DETAILED ACTION
Status of Claims
The status of the claims is as follows:
(a) Claims 1-5 remain pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendments
The Examiner accepts the amendments received on 06/26/2026.
(a) The Applicant, via the claim amendments filed, overcome the 35 U.S.C. 112(b) claim interpretations set forth in the previous Office Action. The Examiner, therefore, withdraws said interpretations.
(b) The Applicant, via the claim amendments filed, overcome the 35 U.S.C. 112(b) claim rejection set forth in the previous Office Action. The Examiner, therefore, withdraws said rejection.
(c) The Applicant, via the claim amendments filed, overcome the 35 U.S.C. 101 claim rejections set forth in the previous Office Action. The Examiner, therefore, withdraws said rejections.
Response to Arguments
Applicant’s arguments with respect to the instant claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 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 (i.e., changing from AIA to pre-AIA ) 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.
Claims 1-5 are rejected under 35 U.S.C. 103 as being unpatentable over Mori U.S. P.G. Publication 2021/0245777 A1 (hereinafter, Mori), in view of Maeda et al. U.S. P.G. Publication 2020/00341470 A1 (hereinafter, Maeda), further in view of Konishi et al. U.S. P.G. Publication 2022/0291014 A1 (hereinafter, Konishi).
Regarding Claim 1, Mori describes an autonomous vehicle that travels (autonomous vehicle that travels, Mori, Paragraph 0005) while estimating a vehicle's own position (determining a vehicle's own position, Mori, Paragraph 0009), comprising:
-a vehicle body including a traction motor that rotates drive wheels for causing a travel movement of the autonomous vehicle (vehicle having an automatic driving function, wherein the vehicle can be an electric vehicle, Mori, Paragraph 0031 and Figure 1);
-a camera that is mounted on the vehicle body and captures images of a road surface of a route while the autonomous vehicle travels (camera which captures images of a road surface of the route while a vehicle travels, Mori, Paragraphs 0003-0005 and 0038 and Figures 1 and 2);
-a map generating circuit that generates a map data set by arranging map-image data points, in which position information items are associated with image data points captured by the camera, according to the position information items (map generating circuit that generates map data based on position information, wherein some of that data can be image data points captured by a camera based on the position of the vehicle, Mori, Paragraphs 0003-0010 and 0038 and Figures 1 and 2); and
-a determining circuit that, during the travel movement of the autonomous vehicle:
…
-performs quality determination for the map data set using a result of quality determination for the … map-image data point and a result of quality determination based on accuracy of estimation of the vehicle's own position made by using the map data set acquired from the map generating circuit (vehicle able to determine quality of map data points using object recognition, including image data points, to generate a more accurate map and vehicle location, Mori, Paragraphs 0038-0039 and Figure 2);
…
Mori does not specifically disclose the autonomous vehicle to include that the determining circuit acquires, from the map generating circuit and among the map data set, one of the map-image data points as a pre-determination map-image data point.
Maeda discloses, teaches, or at least suggests the missing limitation. Maeda describes a vehicle system that includes the ability to acquire pre-determined map image data points, including stored map data points in RAM, and using said information to determine a more accurate map and/or location of the vehicle in relation to the map (Maeda, Paragraphs 0055-0060).
As a result, a person of ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to modify the vehicle of Mori to include a determining circuit that acquires, from the map generating circuit and among the map data set, one of the map-image data points as a pre-determination map-image data point, as disclosed, taught, or at least suggested by Maeda.
It would have been obvious to combine and modify the cited references, with a reasonable expectation of success because gathering image data points helps a vehicle generate a map of the region for the vehicle (Maeda, Paragraph 0002).
Mori and Maeda, however, do not specifically disclose the autonomous vehicle to include that the determining circuit discards the acquired pre-determination map-image data point from the map data set when the acquired pre-determination map-image data point is determined to be defective during the quality determination, stores remaining map-image data points included in the map data set as a modified map data set, estimates the position of the autonomous vehicle using the modified map data set, and controls the traction motor to rotate the drive wheels to cause a movement operation based on the estimated position of the autonomous vehicle.
Konishi discloses, teaches, or at least suggests the missing limitations. Konishi describes a self-driving vehicle that generates and stores an environmental map from feature points extracted from camera images and uses the environmental map to recognize the position of the vehicle (Konishi, Paragraphs 0025 and 0031-0038). Konishi further describes determining that a feature point having a collation result below a predetermined degree is unnecessary data and deleting the feature point from the environmental map (Konishi, Paragraphs 0042 and 0044-0045 and Figure 4). The resulting environmental map is used to recognize the vehicle position, which is used to generate a target path and control traveling actuators, including a traveling motor, to cause the vehicle to travel (Konishi, Paragraphs 0023, 0029-0030 and 0037-0038).
As a result, a person of ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to further modify the vehicle of Mori, as modified by Maeda, to include that the determining circuit discards the acquired pre-determination map-image data point from the map data set when the acquired pre-determination map-image data point is determined to be defective during the quality determination, stores remaining map-image data points included in the map data set as a modified map data set, estimates the position of the autonomous vehicle using the modified map data set, and controls the traction motor to rotate the drive wheels to cause a movement operation based on the estimated position of the autonomous vehicle, as disclosed, taught, or at least suggested by Konishi.
It would have been obvious to combine and modify the cited references, with a reasonable expectation of success because deleting unnecessary map data suppresses errors in matching map data and allows unnecessary data to be removed without deteriorating the accuracy of the map used to recognize the position of the vehicle (Konishi, Paragraphs 0042 and 0047-0048).
Regarding Claim 2, Mori, as modified, describes the autonomous vehicle according to claim 1.
Mori does not specifically disclose the vehicle to include that the determining circuit acquires, as a comparison image data point, an image data point that is different from the image data point associated with the position information item in the pre-determination map-image data point and has been captured at a position close to a position at which the image data point associated with the position information item has been captured, and performs quality determination for the map data set by using a result of quality determination for the pre-determination map-image data point acquired when the acquired comparison image data point and the pre-determination map-image data point are compared with each other, and a result of quality determination based on accuracy of estimation of the vehicle's own position made by using a relative positional relationship between the map data set acquired from the map generating circuit and the image data points.
Maeda discloses, teaches, or at least suggests the missing limitation(s). Maeda describes a vehicle system that includes the ability to acquire pre-determined map image data points (e.g., stored map data points in RAM and the like) (Maeda, Paragraphs 0055-0060). Moreover, Maeda describes using said information to determine a more accurate map and/or location of the vehicle in relation to the map by means of comparing the stored data to newly acquired data (Maeda, Paragraphs 0055-0060 and 0077).
As a result, a person of ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to modify the vehicle of Mori to include the determining circuit acquires, as a comparison image data point, an image data point that is different from the image data point associated with the position information item in the pre-determination map-image data point and has been captured at a position close to a position at which the image data point associated with the position information item has been captured, and performs quality determination for the map data set by using a result of quality determination for the pre-determination map-image data point acquired when the acquired comparison image data point and the pre-determination map-image data point are compared with each other, and a result of quality determination based on accuracy of estimation of the vehicle's own position made by using a relative positional relationship between the map data set acquired from the map generating circuit and the image data points, as disclosed, taught, or at least suggested by Maeda.
It would have been obvious to combine and modify the cited references, with a reasonable expectation of success because gathering image data points and comparing image data points helps a vehicle generate a map of the region for the vehicle (Maeda, Paragraph 0002).
Regarding Claim 3, Mori, as modified, describes the autonomous vehicle according to claim 2.
Mori does not specifically disclose the vehicle to include that the determining circuit acquires, as a comparison image data point, an image data point that is different from the image data point associated with the position information item in the pre-determination map-image data point and has been captured at a position close to a position at which the image data point associated with the position information item has been captured, and performs quality determination for the map data set by using a result of quality determination for the pre-determination map-image data point acquired when the acquired comparison image data point and the pre-determination map-image data point are compared with each other, and a result of quality determination based on accuracy of estimation of the vehicle's own position made by using a relative positional relationship between the map data set acquired from the map generating circuit and the image data points.
Maeda discloses, teaches, or at least suggests the missing limitation(s). Maeda describes a vehicle system that includes the ability to acquire pre-determined map image data points (e.g., stored map data points in RAM and the like) (Maeda, Paragraphs 0055-0060). Moreover, Maeda describes using said information to determine a more accurate map and/or location of the vehicle in relation to the map by means of comparing the stored data to newly acquired data, wherein the comparing is based on a deviation metric (i.e., threshold) for accuracy and updating of the map (Maeda, Paragraphs 0055-0060 and 0077).
As a result, a person of ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to modify the vehicle of Mori to include that the determining circuit determines that the map data set is non-defective when a number of matches between the pre-determination map-image data point and the comparison image data point is greater than or equal to a matching threshold, a reliability of the position information item associated with the pre-determination map-image data point is greater than or equal to a threshold, for the multiple map-image data points forming the map data set, an area of an overlapping range between consecutive ones of the map-image data points is greater than or equal to an area threshold, and accuracy of estimation of the vehicle's own position made based on a relative positional relationship between the map data set and the image data point by using the pre- determination map-image data point as the map data set is greater than or equal to an accuracy threshold, as disclosed, taught, or at least suggested by Maeda.
It would have been obvious to combine and modify the cited references, with a reasonable expectation of success because gathering image data points, comparing, and determining deviations of image data points helps a vehicle generate an accurate map of the region for the vehicle (Maeda, Paragraph 0002).
Regarding Claim 4, Mori, as modified, describes the autonomous vehicle according to claim 1.
Mori does not specifically disclose the vehicle to include a storage circuit that stores a travel route data set in which position information items are associated in advance with image data points of the road surface of the route captured in advance by a camera, wherein the determining circuit acquires a comparison image data point from the travel route data set, and performs quality determination for the map data set by using a result of quality determination when the acquired comparison image data point and the pre-determination map-image data point are compared with each other, and a quality determination result based on an error between the route and a result of estimation of the vehicle's own position made by using a relative positional relationship between the map data set acquired from the map generating circuit and the travel route data set.
Maeda discloses, teaches, or at least suggests the missing limitation(s). Maeda describes a vehicle system that includes the ability to acquire pre-determined map image data points (e.g., stored map data points in RAM and the like) (Maeda, Paragraphs 0055-0060). Moreover, Maeda describes using said information to determine a more accurate map and/or location of the vehicle in relation to the map by means of comparing the stored data to newly acquired data, wherein the comparing is based on a deviation metric (i.e., threshold) for determining accuracy (i.e., error) and updating of the map (Maeda, Paragraphs 0055-0060 and 0077).
As a result, a person of ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to modify the vehicle of Mori to include a storage circuit that stores a travel route data set in which position information items are associated in advance with image data points of the road surface of the route captured in advance by a camera, wherein the determining circuit acquires a comparison image data point from the travel route data set, and performs quality determination for the map data set by using a result of quality determination when the acquired comparison image data point and the pre-determination map-image data point are compared with each other, and a quality determination result based on an error between the route and a result of estimation of the vehicle's own position made by using a relative positional relationship between the map data set acquired from the map generating circuit and the travel route data set, as disclosed, taught, or at least suggested by Maeda.
It would have been obvious to combine and modify the cited references, with a reasonable expectation of success because gathering image data points, comparing, and determining deviations of image data points helps a vehicle generate an accurate map of the region for the vehicle (Maeda, Paragraph 0002).
Regarding Claim 5, Mori, as modified, describes the autonomous vehicle according to claim 1.
Mori does not specifically disclose the vehicle to include that the determining circuit determines that the pre-determination map-image data point is non-defective when a number of matches between the pre-determination map-image data point and the travel route data set is greater than or equal to a matching threshold, a reliability of the position information item associated with the pre-determination map-image data point is greater than or equal to a threshold, an area of an overlapping range between the pre-determination map-image data point and another consecutive map-image data point is greater than or equal to an area threshold, and an error between the route and the vehicle's own position is less than or equal to a threshold, the vehicle's own position being estimated from a relative positional relationship between the map data set and the travel route data set by using the pre-determination map- image data point as the map data set.
Maeda discloses, teaches, or at least suggests the missing limitation(s). Maeda describes a vehicle system that includes the ability to acquire pre-determined map image data points (e.g., stored map data points in RAM and the like) (Maeda, Paragraphs 0055-0060). Moreover, Maeda describes using said information to determine a more accurate map and/or location of the vehicle in relation to the map by means of comparing the stored data to newly acquired data, wherein the comparing is based on a deviation metric (i.e., threshold) for determining accuracy (i.e., error and error threshold) and updating of the map accordingly (i.e., based on map data being accurate enough to pass predetermined thresholds) (Maeda, Paragraphs 0055-0060 and 0077).
As a result, a person of ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to modify the vehicle of Mori to include the determining circuit determines that the pre-determination map-image data point is non-defective when a number of matches between the pre-determination map-image data point and the travel route data set is greater than or equal to a matching threshold, a reliability of the position information item associated with the pre-determination map-image data point is greater than or equal to a threshold, an area of an overlapping range between the pre-determination map-image data point and another consecutive map-image data point is greater than or equal to an area threshold, and an error between the route and the vehicle's own position is less than or equal to a threshold, the vehicle's own position being estimated from a relative positional relationship between the map data set and the travel route data set by using the pre-determination map- image data point as the map data set, as disclosed, taught, or at least suggested by Maeda.
It would have been obvious to combine and modify the cited references, with a reasonable expectation of success because gathering image data points, comparing, and determining deviations of image data points helps a vehicle generate an accurate map of the region for the vehicle (Maeda, Paragraph 0002).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW J CROMER whose telephone number is (313)446-6563. The examiner can normally be reached M-F: ~ 8:15 A.M. - 6:00 P.M..
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/ANDREW J CROMER/Examiner, Art Unit 3667
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