Prosecution Insights
Last updated: July 26, 2026
Application No. 18/424,270

Method and Device for Autonomous Movement of a Vehicle in a Variably Optimized Dynamic Driving State

Non-Final OA §103
Filed
Jan 26, 2024
Priority
Jan 27, 2023 — DE 10 2023 200 693.6
Examiner
SILVA, MICHAEL THOMAS
Art Unit
3663
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
Volkswagen AG
OA Round
3 (Non-Final)
32%
Grant Probability
At Risk
3-4
OA Rounds
11m
Est. Remaining
53%
With Interview

Examiner Intelligence

Grants only 32% of cases
32%
Career Allowance Rate
34 granted / 105 resolved
-19.6% vs TC avg
Strong +21% interview lift
Without
With
+21.0%
Interview Lift
resolved cases with interview
Typical timeline
3y 5m
Avg Prosecution
43 currently pending
Career history
168
Total Applications
across all art units

Statute-Specific Performance

§101
0.3%
-39.7% vs TC avg
§103
93.8%
+53.8% vs TC avg
§102
1.5%
-38.5% vs TC avg
§112
4.2%
-35.8% vs TC avg
Black line = Tech Center average estimate • Based on career data from 105 resolved cases

Office Action

§103
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 . Continued Examination Under 37 CFR 1.114 1. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/24/2026 has been entered. Response to Amendment 2. Claims 1-20 are currently pending. 3. Claims 1, 8, and 10 are currently amended. 4. The 112(b) rejection to Claim 8 has been overcome. Claim Rejections - 35 USC § 103 5. 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. 6. 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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action. 7. 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. 8. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Anderson (US 20100228427 A1) in view of Suzuki (US 20220297676 A1). 9. Regarding Claim 1, Anderson teaches a method for controlling a vehicle by an assistance system, comprising (Anderson: [0013]): Detecting environmental data of a vehicle using a sensor (Anderson: [0014]); Calculating a first travel path of the vehicle to a destination based on the environmental data (Anderson: [0015], [0016], and [0020]); Detecting a driving instruction of a driver using at least one human-machine interface (Anderson: [0020] and [0024]); Determining a correlation value… (Anderson: [0017] and [0021]); Checking whether the correlation value falls below a critical correlation limit value by a second computer apparatus, and if the correlation value falls below the critical correlation limit value (Anderson: [0016]); Converting a data point detected by the human-machine interface that is characteristic of the driving instruction of the driver into a control signal that is not identical to the detected data point (Anderson: [0016], [0022], and [0023]), And wherein the control signal energizes the vehicle or a vehicle component on a second travel path leading to the same destination, wherein the vehicle adopts a stable vehicle state at every point of this second travel path (Anderson: [0011] and [0018]). Anderson fails to explicitly teach determining a correlation value between the driving instruction and the precalculated travel path of the vehicle; and wherein the human-machine interface has an optical signal encoder, where the signal encoder comprises a display element that informs the driver about a deviation between the driver input expected by the assistance system and the input actually made. However, in the same field of endeavor, Suzuki teaches determining a correlation value between the driving instruction and the precalculated travel path of the vehicle (Suzuki: [0073] and [0076]); Checking whether the correlation value falls below a critical correlation limit value by a second computer apparatus (Suzuki: [0028] and [0076] Note that determining the difference between the target parking state and parking execution state is not less than an evaluation threshold is equivalent to checking whether the correlation value falls below a critical correlation limit.), And the human-machine interface has an optical signal encoder, where the signal encoder comprises a display element that informs the driver about a deviation between the driver input expected by the assistance system and the input actually made (Suzuki: [0050] and [0054]). Anderson and Suzuki are considered to be analogous to the claim invention because they are in the same field of vehicle control. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Anderson to incorporate the teachings of Suzuki to determine a correlation value between the driving instruction and the precalculated travel path and to display an element that informs the driver about a deviation between the driver input expected by the assistance system and the input actually made because it provides the benefit of guiding a driver with a parking trajectory by notifying that the driver input has deviated from the target. Suzuki explicitly explains in [0004] and [0005] that this problem is solved because the occupant of the vehicle is guided to know that the parking mode is switched into an automated mode to allow the vehicle to be properly parked in the parking space. 10. Regarding Claim 2, Anderson and Suzuki remains as applied above in Claim 1, and further, Anderson teaches the second travel path is calculated taking into account data regarding a driving mode and/or a driving characteristic of a driver, multiple drivers and/or a driver group, which are loaded from a database (Anderson: [0013] and [0016]). 11. Regarding Claim 3, Anderson and Suzuki remains as applied above in Claim 1, and further, Anderson teaches a signal is electrically transmitted between the human-machine interface and an actuator for controlling the vehicle, wherein the vehicle is controlled by X-by-wire control and a signal for controlling the vehicle is input at the human-machine interface (Anderson: [0012] and [0016]). 12. Regarding Claim 4, Anderson and Suzuki remains as applied above in Claim 1, and further, Anderson teaches a feedback is given to the driver regarding a deviation of his state detected by the human-machine interface and/or his driving instruction detected by the human-machine interface from an expected state and/or an expected input for moving the vehicle on the precalculated travel path (Anderson: [0022]). 13. Regarding Claim 5, Anderson and Suzuki remains as applied above in Claim 1, and further, Anderson teaches the critical correlation limit value is established by the driver or another authorized source (Anderson: [0012] and [0016]). 14. Regarding Claim 6, Anderson and Suzuki remains as applied above in Claim 1, and further, Anderson teaches disadvantageous driving patterns and/or driving characteristics are recognized based on data from a plurality of trips of a driver (Anderson: [0016] and [0025]). 15. Regarding Claim 7, Anderson and Suzuki remains as applied above in Claim 1, and further, Suzuki teaches at least one sensor monitors and/or recognizes the driver and/or a vehicle occupant (Suzuki: [0063]). 16. Regarding Claim 8, Anderson and Suzuki remains as applied above in Claim 1, and further, Anderson teaches the method is at least partially carried out computer-assisted (Anderson: [0013] and [0017]). 17. Regarding Claim 9, Anderson and Suzuki remains as applied above in Claim 1, and further, Anderson teaches a virtual three-dimensional space is set up by the assistance system, the dimensions of which are: a degree of decoupling of a driver input to the human-machine interface from a corresponding actuator signal; a degree of manipulation of the control signal given by the driver in an energizing of a corresponding actuator; and an intensity of a feedback to the driver; wherein each driver is assigned at least one point in this virtual space by the assistance system, wherein this point correlates to a control and/or feedback behavior of the assistance system that is assigned to this driver (Anderson: [0018], [0022], and [0023] Note that Anderson's teaching of the control authority proportion K is equivalent to the degree of decoupling, the remaining authority 1-K is equivalent to the degree of manipulation, and assuming full authority before reaching a critical vehicle state is equivalent to the intensity of the feedback to the driver.). 18. Regarding Claim 10, Anderson teaches an assistance system for at least partially autonomous control of a vehicle, comprising (Anderson: [0013]): A sensor for detecting environmental data (Anderson: [0014]); A first processing circuit for pre-calculating a first travel path of the vehicle to a destination based on the environmental data (Anderson: [0015], [0016], and [0020]); At least one human-machine interface that is intended and configured for detecting a state and/or a driving instruction of a driver (Anderson: [0020] and [0024]); a correlation value determination circuit that is intended and configured for determining a correlation value… (Anderson: [0017] and [0021]); And a second processing circuit that is intended and configured for converting a data point detected by the human-machine interface that is characteristic of the driving instruction of the driver into a control signal, which control signal is not identical to the detected data point (Anderson: [0016], [0022], and [0023]), Wherein the control signal correlates to a second travel path leading to the same destination on which the vehicle can be guided in a stable vehicle state at every point (Anderson: [0011] and [0018]). Anderson fails to explicitly teach determining a correlation value between the driving instruction and the precalculated travel path of the vehicle; and the human-machine interface has an optical signal encoder, where the signal encoder comprises a display element that informs the driver about a deviation between the driver input expected by the assistance system and the input actually made. However, in the same field of endeavor, Suzuki teaches a correlation value determination circuit that is intended and configured for determining a correlation value between the driving instruction and the precalculated travel path of the vehicle (Suzuki: [0073] and [0076]); And the human-machine interface has an optical signal encoder, where the signal encoder comprises a display element that informs the driver about a deviation between the driver input expected by the assistance system and the input actually made (Suzuki: [0050] and [0054]). Anderson and Suzuki are considered to be analogous to the claim invention because they are in the same field of vehicle control. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Anderson to incorporate the teachings of Suzuki to determine a correlation value between the driving instruction and the precalculated travel path and to display an element that informs the driver about a deviation between the driver input expected by the assistance system and the input actually made because it provides the benefit of guiding a driver with a parking trajectory by notifying that the driver input has deviated from the target. Suzuki explicitly explains in [0004] and [0005] that this problem is solved because the occupant of the vehicle is guided to know that the parking mode is switched into an automated mode to allow the vehicle to be properly parked in the parking space. 19. Regarding Claim 11, Anderson and Suzuki remains as applied above in Claim 10, and further, Anderson teaches the human-machine interface has a feedback circuit using which a result of the correlation value determination by the correlation value determination apparatus can be conveyed to a driver (Anderson: [0016]), Wherein a strength of a feedback signal of the feedback apparatus correlates to an amount by which the correlation value is fallen short of (Anderson: [0017]), Wherein a ratio of the strength of the feedback signal to the amount of the falling short can be preset by the driver (Anderson: [0016] and [0027]). 20. Regarding Claim 12, Anderson and Suzuki remains as applied above in Claim 10, and further, Anderson teaches the human-machine interface is in electrical connection to an actuator for controlling the vehicle, wherein the human-machine interface is part of an X-by-wire control of the vehicle (Anderson: [0012] and [0016]). 21. Regarding Claim 13, Anderson and Suzuki remains as applied above in Claim 10, and further, Anderson teaches there is a data connection between the second processing circuit and a database, wherein data regarding driving characteristics of a driver, multiple drivers, a driver group and/or various driving modes are stored in the database (Anderson: [0013] and [0016]), Wherein at least one of the datasets stored in the database correlates to a driving mode selected from a group comprising a comfort driving mode, an energy saving mode, a sport mode, a highway mode, a city traffic mode, a long-distance mode, a working mode, a training mode, a persons transport mode, a goods transport mode and a hazardous goods transport mode (Anderson: [0015] and [0018] Note that under the broadest reasonable interpretation, comfort driving mode is equivalent to safe vehicle control below handling limits.). 22. Regarding Claim 14, Anderson and Suzuki remains as applied above in Claim 10, and further, Anderson teaches the correlation value determination circuit and/or the second processing circuit comprises… wherein the correlation value determination circuit and/or the second processing circuit is provided and configured for recognizing a pattern in the deviation of the correlation between the driving instruction of the driver and the expected driving instruction of the driver for following the pre-calculations of a travel path, and this pattern is stored in a database that forms a foundation for future calculations of a travel path of the vehicle (Anderson: [0016] and [0025]). Anderson does not explicitly teach an artificial intelligence system or has a data connection with the artificial intelligence system at least temporarily. However, Anderson teaches to predictively control the vehicle semi-autonomously to maintain a trajectory of the vehicle in [0016] and [0025]. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date to include an artificial intelligence system as similarly shown in Anderson's [0016] and [0025] use of predictive control to provide the benefit of autonomously controlling the vehicle to avoid collisions. Anderson uses driver preferences, previous threat metrics, and previous control inputs to predict when the vehicle needs to intervene. This intervention is equivalent to an artificial intelligence system. 23. Regarding Claim 15, Anderson and Suzuki remains as applied above in Claim 10, and further, Anderson teaches a vehicle, in particular a motor vehicle, comprising the assistance system of claim 10 (Anderson: [0013]). 24. Regarding Claim 16, Anderson and Suzuki remains as applied above in Claim 2, and further, Anderson teaches the driving modes are selected from a group comprising a comfort driving mode, an energy saving mode, a sport mode, a highway mode, a city traffic mode, a long-distance mode, a working mode, a training mode, a persons transport mode, a goods transport mode and a hazardous goods transport mode (Anderson: [0015] and [0018] Note that under the broadest reasonable interpretation, comfort driving mode is equivalent to safe vehicle control below handling limits.). 25. Regarding Claim 17, Anderson and Suzuki remains as applied above in Claim 4, and further, Anderson teaches a data point correlating to the feedback is stored in a database to detect and/or document a learning and/or training effect of the driver based on a plurality of data correlating to feedbacks (Anderson: [0025]). 26. Regarding Claim 18, Anderson and Suzuki remains as applied above in Claim 4, and further, Anderson teaches this critical correlation limit value can be separately established for individual control signal encoders and/or control signal encoder groups (Anderson: [0016]). 27. Regarding Claim 19, Anderson and Suzuki remains as applied above in Claim 9, and further, Anderson teaches the point assigned to a driver is displaced along a steady curve within this three-dimensional space depending on the driving situation (Anderson: Figs. 5 and 6, [0022], and [0023] Note that Figs. 5 and 6 indicate a steady curve of the amount of assistance required based on the driving situation so the vehicle does not exceed any critical vehicle state (threat metric value).). 28. Regarding Claim 20, Anderson and Suzuki remains as applied above in Claim 10, and further, Anderson teaches the second processing circuit is configured for converting the data point into the control signal on falling below a correlation limit value (Anderson: [0016] and [0017]). Response to Arguments 29. Applicant's arguments filed 2/24/2026 have been fully considered but they are not persuasive. 30. First, the Applicant has alleged "Anderson in view of Suzuki does not teach, nor fairly suggest determining a correlation value between the driving instruction and the precalculated travel path of the vehicle and/or between the driving instruction and the precalculated vehicle state." The Examiner disagrees. Suzuki teaches in at least [0076] to determine a correlation value between the driving instruction and the precalculated travel path of the vehicle. The reference trajectory T0 is equivalent to the precalculated travel path and the actual parking trajectory T1 is equivalent to the driving instruction. Suzuki determines a deviation/difference between T0 and T1 and controls vehicle assistance when it is determined the parking of the subject vehicle has failed (when difference is greater than evaluation threshold). The distance difference between the trajectories T0 and T1 are used to determine a cumulative value, as explained in [0073]. This cumulative value and comparing the value to the evaluation threshold is equivalent to checking whether the correlation value falls below a critical correlation limit value. As a result, Anderson in view of Suzuki teach all the limitations of the claims. 31. Anderson (US 20100228427 A1) in view of Suzuki (US 20220297676 A1) teaches all aspects of the invention. The rejection is modified according to the newly amended language but still maintained with the current prior art of record. 32. Claims 1-20 remain rejected under their respective grounds and rational as cited above, and as stated in the prior office action which is incorporated herein. Also, although not specifically argued, all remaining claims remain rejected under their respective grounds, rationales, and applicable prior art for these reasons cited above, and those mentioned in the prior office action which is incorporated herein. Conclusion 33. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL T SILVA whose telephone number is (571)272-6506. The examiner can normally be reached Mon-Tues: 7AM - 4:30PM ET; Wed-Thurs: 7AM-6PM ET; Fri: OFF. 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, Angela Ortiz can be reached at 571-272-1206. 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. /MICHAEL T SILVA/Examiner, Art Unit 3663
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Prosecution Timeline

Show 3 earlier events
Nov 26, 2025
Final Rejection mailed — §103
Jan 14, 2026
Interview Requested
Jan 14, 2026
Response after Non-Final Action
Jan 22, 2026
Applicant Interview (Telephonic)
Jan 29, 2026
Examiner Interview Summary
Feb 24, 2026
Request for Continued Examination
Mar 12, 2026
Response after Non-Final Action
Apr 23, 2026
Non-Final Rejection mailed — §103 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

3-4
Expected OA Rounds
32%
Grant Probability
53%
With Interview (+21.0%)
3y 5m (~11m remaining)
Median Time to Grant
High
PTA Risk
Based on 105 resolved cases by this examiner. Grant probability derived from career allowance rate.

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