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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/14/2024 has/have been considered by the examiner.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 5-6, and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eguchi Ginga (JP 2019066258 A), hereinafter Ginga in view of Shigetoshi et al (JP 2021135694 A), hereinafter Shigetoshi.
-Regarding claim 1, Ginga discloses a driving assistance device comprising (Abstract; FIGS. 1-16E
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): processing circuitry to calculate line-of-sight information of a subject from a vehicle interior video about inside a vehicle captured by an imaging device including at least one camera (FIG. 1, line-of-sight information acquisition unit 100, line-of-sight detection unit 310; Page 2, Sec. “First Embodiment”, 3rd – 4th paragraphs); to detect a target object present around the vehicle from a vehicle exterior video about outside the vehicle captured by the imaging device and extract target object information that is information of the detected target object (FIG. 3, detection unit 200b, object extraction unit 320; Page 4, 3rd paragraph); to calculate, from the calculated line-of-sight information and the extracted target object information, a visibility indicating an extent of a degree of visual recognition of the detected target object by the subject at each time within a past fixed time period from a current time (FIG. 1, visibility calculation unit 330, instantaneous visibility calculation unit 331, cumulative visibility calculation unit 332; Page 4, 4th – 5th paragraphs), to correct the calculated visibility on a basis of a lapse of time from the current time to each time within the past, to recorrect the corrected visibility on a basis of a state change of the target object, and to calculate a visibility associated to the current time on a basis of the recorrected visibility (FIG. 1, instantaneous visibility calculation unit 331, cumulative visibility calculation unit 332; Page 4, last paragraph; Page 5, 1st paragraph); to calculate a collision possibility indicating an extent of a degree of collision possibility between the detected target object and the vehicle from the extracted target object information (FIG. 1, risk calculation unit 350; Page 5);
Gigna does not disclose calculating an effective collision possibility that decreases in accordance with an increase in the calculated visibility and increases in accordance with an increase in the calculated collision possibility from the calculated visibility and the calculated collision possibility. However, Gigna does disclose that display control unit 360 hides the first warning information when the first cumulative visibility increases. In addition, the display control unit 360 displays warning information associated with an object having a high degree of risk on the display unit 400.
In the same field of endeavor, Shigetoshi teaches a method and device capable of notifying a presence of a person or an obstacle around a vehicle in a manner according to a state of a driver of the vehicle (Shigetoshi: Abstract; FIGS. 1-12), teaches a recognition unit 401 recognizes an object in the image captured by the photographing device 20 (Shigetoshi: FIG. 3), and teaches calculating a collision possibility indicating an extent of a degree of collision possibility between the detected target object and the vehicle from the extracted target object information (Shigetoshi: FIG. 3, recognition unit 401, specific unit 404, estimation unit 407). Shigetoshi further teaches calculating an effective collision possibility that decreases in accordance with an increase in the calculated visibility and increases in accordance with an increase in the calculated collision possibility from the calculated visibility and the calculated collision possibility (Shigetoshi: FIG. 3, specific unit 404, first detection unit 405, first determination unit 406, estimation unit 407, determination unit 408, tagging unit 409; Page 7, 1st – 4th paragraphs, “… unit 406 has the lowest risk associated with tag information when the angle between the reference driver's line-of-sight direction and the direction in which the object exists is 0 degrees … generates a weight that increases the risk associated with the tag information as the angle formed by the driver's line-of-sight direction and the direction in which the object exists increases … ”, 5th -8th paragraphs, “…unit 407 estimates the risk level of the industrial vehicle 1 … can come into contact with the object … estimates the degree of risk according to … calculates the difference (gap) … time until contact …”; Page 8, 1st paragraph, “estimation unit 407 substitutes the gap, the time until contact with the object, and the weight … a predetermined function representing the degree of danger …”; Page 12, 4th paragraph, “… the risk increases as the gap becomes smaller and the risk increases as the time until contact with the object becomes shorter … multiplying the gap by a predetermined weight …”).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Gigna with the teaching of Shigetoshi by calculating an effective collision possibility based on the calculated visibility and the calculated collision possibility in order to improve the performance for the estimation of collision possibility.
-Regarding claim 2, Gigna in view of Shigetoshi teaches the device of claim 1. The combination further teaches wherein the processing circuitry is further configured to determine a content of a warning to be performed for the subject in accordance with the calculated effective collision possibility and control a warning operation by a warning device (Gigna: Page 6, 8th paragraph, “The display control unit 360 hides the first warning information when the first cumulative visibility increases. In addition, the display control unit 360 displays warning information associated with an object having a high degree of risk on the display unit 400”; Page 8, 4th paragraph; See also Shigetoshi: FIG. 3, unit 408, speaker 30).
-Regarding claim 3, Gigna in view of Shigetoshi teaches the device of claim 1. The combination further teaches wherein the processing circuitry is further configured to acquire at least one of a vehicle speed, steering control information, or pedal control information as vehicle information from a control system mounted on the vehicle, and to calculate the collision possibility on a basis of the extracted target object information and the acquired vehicle information (Gigna: FIG. 1; Page 3, 2nd and 4th paragraphs; Page 6, 4th paragraph).
-Regarding claim 5, Gigna in view of Shigetoshi teaches the device of claim 1. The combination further teaches wherein the processing circuitry is further configured to calculate type information indicating a type of the detected target object and state information indicating a state or motion information indicating a motion (Gigna: Page 3, 4th paragraph; Page 16, Sec. “Third Embodiment”, 3rd paragraph, “ … type … of the object …”; FIG. 12).
-Regarding claim 6, Gigna in view of Shigetoshi teaches the device of claim 5. The combination further teaches wherein the processing circuitry is further configured to calculate the collision possibility on a basis of at least one of the calculated state information or motion information according to the calculated type information (Gigna: FIG. 12; Page 16, Sec. “Third Embodiment”, 3rd paragraph).
-Regarding claim 8, Gigna discloses a driven assistance method performed by a driving assistance device, the driving assistance method comprising (Abstract; FIGS. 1-16E): calculating line-of-sight information of a subject from a vehicle interior video about inside a vehicle captured by an imaging device including at least one camera (FIG. 1, line-of-sight information acquisition unit 100, line-of-sight detection unit 310; Page 2, Sec. “First Embodiment”, 3rd – 4th paragraphs); detecting a target object present around the vehicle from a vehicle exterior video about outside the vehicle captured by the imaging device and extract target object information that is information of the detected target object (FIG. 3, detection unit 200b, object extraction unit 320; Page 4, 3rd paragraph); calculating, from the calculated line-of-sight information and the extracted target object information, a visibility indicating an extent of a degree of visual recognition of the detected target object by the subject at each time within a past fixed time period from a current time (FIG. 1, visibility calculation unit 330, instantaneous visibility calculation unit 331, cumulative visibility calculation unit 332; Page 4, 4th – 5th paragraphs), to correct the calculated visibility on a basis of a lapse of time from the current time to each time within the past, to recorrect the corrected visibility on a basis of a state change of the target object, and to calculate a visibility associated to the current time on a basis of the recorrected visibility (FIG. 1, instantaneous visibility calculation unit 331, cumulative visibility calculation unit 332; Page 4, last paragraph; Page 5, 1st paragraph); calculating a collision possibility indicating an extent of a degree of collision possibility between the detected target object and the vehicle from the extracted target object information (FIG. 1, risk calculation unit 350; Page 5);
Gigna does not disclose calculating an effective collision possibility that decreases in accordance with an increase in the calculated visibility and increases in accordance with an increase in the calculated collision possibility from the calculated visibility and the calculated collision possibility. However, Gigna does disclose that display control unit 360 hides the first warning information when the first cumulative visibility increases. In addition, the display control unit 360 displays warning information associated with an object having a high degree of risk on the display unit 400.
In the same field of endeavor, Shigetoshi teaches a method and device capable of notifying a presence of a person or an obstacle around a vehicle in a manner according to a state of a driver of the vehicle (Shigetoshi: Abstract; FIGS. 1-12), teaches a recognition unit 401 recognizes an object in the image captured by the photographing device 20 (Shigetoshi: FIG. 3), and teaches calculating a collision possibility indicating an extent of a degree of collision possibility between the detected target object and the vehicle from the extracted target object information (Shigetoshi: FIG. 3, recognition unit 401, specific unit 404, estimation unit 407). Shigetoshi further teaches calculating an effective collision possibility that decreases in accordance with an increase in the calculated visibility and increases in accordance with an increase in the calculated collision possibility from the calculated visibility and the calculated collision possibility (Shigetoshi: FIG. 3, specific unit 404, first detection unit 405, first determination unit 406, estimation unit 407, determination unit 408, tagging unit 409; Page 7, 1st – 4th paragraphs, “… unit 406 has the lowest risk associated with tag information when the angle between the reference driver's line-of-sight direction and the direction in which the object exists is 0 degrees … generates a weight that increases the risk associated with the tag information as the angle formed by the driver's line-of-sight direction and the direction in which the object exists increases … ”, 5th -8th paragraphs, “…unit 407 estimates the risk level of the industrial vehicle 1 … can come into contact with the object … estimates the degree of risk according to … calculates the difference (gap) … time until contact …”; Page 8, 1st paragraph, “estimation unit 407 substitutes the gap, the time until contact with the object, and the weight … a predetermined function representing the degree of danger …”; Page 12, 4th paragraph, “… the risk increases as the gap becomes smaller and the risk increases as the time until contact with the object becomes shorter … multiplying the gap by a predetermined weight …”).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Gigna with the teaching of Shigetoshi by calculating an effective collision possibility based on the calculated visibility and the calculated collision possibility in order to improve the performance for the estimation of collision possibility.
-Regarding claim 10, Gigna discloses a driving assistance device comprising (Abstract; FIGS. 1-16E): processing circuitry to calculate line-of-sight information of a subject from a vehicle interior video about inside a vehicle captured by an imaging device including at least one camera (FIG. 1, line-of-sight information acquisition unit 100, line-of-sight detection unit 310; Page 2, Sec. “First Embodiment”, 3rd – 4th paragraphs); to detect a target object present around the vehicle from a vehicle exterior video about outside the vehicle captured by the imaging device and extract target object information that is information of the detected target object (FIG. 3, detection unit 200b, object extraction unit 320; Page 4, 3rd paragraph); to calculate, a visibility indicating an extent of a degree of visual recognition of the detected target object by the subject from the calculated line-of-sight information and the extracted target object information (FIG. 1, line-of-sight detection unit 310, object extraction unit 320, visibility calculation unit 330, instantaneous visibility calculation unit 331, cumulative visibility calculation unit 332; Page 4, 4th – 5th paragraphs); to calculate a collision possibility indicating an extent of a degree of collision possibility between the detected target object and the vehicle from the extracted target object information (FIG. 1, risk calculation unit 350; Page 5);
Gigna does not disclose calculating an effective collision possibility that decreases in accordance with an increase in the calculated visibility and increases in accordance with an increase in the calculated collision possibility from the calculated visibility and the calculated collision possibility, wherein the processing circuitry is further configured to calculate a direction matching degree between a direction of a target vector from the vehicle toward the target object and a direction of a line-of-sight vector of the subject at a predetermined timing, to calculate a weighted direction matching degree by adding a weight to a direction matching degree at each time within a past fixed time from a current time, to calculate, when a change in state or motion of the target object occurs within a measurement time, a corrected direction matching degree, which is corrected, by multiplying the calculated, weighted direction matching degree before an occurrence time of the change by a coefficient, and to calculate the visibility by calculating a sum of the calculated, weighted direction matching degrees or the calculated, corrected direction matching degree.
In the same field of endeavor, Shigetoshi teaches a method and device capable of notifying a presence of a person or an obstacle around a vehicle in a manner according to a state of a driver of the vehicle (Shigetoshi: Abstract; FIGS. 1-12), teaches a recognition unit 401 recognizes an object in the image captured by the photographing device 20 (Shigetoshi: FIG. 3), and teaches calculating a collision possibility indicating an extent of a degree of collision possibility between the detected target object and the vehicle from the extracted target object information (Shigetoshi: FIG. 3, recognition unit 401, specific unit 404, estimation unit 407). Shigetoshi further teaches calculating an effective collision possibility that decreases in accordance with an increase in the calculated visibility and increases in accordance with an increase in the calculated collision possibility from the calculated visibility and the calculated collision possibility, wherein the processing circuitry is further configured to calculate a direction matching degree between a direction of a target vector from the vehicle toward the target object and a direction of a line-of-sight vector of the subject at a predetermined timing, to calculate a weighted direction matching degree by adding a weight to a direction matching degree at each time within a past fixed time from a current time, to calculate, when a change in state or motion of the target object occurs within a measurement time, a corrected direction matching degree, which is corrected, by multiplying the calculated, weighted direction matching degree before an occurrence time of the change by a coefficient, and to calculate the visibility by calculating a sum of the calculated, weighted direction matching degrees or the calculated, corrected direction matching degree (Shigetoshi: FIG. 3, specific unit 404, first detection unit 405, first determination unit 406, estimation unit 407, determination unit 408, tagging unit 409; Page 7, 1st – 4th paragraphs, “… unit 406 has the lowest risk associated with tag information when the angle between the reference driver's line-of-sight direction and the direction in which the object exists is 0 degrees … generates a weight that increases the risk associated with the tag information as the angle formed by the driver's line-of-sight direction and the direction in which the object exists increases … ”, 5th -8th paragraphs, “…unit 407 estimates the risk level of the industrial vehicle 1 … can come into contact with the object … estimates the degree of risk according to … calculates the difference (gap) … time until contact …”; Page 8, 1st paragraph, “estimation unit 407 substitutes the gap, the time until contact with the object, and the weight … a predetermined function representing the degree of danger …”; Page 12, 4th paragraph, “… the risk increases as the gap becomes smaller and the risk increases as the time until contact with the object becomes shorter … sum of … multiplying the gap by a predetermined weight … time until contact … sum of the weight …”).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Gigna with the teaching of Shigetoshi by calculating an effective collision possibility based on the calculated visibility and the calculated collision possibility in order to improve the performance for the estimation of collision possibility.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eguchi Ginga (JP 2019066258 A), hereinafter Ginga in view of Shigetoshi et al (JP 2021135694 A), hereinafter Shigetoshi, and further in view of Baig et al (US 20210035442 A1), hereinafter Baig.
-Regarding claim 7, Ginga in view of Shigetoshi teaches the device of claim 1.
Ginga in view of Shigetoshi does not teach a roadside system capable of communicating with the driving assistance device, wherein the roadside system transmits, to the driving assistance device, information of the target object or another target object present around the vehicle different from the target object, and the target object information calculating unit of the driving assistance device extracts the target object information on the basis of the vehicle exterior video and the transmitted information (Note: these are basic features of Vehicle-to-Everything (V2X) technologies).
However, Baig is an analogous art pertinent to the problem to be solved in this application and teaches a method for responding to a public safety alert includes receiving an indication of the public safety alert, wherein the public safety alert comprising a description of a wanted road user and identifying, using sensors of the vehicle (AV) and in response to the public safety alert, road users (Baig: Abstract; FIGS. 1-32). Baig further teaches a roadside system capable of communicating with the driving assistance device, wherein the roadside system transmits, to the driving assistance device, information of the target object or another target object present around the vehicle different from the target object, and the target object information calculating unit of the driving assistance device extracts the target object information on the basis of the vehicle exterior video and the transmitted information (Baig: FIGS. 1-2, 8, 29-32; [0052]; [0059]-[0060]; [0394]; [0399]).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Ginga in view of Shigetoshi with the teaching of Baig by communicating with a roadside system in order to further improve the safety of driving.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jha et al (US 20230110467 A1), hereinafter Jha teaches a method to enhance collective perception service related to connected vehicles, computer-assisted and/or autonomous driving vehicles, Internet of Vehicles (IoV), Intelligent Transportation Systems (ITS), and Vehicle-to-Everything (V2X) technologies.
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/XIAO LIU/Primary Examiner, Art Unit 2664
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