DETAILED ACTION
Claims 1 – 4, 7 – 11 and 14 - 24 have been presented for examination. Claims 5 – 6 and 12 – 13 are cancelled. Claims 1, 7, 9, 14 – 16 and 20 are currently amended. Claims 21 – 24 are new.
This office action is in response to submission of the amendments on 01/05/2026.
The instant office action relies on Matsumoto et al. (US 2020/0276984) and Wang et al. (US 2021/0134154) and Aoude et al. (US 2019/0287395) which are cited on the IDS.
Response to Claim Rejections - 35 USC § 101
Applicant’s arguments have been fully considered. However, the Office does not consider them to be persuasive. The additional elements argued by application do not integrate the abstract idea into a practical application (see Claim Rejections - 35 USC § 101).
Response to Claim Rejections - 35 USC § 103
Applicant’s arguments have been fully considered. However, the Office does not consider them to be persuasive.
Applicant argues: “The Office Action indicates at page 19 that collision avoidance trajectory in Bagnell equals to predicted driving state in the future, and indicates optimized trajectory in Matusomoto equals to optimization parameters. However, the amended claim 1 has recited the driving state comprises at least one of normal, controllable abnormality or uncontrollable abnormality. Thus the simulated episode of locomotion in Bagnell can not be equal to the predicted driving state in the future.”
Applicant presents a conclusory arguments that the simulated episode of locomotion as taught in Bagnell can not be equal to a predicted driving state in the future. Examiner notes that the simulation of Bagnell is explicitly intended to analyze the result of actions, where the results of said actions necessarily occur after performance of said action in the future (a predetermined period of time in future) (see Column 2, Lines 55 – 60).
Applicant argues: “Both of the Matsuomoto and Bagnell fails to teach a simulation difference between the parameter optimization simulation and the warning simulation.”
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Bagnell in view of Aoude et al. (US 2019/0287395) teaches “determining whether the simulation is a parameter optimization simulation or a warning simulation according to the simulation type parameter” (see Claim Rejections - 35 USC § 103).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 24 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With regard to claim 24, it recites “extending the simulation period in response the result of the warning simulation a normal state”. The limitation is unclear since it is not grammatical, and the typographical error appears to encompass multiple words. The limitation is interpreted for examination purposes as the warning simulation indicating any normal state.
Claim Objections
Claim 7 is objected to because of the following informalities: There appears to be a typo in “environment m parallel”. The limitation is interpreted for examination purposes as “environment in parallel”. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 4, 7 – 11 and 14 - 24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Independent claim 1 recites at Step 1 a statutory category (i.e. a process) method for simulating an autonomous vehicle, comprising: performing simulation based on the current information to acquire prediction information of the autonomous vehicle; and wherein performing simulation based on the current information to acquire prediction information of the autonomous vehicle comprising: determining whether the simulation is a parameter optimization simulation or a warning simulation according to the simulation type parameter; in response to the simulation being the parameter optimization simulation, predicting an optimization parameter of the autonomous vehicle in a predetermined period of time in future; and in response to the simulation being the warning simulation, predicting a driving state of the autonomous vehicle in a predetermined period of time in the future, wherein the driving state comprises at least one of normal, controllable abnormality or uncontrollable abnormality. At Step 2A, Prong I the recited limitations, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). For example, the “determining” and “predicting” amounts to modeling actions recited at a high-level of generality, and the “performing simulation” to acquire prediction information requires no more than mental process steps “predicting”. Accordingly, the claim recites an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims: acquiring current information associated with the autonomous vehicle; sending current information associated with the autonomous vehicle to a simulation system; receiving a simulation request comprising a simulation type parameter. The “acquiring” and “sending” and “receiving” amounts to insignificant data gathering since it is recited at a high-level of generality (see MPEP 2106.04(d)). The claim is directed to an abstract idea.
At Step 2B the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. The recited “acquiring” and “sending” and “receiving” cover well-understood, routine, and conventional activity since it is generic and covers receiving and outputting data by any electronics means (see MPEP 2106.05(d)(II) “i. Receiving or transmitting data over a network”). Considering the additional elements in combination does not add anything more than when considering them individually since they all comprise data gathering. For at least these reasons, the claim is not patent eligible.
Dependent claim 2 – 4 recite(s) at Step 1 the same statutory category as the parent claim(s). Accordingly, the claim(s) recite(s) an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims:
Claim 2 wherein the current information comprises at least one of: vehicle state information, environmental information surrounding the autonomous vehicle, and module operating state information of the autonomous vehicle.
Claim 3 wherein performing simulation based on the current information comprises: in response to receiving a simulation request from the autonomous vehicle, performing the simulation in a simulation environment mapped with at least the current information to predict an optimization parameter of the autonomous vehicle or a driving state of the autonomous vehicle in a predetermined period of time in the future
Claim 4 wherein receiving the simulation request comprises at least one of: receiving the simulation request periodically from the autonomous vehicle; or receiving the simulation request from the autonomous vehicle when the autonomous vehicle is in a specific driving state.
For example, the “current information comprises” further modifies a parent claim data gathering steps with specific information gathered. Therefore, it amounts to insignificant data gathering since it does not further limit how the data is gathered. The ”performing the simulation in a simulation environment” amounts to reciting the words “apply it” since it broadly covers all manners of performing the simulation. The “receiving” amounts to insignificant data gathering since it is recited at a high-level of generality, and since the parent claim “generating control information” step relies on the received elements in a generic manner (see MPEP 2106.04(d)). The claim is directed to an abstract idea.
At Step 2B the claim(s) do not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. The recited “current information comprises” and “receiving” cover well-understood, routine, and conventional activity since it is generic and covers receiving and outputting data by any electronics means (see MPEP 2106.05(d)(II) “i. Receiving or transmitting data over a network”). The ”performing the simulation in a simulation environment” amounts to reciting the words “apply it” since it requires no more than ordinary equipment operating in its ordinary capacity. Considering the additional elements in combination does not add anything more than when considering them individually since they require no more than ordinary equipment operating in its ordinary capacity. For at least these reasons, the claim(s) are not patent eligible.
Dependent claim 7 – 8 recite(s) at Step 1 the same statutory category as the parent claim(s), and further recite(s):
Claim 7 determining the optimization parameter based on a simulation result obtained by each simulation environment.
Claim 8 wherein the optimization parameter comprises at least one of: a parameter for a perception module of the autonomous vehicle, a parameter for a planning module of the autonomous vehicle, or a parameter for a control module of the autonomous vehicle.
At Step 2A, Prong I the recited limitations, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). For example, the “determining” and “optimization parameter comprises” amount(s) to modeling actions recited at a high-level of generality. Accordingly, the claim(s) recite(s) an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims: Claim 7 wherein the simulation request comprises a simulation type parameter indicating that the simulation is the parameter optimization simulation, and performing simulation comprises: running multiple simulation environments in parallel mapped with at least the current information, wherein each simulation environment uses a different simulation parameter. The ”simulation request comprises” further limits the parent claim “receiving”, therefore, it amounts to insignificant data gathering. The “running multiple simulation environments” amounts to reciting the words “apply it”. The claim is directed to an abstract idea.
At Step 2B the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. The ”simulation request comprises” further limits the parent claim “receiving” to cover well-understood, routine, and conventional activity since it is generic and covers receiving and outputting data by any electronics means (see MPEP 2106.05(d)(II) “i. Receiving or transmitting data over a network”). The “running multiple simulation environments” amounts to reciting the words “apply it” since it requires no more than ordinary equipment operating in its ordinary capacity. Considering the additional elements in combination does not add anything more than when considering them individually since they require no more than ordinary equipment operating in its ordinary capacity. For at least these reasons, the claim(s) are not patent eligible.
Dependent claim 21 – 24 recite(s) at Step 1 the same statutory category as the parent claim(s), and further recite(s):
Claim 22 wherein the optimization parameter for the control module of the autonomous vehicle comprises optimizing parameters of friction coefficient and engine moment.
Claim 23 wherein the optimization parameter for the perception module of the autonomous vehicle comprises optimizing parameters of a vehicle's maximum recognition distance and minimum recognition precision.
At Step 2A, Prong I the recited limitations, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). For example, the “optimization parameter for” further limits the parent claim “determining”, therefore, it amount(s) to modeling actions recited at a high-level of generality. Accordingly, the claim(s) recite(s) an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims:
Claim 21 wherein the current information comprises module operating state information, the module operating state information comprises at least one of: information indicating that a running result of each module is normal; information indicating that the running result of each module is abnormal; a running frame rate of each module; or a warning log of each module.
Claim 24 shortening a simulation period in response to a result of the warning simulation indicating that the driving state of the autonomous vehicle may be abnormal; and extending the simulation period in response the result of the warning simulation a normal state.
The ”current information comprises” and “module operating state information comprises” further limits the parent claim “acquiring”, therefore, it amounts to insignificant data gathering (see Claim Rejections - 35 USC § 112). The “shortening the simulation” and “extending the simulation period” amount to reciting the words “apply it” since the further limits the parent claim “running multiple simulation environments” by generically achieving an desired affect and without reciting further details. The claim is directed to an abstract idea.
At Step 2B the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. The ”current information comprises” and “module operating state information comprises” to cover well-understood, routine, and conventional activity since it is generic and covers receiving and outputting data by any electronics means (see MPEP 2106.05(d)(II) “i. Receiving or transmitting data over a network”). The “shortening the simulation” and “extending the simulation period” amount to reciting the words “apply it” since it requires no more than ordinary equipment operating in its ordinary capacity. Considering the additional elements in combination does not add anything more than when considering them individually since they require no more than ordinary equipment operating in its ordinary capacity. For at least these reasons, the claim(s) are not patent eligible.
Independent claim 9 recites at Step 1 a statutory category (i.e. a process) method for controlling an autonomous vehicle, comprising: generating control information for the autonomous vehicle based on the prediction information; wherein the simulation system performing the simulation comprises: determining whether the simulation is a parameter optimization simulation or a warning simulation according to the simulation type parameter; in response to the simulation being the parameter optimization simulation, predicting an optimization parameter of the autonomous vehicle in a predetermined period of time in future; and in response to the simulation being the warning simulation, predicting a driving state of the autonomous vehicle in a predetermined period of time in the future, wherein the driving state comprises at least one of normal, controllable abnormality or uncontrollable abnormality. At Step 2A, Prong I the recited limitations, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). For example, the “generating control information” and “determining” and “predicting” amounts to modeling actions recited at a high-level of generality. Accordingly, the claim recites an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims: receiving a simulation request comprising a simulation type parameter; sending current information associated with the autonomous vehicle to a simulation system; acquiring prediction information obtained by the simulation system by performing simulation based on the current information. The “receiving” and “sending” and “acquiring” amounts to insignificant data gathering and outputting since it is recited at a high-level of generality (see MPEP 2106.04(d)). Although the “acquiring” is based on simulation, the limitation recites the acquisition of the information after the simulation is performed. The claim is directed to an abstract idea.
At Step 2B the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. The recited “receiving” and “sending” and “acquiring” cover well-understood, routine, and conventional activity since it is generic and covers receiving and outputting data by any electronics means (see MPEP 2106.05(d)(II) “i. Receiving or transmitting data over a network”). Considering the additional elements in combination does not add anything more than when considering them individually since they all comprise data gathering and outputting. For at least these reasons, the claim is not patent eligible.
Dependent claim 10 – 11 recite(s) at Step 1 the same statutory category as the parent claim(s). Accordingly, the claim(s) recite(s) an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims:
Claim 10 wherein the current information comprises vehicle state information, environmental information surrounding the autonomous vehicle, and module operating state information of the autonomous vehicle;
Claim 11 wherein acquiring prediction information obtained by the simulation system by performing a simulation based on the current information comprises: sending a simulation request to the simulation system periodically or based on the current information; and receiving prediction information obtained by the simulation system in response to the simulation request performing simulation in a simulation environment mapped with at least the current information;
For example, the “current information comprises” further modifies a parent claim data gathering steps with specific information gathered. Therefore, it amounts to insignificant data gathering since it does not further limit how the data is gathered. The “sending” and “receiving” amounts to insignificant data gathering since it is recited at a high-level of generality, and since the parent claim “generating control information” step relies on the received elements in a generic manner (see MPEP 2106.04(d)). The claim is directed to an abstract idea.
At Step 2B the claim(s) do not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. The recited “current information comprises” and “sending” and “receiving” over well-understood, routine, and conventional activity since it is generic and covers receiving and outputting data by any electronics means (see MPEP 2106.05(d)(II) “i. Receiving or transmitting data over a network”). Considering the additional elements in combination does not add anything more than when considering them individually since they all comprise data gathering. For at least these reasons, the claim(s) are not patent eligible.
Dependent claim 14 – 15 recite(s) at Step 1 the same statutory category as the parent claim(s), and further recite(s):
Claim 14 wherein generating control information for the autonomous vehicle based on the prediction information comprises: in responding to the prediction information comprising the predicted driving state of the autonomous vehicle for the predetermined period of time in the future: in response to the predicted driving state comprising a controllable abnormality, generating control information indicating to record the controllable abnormality; and in response to the predicted driving state comprising an uncontrollable abnormality, generating the control information for terminating the autonomous driving state of the autonomous vehicle.
Claim 15 wherein generating control information for the autonomous vehicle based on the prediction information comprises: in response to the prediction information comprising an optimization parameter for the autonomous vehicle, generating the control information for updating a current parameter of the autonomous vehicle with the optimization parameter.
At Step 2A, Prong I the recited limitations, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). For example, the “generating control information” and “generating the control information” amounts to modeling actions recited at a high-level of generality. Accordingly, the claim(s) recite(s) an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention does not further recite any limitations. The claim is directed to an abstract idea.
At Step 2B the claim(s) do not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception since there are no further recited limitations. For at least these reasons, the claim(s) are not patent eligible.
Independent claim 16 recites at Step 1 a statutory category (i.e. a machine) electronic apparatus to execute a method comprising: performing simulation based on the current information to acquire prediction information of the autonomous vehicle; and wherein performing simulation based on the current information to acquire prediction information of the autonomous vehicle comprising: determining whether the simulation is a parameter optimization simulation or a warning simulation according to the simulation type parameter; in response to the simulation being the parameter optimization simulation, predicting an optimization parameter of the autonomous vehicle in a predetermined period of time in future; and in response to the simulation being the warning simulation, predicting a driving state of the autonomous vehicle in a predetermined period of time in the future, wherein the driving state comprises at least one of normal, controllable abnormality or uncontrollable abnormality. At Step 2A, Prong I the recited limitations, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). For example, the “determining” and “predicting” amounts to modeling actions recited at a high-level of generality, and the “performing simulation” to acquire prediction information requires no more than mental process steps “predicting”. Accordingly, the claim recites an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims: at least one processor; and at least one memory, the memory storing an instruction that, when executed by the at least one processor, causes the at least one processor to execute a method comprising; acquiring current information associated with the autonomous vehicle; sending current information associated with the autonomous vehicle to a simulation system; receiving a simulation request comprising a simulation type parameter. The ”processor” and “memory” are recited at a high-level of generality such that they amount to no more than mere application of the judicial exception using generic computer components which does not amount to an improvement in computer functionality (see MPEP 2106.04(a)(I)). The “acquiring” and “sending” and “receiving” amounts to insignificant data gathering since it is recited at a high-level of generality (see MPEP 2106.04(d)). The claim is directed to an abstract idea.
At Step 2B the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the recited “processor” and “memory” amount to no more than mere instructions to apply the judicial exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The recited “acquiring” and “sending” and “receiving” cover well-understood, routine, and conventional activity since it is generic and covers receiving and outputting data by any electronics means (see MPEP 2106.05(d)(II) “i. Receiving or transmitting data over a network”). Considering the additional elements in combination does not add anything more than when considering them individually since they all comprise data gathering and require no more than generic computer functions. For at least these reasons, the claim is not patent eligible.
Dependent claim 17 -19 recite(s) at Step 1 the same statutory category as the parent claim(s), and further recite(s):
Claim 19 wherein the method further comprises: generating control information for the autonomous vehicle based on the prediction information. At Step 2A, Prong I the recited limitations, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). For example, the “generating” amount(s) to modeling actions recited at a high-level of generality. Accordingly, the claim(s) recite(s) an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims:
Claim 17 wherein performing simulation based on the current information comprises: in response to receiving a simulation request from the autonomous vehicle, performing the simulation in a simulation environment mapped with at least the current information to predict an optimization parameter of the autonomous vehicle or a driving state of the autonomous vehicle in a predetermined period of time in the future
Claim 18 wherein the simulation request comprises a simulation type parameter indicating that the simulation is the parameter optimization simulation
Claim 19 wherein the method further comprises: sending current information associated with the autonomous vehicle to a simulation system; acquiring prediction information obtained by a simulation system by performing simulation based on the current information.
For example, the ”performing the simulation in a simulation environment” amounts to reciting the words “apply it” since it broadly covers all manners of performing the simulation. The ”simulation request comprises” further limits the parent claim “receiving”, therefore, it amounts to insignificant data gathering. The “sending” and “acquiring” amount to amounts to insignificant data gathering since it is recited at a high-level of generality (see MPEP 2106.04(d)). The claim is directed to an abstract idea.
At Step 2B the claim(s) do not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. The ”performing the simulation in a simulation environment” amounts to reciting the words “apply it” since it requires no more than ordinary equipment operating in its ordinary capacity. The recited “simulation request comprises” and “acquiring” and “sending” cover well-understood, routine, and conventional activity since it is generic and covers receiving and outputting data by any electronics means (see MPEP 2106.05(d)(II) “i. Receiving or transmitting data over a network”). Considering the additional elements in combination does not add anything more than when considering them individually since they require no more than ordinary equipment operating in its ordinary capacity. For at least these reasons, the claim(s) are not patent eligible.
Independent claim 20 recites at Step 1 a statutory category (i.e. a manufacture) A non-transitory computer-readable storage medium to execute a method comprising: performing simulation based on the current information to acquire prediction information of the autonomous vehicle; and wherein performing simulation based on the current information to acquire prediction information of the autonomous vehicle comprising: determining whether the simulation is a parameter optimization simulation or a warning simulation according to the simulation type parameter; in response to the simulation being the parameter optimization simulation, predicting an optimization parameter of the autonomous vehicle in a predetermined period of time in future; and in response to the simulation being the warning simulation, predicting a driving state of the autonomous vehicle in a predetermined period of time in the future, wherein the driving state comprises at least one of normal, controllable abnormality or uncontrollable abnormality. At Step 2A, Prong I the recited limitations, alone or in combination, amount to steps that, under its broadest reasonable interpretation, cover performance of the limitations in the mind in combination with using a pen and paper (see MPEP 2106.04(a)(2)(III)). For example, the “determining” and “predicting” amounts to modeling actions recited at a high-level of generality, and the “performing simulation” to acquire prediction information requires no more than mental process steps “predicting”. Accordingly, the claim recites an abstract idea.
At Step 2A, Prong II this judicial exception is not integrated into a practical application since the claimed invention further claims: a non-transitory computer-readable storage medium storing a computer instruction, wherein the computer instruction is used to cause the computer to; acquiring current information associated with an autonomous vehicle; sending current information associated with the autonomous vehicle to a simulation system; receiving a simulation request comprising a simulation type parameter. The ”storing a computer instruction” are recited at a high-level of generality such that they amount to no more than mere application of the judicial exception using generic computer components which does not amount to an improvement in computer functionality (see MPEP 2106.04(a)(I)). The “acquiring” and “sending” and “receiving” amounts to insignificant data gathering since it is recited at a high-level of generality (see MPEP 2106.04(d)). The claim is directed to an abstract idea.
At Step 2B the claim does not recite additional elements that, alone or in an ordered combination, are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the recited “storing a computer instruction” amount to no more than mere instructions to apply the judicial exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The recited “acquiring” and “sending” and “receiving” cover well-understood, routine, and conventional activity since it is generic and covers receiving and outputting data by any electronics means (see MPEP 2106.05(d)(II) “i. Receiving or transmitting data over a network”). Considering the additional elements in combination does not add anything more than when considering them individually since they all comprise data gathering and require no more than generic computer functions. For at least these reasons, the claim is not patent eligible.
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter, subject to overcoming the 101 rejection.
None of the prior art of record taken individually or in combination discloses the claim 24 method for simulation an autonomous vehicle, comprising: “shortening a simulation period in response to a result of the warning simulation indicating that the driving state of the autonomous vehicle may be abnormal; and extending the simulation period in response the result of the warning simulation a normal”, in combination with the remaining elements and features of the claim. It is for these reasons that the applicant’s invention defines over the prior art of record.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 – 4, 7 – 11, 16 – 17 and 19 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Matsumoto (984) in view of Bagnell et al. (US 11989020) (henceforth “Bagnell (020)”), and further in view of Aoude et al. (US 2019/0287395) (henceforth “Aoude (395)”). Matsumoto (984) and Bagnell (020) and Aoude (395) are analogous art because they solve the same problem of simulating an autonomous vehicle, and because they are from the same field of autonomous vehicle control.
With regard to claim 1, Matsumoto (984) teaches a method for simulating an autonomous vehicle, comprising: (Paragraph 22 “The trajectory simulator 107 executes, on each of the candidate traveling trajectories generated by the candidate trajectory generation part 102, a trajectory simulation involving use of the characteristic information of the target vehicle stored in the vehicle characteristic information storage part 106.”)
acquiring current information associated with the autonomous vehicle; (Paragraph 28 and Figure 1 “The vehicle-side server coordination part 301 transmits the position information of the target vehicle to the server 100, and receives the information on the traveling trajectory transmitted from the server 100.”
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performing simulation based on the current information to acquire prediction information of the autonomous vehicle; and (Paragraph 17 candidate trajectory uses vehicle position information “For example, the candidate trajectory generation part 102 obtains information indicating a current position of the target vehicle from the onboard device 300, and generates a plurality of candidate traveling trajectories”, and Paragraph 22 trajectory simulation based on candidate trajectories (performing simulation based on the current information) to calculate behaviors (to acquire prediction information) “The trajectory simulator 107 executes, on each of the candidate traveling trajectories generated by the candidate trajectory generation part 102”)
sending the prediction information to the autonomous vehicle. (Paragraph 23 traveling trajectory determined from simulation “determines, as a traveling trajectory of the target vehicle, a candidate traveling trajectory having the lowest evaluation value, i.e., a candidate traveling trajectory that gives the lowest load on the target vehicle”, and Paragraph 28 “The vehicle-side server coordination part 301 transmits the position information of the target vehicle to the server 100, and receives the information on the traveling trajectory transmitted from the server 100.”)
wherein performing simulation based on the current information to acquire prediction information of the autonomous vehicle comprising: in response to the simulation being a parameter optimization simulation, predicting an optimization parameter of the autonomous vehicle in a predetermined period of time in future; and (Matusomoto (984) Paragraph 23 trajectory could be optimized (optimization parameter) “For example, the trajectory evaluation part 108 sets evaluation values on the candidate traveling trajectories based on the behaviors of the target vehicle in the candidate traveling trajectories obtained as a result of the trajectory simulations, and determines, as a traveling trajectory of the target vehicle, a candidate traveling trajectory having the lowest evaluation value, i.e., a candidate traveling trajectory that gives the lowest load on the target vehicle”)
Matsuomoto (984) does not appear to explicitly disclose: receiving a simulation request comprising a simulation type parameter; in response to the simulation being the warning simulation, predicting a driving state of the autonomous vehicle in a predetermined period of time in the future, wherein the driving state comprises at least one of normal, controllable abnormality or uncontrollable abnormality.
However, Bagnell (020) teaches:
receiving a simulation request comprising a simulation type parameter; (Bagnell (020) Col. 18, Lines 43 – 55 type of simulation is used for training (a parameter optimization simulation) and can include collision avoidance (a warning simulation) “the user input detected by the user input engine 290 can include a type of simulation to be performed during the simulated episode of locomotion of the simulated AV based on a given one of the ML model simulated training instance(s). For example, the user input can indicate that the initial demonstrator state instance should correspond to a vehicle beginning to stop at a yellow light, a vehicle beginning to yield to a pedestrian or bicyclist, a vehicle beginning to change lanes, a vehicle maintaining a lane, or a vehicle performing other actions.”)
in response to a simulation being the other action simulation, predicting a driving state of an autonomous vehicle in a predetermined period of time in the future (Bagnell (020) Col. 18, Lines 43 – 55 simulation could show collision avoidance trajectory (predicted driving state in the future) “the user input detected by the user input engine 290 can include a type of simulation to be performed during the simulated episode of locomotion of the simulated AV based on a given one of the ML model simulated training instance(s). For example, the user input can indicate that the initial demonstrator state instance should correspond to a vehicle beginning to stop at a yellow light, a vehicle beginning to yield to a pedestrian or bicyclist, a vehicle beginning to change lanes, a vehicle maintaining a lane, or a vehicle performing other actions.”)
It would have been obvious to one of ordinary skill in the art to combine the method of trajectory simulations disclosed by Matsumoto (984) with the selection of type of simulation to be performed disclosed by Bagnell (020). One of ordinary skill in the art would have been motivated to make this modification in order to desirably perform simulation of desired vehicle scenarios (Bagnell (020) Col. 18, Lines 43 – 55).
Matsumoto (984) in view of Bagnell (020) does not appear to explicitly disclose: determining whether the simulation is a parameter optimization simulation or a warning simulation according to the simulation type parameter; in response to a simulation being the warning simulation, predicting a driving state of an autonomous vehicle in a predetermined period of time in the future wherein the driving state comprises at least one of normal, controllable abnormality or uncontrollable abnormality.
However, Aoude (395) teaches:
a warning simulation; in response to a simulation being the warning simulation, predicting a driving state of an autonomous vehicle in a predetermined period of time in the future wherein the driving state comprises at least one of normal, controllable abnormality or uncontrollable abnormality. (Paragraph 57 the modeling of the specific action of Bagnell (020) can correspond to an early warning of a dangerous situation “Based on the direct use of current sensor data and on the results of applying the artificial intelligence and machine learning to the current sensor data, the system produces early warnings such as alerts of dangerous situations and therefore aids collision avoidance. With respect to early warnings in the form of instructions or commands, the command or instruction could be directed to a specific autonomous or human-driven entity to control the vehicle directly.”)
It would have been obvious to one of ordinary skill in the art to combine the method of trajectory simulations disclosed by Matsumoto (984) in view of Bagnell (020) with the early warning simulation performed disclosed by Aoude (395). One of ordinary skill in the art would have been motivated to make this modification in order to desirably perform simulation of desired vehicle scenarios (Aoude (395) Abstract).
With regard to claim 9, Matsumoto (984) teaches a method for controlling an autonomous vehicle, comprising (Paragraph 45 traveling trajectory received by vehicle used for control (for a control module) “In step S109, the onboard device 300 executes a control for causing the target vehicle to move autonomously following the traveling trajectory, based on the traveling trajectory information received from the server 100 in step S108.”)
sending current information associated with the autonomous vehicle to a simulation system; (Paragraph 28 and Figure 1 “The vehicle-side server coordination part 301 transmits the position information of the target vehicle to the server 100, and receives the information on the traveling trajectory transmitted from the server 100.”
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acquiring prediction information obtained by the simulation system by performing simulation based on the current information; and (Paragraph 17 candidate trajectory uses vehicle position information “For example, the candidate trajectory generation part 102 obtains information indicating a current position of the target vehicle from the onboard device 300, and generates a plurality of candidate traveling trajectories”, and Paragraph 22 trajectory simulation based on candidate trajectories (performing simulation based on the current information) to calculate behaviors (to acquire prediction information) “The trajectory simulator 107 executes, on each of the candidate traveling trajectories generated by the candidate trajectory generation part 102”)
generating control information for the autonomous vehicle based on the prediction information. (Paragraph 45 traveling trajectory received by vehicle used for control (for a control module) “In step S109, the onboard device 300 executes a control for causing the target vehicle to move autonomously following the traveling trajectory, based on the traveling trajectory information received from the server 100 in step S108.”)
wherein performing simulation based on the current information to acquire prediction information of the autonomous vehicle comprising:
in response to the simulation being a parameter optimization simulation, predicting an optimization parameter of the autonomous vehicle in a predetermined period of time in future; and (Matusomoto (984) Paragraph 23 trajectory could be optimized (optimization parameter) “For example, the trajectory evaluation part 108 sets evaluation values on the candidate traveling trajectories based on the behaviors of the target vehicle in the candidate traveling trajectories obtained as a result of the trajectory simulations, and determines, as a traveling trajectory of the target vehicle, a candidate traveling trajectory having the lowest evaluation value, i.e., a candidate traveling trajectory that gives the lowest load on the target vehicle”)
Matsuomoto (984) does not appear to explicitly disclose: receiving a simulation request comprising a simulation type parameter; in response to the simulation being the warning simulation, predicting a driving state of the autonomous vehicle in a predetermined period of time in the future, wherein the driving state comprises at least one of normal, controllable abnormality or uncontrollable abnormality.
However, Bagnell (020) teaches:
receiving a simulation request comprising a simulation type parameter; (Bagnell (020) Col. 18, Lines 43 – 55 type of simulation is used for training (a parameter optimization simulation) and can include collision avoidance (a warning simulation) “the user input detected by the user input engine 290 can include a type of simulation to be performed during the simulated episode of locomotion of the simulated AV based on a given one of the ML model simulated training instance(s). For example, the user input can indicate that the initial demonstrator state instance should correspond to a vehicle beginning to stop at a yellow light, a vehicle beginning to yield to a pedestrian or bicyclist, a vehicle beginning to change lanes, a vehicle maintaining a lane, or a vehicle performing other actions.”)
in response to a simulation being the other action simulation, predicting a driving state of an autonomous vehicle in a predetermined period of time in the future (Bagnell (020) Col. 18, Lines 43 – 55 simulation could show collision avoidance trajectory (predicted driving state in the future) “the user input detected by the user input engine 290 can include a type of simulation to be performed during the simulated episode of locomotion of the simulated AV based on a given one of the ML model simulated training instance(s). For example, the user input can indicate that the initial demonstrator state instance should correspond to a vehicle beginning to stop at a yellow light, a vehicle beginning to yield to a pedestrian or bicyclist, a vehicle beginning to change lanes, a vehicle maintaining a lane, or a vehicle performing other actions.”)
It would have been obvious to one of ordinary skill in the art to combine the method of trajectory simulations disclosed by Matsumoto (984) with the selection of type of simulation to be performed disclosed by Bagnell (020). One of ordinary skill in the art would have been motivated to make this modification in order to desirably perform simulation of desired vehicle scenarios (Bagnell (020) Col. 18, Lines 43 – 55).
Matsumoto (984) in view of Bagnell (020) does not appear to explicitly disclose: determining whether the simulation is a parameter optimization simulation or a warning simulation according to the simulation type parameter; in response to a simulation being the warning simulation, predicting a driving state of an autonomous vehicle in a predetermined period of time in the future wherein the driving state comprises at least one of normal, controllable abnormality or uncontrollable abnormality.
However, Aoude (395) teaches:
a warning simulation; in response to a simulation being the warning simulation, predicting a driving state of an autonomous vehicle in a predetermined period of time in the future wherein the driving state comprises at least one of normal, controllable abnormality or uncontrollable abnormality. (Paragraph 57 the modeling of the specific action of Bagnell (020) can correspond to an early warning of a dangerous situation “Based on the direct use of current sensor data and on the results of applying the artificial intelligence and machine learning to the current sensor data, the system produces early warnings such as alerts of dangerous situations and therefore aids collision avoidance. With respect to early warnings in the form of instructions or commands, the command or instruction could be directed to a specific autonomous or human-driven entity to control the vehicle directly.”)
It would have been obvious to one of ordinary skill in the art to combine the method of trajectory simulations disclosed by Matsumoto (984) in view of Bagnell (020) with the early warning simulation performed disclosed by Aoude (395). One of ordinary skill in the art would have been motivated to make this modification in order to desirably perform simulation of desired vehicle scenarios (Aoude (395) Abstract).
With regard to claim 16, it recites the same steps as claim 1, which is taught by Matsumoto (984) in view of Bagnell (020), and further in view of Aoude (395). Claim 16 further recites: an electronic apparatus, comprising: at least one processor; and at least one memory, the memory storing an instruction that, when executed by the at least one processor, causes the at least one processor to execute a method comprising steps.
Matsumoto (984) teaches:
an electronic apparatus, comprising: at least one processor; and at least one memory, the memory storing an instruction that, when executed by the at least one processor, causes the at least one processor to execute a method comprising steps. (Paragraph 12 “The server 100 has a hardware structure including a CPU, a memory, and a storage (e.g., HDD, SSD), each of which is not illustrated”)
With regard to claim 20, recites the same steps as claim 1, which is taught by Matsumoto (984) in view of Bagnell (020), and further in view of Aoude (395). Claim 20 further recites: a non-transitory computer-readable storage medium storing a computer instruction, wherein the computer instruction is used to cause the computer to execute a method steps.
Matsumoto (984) teaches:
a non-transitory computer-readable storage medium storing a computer instruction, wherein the computer instruction is used to cause the computer to execute a method steps. (Paragraph 12 “The server 100 has a hardware structure including a CPU, a memory, and a storage (e.g., HDD, SSD), each of which is not illustrated”)
With regard to claim 2, Matsumoto (984) in view of Bagnell (020), and further in view of Aoude (395) teaches all the elements of the parent claim 1, and further teaches wherein the current information comprises at least one of: vehicle state information, environmental information surrounding the autonomous vehicle, and module operating state information of the autonomous vehicle. (Matsumoto (984) Paragraph 28 and Figure 1 “The vehicle-side server coordination part 301 transmits the position information of the target vehicle to the server 100, and receives the information on the traveling trajectory transmitted from the server 100.”)
With regard to claim 10, Matsumoto (984) in view of Bagnell (020), and further in view of Aoude (395) teaches all the elements of the parent claim 9, and further teaches: wherein the current information comprises vehicle state information, environmental information surrounding the autonomous vehicle, and module operating state information of the autonomous vehicle. (Matsumoto (984) Paragraph 45 traveling trajectory received by vehicle used for control (for a control module) “In step S109, the onboard device 300 executes a control for causing the target vehicle to move autonomously following the traveling trajectory, based on the traveling trajectory information received from the server 100 in step S108.”)
With regard to claim 11, Matsumoto (984) in view of Bagnell (020), and further in view of Aoude (395) teaches all the elements of the parent claim 9, and further teaches wherein acquiring prediction information obtained by the simulation system by performing a simulation based on the current information comprises:
sending a simulation request to the simulation system periodically or based on the current information; and (Matsumoto (984) Paragraph 25 simulation is re-executed as vehicle situation changes “The resetting determination part 110 determines whether to reset the traveling trajectory determined by the trajectory evaluation part 108, based on the current situation of the target vehicle. If the resetting determination part 110 determines to reset the traveling trajectory, the server 100 executes, via the trajectory simulator 107, trajectory simulations again”)
receiving prediction information obtained by the simulation system in response to the simulation request performing simulation in a simulation environment mapped with at least the current information. (Matsumoto (984) Paragraph 17 candidate trajectory uses vehicle position information (mapped with current information) “For example, the candidate trajectory generation part 102 obtains information indicating a current position of the target vehicle from the onboard device 300, and generates a plurality of candidate traveling trajectories”,
With regard to claim 19, Matsumoto (984) in view of Bagnell (020), and further in view of Aoude (395) teaches all the elements of the parent claim 16, and further teaches:
sending current information associated with the autonomous vehicle to a simulation system; (Matsumoto (984) Paragraph 28 and Figure 1 “The vehicle-side server coordination part 301 transmits the position information of the target vehicle to the server 100, and receives the information on the traveling trajectory transmitted from the server 100.”
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acquiring prediction information obtained by a simulation system by performing simulation based on the current information; and (Matsumoto (984) Paragraph 17 candidate trajectory uses vehicle position information “For example, the candidate trajectory generation part 102 obtains information indicating a current position of the target vehicle from the onboard device 300, and generates a plurality of candidate traveling trajectories”, and Paragraph 22 trajectory simulation based on candidate trajectories (performing simulation based on the current information) to calculate behaviors (to acquire prediction information) “The trajectory simulator 107 executes, on each of the candidate traveling trajectories generated by the candidate trajectory generation part 102”)
generating control information for the autonomous vehicle based on the prediction information. (Matsumoto (984) Paragraph 45 traveling trajectory received by vehicle used for control (for a control module) “In step S109, the onboard device 300 executes a control for causing the target vehicle to move autonomously following the traveling trajectory, based on the traveling trajectory information received from the server 100 in step S108.”)
With regard to claim 3 and 17, Matsumoto (984) in view of Bagnell (020), and further in view of Aoude (395) teaches all the elements of the parent claim 1 and 16, and further teaches wherein performing simulation based on the current information comprises:
performing the simulation in a simulation environment mapped with at least the current information to predict an optimization parameter of the autonomous vehicle or a driving state of the autonomous vehicle in a predetermined period of time in the future. (Matsumoto (984) Paragraph 22 trajectory simulation is on multiple trajectories that predict vehicle position in the future “The trajectory simulator 107 executes, on each of the candidate traveling trajectories generated by the candidate trajectory generation part 102”)
Matsumoto (984) does not appear to explicitly disclose: in response to receiving a simulation request from the autonomous vehicle.
However, Bagnell (020) teaches:
in response to receiving a simulation request from the autonomous vehicle, performing a simulation (Col. 18, Lines 43 – 55 “the user input detected by the user input engine 290 can include a type of simulation to be performed during the simulated episode of locomotion of the simulated AV based on a given one of the ML model simulated training instance(s). For example, the user input can indicate that the initial demonstrator state instance should correspond to a vehicle beginning to stop at a yellow light, a vehicle beginning to yield to a pedestrian or bicyclist, a vehicle beginning to change lanes, a vehicle maintaining a lane, or a vehicle performing other actions.”)
It would have been obvious to one of ordinary skill in the art to combine the method of trajectory simulations disclosed by Matsumoto (984) with the selection of type of simulation to be performed disclosed by Bagnell (020). One of ordinary skill in the art would have been motivated to make this modification in order desirably perform simulation of desired vehicle scenarios (Bagnell (020) Col. 18, Lines 43 – 55).
With regard to claim 4, Matsumoto (984) in view of Bagnell (020), and further in view of Aoude (395) teaches all the elements of the parent claim 3, and further teaches wherein receiving the simulation request comprises at least one of:
receiving the simulation request periodically from the autonomous vehicle; or receiving the simulation request from the autonomous vehicle when the autonomous vehicle is in a specific driving state. (Matsumoto (984) Paragraph 25 simulation is re-executed as vehicle situation changes “The resetting determination part 110 determines whether to reset the traveling trajectory determined by the trajectory evaluation part 108, based on the current situation of the target vehicle. If the resetting determination part 110 determines to reset the traveling trajectory, the server 100 executes, via the trajectory simulator 107, trajectory simulations again”)
With regard to claim 7, Matsumoto (984) in view of Bagnell (020), and further in view of Aoude (395) teaches all the elements of the parent claim 6, and further teaches:
wherein the simulation request comprises a simulation type parameter indicating that the simulation is the parameter optimization simulation, and performing simulation comprises: (Bagnell (020) Col. 18, Lines 43 – 55 type of simulation is used for training and can include collision avoidance)
running multiple simulation environments in parallel mapped with at least the current information, wherein each simulation environment uses a different simulation parameter; and (Matsumoto (984) Paragraph 17 candidate trajectory uses vehicle position information (mapped with current information) “For example, the candidate trajectory generation part 102 obtains information indicating a current position of the target vehicle from the onboard device 300, and generates a plurality of candidate traveling trajectories”, and Paragraph 22 trajectory simulation based on different candidate trajectories (different simulation parameter) to calculate behaviors and current road conditions (mapped with current information) “The trajectory simulator 107 executes, on each of the candidate traveling trajectories generated by the candidate trajectory generation part 102 … The trajectory simulation may be performed in consideration of external factors, such as the conditions of a road surface and/or behaviors of an obstacle existing in its surrounding area”)
determining the optimization parameter based on a simulation result obtained by each simulation environment. (Matusomoto (984) Paragraph 23 optimum trajectory based on simulation is evaluated “For example, the trajectory evaluation part 108 sets evaluation values on the candidate traveling trajectories based on the behaviors of the target vehicle in the candidate traveling trajectories obtained as a result of the trajectory simulations, and determines, as a traveling trajectory of the target vehicle, a candidate traveling trajectory having the lowest evaluation value, i.e., a candidate traveling trajectory that gives the lowest load on the target vehicle”)
It would have been obvious to one of ordinary skill in the art to combine the method of trajectory simulations disclosed by Matsumoto (984) with the selection of type of simulation to be performed disclosed by Bagnell (020). One of ordinary skill in the art would have been motivated to make this modification in order desirably perform simulation of desired vehicle scenarios (Bagnell (020) Col. 18, Lines 43 – 55).
With regard to claim 8, Matsumoto (984) in view of Bagnell (020), and further in view of Aoude (395) teaches all the elements of the parent claim 7, and further teaches: wherein the optimization parameter comprises at least one of: a parameter for a perception module of the autonomous vehicle, a parameter for a planning module of the autonomous vehicle, or a parameter for a control module of the autonomous vehicle. (Matsumoto (984) Paragraph 45 traveling trajectory received by vehicle used for control (for a control module) “In step S109, the onboard device 300 executes a control for causing the target vehicle to move autonomously following the traveling trajectory, based on the traveling trajectory information received from the server 100 in step S108.”)
With regard to claim 12, Matsumoto (984) in view of Bagnell (020), and further in view of Aoude (395) teaches all the elements of the parent claim 11, and further teaches:
a simulation request comprises a simulation type parameter indicating that the simulation is at least one of a warning simulation or a parameter optimization simulation. (Bagnell (020) Col. 18, Lines 43 – 55 type of simulation is used for training (a parameter optimization simulation) and can include collision avoidance (a warning simulation) “the user input detected by the user input engine 290 can include a type of simulation to be performed during the simulated episode of locomotion of the simulated AV based on a given one of the ML model simulated training instance(s). For example, the user input can indicate that the initial demonstrator state instance should correspond to a vehicle beginning to stop at a yellow light, a vehicle beginning to yield to a pedestrian or bicyclist, a vehicle beginning to change lanes, a vehicle maintaining a lane, or a vehicle performing other actions.”)
It would have been obvious to one of ordinary skill in the art to combine the method of trajectory simulations disclosed by Matsumoto (984) with the selection of type of simulation to be performed disclosed by Bagnell (020). One of ordinary skill in the art would have been motivated to make this modification in order desirably perform simulation of desired vehicle scenarios (Bagnell (020) Col. 18, Lines 43 – 55).
With regard to claim 15, Matsumoto (984) in view of Bagnell (020), and further in view of Aoude (395) teaches all the elements of the parent claim 13, and further teaches wherein generating control information for the autonomous vehicle based on the prediction information comprises:
in response to the prediction information comprising an optimization parameter for the autonomous vehicle, generating the control information for updating a current parameter of the autonomous vehicle with the optimization parameter. (Matsumoto (984) Paragraph 45 traveling trajectory received by vehicle used for control (updating a current parameter of autonomous vehicle) “In step S109, the onboard device 300 executes a control for causing the target vehicle to move autonomously following the traveling trajectory, based on the traveling trajectory information received from the server 100 in step S108.”)
Claims 14 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Matsumoto (984) in view of Bagnell (020), and further in view of Aoude (395), and further in view of Wang et al. (US 2021/0001887) (henceforth “Wang (887)”), and further in view of Wang et al. (US 2021/0134154) (henceforth “Wang (154)”). Matsumoto (984) and Bagnell (020) and Aoude (395) and Wang (887) and Wang (154) are analogous art because they solve the same problem of controlling an autonomous vehicle, and because they are from the same field of autonomous vehicle control.
With regard to claim 14, Matsumoto (984) in view of Bagnell (020), and further in view of Aoude (395) teaches all the elements of the parent claim 13, and does not appear to explicitly disclose: wherein generating control information for the autonomous vehicle based on the prediction information comprises: in responding to the prediction information comprising the predicted driving state of the autonomous vehicle for the predetermined period of time in the future: in response to the predicted driving state comprising a controllable abnormality, generating control information indicating to record the controllable abnormality.
However, Wang (887) teaches:
in responding to prediction information comprising predicted driving state of autonomous vehicle for predetermined period of time in the future:
in response to the predicted driving state comprising a controllable abnormality, generating control information indicating to record the controllable abnormality; and (Abstract “in response to determining that the autonomous driving vehicle is in the abnormal operation status, the braking control instruction being used for controlling braking of the autonomous driving vehicle, and the data acquisition instruction being used for acquiring data of a driving recorder in the autonomous driving vehicle.”)
It would have been obvious to one of ordinary skill in the art to combine the method of trajectory simulations disclosed by Matsumoto (984) in view of Bagnell (020) with the recording during an anomaly disclosed by Wang (887). One of ordinary skill in the art would have been motivated to make this modification in order to desirably acquire data during an abnormal operation (Wang (877) Paragraph 36).
Matsumoto (984) in view of Bagnell (020), and further in view of Aoude (395), and further in view of Wang (887) does not appear to explicitly disclose: in response to the predicted driving state comprising an uncontrollable abnormality, generating the control information for terminating the autonomous driving state of the autonomous vehicle.
However, Wang (154) teaches:
in response to a predicted driving state comprising an uncontrollable abnormality, generating control information for terminating autonomous driving state of autonomous vehicle. (Paragraph 36 vehicle failures (uncontrollable anomaly) prompts manual intervention (terminating driving state of autonomous vehicle) “In addition, the current driving control methods for intelligent driving vehicle are focused on manual intervention after vehicle failures, and cannot provide assistance and guidance for driving the intelligent network vehicle based on massive traffic data”)
It would have been obvious to one of ordinary skill in the art to combine the method of trajectory simulations disclosed by Matsumoto (984) in view of Bagnell (020), and further in view of Aoude (395) and further in view of Wang (887) with the manual intervention upon a autonomous vehicle failure disclosed by Wang (154). One of ordinary skill in the art would have been motivated to make this modification in order to address a failure of autonomous vehicle control (Wang (154) Paragraph 36).
With regard to claim 21, Matsumoto (984) in view of Bagnell (020), and further in view of Aoude (395) teaches all the elements of the parent claim 1, and does not appear to explicitly disclose: wherein the current information comprises module operating state information, the module operating state information comprises at least one of: information indicating that a running result of each module is normal; information indicating that the running result of each module is abnormal; a running frame rate of each module; or a warning log of each module.
However, Wang (877) teaches:
wherein current information comprises module operating state information, the module operating state information comprises at least one of:
information indicating that a running result of each module is normal;
information indicating that the running result of each module is abnormal;
a running frame rate of each module; or a warning log of each module. (Wang (877) Abstract “in response to determining that the autonomous driving vehicle is in the abnormal operation status, the braking control instruction being used for controlling braking of the autonomous driving vehicle, and the data acquisition instruction being used for acquiring data of a driving recorder in the autonomous driving vehicle.”)
It would have been obvious to one of ordinary skill in the art to combine the method of trajectory simulations disclosed by Matsumoto (984) in view of Bagnell (020) with the recording during an anomaly disclosed by Wang (887). One of ordinary skill in the art would have been motivated to make this modification in order to desirably acquire data during an abnormal operation (Wang (877) Paragraph 36).
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Matsumoto (984) in view of Bagnell (020), and further in view of Aoude (395), and further in view of Thompson et al. (US 2022/0242422) (henceforth “Thompson (422)”). Matsumoto (984) and Bagnell (020) and Aoude (395) and Thompson (422) are analogous art because they solve the same problem of controlling an autonomous vehicle, and because they are from the same field of autonomous vehicle control.
With regard to claim 22, Matsumoto (984) in view of Bagnell (020), and further in view of Aoude (395) teaches all the elements of the parent claim 8, and further teaches: an engine moment (Matsumoto (984) Paragraph 21 “The characteristic information is information indicating characteristics of the vehicles related to autonomous movement, and includes information indicating the sizes, weights, yaw inertia moments, distances between axles, gravity center positions, and cornering power, for example.”) (Bagnell (020) Col. 12, Lines 47 – 49 “In such implementations, prime mover 104 may include one or more electric motors or an internal combustion engine ( among others”)
Matsumoto (984) in view of Bagnell (020), and further in view of Aoude (395) does not appear to explicitly disclose: wherein the optimization parameter for the control module of the autonomous vehicle comprises optimizing parameters of friction coefficient and engine moment.
However, Thompson (422) teaches: wherein an optimization parameter for a control module of the autonomous vehicle comprises optimizing parameters of friction coefficient and other external parameters. (Thompson (422) Paragraph 5 “The model predictive controller can further operate more effectively by utilizing learned external parameters to consider changes external to the vehicle (such as changes in friction between the vehicle and the road, which could be caused by adverse or favorable weather).”, and Paragraph 8 “The at least one external parameter can be selected from a group consisting of: a friction coefficient between at least one tire and the road, a gravitational constant, a road surface roughness, an external humidity, a wind vector, and an external temperature.”)
It would have been obvious to one of ordinary skill in the art to combine the method of trajectory simulations disclosed by Matsumoto (984) in view of Bagnell (020), and further in view of Aoude (395) with the estimating external parameters disclosed by Thompson (422). One of ordinary skill in the art would have been motivated to make this modification in order to desirably acquire data during a simulation (Thompson (422) Abstract).
With regard to claim 23, Matsumoto (984) in view of Bagnell (020), and further in view of Aoude (395) teaches all the elements of the parent claim 8, and further teaches: wherein the optimization parameter for the perception module of the autonomous vehicle comprises optimizing parameters of a vehicle's maximum recognition distance and minimum recognition precision. (Matsumoto (984) Paragraph 45 traveling trajectory received by vehicle used for control (for a control module), where the rejection does not rely on the perception module of the parent claim in the alternative and which is further limited by claim 23 “In step S109, the onboard device 300 executes a control for causing the target vehicle to move autonomously following the traveling trajectory, based on the traveling trajectory information received from the server 100 in step S108.”)
Examiner General Comments
With regard to the prior art rejection(s), any cited portion of the relied upon reference(s), either by pointing to specific sections or as quotations, is intended to be interpreted in the context of the reference(s) as a whole as would be understood by one of ordinary skill in the art. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention since the entire reference is considered to provide disclosure relating to the cited portions. Further, the claims and only the claims form the metes and bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner’s notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent and spirit of compact prosecution.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Mintz, Y. (US 2022/0319312) teaches network decomposition enabling to run synchronously multiple parts of the network in parallel with the aim to shorten run time of simulated predictions and further to apply more iterations under real time constraints.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ALFRED H. WECHSELBERGER/ExaminerArt Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
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