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
This action is in response to amendments and remarks filed on 03/26/2026. Claim(s) 1-4, 12-13, and 15 have been amended. Claim(s) 1-15 are pending examination. This action is made final.
Response to Arguments
Applicant presents the following argument(s) regarding the previous office action:
Applicant asserts that the 35 USC 103 rejection of claims 1-15 is improper. Applicant asserts that the prior art does not teach all claim limitations of the independent claim 1, and thus should be allowable. Firstly applicant asserts that the high-processing-load mode and low-processing-load mode are not covered by the cited prior art. Applicant further claims that the cited prior art does not teach “a distance between the predicted object information from boundaries of each detection region of the plurality of external recognition sensors.” Lastly applicant asserts that the cited prior art does not teach, “weighting the plurality of pieces of object information based on the distance of the object from the boundaries.”
Applicant's arguments filed 03/26/2026 have been fully considered but they are not persuasive.
Regarding applicant’s argument A, the examiner respectfully disagrees.
Firstly in response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., high-load processing requires inverse matrix operation of the error covariance matrix and the low-processing-load integration mode is a method of averaging positions of objects of each sensor) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). If the applicant wants to include the specific operations of high/low processing modes into the claim as the methods carried out it may provide a differentiation to the cited prior art, but it would require further search and consideration.
Secondly the applicant argues that the use of reference axis and measurements from that are not analogous to the boundaries. The examiner would find that the reference axis would render the boundaries as obvious in light of MPEP2144.04.VI.C. “Rearrangement of Parts,” In re Japiske, and/or “Reversal of Parts,” In re Gazda. The teachings of Braeuchle uses a specifically defined reference axis and measures the distance to the obstacle based on this element. Applicant has merely rearranged where this axis is, i.e. shifted it to the boundary line. Additionally, the applicant merely reversed where the measurement come from, measuring from the outside of the sensor region rather than an internal reference axis. This is merely an obvious modification of Braeuchle.
Lastly the weight based on the distance would be covered by newly cited art, Bozchalooi (US Pat 11,069,161). Looking at Bozchalooi Col. 9, lines 20-31; teaches a system that can take multiple sensor measurements of a given obstacle in the path of a vehicle and assign a series of weights to them based on the distance value measured. This allows for the system to monitor the reliability of the system, i.e. health in lines 33-35. A system that allows itself to constantly monitor the health of the sensors of the system results in better sensor readings and better data fusion.
In light of the above the examiner would not find claim 1 as allowable. It would be rejected as obvious under 35 USC 103 by Suzuki in view of Lee, Braeuchle and Bozchalooi. Claims 2-15 would be rejected at least due to their dependence on rejected subject matter.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-5 and 7-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Suzuki (JP 2006-046962) in view of Lee (US PG Pub 2020/0174113) Braeuchle (US PG Pub 2005/0062615) and Bozchalooi (US Pat 11,069,161).
Regarding claim 1, Suzuki teaches a sensor recognition integration device that integrates a plurality of pieces of object information related to an object around an own vehicle detected by a plurality of external recognition sensors that generate the plurality of pieces of object information at a first time based on a plurality of received signal types, the sensor recognition integration device comprising: ([0047]-[0048], [0096], and [0140]-[0141] teach a vehicle system that can detect a target object in its surroundings and fuse the sensor data collected by a series of external recognition sensors; [0100]-[0101] and [0104] teach the radar devices are two separate kinds of radar with the first being a DBF-FM-CW radar which outputs a single signal, which the second radar is a monopulse radar that outputs five signals in the same time period. [0139]-[0148] teach the system determining a detection of an object over a series of time periods, including a “first time period”)
a prediction update unit that generates predicted object information obtained by predicting an action of the object ([0159] teaches the system predicting the actions of the target vehicle based on the actions of the own vehicle and the previously observed data of the target vehicle) on the basis of an action of the own vehicle estimated from a behavior of the own vehicle ([0096]-[0097] teach controlling the own vehicle) and the object information detected by the external recognition sensors; ([0125] teaches the system to determine the target vehicles “physical quantities” which include relative speed, heading, and distance)
an integration processing mode determination unit that:
determines a distance ([0088]-[0089] teaches the system determining that a target is present based on a detection level. [0095] teaches the system as able to confirm the distance based on the radar used. [0101] furthers this with distances used)
switches an integration processing mode from a low-processing-load integration processing mode ([0095] teaches the use of a radar system with relatively simple calculations that may be used when an object is detected in the non-overlapping zone R2; [0094]-[0096] teach the system as able to use the varying radar systems which have simple and complex calculations based on where the object is detected) to a high-processing load integration processing mode for determining a method of integrating the plurality of pieces of object information based on determining that the distance is within a threshold; ([0094]-[0096] teaches narrowing the collection band of sensors in order to alter the collection time as well as calculations required to and teaches the system as able to use the varying radar systems which have simple and complex calculations based on where the object is detected [0110]-[0114] teaches the system using a first/second determination means which allows for the system to either continuously determine the presence of a tracked object which would take multiple cycles and determine the object at a continuous rate as well as a single computation to compare the data to. [0122]-[0126] teach a second form of data integration i.e. correction where the data is compared between the data collection systems and in the event of an error or other issue the system issues broad corrections to the second radar system. As taught by [0112] the calculations for the first and second radars are performed in parallel when an object is determined to be in the overlapping region of the sensor. This requires a “high-processing load integration process,” as the system is required to determine the results from both sensors and integrate them to determine a relative accuracy, [0122]-[0126]. However, if the target is not detected in the overlapping area the system can switch the radar mode and use a relatively shorter calculation which would require a mode switch, [0096]. [0105] explicitly teaches that the system determines that a given distance/power threshold is used to determine what is present and if a detection result is needed.) and
an integration target information generation unit that integrates the plurality of pieces of object information generated by the external recognition sensors at the first time ([0139]-[0148] teach the system determining a detection of an object over a series of time periods, including a “first time period”) and associated with the predicted object information on the basis of the integration processing mode to generate integrated object information, ([0096] teaches the system using a “second determination means” that achieves a high speed, high accuracy calculation by fusion the sensor data collected)
Suzuki does not teach an association unit that calculates a relationship between the predicted object information and the plurality of pieces of object information; between the predicted object information from the boundaries of each detection region of the plurality of external recognition sensors; and the integration target information generation unit integrates the plurality of pieces of object information by weighting the plurality of pieces of object information based on a distance of the object from boundaries of each of the detection regions of the plurality of external recognition sensors.
However, Lee teaches “that generate the plurality of pieces of object information based on a plurality of received signal types “ (Fig. 1, [0031]-[0036], and [0121] at least teaches the system fusing data received from a camera and radar) and “an association unit that calculates a relationship between the predicted object information and the plurality of pieces of object information” ([0022]-[0024], [0032]-[0034] and [0068] teach the use of computerized systems in order to associate data of a sensed vehicle over a time period and a series of sensor zones; the computer further uses algorithms to avoid sudden changes in speed, location, etc. of the sensed vehicle)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Suzuki with Lee; and have a reasonable expectation of success. Both relate to the determination of sensor information relative to an ego vehicle. The systems may include ways to associate tracking info with the tracked object as it moves relative to the ego vehicle. As Lee teaches in [0022]-[0024] the association of tracking data with a tracked object allows the system to fuse sensor data as the tracked vehicle moves between sensor zones. The association of data allows the ego vehicle system to ensure that the tracked data lines up with each specific tracked vehicle.
The combination of Suzuki and Lee does not teach between the predicted object information from the boundaries of each detection region of the plurality of external recognition sensors, and wherein the integration target information generation unit integrates the plurality of pieces of object information by weighting the plurality of pieces of object information based on a distance of the object from boundaries of each of the detection regions of the plurality of external recognition sensors.
However, Braeuchle teaches “between the predicted object information from the boundaries of each detection region of the plurality of external recognition sensors.” ([0021]-[0026] and [0034] teaches the use of multiple sensors that are able to determine an object’s position. This position is measured as an azimuth angle and transverse distance from a reference axis. The reference axis would be analogous to a boundary of the sensor region. The use of the reference axis is a point that the computer would know and be able to measure from. This serves as the same function as using the boundary of the sensing region. The claim at hand is merely substituting the boundary for a reference action. The system then applies weights to the detected measurements based on the position and reliability of the sensor. The weighting is used to determine that the object is the same for both sensors and allows the system to integrate the sensed data. Additionally, MPEP2144.04.VI. “Rearrangement of Parts,” In re Japiske, and/or “Reversal of Parts,” In re Gazda. The teachings of Braeuchle uses a specifically defined reference axis and measures the distance to the obstacle based on this element. Applicant has merely rearranged where this axis is, i.e. shifted it to the boundary line. Additionally, the applicant merely reversed where the measurement come from, measuring from the outside of the sensor region rather than an internal reference axis. This is merely an obvious modification of Braeuchle)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Suzuki and Lee with Braeuchle; and have a reasonable expectation of success. All relate to the control of vehicle sensor systems. As Braeuchle teaches in [0014]-[0015] the use of overlapping sensor ranges ensures that the system is able to accurately determine the location of an object. The measurement from the reference axis allows the system to know the location of an object relative to a known point in space around the vehicle. The use of the reference axis is analogous to the boundary of the sensor region and would be an obvious reversal and/or rearrangement of an analogous part. The use of Braeuchle would render the claims as obvious.
The combination of Suzuki, Lee, and Braeuchle does not teach wherein the integration target information generation unit integrates the plurality of pieces of object information by weighting the plurality of pieces of object information based on a distance of the object from boundaries of each of the detection regions of the plurality of external recognition sensors.
However, Bozchalooi teaches “wherein the integration target information generation unit integrates the plurality of pieces of object information by weighting the plurality of pieces of object information based on a distance of the object from boundaries of each of the detection regions of the plurality of external recognition sensors.” (Col. 9, lines 20-31; teaches a system that can take multiple sensor measurements of a given obstacle in the path of a vehicle and assign a series of weights to them based on the distance value measured. This is used to fuse the collected sensor data)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Suzuki, Lee, and Braeuchle with Bozchalooi; and have a reasonable expectation of success. All relate to the control of vehicle sensor systems. As Bozchalooi teaches in Col 9, lines 32-67; the use of fusion weights based on distance provide the system with lots of ways to monitor the health of the sensors. If the weights are lower than they were in times past it can indicate an issue occurring in the sensor detection region. This can also lead to decreased reliability of the system. The weighting system provides an early warning and can alert the user to a way to avoid this error if at all possible.
Regarding claim 2, Suzuki teaches the sensor recognition integration device according to claim 1, wherein the threshold is used to determine whether the predicted object information is within a specific region that is an boundary portion in the overlapping region of the detection regions of the plurality of external recognition sensors. ([0096] teaches the system setting an overlapping region as a boundary region between the overlapping sensor data. The different radar detection zones set a distance that the system uses to determine where it is, i.e. a threshold)
Regarding claim 3, Suzuki teaches the sensor recognition integration device according to claim 1, wherein the threshold is used to determine whether the predicted object information is within a specific region that is an overlapping region of the detection regions of the plurality of external recognition sensors located on a travel route of the own vehicle estimated from a behavior of the own vehicle. ([0047] and [0094]-[0096] teach the system as monitoring a specific overlapping region of sensors as the vehicle moves. The different radar detection zones set a distance that the system uses to determine where it is, i.e. a threshold)
Regarding claim 4, Suzuki teaches the sensor recognition integration device according to claim 1, wherein the threshold is used to determine whether the predicted object information is within a specific region that is a region where reliability of the external recognition sensors decreases in an overlapping region of the detection regions of the plurality of external recognition sensors. ([0200] teaches that in the overlap region of the sensors one of the sensors may be more susceptible to noise, i.e. the reliability of the sensor has decreased in the overlap region. The different radar detection zones set a distance that the system uses to determine where it is, i.e. a threshold)
Regarding claim 5, Suzuki teaches the sensor recognition integration device according to claim 1, wherein the integration processing mode includes the high-processing-load integration processing mode, ([0094]-[0096] teach the system using a narrowed beam approach and a combination of radars in order to determine an object in the overlap area additionally, the paragraphs teach the system as able to use the varying radar systems which have simple and complex calculations based on where the object is detected. [0112] teaches processing sensor data from both radars in parallel which would require more processing load) and the low-processing-load integration processing mode. ([0095] teaches the use of a radar system with relatively simple calculations that may be used when an object is detected in the non-overlapping zone R2; [0094]-[0096] teach the system as able to use the varying radar systems which have simple and complex calculations based on where the object is detected)
Regarding claim 7, Suzuki teaches the sensor recognition integration device according to claim 1, wherein the integration processing mode determination unit switches the integration processing mode on the basis of a tracking state of the object. ([0096] and [0161] teach various ways to track the object and determine if the system should perform some variation of sensor fusion on it)
Regarding claim 8, Suzuki teaches the sensor recognition integration device according to claim 7, wherein the tracking state of the object is a tracking time of the object. ([0161] teaches the system tracking the object of a certain number of cycles, i.e. a certain time period the object has been tracked)
Regarding claim 9, Suzuki teaches the sensor recognition integration device according to claim 7, wherein the tracking state of the object is an existence probability of the object. ([0096] teaches altering the tracking mode of the system on the basis of a confirmed presence of the object; it would be analogous that the presence confirmation would be the existence probability. The system is using the total detection of the object to track the object, if it is only detected in one radar it is not possible to confirm its presence, i.e. is existence, the system then alters the tracking range, i.e. state, to determine the object’s existence)
Regarding claim 10, Suzuki teaches the sensor recognition integration device according to claim 1, wherein the integrated object information is continuously changed at a time of switching the integration processing mode. ([0122] and [0125] teaches the system continuously updating the measured data at various time periods)
Regarding claim 11, Suzuki teaches the sensor recognition integration device according to claim 1, further comprising a plan determination unit that plans a planned path for controlling the own vehicle by the integrated object information, ([0096] teaches enabling control of the system on the basis of integrated data)
wherein the integration processing mode determination unit switches the integration processing mode on the basis of the planned path. ([0096] teaches narrowing the sensor area and altering the integration of data in order to enable control of the vehicle)
Regarding claim 12, Suzuki teaches the sensor recognition integration device according to claim 1, wherein
a processing cycle of the high-processing-load integration processing mode is made variable based on a tracking state of the object. ([0096] teaches narrowing the detection band thus reducing the calculation time until a target is confirmed, this would be analogous to making a variable processing cycle. Said narrowing is based on the presence or absence of a vehicle i.e. it’s tracking state. Further [0110]-[0112] teaches the system as altering the amount of processing load based on the parallel or not mode to use both calculations based on where the object is located)
Regarding claim 13, Suzuki teaches the sensor recognition integration device according to claim 1, wherein the threshold is used to determine whether the predicted object information is within a specific region that is an overlapping region of the detection regions of the plurality of external recognition sensors located on a travel route of the own vehicle estimated from a behavior of the own vehicle ([0047] and [0094]-[0096] teach the system as monitoring a specific overlapping region of sensors as the vehicle moves. The different radar detection zones set a distance that the system uses to determine where it is, i.e. a threshold) and
Suzuki does not teach the integration processing mode is switched according to an effect of the object on the travel route of the own vehicle estimated from the behavior of the own vehicle.
However, Lee teaches “wherein the integration processing mode is switched according to an effect of the object on the travel route of the own vehicle estimated from the behavior of the own vehicle.” ([0025]-[0028] teach the need to alter the way a vehicle fusers sensor data based on the way the tracked object moves relative to the vehicle in order to prevent large errors in tracking data)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Suzuki with Lee; and have a reasonable expectation of success. Both relate to the determination of sensor information relative to an ego vehicle. The systems may include ways to associate tracking info with the tracked object as it moves relative to the ego vehicle. As Lee teaches in [0022]-[0024] the association of tracking data with a tracked object allows the system to fuse sensor data as the tracked vehicle moves between sensor zones. The association of data allows the ego vehicle system to ensure that the tracked data lines up with each specific tracked vehicle. This need to alter the fusion mode based on the way an object moves allows for the tracked object to not be mistaken for another object. Altering the fusion style of the data again prevents errors in the system as taught by [0025]-[0028] of Lee.
Regarding claim 14, Suzuki teaches the sensor recognition integration device according to claim 1.
Suzuki does not teach further comprising an association processing mode determination unit that switches association processing of the object in the association unit between the high-processing-load integration processing mode and the low-processing-load integration processing mode.
However, Lee teaches “further comprising an association processing mode determination unit that switches association processing of the object in the association unit between the high-processing-load integration processing mode and the low-processing-load integration processing mode.” ([0062]-[0064] teaches the system having a relatively simple and relatively complex way to associate the sensor data as an object moves, it may fuse the data or merely carry it over which the examiner views as high or load processing load)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Suzuki with Lee; and have a reasonable expectation of success. Both relate to the determination of sensor information relative to an ego vehicle. The systems may include ways to associate tracking info with the tracked object as it moves relative to the ego vehicle. As Lee teaches in [0022]-[0024] the association of tracking data with a tracked object allows the system to fuse sensor data as the tracked vehicle moves between sensor zones. The association of data allows the ego vehicle system to ensure that the tracked data lines up with each specific tracked vehicle. Altering the complexity of the association calculation can result in more or less time spent associating the data which would provide an advantage depending on the situation the ego vehicle finds itself in.
Regarding claim 15, Suzuki teaches the sensor recognition integration device according to claim 1, wherein the integration processing mode determination unit switches the integration processing mode on the basis of a second distance from the own vehicle to the object. ([0095] teaches the system having various distance measuring methods with varying levels of complexity that the system may use when sensing the data. This can be based on detection results from either radars as well as the radar intensity getting stronger, i.e. closer)
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Suzuki, Lee, Braeuchle, and Bozchalooi in view of Liu (US PG Pub 2019/0049958).
Regarding claim 6, the combination of Suzuki, Lee, Braeuchle, and Bozchalooi teaches the sensor recognition integration device according to claim 1.
The combination of Suzuki, Lee, Braeuchle, and Bozchalooi does not teach wherein the integration target information generation unit integrates the plurality of pieces of object information on the basis of the integration processing mode based on an error distribution of each external recognition sensor.
However, Liu teaches “wherein the integration target information generation unit integrates the plurality of pieces of object information on the basis of the integration processing mode based on an error distribution of each external recognition sensor.” ([0071]-[0072], [0159]-[0161], and [0175]-[0176] teach a determination of a sensor quality which is analogous to the sensors error distribution. If the system determines a sensor to be more error prone or unqualified in certain areas of detection the system may ignore the sensor for fusion purposes)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Suzuki, Lee, Braeuchle, and Bozchalooi with Liu; and have a reasonable expectation of success. All relate to sensor fusion systems for vehicles including tracking vehicles in the overlap areas of sensors. As Liu teaches in [0071] the determination of qualified sensors allows for the system to determine which sensors can be used for determining the driving environment. Altering the sensors that are qualified to use results in altering the fusion basis for the sensor data. By examining the errors found in each sensor zone and then determining which data to fuse and by what method it prevents erroneous sensor data from being used by the vehicle system.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kondo (US PG Pub 2023/0008630) teaches an object information acquisition unit acquires object information including an object distance between a radar device and a reflection object and an object azimuth angle at which the reflection object is located. A roadside object extraction unit extracts roadside object information on a roadside object from the object information. An axis deviation angle estimation unit estimates a vertical axis deviation angle from the roadside object information. The vertical axis deviation angle is an angle of deviation of an actual mounting direction from a reference mounting direction in a vertical direction. The actual mounting direction is an actual direction of the radar device, and the reference mounting direction is a direction of the radar device when the radar device is mounted in a reference state.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS STRYKER whose telephone number is (571)272-4659. The examiner can normally be reached Monday-Friday 7:30-5:00.
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/N.S./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665
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