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 1-20 are presented for examination.
Claims 4-5, 11-12, and 19-20 are objected to.
Claims 1-3, 6-10, and 13-18 are rejected.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-3, 6-10, and 13-18 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 102
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.
Claim(s) 1-2, 6, 8, 13, 15, and 17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Vyas (US Pub. No.: 2020/0070817 A1: hereinafter “Vyas”).
Consider claims 1, 13, 15, and 17:
Vyas teaches a processing system (Figs. 3-4 elements 80-100, steps 60-66, “…Systems, apparatuses and methods may provide for technology that conducts a real-time analysis of interior sensor data associated with a vehicle, exterior sensor data associated with the vehicle and environmental data associated with the vehicle…”), a computer-readable non-transitory storage medium storing instructions of a processing program to be executed by a processor (Figs. 3-4 elements 80-100, steps 60-66, “…the method 60 may be implemented as one or more modules in a set of logic instructions stored in a non-transitory machine- or computer-readable storage medium such as random access memory (RAM), read only memory (ROM), programmable ROM (PROM)…”), a processing device mountable to a host moving object (Figs. 3-4 elements 80-100, steps 60-66, “…Systems, apparatuses and methods may provide for technology that conducts a real-time analysis of interior sensor data associated with a vehicle, exterior sensor data associated with the vehicle …”), and a processing method executed by a processor for performing a process related to a driving control of a host moving object (See Vyas, e.g., “…methods may provide for technology that conducts a real-time analysis of interior sensor data associated with a vehicle, exterior sensor data associated with the vehicle and environmental data associated with the vehicle…determine whether a hazard condition exists based on the real-time analysis, wherein the hazard condition includes a deviation of a current behavior waveform from a reference behavior waveform by a predetermined amount…a safety measure is triggered with respect to the vehicle if the hazard condition exists. The safety measure may be selected based on a reaction time constraint associated with the hazard condition…”, of Abstract, ¶ [0014]-¶ [0036], ¶ [0037]-¶ [0043], and Figs. 3-4 elements 80-100, steps 60-66, Figs. 5-6 elements 120-168, Figs. 7-8 elements 180-704), the processing method comprising: monitoring an abnormality in detection information that is generated by detecting an internal and external environment of the host moving object (See Vyas, e.g., “…processing block 62 provides for conducting a real-time analysis of interior sensor data associated with a vehicle, exterior sensor data associated with the vehicle and environmental data associated with the vehicle…A safety measure may be triggered at block 66 with respect to the vehicle if the hazard condition exists, wherein the safety measure may be selected based on a reaction time constraint associated with the hazard condition…processing block 82 provides for collecting interior sensor data while block 84 collects exterior sensor data and block 86 collects environmental data…”, of Abstract, ¶ [0014]-¶ [0036], ¶ [0037]-¶ [0043], and Figs. 3-4 elements 80-100, steps 60-66, Figs. 5-6 elements 120-168, Figs. 7-8 elements 180-704); planning the driving control of the host moving object (See Vyas, e.g., “…Block 90 may fuse the collected data to obtain a current behavior waveform and compare the current behavior waveform with the reference behavior waveform(s). If an anomalous event (e.g., major delta) is detected at block 92, a determination may be made at block 94 as to whether to trigger a collision avoidance measure…”, of Abstract, ¶ [0014]-¶ [0036], ¶ [0037]-¶ [0043], and Figs. 3-4 elements 80-100, steps 60-66, Figs. 5-6 elements 120-168, Figs. 7-8 elements 180-704), wherein the monitoring of the abnormality includes: and in response to determining the abnormality being occurred, setting a constraint or restriction on the driving control to maintain operation of the host moving object within a determined level of risk (See Vyas, e.g., “…If an anomalous event (e.g., major delta) is detected at block 92, a determination may be made at block 94 as to whether to trigger a collision avoidance measure. Block 94 may take into consideration the reaction time constraint of the anomalous event. If there is enough time to avoid the collision, illustrated block 96 notifies the driver, automates a maneuver and/or notifies a remote party…Block 98 may determine whether to trigger a collision mitigation measure. Block 98 may include monitoring for an actual or predicted collision. In order to trigger the collision mitigation measure, illustrated block 100 selects a subset of a plurality of airbags in the vehicle based on predicted trajectories of occupants and/or objects within the vehicle and activates the selected subset of airbags…”, of Abstract, ¶ [0014]-¶ [0036], ¶ [0037]-¶ [0043], and Figs. 3-4 elements 80-100, steps 60-66, Figs. 5-6 elements 120-168, Figs. 7-8 elements 180-704), wherein the constraint or restriction on the driving control is set according to the detection information using a safety model (See Vyas, e.g., “…processing block 82 provides for collecting interior sensor data while block 84 collects exterior sensor data and block 86 collects environmental data. Normal behavior may be modeled at block 88 based on the collected data. For example, current road condition and/or traffic data might be used to retrieve historical acceleration data and/or lane position data, wherein the retrieved data may in turn be used at block 88 to generate a reference behavior waveform such as one or more of the reference behavior waveforms 44, 46…”, of Abstract, ¶ [0014]-¶ [0036], ¶ [0037]-¶ [0043], and Figs. 3-4 elements 80-100, steps 60-66, Figs. 5-6 elements 120-168, Figs. 7-8 elements 180-704), which is in compliance with a driving policy and is generated by modeling safety of intended functionality (See Vyas, e.g., “…Block 98 may determine whether to trigger a collision mitigation measure. Block 98 may include monitoring for an actual or predicted collision. In order to trigger the collision mitigation measure, illustrated block 100 selects a subset of a plurality of airbags in the vehicle based on predicted trajectories of occupants and/or objects within the vehicle and activates the selected subset of airbags. In this regard, block 100 may take into consideration the actual positions of passengers and loose objects based on the interior sensor data…”, of Abstract, ¶ [0014]-¶ [0036], ¶ [0037]-¶ [0043], and Figs. 3-4 elements 80-100, steps 60-66, Figs. 5-6 elements 120-168, Figs. 7-8 elements 180-704) as a computer program that executes a process according to a mathematical model, the driving policy being a strategy or rule defining control behaviors of vehicle acceptable within a vehicle level (See Vyas, e.g., “…processing block 82 provides for collecting interior sensor data while block 84 collects exterior sensor data and block 86 collects environmental data. Normal behavior may be modeled at block 88 based on the collected data. For example, current road condition and/or traffic data might be used to retrieve historical acceleration data and/or lane position data, wherein the retrieved data may in turn be used at block 88 to generate a reference behavior waveform such as one or more of the reference behavior waveforms 44, 46 (FIG. 2), already discussed. Block 90 may fuse the collected data to obtain a current behavior waveform and compare the current behavior waveform with the reference behavior waveform(s). If an anomalous event (e.g., major delta) is detected at block 92, a determination may be made at block 94 as to whether to trigger a collision avoidance measure. Block 94 may take into consideration the reaction time constraint of the anomalous event. If there is enough time to avoid the collision, illustrated block 96 notifies the driver, automates a maneuver and/or notifies a remote party…”, of Abstract, ¶ [0014]-¶ [0036], ¶ [0037]-¶ [0043], and Figs. 3-4 elements 80-100, steps 60-66, Figs. 5-6 elements 120-168, Figs. 7-8 elements 180-704); and intervening in the planned driving control by the set constraint or restriction (See Vyas, e.g., “…Block 98 may determine whether to trigger a collision mitigation measure. Block 98 may include monitoring for an actual or predicted collision. In order to trigger the collision mitigation measure, illustrated block 100 selects a subset of a plurality of airbags in the vehicle based on predicted trajectories of occupants and/or objects within the vehicle and activates the selected subset of airbags. In this regard, block 100 may take into consideration the actual positions of passengers and loose objects based on the interior sensor data…”, of Abstract, ¶ [0014]-¶ [0036], ¶ [0037]-¶ [0043], and Figs. 3-4 elements 80-100, steps 60-66, Figs. 5-6 elements 120-168, Figs. 7-8 elements 180-704).
Consider claim 2:
Vyas teaches everything claimed as implemented in the rejection of claim 1 above. In addition, Vyas teaches wherein in the setting of constraint or restriction, the safety model sets the constraint or restriction based on a reasonable action that is estimated corresponding to an occurrence scene of the abnormality (See Vyas, e.g., “…processing block 82 provides for collecting interior sensor data while block 84 collects exterior sensor data and block 86 collects environmental data. Normal behavior may be modeled at block 88 based on the collected data. For example, current road condition and/or traffic data might be used to retrieve historical acceleration data and/or lane position data, wherein the retrieved data may in turn be used at block 88 to generate a reference behavior waveform such as one or more of the reference behavior waveforms 44, 46 (FIG. 2), already discussed. Block 90 may fuse the collected data to obtain a current behavior waveform and compare the current behavior waveform with the reference behavior waveform(s). If an anomalous event (e.g., major delta) is detected at block 92, a determination may be made at block 94 as to whether to trigger a collision avoidance measure. Block 94 may take into consideration the reaction time constraint of the anomalous event. If there is enough time to avoid the collision, illustrated block 96 notifies the driver, automates a maneuver and/or notifies a remote party…”, of Abstract, ¶ [0014]-¶ [0036], ¶ [0037]-¶ [0043], and Figs. 3-4 elements 80-100, steps 60-66, Figs. 5-6 elements 120-168, Figs. 7-8 elements 180-704).
Consider claim 6:
Vyas teaches everything claimed as implemented in the rejection of claim 1 above. In addition, Vyas teaches wherein the abnormality includes a sensing abnormality of a sensor system, which is equipped to the host moving object and generates the detection information as a generation source (See Vyas, e.g., “…processing block 62 provides for conducting a real-time analysis of interior sensor data associated with a vehicle, exterior sensor data associated with the vehicle and environmental data associated with the vehicle…A safety measure may be triggered at block 66 with respect to the vehicle if the hazard condition exists, wherein the safety measure may be selected based on a reaction time constraint associated with the hazard condition…processing block 82 provides for collecting interior sensor data while block 84 collects exterior sensor data and block 86 collects environmental data…”, of Abstract, ¶ [0014]-¶ [0036], ¶ [0037]-¶ [0043], and Figs. 3-4 elements 80-100, steps 60-66, Figs. 5-6 elements 120-168, Figs. 7-8 elements 180-704).
Consider claim 8:
Vyas teaches everything claimed as implemented in the rejection of claim 6 above. In addition, Vyas teaches wherein the constraint or restriction includes a speed limit value in a longitudinal direction or a lateral direction relative to the host moving object (See Vyas, e.g., “…Normal behavior may be modeled at block 88 based on the collected data. For example, current road condition and/or traffic data might be used to retrieve historical acceleration data and/or lane position data, wherein the retrieved data may in turn be used at block 88 to generate a reference behavior waveform such as one or more of the reference behavior waveforms 44, 46…”, of Abstract, ¶ [0014]-¶ [0036], ¶ [0037]-¶ [0043], and Figs. 3-4 elements 80-100, steps 60-66, Figs. 5-6 elements 120-168, Figs. 7-8 elements 180-704), and the speed limit value is set based on a lane structure that constraints or restricts the host moving object in the longitudinal direction and the lateral direction (See Vyas, e.g., “…Normal behavior may be modeled at block 88 based on the collected data. For example, current road condition and/or traffic data might be used to retrieve historical acceleration data and/or lane position data, wherein the retrieved data may in turn be used at block 88 to generate a reference behavior waveform such as one or more of the reference behavior waveforms 44, 46…”, of Abstract, ¶ [0014]-¶ [0036], ¶ [0037]-¶ [0043], and Figs. 3-4 elements 80-100, steps 60-66, Figs. 5-6 elements 120-168, Figs. 7-8 elements 180-704).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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) 3, 7, 9-10, 14, 16, and 18 is/are rejected under 35 U.S.C. 103 as -being unpatentable over Vyas in view of INOUE.
Consider claim 3, 14, 16, and 18:
Vyas teaches everything claimed as implemented in the rejection of claims 1 13, 15, and 17 above. In addition, Vyas teaches wherein the host moving object is equipped with a sensor system that generates the detection information as a generation source (See Vyas, e.g., “…processing block 62 provides for conducting a real-time analysis of interior sensor data associated with a vehicle, exterior sensor data associated with the vehicle and environmental data associated with the vehicle…A safety measure may be triggered at block 66 with respect to the vehicle if the hazard condition exists, wherein the safety measure may be selected based on a reaction time constraint associated with the hazard condition…processing block 82 provides for collecting interior sensor data while block 84 collects exterior sensor data and block 86 collects environmental data…”, of Abstract, ¶ [0014]-¶ [0036], ¶ [0037]-¶ [0043], and Figs. 3-4 elements 80-100, steps 60-66, Figs. 5-6 elements 120-168, Figs. 7-8 elements 180-704). Vyas further teaches “…Block 98 may determine whether to trigger a collision mitigation measure. Block 98 may include monitoring for an actual or predicted collision. In order to trigger the collision mitigation measure, illustrated block 100 selects a subset of a plurality of airbags in the vehicle based on predicted trajectories of occupants and/or objects within the vehicle and activates the selected subset of airbags. In this regard, block 100 may take into consideration the actual positions of passengers and loose objects based on the interior sensor data…”, of Abstract, ¶ [0014]-¶ [0036], ¶ [0037]-¶ [0043], and Figs. 3-4 elements 80-100, steps 60-66, Figs. 5-6 elements 120-168, Figs. 7-8 elements 180-704. However, Vyas does not explicitly teach and in response to determining the abnormality has occurred in a scene where a target moving object does not exist within a detection range defined by the sensor system, the safety model sets the constraint or restriction based on a reasonable action that does not cause an unreasonable situation between a virtual moving object which is estimated to be located at a detection limit distance of the sensor system.
In an analogous field of endeavor, INOUE teaches in response to determining the abnormality has occurred in a scene where a target moving object does not exist within a detection range defined by the sensor system (See INOUE, e.g., “…when both a pedestrian and a bicycle are assumed as virtual moving objects, the reference velocity calculation unit 13 may select two or more maps from among the plurality of maps. In this case, the reference velocity calculation unit 13 selects the lowest reference velocity from among the two or more reference velocities respectively obtained from the selected two or more maps…” of ¶ [0032]-¶ [0035], ¶ [00049]-¶ [0050], and Fig. 1-3, elements 1-32), the safety model sets the constraint or restriction based on a reasonable action that does not cause an unreasonable situation between a virtual moving object which is estimated to be located at a detection limit distance of the sensor system (See INOUE, e.g., “…A reference velocity is obtained on the assumption that a virtual moving object…rushes out from a blind spot in front of the vehicle 1. It is also assumed that the vehicle 1 and the virtual moving object move straight ahead…A point at which the direction of the vehicle 1 and the direction of the virtual moving object intersect with each other is referred to as collision point…The reference velocity means a velocity at which the vehicle 1 is able to avoid a collision with the virtual moving object in the situation…when the driver of the vehicle 1 recognizes the virtual moving object and then applies sudden braking or when the virtual moving object is recognized by the vehicle 1 and then collision mitigation braking is activated…” of ¶ [0032]-¶ [0035], ¶ [00049]-¶ [0050], and Fig. 1-3, elements 1-32).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine “…methods may provide for technology that conducts a real-time analysis of interior sensor data associated with a vehicle, exterior sensor data associated with the vehicle and environmental data associated with the vehicle…determine whether a hazard condition exists based on the real-time analysis, wherein the hazard condition includes a deviation of a current behavior waveform from a reference behavior waveform by a predetermined amount…a safety measure is triggered with respect to the vehicle if the hazard condition exists. The safety measure may be selected based on a reaction time constraint associated with the hazard condition...”, as disclosed in Vyas with “and in response to determining the abnormality being occurred in a scene where a target moving object does not exist within a detection range defined by the sensor system, the constraint or restriction is set using the safety model in which a virtual moving object is estimated to be located at a detection limit distance of the sensor system”, as taught in INOUE with a reasonable expectation of success to yield a system, and a method that “sets a relative velocity of a host vehicle with respect to a physical object and a lateral movement amount for avoiding the physical object based on the type of the physical object, and controls the host vehicle such that the host vehicle runs so as to achieve the set lateral movement amount.”, as disclosed in ¶ [0003].
Consider claim 7:
Vyas teaches everything claimed as implemented in the rejection of claim 1 above. In addition, Vyas teaches “…methods may provide for technology that conducts a real-time analysis of interior sensor data associated with a vehicle, exterior sensor data associated with the vehicle and environmental data associated with the vehicle…determine whether a hazard condition exists based on the real-time analysis, wherein the hazard condition includes a deviation of a current behavior waveform from a reference behavior waveform by a predetermined amount…a safety measure is triggered with respect to the vehicle if the hazard condition exists. The safety measure may be selected based on a reaction time constraint associated with the hazard condition..”. However, Vyas does not explicitly teach wherein the detection information includes a distance to a target moving object, and the abnormality includes an accuracy abnormality of information related to the distance to the target moving object.
In an analogous field of endeavor, INOUE teaches wherein the detection information includes a distance to a target moving object (See INOUE, e.g., “…A reference velocity is obtained on the assumption that a virtual moving object… The reference velocity means a velocity at which the vehicle 1 is able to avoid a collision with the virtual moving object in the situation…when the driver of the vehicle 1 recognizes the virtual moving object and then applies sudden braking or when the virtual moving object is recognized by the vehicle 1 and then collision mitigation braking is activated…” of ¶ [0032]-¶ [0035], ¶ [00049]-¶ [0050], and Fig. 1-3, elements 1-32), and the abnormality includes an accuracy abnormality of information related to the distance to the target moving object (See INOUE, e.g., “…when both a pedestrian and a bicycle are assumed as virtual moving objects, the reference velocity calculation unit 13 may select two or more maps from among the plurality of maps. In this case, the reference velocity calculation unit 13 selects the lowest reference velocity from among the two or more reference velocities respectively obtained from the selected two or more maps…a reference velocity…calculated based on recognized environment information. The reference velocity V.sub.min is a velocity at which the vehicle 1 is able to avoid a collision with a virtual moving object in the case where a distance between the vehicle 1 and a collision point is D.sub.car…” of ¶ [0032]-¶ [0035], ¶ [00049]-¶ [0050], and Fig. 1-3, elements 1-32).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Vyas with INOUE with a reasonable expectation of success to yield a system, and a method to implement an enhanced, robust, and seamless object detection to mitigate collisions.
Consider claim 9:
Vyas teaches everything claimed as implemented in the rejection of claim 1 above. In addition, Vyas teaches “…methods may provide for technology that conducts a real-time analysis of interior sensor data associated with a vehicle, exterior sensor data associated with the vehicle and environmental data associated with the vehicle…determine whether a hazard condition exists based on the real-time analysis, wherein the hazard condition includes a deviation of a current behavior waveform from a reference behavior waveform by a predetermined amount…a safety measure is triggered with respect to the vehicle if the hazard condition exists. The safety measure may be selected based on a reaction time constraint associated with the hazard condition..”. However, Vyas does not explicitly teach wherein the detection information includes information related to a type of a target moving object, and the abnormality includes a recognition abnormality of the information related to the type of the target moving object.
In an analogous field of endeavor, INOUE teaches wherein the detection information includes information related to a type of a target moving object (See INOUE, e.g., “…A reference velocity is obtained on the assumption that a virtual moving object… The reference velocity means a velocity at which the vehicle 1 is able to avoid a collision with the virtual moving object in the situation…when the driver of the vehicle 1 recognizes the virtual moving object and then applies sudden braking or when the virtual moving object is recognized by the vehicle 1 and then collision mitigation braking is activated…” of ¶ [0032]-¶ [0035], ¶ [00049]-¶ [0050], and Fig. 1-3, elements 1-32), and the abnormality includes a recognition abnormality of the information related to the type of the target moving object (See INOUE, e.g., “…when both a pedestrian and a bicycle are assumed as virtual moving objects, the reference velocity calculation unit 13 may select two or more maps from among the plurality of maps. In this case, the reference velocity calculation unit 13 selects the lowest reference velocity from among the two or more reference velocities respectively obtained from the selected two or more maps…a reference velocity…calculated based on recognized environment information. The reference velocity V.sub.min is a velocity at which the vehicle 1 is able to avoid a collision with a virtual moving object in the case where a distance between the vehicle 1 and a collision point is D.sub.car…” of ¶ [0032]-¶ [0035], ¶ [00049]-¶ [0050], and Fig. 1-3, elements 1-32).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings Vyas with INOUE with a reasonable expectation of success to yield a system, and a method to implement an enhanced, robust, and seamless object detection to mitigate collisions.
Consider claim 10:
Vyas teaches everything claimed as implemented in the rejection of claim 1 above. In addition, Vyas teaches wherein the detection information includes information related to a position of a target object (See Vyas, e.g., “…The interior sensor data may include, for example, driver drowsiness data, seatbelt position data, occupant position data, object position data, driver interface data…”, of Abstract, ¶ [0014]-¶ [0036], ¶ [0037]-¶ [0043], and Figs. 3-4 elements 80-100, steps 60-66, Figs. 5-6 elements 120-168, Figs. 7-8 elements 180-704), and the abnormality includes a localization abnormality of the information related to the position of the target moving object (See Vyas, e.g., “…The interior sensor data may include, for example, driver drowsiness data, seatbelt position data, occupant position data, object position data, driver interface data…”, of Abstract, ¶ [0014]-¶ [0036], ¶ [0037]-¶ [0043], and Figs. 3-4 elements 80-100, steps 60-66, Figs. 5-6 elements 120-168, Figs. 7-8 elements 180-704). However, Vyas does not explicitly teach a target moving object.
In an analogous field of endeavor, INOUE teaches a target moving object (See INOUE, e.g., “…when the driver of the vehicle 1 recognizes the virtual moving object and then applies sudden braking or when the virtual moving object is recognized by the vehicle 1 and then collision mitigation braking is activated…” of ¶ [0032]-¶ [0035], ¶ [00049]-¶ [0050], and Fig. 1-3, elements 1-32),
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings Vyas with INOUE with a reasonable expectation of success to yield a system, and a method to implement an enhanced, robust, and seamless position/location detection to mitigate collisions.
Allowable Subject Matter
Claims 4-5, 11-12, and 19-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Further, the prior art on record fails to teach or suggest, either in singularity or in combination, the claimed subject matter of claims 4-5, 11-12, and 19-20.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
HASE et al. (US Pub. No.: 2020/0233437 A1) teaches “A traveling control device includes: a peripheral recognition unit which recognizes a status around an own vehicle; an abnormality determination unit which determines whether an abnormality has occurred in the recognition function of the peripheral recognition unit; and a control unit which, if the abnormality determination unit has determined that an abnormality has occurred in the recognition function of the peripheral recognition unit, executes procedure modification control to modify an automatic driving procedure in the automatic driving system in accordance with an abnormality recognition direction which is a direction in which the abnormality in the recognition function has occurred.”
SUGAWARA et al. (JP 2009274594 A) teaches “To solve the problem that in a conventional lane change support device, white lane information is used only for calculating the relative information between its own vehicle and a peripheral vehicle, and whether or not the lane change support of its own vehicle is allowed is not determined from road information, so that the lane change to a restricted adjacent lane is supported, and acceleration control is performed on a road with a large curvature which may become dangerous when the vehicle speed is increased. <P>SOLUTION: A lane change support device is provided with: a first control mode for permitting a function for supporting lane change; a second control mode for prohibiting the function for supporting lane change; a road information acquisition part for acquiring road information when its own vehicle is traveling; a relative information acquisition part for acquiring the relative information between its own vehicle and the peripheral vehicle; a selection part for selecting a control mode based on the road information acquired by the road acquisition part; and a control part for controlling its own vehicle based on the relative information acquired by the relative information acquisition part and the control mode selected by the selection part.”
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/BABAR SARWAR/Primary Examiner, Art Unit 3667