NON-FINAL REJECTION
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 .
This action is in response to the original application filed on 03/30/3023.
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. EP22166079.8, filed on 3/31/2022.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference character “s23” in figure 15 has been used to designate both “comparing filtered set of agents with fixed set of agents” labeled “s23” in the drawings and “obtaining fixed set of agents from SA analysis” labeled “s22” in the drawings. 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. 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.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference characters not mentioned in the description: figure 25 labels 25.1, 26.1 and figure 30 label 35. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) 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. 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.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 are rejected under 35 U.S.C 101 because the claimed invention is
directed to an abstract idea without significantly more. The analysis of the claims will
follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50
(“2019 PEG”).
Claim 1
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 1, recites “A computer-implemented method for assisting operation of an agent operating in a dynamic environment involving at least one other agent in the environment of the agent, wherein the method comprises:” therefore it is directed to the statutory category of a process.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“calculating an importance score for each agent of the at least one other agent based on the acquired sensor data and the acquired information on position and motion, wherein the importance score is a quantitative physical value describing a relevance of the particular agent for the operated agent based on the agent's dynamics;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses mathematical concept of utilizing a mathematical formula to perform calculations. A human is able to evaluate data and perform calculations using algorithm in the observed data. This claim discloses a math operation and therefore is ineligible.
“generating a list comprising the at least one other agent based on the calculated importance scores of the at least one other agent;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and place data into a list. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“partitioning the generated list in at least one partition based on the importance score of the at least one other agent, wherein each partition includes at least one other agent;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to partition a list based on observations and evaluations. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“generating a prediction result by predicting a behavior of the at least one agent included in the at least one partition based on the sensor data and the position and motion information using a selected behavior prediction model, wherein the selected behavior prediction model is selected from a plurality of behavior prediction models based on the importance score of the at least one agent included in the at least one partition; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and produce a judgement or prediction based on that data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “acquiring sensor data from at least one sensor physically sensing the environment, including information on the at least one other agent and on physical structures in the environment;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“acquiring data including information on position and motion of the agent;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“outputting the prediction result to a planning and control system for operating the agent.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “acquiring sensor data from at least one sensor physically sensing the environment, including information on the at least one other agent and on physical structures in the environment;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
“acquiring data including information on position and motion of the agent;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
“outputting the prediction result to a planning and control system for operating the agent.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 2
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the plurality of behavior prediction models comprises individual behavior prediction models which differ with respect to at least one of prediction accuracy and computational complexity.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the plurality of behavior prediction models comprises individual behavior prediction models which differ with respect to at least one of prediction accuracy and computational complexity.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 3
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“the method comprises selecting the behavior prediction model for the first partition with a lower computational complexity than the behavior prediction model for the second partition.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate a list and determine a model based on the evaluation of the data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the at least one partition comprise at least a first partition including the other agents having a first range of the importance score and a second partition including the other agents having a second range of the importance score,” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“the first range includes importance scores, which are smaller than the importance scores included in the second range, and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the at least one partition comprise at least a first partition including the other agents having a first range of the importance score and a second partition including the other agents having a second range of the importance score,” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
“the first range includes importance scores, which are smaller than the importance scores included in the second range, and” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 4
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“the method comprises discarding other agents with a calculated importance score below a threshold when generating the list.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and disregard data that does not meet a given threshold. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 5
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“wherein the calculation of the importance score comprises applying a risk shadowing process to the data on the at least one other agent and on the physical structures in the environment.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses mathematical concept of utilizing a mathematical formula to perform calculations. A human is able to perform calculations with given data to produce a result. This claim discloses a math operation and therefore is ineligible.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 6
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“wherein the risk shadowing process comprises performing a reachability analysis based on the sensor data and the position and motion information for determining occupied areas for the agent and the at least one other agent in the environment over time, determining overlapping areas from the occupied areas of the agent and the at least one other agent, and determining a relevance of each of the at least one other agent based on the determined overlapping areas.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate their surroundings and able to determine a vehicles next or current position. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 7
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“computing a continuous relevance score for the at least one other agent by calculating a size of the overlapping area or a distance of the occupied areas.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses mathematical concept of utilizing a mathematical formula to perform calculations. A human is able to perform repetitive calculations based on evolving data. This claim discloses a math operation and therefore is ineligible.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein determining the relevance of each of the at least one other agent includes disregarding the at least one other agent in case the occupied areas of the agent and the at least one other agent do not exist, or” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein determining the relevance of each of the at least one other agent includes disregarding the at least one other agent in case the occupied areas of the agent and the at least one other agent do not exist, or” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 8
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein calculating the importance score comprises decreasing an influence of a first agent included in the at least one other agent based on an influence of at least one second agent of the at least one other agent.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein calculating the importance score comprises decreasing an influence of a first agent included in the at least one other agent based on an influence of at least one second agent of the at least one other agent.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 9
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein calculating the importance score includes applying a risk model that regards an influence of the at least one other agent on the agent for computing the importance score of the at least one other agent.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein calculating the importance score includes applying a risk model that regards an influence of the at least one other agent on the agent for computing the importance score of the at least one other agent.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 10
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“wherein the method comprises calculating the importance score as a first importance score for each of a plurality of other agents by applying a first method, discarding a subset of the plurality other agent based on a first filter criterion for the calculated first importance score for generating a first filtered list of the other agents,” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses mathematical concept of utilizing a mathematical formula to perform calculations. A human is able to perform calculations based on given data. This claim discloses a math operation and therefore is ineligible.
“calculating the importance score as a second importance score by applying a second method for the plurality of other agents, discarding a second subset of the plurality of other agents included in the generated list based on a second filter criterion for the calculated second importance score for generating a second filtered list of the plurality of other agents.” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses mathematical concept of utilizing a mathematical formula to perform calculations. A human is able to perform calculations based on given data. This claim discloses a math operation and therefore is ineligible.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
This claim does not recite any additional limitations which integrate the abstract idea into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible.
Claim 11
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the first method for calculating the first importance score has a lower computational complexity than the second method for calculating the second importance score.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the first method for calculating the first importance score has a lower computational complexity than the second method for calculating the second importance score.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 12
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein calculating the importance score uses a path distance model, a trajectory distance model, a Gaussian model, or a survival analysis model.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein calculating the importance score uses a path distance model, a trajectory distance model, a Gaussian model, or a survival analysis model.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 13
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
A process, as above.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “wherein the agent is a mobile robotic device, and the at least one other agent includes other mobile devices, in particular other mobile robotic devices, or” amounts to no more than generally linking the use of a judicial exception to a particular technology environment or field of use (see MPEP 2106.05(h)).
“the agent is an ego-vehicle, and the at least one other agent is at least one other traffic participant in a road traffic scenario, in particular at least one of other vehicles, cyclists and pedestrians, or” amounts to no more than generally linking the use of a judicial exception to a particular technology environment or field of use (see MPEP 2106.05(h)).
“the operated agent and the at least one other agent are vessels in a maritime traffic scenario, or” amounts to no more than generally linking the use of a judicial exception to a particular technology environment or field of use (see MPEP 2106.05(h)).
“the operated agent and the at least one other agent are air vehicles in an air traffic scenario, or” amounts to no more than generally linking the use of a judicial exception to a particular technology environment or field of use (see MPEP 2106.05(h)).
“the operated agent and the at least one other agent are space vehicles in a space environment.” amounts to no more than generally linking the use of a judicial exception to a particular technology environment or field of use (see MPEP 2106.05(h)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “wherein the agent is a mobile robotic device, and the at least one other agent includes other mobile devices, in particular other mobile robotic devices, or” amounts to no more than generally linking the use of a judicial exception to a particular technology environment or field of use (see MPEP 2106.05(h)).
“the agent is an ego-vehicle, and the at least one other agent is at least one other traffic participant in a road traffic scenario, in particular at least one of other vehicles, cyclists and pedestrians, or” amounts to no more than generally linking the use of a judicial exception to a particular technology environment or field of use (see MPEP 2106.05(h)).
“the operated agent and the at least one other agent are vessels in a maritime traffic scenario, or” amounts to no more than generally linking the use of a judicial exception to a particular technology environment or field of use (see MPEP 2106.05(h)).
“the operated agent and the at least one other agent are air vehicles in an air traffic scenario, or” amounts to no more than generally linking the use of a judicial exception to a particular technology environment or field of use (see MPEP 2106.05(h)).
“the operated agent and the at least one other agent are space vehicles in a space environment.” amounts to no more than generally linking the use of a judicial exception to a particular technology environment or field of use (see MPEP 2106.05(h)).
Each of these limitations fails to meaningfully limit the claims. Each of these limitations merely apply an abstract idea to different technologies in particular environments and fails to provide significantly more than an abstract idea. Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 14
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 14, recites “A computer program product comprising a non-transitory computer readable medium having a computer readable program embodied therein, wherein the computer readable program, when executed on a computing device, causes the computing device to perform a method for assisting operation of an agent operating in a dynamic environment involving at least one other agent in the environment of the agent, wherein the method comprises:” therefore it is directed to the statutory category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“calculating an importance score for each agent of the at least one other agent based on the acquired sensor data and the acquired information on position and motion, wherein the importance score is a quantitative physical value describing a relevance of the particular agent for the operated agent based on the agent's dynamics;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses mathematical concept of utilizing a mathematical formula to perform calculations. A human is able to evaluate data and perform calculations using algorithm in the observed data. This claim discloses a math operation and therefore is ineligible.
“generating a list comprising the at least one other agent based on the calculated importance scores of the at least one other agent;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and place data into a list. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“partitioning the generated list in at least one partition based on the importance score of the at least one other agent, wherein each partition includes at least one other agents;” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to partition a list based on observations and evaluations. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“generating a prediction result by predicting a behavior of the at least one agent included in the at least one partition based on the sensor data and the position and motion information using a selected behavior prediction model, wherein the selected behavior prediction model is selected from a plurality of behavior prediction models based on the importance score of the at least one agent included in the at least one partition; and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and produce a judgement or prediction based on that data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “acquiring sensor data from at least one sensor physically sensing the environment, including information on the at least one other agent and on physical structures in the environment;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“acquiring data including information on position and motion of the agent;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“outputting the prediction result to a planning and control system for operating the agent.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “acquiring sensor data from at least one sensor physically sensing the environment, including information on the at least one other agent and on physical structures in the environment;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
“acquiring data including information on position and motion of the agent;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
“outputting the prediction result to a planning and control system for operating the agent.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 15
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Claim 15, recites “A system for assisting operation of an agent operating in a dynamic scenario involving at least one other agent in an environment of the agent, the system comprises:” therefore it is directed to the statutory category of a machine.
Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural
phenomenon?
The claim recites, inter alia:
“at least one processor configured to: calculate an importance score for each agent of the at least one other agent based on the acquired sensor data and the acquired position and motion data, wherein the importance score is a quantitative physical value describing a relevance of the particular agent for the operated agent based on the agent's dynamics,” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses mathematical concept of utilizing a mathematical formula to perform calculations. A human is able to evaluate data and perform calculations using algorithm in the observed data. This claim discloses a math operation and therefore is ineligible.
“generate a list comprising the at least one other agent based on the calculated importance scores of the at least one other agent,” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and place data into a list. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“partition the generated list in at least one partition based on the importance score of the at least one other agent, wherein each partition includes at least one other agent,” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to partition a list based on observations and evaluations. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
“generate a prediction result by predicting a behavior of the at least one other agent included in the at least one partition based on the sensor data and the position and motion data using a selected behavior prediction model, wherein the selected behavior prediction model is selected from a plurality of behavior prediction models based on the importance score of the at least one other agent included in the at least one partition, and” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate data and produce a judgement or prediction based on that data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c).
Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements, “at least one sensor configured to acquire sensor data from the environment including information on the at least one other agent and on physical structures in the environment;” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“at least one ego-sensor configured to acquire data including information on position and motion on the agent; and” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible.
“to output the prediction result to a planning and control system for operating the agent.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “at least one sensor configured to acquire sensor data from the environment including information on the at least one other agent and on physical structures in the environment;” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
“at least one ego-sensor configured to acquire data including information on position and motion on the agent; and” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”.
“to output the prediction result to a planning and control system for operating the agent.” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
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.
Claims 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Usman et al., (Usman et al., “SYSTEMS AND METHODS FOR PREDICTING AGENT TRAJECTORY”, US 2021/0304018 A1, filed 2020, hereinafter “Usman”) in view of Eggert et al., (Eggert et al., “METHOD FOR ASSISTING A DRIVER, DRIVER ASSISTANCE SYSTEM, AND VEHICLE INCLUDING SUCH DRIVER ASSISTANCE SYSTEM”, US 2020/0231149 A1, Filed 2020, hereinafter “Eggert”).
Regarding claim 1, Usman discloses, “A computer-implemented method for assisting operation of an agent operating in a dynamic environment involving at least one other agent in the environment of the agent, wherein the method comprises:” (Detailed Description, pp. 9, [0062]; “FIG. 6B illustrates an example method 620, according to an embodiment of the present technology. At block 622, a first observed position of an agent at a first time can be determined from sensor data captured. At block 624, a plurality of trajectory prediction algorithms can be applied to the agent at the first observed position to predict respective positions of the agent at a second time as predicted by the plurality of trajectory prediction algorithms. At block 626, a second observed position of the agent at the second time can be determined from the sensor data captured. At block 628, the second observed position and the respective positions can be compared to generate a plurality of performance metrics.” This application discloses a method for operation of an autonomous or semi- autonomous vehicle. This system uses sensors and vehicle data to evaluate the environment and roadway. This system provided assistance to the driver in avoiding obstacles and general operation of the vehicle.)
“generating a list comprising the at least one other agent based on the calculated importance scores of the at least one other agent;” (Detailed Description, pp. 5, [0032]; “The ranked list can be used to prioritize trajectory determination for agents ranked higher in the ranked list. Ranking criteria can be based on relevance, such as importance or criticality, of an identified agent. For example, if an agent is within a threshold distance of a vehicle, that agent may be determined to be of higher relevance to the vehicle than an agent that is not within the threshold distance of the vehicle. In some embodiments, agents can be ranked based on various attributes associated with the agents.” This system will generate a list of agents in the area of the vehicle. This system will rank this list based on the different factors including distance to the agent is to the current vehicle.)
“partitioning the generated list in at least one partition based on the importance score of the at least one other agent, wherein each partition includes at least one other agent;” (Detailed Description, pp. 5, [0032]; “The agent ranking module 206 can be configured to rank (or filter) agents (e.g., agents identified from the sensor data module 204) based on various ranking criteria to generate a ranked list of agents. The ranked list can be used to prioritize trajectory determination for agents ranked higher in the ranked list.” After the list is generated, the system will rank the different agents on the list. This list is used to prioritize different agents in the environment. The higher the score the higher the priority the system assigns.)
“generating a prediction result by predicting a behavior of the at least one agent included in the at least one partition based on the sensor data and the position and motion information using a selected behavior prediction model, wherein the selected behavior prediction model is selected from a plurality of behavior prediction models based on the importance score of the at least one agent included in the at least one partition; and” (Detailed Description, pp. 5, [0035]; “The algorithm selection module 210 can be configured to select a trajectory prediction algorithm, from a plurality of trajectory prediction algorithms, to predict a trajectory for an agent. The selection of a trajectory prediction algorithm can be based on a context associated with the agent as indicated by the agent's attribute values.” This model will use data from the environment and the current system to predict behaviors of other agents. This system uses multiple different trajectory prediction algorithms and one must be selected. This system will rank the agents on a list as stated earlier and this list is used to determine a priority score, which is also considered when using the trajectory prediction algorithm.)
Usman fails to explicitly disclose, “acquiring sensor data from at least one sensor physically sensing the environment, including information on the at least one other agent and on physical structures in the environment;”, “acquiring data including information on position and motion of the agent;”, “calculating an importance score for each agent of the at least one other agent based on the acquired sensor data and the acquired information on position and motion, wherein the importance score is a quantitative physical value describing a relevance of the particular agent for the operated agent based on the agent's dynamics;” and “outputting the prediction result to a planning and control system for operating the agent.”.
However, Eggert discloses, “acquiring sensor data from at least one sensor physically sensing the environment, including information on the at least one other agent and on physical structures in the environment;” (Detailed Description, pp. 3, [0038]; “The image processing module 11 receives the signals from the cameras 4 . . . 7, and identifies the lane of the ego-vehicle 1 and objects, course of the road and traffic signs in the environment of the ego-vehicle 1.” This application discloses an autonomous vehicle which uses sensors. These sensors detect objects in the roadway and traffic signs in the environment.)
“acquiring data including information on position and motion of the agent;” (Detailed Description, pp. 3, [0041]; “The prediction module 16 calculates at least one hypothetical future trajectory for the ego-vehicle 1 based on the status data received from the vehicle controller 10, the information received from the image-processing module 11, the signals received from the front radar 2 and the rear radar 3, and, in the case of autonomously driving, information on the driving route.” This invention will use data from the vehicle, including sensor data and vehicle positioning and location.)
“calculating an importance score for each agent of the at least one other agent based on the acquired sensor data and the acquired information on position and motion, wherein the importance score is a quantitative physical value describing a relevance of the particular agent for the operated agent based on the agent's dynamics;” (Detailed Description, pp. 4, [0047]; “The prediction module 16 predicts a future behavior for the traffic participant 18 based on the selected prediction model, the information received from the image processing module 11 and the signals received from the front radar 2 and the rear radar 3 and calculates a behavior relevant score for ego-vehicle 1 based on the calculated trajectories of ego-vehicle 1 and the traffic participant 18.” The prediction module in this invention will take in data from different sources in order to produce a behavior relevant score.) And (Detailed Description, pp. 4, [0049]; “As shown in FIG. 7, the behavior relevant score BRS is a function over the time within the prediction horizon during which the prediction is considered to remain valid. In FIG. 7, the function of the behavior relevant score BRS indicates a high risk in the distant future (maximum at time t1). In order to determine an ego-vehicle behavior, the BRS for all other traffic participants that are considered for behavior planning, the respective BRS is integrated over the prediction horizon.” The behavior relevant score is a quantitative value used by the system to predict and avoid collisions with other vehicles in the in the driving environment.)
“outputting the prediction result to a planning and control system for operating the agent.” (Detailed Description, pp. 2, [0034]; “FIG. 1 shows a side view of an ego-vehicle 1, equipped with the inventive system for assisting a driver in driving the ego-vehicle 1. The assistance may be provided in the form of information output, especially warnings or recommendations, to the driver in dangerous situations with respect to other traffic participants and/or in the form of autonomously or partially autonomously driving the ego-vehicle 1.” This system will output a result to the system and the driver. This output can be in the form of warnings, sounds and or corrections by the system automatically.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Usman and Eggert. Usman teaches a system which is able to assist or control an autonomous vehicle and handling multiple agents within the systems environment. Eggert teaches a system that assists a driver of a vehicle by collecting data around the primary system and evaluate trajectories and threats. One of ordinary skill would have motivation to combine a system which uses sensors and other data to evaluate a primary systems environment and operate or assist a driver in a vehicle with a system that is able to assist a driver in a vehicle by analyzing the environment using sensors and other data, “It is an object of the present invention to overcome the above-mentioned drawbacks and to provide an improved method for assisting a driver in driving a vehicle. More specifically, it is an object of the invention to provide a method for use in a driver assistance system of an ego-vehicle, a driver assistance system, and a vehicle comprising such driver assistance system, with which the behavior of the ego-vehicle can be planned with low effort and costs, and which produces safe, useful, and comfortable ego-trajectories.” (Eggert, Summary, pp. 1, [0007])
Regarding claim 2, Usman discloses, “wherein the plurality of behavior prediction models comprises individual behavior prediction models which differ with respect to at least one of prediction accuracy and computational complexity.” (Detailed Description, pp. 5, [0035]; “The algorithm selection module 210 can be configured to select a trajectory prediction algorithm, from a plurality of trajectory prediction algorithms, to predict a trajectory for an agent. The selection of a trajectory prediction algorithm can be based on a context associated with the agent as indicated by the agent's attribute values. As mentioned, a one-size-fits-all algorithm may not provide accurate trajectory predictions for all agents in various contexts. For example, a trajectory prediction algorithm may be better suited for quickly predicting linear trajectories. However, some agents may require a more sophisticated trajectory prediction algorithm to predict their complex non-linear trajectories.” This model uses multiple different trajectory models to predict the trajectory of another agent in an environment. Based on a given ranking by the system different trajectory algorithms can be applied to different agents. As stated above the system can evaluate an agent to determine the priority of that agent and apply different algorithms to this agent to predict its trajectory. Agents that meet certain criteria will have use a more complex trajectory algorithm when compared to lower priority agents.)
Regarding claim 3, Usman discloses, “wherein the at least one partition comprise at least a first partition including the other agents having a first range of the importance score and a second partition including the other agents having a second range of the importance score,” (Detailed Description, pp. 5, [0032]; “The agent ranking module 206 can be configured to rank (or filter) agents (e.g., agents identified from the sensor data module 204) based on various ranking criteria to generate a ranked list of agents. The ranked list can be used to prioritize trajectory determination for agents ranked higher in the ranked list.” The system will rank the agents around the current vehicle. This will generate a list of agents and rank them based on different factors. This system can also filter the agent based on certain threshold criteria. This article mentions that some agents can have a higher rank and a lower rank.)
“the first range includes importance scores, which are smaller than the importance scores included in the second range, and” (Detailed Description, pp. 5, [0032]; “Ranking criteria can be based on relevance, such as importance or criticality, of an identified agent. For example, if an agent is within a threshold distance of a vehicle, that agent may be determined to be of higher relevance to the vehicle than an agent that is not within the threshold distance of the vehicle. In some embodiments, agents can be ranked based on various attributes associated with the agents. For example, an agent that has threshold likelihood of interacting with the vehicle can be ranked higher than an agent that does not satisfy the threshold likelihood of interacting with the vehicle. In another example, an agent that is traveling in an opposite direction of the vehicle can be ranked lower than agents traveling in the same direction as the vehicle. By ranking agents for prioritizing trajectory determination, the agent ranking module 206 permits efficient allocation of computational resources to agents that are more likely to impact operation of the vehicle while accounting for any time-based constraints for completing agent trajectory predictions.” This system will rank the agents according to different factors. The agents that have the higher score have a higher chance of collision or proximity to the systems vehicle, therefore that vehicle will have a higher score when compared to a vehicle that is not travelling in the same direction and wont intercept the systems vehicle.)
“the method comprises selecting the behavior prediction model for the first partition with a lower computational complexity than the behavior prediction model for the second partition.” (Detailed Description, pp. 3, [0026]; “In some embodiments, these algorithm properties can also be considered by the vehicle when selecting a trajectory prediction algorithm to predict an agent's trajectory. For example, a first trajectory prediction algorithm may require a lesser amount of time to compute a trajectory for a given object than a second trajectory prediction algorithm. However, the second trajectory prediction algorithm may more accurately predict a trajectory for the object than the first trajectory prediction algorithm. In this example, the vehicle may select the first trajectory prediction to predict the object's trajectory when time is a factor and the second trajectory prediction to predict the object's trajectory when time is not a factor.” This system uses an algorithm to determine which trajectory algorithm to use. Some of the trajectory algorithms are more complex than others and require more resources. Therefore, this system will only use the computationally complex algorithms on high priority, or higher ranked vehicles, and lower-level complexity algorithms on lower priority, or ranked, vehicles.)
Regarding claim 4, Usman discloses, “the method comprises discarding other agents with a calculated importance score below a threshold when generating the list.” (Detailed Description, pp. 7, [0051]; “At block 454, optionally, the vehicle can filter the identified agents based on various criteria. For example, if an agent is a threshold distance away from the vehicle (e.g., > or =100 m, etc.), then the agent may be considered not sufficiently relevant and thus a trajectory need not be determined for the agent.” When analyzing the agents around a vehicle the system can disregard certain objects or actions that do not meet a threshold. This is designed so that the system does not need waster time and energy on non-essential or critical agents.)
Regarding claim 5, Usman fails to explicitly disclose, “wherein the calculation of the importance score comprises applying a risk shadowing process to the data on the at least one other agent and on the physical structures in the environment.”.
However, Eggert discloses, “wherein the calculation of the importance score comprises applying a risk shadowing process to the data on the at least one other agent and on the physical structures in the environment.” (Summary, pp. 2, [0016]; “The behavior relevant score can be calculated as risk and indicate, for the hypothetical future trajectory of the ego-vehicle, collision probability, collision severity, product of collision probability and collision severity, Time-of-Closest-Encounter, Time-To-Closest-Encounter, Position-of-Closest-Encounter or Distance-of-Closest-Encounter.” This model will take into consideration the distance to other vehicles and a probability of collision with the system vehicle. This is taken into consideration when the system is developing the behavior relevant score.)
Regarding claim 6, Usman fails to explicitly disclose, “wherein the risk shadowing process comprises performing a reachability analysis based on the sensor data and the position and motion information for determining occupied areas for the agent and the at least one other agent in the environment over time, determining overlapping areas from the occupied areas of the agent and the at least one other agent, and determining a relevance of each of the at least one other agent based on the determined overlapping areas.”.
However, Eggert discloses, “wherein the risk shadowing process comprises performing a reachability analysis based on the sensor data and the position and motion information for determining occupied areas for the agent and the at least one other agent in the environment over time, determining overlapping areas from the occupied areas of the agent and the at least one other agent, and determining a relevance of each of the at least one other agent based on the determined overlapping areas.” (Detailed Description, pp. 4, [0048]; “The behavior relevant score is relevant to plan/ control the behavior of the ego-vehicle 1 and could be negatively correlated to the safety of the ego-trajectory (i.e., a high collision risk corresponds to a high behavior relevant score). For example, the distance between the ego-vehicle 1 and the traffic participant 18 or a product of collision probability and collision severity for each point in time can be used to calculate the behavior relevant score.” This system uses a behavior relevancy score to determine vehicle behavior and actions. To get the score the system will use data from sensors on the vehicle, the trajectory prediction and other modules. The system will use sensors to evaluate the environment as well as trajectory predations of other agents to determine areas of potential conflict or collision. This includes evaluation of another agents predicted trajectory and to see if it overlaps with the current systems trajectory.)
Regarding claim 7, Usman discloses, “wherein determining the relevance of each of the at least one other agent includes disregarding the at least one other agent in case the occupied areas of the agent and the at least one other agent do not exist, or” (Detailed Description, pp. 7, [0051]; “At block 454, optionally, the vehicle can filter the identified agents based on various criteria. For example, if an agent is a threshold distance away from the vehicle (e.g., > or =100 m, etc.), then the agent may be considered not sufficiently relevant and thus a trajectory need not be determined for the agent.” The system proposed will evaluate different agents and filter out agents that do not meet a threshold. This is used to save on computations. The system will disregard agents based on different attributes such as distance.)
“computing a continuous relevance score for the at least one other agent by calculating a size of the overlapping area or a distance of the occupied areas.” (Detailed Description, pp. 9, [0062]; “FIG. 6B illustrates an example method 620, according to an embodiment of the present technology. At block 622, a first observed position of an agent at a first time can be determined from sensor data captured. At block 624, a plurality of trajectory prediction algorithms can be applied to the agent at the first observed position to predict respective positions of the agent at a second time as predicted by the plurality of trajectory prediction algorithms. At block 626, a second observed position of the agent at the second time can be determined from the sensor data captured. At block 628, the second observed position and the respective positions can be compared to generate a plurality of performance metrics.” This figure discloses two different sets of data on a single agent over a period of time. This teaches that this system will regularly repeat evaluation of agents over time and will alter the metrics based on the updated sensor data after a period of time.)
Regarding claim 8, Usman discloses, “wherein calculating the importance score comprises decreasing an influence of a first agent included in the at least one other agent based on an influence of at least one second agent of the at least one other agent.” (Detailed Description, pp. 5, [0033]; “The agent attributes module 208 can be configured to determine attributes and values of the attributes for agents detected in an environment. Each of the identified agents can be associated with various attributes and values for the attributes. The agent attributes module 208 can determine the values of the attributes based on various information acquired from various sources. For example, the agent attributes module 208 can determine attribute values by processing sensor data obtained by the sensor data module 204 and semantic map data for determining semantic locations of agents in an environment.”) and (Detailed Description, pp. 5, [0037]; “FIG. 3 illustrates example attributes 300 that can be associated with an agent, according to an embodiment of the present technology. Each attribute may include an attribute description 302, an associated value 304, one or more sources 306 from which the attribute value is determined, and/or a priority associated with the attribute 308. As shown, one example attribute 310 can indicate whether an agent should yield. Another example attribute 312 can indicate an agent's direction relative to "ego" (or a vehicle that is predicting a trajectory of the agent). The "Direction relative to Ego" attribute 312 may be associated with a value, which can be one of the values in an enumeration of "Same", "Opposite", or "Crossing." Further, the "Direction relative to Ego" attribute 312 value may be determined from sources including perception data ("pep") describing surroundings of the ego vehicle, position data ("pos") describing positions of the agent and the ego vehicle, and map data ("map") describing geographic and semantic locations of the agent and ego vehicle.” This model will consider the attributes of another agent in the sensor area. These attributes influence the ranking of the agent and can increase/decrease the risk of that agent depending on the agents predicted trajectory and other attributes and agent characteristics.)
Regarding claim 9, Usman discloses, “wherein calculating the importance score includes applying a risk model that regards an influence of the at least one other agent on the agent for computing the importance score of the at least one other agent.” (Detailed Description, pp. 5, [0034]; “The agent attributes module 208 can be configured to determine attributes and values of the attributes for agents detected in an environment. Each of the identified agents can be associated with various attributes and values for the attributes. The agent attributes module 208 can determine the values of the attributes based on various information acquired from various sources. For example, the agent attributes module 208 can determine attribute values by processing sensor data obtained by the sensor data module 204 and semantic map data for determining semantic locations of agents in an environment.”) and (Detailed Description, pp. 5, [0032]; “The agent ranking module 206 can be configured to rank (or filter) agents (e.g., agents identified from the sensor data module 204) based on various ranking criteria to generate a ranked list of agents. The ranked list can be used to prioritize trajectory determination for agents ranked higher in the ranked list. Ranking criteria can be based on relevance, such as importance or criticality, of an identified agent.” This system uses a ranking module to evaluate the different agents based on ranking criteria. This ranking can use the different attributes assigned to agents by the attribute module. This system will also use sensor, motion and positional data to evaluate the risk of another agent.)
Regarding claim 10, Usman discloses, “wherein the method comprises calculating the importance score as a first importance score for each of a plurality of other agents by applying a first method, discarding a subset of the plurality other agent based on a first filter criterion for the calculated first importance score for generating a first filtered list of the other agents,” (Detailed Description, pp. 8, [0058]-[0059]; “In some embodiments, clusters can be determined for similar contexts based on a clustering technique. Any suitable conventional clustering technique can be used. Once clusters are determined, various trajectory prediction algorithms can be applied to the clusters to determine confidence scores associated with the trajectory prediction algorithms with respect to the clusters. Each cluster can be associated with a trajectory prediction algorithm that best predicts trajectories for contexts represented by the cluster. [0059] In the example diagram 500, three clusters are associated with their own trajectory prediction algorithms. A cluster 504 of vehicles with a velocity that falls within a first pre-defined range is associated with algorithm C because algorithm C predicts trajectories for similar contexts in the cluster 504 better than algorithm A and algorithm B (e.g., performance metrics of the algorithm C are superior to the corresponding performance metrics of algorithms A and B).” The system proposed in Usman is able to group and cluster agents accordingly. These different clusters have different risks associated and are ranked by ranking module. The system is able to determine, based on many factors, which trajectory algorithm is to be used. Some of the algorithms are computationally complex and take a disclosed amount of time to complete. Further, this system is able to identify agents that pose little to no risk and are not evaluated by the trajectory algorithms.)
“calculating the importance score as a second importance score by applying a second method for the plurality of other agents, discarding a second subset of the plurality of other agents included in the generated list based on a second filter criterion for the calculated second importance score for generating a second filtered list of the plurality of other agents.” (Detailed Description, pp. 8, [0059]; “A cluster 506 of vehicles with a velocity that falls within a second pre-defined range is associated with the algorithm A, because algorithm A predicts trajectories for contexts represented by the cluster 506 better than algorithm B or algorithm C. A cluster 508 of pedestrians with a velocity that falls within a third pre-defined range is associated with algorithm B, because algorithm B predicts trajectories for the contexts represented by the cluster 508 better than algorithm A or algorithm C. In some cases, it can be determined that none of the trajectory prediction algorithms accurately predict trajectories for some contexts 510, such as a context 502d. The outlier contexts 510 can indicate a need to tune an existing trajectory prediction algorithm or, in some cases, introduce a new trajectory prediction algorithm to more accurately predict trajectories for the outlier contexts 510.” This system uses different trajectory algorithms to predict agents’ movements. This system can cluster agents and a determined trajectory algorithm might be applied to a cluster based on algorithm selection module. Depending on many factors, a more complex algorithm might be applied to one agent in a cluster. Finally, agents may move from cluster to cluster based on updated information, causing the agents to be evaluated by a different less complex algorithm.)
Regarding claim 11, Usman discloses, “wherein the first method for calculating the first importance score has a lower computational complexity than the second method for calculating the second importance score.” (Detailed Description, pp. 5, [0035]; “The algorithm selection module 210 can be configured to select a trajectory prediction algorithm, from a plurality of trajectory prediction algorithms, to predict a trajectory for an agent. The selection of a trajectory prediction algorithm can be based on a context associated with the agent as indicated by the agent's attribute values. As mentioned, a one-size-fits-all algorithm may not provide accurate trajectory predictions for all agents in various contexts. For example, a trajectory prediction algorithm may be better suited for quickly predicting linear trajectories. However, some agents may require a more sophisticated trajectory prediction algorithm to predict their complex non-linear trajectories. For example, a vehicle blinking a right turn signal and decelerating while at a rightmost lane is more likely to make a right turn at an intersection than another vehicle blinking a right turn signal at a leftmost lane and maintaining velocity. Furthermore, some trajectory prediction algorithms may better predict trajectories for certain types of agents with a certain set of attribute-values indicating a certain context.” This model uses multiple trajectory algorithms to predict agents’ movements. Some of the algorithms are more complex and computationally expense when compared to others. As stated above, a one-size-fits- all model does not work in this environment, therefore they developed multiple algorithms of different complexities to perform predictions.)
Regarding claim 12, Usman fails to explicitly disclose, “wherein calculating the importance score uses a path distance model, a trajectory distance model, a Gaussian model, or a survival analysis model.”.
However, Eggert discloses, “wherein calculating the importance score uses a path distance model, a trajectory distance model, a Gaussian model, or a survival analysis model.” (Detailed Description, pp. 4, [0048]; “The behavior relevant score is relevant to plan/ control the behavior of the ego-vehicle 1 and could be negatively correlated to the safety of the ego-trajectory (i.e., a high collision risk corresponds to a high behavior relevant score). For example, the distance between the ego-vehicle 1 and the traffic participant 18 or a product of collision probability and collision severity for each point in time can be used to calculate the behavior relevant score.” This system will calculate a behavior relevancy score. This score will take into consideration trajectory of agents and itself to assist in controlling the vehicle.)
Regarding claim 13, Usman discloses, “wherein the agent is a mobile robotic device, and the at least one other agent includes other mobile devices, in particular other mobile robotic devices, or” (Detailed description, pp. 4, [0029]” In various embodiments, the context-specific prediction module 202 can be implemented by a vehicle being driven semi-autonomously or autonomously. While the vehicle navigates an environment, the context-specific prediction module 202 can detect various agents present in the environment based on sensor data captured by sensors of the vehicle. For example, the context-specific prediction module 202 can detect agents such as other vehicles, pedestrians, bicyclists, animals, among other objects, to name some examples. The context-specific prediction module 202 can also determine related attribute values for the detected agents.” This system, as stated above, can work on fully autonomous vehicles. These systems disclosed are considered mobile autonomous devices which are able to evaluate other mobile autonomous or manual devices in an environment.)
Usman fails to explicitly disclose, “the agent is an ego-vehicle, and the at least one other agent is at least one other traffic participant in a road traffic scenario, in particular at least one of other vehicles, cyclists and pedestrians, or”.
However, Eggert discloses, “the agent is an ego-vehicle, and the at least one other agent is at least one other traffic participant in a road traffic scenario, in particular at least one of other vehicles, cyclists and pedestrians, or” (Detailed Description, pp. 2-3, [0035]; “The ego-vehicle 1 may be any type of vehicle including, but not limited to, cars, trucks, motorcycles, busses, and reacts to surrounding objects (traffic participants), such as pedestrians, automobiles, and bicycles. [0036] In FIG. 1, a front radar 2, a rear radar 3 and cameras 4 ... 7 sensing the environment around the ego-vehicle 1 are mounted on a front surface of the ego-vehicle 1, a rear surface of the ego-vehicle 1, and the roof of the ego-vehicle 1, respectively. The cameras 4 ... 7 preferably are positioned so that a 360° surveillance around the ego-vehicle 1 is possible. Alternatively, or in addition, a stereo camera system and/or lidar sensors can be mounted on the ego vehicle 1. A position sensor 8, e.g. a GPS navigation device, is mounted on the ego-vehicle 1 and detects the position of the ego-vehicle 1. The driver assistance system of the ego-vehicle 1 further comprises a computer 9 that receives or acquires the signals from the front radar 2, the rear radar 3, the cameras 4 ... 7, the position sensor 8, and status data of the ego-vehicle 1, such as vehicle speed, steering angle, engine torque, brake actuation, from of at least one vehicle controller 10 (ECU).” The model in this application is designed to assist a driver of a motor vehicle. This model uses sensor data around the vehicle and other data to assist the driver in normal driving operations. These operations include driving with vehicle and pedestrian traffic on a motorway.)
Both Usman and Eggert fail to explicitly disclose, “the operated agent and the at least one other agent are vessels in a maritime traffic scenario, or”, “the operated agent and the at least one other agent are air vehicles in an air traffic scenario, or” and “the operated agent and the at least one other agent are space vehicles in a space environment.” However, the way the claim has been composed, it designates each of these limitations as potential possibilities of operating environment while using the keyword “or”. Under the broadest reasonable interpretation and use of the keyword “or”, these limitations are optional and therefore have not been mapped to prior art.
Regarding claim 14, Usman discloses, “A computer program product comprising a non-transitory computer readable medium having a computer readable program embodied therein, wherein the computer readable program, when executed on a computing device, causes the computing device to perform a method for assisting operation of an agent operating in a dynamic environment involving at least one other agent in the environment of the agent, wherein the method comprises:” (Detailed Description, pp. 13, [0083]; “In particular embodiments, memory 804 includes main memory for storing instructions for processor 802 to execute or data for processor 802 to operate on. As an example, and not by way of limitation, computer system 800 may load instructions from storage 806 or another source (such as another computer system 800) to memory 804. Processor 802 may then load the instructions from memory 804 to an internal register or internal cache. To execute the instructions, processor 802 may retrieve the instructions from the internal register or internal cache and decode them. During or after execution of the instructions, processor 802 may write one or more results (which may be intermediate or final results) to the internal register or internal cache.” The proposed system uses a computing system similar to a generic computer. This includes using processors which are linked to memory which stores machine code for the processing system. This system is designed to operate in a dynamic environment using sensors to perform different vehicle operations.)
“generating a list comprising the at least one other agent based on the calculated importance scores of the at least one other agent;” (Detailed Description, pp. 5, [0032]; “The ranked list can be used to prioritize trajectory determination for agents ranked higher in the ranked list. Ranking criteria can be based on relevance, such as importance or criticality, of an identified agent. For example, if an agent is within a threshold distance of a vehicle, that agent may be determined to be of higher relevance to the vehicle than an agent that is not within the threshold distance of the vehicle. In some embodiments, agents can be ranked based on various attributes associated with the agents.” This system will generate a list of agents in the area of the vehicle. This system will rank this list based on the different factors including distance to the agent is to the current vehicle.)
“partitioning the generated list in at least one partition based on the importance score of the at least one other agent, wherein each partition includes at least one other agents;” (Detailed Description, pp. 5, [0032]; “The agent ranking module 206 can be configured to rank (or filter) agents (e.g., agents identified from the sensor data module 204) based on various ranking criteria to generate a ranked list of agents. The ranked list can be used to prioritize trajectory determination for agents ranked higher in the ranked list.” After the list is generated, the system will rank the different agents on the list. This list is used to prioritize different agents in the environment. The higher the score the higher the priority the system assigns.)
“generating a prediction result by predicting a behavior of the at least one agent included in the at least one partition based on the sensor data and the position and motion information using a selected behavior prediction model, wherein the selected behavior prediction model is selected from a plurality of behavior prediction models based on the importance score of the at least one agent included in the at least one partition; and” (Detailed Description, pp. 5, [0035]; “The algorithm selection module 210 can be configured to select a trajectory prediction algorithm, from a plurality of trajectory prediction algorithms, to predict a trajectory for an agent. The selection of a trajectory prediction algorithm can be based on a context associated with the agent as indicated by the agent's attribute values.” This model will use data from the environment and the current system to predict behaviors of other agents. This system uses multiple different trajectory prediction algorithms and one must be selected. This system will rank the agents on a list as stated earlier and this list is used to determine a priority score, which is also considered when using the trajectory prediction algorithm.)
Usman fails to explicitly disclose, “acquiring sensor data from at least one sensor physically sensing the environment, including information on the at least one other agent and on physical structures in the environment;”, “acquiring data including information on position and motion of the agent;”, “calculating an importance score for each agent of the at least one other agent based on the acquired sensor data and the acquired information on position and motion, wherein the importance score is a quantitative physical value describing a relevance of the particular agent for the operated agent based on the agent's dynamics;” and “outputting the prediction result to a planning and control system for operating the agent.”.
However, Eggert discloses, “acquiring sensor data from at least one sensor physically sensing the environment, including information on the at least one other agent and on physical structures in the environment;” (Detailed Description, pp. 3, [0038]; “The image processing module 11 receives the signals from the cameras 4 . . . 7, and identifies the lane of the ego-vehicle 1 and objects, course of the road and traffic signs in the environment of the ego-vehicle 1.” This application discloses an autonomous vehicle which uses sensors. These sensors detect objects in the roadway and traffic signs in the environment.)
“acquiring data including information on position and motion of the agent;” (Detailed Description, pp. 3, [0041]; “The prediction module 16 calculates at least one hypothetical future trajectory for the ego-vehicle 1 based on the status data received from the vehicle controller 10, the information received from the image-processing module 11, the signals received from the front radar 2 and the rear radar 3, and, in the case of autonomously driving, information on the driving route.” This invention will use data from the vehicle, including sensor data and vehicle positioning and location.)
“calculating an importance score for each agent of the at least one other agent based on the acquired sensor data and the acquired information on position and motion, wherein the importance score is a quantitative physical value describing a relevance of the particular agent for the operated agent based on the agent's dynamics;” (Detailed Description, pp. 4, [0047]; “The prediction module 16 predicts a future behavior for the traffic participant 18 based on the selected prediction model, the information received from the image processing module 11 and the signals received from the front radar 2 and the rear radar 3 and calculates a behavior relevant score for ego-vehicle 1 based on the calculated trajectories of ego-vehicle 1 and the traffic participant 18.” The prediction module in this invention will take in data from different sources in order to produce a behavior relevant score.) And (Detailed Description, pp. 4, [0049]; “As shown in FIG. 7, the behavior relevant score BRS is a function over the time within the prediction horizon during which the prediction is considered to remain valid. In FIG. 7, the function of the behavior relevant score BRS indicates a high risk in the distant future (maximum at time t1). In order to determine an ego-vehicle behavior, the BRS for all other traffic participants that are considered for behavior planning, the respective BRS is integrated over the prediction horizon.” The behavior relevant score is a quantitative value used by the system to predict and avoid collisions with other vehicles in the in the driving environment.)
“outputting the prediction result to a planning and control system for operating the agent.” (Detailed Description, pp. 2, [0034]; “FIG. 1 shows a side view of an ego-vehicle 1, equipped with the inventive system for assisting a driver in driving the ego-vehicle 1. The assistance may be provided in the form of information output, especially warnings or recommendations, to the driver in dangerous situations with respect to other traffic participants and/or in the form of autonomously or partially autonomously driving the ego-vehicle 1.” This system will output a result to the system and the driver. This output can be in the form of warnings, sounds and or corrections by the system automatically.)
Regarding claim 15, Usman discloses, “A system for assisting operation of an agent operating in a dynamic scenario involving at least one other agent in an environment of the agent, the system comprises:” (Detailed Description, pp. 13, [0081]; “In particular embodiments, computer system 800 includes a processor 802, memory 804, storage 806, an input/output (I/O) interface 808, a communication interface 810, and a bus 812. Although this disclosure describes and illustrates a particular computer system having a particular number of particular components in a particular arrangement, this disclosure contemplates any suitable computer system having any suitable number of any suitable components in any suitable arrangement.” The proposed system uses a computing system similar to a generic computer. This includes using processors which are linked to memory which stores machine code for the processing system. This system is designed to operate in a dynamic environment using sensors to perform different vehicle operations.)
“generate a list comprising the at least one other agent based on the calculated importance scores of the at least one other agent,” (Detailed Description, pp. 5, [0032]; “The ranked list can be used to prioritize trajectory determination for agents ranked higher in the ranked list. Ranking criteria can be based on relevance, such as importance or criticality, of an identified agent. For example, if an agent is within a threshold distance of a vehicle, that agent may be determined to be of higher relevance to the vehicle than an agent that is not within the threshold distance of the vehicle. In some embodiments, agents can be ranked based on various attributes associated with the agents.” This system will generate a list of agents in the area of the vehicle. This system will rank this list based on the different factors including distance to the agent is to the current vehicle.)
partition the generated list in at least one partition based on the importance score of the at least one other agent, wherein each partition includes at least one other agent,” (Detailed Description, pp. 5, [0032]; “The agent ranking module 206 can be configured to rank (or filter) agents (e.g., agents identified from the sensor data module 204) based on various ranking criteria to generate a ranked list of agents. The ranked list can be used to prioritize trajectory determination for agents ranked higher in the ranked list.” After the list is generated, the system will rank the different agents on the list. This list is used to prioritize different agents in the environment. The higher the score the higher the priority the system assigns.)
“generate a prediction result by predicting a behavior of the at least one other agent included in the at least one partition based on the sensor data and the position and motion data using a selected behavior prediction model, wherein the selected behavior prediction model is selected from a plurality of behavior prediction models based on the importance score of the at least one other agent included in the at least one partition, and” (Detailed Description, pp. 5, [0035]; “The algorithm selection module 210 can be configured to select a trajectory prediction algorithm, from a plurality of trajectory prediction algorithms, to predict a trajectory for an agent. The selection of a trajectory prediction algorithm can be based on a context associated with the agent as indicated by the agent's attribute values.” This model will use data from the environment and the current system to predict behaviors of other agents. This system uses multiple different trajectory prediction algorithms and one must be selected. This system will rank the agents on a list as stated earlier and this list is used to determine a priority score, which is also considered when using the trajectory prediction algorithm.)
Usman fails to explicitly disclose, “at least one sensor configured to acquire sensor data from the environment including information on the at least one other agent and on physical structures in the environment;”, “at least one ego-sensor configured to acquire data including information on position and motion on the agent; and”, “at least one processor configured to: calculate an importance score for each agent of the at least one other agent based on the acquired sensor data and the acquired position and motion data, wherein the importance score is a quantitative physical value describing a relevance of the particular agent for the operated agent based on the agent's dynamics,”, and “to output the prediction result to a planning and control system for operating the agent.”.
However, Eggert discloses, “at least one sensor configured to acquire sensor data from the environment including information on the at least one other agent and on physical structures in the environment;” (Detailed Description, pp. 3, [0038]; “The image processing module 11 receives the signals from the cameras 4 . . . 7, and identifies the lane of the ego-vehicle 1 and objects, course of the road and traffic signs in the environment of the ego-vehicle 1.” This application discloses an autonomous vehicle which uses sensors. These sensors detect objects in the roadway and traffic signs in the environment.)
“at least one ego-sensor configured to acquire data including information on position and motion on the agent; and” (Detailed Description, pp. 3, [0041]; “The prediction module 16 calculates at least one hypothetical future trajectory for the ego-vehicle 1 based on the status data received from the vehicle controller 10, the information received from the image-processing module 11, the signals received from the front radar 2 and the rear radar 3, and, in the case of autonomously driving, information on the driving route.” This invention will use data from the vehicle, including sensor data and vehicle positioning and location.)
“at least one processor configured to: calculate an importance score for each agent of the at least one other agent based on the acquired sensor data and the acquired position and motion data, wherein the importance score is a quantitative physical value describing a relevance of the particular agent for the operated agent based on the agent's dynamics,” (Detailed Description, pp. 4, [0047]; “The prediction module 16 predicts a future behavior for the traffic participant 18 based on the selected prediction model, the information received from the image processing module 11 and the signals received from the front radar 2 and the rear radar 3 and calculates a behavior relevant score for ego-vehicle 1 based on the calculated trajectories of ego-vehicle 1 and the traffic participant 18.” The prediction module in this invention will take in data from different sources in order to produce a behavior relevant score.) And (Detailed Description, pp. 4, [0049]; “As shown in FIG. 7, the behavior relevant score BRS is a function over the time within the prediction horizon during which the prediction is considered to remain valid. In FIG. 7, the function of the behavior relevant score BRS indicates a high risk in the distant future (maximum at time t1). In order to determine an ego-vehicle behavior, the BRS for all other traffic participants that are considered for behavior planning, the respective BRS is integrated over the prediction horizon.” The behavior relevant score is a quantitative value used by the system to predict and avoid collisions with other vehicles in the in the driving environment.)
“to output the prediction result to a planning and control system for operating the agent”. (Detailed Description, pp. 2, [0034]; “FIG. 1 shows a side view of an ego-vehicle 1, equipped with the inventive system for assisting a driver in driving the ego-vehicle 1. The assistance may be provided in the form of information output, especially warnings or recommendations, to the driver in dangerous situations with respect to other traffic participants and/or in the form of autonomously or partially autonomously driving the ego-vehicle 1.” This system will output a result to the system and the driver. This output can be in the form of warnings, sounds and or corrections by the system automatically.)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL MICHAEL GALVIN-SIEBENALER whose telephone number is (571)272-1257. The examiner can normally be reached Monday - Friday 8AM to 5PM.
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/PAUL M GALVIN-SIEBENALER/Examiner, Art Unit 2147
/ERIC NILSSON/Primary Examiner, Art Unit 2151