DETAILED ACTION
Response to Amendment
This office action regarding application number 18/564,813, filed November 28, 2023, is in response to the applicants arguments and amendments filed May 6, 2026. Claims 1, and 12-13 have been amended. Claims 1, 3-8 and 10-13 are currently pending and are addressed below.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/6/2026 has been entered.
Response to Arguments
The applicants arguments and amendments to the application have overcome some of the objections and rejections previously set forth in the Final action mailed January 9, 2026. Applicants amendments to the claims have been deemed sufficient to overcome the previous 35 USC 101 mental process rejections through the addition of “the electronic control device outputs traveling control information, including a control instruction value, via the in-vehicle network to one or more vehicle actuators, thereby controlling traveling of the vehicle”, therefore the rejections are withdrawn. Applicants amendments to the drawings have been deemed sufficient to overcome the previous drawing objections through the correction of minor errors, therefore the objections are withdrawn.
However Applicants amendments to claims 1 and 12-13 have NOT been deemed sufficient to overcome the previous 35 USC 103 rejections through the inclusion of “wherein the relationship between the relative position and a detection capability at the at least one external environment sensor is expressed as a detectable distance and a detectable angle range of the at least one external environment sensor, and the electronic control device outputs traveling control information, including a control instruction value, via the in-vehicle network to one or more vehicle actuators, thereby controlling traveling of the vehicle” the examiner finds that the previously cited Akiyama reference teaches these limitations therefore the rejections are maintained with changes to reflect amendment. However as this changes the scope of the claims, new art rejections have been made based on the changes in scope.
Additionally the applicants arguments have been fully considered but are not fully persuasive for the reasons seen below.
On pages 12-14 the applicant argues “In the Office Action ("OA), paragraph [0034] and FIGS. SB, SC, and SD of Akiyama were cited for disclosure of the previously-presented version of Applicants' "relationship ... based on a state of detection ..." limitation. See OA at page 54. As amended, however, Akiyama does not disclose or even suggest the newly added limitation. Instead, Akiyama merely states that the "occlusion area" computation is repeated for multiple surrounding objects; a compliant excerpt is: "in the case where there are a plurality of (n) surrounding objects, the above processing is performed ... to determine occlusion areas Q 1 to Qn ...." See Akiyama at [0034]. That is, Akiyama merely teaches computing multiple occlusion regions (Q 1-Qn) from detection results, i.e., a geometric visibility-blocking construct used to exclude portions of a sensor's detection field due to obstacles. Nothing in Akiyama, however, discloses or even suggests expressing a first sensor's detection capability as a detectable distance and a detectable angle range (i.e., performance-limit parameters), nor does Akiyama teach deriving such distance/angle capability from cross-sensor "state of detection" logic. Id. Likewise, FIGS. SB, SC, and SD of Akiyama merely illustrate (i) an "object detectable area" of the first detector (PA) formed by excluding occlusion area(s), (ii) an "object detectable area" of the second detector (RA), and (iii) an "association possible area" (SA) defined as the overlap of PA and RA for association processing. See id. At Figures SB-SD; see also id. at [0039] and [0040]. In other words, Akiyama merely gates association to regions where both sensors can see (overlap of "detectable areas"), not characterizing the first sensor's detection capability in the claimed distance/angle-range form, and not determining that distance/angle relationship based on whether the first sensor is able vs. unable to detect an element detected by the second sensor (and changes in that state). Therefore, Figs. SB-SD of Akiyama do not disclose or even suggest the newly added "detectable distance" / "detectable angle range" expression of the claimed relationship. Id. Honda does not cure these deficiencies.”, the examiner respectfully disagrees.
Akiyama teaches wherein the relationship between the relative position and a detection capability at the first external environment sensor is expressed as a detectable distance and a detectable angle range of the first external environment sensor (Paragraph [0034], "The number of occlusion areas is one in FIG. 4B, but in the case where there are a plurality of (n) surrounding objects, the above processing is performed for all the object detection results of the first object detection unit 1101, to determine occlusion areas Q1 to Qn for the first object detection unit 1101.") (See Figures 5B-5D) here the system is establishing a relationship between a position of a sensor seen as item 10 in the drawings and its detection capability, this detection capability is seen as different shading in the drawings; (Paragraph [0040], “As shown in FIG. 5D, the association processing unit 1202 determines, as an association possible area SA, a range in which the detectable area PA of the first object detection unit 1101 and the detectable area RA of the second object detection unit 1102 overlap each other (step S5).)”) as seen in Figure 5D the system is determining possible detection areas for the sensors, these areas clearly showing angle different angle ranges for each sensor and distances in which one sensor can detect a further range than the other. Therefore showing a relationship between a position and a detection capability expressed as a detectable distance and detectable angle range.
Therefore the combination of Akiyama, Honda, and Xie teaches wherein the relationship between the relative position and a detection capability at the first external environment sensor is expressed as a detectable distance and a detectable angle range of the first external environment sensor and the 35 USC 103 rejections are maintained.
On pages 14 the applicant argues “Rather than disclosing or even suggesting the newly added limitations, Honda merely describes a detectable area AS that "is determined by the configuration of the sensor," i.e., a sensor-defined detection region, not a computed "relationship" expressed as a detectable distance and a detectable angle range derived/represented as the claimed relationship. Moreover, while Honda may "receive distance" and "receive angle" values (e.g., distances Ra/Rb and angles Ga/Gb) and then calculate reliability by reference to a map, that is merely parameter/reliability processing of detected objects (and/or pixels) and is not the claimed requirement that the relationship between relative position and detection capability is expressed as (i) a detectable distance and (ii) a detectable angle range of the sensor. That is, Honda uses distances/angles as inputs for reliability computations. Honda, however, does not define or output a sensor "relationship" that is expressed as a detectable distance and a detectable angle range of the sensor itself, as now required by the amended claim. Xia does not cure these deficiencies.”, the examiner respectfully disagrees.
MPEP 2142-2144 discusses the requirements for a case of obviousness using 35 USC 103 and provides examples of such cases. MPEP 2111 discusses Broadest Reasonable Interpretation and the interpretation of claims.
As is discussed in the rejection below, the Honda reference is not relied on to teach the limitations of “wherein the relationship between the relative position and a detection capability at the at least one external environment sensor is expressed as a detectable distance and a detectable angle range of the at least one external environment sensor”. These limitation are taught by the Akiyama reference.
However while not relied upon the examiner believe the Honda reference also teaches “wherein the relationship between the relative position and a detection capability at the at least one external environment sensor is expressed as a detectable distance and a detectable angle range of the at least one external environment sensor”.
As discussed in the rejections below Honda teaches a grid map being created by dividing an area into grid patterns on a polar coordinate system having an installation point of the first external environment sensor at a center of the area to express a detection capability level of the first external environment sensor in each unit area (See Figures 3-4, figure 3 showing the area around the vehicle divided into a grid like map with polar coordinates and the vehicle and sensor at the center of the area, and figure 4 expressing a detection capability for the area in front of the vehicle for each unit area) (Column 18, lines 30-35, “The two-dimensional coordinate system may be either a rectangular coordinate system or a polar coordinate system.”) (See Figures 11A and 11B showing two separate polar coordinate systems each having a center at the location of the separate sensors). Here in Honda teaches the relationship between an installation position of the sensor, seen as 32 in figure 4, and a detection capability, this detection capability is seen as the cone extending forward of the position and expressed in the form of the grid. The grid in this case defines a detectable angle range from left to right, and a detectable distances as can be seen in figure 11B in which different detection capabilities for different sensors are defined showing different detectable distances, and different detectable angle range.
Therefore while not relied upon in the instantaneous rejections Honda teaches wherein the relationship between the relative position and a detection capability at the at least one external environment sensor is expressed as a detectable distance and a detectable angle range of the at least one external environment sensor.
Claim Rejections - 35 USC § 103
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 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.
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 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 1, 3-8, and 10-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Akiyama (US-20200225342) in view of Honda (US-7054467) and further in view of Xia (US-20230072637).
Regarding claim 1, Akiyama teaches an electronic control device incorporated in a vehicle, the electronic control device comprising (Paragraph [0012], "An object recognition device according to the present invention includes: first object detection means mounted on a vehicle and configured to detect a surrounding object around the vehicle")
a processing unit and a storage unit, the electronic control device being connected to an external environment sensor group via an in-vehicle network, the electronic control device comprising (Paragraph [0029], “The object recognition processing unit 1200 is composed of a processor 100 and a storage device 101, as shown in FIG. 2 which shows an example of hardware thereof.”) (Paragraph [0002], “detection results are transferred as sensor information to a vehicle control device via the in-vehicle network”)
a sensor detection information acquiring unit implemented by the processing unit that acquires, via the in-vehicle network, detection information from a first external environment sensor and detection information from a second external environment sensor (Paragraph [0027], "The object recognition device includes a first object detection unit 1101, a second object detection unit 1102, and an object recognition processing unit 1200")
the detection information including sensor observation information (Paragraph [0028], “The first object detection unit 1101 is composed of a device such as a camera or a light detection and ranging (lidar) device, which is capable of acquiring an object detection position and in addition, object width information.”)
the first and second environment sensors being incorporated in the vehicle (Abstract, “object recognition device of a vehicle”) (See Figures 4A-5D)
a sensor detection information integrating unit implemented by the processing unit (See Figures 1 and 2, Figure 1 showing various units such as “Association processing unit” that are implemented by a processor as shown in figure 2)
that generates integrated detection information by comparing and integrating the detection information of the first and second external environment sensors to specify a correspondence relationship between environmental elements, the integrated detection information being stored in the storage unit as an integrated detection information data group (Paragraph [0012], “an association processing unit configured to take an association between the first object detection result and the second object detection result in an area excluding the occlusion area determined by the occlusion area detection processing unit “) (Paragraph [0038], "Next, the association between an object detection result of the first object detection unit 1101 for which the occlusion area has been determined as described above, and an object detection result of the second object detection unit 1102, is determined by the association processing unit 1202," here the association processing unit is integrating detection information from the first detection result and the second detection result to determine an association/correspondence relationship between object detections) (See Figures 1 and 2 which shows the functions of the association processing unit implemented by a processor and storage)
and indicating environmental elements detected by the first environmental sensor and the second external environment sensor and for which the correspondence relationship is specified (Paragraph [0038], “the association between each object detection result of the first object detection unit 1101 and each object detection result of the second object detection unit 1102 is determined, and the detection results that exhibit a great association are combined,” here the environmental elements/objects that are detected by both sensors are associated/combined)
a sensor detectable area determining unit implemented by the processing unit that determines a relationship between a relative position and a detection capability at the first external environment sensor (Paragraph [0034], "The number of occlusion areas is one in FIG. 4B, but in the case where there are a plurality of (n) surrounding objects, the above processing is performed for all the object detection results of the first object detection unit 1101, to determine occlusion areas Q1 to Qn for the first object detection unit 1101.") (See Figures 5B-5D)
based on a state of detection by the first external environment sensor of an environmental element detected by the second external environment sensor the state of detection being indicated in the integrated detection information (See Figure 3, steps S3-S5, here the figure shows the system determining object detectable area from a first object detection result, and then determine second object detectable area for the second object detection, and then determine an association possible area from the two separate object detection results in which object can be detected by both sensors)
generates a detectable area for the first external environment sensor (See Figure 5C and 5D showing the system determining detectable areas and occlusion areas for the sensors)
the state of detection being indicated in the integrated detection information (Paragraph [0041], “The update processing unit 1203 determines that the object detection result of the first object detection unit 1101 and the object detection result of the second object detection unit 1102 that are combined with each other in the association processing by the association processing unit 1202 correspond to an identical object, and integrates the detection information therebetween, thereby specifying (recognizing) the position of each of the surrounding objects 20a, 20b, 20c and updating the recognition result for the specified object (step S7),” here the system is updating the recognition result/state of detection in the integrated detection information)
and determines a detectable area for the first external environment sensor based on change in the state of detection indicated by the integrated detection information (Paragraph [0032], “FIG. 4A shows a detection area P of the first object detection unit 1101 when there is no object around the own vehicle 10. In this case, the first object detection unit 1101 can detect all objects in the detection area P, and no occlusion area occurs. Next, FIG. 4B shows a state in which there is a surrounding object 20 in the detection area P. In this case, the surrounding object 20 becomes an obstacle to cause an area (occlusion area Q1) where the first object detection unit 1101 cannot perform detection,” here the system is determining a detectable area for a first sensor based on a change of state from no object detected to an object being detected which forms an occlusion area) (See also figures 5B-5D) (Paragraph [0035], “In the determination for the occlusion area, margins Mx, My may be set in accordance with object detection error in the X-axis direction or the Y-axis direction in the first object detection unit 1101, whereby the condition A and the condition B may be respectively changed into a condition A′ and a condition B′ as shown below, so as to narrow the occlusion area. It is noted that the margins Mx, My are set as positive values,” here the system is determining the detectable area outside of the occlusion area and is updating that area based on changing states of objects according to the integrated detection results of the first and second object detection units)
the relationship between the relative position and a detection capability at the first external environment sensor is expressed as a detectable distance and a detectable angle range of the first external environment sensor (See Figures 5B-5D showing a distance and angle range for each sensor)
and the electronic control device outputs traveling control information, including a control instruction value, via the in-vehicle network to one or more vehicle actuators, thereby controlling traveling of the vehicle (Paragraph [0042], “Using information about the recognized relative positions between the own vehicle 10 and the surrounding objects 20a, 20b, 20c around the own vehicle obtained from the object recognition processing unit 1200 as described above, the vehicle control unit 1300 performs control for a collision damage mitigation brake system for mitigating a damage when the own vehicle 10 collides with a frontward object, an adaptive cruise control system for following a frontward vehicle, and the like. That is, it is possible to perform autonomous driving of the own vehicle on the basis of an object recognition result from the object recognition processing unit 1200.”).
However Akiyama does not explicitly teach generates a detectable area for the first external environment sensor as a grid map stored in the storage unit as a sensor detectable area data group, the grid map being created by dividing an area into grid patterns on a polar coordinate system having an installation point of the first external environment sensor at a center of the area to express a detection capability level of the first external environment sensor in each unit area, determines a detection capability level in each unit area of the grid map, based on the state of detection by the first external environment sensor of an environmental element detected by the second external environment sensor.
Honda teaches an information processing apparatus and method which readily enable integration of information pieces output from sensors including
generates a detectable area for the first external environment sensor as a grid map stored in the storage unit as a sensor detectable area data group (See Figures 3-4, figure 3 showing the area around the vehicle divided into a grid like map, and figure 4 expressing a detection capability for the area in front of the vehicle for each unit area)
the grid map being created by dividing an area into grid patterns on a polar coordinate system having an installation point of the first external environment sensor at a center of the area to express a detection capability level of the first external environment sensor in each unit area (See Figures 3-4, figure 3 showing the area around the vehicle divided into a grid like map with polar coordinates and the vehicle and sensor at the center of the area, and figure 4 expressing a detection capability for the area in front of the vehicle for each unit area) (Column 18, lines 30-35, “The two-dimensional coordinate system may be either a rectangular coordinate system or a polar coordinate system.”) (See Figures 11A and 11B showing two separate polar coordinate systems each having a center at the location of the separate sensors)
determines a detection capability level in each unit area of the grid map (See figure 4 showing reliability information for the sensor for each unit of the grid like map) (See Figure 11A and 11B showing the state detection for each grid section for a plurality of sensors)
based on the state of detection by the first external environment sensor of an environmental element detected by the second external environment sensor (Column 25, lines 25-35, “In the example shown in FIG. 9, the parameter integration section 15 is arranged so as to enable integration of the detection information DS output from the sensor 11A and the detection information DS output from the sensor 11B.” here as can be seen in figure 11B the detection capability level is determined for each unit area of the grid map using the state of detection of a first and second sensor).
Akiyama and Honda are analogous art as they are both generally related to systems for processing sensor information of a vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include generates a detectable area for the first external environment sensor as a grid map stored in the storage unit as a sensor detectable area data group, the grid map being created by dividing an area into grid patterns on a polar coordinate system having an installation point of the first external environment sensor at a center of the area to express a detection capability level of the first external environment sensor in each unit area, determines a detection capability level in each unit area of the grid map, based on the state of detection by the first external environment sensor of an environmental element detected by the second external environment sensor of Honda in the system for determining a detectable area of Akiyama with a reasonable expectation of success in order to convert the various sensor parameters to a single format in order to make the information interchangeable and therefore ease integration and reducing manufacturing costs (Column 3, lines 15-25 “The information processing apparatus includes the parameter conversion means. Hence, the format of the information given to the parameter integration means is made interchangeable, without regard to the format of information output from the sensor means. As a result, detection information can be integrated through use of parameter integration means of single construction, without regard to the combination of sensor means. Hence, the versatility of the parameter integration means is improved, thereby curtailing costs for manufacturing the information processing apparatus.”).
However the combination does not explicitly teach probability-of-presence values as stored in a sensor detection information data group in the storage unit.
Xia teaches a vehicle drivable area detection method, an autonomous driving assistance system, and an autonomous driving vehicle including
probability-of-presence values as stored in a sensor detection information data group in the storage unit (Paragraph [0005], “image data obtained by a camera apparatus, to obtain a first probability distribution of an obstacle; obtaining a second probability distribution of the obstacle based on a time of flight and an echo width of a radar echo signal; and obtaining, based on the first probability distribution of the obstacle and the second probability distribution of the obstacle, a drivable area of a vehicle represented by a probability, where the probability indicates a probability that the vehicle cannot drive through the area,” here the system is determining a probability of presence for an obstacle using each of a plurality of sensors).
Akiyama, Honda, and Xia are analogous art as they are both generally related to systems for processing sensor information of a vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include probability-of-presence values as stored in a sensor detection information data group in the storage unit of Xia in the system for determining a detectable area of Akiyama and Honda with a reasonable expectation of success in order to improve the autonomous driving reliability by accurately determining the probability of obstacles and the associated driving range around a vehicle (Paragraph [0033], “so that a drivable range around the vehicle can be accurately recognized, a new solution is provided for the vehicle drivable area detection method, and support is provided for improving autonomous driving reliability and optimizing driving experience of a user”).
Regarding claim 3, the combination of Akiyama, Honda, and Xia teaches the system as discussed above in claim 1, Akiyama further teaches wherein the sensor detectable area determining unit determines a relationship between a relative position and a detection capability at the first external environment sensor (Paragraph [0032], “FIG. 4A shows a detection area P of the first object detection unit 1101 when there is no object around the own vehicle 10. In this case, the first object detection unit 1101 can detect all objects in the detection area P, and no occlusion area occurs. Next, FIG. 4B shows a state in which there is a surrounding object 20 in the detection area P. In this case, the surrounding object 20 becomes an obstacle to cause an area (occlusion area Q1) where the first object detection unit 1101 cannot perform detection,” here the system is determining a relationship between the position of the sensor and detection capability which forms occlusion zones for the first sensor)
based on a detection position at which a state of detection by the first external environment sensor of an environmental element detected by the second external environment sensor has changed (Paragraph [0050], “When the first object detection unit 1101 detects an object at time t1+Δt, the prediction processing unit 2204 reads the object recognition result for time t1 recorded in the prediction processing unit 2204, and calculates an object prediction result obtained by shifting the read object recognition result to time t1+Δt at which the first object detection unit 1101 detects the object.”) (Paragraph [0051], “As shown in FIG. 8, the second association processing unit 2202 determines, as an association possible area TA, a range in which the object prediction result of the prediction processing unit 2204 described above and the detectable area PA of the first object detection unit 1101 overlap each other (step S9),” here when the system detects an environmental element that has changed position/state at a future time the system can determine that the object has moved by predicting the future state of the object and adjusting the detectable area based on the movement)
the detection position being indicated in time series data of the integrated detection information (Paragraph [0050], “When the first object detection unit 1101 detects an object at time t1+Δt,” here the object detections are recorded as time based data t1 and t1+Δt).
Regarding claim 4, the combination of Akiyama, Honda, and Xia teaches the system as discussed above in claim 1, Akiyama further teaches wherein the sensor detectable area determining unit also estimates a cause by which a state of detection by the first external environment sensor of an environmental element detected by the second external environment sensor has changed and based on the estimated cause, determines a relationship between a relative position and a detection capability at the first external environment sensor (Paragraph [0040], “On the other hand, as shown in FIG. 5C, in the case where the first object detection unit 1101 detects the surrounding objects 20a, 20b, 20c in front of the own vehicle 10, as described in FIG. 4B, the surrounding objects 20a, 20b, 20c become obstacles and thus occlusion areas Q2, Q3 for which the first object detection unit 1101 cannot perform detection, occur.”) (See Figures 5B - 5D, figure 5B shows all of the objects detected by the second sensor, Figure 5C shows some objects detected by the first sensor and object that are causing occlusion areas inhibiting detection of some objects).
Regarding claim 5, the combination of Akiyama, Honda, and Xia teaches the system as discussed above in claim 1, Akiyama further teaches wherein a relationship between the relative position and the detection capability is expressed as a combination of a detectable distance and a detectable angle range (See Figures 5B-5D showing a distance and angle range for each sensor)
and the sensor detectable area determining unit estimates whether a cause of change in the state of detection is a cause related to a detection distance or a cause related to a detection angle (Paragraph [0040], “As shown in FIG. 5D, the association processing unit 1202 determines, as an association possible area SA, a range in which the detectable area PA of the first object detection unit 1101 and the detectable area RA of the second object detection unit 1102 overlap each other (step S5). Then, for the surrounding objects 20a, 20b, 20c detected in the association possible area SA, the association processing unit 1202 compares object detection results of the first object detection unit 1101 and object detection results of the second object detection unit 1102, and as described above, combines the object detection results between which the difference in the distances to the respective detected objects is the smallest (step S6),” the system determines and area which comprises a range and angle and determines an overlap area between the two sensor angles and ranges determines if objects are detectable)
determines a detectable distance for the first external environment sensor, based on the estimated cause related to the detection distance (See Figures 5B-5D showing a distance and angle range for each sensor)
and determines a detectable angle range for the first external environment sensor, based on the estimated cause related to the detection angle (See Figures 5B-5D showing a distance and angle range for each sensor).
Regarding claim 6, the combination of Akiyama, Honda, and Xia teaches the system as discussed above in claim 1, Akiyama further teaches wherein the sensor detectable area determining unit estimates whether a cause of change in the state of detection is occlusion by a different obstacle and does not use information indicating the estimated cause being occlusion by the different obstacle, as information for determining a relationship between a relative position and a detection capability at the first external environment sensor (Paragraph [0043], “in embodiment 1 according to the present invention, associations are taken for only the association possible area excluding the occlusion area, whereby association is suppressed, and further, a different object present in the occlusion area is prevented from being determined to be an identical object. Thus, the possibility of occurrence of an object recognition result based on an erroneous combination (erroneous association) can be decreased,” here the system can determine whether an associated object is within an occlusion area and suppresses association of a different occluded object from being associated with the original object).
Regarding claim 7, the combination of Akiyama, Honda, and Xia teaches the system as discussed above in claim 1, Akiyama further teaches wherein the sensor detectable area determining unit determines detection reliability of the first external environment sensor (Paragraph [0031], “An object detection position measured by the first object detection unit 1101 and object detection position accuracy information thereof are outputted as an object detection position signal and an object detection position accuracy signal,” here the system can determine a detection reliability/accuracy of the first sensor)
by comparing detection position information from the first external environment sensor about detection of the environmental element with detection position information from the second external environment sensor about detection of the environmental element, and determines a state of detection by the first external environment sensor, based on the detection reliability (Paragraph [0041], “combines the object detection results between which the difference in the distances to the respective detected objects is the smallest. The update processing unit 1203 determines that the object detection result of the first object detection unit 1101 and the object detection result of the second object detection unit 1102 that are combined with each other in the association processing by the association processing unit 1202 correspond to an identical object, and integrates the detection information therebetween, thereby specifying (recognizing) the position of each of the surrounding objects 20a, 20b, 20c and updating the recognition result for the specified object (step S7)” here the system can merge the object detection results from the first and second environment sensors to determine if the same objects are being detected and determines the updated recognition for the objects and increasing the reliability/accuracy).
Regarding claim 8, the combination of Akiyama, Honda, and Xia teaches the system as discussed above in claim 1, Akiyama further teaches a vehicle control information generating unit that generates control information on the vehicle based on a detectable area for the first external environment sensor, the detectable area being determined by the sensor detectable area determining unit, and on the integrated detection information (Paragraph [0042], “Using information about the recognized relative positions between the own vehicle 10 and the surrounding objects 20a, 20b, 20c around the own vehicle obtained from the object recognition processing unit 1200 as described above, the vehicle control unit 1300 performs control for a collision damage mitigation brake system for mitigating a damage when the own vehicle 10 collides with a frontward object, an adaptive cruise control system for following a frontward vehicle, and the like. That is, it is possible to perform autonomous driving of the own vehicle on the basis of an object recognition result from the object recognition processing unit 1200.”).
Regarding claim 10, the combination of Akiyama, Honda, and Xia teaches the system as discussed above in claims 1, Akiyama further teaches wherein when a state of detection by the first external environment sensor is detection failure, the sensor detectable area determining unit reduces a detection capability level in a unit area corresponding to a position in a detectable area for the first external environment sensor, the position being indicated in the integrated detection information (See Figures 4B-5D showing occlusion areas in which a detection state for a first sensor in unavailable/failed, these areas are shown as occlusion areas and reduced detection areas).
Regarding claim 11, the combination of Akiyama, Honda, and Xia teaches the system as discussed above in claims 1 and 9, Akiyama does not explicitly teach wherein the grid map is created by dividing an area into grid patterns on a polar coordinate system, the area having an installation point of the first external environment sensor at a center of the area.
However Honda teaches wherein the grid-like map is created by dividing an area into grid-like patterns on a polar coordinate system, the area having an installation point of the first external environment sensor at a center of the area (See Figures 3-4, figure 3 showing the area around the vehicle divided into a grid like map with polar coordinates and the vehicle and sensor at the center of the area, and figure 4 expressing a detection capability for the area in front of the vehicle for each unit area) (Column 18, lines 30-35, “The two-dimensional coordinate system may be either a rectangular coordinate system or a polar coordinate system.”).
Akiyama and Honda are analogous art as they are both generally related to systems for processing sensor information of a vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include wherein the grid-like map is created by dividing an area into grid-like patterns on a polar coordinate system, the area having an installation point of the first external environment sensor at a center of the area of Honda in the system for determining a detectable area of Akiyama with a reasonable expectation of success in order to convert the various sensor parameters to a single format in order to make the information interchangeable and therefore ease integration and reducing manufacturing costs (Column 3, lines 15-25 “The information processing apparatus includes the parameter conversion means. Hence, the format of the information given to the parameter integration means is made interchangeable, without regard to the format of information output from the sensor means. As a result, detection information can be integrated through use of parameter integration means of single construction, without regard to the combination of sensor means. Hence, the versatility of the parameter integration means is improved, thereby curtailing costs for manufacturing the information processing apparatus.”).
Regarding claim 12, Akiyama teaches an electronic control device incorporated in a vehicle, comprising: (Paragraph [0012], "An object recognition device according to the present invention includes: first object detection means mounted on a vehicle and configured to detect a surrounding object around the vehicle")
a processing unit and a storage unit, the electronic control device being connected to an external environment sensor group via an in-vehicle network, the electronic control device comprising (Paragraph [0029], “The object recognition processing unit 1200 is composed of a processor 100 and a storage device 101, as shown in FIG. 2 which shows an example of hardware thereof.”) (Paragraph [0002], “detection results are transferred as sensor information to a vehicle control device via the in-vehicle network”)
a sensor detection information acquiring unit implemented by the processing unit that acquires, via the in-vehicle network, detection information from each of a plurality of external environment sensors with different detection ranges (Paragraph [0027], "The object recognition device includes a first object detection unit 1101, a second object detection unit 1102, and an object recognition processing unit 1200") (See figures 5B-5D showing different detection ranges) (Paragraph [0002], “detection results are transferred as sensor information to a vehicle control device via the in-vehicle network”)
the detection information including sensor observation information (Paragraph [0028], “The first object detection unit 1101 is composed of a device such as a camera or a light detection and ranging (lidar) device, which is capable of acquiring an object detection position and in addition, object width information.”)
the external environment sensors being incorporated in the vehicle (Abstract, “object recognition device of a vehicle”) (See Figures 4A-5D)
a sensor detection information integrating unit implemented by the processing unit that (See Figures 1 and 2, Figure 1 showing various units such as “Association processing unit” that are implemented by a processor as shown in figure 2)
generates integrated detection information by comparing and integrating the detection information of the plurality of external environment sensors to specify correspondence relationships between environmental elements detected by the plurality of external environment sensors, the integrated detection information being stored in the storage unit as an integrated detection information data group (Paragraph [0012], “an association processing unit configured to take an association between the first object detection result and the second object detection result in an area excluding the occlusion area determined by the occlusion area detection processing unit “) (Paragraph [0038], "Next, the association between an object detection result of the first object detection unit 1101 for which the occlusion area has been determined as described above, and an object detection result of the second object detection unit 1102, is determined by the association processing unit 1202," here the association processing unit is integrating detection information from the first detection result and the second detection result to determine an association/correspondence relationship between object detections) (See Figures 1 and 2 which shows the functions of the association processing unit implemented by a processor and storage)
and a sensor detectable area determining unit implemented by the processing unit that compares detection results in an overlapping area of detection ranges of the plurality of external environment sensors indicated by the integrated detection information (Paragraph [0034], "The number of occlusion areas is one in FIG. 4B, but in the case where there are a plurality of (n) surrounding objects, the above processing is performed for all the object detection results of the first object detection unit 1101, to determine occlusion areas Q1 to Qn for the first object detection unit 1101.") (See Figure 5C and 5D showing the system determining detectable areas and occlusion areas for the sensors) (See Figure 3, steps S3-S5, here the figure shows the system determining object detectable area from a first object detection result, and then determine second object detectable area for the second object detection, and then determine an association possible area from the two separate object detection results in which object can be detected by both sensors) (See Figure 3, steps S3-S5, here the figure shows the system determining object detectable area from a first object detection result, and then determine second object detectable area for the second object detection, and then determine an association possible area from the two separate object detection results in which object can be detected by both sensors)
and determines an effective detection range for at least one of the external environment sensors (See Figure 5C and 5D showing the system determining detectable areas and occlusion areas for the sensors)
the state of detection being indicated in the integrated detection information (Paragraph [0041], “The update processing unit 1203 determines that the object detection result of the first object detection unit 1101 and the object detection result of the second object detection unit 1102 that are combined with each other in the association processing by the association processing unit 1202 correspond to an identical object, and integrates the detection information therebetween, thereby specifying (recognizing) the position of each of the surrounding objects 20a, 20b, 20c and updating the recognition result for the specified object (step S7),” here the system is updating the recognition result/state of detection in the integrated detection information)
the relationship between the relative position and a detection capability at the first external environment sensor is expressed as a detectable distance and a detectable angle range of the at least one external environment sensor (See Figures 5B-5D showing a distance and angle range for each sensor)
and the electronic control device outputs traveling control information, including a control instruction value, via the in-vehicle network to one or more vehicle actuators, thereby controlling traveling of the vehicle (Paragraph [0042], “Using information about the recognized relative positions between the own vehicle 10 and the surrounding objects 20a, 20b, 20c around the own vehicle obtained from the object recognition processing unit 1200 as described above, the vehicle control unit 1300 performs control for a collision damage mitigation brake system for mitigating a damage when the own vehicle 10 collides with a frontward object, an adaptive cruise control system for following a frontward vehicle, and the like. That is, it is possible to perform autonomous driving of the own vehicle on the basis of an object recognition result from the object recognition processing unit 1200.”).
However Akiyama does not explicitly teach determines an effective detection range for at least one of the external environment sensors as a grid map stored in the storage unit as a sensor detectable area data group, the grid map being created by dividing an area into grid patterns on a polar coordinate system having an installation point of the at least one external environment sensor at a center of the area to express a detection capability level of the at least one external environment sensor in each unit area, and that determines a detection capability level in each unit area of the grid map, based on a state of detection by the at least one external environment sensor of an environmental element detected by another one of the plurality of external environment sensors.
Honda teaches an information processing apparatus and method which readily enable integration of information pieces output from sensors including
determines an effective detection range for at least one of the external environment sensors as a grid map stored in the storage unit as a sensor detectable area data group (See Figures 3-4, figure 3 showing the area around the vehicle divided into a grid like map, and figure 4 expressing a detection capability for the area in front of the vehicle for each unit area)
the grid map being created by dividing an area into grid patterns on a polar coordinate system having an installation point of the at least one external environment sensor at a center of the area to express a detection capability level of the at least one external environment sensor in each unit area (See Figures 3-4, figure 3 showing the area around the vehicle divided into a grid like map with polar coordinates and the vehicle and sensor at the center of the area, and figure 4 expressing a detection capability for the area in front of the vehicle for each unit area) (Column 18, lines 30-35, “The two-dimensional coordinate system may be either a rectangular coordinate system or a polar coordinate system.”) (See Figures 11A and 11B showing two separate polar coordinate systems each having a center at the location of the separate sensors)
and that determines a detection capability level in each unit area of the grid map (See figure 4 showing reliability information for the sensor for each unit of the grid like map) (See Figure 11A and 11B showing the state detection for each grid section for a plurality of sensors)
based on a state of detection by the at least one external environment sensor of an environmental element detected by another one of the plurality of external environment sensors (Column 25, lines 25-35, “In the example shown in FIG. 9, the parameter integration section 15 is arranged so as to enable integration of the detection information DS output from the sensor 11A and the detection information DS output from the sensor 11B.” here as can be seen in figure 11B the detection capability level is determined for each unit area of the grid map using the state of detection of a first and second sensor).
Akiyama and Honda are analogous art as they are both generally related to systems for processing sensor information of a vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include determines an effective detection range for at least one of the external environment sensors as a grid map stored in the storage unit as a sensor detectable area data group, the grid map being created by dividing an area into grid patterns on a polar coordinate system having an installation point of the at least one external environment sensor at a center of the area to express a detection capability level of the at least one external environment sensor in each unit area, and that determines a detection capability level in each unit area of the grid map, based on a state of detection by the at least one external environment sensor of an environmental element detected by another one of the plurality of external environment sensors of Honda in the system for determining a detectable area of Akiyama with a reasonable expectation of success in order to convert the various sensor parameters to a single format in order to make the information interchangeable and therefore ease integration and reducing manufacturing costs (Column 3, lines 15-25 “The information processing apparatus includes the parameter conversion means. Hence, the format of the information given to the parameter integration means is made interchangeable, without regard to the format of information output from the sensor means. As a result, detection information can be integrated through use of parameter integration means of single construction, without regard to the combination of sensor means. Hence, the versatility of the parameter integration means is improved, thereby curtailing costs for manufacturing the information processing apparatus.”).
However the combination does not explicitly teach probability-of-presence values and being stored in the storage unit as a sensor detection information data group.
Xia teaches a vehicle drivable area detection method, an autonomous driving assistance system, and an autonomous driving vehicle including
probability-of-presence values and being stored in the storage unit as a sensor detection information data group (Paragraph [0005], “image data obtained by a camera apparatus, to obtain a first probability distribution of an obstacle; obtaining a second probability distribution of the obstacle based on a time of flight and an echo width of a radar echo signal; and obtaining, based on the first probability distribution of the obstacle and the second probability distribution of the obstacle, a drivable area of a vehicle represented by a probability, where the probability indicates a probability that the vehicle cannot drive through the area,” here the system is determining a probability of presence for an obstacle using each of a plurality of sensors).
Akiyama, Honda, and Xia are analogous art as they are both generally related to systems for processing sensor information of a vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include probability-of-presence values and being stored in the storage unit as a sensor detection information data group of Xia in the system for determining a detectable area of Akiyama and Honda with a reasonable expectation of success in order to improve the autonomous driving reliability by accurately determining the probability of obstacles and the associated driving range around a vehicle (Paragraph [0033], “so that a drivable range around the vehicle can be accurately recognized, a new solution is provided for the vehicle drivable area detection method, and support is provided for improving autonomous driving reliability and optimizing driving experience of a user”).
Regarding claim 13, Akiyama teaches a control method by an electronic control device incorporated in a vehicle (Paragraph [0012], "An object recognition device according to the present invention includes: first object detection means mounted on a vehicle and configured to detect a surrounding object around the vehicle")
the method being implemented by a processing unit executing instructions stored in a storage unit of the electronic control device the method comprising (Paragraph [0029], “The object recognition processing unit 1200 is composed of a processor 100 and a storage device 101, as shown in FIG. 2 which shows an example of hardware thereof.”)
acquiring, via an in-vehicle network, detection information from a first external environment sensor and detection information from a second external environment sensor (Paragraph [0027], "The object recognition device includes a first object detection unit 1101, a second object detection unit 1102, and an object recognition processing unit 1200") (Paragraph [0002], “detection results are transferred as sensor information to a vehicle control device via the in-vehicle network”)
the first external environment sensor and second external environment sensor being incorporated in the vehicle (Abstract, “object recognition device of a vehicle”) (See Figures 4A-5D)
the detection information including sensor observation information (Paragraph [0028], “The first object detection unit 1101 is composed of a device such as a camera or a light detection and ranging (lidar) device, which is capable of acquiring an object detection position and in addition, object width information.”)
generating, by comparing and integrating the detection information of the first and second external environment sensors, integrated detection information that specifies a correspondence relationship between environmental elements detected by the first external environment sensor and the second external environment sensor, and storing the integrated detection information in the storage unit as an integrated detection information data group (Paragraph [0012], “an association processing unit configured to take an association between the first object detection result and the second object detection result in an area excluding the occlusion area determined by the occlusion area detection processing unit “) (Paragraph [0038], "Next, the association between an object detection result of the first object detection unit 1101 for which the occlusion area has been determined as described above, and an object detection result of the second object detection unit 1102, is determined by the association processing unit 1202," here the association processing unit is integrating detection information from the first detection result and the second detection result to determine an association/correspondence relationship between object detections) (See Figures 1 and 2 which shows the functions of the association processing unit implemented by a processor and storage)
determining, based on a state of detection by the first external environment sensor of an environmental element detected by the second external environment sensor as indicated in the integrated detection information, a relationship between a relative position and a detection capability at the first external environment sensor (Paragraph [0034], "The number of occlusion areas is one in FIG. 4B, but in the case where there are a plurality of (n) surrounding objects, the above processing is performed for all the object detection results of the first object detection unit 1101, to determine occlusion areas Q1 to Qn for the first object detection unit 1101.") (See Figures 5B-5D) (See Figure 3, steps S3-S5, here the figure shows the system determining object detectable area from a first object detection result, and then determine second object detectable area for the second object detection, and then determine an association possible area from the two separate object detection results in which object can be detected by both sensors)
generating a detectable area for the first external environment sensor (See Figure 5C and 5D showing the system determining detectable areas and occlusion areas for the sensors)
the state of detection being indicated in the integrated detection information (Paragraph [0041], “The update processing unit 1203 determines that the object detection result of the first object detection unit 1101 and the object detection result of the second object detection unit 1102 that are combined with each other in the association processing by the association processing unit 1202 correspond to an identical object, and integrates the detection information therebetween, thereby specifying (recognizing) the position of each of the surrounding objects 20a, 20b, 20c and updating the recognition result for the specified object (step S7),” here the system is updating the recognition result/state of detection in the integrated detection information)
and determining a detectable area for the first external environment sensor based on a change in the state of detection indicated by the integrated detection information (Paragraph [0032], “FIG. 4A shows a detection area P of the first object detection unit 1101 when there is no object around the own vehicle 10. In this case, the first object detection unit 1101 can detect all objects in the detection area P, and no occlusion area occurs. Next, FIG. 4B shows a state in which there is a surrounding object 20 in the detection area P. In this case, the surrounding object 20 becomes an obstacle to cause an area (occlusion area Q1) where the first object detection unit 1101 cannot perform detection,” here the system is determining a detectable area for a first sensor based on a change of state from no object detected to an object being detected which forms an occlusion area) (See also figures 5B-5D) (Paragraph [0035], “In the determination for the occlusion area, margins Mx, My may be set in accordance with object detection error in the X-axis direction or the Y-axis direction in the first object detection unit 1101, whereby the condition A and the condition B may be respectively changed into a condition A′ and a condition B′ as shown below, so as to narrow the occlusion area. It is noted that the margins Mx, My are set as positive values,” here the system is determining the detectable area outside of the occlusion area and is updating that area based on changing states of objects according to the integrated detection results of the first and second object detection units)
the relationship between the relative position and a detection capability at the first external environment sensor is expressed as a detectable distance and a detectable angle range of the first external environment sensor (See Figures 5B-5D showing a distance and angle range for each sensor)
and the electronic control device outputs traveling control information, including a control instruction value, via the in-vehicle network to one or more vehicle actuators, thereby controlling traveling of the vehicle (Paragraph [0042], “Using information about the recognized relative positions between the own vehicle 10 and the surrounding objects 20a, 20b, 20c around the own vehicle obtained from the object recognition processing unit 1200 as described above, the vehicle control unit 1300 performs control for a collision damage mitigation brake system for mitigating a damage when the own vehicle 10 collides with a frontward object, an adaptive cruise control system for following a frontward vehicle, and the like. That is, it is possible to perform autonomous driving of the own vehicle on the basis of an object recognition result from the object recognition processing unit 1200.”).
However Akiyama does not explicitly teach generating a detectable area for the first external environment sensor as a grid map created by dividing an area into grid patterns on a polar coordinate system having an installation point of the first external environment sensor at a center of the area to express a detection capability level of the first external environment in each unit area, determines a detection capability level in each unit area of the grid map, based on the state of detection by the first external environment sensor of an environmental element detected by the second external environment sensor.
Honda teaches an information processing apparatus and method which readily enable integration of information pieces output from sensors including
generating a detectable area for the first external environment sensor as a grid map (See Figures 3-4, figure 3 showing the area around the vehicle divided into a grid like map, and figure 4 expressing a detection capability for the area in front of the vehicle for each unit area)
created by dividing an area into grid patterns on a polar coordinate system having an installation point of the at least one external environment sensor at a center of the area to express a detection capability level of the at least one external environment sensor in each unit area and storing the grid map in the storage unit as a sensor detectable area data group (See Figures 3-4, figure 3 showing the area around the vehicle divided into a grid like map with polar coordinates and the vehicle and sensor at the center of the area, and figure 4 expressing a detection capability for the area in front of the vehicle for each unit area) (Column 18, lines 30-35, “The two-dimensional coordinate system may be either a rectangular coordinate system or a polar coordinate system.”) (See Figures 11A and 11B showing two separate polar coordinate systems each having a center at the location of the separate sensors)
determining a detection capability level in each unit area of the grid map (See figure 4 showing reliability information for the sensor for each unit of the grid like map) (See Figure 11A and 11B showing the state detection for each grid section for a plurality of sensors)
based on a state of detection by the first external environment sensor of the environmental element detected by the second external environment sensor (Column 25, lines 25-35, “In the example shown in FIG. 9, the parameter integration section 15 is arranged so as to enable integration of the detection information DS output from the sensor 11A and the detection information DS output from the sensor 11B.” here as can be seen in figure 11B the detection capability level is determined for each unit area of the grid map using the state of detection of a first and second sensor).
Akiyama and Honda are analogous art as they are both generally related to systems for processing sensor information of a vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include generating a detectable area for the first external environment sensor as a grid map created by dividing an area into grid patterns on a polar coordinate system having an installation point of the first external environment sensor at a center of the area to express a detection capability level of the first external environment in each unit area, determines a detection capability level in each unit area of the grid map, based on the state of detection by the first external environment sensor of an environmental element detected by the second external environment sensor of Honda in the system for determining a detectable area of Akiyama with a reasonable expectation of success in order to convert the various sensor parameters to a single format in order to make the information interchangeable and therefore ease integration and reducing manufacturing costs (Column 3, lines 15-25 “The information processing apparatus includes the parameter conversion means. Hence, the format of the information given to the parameter integration means is made interchangeable, without regard to the format of information output from the sensor means. As a result, detection information can be integrated through use of parameter integration means of single construction, without regard to the combination of sensor means. Hence, the versatility of the parameter integration means is improved, thereby curtailing costs for manufacturing the information processing apparatus.”).
However the combination does not explicitly teach probability of presence values, and storing the detection information in the storage unit as a sensor detection information data group.
Xia teaches a vehicle drivable area detection method, an autonomous driving assistance system, and an autonomous driving vehicle including
probability of presence values, and storing the detection information in the storage unit as a sensor detection information data group (Paragraph [0005], “image data obtained by a camera apparatus, to obtain a first probability distribution of an obstacle; obtaining a second probability distribution of the obstacle based on a time of flight and an echo width of a radar echo signal; and obtaining, based on the first probability distribution of the obstacle and the second probability distribution of the obstacle, a drivable area of a vehicle represented by a probability, where the probability indicates a probability that the vehicle cannot drive through the area,” here the system is determining a probability of presence for an obstacle using each of a plurality of sensors).
Akiyama, Honda, and Xia are analogous art as they are both generally related to systems for processing sensor information of a vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include probability of presence values, and storing the detection information in the storage unit as a sensor detection information data group of Xia in the system for determining a detectable area of Akiyama and Honda with a reasonable expectation of success in order to improve the autonomous driving reliability by accurately determining the probability of obstacles and the associated driving range around a vehicle (Paragraph [0033], “so that a drivable range around the vehicle can be accurately recognized, a new solution is provided for the vehicle drivable area detection method, and support is provided for improving autonomous driving reliability and optimizing driving experience of a user”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Li (US-20200064483) teaches an autonomous driving assembly for a vehicle includes a plurality of lidar units configured to be supported by a vehicle body including determining a detectable range and detectable angle for the sensors. Nishijima (US-20160307026) teaches a stereoscopic object detection device including a first detection unit configured to detect a road surface and a stereoscopic object; a second detection unit configured to detect the stereoscopic object by a millimeter-wave radar including determining detectable ranges for the sensors. Ikenouchi (US-20180090006) teaches an automotive target-detection system includes: sensor units each provided on a vehicle; and a central control unit connected to the sensor units including where each sensor has a detectable angle and distance ranges as can be seen in Figure 1.
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/CHRISTOPHER GEORGE FEES/Primary Examiner, Art Unit 3662
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