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 .
Status of Claims
Claims 1-10 were originally presented having a filing date of November 20, 2024 and claiming priority to German Application DE102022113191.2 that was filed on May 25, 2022 and PCT/EP2023/061446 that was filed on May 2, 2023.
A Preliminary Amendment was filed on November 20, 2024.
Claims 1-10 were canceled via Preliminary Amendment.
Claims 11-26 were newly added via Preliminary Amendment.
Claims 11-26 have been examined.
Information Disclosure Statement
The Information Disclosure Statement that was filed on November 20, 2024 is in compliance with 37 CFR 1.97. Accordingly, the IDS has been considered by the Examiner. An initialed copy of the Form 1449 is enclosed herewith.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 20 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 20 depends from claim 17 and they both have exactly the same limitations. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 11-26 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Li, et al. (Publication US 2021/0387650 A1) (hereinafter referred to as “Li”.)
As per claim 11, Li discloses a training method for a driver assistance system for automated lateral guidance of a motor vehicle, the training method comprising:
recording a trajectory of the motor vehicle when driving through a section with the driver assistance system for automated lateral guidance deactivated, wherein the section cannot be managed by the driver assistance system for automated lateral guidance [see at least Li [0019] "By automatically identifying and classifying scenarios encountered by AVs, the system may allow AVs to automatically trigger disengagements and generate disengagement records and triage records based on specific scenarios and vehicle performance metrics. By automatically generating disengagement records and triage records, particular embodiments of the system may provide granular scenario-specific data with insights for why disengagements were triggered and when disengagements should be triggered."]; and
training the driver assistance system for automated lateral guidance based on the recorded trajectory, so that the section can be managed by the driver assistance system for automated lateral guidance after the training [see at least Li [0019] "...By capturing this granular scenario-specific data, particular embodiments of the systems may provide insights on how the AVs respond to particular scenarios through the disengagement data and the human operators' feedback to disengagement data (e.g., whether the human operator changed or adjusted the pre-filled scenario classification, the positive and negative samples from our pre-filled records). Particular embodiments of the system may use the scenario-specific data to train the system to more accurately classify the scenarios as perceived by the AVs and allow the AVs to learn the capability to automatically determine when to disengage from autonomous driving."].
As per claim 12, Li, as shown in the rejection above, discloses all of the limitations of claim 11.
Li discloses … further comprising:
determining that the section lies on a planned route of the motor vehicle at a predetermined distance from the motor vehicle that cannot be managed by the driver assistance system for automated lateral guidance [see at least Li [0019] "...By capturing this granular scenario-specific data,…"; [0025] "...the vehicle system may collect contextual data of the surrounding environment using one or more sensors or sensing systems. In particular embodiments, the vehicle system may collect data related to road conditions or one or more objects of the surrounding environment, for example, but not limited to, road layout, ... locations (e.g., GPS coordination), road conditions (e.g., constructing zones, school zones, wet surfaces, ice surfaces), intersections, ... "; [0028] "...the system may determine a current geographic location using GPS coordinates, a navigation map, a pattern or feature identified from images, a navigation route, etc. The system may determine, based on pre-determined knowledge associated with that particular geographic location, that the vehicle is encountering a particular type of scenario. The system may identify and classify the current scenario into a scenario category corresponding that particular type of scenario. For example, the system may determine that the vehicle is currently near to and approaching an intersection."; [0050] "...For the example as illustrated in FIG. 2D, when the system determines that the distance 234 is smaller than the pre-determined threshold for safely navigate the vehicle, the system may automatically trigger the disengagement operation to disengage the vehicle from autonomous driving."];
driving the motor vehicle with the driver assistance system activated for automated lateral guidance at a time of determining that the section lies on the planned route of the motor vehicle at the predetermined distance from the motor vehicle that cannot be managed by the driver assistance system for automated lateral guidance [see at least Li [0028] "...The system may determine that the vehicle needs to make a right turn at this intersection based the navigation route of the vehicle. The system may determine that the right turn will be an unprotected right turn based on a map of this intersection. The system may determine that the vehicle is encountering an unprotected right turn scenario."; [0029] "At running time, the system may identify the current scenario based on the perception data and compare the identified current scenario to the scenario models accessed from the database."]; and
deactivating the driver assistance system for automated lateral guidance by issuing a takeover request by the motor vehicle before the section is reached by the motor vehicle [see at least Li [0031] "...the rule-based algorithm may identify a series of features of the surrounding environment over time that match a pre-determined series of features associated with a particular scenario that warrants triggering a disengagement operation."].
As per claim 13, Li, as shown in the rejection above, discloses all of the limitations of claim 11.
Li discloses … further comprising:
recording another trajectory of another motor vehicle when driving through the section with the driver assistance system for automated lateral guidance deactivated [see at least Li [0029] "...the scenario models of the pre-determined scenario categories may be generated based on experiential driving data collected by a fleet of autonomous vehicles. In particular embodiments, the scenario models of the pre-determined scenario categories may be generated or adjusted based on experiential knowledge as input by human experts."]; and
training the driver assistance system for automated lateral guidance based on the further recorded trajectory, so that the section can be managed by the driver assistance system for automated lateral guidance after the training [see at least Li [0030] In particular embodiments, the system may generate a new scenario category and a new scenario model each time the system identifies an unknown scenario that does not match any scenario models stored in the database and store the new scenario model in the database. The system may update the corresponding scenario model in the database based on the feedback information from human operators (e.g., safety drivers, teleoperators, triage experts) on this scenario category.].
As per claim 14, Li, as shown in the rejection above, discloses all of the limitations of claim 12.
Li discloses … further comprising:
recording another trajectory of another motor vehicle when driving through the section with the driver assistance system for automated lateral guidance deactivated [see at least Li [0029] "...the scenario models of the pre-determined scenario categories may be generated based on experiential driving data collected by a fleet of autonomous vehicles. In particular embodiments, the scenario models of the pre-determined scenario categories may be generated or adjusted based on experiential knowledge as input by human experts."]; and
training the driver assistance system for automated lateral guidance based on the further recorded trajectory, so that the section can be managed by the driver assistance system for automated lateral guidance after the training [see at least Li [0030] In particular embodiments, the system may generate a new scenario category and a new scenario model each time the system identifies an unknown scenario that does not match any scenario models stored in the database and store the new scenario model in the database. The system may update the corresponding scenario model in the database based on the feedback information from human operators (e.g., safety drivers, teleoperators, triage experts) on this scenario category.].
As per claim 15, Li, as shown in the rejection above, discloses all of the limitations of claim 13.
Li discloses … further comprising:
plausibility checking the trajectory of the motor vehicle with the trajectory of the other motor vehicle [see at least Li [0030] "...The system may update the corresponding scenario model in the database based on the feedback information from human operators (e.g., safety drivers, teleoperators, triage experts) on this scenario category. The feedback information may include additional information related that particular scenario, information that confirms the scenario category, or information that invalidates the scenario category. The feedback information may be received from a human operator through an interaction process which allows the human operator to review and edit the automatically generated disengagement records and triage records. In particular embodiments, with more and more confirmed or invalidated scenario categories and scenario models in the database, the system may more accurately and precisely identify and classify future scenarios based on these pre-determined scenario categories and corresponding scenario models in the database.]; and
training the driver assistance system for automated lateral guidance based on the plausibility-verified trajectory of the motor vehicle so that the driver assistance system for automated lateral guidance can manage the section after the training [see at least Li [0030] "...In particular embodiments, with more and more confirmed or invalidated scenario categories and scenario models in the database, the system may more accurately and precisely identify and classify future scenarios based on these pre-determined scenario categories and corresponding scenario models in the database."; [0032] "...the system may use a machine-learning (ML) model to classify the scenarios encountered by the vehicle when driving in autonomous driving mode. In particular embodiments, the ML model may be trained during a supervised learning process using a number of pre-labeled scenario samples.].
As per claim 16, Li, as shown in the rejection above, discloses all of the limitations of claim 14.
Li discloses … further comprising:
plausibility checking the trajectory of the motor vehicle with the trajectory of the other motor vehicle [see at least Li [0030] "...The system may update the corresponding scenario model in the database based on the feedback information from human operators (e.g., safety drivers, teleoperators, triage experts) on this scenario category. The feedback information may include additional information related that particular scenario, information that confirms the scenario category, or information that invalidates the scenario category. The feedback information may be received from a human operator through an interaction process which allows the human operator to review and edit the automatically generated disengagement records and triage records. In particular embodiments, with more and more confirmed or invalidated scenario categories and scenario models in the database, the system may more accurately and precisely identify and classify future scenarios based on these pre-determined scenario categories and corresponding scenario models in the database.]; and
training the driver assistance system for automated lateral guidance based on the plausibility-verified trajectory of the motor vehicle so that the driver assistance system for automated lateral guidance can manage the section after the training [see at least Li [0030] "...In particular embodiments, with more and more confirmed or invalidated scenario categories and scenario models in the database, the system may more accurately and precisely identify and classify future scenarios based on these pre-determined scenario categories and corresponding scenario models in the database."; [0032] "...the system may use a machine-learning (ML) model to classify the scenarios encountered by the vehicle when driving in autonomous driving mode. In particular embodiments, the ML model may be trained during a supervised learning process using a number of pre-labeled scenario samples.].
As per claim 17, Li, as shown in the rejection above, discloses all of the limitations of claim 11.
Li discloses … wherein the training of the driver assistance system for automated lateral guidance includes specifying a drive-through trajectory for the section starting from a first predetermined lane to a second predetermined lane [see at least Li [0042] "...the operation design domain of the vehicle may include a number of scenarios including, for example, but not limited to, ... merging lanes,…"].
As per claim 18, Li, as shown in the rejection above, discloses all of the limitations of claim 12.
Li discloses … wherein the training of the driver assistance system for automated lateral guidance includes specifying a drive-through trajectory for the section starting from a first predetermined lane to a second predetermined lane [see at least Li [0042] "...the operation design domain of the vehicle may include a number of scenarios including, for example, but not limited to, ... merging lanes,…"].
As per claim 19, Li, as shown in the rejection above, discloses all of the limitations of claim 13.
Li discloses … wherein the training of the driver assistance system for automated lateral guidance includes specifying a drive-through trajectory for the section starting from a first predetermined lane to a second predetermined lane [see at least Li [0042] "...the operation design domain of the vehicle may include a number of scenarios including, for example, but not limited to, ... merging lanes,…"].
As per claim 20, Li, as shown in the rejection above, discloses all of the limitations of claim 17.
Li discloses … wherein the training of the driver assistance system for automated lateral guidance includes specifying a drive-through trajectory for the section starting from a first predetermined lane to a second predetermined lane [see at least Li [0042] "...the operation design domain of the vehicle may include a number of scenarios including, for example, but not limited to, ... merging lanes,…"].
As per claim 21, Li, as shown in the rejection above, discloses all of the limitations of claim 11.
Li discloses … further comprising:
controlling the lateral guidance of the motor vehicle based on the training of the driver assistance system [see at least Li [0057] "...Instead of sending an alert message to the human operator and allowing the human operator to take over the control of the vehicle, the system may send a trigger signal to an automatic co-pilot system to allow the co-pilot system to take over the control the vehicle. The automatic co-pilot system may include navigation algorithms (e.g., ML models) that are trained to handle unknown scenarios or the scenarios that are not included in the operational design domain of the vehicle. The automatic co-pilot may be trained using the scenario data and vehicle operation data included in the disengagement records and triage records generated for former unknown scenarios and scenarios that are not included by the operational design domain."]
As per claim 22, Li, as shown in the rejection above, discloses all of the limitations of claim 21.
Li discloses … wherein before and/or during a drive through the section a check is carried out by the driver assistance system to determine whether an environment and/or a condition of the motor vehicle has changed in a predetermined manner compared to the time when the driver assistance system was trained, and if, compared to a time of training the driver assistance system, the environment and/or condition of the motor vehicle has changed in the predetermined manner, the driver assistance system is not used [see at least Li [0029] "...At running time, the system may identify the current scenario based on the perception data and compare the identified current scenario to the scenario models accessed from the database. The system may determine a probability score of the current scenario belonging to each scenario category based on the comparison between the current scenario and the corresponding scenario model of that category. Then, the system may select the scenario category for the current scenario based on a determination that that scenario category is associated with the highest probability score. In particular embodiments, the scenario models of the pre-determined scenario categories may be generated based on experiential driving data collected by a fleet of autonomous vehicles."; [0067] "The system may include the scenario category label of the identified scenario category in the disengagement record..."].
As per claim 23, Li, as shown in the rejection above, discloses all of the limitations of claim 11.
Li discloses comprising a data processor configured to for carry out a method according to claim 11 [see at least Li [0081] "In particular embodiments, computer system 600 includes a processor 602, memory 604, storage 606, an input/output (I/O) interface 608, a communication interface 610, and a bus 612."].
As per claim 24, Li, as shown in the rejection above, discloses all of the limitations of claim 23.
Li discloses … wherein the data processor is configured to:
determine that the section lies on a planned route of the motor vehicle at a predetermined distance from the motor vehicle that cannot be managed by the driver assistance system for automated lateral guidance [see at least Li [0019] "...By capturing this granular scenario-specific data,…"; [0025] "...the vehicle system may collect contextual data of the surrounding environment using one or more sensors or sensing systems. In particular embodiments, the vehicle system may collect data related to road conditions or one or more objects of the surrounding environment, for example, but not limited to, road layout, ... locations (e.g., GPS coordination), road conditions (e.g., constructing zones, school zones, wet surfaces, ice surfaces), intersections, ... "; [0028] "...the system may determine a current geographic location using GPS coordinates, a navigation map, a pattern or feature identified from images, a navigation route, etc. The system may determine, based on pre-determined knowledge associated with that particular geographic location, that the vehicle is encountering a particular type of scenario. The system may identify and classify the current scenario into a scenario category corresponding that particular type of scenario. For example, the system may determine that the vehicle is currently near to and approaching an intersection."; [0050] "...For the example as illustrated in FIG. 2D, when the system determines that the distance 234 is smaller than the pre-determined threshold for safely navigate the vehicle, the system may automatically trigger the disengagement operation to disengage the vehicle from autonomous driving."];
drive the motor vehicle with the driver assistance system activated for automated lateral guidance at a time of determining that the section lies on the planned route of the motor vehicle at the predetermined distance from the motor vehicle that cannot be managed by the driver assistance system for automated lateral guidance [see at least Li [0028] "...The system may determine that the vehicle needs to make a right turn at this intersection based the navigation route of the vehicle. The system may determine that the right turn will be an unprotected right turn based on a map of this intersection. The system may determine that the vehicle is encountering an unprotected right turn scenario."; [0029] "At running time, the system may identify the current scenario based on the perception data and compare the identified current scenario to the scenario models accessed from the database."]; and
deactivate the driver assistance system for automated lateral guidance by issuing a takeover request by the motor vehicle before the section is reached by the motor vehicle [see at least Li [0031] "...the rule-based algorithm may identify a series of features of the surrounding environment over time that match a pre-determined series of features associated with a particular scenario that warrants triggering a disengagement operation."].
As per claim 25, Li, as shown in the rejection above, discloses all of the limitations of claim 11.
Li discloses a non-transitory computer-readable medium storing commands which, when executed by a computer, cause the computer to carry out a method according to claim 11 [see at least Li [0081] "In particular embodiments, computer system 600 includes a processor 602, memory 604, storage 606, an input/output (I/O) interface 608, a communication interface 610, and a bus 612."].
As per claim 26, Li, as shown in the rejection above, discloses all of the limitations of claim 25.
Li discloses … further comprising commands that cause the computer to:
determine that the section lies on a planned route of the motor vehicle at a predetermined distance from the motor vehicle that cannot be managed by the driver assistance system for automated lateral guidance [see at least Li [0019] "...By capturing this granular scenario-specific data,…"; [0025] "...the vehicle system may collect contextual data of the surrounding environment using one or more sensors or sensing systems. In particular embodiments, the vehicle system may collect data related to road conditions or one or more objects of the surrounding environment, for example, but not limited to, road layout, ... locations (e.g., GPS coordination), road conditions (e.g., constructing zones, school zones, wet surfaces, ice surfaces), intersections, ... "; [0028] "...the system may determine a current geographic location using GPS coordinates, a navigation map, a pattern or feature identified from images, a navigation route, etc. The system may determine, based on pre-determined knowledge associated with that particular geographic location, that the vehicle is encountering a particular type of scenario. The system may identify and classify the current scenario into a scenario category corresponding that particular type of scenario. For example, the system may determine that the vehicle is currently near to and approaching an intersection."; [0050] "...For the example as illustrated in FIG. 2D, when the system determines that the distance 234 is smaller than the pre-determined threshold for safely navigate the vehicle, the system may automatically trigger the disengagement operation to disengage the vehicle from autonomous driving."];
drive the motor vehicle with the driver assistance system activated for automated lateral guidance at a time of determining that the section lies on the planned route of the motor vehicle at the predetermined distance from the motor vehicle that cannot be managed by the driver assistance system for automated lateral guidance [see at least Li [0028] "...The system may determine that the vehicle needs to make a right turn at this intersection based the navigation route of the vehicle. The system may determine that the right turn will be an unprotected right turn based on a map of this intersection. The system may determine that the vehicle is encountering an unprotected right turn scenario."; [0029] "At running time, the system may identify the current scenario based on the perception data and compare the identified current scenario to the scenario models accessed from the database."]; and
deactivate the driver assistance system for automated lateral guidance by issuing a takeover request by the motor vehicle before the section is reached by the motor vehicle [see at least Li [0031] "...the rule-based algorithm may identify a series of features of the surrounding environment over time that match a pre-determined series of features associated with a particular scenario that warrants triggering a disengagement operation."].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAULA L SCHNEIDER whose telephone number is (703)756-4606. The examiner can normally be reached Monday - Friday 9:00 am - 5:00 pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fadey Jabr can be reached at 571-272-1516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/P.L.S/Examiner, Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668