelled and therefore all associated objections and rejections are withdrawn. Applicants amendments to claim 13 have been deemed sufficient to overcome the previous “non-transitory” rejection under 35 USC 101, therefore that rejection is withdrawn. Applicants amendments to the claims have rendered the previous interpretation under 35 USC 112f moot through the removal of the interpreted language, therefore the claim interpretation is withdrawn.
However Applicants amendments to claims 1 and 11-13 have NOT been deemed sufficient to overcome the previous 35 USC 103 rejections through the inclusion of “the vehicle speed abnormality being an abnormality of speed of the vehicle itself” among other amendments, therefore the rejections are maintained with changes to reflect amendments. In particular the examiner finds that the combination of the Katoh and Nonaka references teaches the amended subject matter.
Applicants amendments to claims 1 and 11-13 have NOT been deemed sufficient to overcome the previous 35 USC 101 rejections through the inclusion of “the vehicle speed abnormality being an abnormality of speed of the vehicle itself” among other amendments, in particular the examiner finds that the newly amended subject matter only introduces further mental process steps (calculating, selecting, and in response to determining further calculating) and language that only further defines the mental process steps (wherein the state of the target includes…) therefore the rejections are maintained with changes to reflect amendments.
Additionally the applicants arguments have been fully considered but are not fully persuasive for the reasons seen below.
On page 9 the applicant argues “The Office Action asserts that claims 1-13 are rejected under 35 U.S.C. § 101 because the claims are allegedly directed to non-statutory subject matter. See Office Action, p. 8. Applicant respectfully disagrees with the rejections and disagrees that the claims are directed to an abstract idea, as alleged by the Office Action. However, Applicant has amended claims 1-13 to expedite allowance. Applicant respectfully submits that claims 1-13 have been amended such that the specifics of the §101 rejection are no longer applicable. Reconsideration in view of the above amendments is respectfully requested.”, the examiner respectfully disagrees.
MPEP 2104 discusses requirements under 35 USC 101 and MPEP 2106 discusses Patent subject matter eligibility. MPEP 2106.05(g) provides specific examples of insignificant extra solution activity. MPEP 2111 discusses Broadest Reasonable Interpretation and the interpretation of claims.
The newly amended claims partially overcome the rejections under 35 USC 101 through the inclusion of “non-transitory computer readable”.
However as discussed in the rejections below the examiner finds that the newly amended subject matter only introduces further mental process steps (calculating, selecting, and in response to determining further calculating) and language that only further defines the mental process steps (wherein the state of the target includes…) therefore the rejections are maintained with changes to reflect amendments.
Therefore the rejections under 35 USC 101 are maintained.
On pages 9-10 the applicant argues “Applicant disagrees with aspects of these rejections, but in the interest of expediting prosecution, Applicant has herein amended independent claims 1 and 11-13 to clarify that in the present application, "the vehicle speed abnormality" claimed is "an abnormality of speed of the vehicle itself'. Applicant submits that none of the cited references, whether considered alone or in combination, disclose or suggest at least this feature of independent claims 1 and 11-13. At best, Katoh merely discloses determining "whether or not the ground speed of the object is an outlier that significantly differs from the previous value." Katoh, para. [0016] (emphasis added). Therefore, Katoh relates to object speed abnormalities, not abnormalities of speed of a vehicle itself as presently claimed. Nonaka fails to remedy this defect in Katoh as Nonaka only describes "detecting an abnormality in the vehicle behavior sensor", see Nonaka, para. [0086] (emphasis added), and consequently also does not disclose anything related to an abnormality of speed of the vehicle itself.”, the examiner respectfully disagrees.
MPEP 2142-2144 discusses the requirements for a case of obviousness using 35 USC 103 and provides examples of such cases. MPEP 2111 discusses Broadest Reasonable Interpretation and the interpretation of claims.
As discussed in the rejections below Katoh teaches the detection of an abnormality in a vehicle (Paragraph [0064], “Hereinafter, the abnormal value removal processing (filtering processing) will be described.”); and the resulting changes in vehicle operating methodology if an abnormality is detected or not (Paragraph [0016], “The two determinations allow the determination of the presence or absence of the abnormal value in the ground speed of the object. The relative movement vector calculation unit does not use the ground speed of the object that has a difference from the previous value of the ground speed equal to or greater than the first threshold and has a difference from the speed obtained from the difference between the positions of the object equal to or greater than the second threshold in calculating the relative movement vector,” here the system performs a determination of an abnormal condition and if there is no abnormality the system uses a predicted relative speed based on the radar to calculate movement vector, and if an abnormality is determined the system modifies this process). While Katoh does not explicitly teach that this abnormality is a vehicle speed abnormality, this limitation is taught by Nonaka.
Nonaka teaches an image-capturing device capable of fulfilling a complementary role to a vehicle behavior sensor when a failure occurs in the vehicle behavior sensor including determine whether a vehicle speed abnormality has occurred (Paragraph [0086], “an abnormality detection unit (e.g. vehicle behavior sensor information diagnosis and behavior information selection unit 108) for detecting an abnormality in the vehicle behavior sensor by comparing the behavior information acquired by the vehicle behavior acquisition unit and the behavior information inferred by the vehicle behavior inference unit”) (Paragraph [0053], “The vehicle behavior sensor information diagnosis and behavior information selection unit 108 is capable of performing similar diagnosis on information from other vehicle behavior sensors such as a vehicle speed sensor,” here the system can determine an abnormality in a speed of the vehicle by comparing a vehicle speed value as output by a sensor to an inferred value, the comparison can reveal an abnormality in the speed of the vehicle).
Here Nonaka is using the abnormality detection unit to detect an abnormality in the vehicle by comparing vehicle sensor information to inferred vehicle information, this comparison results in a determination of an abnormality in a vehicle behavior such as a speed, when a vehicle speed as determined by the speed sensor does not match the inferred vehicle speed. This is a vehicle speed abnormality in the speed of the vehicle itself.
Therefore the combination of Katoh and Nonaka teaches an abnormality of speed of the vehicle itself, and the rejections under 35 USC 103 are maintained.
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, 3-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1,
Under Step 1:
Claim 1 is an apparatus claim comprising a information processing device. (thus the claims are to an apparatus Step 1: yes)
Under Step 2A - Prong 1:
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b)certain methods of organizing human activity, and/or c) mental processes.
Independent Claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
An information processing device for a vehicle, the information processing device comprising a processor and a memory storing instructions, wherein the processor is configured to execute the instructions to:
acquire an observation signal output from a sensor transmitting and receiving radar waves;
detect, based on the observation signal, at least one observed value related to at least one target around the vehicle;
calculate an estimated value which indicates a state of the target at predetermined processing cycles; and
determine whether a vehicle speed abnormality has occurred, the vehicle speed abnormality being an abnormality of speed of the vehicle itself,
wherein the state of the target includes at least one of a distance from the vehicle to the target, an azimuth of the target with respect to the vehicle, and a relative speed of the target with respect to the vehicle
the calculation of the estimated value includes calculating a current predicted value from an estimated value calculated in the past selecting the observed value which is in a predicted range set based on the current predicted value and calculating a current estimated value based on the selected observed value and the current predicted value and
the calculation of the estimated value further includes one or more of:
in response to determining that no abnormality has occurred performing a first process using the predicted value of a relative speed based on a detection result of a vehicle speed to calculate the current estimated value and
in response to determining that the vehicle speed abnormality has occurred, performing a second process different from the first process to calculate the current estimated value.
The examiner submits that the foregoing bolded limitations constitute a “mental process” because as drafted, the limitations are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components (i.e. “information processing device”). Specifically, but for the “information processing device” language, “detect, based on the observation signal, at least one observed value related to at least one target around the vehicle; calculate an estimated value which indicates a state of the target at predetermined processing cycles; and determine whether a vehicle speed abnormality has occurred, the vehicle speed abnormality being an abnormality of speed of the vehicle itself, wherein the state of the target includes at least one of a distance from the vehicle to the target, an azimuth of the target with respect to the vehicle, and a relative speed of the target with respect to the vehicle; the calculation of the estimated value includes calculating a current predicted value from an estimated value calculated in the past selecting the observed value which is in a predicted range set based on the current predicted value and calculating a current estimated value based on the selected observed value and the current predicted value and the calculation of the estimated value further includes one or more of: in response to determining that no abnormality has occurred performing a first process using the predicted value of a relative speed based on a detection result of a vehicle speed to calculate the current estimated value and in response to determining that the vehicle speed abnormality has occurred, performing a second process different from the first process to calculate the current estimated value.” in the context of this claim encompasses the user mentally or with a pen and paper determining from sensor data an observed state of an object, using that determined state to determine a current predicted value, and further calculating a current estimated value from an observed value and the predicted value, the system will then determine if there is an abnormality, and based on the abnormality determination the system will perform specific calculations. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with an pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Under Step 2A - Prong 2:
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, 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 area as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
An information processing device for a vehicle, the information processing device comprising a processor and a memory storing instructions, wherein the processor is configured to execute the instructions to:
acquire an observation signal output from a sensor transmitting and receiving radar waves;
detect, based on the observation signal, at least one observed value related to at least one target around the vehicle;
calculate an estimated value which indicates a state of the target at predetermined processing cycles; and
determine whether a vehicle speed abnormality has occurred, the vehicle speed abnormality being an abnormality of speed of the vehicle itself,
wherein the state of the target includes at least one of a distance from the vehicle to the target, an azimuth of the target with respect to the vehicle, and a relative speed of the target with respect to the vehicle
the calculation of the estimated value includes calculating a current predicted value from an estimated value calculated in the past selecting the observed value which is in a predicted range set based on the current predicted value and calculating a current estimated value based on the selected observed value and the current predicted value and
the calculation of the estimated value further includes one or more of:
in response to determining that no abnormality has occurred performing a first process using the predicted value of a relative speed based on a detection result of a vehicle speed to calculate the current estimated value and
in response to determining that the vehicle speed abnormality has occurred, performing a second process different from the first process to calculate the current estimated value.
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 limitations of “acquire an observation signal output from a sensor transmitting and receiving radar waves” the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (information processing device) to perform the process. In particular, the acquiring steps from the sensors are recited at a high level of generality (i.e. as a general means of gathering data for use in the later determining steps), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. See MPEP 2106.05(g).
Regarding the additional limitations of “An information processing device for a vehicle, the information processing device comprising a processor and a memory storing instructions, wherein the processor is configured to execute the instructions to” the examiner submits that these limitations are an attempt to generally link additional elements to a technological environment. In particular, information processing device and units are recited at a high level of generality and merely automates the determining steps, therefore acting as a generic computer to perform the abstract idea. The information processing device is claimed generically and is operating in its ordinary capacity and does not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. The additional limitation is no more than mere instructions to apply the exception using generic computer components (the information processing device).
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 (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.
Under 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 element of “An information processing device for a vehicle, the information processing device comprising a processor and a memory storing instructions, wherein the processor is configured to execute the instructions to” amounts to nothing more than mere instructions to apply the exception using a generic computer component and generally link the claim to a technological environment. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of “acquire an observation signal output from a sensor transmitting and receiving radar waves” the examiner submits that these limitations are insignificant extra-solution activities. Hence, the claim is not patent eligible.
Therefore claim 1 is ineligible under 35 USC 101.
Regarding dependent claims 3-10
Under Step 1:
Claims 3-10 are to a method comprising the steps of “the first process includes calculating the current predicted value of a relative speed using the vehicle speed” (Claim 3), “includes setting the prediction range to be larger than in the first process” (Claim 4), “includes setting a contribution of the relative speed to be used in calculation of an association cost” (Claim 5), “includes setting a contribution of the predicted value of the relative speed to be used in calculation of the estimated value of the relative speed to be lower” (Claim 6), “that the vehicle speed abnormality has occurred” (Claim 7), “the vehicle speed abnormality has occurred for a state in which a prediction residual, … is a predetermined prediction residual threshold value or more” (Claim 8), “determining that the vehicle speed abnormality has occurred for a state in which the prediction residual … is the predetermined prediction residual threshold value or more” (Claim 9), and “the number of stationary objects is the predetermined stationary target number or more, determining that the vehicle speed abnormality has occurred” (Claim 10) (thus the claims are to an method, Step 1: yes).
Under Step 2A – Prong 1:
Claims 3-10 depend on claim 1 and recite the limitations of “the first process includes calculating the current predicted value of a relative speed using the vehicle speed” (Claim 3), “includes setting the prediction range to be larger than in the first process” (Claim 4), “includes setting a contribution of the relative speed to be used in calculation of an association cost” (Claim 5), “includes setting a contribution of the predicted value of the relative speed to be used in calculation of the estimated value of the relative speed to be lower” (Claim 6), “that the vehicle speed abnormality has occurred” (Claim 7), “the vehicle speed abnormality has occurred for a state in which a prediction residual, … is a predetermined prediction residual threshold value or more” (Claim 8), “determining that the vehicle speed abnormality has occurred for a state in which the prediction residual … is the predetermined prediction residual threshold value or more” (Claim 9), and “the number of stationary objects is the predetermined stationary target number or more, determining that the vehicle speed abnormality has occurred” (Claim 10), These claims recite an abstract idea which is directed to mental process.
Under Step 2A – Prong 2:
This judicial exception is not integrated into a practical application, the claims do not includes any additional elements that integrate the abstract idea into a practical application. Claims 3-6 only further define the mental process steps of claims 1 for example defining how the prediction unit calculates and the association unit sets values. Claims 7-10 only further define the mental process step of the abnormality determination unit, for example defining that the abnormality determination is based on an acceleration or a threshold.
Under Step 2B:
Step 2B, the claims 3-10 do not include any additional elements that are sufficient to amount to significantly more than the judicial exception for similar reasons as that discussed in Step 2A Prong Two.
The additional limitations recited in the dependent claims 3-10 fail to establish that the dependent claims are not directed to an abstract idea. The additional limitations of the dependent claims, when considered individually and in combination, do not amount to significantly more than the abstract idea. Accordingly, claims 3-10 are not patent eligible.
Regarding claim 11,
Under Step 1:
Claim 11 is an apparatus claim comprising an object tracking device. (thus the claims are to an apparatus Step 1: yes)
Under Step 2A - Prong 1:
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b)certain methods of organizing human activity, and/or c) mental processes.
Independent Claim 11 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 11 recites:
An object tracking device for a vehicle, the device comprising a processor and a memory storing instructions, wherein the processor is configured to execute the instructions to:
acquire an observation signal output from a sensor transmitting and receiving radar waves;
detect, based on the observation signal, at least one observed value related to at least one target around the vehicle;
calculate an estimated value which indicates a state of the target at predetermined processing cycles; and
determine whether a vehicle speed abnormality has occurred, the vehicle speed abnormality being an abnormality of speed of the vehicle itself,
wherein the state of the target includes at least one of a distance from the vehicle to the target, an azimuth of the target with respect to the vehicle, and a relative speed of the target with respect to the vehicle
the calculation of the estimated value includes calculating a current predicted value from an estimated value calculated in the past selecting the observed value which is in a predicted range set based on the current predicted value and calculating a current estimated value based on the selected observed value and the current predicted value and
the calculation of the estimated value further includes one or more of:
in response to determining that no abnormality has occurred performing a first process using the predicted value of a relative speed based on a detection result of a vehicle speed to calculate the current estimated value and
in response to determining that the vehicle speed abnormality has occurred, performing a second process different from the first process to calculate the current estimated value.
The examiner submits that the foregoing bolded limitations constitute a “mental process” because as drafted, the limitations are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components (i.e. “object tracking device”). Specifically, but for the “object tracking device” language, “detect, based on the observation signal, at least one observed value related to at least one target around the vehicle; calculate an estimated value which indicates a state of the target at predetermined processing cycles; and determine whether a vehicle speed abnormality has occurred, the vehicle speed abnormality being an abnormality of speed of the vehicle itself, wherein the state of the target includes at least one of a distance from the vehicle to the target, an azimuth of the target with respect to the vehicle, and a relative speed of the target with respect to the vehicle; the calculation of the estimated value includes calculating a current predicted value from an estimated value calculated in the past selecting the observed value which is in a predicted range set based on the current predicted value and calculating a current estimated value based on the selected observed value and the current predicted value and the calculation of the estimated value further includes one or more of: in response to determining that no abnormality has occurred performing a first process using the predicted value of a relative speed based on a detection result of a vehicle speed to calculate the current estimated value and in response to determining that the vehicle speed abnormality has occurred, performing a second process different from the first process to calculate the current estimated value.” in the context of this claim encompasses the user mentally or with a pen and paper determining from sensor data an observed state of an object, using that determined state to determine a current predicted value, and further calculating a current estimated value from an observed value and the predicted value, the system will then determine if there is an abnormality, and based on the abnormality determination the system will perform specific calculations. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with an pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Under Step 2A - Prong 2:
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, 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 area as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
An object tracking device for a vehicle, the device comprising a processor and a memory storing instructions, wherein the processor is configured to execute the instructions to:
acquire an observation signal output from a sensor transmitting and receiving radar waves;
detect, based on the observation signal, at least one observed value related to at least one target around the vehicle;
calculate an estimated value which indicates a state of the target at predetermined processing cycles; and
determine whether a vehicle speed abnormality has occurred, the vehicle speed abnormality being an abnormality of speed of the vehicle itself,
wherein the state of the target includes at least one of a distance from the vehicle to the target, an azimuth of the target with respect to the vehicle, and a relative speed of the target with respect to the vehicle
the calculation of the estimated value includes calculating a current predicted value from an estimated value calculated in the past selecting the observed value which is in a predicted range set based on the current predicted value and calculating a current estimated value based on the selected observed value and the current predicted value and
the calculation of the estimated value further includes one or more of:
in response to determining that no abnormality has occurred performing a first process using the predicted value of a relative speed based on a detection result of a vehicle speed to calculate the current estimated value and
in response to determining that the vehicle speed abnormality has occurred, performing a second process different from the first process to calculate the current estimated value.
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 limitations of “acquire an observation signal output from a sensor transmitting and receiving radar waves” the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (object tracking device) to perform the process. In particular, the acquiring steps from the sensors are recited at a high level of generality (i.e. as a general means of gathering data for use in the later determining steps), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. See MPEP 2106.05(g).
Regarding the additional limitations of “An object tracking device for a vehicle, the device comprising a processor and a memory storing instructions, wherein the processor is configured to execute the instructions to:” the examiner submits that these limitations are an attempt to generally link additional elements to a technological environment. In particular, object tracking device and units are recited at a high level of generality and merely automates the determining steps, therefore acting as a generic computer to perform the abstract idea. The object tracking device is claimed generically and is operating in its ordinary capacity and does not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. The additional limitation is no more than mere instructions to apply the exception using generic computer components (the object tracking device).
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 (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.
Under 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 element of “An object tracking device for a vehicle, the device comprising a processor and a memory storing instructions, wherein the processor is configured to execute the instructions to:” amounts to nothing more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of “acquire an observation signal output from a sensor transmitting and receiving radar waves” the examiner submits that these limitations are insignificant extra-solution activities. Hence, the claim is not patent eligible.
Therefore claim 11 is ineligible under 35 USC 101.
Regarding claim 12,
Under Step 1:
Claim 12 is an method claim comprising the steps of acquiring, tracking, and determining. (thus the claims are to an method Step 1: yes)
Under Step 2A - Prong 1:
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b)certain methods of organizing human activity, and/or c) mental processes.
Independent Claim 12 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 12 recites:
A tracking method for an information processing device for a vehicle, the tracking method comprising:
acquiring an observation signal output from a sensor transmitting and receiving radar waves;
detecting, based on the observation signal, at least one observed value related to at least one target around the vehicle;
calculating an estimated value which indicates a state of the target at predetermined processing cycles; and
determining whether a vehicle speed abnormality has occurred, the vehicle speed abnormality being an abnormality of speed of the vehicle itself,
wherein the state of the target includes at least one of a distance from the vehicle to the target, an azimuth of the target with respect to the vehicle, and a relative speed of the target with respect to the vehicle
the calculation of the estimated value includes calculating a current predicted value from an estimated value calculated in the past selecting the observed value which is in a predicted range set based on the current predicted value and calculating a current estimated value based on the selected observed value and the current predicted value and
the calculation of the estimated value further includes one or more of:
in response to determining that no abnormality has occurred performing a first process using the predicted value of a relative speed based on a detection result of a vehicle speed to calculate the current estimated value and
in response to determining that the vehicle speed abnormality has occurred, performing a second process different from the first process to calculate the current estimated value.
The examiner submits that the foregoing bolded limitations constitute a “mental process” because as drafted, the limitations are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components (i.e. “information processing device”). Specifically, but for the “information processing device” language, “detecting, based on the observation signal, at least one observed value related to at least one target around the vehicle; calculating an estimated value which indicates a state of the target at predetermined processing cycles; and determining whether a vehicle speed abnormality has occurred, the vehicle speed abnormality being an abnormality of speed of the vehicle itself, wherein the state of the target includes at least one of a distance from the vehicle to the target, an azimuth of the target with respect to the vehicle, and a relative speed of the target with respect to the vehicle; the calculation of the estimated value includes calculating a current predicted value from an estimated value calculated in the past selecting the observed value which is in a predicted range set based on the current predicted value and calculating a current estimated value based on the selected observed value and the current predicted value and the calculation of the estimated value further includes one or more of: in response to determining that no abnormality has occurred performing a first process using the predicted value of a relative speed based on a detection result of a vehicle speed to calculate the current estimated value and in response to determining that the vehicle speed abnormality has occurred, performing a second process different from the first process to calculate the current estimated value” in the context of this claim encompasses the user mentally or with a pen and paper determining from sensor data an observed state of an object, using that determined state to determine a current predicted value, and further calculating a current estimated value from an observed value and the predicted value, the system will then determine if there is an abnormality, and based on the abnormality determination the system will perform specific calculations. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with an pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Under Step 2A - Prong 2:
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, 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 area as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A tracking method for an information processing device for a vehicle, the tracking method comprising:
acquiring an observation signal output from a sensor transmitting and receiving radar waves;
detecting, based on the observation signal, at least one observed value related to at least one target around the vehicle;
calculating an estimated value which indicates a state of the target at predetermined processing cycles; and
determining whether a vehicle speed abnormality has occurred, the vehicle speed abnormality being an abnormality of speed of the vehicle itself,
wherein the state of the target includes at least one of a distance from the vehicle to the target, an azimuth of the target with respect to the vehicle, and a relative speed of the target with respect to the vehicle
the calculation of the estimated value includes calculating a current predicted value from an estimated value calculated in the past selecting the observed value which is in a predicted range set based on the current predicted value and calculating a current estimated value based on the selected observed value and the current predicted value and
the calculation of the estimated value further includes one or more of:
in response to determining that no abnormality has occurred performing a first process using the predicted value of a relative speed based on a detection result of a vehicle speed to calculate the current estimated value and
in response to determining that the vehicle speed abnormality has occurred, performing a second process different from the first process to calculate the current estimated value.
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 limitations of “acquiring an observation signal output from a sensor transmitting and receiving radar waves” the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (information processing device) to perform the process. In particular, the acquiring steps from the sensors are recited at a high level of generality (i.e. as a general means of gathering data for use in the later determining steps), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. See MPEP 2106.05(g).
Regarding the additional limitations of “A tracking method for an information processing device for a vehicle, the tracking method comprising” the examiner submits that these limitations are an attempt to generally link additional elements to a technological environment. In particular, information processing device is recited at a high level of generality and merely automates the determining steps, therefore acting as a generic computer to perform the abstract idea. The information processing device is claimed generically and is operating in its ordinary capacity and does not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. The additional limitation is no more than mere instructions to apply the exception using generic computer components (information processing device).
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 (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.
Under 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 element of “A tracking method for an information processing device for a vehicle, the tracking method comprising” amounts to nothing more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of “acquiring an observation signal output from a sensor transmitting and receiving radar waves” the examiner submits that these limitations are insignificant extra-solution activities. Hence, the claim is not patent eligible.
Therefore claim 12 is ineligible under 35 USC 101.
Regarding claim 13,
Under Step 1:
Claim 13 recites an apparatus including a computer-readable medium that is not limited to non-transitory tangible media. (thus the claims are to non-statutory subject matter Step 1: NO, See rejection above)
Under Step 2A - Prong 1:
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b)certain methods of organizing human activity, and/or c) mental processes.
Independent Claim 13 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 13 recites:
A non-transitory computer readable storage medium in which a program is stored that, when executed by one or more processors, causes a computer, which configures an information processing device for a vehicle, to perform operations the operations comprising:
acquiring an observation signal output from a sensor transmitting and receiving radar waves;
detecting, based on the observation signal, at least one observed value related to at least one target around the vehicle;
calculating an estimated value which indicates a state of the target at predetermined processing cycles; and
determining whether a vehicle speed abnormality has occurred, the vehicle speed abnormality being an abnormality of speed of the vehicle itself,
wherein the state of the target includes at least one of a distance from the vehicle to the target, an azimuth of the target with respect to the vehicle, and a relative speed of the target with respect to the vehicle
the calculation of the estimated value includes calculating a current predicted value from an estimated value calculated in the past selecting the observed value which is in a predicted range set based on the current predicted value and calculating a current estimated value based on the selected observed value and the current predicted value and
the calculation of the estimated value further includes one or more of:
in response to determining that no abnormality has occurred performing a first process using the predicted value of a relative speed based on a detection result of a vehicle speed to calculate the current estimated value and
in response to determining that the vehicle speed abnormality has occurred, performing a second process different from the first process to calculate the current estimated value
acquire an observation signal output from a sensor transmitting and receiving radar waves and detect, from the observation signal, at least one observed value related to at least one target around the vehicle; a tracking unit configured to track the target by calculating a current predicted value from a past estimated value which indicates a state of the target and calculating a current estimated value from a current observed value and the current predicted value at predetermined processing cycles; and an abnormality determination unit configured to determine whether a vehicle speed abnormality has occurred, wherein
if it is determined that no vehicle speed abnormality has occurred, the tracking unit performs a first process using the predicted value of a relative speed based on a detection result of a vehicle speed to calculate the current estimated value, and
if it is determined that the vehicle speed abnormality has occurred, the tracking unit performs a second process different from the first process to calculate the current estimated value.
The examiner submits that the foregoing bolded limitations constitute a “mental process” because as drafted, the limitations are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components (i.e. “a computer”). Specifically, but for the “a computer” language, “detecting, based on the observation signal, at least one observed value related to at least one target around the vehicle; calculating an estimated value which indicates a state of the target at predetermined processing cycles; and determining whether a vehicle speed abnormality has occurred, the vehicle speed abnormality being an abnormality of speed of the vehicle itself, wherein the state of the target includes at least one of a distance from the vehicle to the target, an azimuth of the target with respect to the vehicle, and a relative speed of the target with respect to the vehicle; the calculation of the estimated value includes calculating a current predicted value from an estimated value calculated in the past selecting the observed value which is in a predicted range set based on the current predicted value and calculating a current estimated value based on the selected observed value and the current predicted value and the calculation of the estimated value further includes one or more of: in response to determining that no abnormality has occurred performing a first process using the predicted value of a relative speed based on a detection result of a vehicle speed to calculate the current estimated value and in response to determining that the vehicle speed abnormality has occurred, performing a second process different from the first process to calculate the current estimated value” in the context of this claim encompasses the user mentally or with a pen and paper determining from sensor data an observed state of an object, using that determined state to determine a current predicted value, and further calculating a current estimated value from an observed value and the predicted value, the system will then determine if there is an abnormality, and based on the abnormality determination the system will perform specific calculations. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with an pen and paper but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Under Step 2A - Prong 2:
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, 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 area as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A non-transitory computer readable storage medium in which a program is stored that, when executed by one or more processors, causes a computer, which configures an information processing device for a vehicle, to perform operations the operations comprising:
acquiring an observation signal output from a sensor transmitting and receiving radar waves;
detecting, based on the observation signal, at least one observed value related to at least one target around the vehicle;
calculating an estimated value which indicates a state of the target at predetermined processing cycles; and
determining whether a vehicle speed abnormality has occurred, the vehicle speed abnormality being an abnormality of speed of the vehicle itself,
wherein the state of the target includes at least one of a distance from the vehicle to the target, an azimuth of the target with respect to the vehicle, and a relative speed of the target with respect to the vehicle
the calculation of the estimated value includes calculating a current predicted value from an estimated value calculated in the past selecting the observed value which is in a predicted range set based on the current predicted value and calculating a current estimated value based on the selected observed value and the current predicted value and
the calculation of the estimated value further includes one or more of:
in response to determining that no abnormality has occurred performing a first process using the predicted value of a relative speed based on a detection result of a vehicle speed to calculate the current estimated value and
in response to determining that the vehicle speed abnormality has occurred, performing a second process different from the first process to calculate the current estimated value
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 limitations of “acquiring an observation signal output from a sensor transmitting and receiving radar waves” the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (computer) to perform the process. In particular, the acquiring steps from the sensors are recited at a high level of generality (i.e. as a general means of gathering data for use in the later determining steps), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. See MPEP 2106.05(g).
Regarding the additional limitations of “A non-transitory computer readable storage medium in which a program is stored that, when executed by one or more processors, causes a computer, which configures an information processing device for a vehicle, to perform operations the operations comprising:” the examiner submits that these limitations are an attempt to generally link additional elements to a technological environment. In particular, information processing device and units are recited at a high level of generality and merely automates the determining steps, therefore acting as a generic computer to perform the abstract idea. The information processing device is claimed generically and is operating in its ordinary capacity and does not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. The additional limitation is no more than mere instructions to apply the exception using generic computer components (the information processing device).
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 (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.
Under 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 element of “A non-transitory computer readable storage medium in which a program is stored that, when executed by one or more processors, causes a computer, which configures an information processing device for a vehicle, to perform operations the operations comprising:” amounts to nothing more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of “acquiring an observation signal output from a sensor transmitting and receiving radar waves” the examiner submits that these limitations are insignificant extra-solution activities. Hence, the claim is not patent eligible.
Therefore claim 13 is ineligible under 35 USC 101.
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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3, and 5-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Katoh (US 20160101779) in view of Nonaka (US-20190303690).
Regarding claim 1, Katoh teaches an information processing device for a vehicle, the information processing device comprising (Paragraph [0008], “In the movement trajectory predicting device, the object detection unit detects the object and acquires the position of the object (relative position with respect to the vehicle).”)
a processor and a memory storing instructions, wherein the processor is configured to execute the instructions to (Paragraph [0058], “A central processing unit (CPU), a read-only memory (ROM), a random access memory (RAM), and the like constitute the system ECU 30.”)
acquire an observation signal output from a sensor transmitting and receiving radar waves (Paragraph [0089], “the external sensor that detects the object around the subject vehicle. However, other types of external sensors such as a single-lens camera-based image sensor (one capable of detecting even the distance and the lateral position of the object with a single lens) and a radar sensor such as a laser radar and a millimeter wave radar may also be used as the external sensor”)
detect, based on the observation signal, at least one observed value related to at least one target around the vehicle (Paragraph [0017], “a speed acquiring step for acquiring a ground speed of the object”)
calculate an estimated value which indicates a state of the target (Paragraph [0037], “The trajectory prediction vector (slope, in particular) will be described with reference to FIG. 2. The trajectory prediction vector PV is obtained as a relative vector between the vehicle speed Vc of the subject vehicle and the ground speed Vp of the object.”) (Paragraph [0007], “a movement trajectory predicting device predicting a movement trajectory of an object around a vehicle, the device including an object detection unit that acquires a position of the object around the vehicle, a speed acquisition unit that acquires a ground speed of the object, a relative movement vector calculation unit that calculates a relative movement vector of the object with respect to the vehicle by using speed information on the vehicle and the ground speed of the object acquired by the speed acquisition unit … and a movement trajectory prediction unit that predicts the movement trajectory of the object based on a plurality of positions of the object included in at least one of the groups classified by the classification unit.”) (Paragraph [0083], “The system ECU 30 predicts, for each object, the position of the collision with the subject vehicle from the movement trajectory of the subject vehicle and the movement trajectory of the object (S10),” here the system can calculate a current estimated value/speed which indicates a state of the target)
at predetermined processing cycles (See figures 3, 5, and 7-8 showing a series of processing cycles for received data points) (Paragraph [0062], “The predetermined time is set in advance by adaptation and examples thereof include a step of update time.”)
determining whether an abnormality has occurred (Paragraph [0064], “Hereinafter, the abnormal value removal processing (filtering processing) will be described.”)
wherein the state of the target includes at least one of a distance from the vehicle to the target, an azimuth of the target with respect to the vehicle, and a relative speed of the target with respect to the vehicle (Paragraph [0048], “Examples of the information on the object include the relative distance in the depth direction from the stereo camera 10 (subject vehicle) to the object,” here a state of the target includes a distance) (Paragraph [0037], “The trajectory prediction vector PV is obtained as a relative vector between the vehicle speed Vc of the subject vehicle and the ground speed Vp of the object. The slope θ of the trajectory prediction vector PV is obtained as the slope (angle) of the movement of the object with respect to the traveling direction of the subject vehicle (basically, straight-driving direction),” here the slope/azimuth of the trajectory of the target with respect of the vehicle is predicted) (Paragraph [0007], “a relative movement vector calculation unit that calculates a relative movement vector of the object with respect to the vehicle by using speed information on the vehicle and the ground speed of the object acquired by the speed acquisition unit,” here the system is determining the relative speed vector of the target)
the calculation of the estimated value includes calculating a current predicted value from an estimated value calculated in the past (Paragraph [0007], “a movement trajectory predicting device predicting a movement trajectory of an object around a vehicle, the device including an object detection unit that acquires a position of the object around the vehicle, a speed acquisition unit that acquires a ground speed of the object, a relative movement vector calculation unit that calculates a relative movement vector of the object with respect to the vehicle by using speed information on the vehicle and the ground speed of the object acquired by the speed acquisition unit … and a movement trajectory prediction unit that predicts the movement trajectory of the object based on a plurality of positions of the object included in at least one of the groups classified by the classification unit.”)
selecting the observed value which is in a predicted range set based on the current predicted value (See Figure 7 showing a series of observed values both inside and outside the predicted range) (Paragraph [0037], “The trajectory prediction vector (slope, in particular) will be described with reference to FIG. 2. The trajectory prediction vector PV is obtained as a relative vector between the vehicle speed Vc of the subject vehicle and the ground speed Vp of the object.”)
and calculating a current estimated value based on the selected observed value and the current predicted value (Paragraph [0037], “The trajectory prediction vector (slope, in particular) will be described with reference to FIG. 2. The trajectory prediction vector PV is obtained as a relative vector between the vehicle speed Vc of the subject vehicle and the ground speed Vp of the object.”) (Paragraph [0007], “a movement trajectory predicting device predicting a movement trajectory of an object around a vehicle, the device including an object detection unit that acquires a position of the object around the vehicle, a speed acquisition unit that acquires a ground speed of the object, a relative movement vector calculation unit that calculates a relative movement vector of the object with respect to the vehicle by using speed information on the vehicle and the ground speed of the object acquired by the speed acquisition unit … and a movement trajectory prediction unit that predicts the movement trajectory of the object based on a plurality of positions of the object included in at least one of the groups classified by the classification unit.”)
and the calculation of the estimated value further includes one or more of: in response to determining that no abnormality has occurred performing a first process using the predicted value of a relative speed based on a detection result of a vehicle speed to calculate the current estimated value and in response to determining that the vehicle speed abnormality has occurred, performing a second process different from the first process to calculate the current estimated value (Paragraph [0016], “The two determinations allow the determination of the presence or absence of the abnormal value in the ground speed of the object. The relative movement vector calculation unit does not use the ground speed of the object that has a difference from the previous value of the ground speed equal to or greater than the first threshold and has a difference from the speed obtained from the difference between the positions of the object equal to or greater than the second threshold in calculating the relative movement vector,” here the system performs a determination of an abnormal condition and if there is no abnormality the system uses a predicted relative speed based on the radar to calculate movement vector, and if an abnormality is determined the system modifies this process).
However Katoh does not explicitly teach to determine whether a vehicle speed abnormality has occurred the vehicle speed abnormality being an abnormality of speed of the vehicle itself.
Nonaka teaches an image-capturing device capable of fulfilling a complementary role to a vehicle behavior sensor when a failure occurs in the vehicle behavior sensor including
determine whether a vehicle speed abnormality has occurred (Paragraph [0086], “an abnormality detection unit (e.g. vehicle behavior sensor information diagnosis and behavior information selection unit 108) for detecting an abnormality in the vehicle behavior sensor by comparing the behavior information acquired by the vehicle behavior acquisition unit and the behavior information inferred by the vehicle behavior inference unit”) (Paragraph [0053], “The vehicle behavior sensor information diagnosis and behavior information selection unit 108 is capable of performing similar diagnosis on information from other vehicle behavior sensors such as a vehicle speed sensor,” here the system can determine an abnormality in a vehicle sensor such as a speed sensor)
the vehicle speed abnormality being an abnormality of speed of the vehicle itself (Paragraph [0086], “an abnormality detection unit (e.g. vehicle behavior sensor information diagnosis and behavior information selection unit 108) for detecting an abnormality in the vehicle behavior sensor by comparing the behavior information acquired by the vehicle behavior acquisition unit and the behavior information inferred by the vehicle behavior inference unit”) (Paragraph [0053], “The vehicle behavior sensor information diagnosis and behavior information selection unit 108 is capable of performing similar diagnosis on information from other vehicle behavior sensors such as a vehicle speed sensor,” here the system can determine an abnormality in a speed of the vehicle by comparing a vehicle speed value as output by a sensor to an inferred value, the comparison can reveal an abnormality in the speed of the vehicle).
Katoh and Nonaka are analogous art as they are both generally related to systems for processing sensor information of vehicles.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include to determine whether a vehicle speed abnormality has occurred the vehicle speed abnormality being an abnormality of speed of the vehicle itself of Nonaka in the system for processing information of a vehicle of Katoh with a reasonable expectation of success in order improve the accuracy of vehicle sensor information so as to improve the control of the vehicle (Paragraph [0006-0007], “In particular, in an environment where a coefficient of friction between tires and a road surface is small, the amount of slippage increases, and thus an error increases between the yaw rate obtained by the speed sensors of the wheel speed as an alternative means and the actual yaw rate. Therefore, there are cases where an incorrect locus prediction result is obtained when information obtained by the alternative means is used as it is for example for the locus prediction of the vehicle. Thus, there is a problem that sufficient effects cannot be obtained in driving assistance or automatic driving for predicting and avoiding collision or deviation in advance on the basis of the locus prediction result. An object of the present invention is to provide an image-capturing device capable of fulfilling a complementary role to a vehicle behavior sensor when a failure occurs in the vehicle behavior sensor.”).
Regarding claim 3, the combination of Katoh and Nonaka teaches the system as discussed above in claim 1, Katoh further teaches wherein the first process includes calculating the current predicted value of a relative speed using the vehicle speed (Paragraph [0016], “The two determinations allow the determination of the presence or absence of the abnormal value in the ground speed of the object. The relative movement vector calculation unit does not use the ground speed of the object that has a difference from the previous value of the ground speed equal to or greater than the first threshold and has a difference from the speed obtained from the difference between the positions of the object equal to or greater than the second threshold in calculating the relative movement vector,” here the system performs a determination of an abnormal condition and if there is no abnormality the system uses a predicted relative speed based on the radar to calculate movement vector, and if an abnormality is determined the system modifies this process).
However Katoh does not explicitly teach an abnormality determination unit configured to determine whether a vehicle speed abnormality has occurred and the second process includes calculating the predicted value of a relative speed without using the vehicle speed.
Nonaka teaches an image-capturing device capable of fulfilling a complementary role to a vehicle behavior sensor when a failure occurs in the vehicle behavior sensor including
determine whether a vehicle speed abnormality has occurred (Paragraph [0086], “an abnormality detection unit (e.g. vehicle behavior sensor information diagnosis and behavior information selection unit 108) for detecting an abnormality in the vehicle behavior sensor by comparing the behavior information acquired by the vehicle behavior acquisition unit and the behavior information inferred by the vehicle behavior inference unit”) (Paragraph [0053], “The vehicle behavior sensor information diagnosis and behavior information selection unit 108 is capable of performing similar diagnosis on information from other vehicle behavior sensors such as a vehicle speed sensor,” here the system can determine an abnormality in a vehicle sensor such as a speed sensor)
the second process includes calculating the predicted value of a relative speed without using the vehicle speed (Paragraph [0036], “behavior information selection unit 108 determines that there is an abnormality in an output value of a vehicle behavior sensor … and outputs, to the vehicle control method determination unit 109, substitute … information … calculated from detection results of other vehicle behavior sensors having correlation (for example, left and right wheel speed sensors) or … information … that is the vehicle behavior information inferred by the vehicle behavior information inference unit 106 and further notifies the vehicle control method determination unit 109 that there is a problem in the state of the vehicle behavior sensor as a diagnosis result,” here when a abnormality in a vehicle speed is determined the system will use substitute information instead of the abnormal information/vehicle speed information such as the inferred information) (Paragraph [0053], “The vehicle behavior sensor information diagnosis and behavior information selection unit 108 is capable of performing similar diagnosis on information from other vehicle behavior sensors such as a vehicle speed sensor,” here the system can determine an abnormality in a vehicle sensor such as a speed sensor).
Katoh and Nonaka are analogous art as they are both generally related to systems for processing sensor information of vehicles.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include an abnormality determination unit configured to determine whether a vehicle speed abnormality has occurred and the second process includes calculating the predicted value of a relative speed without using the vehicle speed of Nonaka in the system for processing information of a vehicle of Katoh with a reasonable expectation of success in order improve the accuracy of vehicle sensor information so as to improve the control of the vehicle (Paragraph [0006-0007], “In particular, in an environment where a coefficient of friction between tires and a road surface is small, the amount of slippage increases, and thus an error increases between the yaw rate obtained by the speed sensors of the wheel speed as an alternative means and the actual yaw rate. Therefore, there are cases where an incorrect locus prediction result is obtained when information obtained by the alternative means is used as it is for example for the locus prediction of the vehicle. Thus, there is a problem that sufficient effects cannot be obtained in driving assistance or automatic driving for predicting and avoiding collision or deviation in advance on the basis of the locus prediction result. An object of the present invention is to provide an image-capturing device capable of fulfilling a complementary role to a vehicle behavior sensor when a failure occurs in the vehicle behavior sensor.”).
Regarding claim 5, the combination of Katoh and Nonaka teaches the system as discussed above in claim 1, Katoh further teaches wherein the second process includes setting a contribution of the relative speed to be used in calculation of an association cost, which is an indicator indicating a degree of divergence between the predicted value and the observed value, to be lower than in the first process (Paragraph [0013], “In the movement trajectory predicting device according to the invention, it is preferable that the classification unit performs the classification while increasing a weight for the position among the recorded positions of the object as the position becomes closer in time to the present.”) (Paragraph [0016], “The two determinations allow the determination of the presence or absence of the abnormal value in the ground speed of the object. The relative movement vector calculation unit does not use the ground speed of the object that has a difference from the previous value of the ground speed equal to or greater than the first threshold and has a difference from the speed obtained from the difference between the positions of the object equal to or greater than the second threshold in calculating the relative movement vector,” here the system performs a determination of an abnormal condition and if there is no abnormality the system uses a predicted relative speed based on the radar to calculate movement vector, and if a abnormality is determined the system modifies this process to lower the contribution/exclude the abnormal relative speed including in the calculations using the cost/weight).
Regarding claim 6, the combination of Katoh and Nonaka teaches the system as discussed above in claim 1, Katoh further teaches wherein the second process includes setting a contribution of the predicted value of the relative speed to be used in calculation of the estimated value of the relative speed to be lower than in the first (Paragraph [0016], “The two determinations allow the determination of the presence or absence of the abnormal value in the ground speed of the object. The relative movement vector calculation unit does not use the ground speed of the object that has a difference from the previous value of the ground speed equal to or greater than the first threshold and has a difference from the speed obtained from the difference between the positions of the object equal to or greater than the second threshold in calculating the relative movement vector,” here the system performs a determination of an abnormal condition and if there is no abnormality the system uses a predicted relative speed based on the radar to calculate movement vector, and if a abnormality is determined the system modifies this process to lower the contribution/exclude the abnormal relative speed).
Regarding claim 7, the combination of Katoh and Nonaka teaches the system as discussed above in claim 1, Katoh further teaches wherein the determination whether the vehicle speed abnormality has occurred includes determining based on a magnitude of an acceleration of the vehicle, that the vehicle speed abnormality has occurred for a state in which the acceleration is a predetermined acceleration threshold value or more (Paragraph [0012], “In a case where the vehicle is subjected to at least certain additional steering or cutback, the vehicle turns due to the additional steering or the cutback and the traveling direction changes. Accordingly, the relative position of the object changes in response thereto. In this case, the apparent shape of the object may change and the accuracy of the detection of the ground speed of the object may also be reduced. In this regard, in the movement trajectory predicting device, the classification unit does not perform the classification in a case where the steering amount of the vehicle is equal to or greater than the predetermined amount,” here in a case when the vehicle has a large degree of steering, and therefore a large yaw rate acceleration, the system will determine the abnormality has occurred).
Regarding claim 8, the combination of Katoh and Nonaka teaches the system as discussed above in claim 1, Katoh further teaches wherein the determination whether the vehicle speed abnormality has occurred includes determining that the vehicle abnormality has occurred for a state in which a prediction residual, which is a difference between the predicted value and the observed value, related to the relative speed is a predetermined prediction residual threshold value or more (Paragraph [0064], “ECU 30 determines whether or not the absolute value of the difference between the current value and the previous value of the ground speed is equal to or greater than a first threshold. The first threshold, which is set in advance by adaptation, is a threshold that is used in determining whether or not the ground speed of the object becomes the outlier differing from the previous value. In a case where the absolute value is determined to be equal to or greater than the first threshold (in a case where the current value of the ground speed is the outlier), the system ECU 30 calculates the speed by dividing the difference between the current value and the previous value of the detected position of the object by the update time and determines whether or not the absolute value of the difference between the speed and the current value of the ground speed is equal to or greater than a second threshold”).
Regarding claim 9, the combination of Katoh and Nonaka teaches the system as discussed above in claim 1, Katoh further teaches wherein the determination whether the vehicle speed abnormality has occurred includes that the vehicle speed abnormality has occurred for a state in which the prediction residual, related to the relative speed of a stationary object of the at least one target is the predetermined prediction residual threshold value or more (Paragraph [0033], “The object includes a moving object and a stationary object. Examples of the moving object include a pedestrian, a bicycle, and a vehicle. Examples of the stationary object include a utility pole and a traffic sign,” here the object can be a stationary object) (Paragraph [0064], “ECU 30 determines whether or not the absolute value of the difference between the current value and the previous value of the ground speed is equal to or greater than a first threshold. The first threshold, which is set in advance by adaptation, is a threshold that is used in determining whether or not the ground speed of the object becomes the outlier differing from the previous value. In a case where the absolute value is determined to be equal to or greater than the first threshold (in a case where the current value of the ground speed is the outlier), the system ECU 30 calculates the speed by dividing the difference between the current value and the previous value of the detected position of the object by the update time and determines whether or not the absolute value of the difference between the speed and the current value of the ground speed is equal to or greater than a second threshold”).
Regarding claim 10, the combination of Katoh and Nonaka teaches the system as discussed above in claim 1, Katoh further teaches determining that the vehicle speed abnormality has occurred for a state in which the prediction residual related to the relative speed of at least one of the stationary objects is the predetermined prediction residual threshold value or more (Paragraph [0033], “The object includes a moving object and a stationary object. Examples of the moving object include a pedestrian, a bicycle, and a vehicle. Examples of the stationary object include a utility pole and a traffic sign,” here the object can be a stationary object) (Paragraph [0064], “ECU 30 determines whether or not the absolute value of the difference between the current value and the previous value of the ground speed is equal to or greater than a first threshold. The first threshold, which is set in advance by adaptation, is a threshold that is used in determining whether or not the ground speed of the object becomes the outlier differing from the previous value. In a case where the absolute value is determined to be equal to or greater than the first threshold (in a case where the current value of the ground speed is the outlier), the system ECU 30 calculates the speed by dividing the difference between the current value and the previous value of the detected position of the object by the update time and determines whether or not the absolute value of the difference between the speed and the current value of the ground speed is equal to or greater than a second threshold”).
However Katoh does not explicitly teach wherein the determination whether the vehicle speed abnormality has occurred includes for a state in which the number of stationary object is the predetermined stationary target number or more.
Nonaka teaches wherein the determination whether the vehicle speed abnormality has occurred includes for a state in which the number of stationary object is the predetermined stationary target number or more (Paragraph [0027], “FIG. 2 is a diagram illustrating a positional relationship between stationary objects and a vehicle when two stationary objects are captured.”) (Paragraph [0051], “As described above, there are cases where the yaw rate information Yrp cannot be calculated due to factors such as a capturing state of a stationary object on the basis of the images captured by the image-capturing units 101a and 101b,” here the system determines if there is case in which the inferred information cannot be calculated, for example if the capturing state stationary objects are below a threshold value, in this case the threshold would be one).
Katoh and Nonaka are analogous art as they are both generally related to systems for processing sensor information of vehicles.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include wherein the determination whether the vehicle speed abnormality has occurred includes for a state in which the number of stationary object is the predetermined stationary target number or more of Nonaka in the system for processing information of a vehicle of Katoh with a reasonable expectation of success in order improve the accuracy of vehicle sensor information so as to improve the control of the vehicle (Paragraph [0006-0007], “In particular, in an environment where a coefficient of friction between tires and a road surface is small, the amount of slippage increases, and thus an error increases between the yaw rate obtained by the speed sensors of the wheel speed as an alternative means and the actual yaw rate. Therefore, there are cases where an incorrect locus prediction result is obtained when information obtained by the alternative means is used as it is for example for the locus prediction of the vehicle. Thus, there is a problem that sufficient effects cannot be obtained in driving assistance or automatic driving for predicting and avoiding collision or deviation in advance on the basis of the locus prediction result. An object of the present invention is to provide an image-capturing device capable of fulfilling a complementary role to a vehicle behavior sensor when a failure occurs in the vehicle behavior sensor.”).
Regarding claim 11, Katoh teaches an object tracking device for a vehicle, the device comprising (Paragraph [0008], “In the movement trajectory predicting device, the object detection unit detects the object and acquires the position of the object (relative position with respect to the vehicle).”)
a processor and a memory storing instructions, wherein the processor is configured to execute the instructions to: (Paragraph [0058], “A central processing unit (CPU), a read-only memory (ROM), a random access memory (RAM), and the like constitute the system ECU 30.”)
acquire an observation signal output from a sensor transmitting and receiving radar waves (Paragraph [0089], “the external sensor that detects the object around the subject vehicle. However, other types of external sensors such as a single-lens camera-based image sensor (one capable of detecting even the distance and the lateral position of the object with a single lens) and a radar sensor such as a laser radar and a millimeter wave radar may also be used as the external sensor”)
detect, based on the observation signal, at least one observed value related to at least one target around the vehicle (Paragraph [0017], “a speed acquiring step for acquiring a ground speed of the object”)
calculate an estimated value which indicates a state of the target (Paragraph [0037], “The trajectory prediction vector (slope, in particular) will be described with reference to FIG. 2. The trajectory prediction vector PV is obtained as a relative vector between the vehicle speed Vc of the subject vehicle and the ground speed Vp of the object.”) (Paragraph [0007], “a movement trajectory predicting device predicting a movement trajectory of an object around a vehicle, the device including an object detection unit that acquires a position of the object around the vehicle, a speed acquisition unit that acquires a ground speed of the object, a relative movement vector calculation unit that calculates a relative movement vector of the object with respect to the vehicle by using speed information on the vehicle and the ground speed of the object acquired by the speed acquisition unit … and a movement trajectory prediction unit that predicts the movement trajectory of the object based on a plurality of positions of the object included in at least one of the groups classified by the classification unit.”) (Paragraph [0083], “The system ECU 30 predicts, for each object, the position of the collision with the subject vehicle from the movement trajectory of the subject vehicle and the movement trajectory of the object (S10),” here the system can calculate a current estimated value/speed which indicates a state of the target)
at predetermined processing cycles (See figures 3, 5, and 7-8 showing a series of processing cycles for received data points) (Paragraph [0062], “The predetermined time is set in advance by adaptation and examples thereof include a step of update time.”)
determining whether an abnormality has occurred (Paragraph [0064], “Hereinafter, the abnormal value removal processing (filtering processing) will be described.”)
wherein the state of the target includes at least one of a distance from the vehicle to the target, an azimuth of the target with respect to the vehicle, and a relative speed of the target with respect to the vehicle (Paragraph [0048], “Examples of the information on the object include the relative distance in the depth direction from the stereo camera 10 (subject vehicle) to the object,” here a state of the target includes a distance) (Paragraph [0037], “The trajectory prediction vector PV is obtained as a relative vector between the vehicle speed Vc of the subject vehicle and the ground speed Vp of the object. The slope θ of the trajectory prediction vector PV is obtained as the slope (angle) of the movement of the object with respect to the traveling direction of the subject vehicle (basically, straight-driving direction),” here the slope/azimuth of the trajectory of the target with respect of the vehicle is predicted) (Paragraph [0007], “a relative movement vector calculation unit that calculates a relative movement vector of the object with respect to the vehicle by using speed information on the vehicle and the ground speed of the object acquired by the speed acquisition unit,” here the system is determining the relative speed vector of the target)
the calculation of the estimated value includes calculating a current predicted value from an estimated value calculated in the past (Paragraph [0007], “a movement trajectory predicting device predicting a movement trajectory of an object around a vehicle, the device including an object detection unit that acquires a position of the object around the vehicle, a speed acquisition unit that acquires a ground speed of the object, a relative movement vector calculation unit that calculates a relative movement vector of the object with respect to the vehicle by using speed information on the vehicle and the ground speed of the object acquired by the speed acquisition unit … and a movement trajectory prediction unit that predicts the movement trajectory of the object based on a plurality of positions of the object included in at least one of the groups classified by the classification unit.”)
selecting the observed value which is in a predicted range set based on the current predicted value (See Figure 7 showing a series of observed values both inside and outside the predicted range) (Paragraph [0037], “The trajectory prediction vector (slope, in particular) will be described with reference to FIG. 2. The trajectory prediction vector PV is obtained as a relative vector between the vehicle speed Vc of the subject vehicle and the ground speed Vp of the object.”)
and calculating a current estimated value based on the selected observed value and the current predicted value (Paragraph [0037], “The trajectory prediction vector (slope, in particular) will be described with reference to FIG. 2. The trajectory prediction vector PV is obtained as a relative vector between the vehicle speed Vc of the subject vehicle and the ground speed Vp of the object.”) (Paragraph [0007], “a movement trajectory predicting device predicting a movement trajectory of an object around a vehicle, the device including an object detection unit that acquires a position of the object around the vehicle, a speed acquisition unit that acquires a ground speed of the object, a relative movement vector calculation unit that calculates a relative movement vector of the object with respect to the vehicle by using speed information on the vehicle and the ground speed of the object acquired by the speed acquisition unit … and a movement trajectory prediction unit that predicts the movement trajectory of the object based on a plurality of positions of the object included in at least one of the groups classified by the classification unit.”)
and the calculation of the estimated value further includes one or more of: in response to determining that no vehicle speed abnormality has occurred, performing a first process using the predicted value of a relative speed based on a detection result of a vehicle speed to calculate the current estimated value and in response to determining that the vehicle speed abnormality has occurred, performing a second process different from the first process to calculate the current estimated value (Paragraph [0016], “The two determinations allow the determination of the presence or absence of the abnormal value in the ground speed of the object. The relative movement vector calculation unit does not use the ground speed of the object that has a difference from the previous value of the ground speed equal to or greater than the first threshold and has a difference from the speed obtained from the difference between the positions of the object equal to or greater than the second threshold in calculating the relative movement vector,” here the system performs a determination of an abnormal condition and if there is no abnormality the system uses a predicted relative speed based on the radar to calculate movement vector, and if a abnormality is determined the system modifies this process).
However Katoh does not explicitly teach to determine whether a vehicle speed abnormality has occurred the vehicle speed abnormality being an abnormality of speed of the vehicle itself.
Nonaka teaches an image-capturing device capable of fulfilling a complementary role to a vehicle behavior sensor when a failure occurs in the vehicle behavior sensor including
determine whether a vehicle speed abnormality has occurred (Paragraph [0086], “an abnormality detection unit (e.g. vehicle behavior sensor information diagnosis and behavior information selection unit 108) for detecting an abnormality in the vehicle behavior sensor by comparing the behavior information acquired by the vehicle behavior acquisition unit and the behavior information inferred by the vehicle behavior inference unit”) (Paragraph [0053], “The vehicle behavior sensor information diagnosis and behavior information selection unit 108 is capable of performing similar diagnosis on information from other vehicle behavior sensors such as a vehicle speed sensor,” here the system can determine an abnormality in a vehicle sensor such as a speed sensor)
the vehicle speed abnormality being an abnormality of speed of the vehicle itself (Paragraph [0086], “an abnormality detection unit (e.g. vehicle behavior sensor information diagnosis and behavior information selection unit 108) for detecting an abnormality in the vehicle behavior sensor by comparing the behavior information acquired by the vehicle behavior acquisition unit and the behavior information inferred by the vehicle behavior inference unit”) (Paragraph [0053], “The vehicle behavior sensor information diagnosis and behavior information selection unit 108 is capable of performing similar diagnosis on information from other vehicle behavior sensors such as a vehicle speed sensor,” here the system can determine an abnormality in a speed of the vehicle by comparing a vehicle speed value as output by a sensor to an inferred value, the comparison can reveal an abnormality in the speed of the vehicle).
Katoh and Nonaka are analogous art as they are both generally related to systems for processing sensor information of vehicles.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include to determine whether a vehicle speed abnormality has occurred the vehicle speed abnormality being an abnormality of speed of the vehicle itself of Nonaka in the system for processing information of a vehicle of Katoh with a reasonable expectation of success in order improve the accuracy of vehicle sensor information so as to improve the control of the vehicle (Paragraph [0006-0007], “In particular, in an environment where a coefficient of friction between tires and a road surface is small, the amount of slippage increases, and thus an error increases between the yaw rate obtained by the speed sensors of the wheel speed as an alternative means and the actual yaw rate. Therefore, there are cases where an incorrect locus prediction result is obtained when information obtained by the alternative means is used as it is for example for the locus prediction of the vehicle. Thus, there is a problem that sufficient effects cannot be obtained in driving assistance or automatic driving for predicting and avoiding collision or deviation in advance on the basis of the locus prediction result. An object of the present invention is to provide an image-capturing device capable of fulfilling a complementary role to a vehicle behavior sensor when a failure occurs in the vehicle behavior sensor.”).
Regarding claim 12, Katoh teaches a tracking method for an information processing device for a vehicle, the tracking method comprising: (Paragraph [0008], “In the movement trajectory predicting device, the object detection unit detects the object and acquires the position of the object (relative position with respect to the vehicle).”)
acquiring an observation signal output from a sensor transmitting and receiving radar waves (Paragraph [0089], “the external sensor that detects the object around the subject vehicle. However, other types of external sensors such as a single-lens camera-based image sensor (one capable of detecting even the distance and the lateral position of the object with a single lens) and a radar sensor such as a laser radar and a millimeter wave radar may also be used as the external sensor”)
detecting, based on the observation signal, at least one observed value related to at least one target around the vehicle (Paragraph [0017], “a speed acquiring step for acquiring a ground speed of the object”)
calculating an estimated value which indicates a state of the target (Paragraph [0037], “The trajectory prediction vector (slope, in particular) will be described with reference to FIG. 2. The trajectory prediction vector PV is obtained as a relative vector between the vehicle speed Vc of the subject vehicle and the ground speed Vp of the object.”) (Paragraph [0007], “a movement trajectory predicting device predicting a movement trajectory of an object around a vehicle, the device including an object detection unit that acquires a position of the object around the vehicle, a speed acquisition unit that acquires a ground speed of the object, a relative movement vector calculation unit that calculates a relative movement vector of the object with respect to the vehicle by using speed information on the vehicle and the ground speed of the object acquired by the speed acquisition unit … and a movement trajectory prediction unit that predicts the movement trajectory of the object based on a plurality of positions of the object included in at least one of the groups classified by the classification unit.”) (Paragraph [0083], “The system ECU 30 predicts, for each object, the position of the collision with the subject vehicle from the movement trajectory of the subject vehicle and the movement trajectory of the object (S10),” here the system can calculate a current estimated value/speed which indicates a state of the target)
at predetermined processing cycles (See figures 3, 5, and 7-8 showing a series of processing cycles for received data points) (Paragraph [0062], “The predetermined time is set in advance by adaptation and examples thereof include a step of update time.”)
and determining whether an abnormality has occurred (Paragraph [0064], “Hereinafter, the abnormal value removal processing (filtering processing) will be described.”)
wherein the state of the target includes at least one of a distance from the vehicle to the target, an azimuth of the target with respect to the vehicle, and a relative speed of the target with respect to the vehicle (Paragraph [0048], “Examples of the information on the object include the relative distance in the depth direction from the stereo camera 10 (subject vehicle) to the object,” here a state of the target includes a distance) (Paragraph [0037], “The trajectory prediction vector PV is obtained as a relative vector between the vehicle speed Vc of the subject vehicle and the ground speed Vp of the object. The slope θ of the trajectory prediction vector PV is obtained as the slope (angle) of the movement of the object with respect to the traveling direction of the subject vehicle (basically, straight-driving direction),” here the slope/azimuth of the trajectory of the target with respect of the vehicle is predicted) (Paragraph [0007], “a relative movement vector calculation unit that calculates a relative movement vector of the object with respect to the vehicle by using speed information on the vehicle and the ground speed of the object acquired by the speed acquisition unit,” here the system is determining the relative speed vector of the target)
the calculation of the estimated value includes calculating a current predicted value from an estimated value calculated in the past (Paragraph [0007], “a movement trajectory predicting device predicting a movement trajectory of an object around a vehicle, the device including an object detection unit that acquires a position of the object around the vehicle, a speed acquisition unit that acquires a ground speed of the object, a relative movement vector calculation unit that calculates a relative movement vector of the object with respect to the vehicle by using speed information on the vehicle and the ground speed of the object acquired by the speed acquisition unit … and a movement trajectory prediction unit that predicts the movement trajectory of the object based on a plurality of positions of the object included in at least one of the groups classified by the classification unit.”)
selecting the observed value which is in a predicted range set based on the current predicted value (See Figure 7 showing a series of observed values both inside and outside the predicted range) (Paragraph [0037], “The trajectory prediction vector (slope, in particular) will be described with reference to FIG. 2. The trajectory prediction vector PV is obtained as a relative vector between the vehicle speed Vc of the subject vehicle and the ground speed Vp of the object.”)
and calculating a current estimated value based on the selected observed value and the current predicted value (Paragraph [0037], “The trajectory prediction vector (slope, in particular) will be described with reference to FIG. 2. The trajectory prediction vector PV is obtained as a relative vector between the vehicle speed Vc of the subject vehicle and the ground speed Vp of the object.”) (Paragraph [0007], “a movement trajectory predicting device predicting a movement trajectory of an object around a vehicle, the device including an object detection unit that acquires a position of the object around the vehicle, a speed acquisition unit that acquires a ground speed of the object, a relative movement vector calculation unit that calculates a relative movement vector of the object with respect to the vehicle by using speed information on the vehicle and the ground speed of the object acquired by the speed acquisition unit … and a movement trajectory prediction unit that predicts the movement trajectory of the object based on a plurality of positions of the object included in at least one of the groups classified by the classification unit.”)
and the calculation of the estimated value further includes one or more of: in response to determining that no vehicle speed abnormality has occurred, a first process is performed using the predicted value of a relative speed based on a detection result of a vehicle speed to calculate the current estimated value and in response to determining that the vehicle speed abnormality has occurred, a second process different from the first process is performed to calculate the current estimated value (Paragraph [0016], “The two determinations allow the determination of the presence or absence of the abnormal value in the ground speed of the object. The relative movement vector calculation unit does not use the ground speed of the object that has a difference from the previous value of the ground speed equal to or greater than the first threshold and has a difference from the speed obtained from the difference between the positions of the object equal to or greater than the second threshold in calculating the relative movement vector,” here the system performs a determination of an abnormal condition and if there is no abnormality the system uses a predicted relative speed based on the radar to calculate movement vector, and if a abnormality is determined the system modifies this process).
However Katoh does not explicitly teach determining whether a vehicle speed abnormality has occurred the vehicle speed abnormality being an abnormality of speed of the vehicle itself.
Nonaka teaches an image-capturing device capable of fulfilling a complementary role to a vehicle behavior sensor when a failure occurs in the vehicle behavior sensor including
determining whether a vehicle speed abnormality has occurred (Paragraph [0086], “an abnormality detection unit (e.g. vehicle behavior sensor information diagnosis and behavior information selection unit 108) for detecting an abnormality in the vehicle behavior sensor by comparing the behavior information acquired by the vehicle behavior acquisition unit and the behavior information inferred by the vehicle behavior inference unit”) (Paragraph [0053], “The vehicle behavior sensor information diagnosis and behavior information selection unit 108 is capable of performing similar diagnosis on information from other vehicle behavior sensors such as a vehicle speed sensor,” here the system can determine an abnormality in a vehicle sensor such as a speed sensor)
the vehicle speed abnormality being an abnormality of speed of the vehicle itself (Paragraph [0086], “an abnormality detection unit (e.g. vehicle behavior sensor information diagnosis and behavior information selection unit 108) for detecting an abnormality in the vehicle behavior sensor by comparing the behavior information acquired by the vehicle behavior acquisition unit and the behavior information inferred by the vehicle behavior inference unit”) (Paragraph [0053], “The vehicle behavior sensor information diagnosis and behavior information selection unit 108 is capable of performing similar diagnosis on information from other vehicle behavior sensors such as a vehicle speed sensor,” here the system can determine an abnormality in a speed of the vehicle by comparing a vehicle speed value as output by a sensor to an inferred value, the comparison can reveal an abnormality in the speed of the vehicle).
Katoh and Nonaka are analogous art as they are both generally related to systems for processing sensor information of vehicles.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include determining whether a vehicle speed abnormality has occurred the vehicle speed abnormality being an abnormality of speed of the vehicle itself of Nonaka in the system for processing information of a vehicle of Katoh with a reasonable expectation of success in order improve the accuracy of vehicle sensor information so as to improve the control of the vehicle (Paragraph [0006-0007], “In particular, in an environment where a coefficient of friction between tires and a road surface is small, the amount of slippage increases, and thus an error increases between the yaw rate obtained by the speed sensors of the wheel speed as an alternative means and the actual yaw rate. Therefore, there are cases where an incorrect locus prediction result is obtained when information obtained by the alternative means is used as it is for example for the locus prediction of the vehicle. Thus, there is a problem that sufficient effects cannot be obtained in driving assistance or automatic driving for predicting and avoiding collision or deviation in advance on the basis of the locus prediction result. An object of the present invention is to provide an image-capturing device capable of fulfilling a complementary role to a vehicle behavior sensor when a failure occurs in the vehicle behavior sensor.”).
Regarding claim 13, Katoh teaches a non-transitory computer-readable storage medium in which a program is stored that, when executed by one or more processors, causes a computer, which configures an information processing device for a vehicle, to perform operations, the operations comprising (Paragraph [0008], “In the movement trajectory predicting device, the object detection unit detects the object and acquires the position of the object (relative position with respect to the vehicle).”) (Paragraph [0058], “A central processing unit (CPU), a read-only memory (ROM), a random access memory (RAM), and the like constitute the system ECU 30.”)
acquiring an observation signal output from a sensor transmitting and receiving radar waves (Paragraph [0089], “the external sensor that detects the object around the subject vehicle. However, other types of external sensors such as a single-lens camera-based image sensor (one capable of detecting even the distance and the lateral position of the object with a single lens) and a radar sensor such as a laser radar and a millimeter wave radar may also be used as the external sensor”)
detecting based on the observation signal, at least one observed value related to at least one target around the vehicle (Paragraph [0017], “a speed acquiring step for acquiring a ground speed of the object”)
calculating an estimated value which indicates a state of the target (Paragraph [0037], “The trajectory prediction vector (slope, in particular) will be described with reference to FIG. 2. The trajectory prediction vector PV is obtained as a relative vector between the vehicle speed Vc of the subject vehicle and the ground speed Vp of the object.”) (Paragraph [0007], “a movement trajectory predicting device predicting a movement trajectory of an object around a vehicle, the device including an object detection unit that acquires a position of the object around the vehicle, a speed acquisition unit that acquires a ground speed of the object, a relative movement vector calculation unit that calculates a relative movement vector of the object with respect to the vehicle by using speed information on the vehicle and the ground speed of the object acquired by the speed acquisition unit … and a movement trajectory prediction unit that predicts the movement trajectory of the object based on a plurality of positions of the object included in at least one of the groups classified by the classification unit.”) (Paragraph [0083], “The system ECU 30 predicts, for each object, the position of the collision with the subject vehicle from the movement trajectory of the subject vehicle and the movement trajectory of the object (S10),” here the system can calculate a current estimated value/speed which indicates a state of the target)
at predetermined processing cycles (See figures 3, 5, and 7-8 showing a series of processing cycles for received data points) (Paragraph [0062], “The predetermined time is set in advance by adaptation and examples thereof include a step of update time.”)
and determining whether an abnormality has occurred (Paragraph [0064], “Hereinafter, the abnormal value removal processing (filtering processing) will be described.”)
wherein the state of the target includes at least one of a distance from the vehicle to the target, an azimuth of the target with respect to the vehicle, and a relative speed of the target with respect to the vehicle (Paragraph [0048], “Examples of the information on the object include the relative distance in the depth direction from the stereo camera 10 (subject vehicle) to the object,” here a state of the target includes a distance) (Paragraph [0037], “The trajectory prediction vector PV is obtained as a relative vector between the vehicle speed Vc of the subject vehicle and the ground speed Vp of the object. The slope θ of the trajectory prediction vector PV is obtained as the slope (angle) of the movement of the object with respect to the traveling direction of the subject vehicle (basically, straight-driving direction),” here the slope/azimuth of the trajectory of the target with respect of the vehicle is predicted) (Paragraph [0007], “a relative movement vector calculation unit that calculates a relative movement vector of the object with respect to the vehicle by using speed information on the vehicle and the ground speed of the object acquired by the speed acquisition unit,” here the system is determining the relative speed vector of the target)
the calculation of the estimated value includes calculating a current predicted value from an estimated value calculated in the past (Paragraph [0007], “a movement trajectory predicting device predicting a movement trajectory of an object around a vehicle, the device including an object detection unit that acquires a position of the object around the vehicle, a speed acquisition unit that acquires a ground speed of the object, a relative movement vector calculation unit that calculates a relative movement vector of the object with respect to the vehicle by using speed information on the vehicle and the ground speed of the object acquired by the speed acquisition unit … and a movement trajectory prediction unit that predicts the movement trajectory of the object based on a plurality of positions of the object included in at least one of the groups classified by the classification unit.”)
selecting the observed value which is in a predicted range set based on the current predicted value (See Figure 7 showing a series of observed values both inside and outside the predicted range) (Paragraph [0037], “The trajectory prediction vector (slope, in particular) will be described with reference to FIG. 2. The trajectory prediction vector PV is obtained as a relative vector between the vehicle speed Vc of the subject vehicle and the ground speed Vp of the object.”)
and calculating a current estimated value based on the selected observed value and the current predicted value (Paragraph [0037], “The trajectory prediction vector (slope, in particular) will be described with reference to FIG. 2. The trajectory prediction vector PV is obtained as a relative vector between the vehicle speed Vc of the subject vehicle and the ground speed Vp of the object.”) (Paragraph [0007], “a movement trajectory predicting device predicting a movement trajectory of an object around a vehicle, the device including an object detection unit that acquires a position of the object around the vehicle, a speed acquisition unit that acquires a ground speed of the object, a relative movement vector calculation unit that calculates a relative movement vector of the object with respect to the vehicle by using speed information on the vehicle and the ground speed of the object acquired by the speed acquisition unit … and a movement trajectory prediction unit that predicts the movement trajectory of the object based on a plurality of positions of the object included in at least one of the groups classified by the classification unit.”)
and the calculation of the estimated value further includes one or more of: in response to determining that no vehicle speed abnormality has occurred, performing a first process using the predicted value of a relative speed based on a detection result of a vehicle speed to calculate the current estimated value and in response to determining that the vehicle speed abnormality has occurred, performing a second process different from the first process to calculate the current estimated value (Paragraph [0016], “The two determinations allow the determination of the presence or absence of the abnormal value in the ground speed of the object. The relative movement vector calculation unit does not use the ground speed of the object that has a difference from the previous value of the ground speed equal to or greater than the first threshold and has a difference from the speed obtained from the difference between the positions of the object equal to or greater than the second threshold in calculating the relative movement vector,” here the system performs a determination of an abnormal condition and if there is no abnormality the system uses a predicted relative speed based on the radar to calculate movement vector, and if a abnormality is determined the system modifies this process).
However Katoh does not explicitly teach to determine whether a vehicle speed abnormality has occurred the vehicle speed abnormality being an abnormality of speed of the vehicle itself.
Nonaka teaches an image-capturing device capable of fulfilling a complementary role to a vehicle behavior sensor when a failure occurs in the vehicle behavior sensor including
determine whether a vehicle speed abnormality has occurred (Paragraph [0086], “an abnormality detection unit (e.g. vehicle behavior sensor information diagnosis and behavior information selection unit 108) for detecting an abnormality in the vehicle behavior sensor by comparing the behavior information acquired by the vehicle behavior acquisition unit and the behavior information inferred by the vehicle behavior inference unit”) (Paragraph [0053], “The vehicle behavior sensor information diagnosis and behavior information selection unit 108 is capable of performing similar diagnosis on information from other vehicle behavior sensors such as a vehicle speed sensor,” here the system can determine an abnormality in a vehicle sensor such as a speed sensor)
the vehicle speed abnormality being an abnormality of speed of the vehicle itself (Paragraph [0086], “an abnormality detection unit (e.g. vehicle behavior sensor information diagnosis and behavior information selection unit 108) for detecting an abnormality in the vehicle behavior sensor by comparing the behavior information acquired by the vehicle behavior acquisition unit and the behavior information inferred by the vehicle behavior inference unit”) (Paragraph [0053], “The vehicle behavior sensor information diagnosis and behavior information selection unit 108 is capable of performing similar diagnosis on information from other vehicle behavior sensors such as a vehicle speed sensor,” here the system can determine an abnormality in a speed of the vehicle by comparing a vehicle speed value as output by a sensor to an inferred value, the comparison can reveal an abnormality in the speed of the vehicle).
Katoh and Nonaka are analogous art as they are both generally related to systems for processing sensor information of vehicles.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include to determine whether a vehicle speed abnormality has occurred the vehicle speed abnormality being an abnormality of speed of the vehicle itself of Nonaka in the system for processing information of a vehicle of Katoh with a reasonable expectation of success in order improve the accuracy of vehicle sensor information so as to improve the control of the vehicle (Paragraph [0006-0007], “In particular, in an environment where a coefficient of friction between tires and a road surface is small, the amount of slippage increases, and thus an error increases between the yaw rate obtained by the speed sensors of the wheel speed as an alternative means and the actual yaw rate. Therefore, there are cases where an incorrect locus prediction result is obtained when information obtained by the alternative means is used as it is for example for the locus prediction of the vehicle. Thus, there is a problem that sufficient effects cannot be obtained in driving assistance or automatic driving for predicting and avoiding collision or deviation in advance on the basis of the locus prediction result. An object of the present invention is to provide an image-capturing device capable of fulfilling a complementary role to a vehicle behavior sensor when a failure occurs in the vehicle behavior sensor.”).
Claim 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Katoh (US 20160101779) in view of Nonaka (US-20190303690) and further in view of Sakai (US-20110313664).
Regarding claim 4, the combination of Katoh and Nonaka teaches the system as discussed above in claim 1, however Katoh does not explicitly teach an abnormality determination unit configured to determine whether a vehicle speed abnormality has occurred.
Nonaka teaches an image-capturing device capable of fulfilling a complementary role to a vehicle behavior sensor when a failure occurs in the vehicle behavior sensor including an abnormality determination unit configured to determine whether a vehicle speed abnormality has occurred (Paragraph [0086], “an abnormality detection unit (e.g. vehicle behavior sensor information diagnosis and behavior information selection unit 108) for detecting an abnormality in the vehicle behavior sensor by comparing the behavior information acquired by the vehicle behavior acquisition unit and the behavior information inferred by the vehicle behavior inference unit”) (Paragraph [0053], “The vehicle behavior sensor information diagnosis and behavior information selection unit 108 is capable of performing similar diagnosis on information from other vehicle behavior sensors such as a vehicle speed sensor,” here the system can determine an abnormality in a vehicle sensor such as a speed sensor).
Katoh and Nonaka are analogous art as they are both generally related to systems for processing sensor information of vehicles.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include to determine whether a vehicle speed abnormality has occurred of Nonaka in the system for processing information of a vehicle of Katoh with a reasonable expectation of success in order improve the accuracy of vehicle sensor information so as to improve the control of the vehicle (Paragraph [0006-0007], “In particular, in an environment where a coefficient of friction between tires and a road surface is small, the amount of slippage increases, and thus an error increases between the yaw rate obtained by the speed sensors of the wheel speed as an alternative means and the actual yaw rate. Therefore, there are cases where an incorrect locus prediction result is obtained when information obtained by the alternative means is used as it is for example for the locus prediction of the vehicle. Thus, there is a problem that sufficient effects cannot be obtained in driving assistance or automatic driving for predicting and avoiding collision or deviation in advance on the basis of the locus prediction result. An object of the present invention is to provide an image-capturing device capable of fulfilling a complementary role to a vehicle behavior sensor when a failure occurs in the vehicle behavior sensor.”).
However the combination does not explicitly teach the second process includes setting the prediction range to be larger than in the first process in a case in which it is determined that no vehicle abnormality has occurred.
Sakai teaches a movement region prediction apparatus includes a mobile body detection device that detects a mobile body around a host vehicle including
the second process includes setting the prediction range to be larger than in the first process (See Figures 2A and 2B) (Paragraph [0062], “FIG. 2A shows a case where the motion prediction is performed assuming that all the other vehicles are abnormal vehicles. In this case, broad ranges of existence AA1 and AA2 in which the other vehicles PV1 and PV2 are to exist after a predetermined time are predicted”) (Paragraph [0063], “FIG. 2B shows a case where the motion prediction is performed assuming that all the other vehicles are normal vehicles,” here you can see two different sized prediction range, a smaller range for normal prediction state, and a larger range for an abnormal prediction state, while the system here is determining abnormality on the basis of the target vehicle, this system could reasonably be applied to the abnormal/normal determination as discussed above in the combination of Katoh and Nonaka).
Katoh and Nonaka are analogous art as they are both generally related to systems for processing sensor information of vehicles.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include the second process includes setting the prediction range to be larger than in the first process of Sakai in the system for processing information of a vehicle of Katoh and Nonaka with a reasonable expectation of success in order improve the safety of the system by adjusting a prediction in response to an abnormality in order maintain reliability of prediction results (Paragraph [0025], “the prediction with a short prediction time is performed using a movement prediction model prepared for a mobile body that is abnormal in the situation of movement, so that it becomes possible to cope with the case where the mobile body suddenly falls into an abnormal situation of movement without losing the reliability of the prediction results. Thus, safety can be further improved.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Kang (US-20230007183) teaches a security camera having a built-in radar, and the camera is enabled to track the target according to the moving direction and specific signs of the target after the target is identified as a person and a vehicle sequentially according to a decision priority order. Miyazaki (US 20180217232) teaches a detection unit that performs a detection processing of detecting a target iteratively at a predetermined cycle; a speed deriving unit that derives a speed of the target detected in a current iteration of the detection processing; a region setting unit that sets a prediction region where the target having temporal continuity with and being identical to the target detected in the current iteration of the detection processing is expected to be detected in a next iteration of the detection processing. Maekawa (US 20220026526) teaches a radar device that is able to detect abnormalities including those in a radar dome, which may influence the measurement accuracy of the radar device.
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A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/CHRISTOPHER GEORGE FEES/Primary Examiner, Art Unit 3662