Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/27/2024 is being considered by the examiner.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification (MPEP 608.01, ¶6.31).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-10 and 12-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ye (US20220355808A1) in view of Jang (US20230391369A1).
Regarding claim 1, Ye teaches;
An autonomy computing system of an autonomous vehicle (taught as a system for autonomous vehicle operation, element 100), the system comprising:
at least one memory device storing computer-executable instructions (taught as the system including a computing system, element 120, including memory to execute computer instructions, paragraph 0045); and
at least one processor (taught as the system being implemented on processors, paragraph 0139) comprising a minimal risk maneuver (MRM) module (taught as the computing system interfacing with/includes a fallback planner, paragraph 0048; while not explicitly an MRM, fallback plans effectively cover the scope of MRM by providing emergency response trajectories, plans etc. for example, high-level guidance and modifications for objectives, e.g. paragraph 0076), the MRM module comprising:
a plurality of executable MRM generators (taught as a high-level safety platform/fallback planner of the computing system configured to generate plans and trajectories, paragraph 0048, and wherein multiple computing systems to generate different trajectories are used, paragraph 0044, implemented with multiple embedded fallback controllers, paragraph 0078), each MRM generator associated with a respective MRM to be performed by the autonomous vehicle (taught as generating a fallback plan, such as waypoints, trajectories, in response to conditions, paragraph 0048); and
a task handler programmed to provide input data to and receive output data from the plurality of MRM generators (taught as a low-level safety platform element 130, including a watchdog and fallback controller, and receives input from the computing system and provides output [through the vehicle communication network] back, as shown in Fig 1) ; and
wherein the at least one processor is programmed, by the computer-executable instructions to:
receive, at the task handler, a notification of component failure of at least one component of the autonomous vehicle (taught as the watchdog monitoring health system components, which sends an alert/message to trigger a fallback condition, paragraphs 0078 and 0128);
in response to receiving the notification, input, via the task handler, the input data to at least one MRM generator of the plurality of MRM generators (taught as, upon receiving the alert/message from the watchdog, determining a fallback condition, and trigger fallback plans associated with the set of outputs, paragraph 0129);
execute the at least one MRM generator with the input data to generate the output data (taught as, upon receiving the alert/message from the watchdog, determining a fallback condition, and trigger fallback plans associated with the set of outputs, paragraph 0129);
based on the output data from the at least one MRM generator, identify, via the task handler, an output MRM to be performed by the autonomous vehicle (taught as determining a fallback plan associated with the set of outputs, paragraph 0129), [[based on a hierarchical relationship of the MRMs associated with the plurality of MRM generators]]; and
transmit instructions to one or more control components of the autonomous vehicle to implement the output MRM (taught as outputting control commands and other suitable instructions to navigate the vehicle, paragraph 0129, such as implementing trajectories, stops etc. e.g. paragraph 0074, 0075).
However, Ye does not explicitly teach; based on a hierarchical relationship of the MRMs associated with the plurality of MRM generators.
Jang teaches; based on the output data from the at least one MRM generator, identify, via the task handler (taught as the system selecting the most appropriate MRM type, considering various conditions [such as sensor data, positions etc. paragraph 0124), an output MRM to be performed by the autonomous vehicle based on a hierarchical relationship of the MRMs associated with the plurality of MRM generators (taught as determining MRM types between levels 1-5 [where 5 has the safest general parking condition, and 1 has the more dangerous hard stop condition], paragraphs 0149-0153).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use hierarchical relationships regarding MRMs to improve safety. As suggested by Jang, even in at risk events, converting the situation/condition of the vehicle to correspond to a minimal risk condition to perform an MRM helps ensure the driving stability of the vehicle (paragraphs 0006-0007). By implementing different hierarchies of maneuvers/plans, one can ensure the safest available option is chosen based on the data/conditions. In the system of Ye, one of ordinary skill in the art would find it obvious to categorize different stored fallback plans as the different levels of MRM taught by Jang in order to improve the selection of fallback plan.
Regarding claim 2, Ye as modified by Jang teaches;
The autonomy computing system of claim 1 (see claim 1 rejection). However, Ye does not explicitly teach; wherein the at least one processor is further programmed to:
execute the at least one MRM generator of the plurality of MRM generators by executing the plurality of MRM generators in a sequential order defined by the hierarchical relationship.
Jang teaches; wherein the at least one processor is further programmed to:
execute the at least one MRM generator of the plurality of MRM generators by executing the plurality of MRM generators in a sequential order defined by the hierarchical relationship (taught as MRM/other safety functions being performed sequentially, paragraph 0054).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to sequentially compute/compare MRM levels as taught by Jang int h system taught by Ye in order to improve fallback selection. As taught by Jang, such a system allows for MRM type transitions to lower levels that can be performed more immediately (paragraph 0154), and thus allows for the safest available fallback option to be selected.
Regarding claim 3, Ye as modified by Jang teaches;
The autonomy computing system of claim 2 (see claim 2 rejection). However, Ye does not explicitly teach; wherein the at least one processor is further programmed to:
receive, at the task handler, the output data from a first-executed MRM generator, the output data including a maneuver success result; and
identify, via the task handler, the output MRM as the MRM associated with the first-executed MRM generator.
Jang teaches; receive, [[at the task handler]], the output data from a subsequent MRM generator, the output from the subsequent MRM generator including a maneuver success result (indicated fig 14; wherein a ‘success’ indicates that a level of MRM can be performed and is selected, and an ‘error’ indicates a maneuver cannot be performed and is not selected); and
identify, via the task handler, the output MRM as the MRM associated with the subsequent MRM generator (indicated in selecting the most suitable MRM type based on the internal and external information, paragraph 0144, Fig 14).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to sequentially compute/compare MRM levels as taught by Jang int h system taught by Ye in order to improve fallback selection. As taught by Jang, such a system, with execution time thresholds, allow for MRM type transitions to lower levels that can be performed more immediately (paragraph 0154), and thus allows for the safest available fallback option to be selected.
Regarding claim 4, Ye as modified by Jang teaches;
The autonomy computing system of claim 2 (see claim 2 rejection), wherein the at least one processor is further programmed to:
receive, at the task handler, the output data from a first-executed MRM generator, the output data including an error [interpreted to be a result such that the MRM is not viable, based on paragraph 0054 of the specification] result (taught as the low-level safety platform receiving a trigger condition, such as errors sensed, paragraph 0084, or general health of systems from the watchdog, paragraph 0128).
However, Ye does not explicitly teach; the output from the subsequent MRM generator including a maneuver success result; and
identify, via the task handler, the output MRM as the MRM associated with the subsequent MRM generator.
Jang teaches; receive, at the task handler, the output data from a subsequent MRM generator, the output from the subsequent MRM generator including a maneuver success [interpreted to be a result such that the MRM is viable based on paragraph 0054] result (indicated fig 14; wherein a ‘success’ indicates that a level of MRM can be performed and is selected, and an ‘error’ indicates a maneuver cannot be performed and is not selected); and
identify, via the task handler, the output MRM as the MRM associated with the subsequent MRM generator (indicated in selecting the most suitable MRM type based on the internal and external information, paragraph 0144).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to sequentially compute/compare MRM levels as taught by Jang int h system taught by Ye in order to improve fallback selection. As taught by Jang, such a system, with execution time thresholds, allow for MRM type transitions to lower levels that can be performed more immediately (paragraph 0154), and thus allows for the safest available fallback option to be selected.
Regarding claim 5, Ye as modified by Jang teaches;
The autonomy computing system of claim 1 (see claim 1 rejection). However, Ye does not explicitly teach; wherein the at least one processor is further programmed to:
execute the at least one MRM generator of the plurality of MRM generators by executing the plurality of MRM generators in parallel.
Jang teaches; wherein the at least one processor is further programmed to:
execute the at least one MRM generator of the plurality of MRM generators by executing the plurality of MRM generators in parallel (taught as MRM and safety functions of the vehicle being performed in parallel, paragraph 0054).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to sequentially compute/compare MRM levels as taught by Jang int h system taught by Ye in order to improve fallback selection. As taught by Jang, the MRM does not inhibit other safety functions of the vehicle, and thus can be performed/determined in parallel (paragraph 0054), and thus performing in parallel can, in some cases, improve the speed of determining and implementing the most effective/safest MRM.
Regarding claim 6, Ye as modified by Jang teaches;
The autonomy computing system of claim 5 (see claim 5 rejection). However, Ye does not explicitly teach; wherein the at least one processor is further programmed to:
receive, at the task handler, the respective output data from each of the plurality of MRM generators, including at least one maneuver success result; and
identify, via the task handler, the output MRM by identifying the output MRM as an MRM having a highest level in the hierarchical relationship and being associated with one of the plurality of MRM generators having output a maneuver success result.
Jang teaches; wherein the at least one processor is further programmed to receive, at the task handler, the respective output data from each of the plurality of MRM generators, including at least one maneuver success result (indicated fig 14; wherein a ‘success’ indicates that a level of MRM can be performed and is selected, and an ‘error’ indicates a maneuver cannot be performed and is not selected); and
identify, via the task handler, the output MRM by identifying the output MRM as an MRM having a highest level in the hierarchical relationship and being associated with one of the plurality of MRM generators having output a maneuver success result (indicated in Fig 14, wherein the highest level MRM is selected based on what can be performed [evaluated first], and further that transitioning to a high level type when possible, paragraph 0157).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to sequentially compute/compare MRM levels as taught by Jang int h system taught by Ye in order to improve fallback selection. As taught by Jang, the MRM does not inhibit other safety functions of the vehicle, and thus can be performed/determined in parallel (paragraph 0054), and thus performing in parallel can, in some cases, improve the speed of determining and implementing the most effective/safest MRM.
Regarding claim 7, Ye as modified by Jang teaches;
The autonomy computing system of claim 5 (see claim 5 rejection). Ye further teaches; wherein the at least one processor is further programmed to:
receive a second notification of further component failure while executing the plurality of MRM generators (indicated in the watchdog sending an alert or message based on the active determination that a system has failed, paragraph 0128; if another system fails, another message would be sent).
However, Ye does not explicitly teach;
re-execute the plurality of MRM generators;
based on updated output data from the plurality of MRM generators, update the output MRM to be performed by the autonomous vehicle; and
transmit instructions to the one or more control components of the autonomous vehicle to implement updated output MRM.
Jang teaches; re-execute the plurality of MRM generators (indicated in an MRM type transition; when the system is updated, such as by a temporary or newly generated defect or the like, transitioning to a lower level MRM, paragraph 0158);
based on updated output data from the plurality of MRM generators, update the output MRM to be performed by the autonomous vehicle (taught as transitioning to a lower level of MRM based on the newly determined capabilities of the vehicle, paragraph 0158); and
transmit instructions to the one or more control components of the autonomous vehicle to implement updated output MRM (taught as transitioning to the lower level MRM based on the information, such that the highest level type possible is selected/preferred, paragraph 0158, and implementing the most suited MRM, paragraph 0144).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to sequentially compute/compare MRM levels as taught by Jang int h system taught by Ye in order to improve fallback selection. As taught by Jang, the MRM does not inhibit other safety functions of the vehicle, and thus can be performed/determined in parallel (paragraph 0054), and thus performing in parallel can, in some cases, improve the speed of determining and implementing the most effective/safest MRM.
Regarding claim 8, Ye as modified by Jang teaches;
The autonomy computing system of claim 1 (see claim 1 rejection). However, Ye does not explicitly teach; wherein the at least one processor is further programmed to:
receive a restoration notification of restoration of component capability;
re-execute at least one MRM generator of the plurality of MRM generators;
receive respective updated output data from the at least one MRM generator; and
update the output MRM to be implemented by the autonomous vehicle by replacing the output MRM with an updated output MRM having a higher level in the hierarchical relationship than an MRM being implemented by the autonomous vehicle.
Jang teaches; wherein the at least one processor is further programmed to:
receive a restoration notification of restoration of component capability (taught as determining a repaired/resolved temporary defect, paragraph 0157);
re-execute at least one MRM generator of the plurality of MRM generators (taught as, upon determining that the defect is repaired, transitioning to a higher level MRM, using current state information of the components and external surrounding information, paragraph 0157, which indicates a re-evaluation to maintain current considerations);
receive respective updated output data from the at least one MRM generator (taught as transitioning to a higher level MRM, paragraph 0157); and
update the output MRM to be implemented by the autonomous vehicle by replacing the output MRM with an updated output MRM having a higher level in the hierarchical relationship than an MRM being implemented by the autonomous vehicle (taught as transitioning to the higher level MRM based on the updated information, such that the highest level type possible is selected/preferred, paragraph 0158, and implementing the most suited MRM, paragraph 0144).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to sequentially compute/compare MRM levels as taught by Jang int h system taught by Ye in order to improve fallback selection. As taught by Jang, the MRM does not inhibit other safety functions of the vehicle, and thus can be performed/determined in parallel (paragraph 0054), and thus performing in parallel can, in some cases, improve the speed of determining and implementing the most effective/safest MRM.
Regarding claim 9, Ye as modified by Jang teaches;
The autonomy computing system of claim 1 (see claim 1 rejection). However, Ye does not explicitly teach; wherein each MRM generator, when executed, determines whether the autonomous vehicle is capable of performing the respective MRM.
Jang teaches; wherein each MRM generator, when executed, determines whether the autonomous vehicle is capable of performing the respective MRM (taught as, evaluating MRMs, determining whether a maneuver function is possible, as shown in Fig 14 [steering function possible, road condition detection possible etc.])
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use hierarchical relationships regarding MRMs to improve safety. As suggested by Jang, even in at risk events, converting the situation/condition of the vehicle to correspond to a minimal risk condition to perform an MRM helps ensure the driving stability of the vehicle (paragraphs 0006-0007). By implementing different hierarchies of maneuvers/plans, one can ensure the safest available option is chosen based on the data/conditions. In the system of Ye, one of ordinary skill in the art would find it obvious to categorize different stored fallback plans as the different levels of MRM taught by Jang in order to improve the selection of fallback plan.
Regarding claim 10, Ye as modified by Jang teaches;
The autonomy computing system of claim 1 (see claim 1 rejection). However, Ye does not explicitly teach; wherein the MRM module defines a hierarchical and nested relationship of the MRMs associated with the plurality of MRM generators, wherein the at least one processor is further programmed to:
identify, by the task handler, the output MRM as the MRM associated with one of the plurality of MRM generators based on the output data in an order based on the hierarchical and nested relationship.
Jang teaches; wherein the MRM module defines a hierarchical and nested relationship of the MRMs associated with the plurality of MRM generators (taught as determining MRM types between levels 1-5 [where 5 has the safest general parking condition, and 1 has the more dangerous hard stop condition], paragraphs 0149-0153; the nesting relationship includes the process of checking the highest level, then the next highest etc., e.g. paragraph 0154), wherein the at least one processor is further programmed to:
identify, by the task handler, the output MRM as the MRM associated with one of the plurality of MRM generators based on the output data in an order based on the hierarchical and nested relationship (indicated in the execution of MRM types; wherein the highest level MRM is presented first, then the next highest [if the previous level of MRM is unavailable/can’t be performed] etc. paragraph 0154, so that the highest possible MRM level can be chosen).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use hierarchical relationships regarding MRMs to improve safety. As suggested by Jang, even in at risk events, converting the situation/condition of the vehicle to correspond to a minimal risk condition to perform an MRM helps ensure the driving stability of the vehicle (paragraphs 0006-0007). By implementing different hierarchies of maneuvers/plans, one can ensure the safest available option is chosen based on the data/conditions. In the system of Ye, one of ordinary skill in the art would find it obvious to categorize different stored fallback plans as the different levels of MRM taught by Jang in order to improve the selection of fallback plan.
Regarding claims 12-20, it has been determined that no further limitations exist apart from those previously addressed in claims 1-10. Therefore, claims 12-20 are rejected under the same rationale as claims 1-10, wherein;
Claims 12 and 18 correspond to claim 1,
Claims 13 and 19 correspond to claim 2,
Claims 14 and 20 correspond to claim 5,
Claim 15 corresponds to claim 7,
Claim 16 corresponds to claim 9,
Claim 17 corresponds to claim 10.
Claim(s) 11 is rejected under 35 U.S.C. 103 as being unpatentable over Ye (US20220355808A1) in view of Jang (US20230391369A1) and in view of the examiner’s official notice.
Regarding claim 11, Ye as modified by Jag teaches;
The autonomy computing system of claim 1 (see claim 1 rejection). However, Ye does not explicitly teach; wherein the at least one processor is further programmed to:
store a temporary value of a variable representing the output MRM being implemented ; and
when a different output MRM is implemented at the autonomous vehicle, update the temporary value of the variable with a value corresponding to the different output MRM.
While such temporary values of a variable are not explicitly taught in Ye or Jang, this is merely a feature of the programming required to implement software described in the prior art. All variables in programming will have an initialized value in its definition step (e.g. defining a carriable called X1 would, for example, be initialized as; double X1 = (0)). Even if not explicitly written in the code, such variables would have implicit values, such as null, 0, empty etc. the process of calling or defining a variable would, at the very least, involve an initialization value. Thus, the examiner takes official notice that, by the nature of programming, assigning a temporary value to define a variable [to be replaced later when the variable is called/defined] is just how one defines variables in coding, and thus would implicitly be used in the methods taught by Ye and Jang, and further in vehicle programming in general.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US20190056735A1 for general MRM selection and safety architecture, pertaining to the independent claims and recursive/parallel decision making in claim 10.
US20200110414A1 for recursive/parallel decision making in claim 10.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GABRIEL ANFINRUD whose telephone number is (571)270-3401. The examiner can normally be reached M-F 9:30-5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jelani Smith can be reached at (571)270-3969. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GABRIEL ANFINRUD/Examiner, Art Unit 3662
/JELANI A SMITH/Supervisory Patent Examiner, Art Unit 3662
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