DETAILED ACTION
This communication is a Non-Final Office Action on the Merits. Claims 1-20 as originally filed are pending and have been considered as follows.
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 .
Drawings
The drawings are objected to because Figure 14 includes a foreign language character instead of “No” between “Whether the predicted obstacle …” and “Adjusting the predicted obstacle state …”.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
Applicant is reminded of proper language and format for an abstract (see MPEP § 608.01(b)):
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because “An embodiment of the present disclosure discloses” is a phrase which can be implied and should be avoided. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text.
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” in that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim Objections
Claim 14 is objected to because of the following informalities: “instruction” in line 5 should be “instructions” consistent with lines 3 and 4. Appropriate correction is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1 and 13-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Van Den Broek (US Patent No. 8,626,398).
As per Claim 1, Van Den Broek discloses a method (Fig. 3) for determining a collision risk state (as per 37) (Fig. 3; 6:46-8:64), comprising:
determining first ego vehicle state information (as per 31) of a vehicle (20) at a current time frame (as per “current time” in 5:8, as per “initial values” in 6:54) and first obstacle state information (as per 34) of an obstacle (22) around the vehicle (20) at the current time frame (as per “current time” in 5:8, as per “initial values” in 6:54) (Figs. 1-3; 4:15-60, 5:1-26, 6:46-59);
predicting, based on the first ego vehicle state information (as per 31), first position probability distribution information (as per 32) of the vehicle (20) at a future time frame (as per “for a future time” in 5:17, as per “for a future time point” in 6:53) (Figs. 1-3; 4:15-60, 5:1-26, 6:46-59);
predicting, based on the first obstacle state information (as per 34), second position probability distribution information (as per 35) of the obstacle (22) at the future time frame (as per “for a future time” in 5:17, as per “at the future time point” in 8:15-16) (Figs. 1-3; 4:15-60, 5:1-26, 6:46-8:18); and
determining the collision risk state (as per 37) of the vehicle (20) with the obstacle (22) based on the first position probability distribution information (as per 31) and the second position probability distribution information (as per 35) corresponding to the future time frame (as per “for a future time” in 5:17, as per “for a future time point” in 6:53, as per “at the future time point” in 8:15-16) (Figs. 1-3; 4:15-60, 5:1-26, 6:46-8:64).
As per Claim 13, Van Den Broek discloses a computer-readable storage medium (as per “program memory” in 11:39-40) having stored thereon a computer program (as per “program” in 11:40), which, when executed by a processor (12), cause the processor (12) to implement a method (Fig. 3) for determining a collision risk state (as per 37) (Figs. 1, 3; 6:46-8:64, 11:27-51), comprising:
determining first ego vehicle state information (as per 31) of a vehicle (20) at a current time frame (as per “current time” in 5:8, as per “initial values” in 6:54) and first obstacle state information (as per 34) of an obstacle (22) around the vehicle (20) at the current time frame (as per “current time” in 5:8, as per “initial values” in 6:54) (Figs. 1-3; 4:15-60, 5:1-26, 6:46-59);
predicting, based on the first ego vehicle state information (as per 31), first position probability distribution information (as per 32) of the vehicle (20) at a future time frame (as per “for a future time” in 5:17, as per “for a future time point” in 6:53) (Figs. 1-3; 4:15-60, 5:1-26, 6:46-59);
predicting, based on the first obstacle state information (as per 34), second position probability distribution information (as per 35) of the obstacle (22) at the future time frame (as per “for a future time” in 5:17, as per “at the future time point” in 8:15-16) (Figs. 1-3; 4:15-60, 5:1-26, 6:46-8:18); and
determining the collision risk state (as per 37) of the vehicle (20) with the obstacle (22) based on the first position probability distribution information (as per 31) and the second position probability distribution information (as per 35) corresponding to the future time frame (as per “for a future time” in 5:17, as per “for a future time point” in 6:53, as per “at the future time point” in 8:15-16) (Figs. 1-3; 4:15-60, 5:1-26, 6:46-8:64).
As per Claim 14, Van Den Broek discloses an electronic device (Fig. 1; 4:15-38, 11:27-51), comprising:
a processor (12) (Fig. 1; 4:15-38, 11:27-51); and
a memory (as per “program memory” in 11:39-40) configured for storing executable instructions (as per “program” in 11:40) of the processor (12) (Fig. 1; 4:15-38, 11:27-51),
wherein the processor (12) is configured to read the executable instructions (as per “program” in 11:40) from the memory (as per “program memory” in 11:39-40) and execute the instruction (as per “program” in 11:40) to implement a method (Fig. 3) for determining a collision risk state (as per 37) (Figs. 1, 3; 6:46-8:64, 11:27-51), comprising:
determining first ego vehicle state information (as per 31) of a vehicle (20) at a current time frame (as per “current time” in 5:8, as per “initial values” in 6:54) and first obstacle state information (as per 34) of an obstacle (22) around the vehicle (20) at the current time frame (as per “current time” in 5:8, as per “initial values” in 6:54) (Figs. 1-3; 4:15-60, 5:1-26, 6:46-59);
predicting, based on the first ego vehicle state information (as per 31), first position probability distribution information (as per 32) of the vehicle (20) at a future time frame (as per “for a future time” in 5:17, as per “for a future time point” in 6:53) (Figs. 1-3; 4:15-60, 5:1-26, 6:46-59);
predicting, based on the first obstacle state information (as per 34), second position probability distribution information (as per 35) of the obstacle (22) at the future time frame (as per “for a future time” in 5:17, as per “at the future time point” in 8:15-16) (Figs. 1-3; 4:15-60, 5:1-26, 6:46-8:18); and
determining the collision risk state (as per 37) of the vehicle (20) with the obstacle (22) based on the first position probability distribution information (as per 31) and the second position probability distribution information (as per 35) corresponding to the future time frame (as per “for a future time” in 5:17, as per “for a future time point” in 6:53, as per “at the future time point” in 8:15-16) (Figs. 1-3; 4:15-60, 5:1-26, 6:46-8:64).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 2-5, 8-12, 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Van Den Broek (US Patent No. 8,626,398) in view of Berntorp (US Pub. No. 2018/0284785), further in view of Kamann (WO 2021/083464 A1; citations to corresponding US Pub. No. 2023/0034560).
As per Claim 2, Van Den Broek discloses all limitations of Claim 1. Van Den Broek does not expressly disclose wherein the predicting, based on the first ego vehicle state information, first position probability distribution information of the vehicle at a future time frame comprises:
determining, based on the first ego vehicle state information, a pre-set quantity of first sampling states corresponding to the current time frame and a first weights respectively corresponding to the first sampling states by using an unscented transformation;
for each of the first sampling states, determining a first predicted state corresponding to the first sampling state at the future time frame based on the first sampling state and an ego vehicle state transition function of the vehicle; and
determining the first position probability distribution information of the vehicle at the future time frame based on the first predicted state and the first weight respectively corresponding to each of the first sampling states.
Berntorp discloses a control system (199) for controlling a host vehicle (010/200) traveling in an environment shared with other vehicles (090) (Figs. 1A, 2A; ¶47, 55-58). The control system (199) includes: a motion-planning system (240) for determining control inputs corresponding to future motion of the vehicle (010/200); and a threat assessment system (220) that operates to determine feasible trajectories of vehicles traveling around the host vehicle (010/200) in view of information (244) including state transitions of the host vehicle (Figs. 2A, 6A-B; ¶55-60, ¶88-89). Evaluating the trajectories of such vehicles involves determining a set of sampled states (600) in order to confirm that the information (244) satisfies specified constraints of the vehicle (Fig. 6A-B; ¶87-92). Like Van Den Broek, Berntorp is concerned with vehicle control systems.
Kamann discloses a motor vehicle (1) that includes a collision device for the detection of an imminent collision with a remote target vehicle (2) (Fig. 1; ¶93-94). The collision device performs operations (100) including receiving data (101) describing the environment of the vehicle (1) and estimating (108) the current position of the target vehicle (2) (Fig. 2; ¶96-98, 118-123). Estimating (108) the current position of the target vehicle (2) involves a tracking filter featuring multiple unscented Kalman filters each having a specified weighting (¶118). In this way, the system is adapted to address different possible situations and take account of these accordingly so that a flexible adaptation to a specific situation is advantageously brought about and tracking which is as accurate as possible is achieved (¶123). Like Van Den Broek, Kamann is concerned with vehicle control systems.
Therefore, from these teachings of Van Den Broek, Berntorp, and Kamann, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Berntorp and Kamann to the system of Van Den Broek since doing so would enhance the system by: providing data that satisfies specified constraints of the vehicle; improving accuracy of information for tracking. Applying the teachings of Berntorp and Kamann to the system of Van Den Broek would result in a system that operates wherein the predicting, based on the first ego vehicle state information, first position probability distribution information of the vehicle at a future time frame comprises:
“determining, based on the first ego vehicle state information, a pre-set quantity of first sampling states corresponding to the current time frame and a first weights respectively corresponding to the first sampling states by using an unscented transformation” in that data informing operation of the system of Van Den Broek would be sampled as per Berntorp and evaluated in view of Kalman filters with appropriate weights as per Kamann;
“for each of the first sampling states, determining a first predicted state corresponding to the first sampling state at the future time frame based on the first sampling state and an ego vehicle state transition function of the vehicle” in that data informing operation of the system of Van Den Broek would be sampled as per Berntorp; and
“determining the first position probability distribution information of the vehicle at the future time frame based on the first predicted state and the first weight respectively corresponding to each of the first sampling states” in that data informing operation of the system of Van Den Broek would be sampled as per Berntorp and evaluated in view of Kalman filters with appropriate weights as per Kamann.
As per Claim 3, the combination of Van Den Broek, Berntorp, and Kamann teaches or suggests all limitations of Claim 2. Van Den Broek further discloses determining a probability density function involves a covariance matrix (4:61-67). Van Den Broek does not expressly disclose wherein the first weight comprises a first mean weight and a first variance weight; and
the determining the first position probability distribution information of the vehicle at the future time frame based on the first predicted state and the first weight respectively corresponding to each of the first sampling states comprises:
determining the first state mean of the vehicle at the future time frame based on the first predicted state and the first mean weight respectively corresponding to each of the first sampling states;
determining a first covariance matrix of the vehicle at the future time frame based on the first state mean, the first predicted state respectively corresponding to each of the first sampling states and the first variance weight; and
determining the first position probability distribution information of the vehicle at the future time frame based on the first state mean and the first covariance matrix.
See rejection of Claim 2 for discussion of teachings of Berntorp. Berntorp further discloses wherein determining trajectory information includes a neural network trained by statistical values including mean and variance (¶111-114).
See rejection of Claim 2 for discussion of teachings of Kamann.
Therefore, from these teachings of Van Den Broek, Berntorp, and Kamann, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Berntorp and Kamann to the system of Van Den Broek since doing so would enhance the system by: providing data that satisfies specified constraints of the vehicle; improving accuracy of information for tracking.
Applying the teachings of Berntorp and Kamann to the system of Van Den Broek would result in a system that operates: “wherein the first weight comprises a first mean weight and a first variance weight” in that data informing operation of the system of Van Den Broek would be sampled and trained as per Berntorp and evaluated in view of Kalman filters with appropriate weights as per Kamann.
Further, applying the teachings of Berntorp and Kamann to the system of Van Den Broek would result in a system that operates wherein the determining the first position probability distribution information of the vehicle at the future time frame based on the first predicted state and the first weight respectively corresponding to each of the first sampling states comprises:
“determining the first state mean of the vehicle at the future time frame based on the first predicted state and the first mean weight respectively corresponding to each of the first sampling states” in that data informing operation of the system of Van Den Broek would be sampled and trained as per Berntorp and evaluated in view of Kalman filters with appropriate weights as per Kamann;
“determining a first covariance matrix of the vehicle at the future time frame based on the first state mean, the first predicted state respectively corresponding to each of the first sampling states and the first variance weight” in that data informing operation of the system of Van Den Broek would be sampled and trained as per Berntorp and evaluated in view of Kalman filters with appropriate weights as per Kamann; and
“determining the first position probability distribution information of the vehicle at the future time frame based on the first state mean and the first covariance matrix” in that data informing operation of the system of Van Den Broek would be sampled and trained as per Berntorp and evaluated in view of Kalman filters with appropriate weights as per Kamann.
As per Claim 4, the combination of Van Den Broek, Berntorp, and Kamann teaches or suggests all limitations of Claim 2. Van Den Broek does not expressly disclose wherein after determining a first predicted state corresponding to the first sampling state at the future time frame based on the first sampling state and an ego vehicle state transition function of the vehicle, the method further comprises:
taking any of the first sampling states as a target first sampling state, and determining whether the first predicted state corresponding to the target first sampling state at the future time frame satisfies a first pre-set condition; and
in response to the first predicted state not satisfying the first pre-set condition, adjusting the first predicted state based on the first pre-set condition to take the adjusted predicted state as the first predicted state.
See rejection of Claim 2 for discussion of teachings of Berntorp. Berntorp further discloses wherein states generated from a noise source are corrected to better satisfy the intentions (¶93).
See rejection of Claim 2 for discussion of teachings of Kamann.
Therefore, from these teachings of Van Den Broek, Berntorp, and Kamann, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Berntorp and Kamann to the system of Van Den Broek since doing so would enhance the system by: providing data that satisfies specified constraints of the vehicle; improving accuracy of information for tracking. Applying the teachings of Berntorp and Kamann to the system of Van Den Broek would result in a system that operates wherein after determining a first predicted state corresponding to the first sampling state at the future time frame based on the first sampling state and an ego vehicle state transition function of the vehicle, the method further comprises:
“taking any of the first sampling states as a target first sampling state, and determining whether the first predicted state corresponding to the target first sampling state at the future time frame satisfies a first pre-set condition” in that data informing operation of the system of Van Den Broek would be sampled and evaluated as per Berntorp; and
“in response to the first predicted state not satisfying the first pre-set condition, adjusting the first predicted state based on the first pre-set condition to take the adjusted predicted state as the first predicted state” in that data informing operation of the system of Van Den Broek would be sampled and corrected as per Berntorp.
As per Claim 5, Van Den Broek discloses all limitations of Claim 1. Van Den Broek does not expressly disclose wherein the predicting, based on the first obstacle state information of the obstacle, second position probability distribution information of the obstacle at the future time frame comprises:
determining, based on the first state information, a pre-set quantity of second sampling states corresponding to the current time frame and a second weights respectively corresponding to the second sampling states by using an unscented transformation;
for each of the second sampling states, determining a second predicted state corresponding to the second sampling state at the future time frame based on the second sampling state and a target state transition function of the obstacle; and
determining the second position probability distribution information of the obstacle at the future time frame based on the second predicted state and the second weight respectively corresponding to each of the second sampling states.
See rejection of Claim 2 for discussion of teachings of Berntorp. Berntorp further discloses wherein states generated from a noise source are corrected to better satisfy the intentions (¶93).
See rejection of Claim 2 for discussion of teachings of Kamann.
Therefore, from these teachings of Van Den Broek, Berntorp, and Kamann, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Berntorp and Kamann to the system of Van Den Broek since doing so would enhance the system by: providing data that satisfies specified constraints of the vehicle; improving accuracy of information for tracking.
Applying the teachings of Berntorp and Kamann to the system of Van Den Broek would result in a system that operates wherein the predicting, based on the first obstacle state information of the obstacle, second position probability distribution information of the obstacle at the future time frame comprises:
“determining, based on the first state information, a pre-set quantity of second sampling states corresponding to the current time frame and a second weights respectively corresponding to the second sampling states by using an unscented transformation” in that data informing operation of the system of Van Den Broek would be sampled and evaluated as per Berntorp and evaluated in view of Kalman filters with appropriate weights as per Kamann;
“for each of the second sampling states, determining a second predicted state corresponding to the second sampling state at the future time frame based on the second sampling state and a target state transition function of the obstacle” in that data informing operation of the system of Van Den Broek would be sampled and evaluated as per Berntorp; and
“determining the second position probability distribution information of the obstacle at the future time frame based on the second predicted state and the second weight respectively corresponding to each of the second sampling states” in that data informing operation of the system of Van Den Broek would be sampled and evaluated as per Berntorp and evaluated in view of Kalman filters with appropriate weights as per Kamann.
As per Claim 8, the combination of Van Den Broek, Berntorp, and Kamann teaches or suggests all limitations of Claim 5. Van Den Broek does not expressly disclose wherein after the determining a second predicted state corresponding to the second sampling state at the future time frame based on the second sampling state and a target state transition function of the obstacle, the method further comprises:
with regard to any of the second predicted states, determining whether the second predicted state satisfies a second pre-set condition; and
in response to the second predicted state not satisfying the second pre-set condition, adjusting the second predicted state based on the second pre-set condition to take the adjusted predicted state as the second predicted state.
See rejection of Claim 2 for discussion of teachings of Berntorp. Berntorp further discloses wherein states generated from a noise source are corrected to better satisfy the intentions (¶93).
See rejection of Claim 2 for discussion of teachings of Kamann.
Therefore, from these teachings of Van Den Broek, Berntorp, and Kamann, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Berntorp and Kamann to the system of Van Den Broek since doing so would enhance the system by: providing data that satisfies specified constraints of the vehicle; improving accuracy of information for tracking. Applying the teachings of Berntorp and Kamann to the system of Van Den Broek would result in a system that operates wherein after the determining a second predicted state corresponding to the second sampling state at the future time frame based on the second sampling state and a target state transition function of the obstacle, the method further comprises:
“with regard to any of the second predicted states, determining whether the second predicted state satisfies a second pre-set condition” in that data informing operation of the system of Van Den Broek would be sampled and evaluated as per Berntorp; and
“in response to the second predicted state not satisfying the second pre-set condition, adjusting the second predicted state based on the second pre-set condition to take the adjusted predicted state as the second predicted state” in that data informing operation of the system of Van Den Broek would be sampled and corrected as per Berntorp.
As per Claim 9, Van Den Broek discloses all limitations of Claim 1. Van Den Broek does not expressly disclose wherein the determining first ego vehicle state information of a vehicle at a current time frame and first obstacle state information of an obstacle around the vehicle at the current time frame comprises:
determining second ego vehicle state information of the vehicle at the current time frame and second obstacle state information of the obstacle at the current time frame based on sensor data collected by a sensor on the vehicle;
performing noise reduction processing on the second ego vehicle state information and the second obstacle state information to obtain the noise-reduced third ego vehicle state information and third obstacle state information;
performing a completeness check on the third ego vehicle state information and the third obstacle state information to obtain a check result; and
determining the first ego vehicle state information of the vehicle and the first obstacle state information of the obstacle based on the third ego vehicle state information, the third obstacle state information, and the check result.
See rejection of Claim 2 for discussion of teachings of Berntorp. Berntorp further discloses wherein states generated from a noise source are corrected to better satisfy the intentions (¶93).
See rejection of Claim 2 for discussion of teachings of Kamann.
Therefore, from these teachings of Van Den Broek, Berntorp, and Kamann, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Berntorp and Kamann to the system of Van Den Broek since doing so would enhance the system by: providing data that satisfies specified constraints of the vehicle; improving accuracy of information for tracking. Applying the teachings of Berntorp and Kamann to the system of Van Den Broek would result in a system that operates wherein the determining first ego vehicle state information of a vehicle at a current time frame and first obstacle state information of an obstacle around the vehicle at the current time frame comprises:
“determining second ego vehicle state information of the vehicle at the current time frame and second obstacle state information of the obstacle at the current time frame based on sensor data collected by a sensor on the vehicle” in that data informing operation of the system of Van Den Broek would be sampled and evaluated as per Berntorp;
“performing noise reduction processing on the second ego vehicle state information and the second obstacle state information to obtain the noise-reduced third ego vehicle state information and third obstacle state information” in that data informing operation of the system of Van Den Broek would be sampled and corrected as per Berntorp;
“performing a completeness check on the third ego vehicle state information and the third obstacle state information to obtain a check result” in that data informing operation of the system of Van Den Broek would be sampled and corrected as per Berntorp; and
“determining the first ego vehicle state information of the vehicle and the first obstacle state information of the obstacle based on the third ego vehicle state information, the third obstacle state information, and the check result” in that data informing operation of the system of Van Den Broek would be sampled and corrected as per Berntorp.
As per Claim 10, the combination of Van Den Broek, Berntorp, and Kamann teaches or suggests all limitations of Claim 9. Van Den Broek does not expressly disclose wherein the performing noise reduction processing on the second ego vehicle state information and the second obstacle state information of the obstacle to obtain the noise-reduced third ego vehicle state information and third obstacle state information of the obstacle comprises:
performing noise reduction processing on the second ego vehicle state information based on an unscented Kalman filtering to obtain the third ego vehicle state information; and
performing noise reduction processing on the second obstacle state information based on the unscented Kalman filtering to obtain the third obstacle state information.
See rejection of Claim 2 for discussion of teachings of Berntorp. Berntorp further discloses wherein states generated from a noise source are corrected to better satisfy the intentions (¶93).
See rejection of Claim 2 for discussion of teachings of Kamann.
Therefore, from these teachings of Van Den Broek, Berntorp, and Kamann, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Berntorp and Kamann to the system of Van Den Broek since doing so would enhance the system by: providing data that satisfies specified constraints of the vehicle; improving accuracy of information for tracking. Applying the teachings of Berntorp and Kamann to the system of Van Den Broek would result in a system that operates wherein the performing noise reduction processing on the second ego vehicle state information and the second obstacle state information of the obstacle to obtain the noise-reduced third ego vehicle state information and third obstacle state information of the obstacle comprises:
“performing noise reduction processing on the second ego vehicle state information based on an unscented Kalman filtering to obtain the third ego vehicle state information” in that data informing operation of the system of Van Den Broek would be sampled and corrected as per Berntorp and evaluated in view of Kalman filters as per Kamann; and
“performing noise reduction processing on the second obstacle state information based on the unscented Kalman filtering to obtain the third obstacle state information” in that data informing operation of the system of Van Den Broek would be sampled and corrected as per Berntorp and evaluated in view of Kalman filters as per Kamann.
As per Claim 11, the combination of Van Den Broek, Berntorp, and Kamann teaches or suggests all limitations of Claim 10. Van Den Broek does not expressly disclose wherein before the performing noise reduction processing on the second ego vehicle state information based on an unscented Kalman filtering to obtain the third ego vehicle state information, the method further comprises:
performing data cleaning on the second ego vehicle state information to obtain the cleaned ego vehicle state information;
performing data cleaning on the second obstacle state information to obtain the cleaned obstacle state information; and
the performing noise reduction processing on the second ego vehicle state information based on an unscented Kalman filtering to obtain the third ego vehicle state information comprises:
performing filtering processing on the cleaned ego vehicle state information based on the unscented Kalman filtering to obtain the third ego vehicle state information; an
the performing noise reduction processing on the second obstacle state information based on the unscented Kalman filtering to obtain the third obstacle state information comprises: performing filtering processing on the cleaned obstacle state information based on the unscented Kalman filtering to obtain the third obstacle state information.
See rejection of Claim 2 for discussion of teachings of Berntorp. Berntorp further discloses wherein states generated from a noise source are corrected to better satisfy the intentions (¶93).
See rejection of Claim 2 for discussion of teachings of Kamann.
Therefore, from these teachings of Van Den Broek, Berntorp, and Kamann, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Berntorp and Kamann to the system of Van Den Broek since doing so would enhance the system by: providing data that satisfies specified constraints of the vehicle; improving accuracy of information for tracking.
Applying the teachings of Berntorp and Kamann to the system of Van Den Broek would result in a system that operates wherein before the performing noise reduction processing on the second ego vehicle state information based on an unscented Kalman filtering to obtain the third ego vehicle state information, the method further comprises:
“performing data cleaning on the second ego vehicle state information to obtain the cleaned ego vehicle state information” in that data informing operation of the system of Van Den Broek would be sampled and corrected as per Berntorp; and
“performing data cleaning on the second obstacle state information to obtain the cleaned obstacle state information” in that data informing operation of the system of Van Den Broek would be sampled and corrected as per Berntorp.
Further, applying the teachings of Berntorp and Kamann to the system of Van Den Broek would result in a system that operates wherein the performing noise reduction processing on the second ego vehicle state information based on an unscented Kalman filtering to obtain the third ego vehicle state information comprises: “performing filtering processing on the cleaned ego vehicle state information based on the unscented Kalman filtering to obtain the third ego vehicle state information” in that data informing operation of the system of Van Den Broek would be sampled and corrected as per Berntorp and evaluated in view of Kalman filters as per Kamann.
In addition, applying the teachings of Berntorp and Kamann to the system of Van Den Broek would result in a system that operates wherein the performing noise reduction processing on the second obstacle state information based on the unscented Kalman filtering to obtain the third obstacle state information comprises “performing filtering processing on the cleaned obstacle state information based on the unscented Kalman filtering to obtain the third obstacle state information” in that data informing operation of the system of Van Den Broek would be sampled and corrected as per Berntorp and evaluated in view of Kalman filters as per Kamann.
As per Claim 12, the combination of Van Den Broek, Berntorp, and Kamann teaches or suggests all limitations of Claim 9. Van Den Broek does not expressly disclose wherein the performing noise reduction processing on the second ego vehicle state information and the second obstacle state information to obtain the noise-reduced third ego vehicle state information and third obstacle state information comprises:
determining whether at least one of the second ego vehicle state information and the second obstacle state information satisfies a Gaussian distribution; and
in response to at least one of the second ego vehicle state information and the second obstacle state information not satisfying the Gaussian distribution, based on a particle filtering, performing noise reduction processing on the second ego vehicle state information and the second obstacle state information to obtain the noise-reduced third ego vehicle state information and the third obstacle state information.
See rejection of Claim 2 for discussion of teachings of Berntorp. Berntorp further discloses: wherein states generated from a noise source are corrected to better satisfy the intentions (¶93); wherein a Gaussian distribution is implemented to correct a noise source (¶92-94); and wherein a particle filter is implemented to determine driver intention (¶111-114).
See rejection of Claim 2 for discussion of teachings of Kamann.
Therefore, from these teachings of Van Den Broek, Berntorp, and Kamann, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Berntorp and Kamann to the system of Van Den Broek since doing so would enhance the system by: providing data that satisfies specified constraints of the vehicle; improving accuracy of information for tracking.
Applying the teachings of Berntorp and Kamann to the system of Van Den Broek would result in a system that operates wherein the performing noise reduction processing on the second ego vehicle state information and the second obstacle state information to obtain the noise-reduced third ego vehicle state information and third obstacle state information comprises:
“determining whether at least one of the second ego vehicle state information and the second obstacle state information satisfies a Gaussian distribution” in that data informing operation of the system of Van Den Broek would be sampled and evaluated as per Berntorp; and
“in response to at least one of the second ego vehicle state information and the second obstacle state information not satisfying the Gaussian distribution, based on a particle filtering, performing noise reduction processing on the second ego vehicle state information and the second obstacle state information to obtain the noise-reduced third ego vehicle state information and the third obstacle state information” in that data informing operation of the system of Van Den Broek would be sampled and evaluated as per Berntorp.
As per Claim 15, Van Den Broek discloses all limitations of Claim 14. Van Den Broek does not expressly disclose wherein the predicting, based on the first ego vehicle state information, first position probability distribution information of the vehicle at a future time frame comprises:
determining, based on the first ego vehicle state information, a pre-set quantity of first sampling states corresponding to the current time frame and a first weights respectively corresponding to the first sampling states by using an unscented transformation;
for each of the first sampling states, determining a first predicted state corresponding to the first sampling state at the future time frame based on the first sampling state and an ego vehicle state transition function of the vehicle; and
determining the first position probability distribution information of the vehicle at the future time frame based on the first predicted state and the first weight respectively corresponding to each of the first sampling states.
See rejection of Claim 2 for discussion of teachings of Berntorp.
See rejection of Claim 2 for discussion of teachings of Kamann.
Therefore, from these teachings of Van Den Broek, Berntorp, and Kamann, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Berntorp and Kamann to the system of Van Den Broek since doing so would enhance the system by: providing data that satisfies specified constraints of the vehicle; improving accuracy of information for tracking. Applying the teachings of Berntorp and Kamann to the system of Van Den Broek would result in a system that operates wherein the predicting, based on the first ego vehicle state information, first position probability distribution information of the vehicle at a future time frame comprises:
“determining, based on the first ego vehicle state information, a pre-set quantity of first sampling states corresponding to the current time frame and a first weights respectively corresponding to the first sampling states by using an unscented transformation” in that data informing operation of the system of Van Den Broek would be sampled as per Berntorp and evaluated in view of Kalman filters with appropriate weights as per Kamann;
“for each of the first sampling states, determining a first predicted state corresponding to the first sampling state at the future time frame based on the first sampling state and an ego vehicle state transition function of the vehicle” in that data informing operation of the system of Van Den Broek would be sampled as per Berntorp; and
“determining the first position probability distribution information of the vehicle at the future time frame based on the first predicted state and the first weight respectively corresponding to each of the first sampling states” in that data informing operation of the system of Van Den Broek would be sampled as per Berntorp and evaluated in view of Kalman filters with appropriate weights as per Kamann.
As per Claim 16, the combination of Van Den Broek, Berntorp, and Kamann teaches or suggests all limitations of Claim 15. Van Den Broek further discloses determining a probability density function involves a covariance matrix (4:61-67). Van Den Broek does not expressly disclose wherein the first weight comprises a first mean weight and a first variance weight; and
the determining the first position probability distribution information of the vehicle at the future time frame based on the first predicted state and the first weight respectively corresponding to each of the first sampling states comprises:
determining the first state mean of the vehicle at the future time frame based on the first predicted state and the first mean weight respectively corresponding to each of the first sampling states;
determining a first covariance matrix of the vehicle at the future time frame based on the first state mean, the first predicted state respectively corresponding to each of the first sampling states and the first variance weight; and
determining the first position probability distribution information of the vehicle at the future time frame based on the first state mean and the first covariance matrix.
See rejection of Claim 2 for discussion of teachings of Berntorp. Berntorp further discloses wherein determining trajectory information includes a neural network trained by statistical values including mean and variance (¶111-114).
See rejection of Claim 2 for discussion of teachings of Kamann.
Therefore, from these teachings of Van Den Broek, Berntorp, and Kamann, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Berntorp and Kamann to the system of Van Den Broek since doing so would enhance the system by: providing data that satisfies specified constraints of the vehicle; improving accuracy of information for tracking.
Applying the teachings of Berntorp and Kamann to the system of Van Den Broek would result in a system that operates: “wherein the first weight comprises a first mean weight and a first variance weight” in that data informing operation of the system of Van Den Broek would be sampled and trained as per Berntorp and evaluated in view of Kalman filters with appropriate weights as per Kamann.
Further, applying the teachings of Berntorp and Kamann to the system of Van Den Broek would result in a system that operates wherein the determining the first position probability distribution information of the vehicle at the future time frame based on the first predicted state and the first weight respectively corresponding to each of the first sampling states comprises:
“determining the first state mean of the vehicle at the future time frame based on the first predicted state and the first mean weight respectively corresponding to each of the first sampling states” in that data informing operation of the system of Van Den Broek would be sampled and trained as per Berntorp and evaluated in view of Kalman filters with appropriate weights as per Kamann;
“determining a first covariance matrix of the vehicle at the future time frame based on the first state mean, the first predicted state respectively corresponding to each of the first sampling states and the first variance weight” in that data informing operation of the system of Van Den Broek would be sampled and trained as per Berntorp and evaluated in view of Kalman filters with appropriate weights as per Kamann; and
“determining the first position probability distribution information of the vehicle at the future time frame based on the first state mean and the first covariance matrix” in that data informing operation of the system of Van Den Broek would be sampled and trained as per Berntorp and evaluated in view of Kalman filters with appropriate weights as per Kamann.
As per Claim 17, the combination of Van Den Broek, Berntorp, and Kamann teaches or suggests all limitations of Claim 15. Van Den Broek does not expressly disclose wherein after determining a first predicted state corresponding to the first sampling state at the future time frame based on the first sampling state and an ego vehicle state transition function of the vehicle, the method further comprises:
taking any of the first sampling states as a target first sampling state, and determining whether the first predicted state corresponding to the target first sampling state at the future time frame satisfies a first pre-set condition; and
in response to the first predicted state not satisfying the first pre-set condition, adjusting the first predicted state based on the first pre-set condition to take the adjusted predicted state as the first predicted state.
See rejection of Claim 2 for discussion of teachings of Berntorp. Berntorp further discloses wherein states generated from a noise source are corrected to better satisfy the intentions (¶93).
See rejection of Claim 2 for discussion of teachings of Kamann.
Therefore, from these teachings of Van Den Broek, Berntorp, and Kamann, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Berntorp and Kamann to the system of Van Den Broek since doing so would enhance the system by: providing data that satisfies specified constraints of the vehicle; improving accuracy of information for tracking. Applying the teachings of Berntorp and Kamann to the system of Van Den Broek would result in a system that operates wherein after determining a first predicted state corresponding to the first sampling state at the future time frame based on the first sampling state and an ego vehicle state transition function of the vehicle, the method further comprises:
“taking any of the first sampling states as a target first sampling state, and determining whether the first predicted state corresponding to the target first sampling state at the future time frame satisfies a first pre-set condition” in that data informing operation of the system of Van Den Broek would be sampled and evaluated as per Berntorp; and
“in response to the first predicted state not satisfying the first pre-set condition, adjusting the first predicted state based on the first pre-set condition to take the adjusted predicted state as the first predicted state” in that data informing operation of the system of Van Den Broek would be sampled and corrected as per Berntorp.
As per Claim 18, Van Den Broek discloses all limitations of Claim 14. Van Den Broek does not expressly disclose wherein the predicting, based on the first obstacle state information of the obstacle, second position probability distribution information of the obstacle at the future time frame comprises:
determining, based on the first state information, a pre-set quantity of second sampling states corresponding to the current time frame and a second weights respectively corresponding to the second sampling states by using an unscented transformation;
for each of the second sampling states, determining a second predicted state corresponding to the second sampling state at the future time frame based on the second sampling state and a target state transition function of the obstacle; and
determining the second position probability distribution information of the obstacle at the future time frame based on the second predicted state and the second weight respectively corresponding to each of the second sampling states.
See rejection of Claim 2 for discussion of teachings of Berntorp. Berntorp further discloses wherein states generated from a noise source are corrected to better satisfy the intentions (¶93).
See rejection of Claim 2 for discussion of teachings of Kamann.
Therefore, from these teachings of Van Den Broek, Berntorp, and Kamann, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Berntorp and Kamann to the system of Van Den Broek since doing so would enhance the system by: providing data that satisfies specified constraints of the vehicle; improving accuracy of information for tracking.
Applying the teachings of Berntorp and Kamann to the system of Van Den Broek would result in a system that operates wherein the predicting, based on the first obstacle state information of the obstacle, second position probability distribution information of the obstacle at the future time frame comprises:
“determining, based on the first state information, a pre-set quantity of second sampling states corresponding to the current time frame and a second weights respectively corresponding to the second sampling states by using an unscented transformation” in that data informing operation of the system of Van Den Broek would be sampled and evaluated as per Berntorp and evaluated in view of Kalman filters with appropriate weights as per Kamann;
“for each of the second sampling states, determining a second predicted state corresponding to the second sampling state at the future time frame based on the second sampling state and a target state transition function of the obstacle” in that data informing operation of the system of Van Den Broek would be sampled and evaluated as per Berntorp; and
“determining the second position probability distribution information of the obstacle at the future time frame based on the second predicted state and the second weight respectively corresponding to each of the second sampling states” in that data informing operation of the system of Van Den Broek would be sampled and evaluated as per Berntorp and evaluated in view of Kalman filters with appropriate weights as per Kamann.
Claim 6-7 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Van Den Broek (US Patent No. 8,626,398) in view of Berntorp (US Pub. No. 2018/0284785), further in view of Kamann (WO 2021/083464 A1; citations to corresponding US Pub. No. 2023/0034560), further in view of Nister (US Pub. No. 2019/0243371).
As per Claim 6, the combination of Van Den Broek, Berntorp, and Kamann teaches or suggests all limitations of Claim 5. Van Den Broek does not expressly disclose wherein the determining a second predicted state corresponding to the second sampling state at the future time frame based on the second sampling state and a target state transition function of the obstacle comprises:
determining an object type of the obstacle;
determining the target state transition function of the obstacle based on the object type of the obstacle and a relationship between different types and the state transition function; and
determining the second predicted state corresponding to each of the second sampling states at the future time frame respectively based on each of the second sampling states and the target state transition function.
See rejection of Claim 2 for discussion of teachings of Berntorp.
See rejection of Claim 2 for discussion of teachings of Kamann.
Nister discloses a vehicle (102) having obstacle avoidance components (128) including a trajectory generator (138) that generates trajectories (306, 308) for the vehicle (102) and trajectories (312, 314) for an object (106) sensed by the vehicle (102) (Figs. 1, 3D; ¶57-58, 62-63, 75, 111-112). Trajectories (312, 324) for the object (106) are informed by information about the object (106) including type, size, and other information about the object (106) as perceived and/or determined by the vehicle (102) (Fig. 3D; ¶111). In this way, safety procedures appropriate for different types of objects are implemented (Figs. 4E; ¶123). Like Van Den Broek, Nister is concerned with vehicle control systems.
Therefore, from these teachings of Van Den Broek, Berntorp, Kamann, and Nister, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Berntorp, Kamann, and Nister to the system of Van Den Broek since doing so would enhance the system by: providing data that satisfies specified constraints of the vehicle; improving accuracy of information for tracking; and providing appropriate safety procedures. Applying the teachings of Berntorp, Kamann, and Nister to the system of Van Den Broek would result in a system that operates wherein the determining a second predicted state corresponding to the second sampling state at the future time frame based on the second sampling state and a target state transition function of the obstacle comprises:
“determining an object type of the obstacle” in that data informing operation of the system of Van Den Broek would be informed to determine object type as per Nister;
“determining the target state transition function of the obstacle based on the object type of the obstacle and a relationship between different types and the state transition function” in that data informing operation of the system of Van Den Broek would be informed to determine object type as per Nister and sampled as per Berntorp; and
“determining the second predicted state corresponding to each of the second sampling states at the future time frame respectively based on each of the second sampling states and the target state transition function” in that data informing operation of the system of Van Den Broek would be informed to determine object type as per Nister and sampled as per Berntorp.
As per Claim 7, the combination of Van Den Broek, Berntorp, Kamann, and Nister teaches or suggests all limitations of Claim 6. Van Den Broek does not expressly disclose wherein the determining an object type of the obstacle comprises:
determining a first type of the obstacle using a classification network model based on perceptual sensor data at the current time frame;
determining a confidence corresponding to the first type using a classification confidence prediction model based on the first state data of the obstacle; and
determining the object type of the obstacle based on the first type and the confidence.
See rejection of Claim 2 for discussion of teachings of Berntorp.
See rejection of Claim 2 for discussion of teachings of Kamann.
See rejection of Claim 6 for discussion of teachings of Nister. Nister further discloses wherein a machine learning model involving a convolutional neural network is employed to evaluate the objects (106) (¶85) and wherein confidence intervals are provided for all metrics needed to calculate constaints of the vehicle safety system (¶182, 260).
Therefore, from these teachings of Van Den Broek, Berntorp, Kamann, and Nister, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Berntorp, Kamann, and Nister to the system of Van Den Broek since doing so would enhance the system by: providing data that satisfies specified constraints of the vehicle; improving accuracy of information for tracking; and providing appropriate safety procedures. Applying the teachings of Berntorp, Kamann, and Nister to the system of Van Den Broek would result in a system that operates wherein the determining an object type of the obstacle comprises:
“determining a first type of the obstacle using a classification network model based on perceptual sensor data at the current time frame” in that data informing operation of the system of Van Den Broek would be informed to determine object type as per Nister;
“determining a confidence corresponding to the first type using a classification confidence prediction model based on the first state data of the obstacle” in that data informing operation of the system of Van Den Broek would be informed to determine object type as per Nister;
“determining the object type of the obstacle based on the first type and the confidence” in that data informing operation of the system of Van Den Broek would be informed to determine object type as per Nister.
As per Claim 19, the combination of Van Den Broek, Berntorp, and Kamann teaches or suggests all limitations of Claim 18. Van Den Broek does not expressly disclose wherein the determining a second predicted state corresponding to the second sampling state at the future time frame based on the second sampling state and a target state transition function of the obstacle comprises:
determining an object type of the obstacle;
determining the target state transition function of the obstacle based on the object type of the obstacle and a relationship between different types and the state transition function; and
determining the second predicted state corresponding to each of the second sampling states at the future time frame respectively based on each of the second sampling states and the target state transition function.
See rejection of Claim 2 for discussion of teachings of Berntorp.
See rejection of Claim 2 for discussion of teachings of Kamann.
See rejection of Claim 6 for discussion of teachings of Nister.
Therefore, from these teachings of Van Den Broek, Berntorp, Kamann, and Nister, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Berntorp, Kamann, and Nister to the system of Van Den Broek since doing so would enhance the system by: providing data that satisfies specified constraints of the vehicle; improving accuracy of information for tracking; and providing appropriate safety procedures. Applying the teachings of Berntorp, Kamann, and Nister to the system of Van Den Broek would result in a system that operates wherein the determining a second predicted state corresponding to the second sampling state at the future time frame based on the second sampling state and a target state transition function of the obstacle comprises:
“determining an object type of the obstacle” in that data informing operation of the system of Van Den Broek would be informed to determine object type as per Nister;
“determining the target state transition function of the obstacle based on the object type of the obstacle and a relationship between different types and the state transition function” in that data informing operation of the system of Van Den Broek would be informed to determine object type as per Nister and sampled as per Berntorp; and
“determining the second predicted state corresponding to each of the second sampling states at the future time frame respectively based on each of the second sampling states and the target state transition function” in that data informing operation of the system of Van Den Broek would be informed to determine object type as per Nister and sampled as per Berntorp.
As per Claim 20, the combination of Van Den Broek, Berntorp, Kamann, and Nister teaches or suggests all limitations of Claim 19. Van Den Broek does not expressly disclose wherein the determining an object type of the obstacle comprises:
determining a first type of the obstacle using a classification network model based on perceptual sensor data at the current time frame;
determining a confidence corresponding to the first type using a classification confidence prediction model based on the first state data of the obstacle; and
determining the object type of the obstacle based on the first type and the confidence.
See rejection of Claim 2 for discussion of teachings of Berntorp.
See rejection of Claim 2 for discussion of teachings of Kamann.
See rejection of Claim 6 for discussion of teachings of Nister. Nister further discloses wherein a machine learning model involving a convolutional neural network is employed to evaluate the objects (106) (¶85) and wherein confidence intervals are provided for all metrics needed to calculate constaints of the vehicle safety system (¶182, 260).
Therefore, from these teachings of Van Den Broek, Berntorp, Kamann, and Nister, one of ordinary skill in the art before the effective filing date would have found it obvious to apply the teachings of Berntorp, Kamann, and Nister to the system of Van Den Broek since doing so would enhance the system by: providing data that satisfies specified constraints of the vehicle; improving accuracy of information for tracking; and providing appropriate safety procedures. Applying the teachings of Berntorp, Kamann, and Nister to the system of Van Den Broek would result in a system that operates wherein the determining an object type of the obstacle comprises:
“determining a first type of the obstacle using a classification network model based on perceptual sensor data at the current time frame” in that data informing operation of the system of Van Den Broek would be informed to determine object type as per Nister;
“determining a confidence corresponding to the first type using a classification confidence prediction model based on the first state data of the obstacle” in that data informing operation of the system of Van Den Broek would be informed to determine object type as per Nister;
“determining the object type of the obstacle based on the first type and the confidence” in that data informing operation of the system of Van Den Broek would be informed to determine object type as per Nister.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Matsunaga (US Pub. No. 2021/0300420), Stelzer (WO 2022/089990 A1), and Probst (US Pub. No. 2022/0315047) disclose vehicle control systems.
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/STEPHEN HOLWERDA/Primary Examiner, Art Unit 3656
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