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 Amendment
1. Claims 1-20 are currently pending.
Claim Rejections - 35 USC § 103
2. 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.
3. 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.
4. 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.
5. 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).
6. Regarding Claim 1, Anderson teaches a method for controlling a vehicle by an assistance system, comprising (Anderson: [0013] semi-autonomous active navigation system):
Detecting environmental data of a vehicle using a sensor (Anderson: [0014] Real time sensors detect “the position of road edges, static obstacles (e.g. trees, road-side signs), and dynamic obstacles (e.g. other vehicles, pedestrians).”);
Calculating a first travel path of the vehicle to a destination based on the environmental data (Anderson: [0015], [0016], and [0020] "The predicted safe vehicle trajectory (and associated control inputs to yield such a trajectory) is found such that it satisfies a configurable set of trajectory requirements…”);
Detecting a driving instruction of a driver using at least one human-machine interface (Anderson: [0020] and [0024] “Considerations for this step include, for example, (but are not limited to)… driver inputs”);
Determining a correlation value… (Anderson: [0017] and [0021] metric value);
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] “if the threat metric value is low, the control system intervention is low (i.e. the driver commands the vehicle with little or no computer-controlled intervention); if the threat metric value is high, the control system intervention is high”);
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] Fig. 6),
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] “the intervention law can be designed such that it assumes full authority by the time the predicted safe trajectory reaches the limit of any pre-defined critical vehicle states. This corresponds to a situation where only an optimal set of inputs would result in a safe vehicle trajectory”).
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] The cumulative value integrating the distance D2 with the time the vehicle V1 takes to reach target position is equivalent to determining a correlation value between the driving instruction and precalculated travel path.);
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] “the parking assistance device 100 does not output the guidance display for switching the parking mode to the display 13 until a determination is made that the parking has failed”).
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.
7. Regarding Claim 2, Anderson and Suzuki remain 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] past driver performance).
8. Regarding Claim 3, Anderson and Suzuki remain 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] computer controlled actuator).
9. Regarding Claim 4, Anderson and Suzuki remain 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] “FIG. 5 shows, graphically, the results of a simulated test illustrating system response when a driver fails to navigate a curve in the road, shown by a light gray line... Note that in this embodiment of the invention, K represents proportion of control authority given to the autonomous system, with the driver allowed the remaining (1-K).”).
10. Regarding Claim 5, Anderson and Suzuki remain 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] The control authority exerted by the system to then determine a function of the computed threat is equivalent to the critical correlation limit value established by an authorized source.).
11. Regarding Claim 6, Anderson and Suzuki remain 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] “In addition to considering past and current vehicle and driver actions to assess threat and determine control authority,”).
12. Regarding Claim 7, Anderson and Suzuki remain 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] detecting driver’s gesture).
13. Regarding Claim 8, Anderson and Suzuki remain as applied above in Claim 1, and further, Anderson teaches the method is at least partially carried out computer-assisted (Anderson: [0013] and [0017] computer controlled intervention).
14. 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.).
15. Regarding Claim 10, Anderson teaches an assistance system for at least partially autonomous control of a vehicle, comprising (Anderson: [0013] semi-autonomous active navigation system):
A sensor for detecting environmental data (Anderson: [0014] Real time sensors detect “the position of road edges, static obstacles (e.g. trees, road-side signs), and dynamic obstacles (e.g. other vehicles, pedestrians).”);
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] "The predicted safe vehicle trajectory (and associated control inputs to yield such a trajectory) is found such that it satisfies a configurable set of trajectory requirements…”);
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] “Considerations for this step include, for example, (but are not limited to)… driver inputs”);
A correlation value determination circuit that is intended and configured for determining a correlation value… (Anderson: [0017] and [0021] metric value);
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] Fig. 6),
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] “the intervention law can be designed such that it assumes full authority by the time the predicted safe trajectory reaches the limit of any pre-defined critical vehicle states. This corresponds to a situation where only an optimal set of inputs would result in a safe vehicle trajectory”).
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] The cumulative value integrating the distance D2 with the time the vehicle V1 takes to reach target position is equivalent to determining a correlation value between the driving instruction and precalculated travel path.);
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] “the parking assistance device 100 does not output the guidance display for switching the parking mode to the display 13 until a determination is made that the parking has failed”).
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.
16. Regarding Claim 11, Anderson and Suzuki remain 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] “The form of the intervention law modulating this control system authority is configurable and can differ for different actuators (i.e. a vehicle with both active steering and braking can have distinct intervention laws defined for the steering actuator and the braking actuators)”),
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] “as the threat metric value increases, indicating that the predicted vehicle trajectory will near a pre-defined critical vehicle state(s) (such as spatial location, lateral acceleration, or tire friction saturation), the control system begins to assume control authority to preempt an unsafe maneuver”),
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] driver preference).
17. 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] computer controlled actuator).
18. 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.).
19. 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] "Significant advantages stem from the predictive nature of this solution. In addition to considering past and current vehicle and driver actions to assess threat and determine control authority, the current solution predicts a future vehicle trajectory and associated threat, and uses this prediction to schedule control authority.").
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.
20. Regarding Claim 15, Anderson and Suzuki remain 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] semi-autonomous active navigation system).
21. Regarding Claim 16, Anderson and Suzuki remain 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.).
22. Regarding Claim 17, Anderson and Suzuki remain 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] “In addition to considering past and current vehicle and driver actions to assess threat and determine control authority”).
23. Regarding Claim 18, Anderson and Suzuki remain 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] “The form of the intervention law modulating this control system authority is configurable and can differ for different actuators (i.e. a vehicle with both active steering and braking can have distinct intervention laws defined for the steering actuator and the braking actuators)”).
24. Regarding Claim 19, Anderson and Suzuki remain 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).).
25. Regarding Claim 20, Anderson and Suzuki remain 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] "The control authority exerted by the system is then determined as a function of this computed threat: generally speaking, if the threat metric value is low, the control system intervention is low (i.e. the driver commands the vehicle with little or no computer-controlled intervention); if the threat metric value is high, the control system intervention is high.”).
Response to Arguments
26. Applicant's arguments filed 7/13/2026 have been fully considered but they are not persuasive.
27. First, the Applicant has alleged "the Examiner's assertion 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 fails below a critical correlation limit' is erroneous." 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 because the reference trajectory is calculated from a preliminary stored parking program (see [0035]). The actual parking trajectory T1 is equivalent to the driving instruction because the actual parking trajectory is based on an actual steering angle of the vehicle (see [0070]). The actual steering angle is a driving instruction for the vehicle that is input to control the path of the vehicle.
Further, Suzuki determines a deviation or 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 is equivalent to determining a correlation value between the driving instruction and precalculated travel path. The cumulative value being compared to an evaluation threshold in Suzuki is equivalent to checking the correlation value.
Therefore, the combination of Anderson in view of Suzuki is reasonable because the driving instruction and precalculated travel path are compared. The fact that Suzuki determines this difference in a parking lot is irrelevant because there is no claim language that distinguishes the environment in which the predetermined and actual trajectories that end at a destination (parking spot) are compared. A parking trajectory is equivalent to a travel path because the vehicle is still being controlled to the destination.
Additionally, it appears that the Applicant is arguing the references individually. One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). As was specifically stated in the Nonfinal Office Action mailed 4/23/2026, Suzuki was only used to disclose determining a correlation value between the driving instruction and the precalculated travel path of the vehicle. Anderson was used to teach detecting a driving instruction of a driver using at least one human machine interface and checking whether the correlation value falls below a critical correlation limit value. Thus, Suzuki still teaches the features for which it was cited for determining a correlation value.
28. Second, the Applicant has alleged "Suzuki describes a mode-switch prompt to a user, not feedback that 'informs the driver about a deviation between the drive input expected by the assistance system and the input actually made,' as recited in Claim 1 and similar elements of Claim 10." The Examiner disagrees.
Suzuki teaches in [0050] and [0054] that the guidance display is output when the determination is made that the manual parking has failed. The guidance display is only output after a determination is made that the parking has failed (correlation value greater than a threshold) is equivalent to informing the driver about a deviation between driver input and input actually made. The guidance display outputs information so the user can change the vehicle from manual mode to automated mode because of the deviation between the target and actual trajectories.
As currently claimed, there is no language that describes what kind of display element is used to inform the driver about a deviation between input expected and actual input. There is also no indication when the display element informs the driver. The Applicant alleges that Suzuki does not give feedback that informs the driver. However, displaying the guidance display to change to an automated is feedback that the deviation between target and actual trajectories was greater than a threshold.
As a result, Anderson in view of Suzuki teaches all the limitations of the claims for which it was cited.
29. Anderson (US 20100228427 A1) in view of Suzuki (US 20220297676 A1) teaches all aspects of the invention. The rejection is still maintained with the current prior art of record.
30. 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
31. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
32. 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.
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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.
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/MICHAEL T SILVA/Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663
