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 .
Response to Arguments
Claim Rejections - 35 USC § 101:
Applicant's arguments filed 12/04/2025 have been fully considered but they are not persuasive. The amendment to independent claims 15, 23 and 26-27 does not overcome the rejection. The amended additional limitations of “obtaining, from each of the automated vehicles…”, “generating…a respective relevance distribution vector…”, “comparing…a relevance distribution vector…” and “updating…the plurality of automated vehicles…”; the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer to perform the process. In particular, the obtaining steps from external sources is recited at a high level of generality (i.e. as a general means of gathering scenario and safety metric data for use in the determining step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The generating and comparing steps are also recited at a high level of generality (i.e. as a general means of processing and transmitting data from the determining steps ), and amounts to mere post solution, which is a form of insignificant extra-solution activity. Additionally, the updating step is also recited at a high level of generality (i.e. as a general means of data output from the determining step), and amounts to mere post solution, which is a form of insignificant post-solution activity. Lastly, the computer merely describes how to generally “apply” the otherwise mental judgements in a generic or general purpose vehicle control environment.
Taken alone, the amended additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular vehicle navigation or control problem, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Thus, the rejection is maintained.
Claim Rejections - 35 USC § 103:
Applicant’s arguments with respect to independent claims 15, 23 and 26-27 have been considered but are moot because the new ground of rejection does not rely on the Sadeghi et al. (US 20250225051 A1) reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 112
Claims 15 and 18-27 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent claims 15, 23 and 26-27 recited subject matter regarding a distribution vector that is not described in the specification as filed (e.g. a respective relevance distribution vector formed of a plurality of vector components).
Claims 16-22 and 24-25 are rejected based on dependence on independent claims 15 and 23.
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 15 and 18-27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 15 is directed to a method for improving safety precautions for vehicles (i.e., a process). Therefore, claim 15 is within at least one of the four statutory categories.
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed
to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Claim 15 includes limitations that recite an abstract idea (emphasized below)
and will be used as a representative claim for the remainder of the 101 rejection. Claim 15 recites:
A method, performed by a central server in communication with a plurality of automated vehicles moving in an at least partially automated manner, for improving safety precautions of the plurality of automated vehicles, the method comprising the following steps:
obtaining, from each of the automated vehicles, data representing instances of scenarios detected by a sensor system of the vehicle; for each of the instances of the scenarios for which the data is obtained,
obtaining, from the each of the automated vehicles, information indicating, for each of a plurality of predefined vehicle operational safety metrics, whether fulfillment of the respective safety metric coincided with the respective instance of the scenario the predefined vehicle operational safety metrics including: exceedance of a first threshold, falling below a second threshold, and/or existence of a predefined state; for each of the scenarios
generating, by the central server, a respective relevance distribution vector formed of a plurality of vector components, wherein each of the vector components indicates a percentage of the instances of the respective scenario in which fulfillment of a corresponding one of the plurality of predefined vehicle operational safety metrics has been recorded to coincide with the instance of the scenario;
comparing, by the central server, a relevance distribution vector of a particular one of the scenarios to other stored relevance distribution vectors of other different scenarios which have been set as respective trigger events for triggering an automated vehicle operation; based on the comparison,
determining, by the central server, that the relevance distribution vector of the particular scenario is classified as a match, within predefined limits, to one or more of the other stored relevance distribution vectors that are stored in association with those of the different scenarios whose occurrence has been set as trigger events,
in response to the match, updating, by the central server, the plurality of automated vehicles to set the particular scenario as a new trigger event for the automated vehicle operation.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “determining” in the context of this claim encompasses a person looking at data collected and forming a simple judgement. Accordingly, the claim recites at least one abstract idea.
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a practical application.
In the present case, the additional limitations beyond the above-noted abstract idea are as
follows (where the underlined portions are the “additional limitations” while the bolded portions
continue to represent the “abstract idea”:
A method, performed by a central server in communication with a plurality of automated vehicles moving in an at least partially automated manner, for improving safety precautions of the plurality of automated vehicles, the method comprising the following steps:
obtaining, from each of the automated vehicles, data representing instances of scenarios detected by a sensor system of the vehicle; for each of the instances of the scenarios for which the data is obtained,
obtaining, from the each of the automated vehicles, information indicating, for each of a plurality of predefined vehicle operational safety metrics, whether fulfillment of the respective safety metric coincided with the respective instance of the scenario the predefined vehicle operational safety metrics including: exceedance of a first threshold, falling below a second threshold, and/or existence of a predefined state; for each of the scenarios
generating, by the central server, a respective relevance distribution vector formed of a plurality of vector components, wherein each of the vector components indicates a percentage of the instances of the respective scenario in which fulfillment of a corresponding one of the plurality of predefined vehicle operational safety metrics has been recorded to coincide with the instance of the scenario;
comparing, by the central server, a relevance distribution vector of a particular one of the scenarios to other stored relevance distribution vectors of other different scenarios which have been set as respective trigger events for triggering an automated vehicle operation; based on the comparison,
determining, by the central server, that the relevance distribution vector of the particular scenario is classified as a match, within predefined limits, to one or more of the other stored relevance distribution vectors that are stored in association with those of the different scenarios whose occurrence has been set as trigger events,
in response to the match, updating, by the central server, the plurality of automated vehicles to set the particular scenario as a new trigger event for the automated vehicle operation.
For the following reason(s), the examiner submits that the above identified additional
limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “obtaining, from each of the automated vehicles…”, “generating…a respective relevance distribution vector…”, “comparing…a relevance distribution vector…” and “updating…the plurality of automated vehicles…”; the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer to perform the process. In particular, the obtaining steps from external sources is recited at a high level of generality (i.e. as a general means of gathering scenario and safety metric data for use in the determining step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The generating and comparing steps are also recited at a high level of generality (i.e. as a general means of processing and transmitting data from the determining steps ), and amounts to mere post solution, which is a form of insignificant extra-solution activity. Additionally, the updating step is also recited at a high level of generality (i.e. as a general means of data output from the determining step), and amounts to mere post solution, which is a form of insignificant post-solution activity. Lastly, the computer merely describes how to generally “apply” the otherwise mental judgements in a generic or general purpose vehicle control environment. The method for improving safety precautions for vehicles is recited at a high level of generality and merely automates the defines and determining step.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular vehicle navigation or control problem, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Regarding Step 2B of the 2019 PEG, representative independent claim 15 does not include
additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer to perform the defines and determining… amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of “obtaining, from each of the automated vehicles…”, “generating…a respective relevance distribution vector…”, “comparing…a relevance distribution vector…” and “updating…the plurality of automated vehicles…”; the examiner submits that these limitations are insignificant extra-solution activities.
Further, a conclusion that an additional element is insignificant extra-solution activity in
Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well understood, routine, conventional activity in the field. The additional limitations of “obtaining, from each of the automated vehicles…”, “generating…a respective relevance distribution vector…”, “comparing…a relevance distribution vector…” and “updating…the plurality of automated vehicles…” are well-understood, routine, and conventional activities, and the specification does not provide any indication that the computer is anything other than a conventional computer network component. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Hence, the claim is not patent eligible.
Same analysis applied to independent claims 23, 26 and 27.
Dependent claims 18-22 and 24-25 do not recite any further limitations that cause the claim to
be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 18-22 and 24-25 are not patent eligible under the same rationale as provided for in the rejection of Claim 15. Therefore, claims 15 and 18-27 are ineligible under 35 USC §101.
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 15 and 18-27 are rejected under 35 U.S.C. 103 as being unpatentable over Hendy et al. (US 11912301 B1; hereinafter Hendy) in view of Bai et al. (US 20170268896 A1; hereinafter Bai).
Regarding claim 15, Hendy teaches a method, performed by a central server (see at least, Fig 9, Network-934) in communication with a plurality of automated vehicles moving in an at least partially automated manner (see at least, Col 9 lines 65-66, log data collected by a fleet of autonomous vehicles operating in real-world environments), for improving safety precautions of the plurality of automated vehicles (see at least, (Col 19 lines 45-50, scenario analysis and the identification and/or generation of similar scenarios…may assist with improving overall operations of the autonomous vehicles when encountering new or unexpected situations), the method comprising the following steps: obtaining, from each of the automated vehicles, data representing instances of scenarios detected by a sensor system of the vehicle; for each of the instances of the scenarios for which the data is obtained (see at least, (Col 10 lines 18-23, If the scenario currently encountered by the autonomous vehicle is within a similarity threshold of one or more other scenarios labeled as having a risk value greater than a threshold (e.g., vectors are within a predetermined distance in the multi-dimensional space), the autonomous vehicle may determine a vehicle control action to be performed), obtaining, from the each of the automated vehicles, information indicating, for each of a plurality of predefined vehicle operational safety metrics, whether fulfillment of the respective safety metric coincided with the respective instance of the scenario (see at least, (Col 10 lines 13-18, The repository of previously encountered driving scenarios may be stored onboard the autonomous vehicle or in a separate remote server, and the previous driving scenarios may be labeled based on a risk or probability level of a collision, injury, or other safety-related risk) the predefined vehicle operational safety metrics including: exceedance of a first threshold, falling below a second threshold, and/or existence of a predefined state; for each of the scenarios (see at least, (Col 10 lines 18-23, If the scenario currently encountered by the autonomous vehicle is within a similarity threshold of one or more other scenarios labeled as having a risk value greater than a threshold (e.g., vectors are within a predetermined distance in the multi-dimensional space) generating, by the central server, a respective relevance distribution vector formed of a plurality of vector components (see at least, Fig 7, Col 21 lines 22-28, the scenario analysis system 300 may use a similarity threshold in operation 812…to select all vectors and retrieve the associated scenarios that are within the threshold distance to the input vector in the multi-dimensional space), wherein each of the vector components indicates a percentage of the instances of the respective scenario in which fulfillment of a corresponding one of the plurality of predefined vehicle operational safety metrics has been recorded to coincide with the instance of the scenario (see at least, Col 21 lines 9-17, the scenario retrieval component 314 may determine the exposure metric in operation 810 as a raw number or a percentage of the driving scenarios that are similar to the driving scenario input in operation 802…may provide an indication of the number of times and/or frequency that a particular vehicle system failure or other vehicle behavior is likely to manifest for one or more autonomous vehicles operating in real-world environments); comparing, by the central server, a relevance distribution vector of a particular one of the scenarios to other stored relevance distribution vectors of other different scenarios which have been set as respective trigger events for triggering an automated vehicle operation (see at least, Col 10 lines 9-24, the scenario analysis system executing within the autonomous vehicle…may…compare the current scenario to a repository of previously encountered driving scenarios…may be stored…in a separate remote server, and the previous driving scenarios may be labeled based on a risk or probability level of a collision, …may determine a vehicle control action to be performed, such as the activation of a collision avoidance system (CAS)); based on the comparison, determining, by the central server, that the relevance distribution vector of the particular scenario is classified as a match, within predefined limits, to one or more of the other stored relevance distribution vectors that are stored in association with those of the different scenarios whose occurrence has been set as trigger events (see at least, Col 10 lines 32-35, the similarity of the current scenario of the autonomous vehicle to one or more similar high-risk scenarios may be used as a determining factor for activating or not activating a CAS, or may be used as weight value or an input into a separate model configured to determine when to activate a CAS or teleoperations device…may be used to determine additional vehicle control actions).
Hendy does not explicitly teach in response to the match, updating, by the central server, the plurality of automated vehicles to set the particular scenario as a new trigger event for the automated vehicle operation. However, Bai teaches this limitation.
Bai teaches in response to the match, updating, by the central server (see at least, [0040] CWS 202 may transmit and receive information directly or indirectly to and from a service provider 212 over a wireless communication network 204…includes a remote server 214), the plurality of automated vehicles (see at least, [0030] a vehicle communication… can include…automated drive vehicles) to set the particular scenario as a new trigger event for the automated vehicle operation (see at least, [0088] If comparison calculator 420 determines that newly-collected event data has a statistical match to historical event data, then the comparison calculator 420 stores the new event data into collision event database 422 with the matched prior event data. If the comparison calculator 420 determines that the new event data is not a statistical match to historical event data, new event data is stored into collision event database 422 as a single event).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hendy to include as taught by Bai on order to assist the driver of vehicle or another vehicle in communication with vehicle to avoid a road hazard or potential collision (Bai, [0068]).
Regarding claim 18, the combination of Hendy and Bai teaches the method according to claim 15. Hendy further teaches wherein each scenario is defined by respective values for one or more is defined by respective values for one or more information characterizing the scenario present at the time point includes operating data of the vehicle parameters and/or environmental parameters (see at least, Col 5 lines 20-33, the scenario analysis system may partition the environment around the vehicle into a number of discreet regions, and may determine a set of characteristics (e.g., occupancies) for each the region of the environment at different time intervals during the scenario).
Regarding claim 19, the combination of Hendy and Bai teaches the method according to claim 18. Hendy further teaches wherein the parameters (see at least, Col 5 lines 7-9, the perception system can determine, based on the sensor data, movement information about the agents in the environment) are determined from a recorded video or radar data, from the sensor system of the vehicle (Col 4 lines 50-51, Sensor data captured by the vehicle 114 can include LIDAR data, RADAR data).
Regarding claim 20, the combination of Hendy and Bai teaches the method according to claim 15. Hendy further teaches comprising, for each of the scenario instances for which the data is obtained: checking whether any instance of the respective scenario has already been stored, and, when the scenario has not already been stored, storing the scenario and/or when the scenario has already been stored, updating the relevance distribution vector of the scenario (see at least, Col 10 lines 9-17, the scenario analysis system executing within the autonomous vehicle…may…compare the current scenario to a repository of previously encountered driving scenarios…may be stored…in a separate remote server, and the previous driving scenarios may be labeled based on a risk or probability level of a collision).
Regarding claim 21, the combination of Hendy and Bai teaches the method according to claim 15. Hendy further teaches comprising, in response to obtained data representing an instance of a scenario that has already been defined as a trigger event, triggering a check of the vehicle (Col 10 lines 11-37, compare the current scenario to a repository of previously encountered driving scenarios…If the scenario currently encountered by the autonomous vehicle is within a similarity threshold of one or more other scenarios labeled as having a risk value greater than a threshold….The similarity data between the current scenario and previously stored and labeled scenarios also may be used to determine additional vehicle control actions).
Regarding claim 22, the combination of Hendy and Bai teaches the method according to claim 21. Hendy further teaches further comprising, in response to obtained data representing an instance of a scenario that has already been defined as a trigger event, triggering as automatic bringing of the vehicle into a safe state (Col 10 lines 13-26, If the scenario currently encountered by the autonomous vehicle is within a similarity threshold of one or more other scenarios labeled as having a risk value greater than a threshold...the autonomous vehicle may determine a vehicle control action to be performed, such as the activation of a collision avoidance system (CAS) or the initiation of a remote teleoperations computing device at the autonomous vehicle).
Regarding claim 23, Hendy teaches a method, performed by an automated vehicle of a plurality of automated vehicles moving in an at least partially automated manner (see at least, Col 9 lines 65-66, log data collected by a fleet of autonomous vehicles operating in real-world environments and in communication with a central server (see at least, Fig 9, Network-934), for improving safety precautions of the plurality of automated vehicles (see at least, (Col 19 lines 45-50, scenario analysis and the identification and/or generation of similar scenarios…may assist with improving overall operations of the autonomous vehicles when encountering new or unexpected situations), the method comprising the following steps: detecting by a sensor system of the automated vehicle during operation of the vehicle, instances of scenarios, each scenario including values for one or more vehicle and/or environmental parameters (see at least, Col 10 lines 9-17, the scenario analysis system executing within the autonomous vehicle…may…compare the current scenario to a repository of previously encountered driving scenarios…may be stored…in a separate remote server, and the previous driving scenarios may be labeled based on a risk or probability level of a collision); for each of the detected scenario instances: checking, by the automated vehicle, whether one or more predefined vehicle operational safety metrics are is fulfilled (see at least, (Col 10 lines 13-18, The repository of previously encountered driving scenarios may be stored onboard the autonomous vehicle or in a separate remote server, and the previous driving scenarios may be labeled based on a risk or probability level of a collision, injury, or other safety-related risk), the predefined vehicle operational safety metrics including: exceedance of a first threshold, falling below a second threshold, and/or existence of a predefined state (see at least, (Col 10 lines 18-23, If the scenario currently encountered by the autonomous vehicle is within a similarity threshold of one or more other scenarios labeled as having a risk value greater than a threshold (e.g., vectors are within a predetermined distance in the multi-dimensional space); and generating, in association with the respective detected scenario instance, safety metric information indicating, for each of the plurality of predefined vehicle operational safety metrics, whether fulfillment of the respective safety metric coincided with the scenario instance (see at least, Fig 7, Col 21 lines 22-28, the scenario analysis system 300 may use a similarity threshold in operation 812…to select all vectors and retrieve the associated scenarios that are within the threshold distance to the input vector in the multi-dimensional space); transmitting, from the automated vehicle to the central server the scenario information characterizing each detected scenario instance together with the associated safety metric information (see at least, Col 21 lines 9-17, the scenario retrieval component 314 may determine the exposure metric in operation 810 as a raw number or a
percentage of the driving scenarios that are similar to the driving scenario input in operation 802
…may provide an indication of the number of times and/or frequency that a particular vehicle system failure or other vehicle behavior is likely to manifest for one or more autonomous vehicles operating in real-world environments), the central server being configured to respond to receipt of the transmitted scenario information and associated safety metric information by (I) generating relevance distribution vectors for the scenarios formed of a plurality of vector components that each indicates a percentage of the instances of the respective scenario in which fulfillment of a corresponding one of the plurality of predefined vehicle operational safety metrics has been recorded to coincide with the instance of the scenario (see at least, Col 21 lines 9-17, the scenario retrieval component 314 may determine the exposure metric in operation 810 as a raw number or a percentage of the driving scenarios that are similar to the driving scenario input in operation 802…may provide an indication of the number of times and/or frequency that a particular vehicle system failure or other vehicle behavior is likely to manifest for one or more autonomous vehicles operating in real-world environments), (II) comparing the relevance distribution vectors generated for the detected scenario instances of the scenario information to other stored relevance distribution vectors of other different scenarios which have been set as respective trigger events for triggering an automated vehicle operation (see at least, Col 10 lines 9-24, the scenario analysis system executing within the autonomous vehicle…may…compare the current scenario to a repository of previously encountered driving scenarios…may be stored…in a separate remote server, and the previous driving scenarios may be labeled based on a risk or probability level of a collision, …may determine a vehicle control action to be performed, such as the activation of a collision avoidance system (CAS)); (III) in response to a result of the comparing being that a detected scenario instance is classified as a match, within predefined limits, to one or more of the other stored relevance distribution vectors that are stored in association with those of the different scenarios whose occurrence has been set as trigger events (see at least, Col 10 lines 32-35, the similarity of the current scenario of the autonomous vehicle to one or more similar high-risk scenarios may be used as a determining factor for activating or not activating a CAS, or may be used as weight value or an input into a separate model configured to determine when to activate a CAS or teleoperations device…may be used to determine additional vehicle control actions).
Hendy does not explicitly teach transmitting to the plurality of automated vehicles an indication of the matching detected scenario instance as being a new trigger event for an automated vehicle operation; and in response to receipt, by the automated vehicle, of the indication transmitted by the central server, responding to a new detection of the matching detected scenario instance by triggering the automated vehicle operation. However, Bai teaches this limitation.
Bai teaches transmitting (see at least, [0040] CWS 202 may transmit and receive information directly or indirectly to and from a service provider 212 over a wireless communication network 204…includes a remote server 214) to the plurality of automated vehicles (see at least, [0030] a vehicle
communication… can include…automated drive vehicles)an indication of the matching detected scenario instance as being a new trigger event for an automated vehicle operation; and in response to receipt, by the automated vehicle, of the indication transmitted by the central server, responding to a new detection of the matching detected scenario instance by triggering the automated vehicle operation (see at least, [0088] If comparison calculator 420 determines that newly-collected event data has a statistical match to historical event data, then the comparison calculator 420 stores the new event data into collision event database 422 with the matched prior event data. If the comparison calculator 420 determines that the new event data is not a statistical match to historical event data, new event data is stored into collision event database 422 as a single event).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hendy to include transmitting to the plurality of automated vehicles an indication of the matching detected scenario instance as being a new trigger event for an automated vehicle operation; and in response to receipt, by the automated vehicle, of the indication transmitted by the central server, responding to a new detection of the matching detected scenario instance by triggering the automated vehicle operation as taught by Bai on order to assist the driver of vehicle or another vehicle in communication with vehicle to avoid a road hazard or potential collision (Bai, [0068]).
Regarding claim 24, the combination of Hendy and Bai teaches the method according to claim 23. Hendy further teaches comprising, prior to transmitting, checking whether a trigger event for the scenario has already been defined, wherein, when a result of the check for a detected instance of one of the scenarios is that the scenario has already been defined as a trigger event, the transmitting is not performed for the detected instance (see at least, Col 10 lines 9-24, the scenario analysis system executing within the autonomous vehicle…may…compare the current scenario to a repository of previously encountered driving scenarios…may be stored…in a separate remote server, and the previous driving scenarios may be labeled based on a risk or probability level of a collision, …may determine a vehicle control action to be performed, such as the activation of a collision avoidance system (CAS)).
Regarding claim 25, the combination of Hendy and Bai teaches the method according to claim 15. Hendy further teaches wherein the scenarios include values of one or more of the following vehicle and/or environmental parameters: road type, speed, position, time of day, weather, recognized objects (Col 2 lines 32-37, the scenarios may include the data representing a road configuration around the vehicle, road conditions, weather conditions, lighting conditions, and various relevant agents and other objects in the environment).
Regarding claim 26, Hendy teaches a central server (see at least, Fig 9, Network-934) comprising: a processor (see at least, Fig 9, Processor-940); and a communication interface via which the processor is in communication with a plurality of automated vehicles moving in an at least partially automated manner (see at least, Col 9 lines 65-66, log data collected by a fleet of autonomous vehicles operating in real-world environments),wherein, for improving safety precautions of the plurality of vehicles (see at least, Col 19 lines 45-50, scenario analysis and the identification and/or generation of similar scenarios…may assist with improving overall operations of the autonomous vehicles when encountering new or unexpected situations), the processor is configured to: obtain, from each of the automated vehicles, data representing instances of scenarios detected by a sensor system of the vehicle; for each of the instances of the scenarios for which the data is obtained (see at least, (Col 10 lines 18-23, If the scenario currently encountered by the autonomous vehicle is within a similarity threshold of one or more other scenarios labeled as having a risk value greater than a threshold (e.g., vectors are within a predetermined distance in the multi-dimensional space), the autonomous vehicle may determine a vehicle control action to be performed), obtain, from the each of the automated vehicles, information indicating, for each of a plurality of predefined vehicle operational safety metrics, whether is fulfillment of the respective safety metric coincided with the respective instance of the scenario (see at least, (Col 10 lines 13-18, The repository of previously encountered driving scenarios may be stored onboard the autonomous vehicle or in a separate remote server, and the previous driving scenarios may be labeled based on a risk or probability level of a collision, injury, or other safety-related risk), the predefined vehicle operational safety metrics including: exceedance of a first threshold, falling below a second threshold, and/or existence of a predefined state; for each of the scenarios (see at least, (Col 10 lines 18-23, If the scenario currently encountered by the autonomous vehicle is within a similarity threshold of one or more other scenarios labeled as having a risk value greater than a threshold (e.g., vectors are within a predetermined distance in the multi-dimensional space), generate a respective relevance distribution vector formed of a plurality of vector components (see at least, Fig 7, Col 21 lines 22-28, the scenario analysis system 300 may use a similarity threshold in operation 812…to select all vectors and retrieve the associated scenarios that are within the threshold distance to the input vector in the multi-dimensional space), wherein each of the vector components indicates a percentage of the instances of the respective scenario in which fulfillment of a corresponding one of the plurality of predefined vehicle operational distribution indicates how often the safety metrics is or has been recorded to coincide with the instance of fulfilled in the scenario (see at least, Col 21 lines 9-17, the scenario retrieval component 314 may determine the exposure metric in operation 810 as a raw number or a percentage of the driving scenarios that are similar to the driving scenario input in operation 802…may provide an indication of the number of times and/or frequency that a particular vehicle system failure or other vehicle behavior is likely to manifest for one or more autonomous vehicles operating in real-world environments); compare a relevance distribution vector of a particular one of the scenarios to other stored relevance distribution vectors of other different which have been set as respective trigger events for triggering an automated vehicle operation (see at least, Col 10 lines 9-24, the scenario analysis system executing within the autonomous vehicle…may…compare the current scenario to a repository of previously encountered driving scenarios…may be stored…in a separate remote server, and the previous driving scenarios may be labeled based on a risk or probability level of a collision, …may determine a vehicle control action to be performed, such as the activation of a collision avoidance system (CAS)); based on the comparison, determine that the relevance distribution vector of the particular scenario is classified as a match within predefined limits, to one or more of the other stored relevance distribution vectors that are stored in association with those of the different scenarios whose occurrence has been set as trigger events (see at least, Col 10 lines 32-35, the similarity of the current scenario of the autonomous vehicle to one or more similar high-risk scenarios may be used as a determining factor for activating or not activating a CAS, or may be used as weight value or an input into a separate model configured to determine when to activate a CAS or teleoperations device…may be used to determine additional vehicle control actions).
Hendy does not explicitly teach in response to the match, update the plurality of automated vehicles to set the particular scenario as a new trigger event for the automated vehicle operation. However, Bai teaches this limitation.
Bai teaches in response to the match, update (see at least, [0040] CWS 202 may transmit and receive information directly or indirectly to and from a service provider 212 over a wireless communication network 204…includes a remote server 214), the plurality of automated vehicles (see at least, [0030] a vehicle communication… can include…automated drive vehicles) to set the particular scenario as a new trigger event for the automated vehicle operation (see at least, [0088] If comparison calculator 420 determines that newly-collected event data has a statistical match to historical event data, then the comparison calculator 420 stores the new event data into collision event database 422 with the matched prior event data. If the comparison calculator 420 determines that the new event data is not a statistical match to historical event data, new event data is stored into collision event database 422 as a single event).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hendy to include in response to the match, update the plurality of automated vehicles to set the particular scenario as a new trigger event for the automated vehicle operation as taught by Bai on order to assist the driver of vehicle or another vehicle in communication with vehicle to avoid a road hazard or potential collision (Bai, [0068]).
Regarding claim 27, Hendy teaches a non-transitory machine-readable storage medium (see at least, Fig 9, Memory-920) on which is stored a computer program that is executable by a processor (see at least, Fig 9, Processor-940) of a central server (see at least, Fig 9, Network-934) in communication with a plurality of automated vehicles moving in an at least partially automated manner (see at least, Col 9 lines 65-66, log data collected by a fleet of autonomous vehicles operating in real-world environments), and that, when executed by the processor, causes the processor to perform a method for improving safety precautions of the plurality of automated vehicles (see at least, (Col 19 lines 45-50, scenario analysis and the identification and/or generation of similar scenarios…may assist with
improving overall operations of the autonomous vehicles when encountering new or unexpected situations), the method comprising the following steps: obtaining, from each of the automated vehicles, data representing instances of scenarios detected by a sensor system of the vehicle; for each of the instances of the scenarios for which the data is obtained (see at least, (Col 10 lines 18-23, If the scenario currently encountered by the autonomous vehicle is within a similarity threshold of one or more other scenarios labeled as having a risk value greater than a threshold (e.g., vectors are within a predetermined distance in the multi-dimensional space), the autonomous vehicle may determine a vehicle control action to be performed), obtaining, from the each of the automated vehicles, information indicating, for each of a plurality of predefined vehicle operational safety metrics, whether fulfillment of the respective safety metric coincided with the respective instance of the scenario (see at least, (Col 10 lines 13-18, The repository of previously encountered driving scenarios may be stored onboard the autonomous vehicle or in a separate remote server, and the previous driving scenarios may be labeled based on a risk or probability level of a collision, injury, or other safety-related risk) the predefined vehicle operational safety metrics including: exceedance of a first threshold, falling below a second threshold, and/or existence of a predefined state; for each of the scenarios (see at least, (Col 10 lines 18-23, If the scenario currently encountered by the autonomous vehicle is within a similarity threshold of one or more other scenarios labeled as having a risk value greater than a threshold (e.g., vectors are within a predetermined distance in the multi-dimensional space) generating, by the central server, a respective relevance distribution vector formed of a plurality of vector components (see at least, Fig 7, Col 21 lines 22-28, the scenario analysis system 300 may use a similarity threshold in operation 812…to select all vectors and retrieve the associated scenarios that are within the threshold distance to the input vector in the multi-dimensional space), wherein each of the vector components indicates a percentage of the instances of the respective scenario in which fulfillment of a corresponding one of the plurality of predefined vehicle operational safety metrics has been recorded to coincide with the instance of the scenario (see at least, Col 21 lines 9-17, the scenario retrieval component 314 may determine the exposure metric in operation 810 as a raw number or a percentage of the driving scenarios that are similar to the driving scenario input in operation 802…may provide an indication of the number of times and/or frequency that a particular vehicle system failure or other vehicle behavior is likely to manifest for one or more autonomous vehicles operating in real-world environments); comparing, by the central server, a relevance distribution vector of a particular one of the scenarios to other stored relevance distribution vectors of other different scenarios which have been set as respective trigger events for triggering an automated vehicle operation (see at least, Col 10 lines 9-24, the scenario analysis system executing within the autonomous vehicle…may…compare the current scenario to a repository of previously encountered driving scenarios…may be stored…in a separate remote server, and the previous driving scenarios may be labeled based on a risk or probability level of a collision, …may determine a vehicle control action to be performed, such as the activation of a collision avoidance system (CAS)); based on the comparison, determining, by the central server, that the relevance distribution vector of the particular scenario is classified as a match, within predefined limits, to one or more of the other stored relevance distribution vectors that are stored in association with those of the different scenarios whose occurrence has been set as trigger events (see at least, Col 10 lines 32-35, the similarity of the current scenario of the autonomous vehicle to one or more similar high-risk scenarios may be used as a determining factor for activating or not activating a CAS, or may be used as weight value or an input into a separate model configured to determine when to activate a CAS or teleoperations device…may be used to determine additional vehicle control actions).
Hendy does not explicitly teach in response to the match, updating the plurality of automated vehicles to set the particular scenario as a new trigger event for the automated vehicle operation. However, Bai teaches this limitation.
Bai teaches in response to the match, update (see at least, [0040] CWS 202 may transmit and receive information directly or indirectly to and from a service provider 212 over a wireless communication network 204…includes a remote server 214), the plurality of automated vehicles (see at least, [0030] a vehicle communication… can include…automated drive vehicles) to set the particular scenario as a new trigger event for the automated vehicle operation (see at least, [0088] If comparison calculator 420 determines that newly-collected event data has a statistical match to historical event data, then the comparison calculator 420 stores the new event data into collision event database 422 with the matched prior event data. If the comparison calculator 420 determines that the new event data is not a statistical match to historical event data, new event data is stored into collision event database 422 as a single event).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hendy to include in response to the match, update the plurality of automated vehicles to set the particular scenario as a new trigger event for the automated vehicle operation as taught by Bai on order to assist the driver of vehicle or another vehicle in communication with vehicle to avoid a road hazard or potential collision (Bai, [0068]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/TOYA PETTIEGREW/Primary Examiner, Art Unit 3662