DETAILED ACTION
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 .
Introduction
This is a response to applicant’s submissions filed on December 18, 2025. Claims 1-4, 6, 8-14, 16, and 18-20 are pending.
Examiner' s Note
Examiner has cited particular paragraphs / columns and line numbers or figures in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, in preparing the responses, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. Applicant is reminded that the Examiner is entitled to give the broadest reasonable interpretation to the language of the claims. Furthermore, the Examiner is not limited to Applicants' definition which is not specifically set forth in the disclosure.
Response to Arguments
All of applicant’s arguments have been considered.
Regarding applicant’s argument that the cited references merely acknowledge that laws exist (Applicant’s Response, pg. 14), the examiner respectfully disagrees. Parasuram takes into account common laws and determines what the other vehicle will do according to those common laws.
Regarding applicant’s argument that the amendment defines a specific database-driven path filtering mechanism which uses a deterministic selection process that uses a structured databased to filter candidate paths (Applicant’s Response, pg. 14). It is noted that these limitations are not recited in the claim. The claim recites “selecting, from a plurality of sampled surrounding vehicle paths, one or more paths that comply with the traffic regulations according to a database of national laws and regulations”, which under broadest reasonable interpretation is determining a path based on the path complying with laws and regulations. Further, “limitations from the specification are not read into the claims”. See MPEP 2145 VI.
Regarding applicant’s argument that Parasuram fails to teach the Database-Driven Path filtering Mechanism (Applicant’s Response, pg. 15). It is noted that this limitation is not recited in the claim. The claim recites “selecting, from a plurality of sampled surrounding vehicle paths, one or more paths that comply with the traffic regulations according to a database of national laws and regulations”, which under broadest reasonable interpretation is determining a path based on the path complying with laws and regulations. Parasuram takes into account common laws and determines what the other vehicle will do according to those common laws.
Regarding applicant’s argument that Parasuram acknowledges the existence of legal requirements but fails to disclose the technical means for an autonomous system to determine compliance (Applicant’s Response, pg. 15). It is noted that this limitation is not recited in the claim. The claim recites “selecting, from a plurality of sampled surrounding vehicle paths, one or more paths that comply with the traffic regulations according to a database of national laws and regulations”, which under broadest reasonable interpretation is determining a path based on the path complying with laws and regulations. Parasuram takes into account common laws and determines what the other vehicle will do according to those common laws.
Regarding applicant’s argument that Parasuram does not teach accessing a database and selecting/filtering sample paths based on that database to derive the final state (Applicant’s Response, pg. 15). It is noted that this limitation is not recited in the claim. The claim recites “selecting, from a plurality of sampled surrounding vehicle paths, one or more paths that comply with the traffic regulations according to a database of national laws and regulations”, which under broadest reasonable interpretation is determining a path based on the path complying with laws and regulations. Parasuram takes into account common laws and determines what the other vehicle will do according to those common laws.
Regarding applicant’s argument that Parasuram predicts what a vehicle might do based on learned patterns (Applicant’s Response, pg. 15), the examiner respectfully disagrees. Parasuram predicts what a vehicle might do according to common laws, not what the vehicle had done previously.
Regarding applicant’s argument that Tseng does not teach “selecting…paths…according to a database” (Applicant’s Response, pg. 16). It is noted that Tseng is not relied upon to meet this limitation.
Regarding applicant’s argument that Xu does not teach sampling multiple paths, querying a specific database of national laws and regulations, and filtering those paths to find compliant one to establish a final state (Applicant’s Response, pg. 16). It is noted that Xu is not relied upon to meet this limitation.
Regarding applicant’s argument that there is no motivation to incorporate the database-driven path filtering mechanism (Applicant’s Response, pg. 16). It is noted that this limitation is not recited in the claim. The claim recites “selecting, from a plurality of sampled surrounding vehicle paths, one or more paths that comply with the traffic regulations according to a database of national laws and regulations”, which under broadest reasonable interpretation is determining a path based on the path complying with laws and regulations.
Regarding applicant’s argument that there is no suggestion to introduce a rigid, rule-based database of national laws and regulations to filter paths (Applicant’s Response, pg. 16-17). It is noted that this limitation is not recited in the claim. The claim recites “selecting, from a plurality of sampled surrounding vehicle paths, one or more paths that comply with the traffic regulations according to a database of national laws and regulations”, which under broadest reasonable interpretation is determining a path based on the path complying with laws and regulations.
Specification
Amendments to the specification were received on December 18, 2025.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
Determining module in claim 1.
Path prediction module in claims 1, 3, 6, 8, and 10.
Emergency decision module in claim 1.
Control module in claim 1.
General decision module in claim 4.
Probability calculation unit in claims 6 and 10.
Path optimization unit in claims 8 and 9.
The determining module, path prediction module, emergency decision module, control module, general decision module, probability calculation unit, and path optimization unit are interpreted as parts of a processor as disclosed in paragraphs 18, 31, and 39 and structural equivalents thereof.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Parasuram (US 2023/0139578) in view of Tseng (US 2017/0192429).
Regarding claims 1 and 11, Parasuram discloses an autonomous vehicle path prediction system and method, suitable for an autonomous vehicle (Parasuram, [0012] regarding an on-board system of an autonomous vehicle that can generate trajectory predictions for agents in its vicinity), the autonomous vehicle path prediction system comprising:
a sensor, used for sensing a plurality of vehicles driving on a road and generating sensing signals corresponding to the vehicles, wherein the vehicles comprise at least one surrounding vehicle (Parasuram, [0024], regarding a sensor system which enables the on-board system to "see" the environment in the vicinity of the vehicle & Fig. 1a regarding another vehicle being in the vicinity of the vehicle); and
a processor, coupled to the sensor, the processor comprising:
a determining module, used for determining whether the vehicles further comprise an emergency vehicle according to the sensing signals of the vehicles (Parasuram, [0028] regarding using raw sensor data to generate scene feature data & [0029] regarding the scene feature data including whether the agent is an emergency vehicle);
a path prediction module, configured to:
obtain a current state of the at least one surrounding vehicle and a current state of the emergency vehicle, wherein the current state comprises position, acceleration, and steering angle (Parasuram, [0029] regarding the scene feature data including the acceleration, location, and trajectory of each agent),
in response to determining that an emergency vehicle is present, determine a final state of each surrounding vehicle based on traffic regulations for emergency vehicle encounters (Parasuram, [0013] regarding expecting the other vehicle to pull over to the side of the road when an active emergency vehicle is in the vicinity which is required by law), comprising:
selecting, from a plurality of sampled surrounding vehicle paths, one or more paths that comply with the traffic regulations according to a database of national laws and regulations (Parasuram, [0019] regarding a trajectory that the other vehicle might following being required by law); and
obtaining the final state based on the selected one or more paths (Parasuram, [0034] regarding generating trajectory predictions for other vehicles when there is an active emergency vehicle in the vicinity & [0013] regarding expecting the other vehicle to pull over to the side of the road when an active emergency vehicle is in the vicinity which is required by law); and
perform an emergency path prediction corresponding to the emergency vehicle, wherein the emergency path prediction considers predicted behaviors of the at least one surrounding vehicles transitioning from their current states to their final states in compliance with the traffic regulations (Parasuram, [0034] regarding generating trajectory predictions for other vehicles when there is an active emergency vehicle in the vicinity);
an emergency decision module, configured to:
generate an emergency autonomous driving decision according to the emergency path prediction and the final states of the at least one surrounding vehicles, and
provide autonomous vehicle path planning corresponding to the emergency autonomous driving decision (Parasuram, [0036] regarding the planning system using the trajectory prediction output data to update a planned trajectory for the vehicle), and
a control module, used for controlling the autonomous vehicle to change a driving path and a driving mode on the road according to the autonomous vehicle path planning (Parasuram, [0037] regarding implementing the fully-autonomous driving decisions generated by the planning system using a control system of the vehicle).
Parasuram does not disclose determining whether the vehicles further comprise an emergency vehicle according to a vehicle-to-everything (V2X).
Tseng teaches determining whether the vehicles further comprise an emergency vehicle according to a vehicle-to-everything (V2X) (Tseng, [0013] regarding the detection system 105 detecting emergency vehicles via vehicle-to-infrastructure and vehicle-to-vehicle and sensors).
Parasuram and Tseng are considered to be analogous to the claimed invention because they are in the same field of emergency vehicle detection. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Parasuram to incorporate detecting an emergency vehicle using vehicle-to-infrastructure, as disclosed by Tseng, with a reasonable expectation of success because doing so would yield the predictable result of have a way to verify the sensor detection is accurate.
Claims 2-4, 6, 8-10, 12-14, 16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Parasuram in view of Tseng, and further in view of Xu (US 2020/0339116).
Regarding claim 2, Parasuram in view of Tseng teaches the autonomous path prediction system as claimed in claim 1. Parasuram further teaches wherein the plurality of sampled surrounding vehicle paths are sampled according to the current state of the at least one surrounding vehicle (Parasuram, [0034] regarding generating trajectory predictions for other vehicles when there is an active emergency vehicle in the vicinity).
Parasuram does not explicitly disclose wherein the plurality of sampled surrounding vehicle paths are sampled according to map information.
Xu teaches wherein the plurality of sampled surrounding vehicle paths are sampled according to map information (Xu, [0039] regarding the prediction module 303 prediction being in view of map information 311).
Parasuram and Xu are considered to be analogous to the claimed invention because they are in the same field of vehicle path prediction. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Parasuram, as modified, to incorporate using map information to predict vehicle paths, as disclosed by Xu, with a reasonable expectation of success because doing so would yield the predictable result of using the map to aid in the determination of possible paths of another vehicle.
Regarding claim 3, Parasuram in view of Tseng and Xu teaches the autonomous path prediction system as claimed in claim 2. Xu further teaches wherein the path prediction module performs a general path prediction corresponding to the at least one surrounding vehicle when the determining module determines that the emergency vehicle is not present in the vehicles (Xu, [0065] regarding the prediction module 303 determining one or more possible object paths & [0039] regarding the prediction module 303 prediction being in view of map information 311 and traffic rules 312).
Parasuram and Xu are considered to be analogous to the claimed invention because they are in the same field of vehicle path prediction. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Parasuram, as modified, to incorporate predicting the paths of other vehicles, as disclosed by Xu, with a reasonable expectation of success because doing so would yield the predictable result of using the prediction to control the vehicle safely.
Regarding claim 4, Parasuram in view of Tseng and Xu teaches the autonomous path prediction system as claimed in claim 3. Xu further teaches wherein the processor further comprises:
a general decision module, configured to generate a general autonomous driving decision according to the general path prediction, and provide the autonomous vehicle path planning corresponding to the general autonomous driving decision (Xu, [0065] regarding the vehicle planning a path to navigate and avoid collision with the moving object based on the lowest trajectory costs associated with the possible object paths).
Parasuram and Xu are considered to be analogous to the claimed invention because they are in the same field of vehicle path prediction. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Parasuram, as modified, to incorporate operating the vehicle based on the predicted path of another vehicle, as disclosed by Xu, with a reasonable expectation of success because doing so would yield the predictable result of increasing safety by choosing controlling the vehicle to avoid the other vehicle’s most likely path.
Regarding claim 6, Parasuram in view of Tseng teaches the autonomous path prediction system as claimed in claim 1. Parasuram further teaches wherein the path prediction module further comprises a probability calculation unit configured to calculate a plurality of path probabilities of the at least one surrounding vehicle (Parasuram, [0065] regarding a trajectory evaluation machine learning model being used to determine the likelihood that an agent will follow the trajectory),
wherein the path probabilities are between 0 and 1, and a sum of the path probabilities is 1 (Parasuram, [0065] regarding the probability of occurrence of each trajectory is between 0 and 1. The sum of all probabilities has to equal 1.).
Parasuram does not explicitly teach wherein the path prediction module further comprises a probability calculation unit configured to calculate a plurality of path probabilities of the at least one surrounding vehicle according to path costs of the at least one surrounding vehicle.
Xu teaches wherein the path prediction module further comprises a probability calculation unit configured to calculate a plurality of path probabilities of the at least one surrounding vehicle according to path costs of the at least one surrounding vehicle (Xu, [0048] regarding a cost calculator 403 for calculating a cost of each trajectory & [0059] regarding a likelihood probability being determined based on the trajectory cost).
Parasuram and Xu are considered to be analogous to the claimed invention because they are in the same field of vehicle path prediction. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Parasuram, as modified, to incorporate using trajectory costs to determine a trajectory probability, as disclosed by Xu, with a reasonable expectation of success because doing so would yield the predictable result of weighing factors that are more significant to get a more accurate probability.
Regarding claim 8, Parasuram in view of Tseng teaches the autonomous path prediction system as claimed in claim 1. Parasuram further teaches wherein the path prediction module further comprises a path optimization unit configured to calculate a plurality of surrounding vehicle paths of the at least one surrounding vehicle (Parasuram, [0034] regarding generating trajectory predictions for other vehicles when there is an active emergency vehicle in the vicinity).
Parasuram does not teach wherein the path prediction module further comprises a path optimization unit configured to calculate a plurality of surrounding vehicle paths of the at least one surrounding vehicle and corresponding path costs according to the current state of the at least one surrounding vehicle and the final states of the at least one surrounding vehicle,
wherein the path optimization unit calculates a plurality of expended times that is expended for each of the surrounding vehicle paths,
segments each of the expended times into a plurality of time units and forming a plurality of time sets, and
calculates the path costs corresponding to the surrounding vehicle paths in each of the time sets with each of the time units as a calculation unit.
Xu teaches wherein the path prediction module further comprises a path optimization unit configured to calculate a plurality of surrounding vehicle paths of the at least one surrounding vehicle and corresponding path costs according to the current state of the at least one surrounding vehicle and the final states of the at least one surrounding vehicle (Xu, [0048] regarding a cost calculator 403 for calculating a cost of each trajectory),
wherein the path optimization unit calculates a plurality of expended times that is expended for each of the surrounding vehicle paths (Xu, [0061] regarding calculating acceleration and collision costs over the course of 8 seconds in 1 second increments),
segments each of the expended times into a plurality of time units and forming a plurality of time sets (Xu, [0061] regarding calculating acceleration and collision costs over the course of 8 seconds in 1 second increments), and
calculates the path costs corresponding to the surrounding vehicle paths in each of the time sets with each of the time units as a calculation unit (Xu, [0061] regarding calculating acceleration and collision costs over the course of 8 seconds in 1 second increments).
Parasuram and Xu are considered to be analogous to the claimed invention because they are in the same field of vehicle path prediction. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Parasuram, as modified, to incorporate calculating the trajectory costs over small increments, as disclosed by Xu, with a reasonable expectation of success because doing so would yield the predictable result of calculating a more accurate cost and probability for each trajectory.
Regarding claim 9, Parasuram in view of Tseng and Xu teaches the autonomous path prediction system as claimed in claim 8. Xu further teaches wherein the path optimization unit obtains a position, a velocity vector, and an acceleration vector of the at least one surrounding vehicle in each of the time units in each of the time sets (Xu, [0037] regarding position of another vehicle & [0056] regarding speed and acceleration based on speed of the moving object), and
calculates the path costs corresponding to the surrounding vehicle paths (Xu, [0058] regarding calculating trajectory costs).
Parasuram and Xu are considered to be analogous to the claimed invention because they are in the same field of vehicle path prediction. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Parasuram, as modified, to incorporate using position, velocity, and acceleration to determine trajectory costs, as disclosed by Xu, with a reasonable expectation of success because doing so would yield the predictable result of calculating a more accurate cost and probability for each trajectory.
Regarding claim 10, Parasuram in view of Tseng teaches the autonomous path prediction system as claimed in claim 1. Parasuram further teaches wherein the path prediction module further comprises a probability calculation unit configured to calculate a plurality of path probabilities of the at least one surrounding vehicle (Parasuram, [0065] regarding a trajectory evaluation machine learning model being used to determine the likelihood that an agent will follow the trajectory).
Parasuram does not explicitly teach wherein the path prediction module further comprises a probability calculation unit configured to calculate a plurality of path probabilities of the at least one surrounding vehicle according to path costs of the at least one surrounding vehicle,
wherein the path prediction module
selects a surrounding vehicle path corresponding to a maximum path probability of the plurality of path probabilities of the at least one surrounding vehicle, and
performs the emergency path prediction corresponding to the emergency vehicle or the general path prediction corresponding to the at least one surrounding vehicle according to the selected surrounding vehicle path corresponding to the maximum path probability of the plurality of path probabilities of the at least one surrounding vehicle.
Xu teaches wherein the path prediction module further comprises a probability calculation unit configured to calculate a plurality of path probabilities of the at least one surrounding vehicle according to path costs of the at least one surrounding vehicle (Xu, [0048] regarding a cost calculator 403 for calculating a cost of each trajectory & [0059] regarding a likelihood probability being determined based on the trajectory cost),
wherein the path prediction module
selects a surrounding vehicle path corresponding to a maximum path probability of the plurality of path probabilities of the at least one surrounding vehicle (Xu, [0061] regarding the highest moving probability being utilized as the final predicted movement), and
performs the emergency path prediction corresponding to the emergency vehicle or the general path prediction corresponding to the at least one surrounding vehicle according to the selected surrounding vehicle path corresponding to the maximum path probability of the plurality of path probabilities of the at least one surrounding vehicle (Xu, Fig. 8 regarding planning a path to avoid collision with the moving object based on the lowest trajectory costs).
Parasuram and Xu are considered to be analogous to the claimed invention because they are in the same field of vehicle path prediction. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Parasuram, as modified, to incorporate controlling the vehicle based on the highest probability trajectory of the other vehicle, as disclosed by Xu, with a reasonable expectation of success because doing so would yield the predictable result of increasing the safety of the action taken by the vehicle.
Regarding claim 12, Parasuram in view of Tseng teaches the autonomous vehicle path prediction method as claimed in claim 11. Parasuram does not teach performing a general path prediction corresponding to the at least one surrounding vehicle when the emergency vehicle is not present in the vehicles.
Xu teaches performing a general path prediction corresponding to the at least one surrounding vehicle when the emergency vehicle is not present in the vehicles (Xu, [0065] regarding the prediction module 303 determining one or more possible object paths & [0039] regarding the prediction module 303 prediction being in view of map information 311 and traffic rules 312).
Parasuram and Xu are considered to be analogous to the claimed invention because they are in the same field of vehicle path prediction. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Parasuram, as modified, to incorporate predicting the paths of other vehicles, as disclosed by Xu, with a reasonable expectation of success because doing so would yield the predictable result of using the prediction to control the vehicle safely.
Regarding claim 13, Parasuram in view of Tseng and Xu teaches the autonomous vehicle path prediction method as claimed in claim 12. Xu further teaches generating a general autonomous driving decision according to the general path prediction, and providing the autonomous vehicle path planning corresponding to the general autonomous driving decision (Xu, [0065] regarding the vehicle planning a path to navigate and avoid collision with the moving object based on the lowest trajectory costs associated with the possible object paths).
Parasuram and Xu are considered to be analogous to the claimed invention because they are in the same field of vehicle path prediction. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Parasuram, as modified, to incorporate operating the vehicle based on the predicted path of another vehicle, as disclosed by Xu, with a reasonable expectation of success because doing so would yield the predictable result of increasing safety by choosing controlling the vehicle to avoid the other vehicle’s most likely path.
Regarding claim 14, Parasuram in view of Tseng teaches the autonomous vehicle path prediction method as claimed in claim 11. Parasuram further teaches wherein the plurality of sampled surrounding vehicle paths of the at least one surrounding vehicle are sampled according to the current state of the at least one surrounding vehicle (Parasuram, [0034] regarding generating trajectory predictions for other vehicles when there is an active emergency vehicle in the vicinity).
Parasuram does not explicitly teach wherein the plurality of sampled surrounding vehicle paths of the at least one surrounding vehicle are sampled according to map information.
Xu further teaches wherein the plurality of sampled surrounding vehicle paths of the at least one surrounding vehicle are sampled according to map information and the current state of the at least one surrounding vehicle (Xu, [0039] regarding the prediction module 303 prediction being in view of map information 311 and traffic rules 312).
Xu teaches sampling a plurality of surrounding vehicle paths according to map information (Xu, [0039] regarding the prediction module 303 prediction being in view of map information 311).
Parasuram and Xu are considered to be analogous to the claimed invention because they are in the same field of vehicle path prediction. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Parasuram, as modified, to incorporate using map information to predict vehicle paths, as disclosed by Xu, with a reasonable expectation of success because doing so would yield the predictable result of using the map to aid in the determination of possible paths of another vehicle.
Regarding claim 16, Parasuram in view of Tseng teaches the autonomous vehicle path prediction method as claimed in claim 11. Parasuram further teaches calculating a plurality of path probabilities of the at least one surrounding vehicle (Parasuram, [0065] regarding a trajectory evaluation machine learning model being used to determine the likelihood that an agent will follow the trajectory),and
wherein the path probabilities are between 0 and 1, and a sum of the path probabilities is 1 (Parasuram, [0065] regarding the probability of occurrence of each trajectory is between 0 and 1. The sum of all probabilities has to equal 1.).
Parasuram does not explicitly teach calculating a plurality of path probabilities of the at least one surrounding vehicle according to path costs of the at least one surrounding vehicle,
wherein the path costs and the path probabilities are in an inverse relationship.
Xu teaches calculating a plurality of path probabilities of the at least one surrounding vehicle according to path costs of the at least one surrounding vehicle (Xu, [0048] regarding a cost calculator 403 for calculating a cost of each trajectory & [0059] regarding a likelihood probability being determined based on the trajectory cost),
wherein the path costs and the path probabilities are in an inverse relationship (Xu, [0059] regarding likelihood probability being determined based on the lowest trajectory cost).
Parasuram and Xu are considered to be analogous to the claimed invention because they are in the same field of vehicle path prediction. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Parasuram, as modified, to incorporate using trajectory costs to determine a trajectory probability, as disclosed by Xu, with a reasonable expectation of success because doing so would yield the predictable result of weighing factors that are more significant to get a more accurate probability.
Regarding claim 18, Parasuram in view of Tseng teaches the autonomous vehicle path prediction method as claimed in claim 11. Parasuram further teaches calculating a plurality of surrounding vehicle paths of the at least one surrounding vehicle (Parasuram, [0034] regarding generating trajectory predictions for other vehicles when there is an active emergency vehicle in the vicinity).
Parasuram does not teach calculating a plurality of surrounding vehicle paths of the at least one surrounding vehicle and corresponding path costs according to the current state of the at least one surrounding vehicle and the final states of the at least one surrounding vehicle;
calculating a plurality of expended times that is expended for each of the surrounding vehicle paths;
segmenting each of the expended times into a plurality of time units and forming a plurality of time sets; and
calculating the path costs corresponding to the surrounding vehicle paths in each of the time sets with each of the time units as a calculation unit.
Xu teaches calculating a plurality of surrounding vehicle paths of the at least one surrounding vehicle and corresponding path costs according to the current state of the at least one surrounding vehicle and the final states of the at least one surrounding vehicle (Xu, [0048] regarding a cost calculator 403 for calculating a cost of each trajectory);
calculating a plurality of expended times that is expended for each of the surrounding vehicle paths (Xu, [0061] regarding calculating acceleration and collision costs over the course of 8 seconds in 1 second increments);
segmenting each of the expended times into a plurality of time units and forming a plurality of time sets (Xu, [0061] regarding calculating acceleration and collision costs over the course of 8 seconds in 1 second increments); and
calculating the path costs corresponding to the surrounding vehicle paths in each of the time sets with each of the time units as a calculation unit (Xu, [0061] regarding calculating acceleration and collision costs over the course of 8 seconds in 1 second increments).
Parasuram and Xu are considered to be analogous to the claimed invention because they are in the same field of vehicle path prediction. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Parasuram, as modified, to incorporate calculating the trajectory costs over small increments, as disclosed by Xu, with a reasonable expectation of success because doing so would yield the predictable result of calculating a more accurate cost and probability for each trajectory.
Regarding claim 19, Parasuram in view of Tseng and Xu teaches the autonomous vehicle path prediction method as claimed in claim 18. Xu further teaches obtaining a position, a velocity vector, and an acceleration vector of the at least one surrounding vehicle in each of the time units in each of the time sets, and calculating the path costs corresponding to the surrounding vehicle paths (Xu, [0037] regarding position of another vehicle, [0056] regarding speed and acceleration based on speed of the moving object & [0058] regarding calculating trajectory costs).
Parasuram and Xu are considered to be analogous to the claimed invention because they are in the same field of vehicle path prediction. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Parasuram, as modified, to incorporate using position, velocity, and acceleration to determine trajectory costs, as disclosed by Xu, with a reasonable expectation of success because doing so would yield the predictable result of calculating a more accurate cost and probability for each trajectory.
Regarding claim 20, Parasuram in view of Tseng teaches the autonomous vehicle path prediction method as claimed in claim 11. Parasuram further teaches calculating a plurality of path probabilities of the at least one surrounding vehicle (Parasuram, [0065] regarding a trajectory evaluation machine learning model being used to determine the likelihood that an agent will follow the trajectory).
Parasuram does not teach calculating a plurality of path probabilities of the at least one surrounding vehicle according to path costs of the at least one surrounding vehicle, wherein the path costs and the path probabilities are in an inverse relationship;
selecting a surrounding vehicle path corresponding to a maximum path probability of the plurality of path probabilities of the at least one surrounding vehicle; and
performing the emergency path prediction corresponding to the emergency vehicle or the general path prediction corresponding to the at least one surrounding vehicle according to the selected surrounding vehicle path corresponding to the maximum path probability of the plurality of path probabilities of the at least one surrounding vehicle.
Xu teaches calculating a plurality of path probabilities of the at least one surrounding vehicle according to path costs of the at least one surrounding vehicle, wherein the path costs and the path probabilities are in an inverse relationship (Xu, [0048] regarding a cost calculator 403 for calculating a cost of each trajectory & [0059] regarding a likelihood probability being determined based on the lowest trajectory cost);
selecting a surrounding vehicle path corresponding to a maximum path probability of the plurality of path probabilities of the at least one surrounding vehicle (Xu, [0061] regarding the highest moving probability being utilized as the final predicted movement); and
performing the emergency path prediction corresponding to the emergency vehicle or the general path prediction corresponding to the at least one surrounding vehicle according to the selected surrounding vehicle path corresponding to the maximum path probability of the plurality of path probabilities of the at least one surrounding vehicle (Xu, Fig. 8 regarding planning a path to avoid collision with the moving object based on the lowest trajectory costs).
Parasuram and Xu are considered to be analogous to the claimed invention because they are in the same field of vehicle path prediction. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Parasuram, as modified, to incorporate controlling the vehicle based on the highest probability trajectory of the other vehicle, as disclosed by Xu, with a reasonable expectation of success because doing so would yield the predictable result of increasing the safety of the action taken by the vehicle.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ALEX B GRIFFIN/Examiner, Art Unit 3665
/Erin D Bishop/Supervisory Patent Examiner, Art Unit 3665