DETAIL ACTIONS
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
This office action is in response to the amendments/arguments submitted by the Applicant(s) on 09/09/2026.
Status of the Claims
Claims 1- 7 are pending.
Claims 1 and 5 are amended.
Claims 6-7 are new.
Response to Arguments
Rejections Under 35 U.S.C.§101
Applicant's arguments, see remarks pages 5-7, filed 09/09/2026.
with respect to the rejection(s) of Claims under 35 U.S.C. §101 has been considered and are persuasive. Therefore, the rejections under 35 U.S.C. §101have been withdrawn.
Rejections Under 35 U.S.C. §102(a)(1)
Applicant argument in the remarks pages 7-8, filed 09/09/2026 with respect to the rejection(s) of Claims under 35 U.S.C§102(a)(1) has been considered, and are moot because the amendment has necessitated a new ground of rejections. The new rejections
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.
Claims 1-2, and 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over Horiguchi et al. (US 2020/0070847 A1, hereinafter Horiguchi, IDS ref. previously cited) and in view of Horiguchi et al., (US 2021/0053580 A1, hereinafter Horiguchi’580).
Regarding Claim 1, Horiguchi teaches
A prediction accuracy evaluation method executed by a computer, the prediction accuracy evaluation (Horiguchi, Figure 1, [0009] an action prediction unit comprising:
acquiring detection information (Horiguchi, Figure 1, Figure 3, external information detection unit (Figure 1, external information detection unit 20 includes a millimeter wave radar 21 and a camera 22.) acquires information about objects surrounding the vehicle(Horiguchi, Figure 4, 103 the bicycle, 104 pedestrian, 102 another vehicle).)” indicating a detected position, a detected velocity (Horiguchi, Figure 4, [0033] FIG. 4 illustrates an example plotting objects, using the sensor fusion processing S1, on a map of object around the own vehicle M by integrating information of positions, size, and moving speed of the objects around the own vehicle detected with the millimeter wave radar 21 and the stereo camera 22”NOTE: “moving speed” reads on “ detected velocity”), an error range of the detected position(Horiguchi, Figure 4, and Figure 6, [0049], “position error”) of an obstacle detected (Horiguchi, Figure 4, 103 the bicycle, 104 pedestrian, 102 another vehicle) by using a sensor (Horiguchi ,Figure 1, the millimeter wave radar 21 and the stereo camera 22”) mounted on a moving body (Horiguchi ,Figure 1, Vehicle, Figure 4, 101 own vehicle).
a predicted distribution generation process that generates a predicted distribution of a position of the obstacle at a second time later than a first time, (Horiguchi, FIG. 1, 6 [0048] It is assumed that the current time is T2.”The microcomputer 11 acquires the surrounding object prediction map M2(T1) based on first detection information that is the detection information at the first time ([0048] “predicted at time T1 from the surrounding object action prediction processing S2 (S41)”),; and
a prediction abnormality determination process that determines whether or not the predicted distribution is abnormal based on the predicted distribution and a second detected position that is the detected position of the obstacle at the second time.(Horiguchi, Figures 1, 4, and 6, [0048] By comparing the prediction map around the own vehicle M2(T1) predicted at time T1 to the sensor data (sensor data after time synchronization processing) acquired from the sensors 21 and 22 at time T2 (S45), it is possible to determine whether the sensors 21 and 22 have an abnormality.[0053] Similar to the above-described abnormality determination (S43) of the sensor fusion processing S1, the presence or absence of abnormality of the sensor data can be determined using the threshold ”).
and a control process that transmits instructions (Horiguchi, Figure 1, [0026] the automatic driving ECU 10 transmits a control command value to the lower-level ECU 40-43) to control the moving body based on the predicted distribution being abnormal. (Horiguchi, Figure 6, Step48, Step 50 “[0059] Refer to FIG. 6 again. The microcomputer 11 determines whether the comprehensive determination result is "abnormal" (S47) and, if the abnormality is determined (S47: YES), notifies the driver in the vehicle 1 of the abnormality being detected (S48).” [0061] After notifying the driver in step S48, the microcomputer 11 determines whether the abnormal state continues (S49). (S49: YES), the microcomputer 11 starts fallback operation processing (S50 Start Transitioning process to manual driving, Fallback operation using another sensor)”).
Horiguchi teaches generating a map of objects around the own vehicle M by integrating information of positions, size, and moving speed of the objects around the own vehicle detected with the millimeter wave radar 21 and the stereo camera 22. See [0033] and figure 5. Figure 4. In the comparison processing in step S45, calculate the difference between the synchronized millimeter wave radar data and the actual position of each surrounding object indicated by the synchronized camera data is compared to a predetermined threshold.
Horiguchi is silent on an error range of the detected velocity of an obstacle detected.
However, Horiguchi”580 teaches an error range of the detected velocity of an obstacle detected. (Horiguchi”580, Figures 2-4, [0051] Subsequently, “The behavior prediction processing unit 5 performs behavior prediction of various surrounding objects mapped on the own vehicle surrounding map 7. The prediction methods include, for example, a method of extrapolating a future position based on the current position and speed of each surrounding object, but a variety of other publicly known methods can be used”. [0065] Similar to the processing in the first comparison unit 102, in the processing in the second comparison unit 103, by performing, on two data, consistency determination that allows an error such as that in the processing in the first comparison unit 102, the second comparison unit output 106 indicating whether or not the two data have been consistent with each other within a range of a predetermined threshold value is obtained also see[0066] ). NOTE: comparison unit 102 and 103 compares sensor data (position and speed) with a threshold value to determine consistency of data and error estimation.).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Horiguchi’s method for generating moving map with the location of moving object to incorporate Horiguchi”580 processing method of integrating velocity value deviation between the observation value and a prediction value as taught by Horiguchi’s with the benefit of obtaining an accurate abnormality value detection occurring in arithmetic processing in the sensors and the ECU with a low arithmetic load without using the multiplexed, additional sensors (Horiguchi”580,[0014] [0051]-[0065]). It would have been obvious to a person of ordinary skill to include the well-known abnormality prediction process for moving object detection using know arithmetic process and model, in order to yield the predicted results of generating accurate map detection, yet with higher accuracy (KSR).
Regarding Claim 2, combination of Horiguchi and Horiguchi”580 teaches the prediction accuracy evaluation method according to claim 1,
Horiguchi further teaches further comprising a prediction accuracy calculation process that calculates accuracy of the predicted distribution by comparing the second detected position with the predicted distribution, wherein the prediction abnormality determination process includes determining that the predicted distribution is abnormal when the accuracy of the predicted distribution is lower than a predetermined level. (Horiguchi, [0049] For example, there is a method of comparing the deviation width of the position of each surrounding object with a predetermined threshold. There is a position error of less than one grid on the drawing between the predicted position 103(2) of the bicycle at time T2 calculated at time Tl in FIG. 4(2) and the position 103(3) of the bicycle at time T2 in FIG. 4(3). [0050 it can be determined that an abnormality has occurred in the bicycle position 103(3) at time T2. This makes it possible to detect the presence or absence of abnormality that has occurred before the sensor fusion processing S1 is done in the main function processing flow of the automatic driving ECU 10. The determination result of step S43 is sent to the comprehensive determination step S46”).
Regarding Claim 5, Horiguchi teaches,
A prediction accuracy evaluation system comprising processing circuitry (Horiguchi, Figure 1, [0009] an action prediction unit comprising:
acquiring detection information (Horiguchi, Figure 1, Figure 3), external information detection unit (Figure 1, external information detection unit 20 includes a millimeter wave radar 21 and a camera 22.) acquires information about objects surrounding the vehicle(Horiguchi, Figure 4, 103 the bicycle, 104 pedestrian, 102 another vehicle), indicating a detected position, a detected velocity (Horiguchi, Figure 4, [0033] FIG. 4 illustrates an example plotting objects, using the sensor fusion processing S1, on a map of object around the own vehicle M by integrating information of positions, size, and moving speed of the objects around the own vehicle detected with the millimeter wave radar 21 and the stereo camera 22”NOTE: “moving speed” reads on “ detected velocity”), an error range of the detected position(Horiguchi, Figure 4, and Figure 6, [0049], “position error”) and an error range of the detected velocity (Horiguchi, [0033] FIG. 4 moving speed of the objects around the own vehicle detected” Figure 6, stepS45,[0045] comparing the prediction map around the own vehicle M2(Tl) predicted at time Tl to the sensor data (sensor data after time synchronization processing) acquired from the sensors 21 and 22 at time T2 (S45of an obstacle detected (Horiguchi, Figure 4, 103 the bicycle, 104 pedestrian, 102 another vehicle) by using a sensor (Horiguchi ,Figure 1, the millimeter wave radar 21 and the stereo camera 22”) mounted on a moving body (Horiguchi ,Figure 1, Vehicle, Figure 4, 101 own vehicle”)
a predicted distribution generation process that generates a predicted distribution of a position of the obstacle at a second time later than a first time, (Horiguchi, FIG. 1, 6 [0048] It is assumed that the current time is T2.”The microcomputer 11 acquires the surrounding object prediction map M2(T1) based on first detection information that is the detection information at the first time ([0048] “predicted at time T1 from the surrounding object action prediction processing S2 (S41)”),; and
a prediction abnormality determination process that determines whether or not the predicted distribution is abnormal based on the predicted distribution and a second detected position that is the detected position of the obstacle at the second time.(Horiguchi, Figures 1, 4, and 6, [0048] By comparing the prediction map around the own vehicle M2(T1) predicted at time T1 to the sensor data (sensor data after time synchronization processing) acquired from the sensors 21 and 22 at time T2 (S45), it is possible to determine whether the sensors 21 and 22 have an abnormality.[0053] Similar to the above-described abnormality determination (S43) of the sensor fusion processing S1, the presence or absence of abnormality of the sensor data can be determined using the threshold ”).
and a control process that transmits instructions (Horiguchi, Figure 1, [0026] the automatic driving ECU 10 transmits a control command value to the lower-level ECU 40-43) to control the moving body based on the predicted distribution being abnormal. (Horiguchi, Figure 6, Step48, Step 50 “[0059] Refer to FIG. 6 again. The microcomputer 11 determines whether the comprehensive determination result is "abnormal" (S47) and, if the abnormality is determined (S47: YES), notifies the driver in the vehicle 1 of the abnormality being detected (S48).” [0061] After notifying the driver in step S48, the microcomputer 11 determines whether the abnormal state continues (S49). (S49: YES), the microcomputer 11 starts fallback operation processing (S50 Start Transitioning process to manual driving, Fallback operation using another sensor)”).
Horiguchi teaches generating a map of objects around the own vehicle M by integrating information of positions, size, and moving speed of the objects around the own vehicle detected with the millimeter wave radar 21 and the stereo camera 22. See [0033] and figure 5. Figure 4. In the comparison processing in step S45, calculate the difference between the synchronized millimeter wave radar data and the actual position of each surrounding object indicated by the synchronized camera data is compared to a predetermined threshold.
Horiguchi is silent on an error range of the detected velocity of an obstacle detected.
However, Horiguchi”580 teaches an error range of the detected velocity of an obstacle detected. (Horiguchi”580, Figures 2-4, [0051] Subsequently, “The behavior prediction processing unit 5 performs behavior prediction of various surrounding objects mapped on the own vehicle surrounding map 7. The prediction methods include, for example, a method of extrapolating a future position based on the current position and speed of each surrounding object, but a variety of other publicly known methods can be used”. [0065] Similar to the processing in the first comparison unit 102, in the processing in the second comparison unit 103, by performing, on two data, consistency determination that allows an error such as that in the processing in the first comparison unit 102, the second comparison unit output 106 indicating whether or not the two data have been consistent with each other within a range of a predetermined threshold value is obtained also see[0066] ). NOTE: comparison unit 102 and 103 compares sensor data (position and speed) with a threshold value to determine consistency of data and error estimation.).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Horiguchi’s method for generating moving map with the location of moving object to incorporate Horiguchi”580 processing method of integrating velocity value deviation between the observation value and a prediction value as taught by Horiguchi’s with the benefit of obtaining an accurate abnormality value detection occurring in arithmetic processing in the sensors and the ECU with a low arithmetic load without using the multiplexed, additional sensors (Horiguchi”580,[0014] [0051]-[0065]). It would have been obvious to a person of ordinary skill to include the well-known abnormality prediction process for moving object detection using know arithmetic process and model, in order to yield the predicted results of generating accurate map detection, yet with higher accuracy (KSR).
Regarding Claim 6, combination of Horiguchi and Horiguchi”580 teaches the prediction accuracy evaluation method according to claim 1,
Horiguchi further teaches wherein the instructions to control the moving body are at least one of: a deceleration control of the moving body; or a stopping control of the moving body. (Horiguchi, Figure 6, [0061] After notifying the driver in step S48, the microcomputer 11 determines whether the abnormal state continues (S49). For example, when the comprehensive determination result continues to be determined as "abnormal" for at least another predetermined number of times (S49: YES), the microcomputer 11 starts fallback operation processing (S50). [0062] Although not illustrated, the fallback operation processing, for example, turns on a hazard lamp, changes the traveling control to enter a predetermined stop operation”).
Regarding Claim 7, combination of Horiguchi and Horiguchi”580 teaches the prediction evaluation system according to claim 5,
Horiguchi further teaches wherein the instructions to control the moving body are at least one of: a deceleration control of the moving body; or a stopping control of the moving body. (Horiguchi, Figure 6, [0061] After notifying the driver in step S48, the microcomputer 11 determines whether the abnormal state continues (S49). For example, when the comprehensive determination result continues to be determined as "abnormal" for at least another predetermined number of times (S49: YES), the microcomputer 11 starts fallback operation processing (S50). [0062] Although not illustrated, the fallback operation processing, for example, turns on a hazard lamp, changes the traveling control to enter a predetermined stop operation”).
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Horiguchi and in view of Horiguchi”580 as applied to claim 1 and in further view of Hiroi et al. (US 2021/0333387 A1, hereinafter Hiroi, IDS ref. previously cited).
Regarding Claim 3, combination of Horiguchi and Horiguchi”580 teaches the prediction accuracy evaluation method according to claim 2,
Horiguchi is silent on wherein the prediction accuracy calculation process includes: calculating a Mahalanobis' distance between a center position of the predicted distribution and the second detected position; and acquiring an evaluation value that increases as the Mahalanobis' distance increases, as an index indicating the accuracy of the predicted distribution, and the prediction abnormality determination process is performed based on the evaluation value.
However, Hiroi teaches wherein the prediction accuracy calculation process includes: calculating a Mahalanobis' distance between a center position of the predicted distribution and the second detected position; and acquiring an evaluation value that increases as the Mahalanobis' distance increases, as an index indicating the accuracy of the predicted distribution, and the prediction abnormality determination process is performed based on the evaluation value. (Hiroi, [0011] a reliability calculation unit to take, as a subject sensor, each of the plurality of sensors, and to calculate a reliability of the detection value that is calculated on the basis of the observation value obtained with the subject sensor, by using a Kalman gain in addition to a Mahalanobis distance between the observation value and a prediction value, the observation value being obtained with the subject sensor, the prediction value being a value of the detection item of the object at the subject time which is predicted at a time before the subject time, the prediction value being used in calculation of calculating the detection value by the tracking unit on the basis of the observation value, the Kalman gain being obtained in the calculation”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Horiguchi’s method for calculating accurate object position and velocity to incorporate Hiroi’s calculation method of measuring Mahalanobis distance between the observation value and a prediction value as taught by Hiroi and obtain an accurate abnormality values (Hiroi, [0011]-[0013], [0050]-[0055]). It would have been obvious to a person of ordinary skill to include the well-known Mahalanobis distance along with the other analysis, in order to yield the predicted results of generating accurate detection value in consideration of both a high reliability of most recent information and a high reliability of time-series information, yet with higher accuracy (KSR).
Regarding Claim 4, combination of Horiguchi and Horiguchi”580 and Hiroi teaches the prediction accuracy evaluation method according to claim 3,
Horiguchi teaches generating a histogram of the evaluation value for each elapsed time from the first time to the second time; acquiring, as a degree of abnormality, a number or a percentage of samples whose evaluation value exceeds a threshold value in the histogram; and determining that the predicted distribution is abnormal when the degree of abnormality exceeds an abnormality degree threshold (Horiguchi, Figure 4, and Figure 6, [0049] Here, an example of the comparison method in step S43 is described. For example, there is a method of comparing the deviation width of the position of each surrounding object with a predetermined threshold. There is a position error of less than one grid on the drawing between the predicted position 103(2) of the bicycle at time T2 calculated at time Tl in FIG. 4(2) and the position 103(3) of the bicycle at time T2 in FIG. 4(3) [0059] Refer to FIG. 6 again. Microcomputer 11 determines whether the comprehensive determination result is "abnormal" (S47) and, if the abnormality is determined (S47: YES), notifies the driver in vehicle 1 of the abnormality being detected (S48). For example, the microcomputer 11 provides voice output or displays a message such as "abnormality is detected in the automatic driving system".” NOTE: the evaluation result can be presented as histogram data. Displaying evaluation data as a histogram is a design choice, not an inventive step).
Conclusion
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Shogo Matsunga (US 2018/0374352 A1) “moving object control apparatus includes an object detecting device that detects an object present around a moving object. The moving object control apparatus performs predetermined control based on a detection result of the object detecting device while the moving object is moving. In the moving object control apparatus, a trajectory of a stationary object relative to the moving object is acquired and a moving direction of the moving object is acquired. An angle of axial displacement of a center axis of the object detecting device is calculated, as axial displacement information, based on the trajectory and the moving direction. The predetermined
control is performed based on axial displacement information. A regulating unit regulates reflecting the axial displacement information in the predetermined control if an absolute value of an angular acceleration around a center of gravity of the moving object is larger than a preset threshold value” (abstract).
Ryuzo Okada (US 2005/0165550 A1) Discloses “An obstacle detection apparatus includes an image input unit inputting a image sequence ctptured by an image sensor, a velocity detector deriving velocity vectors representing
motions of a to-be-detected point and a to-be-compared point on each image of the image sequence. a predictive velocity vector calculator calculating a predictive velocity vector of the to-be-detected point using a detected velocity
vector of the to-he-compared point, an obstacle detector detecting the to-be-detected point as a point on the obstacle when a horizontal component of a detected velocity vector of the to-be-detected point is larger than a horizontal component of the predictive velocity vector by a given threshold, and an obstacle information output unit outputting information on the to-be-detected point detected as the obstacle. (abstract).
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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/DILARA SULTANA/Examiner, Art Unit 2858
10/01/2026
/SON T LE/Primary Examiner, Art Unit 2858
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