DETAILED ACTION
This action is in response to the application filed on 04/11/2024. Claims 1-20 are pending and have been examined.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/20/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claim 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1:
Subject Matter of Eligibility Analysis Step 1:
Claim 1 recites a method, which is directed to a process, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Claim 1 recites
performing one or more operations to determine a performance of one or more predefined rules based on data that is received (this limitation is a mental process as it encompasses a human mentally determining the performance on the predefined rules, if the equation was given).
one or more first predictions generated using the one or more predefined rules (this limitation is a mental process as it encompasses a human mentally creating predictions based on predefined rules).
performing one or more operations to determine a performance of a trained machine learning model based on the data (this limitation is a mental process as it encompasses a human mentally determining the performance of a machine learning model, if the equation was given).
processing the data using the one or more predefined rules to generate one or more third predictions (this limitation is a mental process as it encompasses a human mentally creating predictions based on predefined rules).
generating one or more fifth predictions based on the one or more third predictions, the one or more fourth predictions, the performance of the one or more predefined rules, and the performance of the trained machine learning model (this limitation is a mental process as it encompasses a human mentally creating predictions based on other predictions and performances of predefined rules and machine learning model).
Therefore, claim 1 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 1 further recites additional elements of
one or more second predictions generated using the trained machine learning model (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
processing the data using the trained machine learning model to generate one or more fourth predictions (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
Therefore, claim 1 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 1 do not provide significantly more than the abstract idea itself, taken alone and in combination because
one or more second predictions generated using the trained machine learning model is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f).
processing the data using the trained machine learning model to generate one or more fourth predictions is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f).
Therefore, claim 1 is subject-matter ineligible.
Regarding claim 2:
Subject Matter of Eligibility Analysis Step 1:
Claim 2 recites a method, which is directed to a process, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Because claim 2 is dependent on claim 1, the Subject Matter of Eligibility Analysis Step 2A Prong 1 from claim 1 is applied here. Therefore claim 2 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 2 further recites additional elements of
performing one or more Bayes rule update operations to determine a belief based on the performance of the one or more predefined rules, the performance of the trained machine learning model, and a previous belief (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
the one or more fifth predictions are generated based on the one or more third predictions, the one or more fourth predictions, and the belief (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
Therefore, claim 2 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 2 do not provide significantly more than the abstract idea itself, taken alone and in combination because
performing one or more Bayes rule update operations to determine a belief based on the performance of the one or more predefined rules, the performance of the trained machine learning model, and a previous belief is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f).
the one or more fifth predictions are generated based on the one or more third predictions, the one or more fourth predictions, and the belief is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f).
Therefore, claim 2 is subject-matter ineligible.
Regarding claim 3:
Subject Matter of Eligibility Analysis Step 1:
Claim 3 recites a method, which is directed to a process, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Because claim 3 is dependent on claim 2, the Subject Matter of Eligibility Analysis Step 2A Prong 1 from claim 2 is applied here. Therefore claim 3 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 3 further recites additional elements of
the belief is further determined based on a predefined prior belief (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
Therefore, claim 3 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 3 do not provide significantly more than the abstract idea itself, taken alone and in combination because
the belief is further determined based on a predefined prior belief is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f).
Therefore, claim 3 is subject-matter ineligible.
Regarding claim 4:
Subject Matter of Eligibility Analysis Step 1:
Claim 4 recites a method, which is directed to a process, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Claim 4 recites
the one or more first predictions and the one or more second predictions were generated during a previous time step (This limitation is a mental process as it encompasses a human mentally creating predictions from a different time step).
Therefore claim 4 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 4 does not further recite any additional elements. Therefore, claim 4 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
Since there are no additional elements, claim 4 does not provide significantly more than the abstract idea itself, taken alone or in combination. Therefore, claim 4 is subject matter ineligible.
Regarding claim 5:
Subject Matter of Eligibility Analysis Step 1:
Claim 5 recites a method, which is directed to a process, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Claim 5 recites
each of the performance of the one or more predefined rules and the performance of the trained machine learning model is determined based on a loss function (this limitation is a mental process as it encompasses a human mentally calculating the performance if the loss function was given).
Therefore claim 5 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 5 does not further recite any additional elements. Therefore, claim 5 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
Since there are no additional elements, claim 5 does not provide significantly more than the abstract idea itself, taken alone or in combination. Therefore, claim 5 is subject matter ineligible.
Regarding claim 6:
Subject Matter of Eligibility Analysis Step 1:
Claim 6 recites a method, which is directed to a process, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Claim 6 recites
the loss function computes at least one of an average displacement error, a final displacement error, a likelihood of kernel density estimate, or a downstream planning cost (This limitation is a mental process as it encompasses a human mentally calculating an error if the loss function was given).
Therefore claim 6 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 6 does not further recite any additional elements. Therefore, claim 6 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
Since there are no additional elements, claim 6 does not provide significantly more than the abstract idea itself, taken alone or in combination. Therefore, claim 6 is subject matter ineligible.
Regarding claim 7:
Subject Matter of Eligibility Analysis Step 1:
Claim 7 recites a method, which is directed to a process, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Claim 7 recites
generating the one or more fifth predictions comprises sampling from the one or more third predictions and the one or more fourth predictions based on the performance of the one or more predefined rules and the performance of the trained machine learning model (this limitation is a mental process as it encompasses a human mentally creating predictions based on other predictions and performances of predefined rules and machine learning model).
Therefore, claim 7 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 7 does not further recite any additional elements. Therefore, claim 7 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
Since there are no additional elements, claim 7 does not provide significantly more than the abstract idea itself, taken alone or in combination. Therefore, claim 7 is subject matter ineligible.
Regarding claim 8:
Subject Matter of Eligibility Analysis Step 1:
Claim 8 recites a method, which is directed to a process, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Claim 8 recites
the one or more rules include a plurality of rules within a hierarchy of rules ordered based on one or more priorities (This limitation is a mental process as it encompasses a human mentally creating rules with a hierarchy based on priorities).
Therefore claim 8 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 8 does not further recite any additional elements. Therefore, claim 8 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
Since there are no additional elements, claim 8 does not provide significantly more than the abstract idea itself, taken alone or in combination. Therefore, claim 8 is subject matter ineligible.
Regarding claim 9:
Subject Matter of Eligibility Analysis Step 1:
Claim 9 recites a method, which is directed to a process, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Claim 9 recites
each of the one or more first predictions, the one or more second predictions, the one or more third predictions, and the one or more fourth predictions includes one or more trajectories (This limitation is a mental process as it encompasses a human mentally creating predictions that are trajectories).
Therefore claim 9 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 9 further recites additional elements of
Therefore, claim 9 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 9 do not provide significantly more than the abstract idea itself, taken alone and in combination because
each of the one or more first predictions, the one or more second predictions, the one or more third predictions, and the one or more fourth predictions includes one or more trajectories recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h).
Therefore, claim 9 is subject-matter ineligible.
Regarding claim 10:
Subject Matter of Eligibility Analysis Step 1:
Claim 10 recites a method, which is directed to a process, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Because claim 10 is dependent on claim 1 the Subject Matter of Eligibility Analysis Step 2A Prong 1 from claim 1 is applied here. Therefore claim 10 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 10 further recites additional elements of
performing one or more operations to control a vehicle based on the one or more fifth predictions (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
Therefore, claim 10 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 10 do not provide significantly more than the abstract idea itself, taken alone and in combination because
performing one or more operations to control a vehicle based on the one or more fifth predictions is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f).
Therefore, claim 10 is subject-matter ineligible.
Regarding claim 11:
Subject Matter of Eligibility Analysis Step 1:
Claim 11 recites a non-transitory computer-readable medium, which is directed to a manufacture, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Claim 11 recites
performing one or more operations to determine a performance of one or more predefined rules based on data that is received (this limitation is a mental process as it encompasses a human mentally determining the performance on the predefined rules, if the equation was given).
one or more first predictions generated using the one or more predefined rules (this limitation is a mental process as it encompasses a human mentally creating predictions based on predefined rules).
performing one or more operations to determine a performance of a trained machine learning model based on the data (this limitation is a mental process as it encompasses a human mentally determining the performance of a machine learning model, if the equation was given).
processing the data using the one or more predefined rules to generate one or more third predictions (this limitation is a mental process as it encompasses a human mentally creating predictions based on predefined rules).
generating one or more fifth predictions based on the one or more third predictions, the one or more fourth predictions, the performance of the one or more predefined rules, and the performance of the trained machine learning model (this limitation is a mental process as it encompasses a human mentally creating predictions based on other predictions and performances of predefined rules and machine learning model).
Therefore, claim 11 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 11 further recites additional elements of
One or more non-transitory computer-readable media storing instructions that, when executed by at least one processor, cause the at least one processor to perform (this element does not integrate the abstract idea into a practical application because it a generic computing component on which to perform the abstract idea (see MPEP 2106.05(f))).
one or more second predictions generated using the trained machine learning model (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
processing the data using the trained machine learning model to generate one or more fourth predictions (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
Therefore, claim 11 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 11 do not provide significantly more than the abstract idea itself, taken alone and in combination because
One or more non-transitory computer-readable media storing instructions that, when executed by at least one processor, cause the at least one processor to perform uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f))
one or more second predictions generated using the trained machine learning model is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f).
processing the data using the trained machine learning model to generate one or more fourth predictions is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f).
Therefore, claim 11 is subject-matter ineligible.
Regarding claim 12:
Subject Matter of Eligibility Analysis Step 1:
Claim 12 recites a non-transitory computer-readable medium, which is directed to a manufacture, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Because claim 12 is dependent on claim 11, the Subject Matter of Eligibility Analysis Step 2A Prong 1 from claim 11 is applied here. Therefore claim 12 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 12 further recites additional elements of
performing one or more Bayes rule update operations to determine a belief based on the performance of the one or more predefined rules, the performance of the trained machine learning model, and a previous belief (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
the one or more fifth predictions are generated based on the one or more third predictions, the one or more fourth predictions, and the belief (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
Therefore, claim 12 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 12 do not provide significantly more than the abstract idea itself, taken alone and in combination because
performing one or more Bayes rule update operations to determine a belief based on the performance of the one or more predefined rules, the performance of the trained machine learning model, and a previous belief is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f).
the one or more fifth predictions are generated based on the one or more third predictions, the one or more fourth predictions, and the belief is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f).
Therefore, claim 12 is subject-matter ineligible.
Regarding claim 13:
Subject Matter of Eligibility Analysis Step 1:
Claim 13 recites a non-transitory computer-readable medium, which is directed to a manufacture, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Because claim 13 is dependent on claim 12, the Subject Matter of Eligibility Analysis Step 2A Prong 1 from claim 112 is applied here. Therefore claim 13 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 13 further recites additional elements of
the belief is further determined based on a predefined prior belief that gives equal weight to the one or more predefined rules and the trained machine learning model (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
Therefore, claim 13 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 13 do not provide significantly more than the abstract idea itself, taken alone and in combination because
the belief is further determined based on a predefined prior belief that gives equal weight to the one or more predefined rules and the trained machine learning model is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f).
Therefore, claim 13 is subject-matter ineligible.
Regarding claim 14:
Subject Matter of Eligibility Analysis Step 1:
Claim 14 recites a non-transitory computer-readable medium, which is directed to a manufacture, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Claim 14 recites
the one or more first predictions and the one or more second predictions were generated during a previous time step (This limitation is a mental process as it encompasses a human mentally creating predictions from a different time step).
Therefore claim 14 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 14 does not further recite any additional elements. Therefore, claim 14 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
Since there are no additional elements, claim 14 does not provide significantly more than the abstract idea itself, taken alone or in combination. Therefore, claim 14 is subject matter ineligible.
Regarding claim 15:
Subject Matter of Eligibility Analysis Step 1:
Claim 15 recites a non-transitory computer-readable medium, which is directed to a manufacture, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Claim 15 recites
each of the performance of the one or more predefined rules and the performance of the trained machine learning model is determined based on a loss function (this limitation is a mental process as it encompasses a human mentally calculating the performance if the loss function was given).
Therefore claim 15 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 15 does not further recite any additional elements. Therefore, claim 15 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
Since there are no additional elements, claim 15 does not provide significantly more than the abstract idea itself, taken alone or in combination. Therefore, claim 15 is subject matter ineligible.
Regarding claim 16:
Subject Matter of Eligibility Analysis Step 1:
Claim 16 recites a non-transitory computer-readable medium, which is directed to a manufacture, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Claim 16 recites
the loss function computes at least one of an average displacement error, a final displacement error, a likelihood of kernel density estimate, or a downstream planning cost (This limitation is a mental process as it encompasses a human mentally calculating an error if the loss function was given).
Therefore claim 16 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 16 does not further recite any additional elements. Therefore, claim 16 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
Since there are no additional elements, claim 16 does not provide significantly more than the abstract idea itself, taken alone or in combination. Therefore, claim 16 is subject matter ineligible.
Regarding claim 17:
Subject Matter of Eligibility Analysis Step 1:
Claim 17 recites a non-transitory computer-readable medium, which is directed to a manufacture, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Claim 17 recites
generating the one or more fifth predictions comprises sampling from the one or more third predictions and the one or more fourth predictions based on the performance of the one or more predefined rules and the performance of the trained machine learning model (this limitation is a mental process as it encompasses a human mentally creating predictions based on other predictions and performances of predefined rules and machine learning model).
Therefore, claim 17 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 17 does not further recite any additional elements. Therefore, claim 17 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
Since there are no additional elements, claim 17 does not provide significantly more than the abstract idea itself, taken alone or in combination. Therefore, claim 17 is subject matter ineligible.
Regarding claim 18:
Subject Matter of Eligibility Analysis Step 1:
Claim 18 recites a non-transitory computer-readable medium, which is directed to a manufacture, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Because claim 18 is dependent on claim 11, the Subject Matter of Eligibility Analysis Step 2A Prong 1 from claim 11 is applied here. Therefore claim 18 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 18 further recites additional elements of
the one or more rules include a plurality of rules for operating a vehicle within a hierarchy of rules ordered based on one or more priorities (this element does not integrate an abstract idea because it recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h))).
Therefore, claim 18 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 18 do not provide significantly more than the abstract idea itself, taken alone and in combination because
the one or more rules include a plurality of rules for operating a vehicle within a hierarchy of rules ordered based on one or more priorities recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h).
Therefore, claim 18 is subject-matter ineligible.Regarding claim 19:
Subject Matter of Eligibility Analysis Step 1:
Claim 19 recites a non-transitory computer-readable medium, which is directed to a manufacture, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Because claim 19 is dependent on claim 11, the Subject Matter of Eligibility Analysis Step 2A Prong 1 from claim 11 is applied here. Therefore claim 19 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 19 further recites additional elements of
the hierarchy of rules includes at least one of one or more rules for collision avoidance, one or more rules for following a center polyline of a lane, one or more rules for orienting along the center polyline, or one or more rules for following a speed limit (this element does not integrate an abstract idea because it recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h))).
Therefore, claim 19 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 19 do not provide significantly more than the abstract idea itself, taken alone and in combination because
the hierarchy of rules includes at least one of one or more rules for collision avoidance, one or more rules for following a center polyline of a lane, one or more rules for orienting along the center polyline, or one or more rules for following a speed limit recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h).
Therefore, claim 19 is subject-matter ineligible.
Regarding claim 20:
Subject Matter of Eligibility Analysis Step 1:
Claim 20 recites a system, which is directed to a machine, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Claim 20 recites
perform one or more operations to determine a performance of one or more predefined rules based on data that is received (this limitation is a mental process as it encompasses a human mentally determining the performance on the predefined rules, if the equation was given).
one or more first predictions generated using the one or more predefined rules (this limitation is a mental process as it encompasses a human mentally creating predictions based on predefined rules).
perform one or more operations to determine a performance of a trained machine learning model based on the data (this limitation is a mental process as it encompasses a human mentally determining the performance of a machine learning model, if the equation was given).
process the data using the one or more predefined rules to generate one or more third predictions (this limitation is a mental process as it encompasses a human mentally creating predictions based on predefined rules).
generate one or more fifth predictions based on the one or more third predictions, the one or more fourth predictions, the performance of the one or more predefined rules, and the performance of the trained machine learning model (this limitation is a mental process as it encompasses a human mentally creating predictions based on other predictions and performances of predefined rules and machine learning model).
Therefore, claim 20 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 20 further recites additional elements of
A system, comprising: one or more memories storing instructions; and one or more processors that are coupled to the one or more memories (this element does not integrate the abstract idea into a practical application because it a generic computing component on which to perform the abstract idea (see MPEP 2106.05(f))).
one or more second predictions generated using the trained machine learning model (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
process the data using the trained machine learning model to generate one or more fourth predictions (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
Therefore, claim 20 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 11 do not provide significantly more than the abstract idea itself, taken alone and in combination because
A system, comprising: one or more memories storing instructions; and one or more processors that are coupled to the one or more memories uses a computer as a tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f))
one or more second predictions generated using the trained machine learning model is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f).
process the data using the trained machine learning model to generate one or more fourth predictions is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f).
Therefore, claim 20 is subject-matter ineligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1 and 9 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kim et al. (Hybrid Approach for Vehicle Trajectory Prediction Using Weighted Integration of Multiple Models) (hereafter referred to as Kim).
Regarding claim 1, Kim teaches
performing one or more operations to determine a performance of one or more predefined rules based on data that is received and one or more first predictions generated using the one or more predefined rules (Kim, Section I, “In this study, an integrated model that combines the trajectory results from physics-, maneuver-, and learning-based method is proposed to improve trajectory prediction performance in various environments. Each model predicts the mean and variance at each time step” and “In the prediction module, the physics-, maneuver-, and learning-based models predict future trajectory positions (µx,µy) and their uncertainties (σx,σy) using the map and the history information of each vehicle” (Kim, Section II). Examiner notes that the mean and variance is mapped to the performance and the physics-based and maneuver-based methods map to the predefined rules).
performing one or more operations to determine a performance of a trained machine learning model based on the data and one or more second predictions generated using the trained machine learning model (Kim, Section I, “In this study, an integrated model that combines the trajectory results from physics-, maneuver-, and learning-based method is proposed to improve trajectory prediction performance in various environments. Each model predicts the mean and variance at each time step” and “In this study, we focused on improving the performance using the hybrid model rather than emphasizing the performance of the network model itself. Therefore, a simple network modelResnet18 with an additional fully connected layer, as shown in Fig. 2, is used” (Kim, Section III C.1)).
processing the data using the one or more predefined rules to generate one or more third predictions; processing the data using the trained machine learning model to generate one or more fourth predictions (Kim, Section II, “In the prediction module, the physics-, maneuver-, and learning-based models predict future trajectory positions (µx,µy) and their uncertainties (σx,σy) using the map and the history information of each vehicle”).
generating one or more fifth predictions based on the one or more third predictions, the one or more fourth predictions, the performance of the one or more predefined rules, and the performance of the trained machine learning model (Kim, Section IV, “The prediction results of the three models are used to generate the final future trajectory. This process comprises of two steps. The first step is to decide whether to use the hybrid model based on the uncertainty of the network output. If the uncertainty is below the threshold, the network output is accepted as the final trajectory without further changes. However, if the uncertainty exceeds the threshold, the next step is performed”).
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Regarding claim 9, Kim teaches
each of the one or more first predictions, the one or more second predictions, the one or more third predictions, and the one or more fourth predictions includes one or more trajectories (Kim, Section I, “In this study, an integrated model that combines the trajectory results from physics-, maneuver-, and learning-based method is proposed to improve trajectory prediction performance in various environments”).
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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(s) 2-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Blom et al. (The Interacting Multiple Model Algorithm for Systems with Markovian Switching Coefficients) (hereafter referred to as Blom).
Regarding claim 2, Kim teaches the method of claim 1, Kim does not teach, but Blom does teach
performing one or more Bayes rule update operations to determine a belief based on the performance of the one or more predefined rules, the performance of the trained machine learning model, and a previous belief, wherein the one or more fifth predictions are generated based on the one or more third predictions, the one or more fourth predictions, and the belief (Blom, Section II, “Let us take a closer look at the derivation of the above cycle. As u_t and w_t, are mutually independent, the Bayes formula, which represents (6) and (7), follows easily from (2)”. Examiner notes that the Bayes formula maps to the Bayes rule update operation).
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(Blom, Section III)
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Examiner notes that
p
̂
_i(t-1) maps to the previous belief.
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Examiner notes that equation 15 maps to the determined belief. Equation 16 maps to the performance of the rules and model. Equation 18 maps to the fifth predictions, where
p
^
_
i
(t) is the belief and
x
^
_
i
(t) is the third and fourth predictions.
Kim and Blom are considered analogous to the claimed invention because they deal with multiple predictor models. It would have been obvious to one having ordinary skill in the art prior to the effective filling date to have modified Kim to use the Interacting Multiple Model (IMM) Algorithm from Blom. Blom teaches that the “Evaluation of the IMM algorithm makes it clear that it performs very well at a relatively low computational load(Blom, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Regarding claim 3, Kim teaches the method of claim 2, Kim does not teach, but Blom does teach
the belief is further determined based on a predefined prior belief (Blom, Section II, “To show the possibilities of timing the hypothesis reduction, we start with a filter cycle from one measurement update up to and including the next measurement update. For this, we take a cycle of recursions for the evolution of the conditional probability measure of our hybrid state Markov process (x_t, 0_t)” and “In all 19 cases both xr and y, are scalar processes, which satisfy x_t = a(Θ_t)x_t-1 + b(Θ_t)w_t + u(t) and y_t = h(Θ_t)x_t + g(Θ_t)u_t, with Θ_1:Ω => {0, 1}, u(f) = 10. cos {2πt/100), x_0 a Gaussian variable with expectation 10 and variance 10, P{Θ_0 = 1) = P{Θ_0 = 0) = ½“ (Blom, Section IV). Examiner notes that P{Θ_0 = 1) = P{Θ_0 = 0) = ½ maps to the predefines prior belief).
Kim and Blom are considered analogous to the claimed invention because they deal with multiple predictor models. It would have been obvious to one having ordinary skill in the art prior to the effective filling date to have modified Kim to use the Interacting Multiple Model (IMM) Algorithm from Blom. Blom teaches that the “Evaluation of the IMM algorithm makes it clear that it performs very well at a relatively low computational load(Blom, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Regarding claim 4, Kim teaches the method of claim 2, Kim does not teach, but Blom does teach
the one or more first predictions and the one or more second predictions were generated during a previous time step (Blom, Section III)
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Examiner notes that
x
̂
_i(t-1) maps to the previous time step.
Kim and Blom are considered analogous to the claimed invention because they deal with multiple predictor models. It would have been obvious to one having ordinary skill in the art prior to the effective filling date to have modified Kim to use the Interacting Multiple Model (IMM) Algorithm from Blom. Blom teaches that the “Evaluation of the IMM algorithm makes it clear that it performs very well at a relatively low computational load(Blom, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Claim(s) 5 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Sun et al. (On Complementing End-to-End Human Behavior Predictors with Planning) (hereafter referred to as Sun).
Regarding claim 5, Kim teaches the method of claim 1, Kim does not teach, but Sun does teach
each of the performance of the one or more predefined rules and the performance of the trained machine learning model is determined based on a loss function (Sun, Section II-F, “The third approach is to simply train a classifier to explicitly distinguish whether a prediction is good enough ...The classifier tries to learn a function σ : (x,h, fe2e(x,h))→ {0,1} from training data D classifier that we auto-label based on the average distance error (ADE) between the predicted trajectory and the ground-truth trajectory in the predictor training data. We label all predicted trajectories that generate large ADEs (2 sigmas beyond the mean ADE of the training set) as bad predictions”. Examiner notes that ADE is mapped to the loss function)
Kim and Sun are considered analogous to the claimed invention because they deal with autonomous vehicles. It would have been obvious to one having ordinary skill in the art prior to the effective filling date to have modified Kim to use a loss function from Sun to determine performance. One of the ordinary skill in the art would have known to apply the known technique of using a loss function to determine performance. Therefore, applying Sun’s technique would yield the predicable result of determining the degree of error a machine model contains(See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predicable results).
Regarding claim 6, Kim teaches the method of claim 5, Kim does not teach, but Sun does teach
the loss function computes at least one of an average displacement error, a final displacement error, a likelihood of kernel density estimate, or a downstream planning cost (Sun, Section II-E, “The third approach is to simply train a classifier to explicitly distinguish whether a prediction is good enough ...The classifier tries to learn a function σ : (x,h, fe2e(x,h))→ {0,1} from training data D classifier that we auto-label based on the average distance error (ADE) between the predicted trajectory and the ground-truth trajectory in the predictor training data. We label all predicted trajectories that generate large ADEs (2 sigmas beyond the mean ADE of the training set) as bad predictions”).
Kim and Sun are considered analogous to the claimed invention because they deal with autonomous vehicles. It would have been obvious to one having ordinary skill in the art prior to the effective filling date to have modified Kim to use the average distance error (ADE) from Sun to determine performance. One of the ordinary skill in the art would have known to apply the known technique of using an ADE to determine performance. Therefore, applying Sun’s technique would yield the predicable result of determining the degree of error a machine model contains(See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predicable results).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Ikonomovska et al. (Real-Time Bid Prediction using Thompson Sampling-Based Expert Selection) (hereafter referred to as Ikonomovska).
Regarding claim 7, Kim teaches the method of claim 1, Kim does not teach, but Ikonomovska does teach
generating the one or more fifth predictions comprises sampling from the one or more third predictions and the one or more fourth predictions based on the performance of the one or more predefined rules and the performance of the trained machine learning model (Ikonomovska, Abstract, “We study online meta-learners for real-time bid prediction that predict by selecting a single best predictor among several subordinate prediction algorithms, here called “experts”. These predictors belong to the family of context-dependent past performance estimators that make a prediction only when the instance to be predicted falls within their areas of expertise” and “The pseudo-code of our proposed Thompson Sampling algorithm is given in Algorithm 1.The algorithm starts from its prior beliefs on the expected success rate and the expected cost, implemented with a Beta and a Gaussian prior distribution, respectively. In each trial, the algorithm first observes the set of available experts S_t and their estimates ˆxj t (line3), and performs a Monte Carlo simulation to calculate the allocation probabilities using Eq.6 (line4).The simulation consists of drawing independent samples from the posterior distributions of θ and m. Then the algorithm chooses to play an expert according to the allocation probabilities w_t (line5)” (Ikonomovska, Section 4.2)).
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Examiner notes that S_t are experts, which maps to the predictors. In line 5 of Algorithm 1, choosing an expert randomly maps to the sampling of predictions based on performance.
Kim and Ikonomovska are considered analogous to the claimed invention because they deal with multiple predictor models. It would have been obvious to one having ordinary skill in the art prior to the effective filling date to have modified Kim to use Thompson Sampling from Ikonomovska. Ikonomovska teaches “using randomized probability matching as a superior algorithm, and the industry standard, due to its performance both in terms of accuracy, as well as speed, broad applicability and ease-of-use” (Ikonomovska, Section 9) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Claim(s) 8 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Xiao et al. (Rule-based Optimal Control for Autonomous Driving) (hereafter referred to as Xiao).
Regarding claim 8, Kim teaches the method of claim 1, Kim does not teach, but Xiao does teach
the one or more rules include a plurality of rules within a hierarchy of rules ordered based on one or more priorities (Xiao, Section 4.2, “Consider the driving scenario from Fig- 2(a) and a priority structure (R, ~p, <= p) in Fig 2(b), where R = {r1,r2,r3,r4}, and r1: “No collision”, r2: “Lane keeping”, r3: “Speed limit” and r4: “Comfort”. There are 3 equivalence classes given by O1 = {r4}, O2 = {r2,r3} and O3 = {r1}. Rule r4 has priority 1, r2 and r3 have priority 2, and r1 has priority 3”)
Kim and Xiao are considered analogous to the claimed invention because they deal with autonomous vehicles. It would have been obvious to one having ordinary skill in the art prior to the effective filling date to have modified Kim to use the priority structure from Xiao. One of the ordinary skill in the art would have known to apply the known technique of ordering rules based on priority. Therefore, applying Xiao’s technique would yield the predicable result of reducing complexity and improving efficiency (See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predicable results).
Regarding claim 10, Kim teaches the method of claim 1, Kim further teaches
performing one or more operations to control a vehicle based on the one or more fifth predictions (Kim, Section IV, “The prediction results of the three models are used to generate the final future trajectory. This process comprises of two steps. The first step is to decide whether to use the hybrid model based on the uncertainty of the network output. If the uncertainty is below the threshold, the network output is accepted as the final trajectory without further changes. However, if the uncertainty exceeds the threshold, the next step is performed”).
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Kim does not teach, but Xiao does teach
performing one or more operations to control a vehicle based on the one or more fifth predictions (Xiao, Abstract, “We develop optimal control strategies for Autonomous Vehicles (AVs) that are requires to meet complex specifications imposed by traffic laws and cultural expectations of reasonable driving behavior”).
Kim and Xiao are considered analogous to the claimed invention because they deal with autonomous vehicles. It would have been obvious to one having ordinary skill in the art prior to the effective filling date to have modified Kim to be applied to AVs from Xiao. One of the ordinary skill in the art would have known to apply the known technique of performing optimal control strategies on AVs. Therefore, applying Xiao’s technique would yield the predicable result of AVs determining the best decision to take (See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predicable results).
Claim(s) 11 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Ma et al. (US 11745762 B2) (hereafter referred to as Ma).
Regarding claim 11, Kim teaches
performing one or more operations to determine a performance of one or more predefined rules based on data that is received and one or more first predictions generated using the one or more predefined rules (Kim, Section I, “In this study, an integrated model that combines the trajectory results from physics-, maneuver-, and learning-based method is proposed to improve trajectory prediction performance in various environments. Each model predicts the mean and variance at each time step” and “In the prediction module, the physics-, maneuver-, and learning-based models predict future trajectory positions (µx,µy) and their uncertainties (σx,σy) using the map and the history information of each vehicle” (Kim, Section II). Examiner notes that the mean and variance is mapped to the performance and the physics-based and maneuver-based methods map to the predefined rules).
performing one or more operations to determine a performance of a trained machine learning model based on the data and one or more second predictions generated using the trained machine learning model (Kim, Section I, “In this study, an integrated model that combines the trajectory results from physics-, maneuver-, and learning-based method is proposed to improve trajectory prediction performance in various environments. Each model predicts the mean and variance at each time step” and “In this study, we focused on improving the performance using the hybrid model rather than emphasizing the performance of the network model itself. Therefore, a simple network modelResnet18 with an additional fully connected layer, as shown in Fig. 2, is used” (Kim, Section III C.1)).
processing the data using the one or more predefined rules to generate one or more third predictions; processing the data using the trained machine learning model to generate one or more fourth predictions (Kim, Section II, “In the prediction module, the physics-, maneuver-, and learning-based models predict future trajectory positions (µx,µy) and their uncertainties (σx,σy) using the map and the history information of each vehicle”).
generating one or more fifth predictions based on the one or more third predictions, the one or more fourth predictions, the performance of the one or more predefined rules, and the performance of the trained machine learning model (Kim, Section IV, “The prediction results of the three models are used to generate the final future trajectory. This process comprises of two steps. The first step is to decide whether to use the hybrid model based on the uncertainty of the network output. If the uncertainty is below the threshold, the network output is accepted as the final trajectory without further changes. However, if the uncertainty exceeds the threshold, the next step is performed”).
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Kim does not teach, but Ma does teach
One or more non-transitory computer-readable media storing instructions that, when executed by at least one processor, cause the at least one processor to perform (Ma, paragraph 0107, “By way of example, and not limitation, such computer-readable storage media may include non-transitory computer-readable storage media including Random Access Memory (RAM), Read-Only Memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Compact Disc Read-Only Memory (CD-ROM) or other optical disk storage, magnetic disk storage or other magnetic storage devices, flash memory devices (e.g., solid state memory devices), or any other storage medium which may be used to store desired program code in the form of computer-executable instructions or data structures and which may be accessed by a general-purpose or special-purpose computer”).
Kim and Ma are considered analogous to the claimed invention because they deal with autonomous vehicles. It would have been obvious to one having ordinary skill in the art prior to the effective filling date to have modified Kim to include a non-transitory computer-readable medium from Ma. One of the ordinary skill in the art would have known to apply the known technique to apply Ma’s technique of a non-transitory computer-readable medium to perform the instructions from Kim. Therefore, applying Ma’s technique would yield the predicable result of running instructions on a non-transitory computer-readable medium (See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predicable results).
Regarding claim 20, Kim teaches
perform one or more operations to determine a performance of one or more predefined rules based on data that is received and one or more first predictions generated using the one or more predefined rules (Kim, Section I, “In this study, an integrated model that combines the trajectory results from physics-, maneuver-, and learning-based method is proposed to improve trajectory prediction performance in various environments. Each model predicts the mean and variance at each time step” and “In the prediction module, the physics-, maneuver-, and learning-based models predict future trajectory positions (µx,µy) and their uncertainties (σx,σy) using the map and the history information of each vehicle” (Kim, Section II). Examiner notes that the mean and variance is mapped to the performance and the physics-based and maneuver-based methods map to the predefined rules).
perform one or more operations to determine a performance of a trained machine learning model based on the data and one or more second predictions generated using the trained machine learning model (Kim, Section I, “In this study, an integrated model that combines the trajectory results from physics-, maneuver-, and learning-based method is proposed to improve trajectory prediction performance in various environments. Each model predicts the mean and variance at each time step” and “In this study, we focused on improving the performance using the hybrid model rather than emphasizing the performance of the network model itself. Therefore, a simple network modelResnet18 with an additional fully connected layer, as shown in Fig. 2, is used” (Kim, Section III C.1)).
process the data using the one or more predefined rules to generate one or more third predictions; processing the data using the trained machine learning model to generate one or more fourth predictions (Kim, Section II, “In the prediction module, the physics-, maneuver-, and learning-based models predict future trajectory positions (µx,µy) and their uncertainties (σx,σy) using the map and the history information of each vehicle”).
generate one or more fifth predictions based on the one or more third predictions, the one or more fourth predictions, the performance of the one or more predefined rules, and the performance of the trained machine learning model (Kim, Section IV, “The prediction results of the three models are used to generate the final future trajectory. This process comprises of two steps. The first step is to decide whether to use the hybrid model based on the uncertainty of the network output. If the uncertainty is below the threshold, the network output is accepted as the final trajectory without further changes. However, if the uncertainty exceeds the threshold, the next step is performed”).
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Kim does not teach, but Ma does teach
A system, comprising: one or more memories storing instructions; and one or more processors that are coupled to the one or more memories (Ma, paragraph 0106, “The memory 1020 and the data storage 1030 may include computer-readable storage media or one or more computer-readable storage mediums for having computer-executable instructions or data structures stored thereon. Such computer-readable storage media may be any available media that may be accessed by a general-purpose or special-purpose computer, such as the processor 1010. For example, the memory 1020 and/or the data storage 1030 may store the contextual object list, the relevant objects, the prediction type context, and/or the predicted trajectories. In some embodiments, the computing system 1000 may or may not include either of the memory 1020 and the data storage 1030.”).
Kim and Ma are considered analogous to the claimed invention because they deal with autonomous vehicles. It would have been obvious to one having ordinary skill in the art prior to the effective filling date to have modified Kim to include the computing system from Ma. One of the ordinary skill in the art would have known to apply the known technique to apply Ma’s technique of using a computer system to perform the instructions from Kim. Therefore, applying Ma’s technique would yield the predicable result of running instructions on a computer system (See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predicable results).
Claim(s) 12-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Ma, and Blom.
Regarding claim 12, Kim and Ma teaches the non-transitory computer-readable medium of claim 11, Kim and Ma do not teach, but Blom does teach
performing one or more Bayes rule update operations to determine a belief based on the performance of the one or more predefined rules, the performance of the trained machine learning model, and a previous belief, wherein the one or more fifth predictions are generated based on the one or more third predictions, the one or more fourth predictions, and the belief (Blom, Section II, “Let us take a closer look at the derivation of the above cycle. As u_t and w_t, are mutually independent, the Bayes formula, which represents (6) and (7), follows easily from (2)”. Examiner notes that the Bayes formula maps to the Bayes rule update operation).
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(Blom, Section III)
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Examiner notes that
p
̂
_i(t-1) maps to the previous belief.
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Examiner notes that equation 15 maps to the determined belief. Equation 16 maps to the performance of the rules and model. Equation 18 maps to the fifth predictions, where
p
^
_
i
(t) is the belief and
x
^
_
i
(t) is the third and fourth predictions.
Kim, Ma, and Blom are considered analogous to the claimed invention because they deal with multiple predictor models. It would have been obvious to one having ordinary skill in the art prior to the effective filling date to have modified Kim and Ma to use the Interacting Multiple Model (IMM) Algorithm from Blom. Blom teaches that the “Evaluation of the IMM algorithm makes it clear that it performs very well at a relatively low computational load(Blom, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Regarding claim 13, Kim and Ma teaches the non-transitory computer-readable medium of claim 12, Kim and Ma do not teach, but Blom does teach
the belief is further determined based on a predefined prior belief that gives equal weight to the one or more predefined rules and the trained machine learning model (Blom, Section II, “To show the possibilities of timing the hypothesis reduction, we start with a filter cycle from one measurement update up to and including the next measurement update. For this, we take a cycle of recursions for the evolution of the conditional probability measure of our hybrid state Markov process (x_t, 0_t)” and “In all 19 cases both xr and y, are scalar processes, which satisfy x_t = a(Θ_t)x_t-1 + b(Θ_t)w_t + u(t) and y_t = h(Θ_t)x_t + g(Θ_t)u_t, with Θ_1:Ω => {0, 1}, u(f) = 10. cos {2πt/100), x_0 a Gaussian variable with expectation 10 and variance 10, P{Θ_0 = 1) = P{Θ_0 = 0) = ½“ (Blom, Section IV). Examiner notes that P{Θ_0 = 1) = P{Θ_0 = 0) = ½ maps to the predefines prior belief).
Kim, Ma, and Blom are considered analogous to the claimed invention because they deal with multiple predictor models. It would have been obvious to one having ordinary skill in the art prior to the effective filling date to have modified Kim and Ma1 to use the Interacting Multiple Model (IMM) Algorithm from Blom. Blom teaches that the “Evaluation of the IMM algorithm makes it clear that it performs very well at a relatively low computational load(Blom, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Regarding claim 14, Kim and Ma teaches the non-transitory computer-readable medium of claim 11, Kim and Ma do not teach, but Blom does teach
the one or more first predictions and the one or more second predictions were generated during a previous time step (Blom, Section III)
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Examiner notes that
x
̂
_i(t-1) maps to the previous time step.
Kim, Ma, and Blom are considered analogous to the claimed invention because they deal with multiple predictor models. It would have been obvious to one having ordinary skill in the art prior to the effective filling date to have modified Kim and Ma to use the Interacting Multiple Model (IMM) Algorithm from Blom. Blom teaches that the “Evaluation of the IMM algorithm makes it clear that it performs very well at a relatively low computational load(Blom, Abstract) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Claim(s) 15 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Ma and Sun.
Regarding claim 15, Kim and Ma teaches the non-transitory computer-readable medium of claim 11, Kim and Ma do not teach, but Sun does teach
each of the performance of the one or more predefined rules and the performance of the trained machine learning model is determined based on a loss function (Sun, Section II-F, “The third approach is to simply train a classifier to explicitly distinguish whether a prediction is good enough ...The classifier tries to learn a function σ : (x,h, fe2e(x,h))→ {0,1} from training data D classifier that we auto-label based on the average distance error (ADE) between the predicted trajectory and the ground-truth trajectory in the predictor training data. We label all predicted trajectories that generate large ADEs (2 sigmas beyond the mean ADE of the training set) as bad predictions”. Examiner notes that ADE is mapped to the loss function)
Kim, Ma, and Sun are considered analogous to the claimed invention because they deal with autonomous vehicles. It would have been obvious to one having ordinary skill in the art prior to the effective filling date to have modified Kim and Ma to use a loss function from Sun to determine performance. One of the ordinary skill in the art would have known to apply the known technique of using a loss function to determine performance. Therefore, applying Sun’s technique would yield the predicable result of determining the degree of error a machine model contains(See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predicable results).
Regarding claim 16, Kim and Ma teaches the non-transitory computer-readable medium of claim 15, Kim and Ma do not teach, but Sun does teach
the loss function computes at least one of an average displacement error, a final displacement error, a likelihood of kernel density estimate, or a downstream planning cost (Sun, Section II-E, “The third approach is to simply train a classifier to explicitly distinguish whether a prediction is good enough ...The classifier tries to learn a function σ : (x,h, fe2e(x,h))→ {0,1} from training data D classifier that we auto-label based on the average distance error (ADE) between the predicted trajectory and the ground-truth trajectory in the predictor training data. We label all predicted trajectories that generate large ADEs (2 sigmas beyond the mean ADE of the training set) as bad predictions”).
Kim, Ma, and Sun are considered analogous to the claimed invention because they deal with autonomous vehicles. It would have been obvious to one having ordinary skill in the art prior to the effective filling date to have modified Kim and Ma to use the average distance error (ADE) from Sun to determine performance. One of the ordinary skill in the art would have known to apply the known technique of using an ADE to determine performance. Therefore, applying Sun’s technique would yield the predicable result of determining the degree of error a machine model contains(See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predicable results).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Ma and Ikonomovska.
Regarding claim 17, Kim and Ma teach the non-transitory computer-readable medium of claim 11, Kim and Ma do not teach, but Ikonomovska does teach
generating the one or more fifth predictions comprises sampling from the one or more third predictions and the one or more fourth predictions based on the performance of the one or more predefined rules and the performance of the trained machine learning model (Ikonomovska, Abstract, “We study online meta-learners for real-time bid prediction that predict by selecting a single best predictor among several subordinate prediction algorithms, here called “experts”. These predictors belong to the family of context-dependent past performance estimators that make a prediction only when the instance to be predicted falls within their areas of expertise” and “The pseudo-code of our proposed Thompson Sampling algorithm is given in Algorithm 1.The algorithm starts from its prior beliefs on the expected success rate and the expected cost, implemented with a Beta and a Gaussian prior distribution, respectively. In each trial, the algorithm first observes the set of available experts S_t and their estimates ˆxj t (line3), and performs a Monte Carlo simulation to calculate the allocation probabilities using Eq.6 (line4).The simulation consists of drawing independent samples from the posterior distributions of θ and m. Then the algorithm chooses to play an expert according to the allocation probabilities w_t (line5)” (Ikonomovska, Section 4.2)).
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Examiner notes that S_t are experts, which maps to the predictors. In line 5 of Algorithm 1, choosing an expert randomly maps to the sampling of predictions based on performance.
Kim, Ma, and Ikonomovska are considered analogous to the claimed invention because they deal with multiple predictor models. It would have been obvious to one having ordinary skill in the art prior to the effective filling date to have modified Kim and Ma to use Thompson Sampling from Ikonomovska. Ikonomovska teaches “using randomized probability matching as a superior algorithm, and the industry standard, due to its performance both in terms of accuracy, as well as speed, broad applicability and ease-of-use” (Ikonomovska, Section 9) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention).
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Ma and Xiao.
Regarding claim 18, Kim and Ma teach the non-transitory computer-readable medium of claim 11, Kim and Ma do not teach, but Xiao does teach
the one or more rules include a plurality of rules for operating a vehicle within a hierarchy of rules ordered based on one or more priorities (Xiao, Section 4.2, “Consider the driving scenario from Fig- 2(a) and a priority structure (R, ~p, <= p) in Fig 2(b), where R = {r1,r2,r3,r4}, and r1: “No collision”, r2: “Lane keeping”, r3: “Speed limit” and r4: “Comfort”. There are 3 equivalence classes given by O1 = {r4}, O2 = {r2,r3} and O3 = {r1}. Rule r4 has priority 1, r2 and r3 have priority 2, and r1 has priority 3”)
Kim, Ma and Xiao are considered analogous to the claimed invention because they deal with autonomous vehicles. It would have been obvious to one having ordinary skill in the art prior to the effective filling date to have modified Kim and Ma to use the priority structure from Xiao. One of the ordinary skill in the art would have known to apply the known technique of ordering rules based on priority. Therefore, applying Xiao’s technique would yield the predicable result of reducing complexity and improving efficiency (See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predicable results).
Regarding claim 19, Kim, Ma, and Xiao teach the non-transitory computer-readable medium of claim 18, Kim and Ma do not teach, but Xiao further teaches
the hierarchy of rules includes at least one of one or more rules for collision avoidance, one or more rules for following a center polyline of a lane, one or more rules for orienting along the center polyline, or one or more rules for following a speed limit (Xiao, Section 4.2, “Consider the driving scenario from Fig- 2(a) and a priority structure (R, ~p, <= p) in Fig 2(b), where R = {r1,r2,r3,r4}, and r1: “No collision”, r2: “Lane keeping”, r3: “Speed limit” and r4: “Comfort”. There are 3 equivalence classes given by O1 = {r4}, O2 = {r2,r3} and O3 = {r1}. Rule r4 has priority 1, r2 and r3 have priority 2, and r1 has priority 3”)
Kim, Ma and Xiao are considered analogous to the claimed invention because they deal with autonomous vehicles. It would have been obvious to one having ordinary skill in the art prior to the effective filling date to have modified Kim and Ma to use the priority structure from Xiao. One of the ordinary skill in the art would have known to apply the known technique of ordering rules based on priority. Therefore, applying Xiao’s technique would yield the predicable result of reducing complexity and improving efficiency (See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predicable results).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Kamenev et al. (PredictionNet: Real-Time Joint Probabilistic Traffic Prediction for Planning, Control, and Simulation) discloses PredictionNet, a deep neural network (DNN) that predicts the motion of all surrounding traffic agents together with the ego-vehicle’s motion. Bhattacharyya et al. (A Hybrid Rule-Based and Data-Driven Approach to Driver Modeling Through Particle Filtering) discloses driver modeling experiments on the task of highway driving and merging using data from three real-world driving demonstration datasets. Sieberg et al. (Ensuring the Reliability of Virtual Sensors Based on Artificial Intelligence within Vehicle Dynamics Control Systems) discloses a hybrid method that safeguards the reliability of artificial intelligence-based estimations.
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/S.V./ Examiner, Art Unit 2148 /MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
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