Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Amendments filed on 11/20/2025 have been fulling considered and entered.
Response to Arguments
Applicant's arguments filed 11/20/2025 have been fully considered but they are not persuasive. In response to applicant’s arguments that Tanaka does not teach the newly added limitation of “wherein the position estimation error factor on a road surface includes at least one of a road marking on the road surface or a structure on the road surface”, the Examiner respectfully disagrees. It is noted that Tanaka teaches detection of road construction which is interpreted to be the claimed structure on the road surface.
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.
Claims 1-6 and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Tanaka et al. (US 20230289994 A1 hereinafter “Tanaka”) in view of Lin et al. (EP 1959414 A1 hereinafter “Lin”).
As to claim 1, Tanaka teaches an information processing device (object position detection apparatus; Title) comprising: at least one processor or circuit (processor 90 such as CPU; [0032]) and at least one memory ([0033]), including instructions stored thereon, which when executed by the processor (processor 90 such as CPU; [0032]), cause the information processing device to acquire an image (The image acquisition unit 31 acquires an image from the roadside monitoring camera; [0039]); detect an object position in the image based on the image (The object detection unit 32 sets an arbitrary representative position of the existence area of the object as the position in image of the object; [0044]); transform the object position in the image into an object position in world coordinates (The object position calculation unit 34 acquires the coordinate transformation equation of the image area corresponding to the position in image of the detected object, from the object information map data; and transforms the position in image of the detected object into the position of the real world coordinates; [0056]); calculate a reliability of the object position in the world coordinates (the possibility of the existence of the detected object is determined in the real world coordinates; [0054]-[0055]); determine whether the object position detected by the object position detection unit is influenced by a position estimation error factor on a road surface (Accordingly, when the object type of detection target, such as the vehicle, is detected in this area, a possibility of erroneous detection is high. In an area where a floor on which the vehicle cannot travel is imaged, such as arable land, a possibility of existence of the vehicle is normally low. On the other hand, in an area where the road surface is imaged, a possibility of existence of the vehicle and the person is high.; [0054] the erroneous detection determination unit 35 acquires the information on existence possibility of object corresponding to the detected position in image of the object and the detected object type, from the object information map data; [0059]); wherein the position estimation error factor on a road surface includes at least one of a road marking on the road surface or a structure on the road surface (building and wall surface, is changed by construction and the like; [0055] construction is interpreted to be structure on the road surface); change the reliability of the object position according to a determination result from the error factor presence determination (It is desirable that the object information map data stored in the storage apparatus 91 can be rewritten from the outside. Accordingly, when the road shape is changed or the shape of structure, such as building and wall surface, is changed by construction and the like, the coordinate transformation equation, the information on size limitation, and the information on existence possibility of object of each image area can be changed, and the detection accuracy can be kept; [0055]).
Tanaka teaches a processor 90 such as CPU ([0032]) for implementation of automatic driving in a specific area, it is studied that the roadside machine which detects the object in the area and distributes object information to a vehicle, a person, a dynamic map, and the like is installed on the road ([0003]) and provide an object position detection apparatus which can calculate a position of object in the real world with good accuracy from the image imaged by the roadside monitoring camera, while suppressing erroneous detection of object, even for geographical feature where height of floor changes variously ([0009]). However, Tanaka does not explicitly teach estimate a speed of an object by using a past history of the object position in the world coordinates and a past history of the reliability after the change. Lin teaches an apparatus for estimating travel time (Title) where roadside sensors are installed to collect movement data where the travel time is determined using velocity of the vehicle ([0009]) and to increase accuracy, historical data specifying how long other vehicles required for travelling the distance in the past was incorporated. It would have been obvious for one ordinary skilled in the art before the time of filing to have combined the roadside machine which accurately detect vehicles with the travel time estimation using past data of Lin since they are both within the same field of endeavor.
As to claim 2, Tanaka and Lin teach the information processing device according to claim 1, wherein the position estimation error factor includes a road marking (shape of road; Tanaka [0046]).
As to claim 3, Tanaka and Lin teach the information processing device according to claim 1, wherein the instructions when executed by the at least one processor cause the information processing device to: decide in advance a determination region for determining whether or not there is an influence of the position estimation error factor on the road surface, and perform the determination based on whether the detected object position is within a range of the determination region (FIG. 9 shows an example of cutting-out of the image. The outside of an area enclosed by a thick frame line is an area which is set that there is no possibility of existence of the object; Tanaka [0075]).
As to claim 4, Tanaka and Lin teach the rectangular cut out area of any size may be set (Tanaka 0075) but does not explicitly teach the region can be set by a user. It is well known in the art that automation is generally an improvement upon a manual step and allowing for additional manual manipulation over an automated step is allowed. Therefore, it would have been obvious for one ordinary skilled in the art before the effective filing date to have allowed for the option of a user selectable cut out area to allow for additional flexibility in the region setting.
As to claim 5, Tanaka and Lin teach the information processing device according to claim 3, wherein the instructions when executed by the at least one processor cause the information processing device to: detect a road marking from the image, and the determination region is calculated in advance according to a detection result (cutting out area with existence possibility from image occurs before any object detection; Tanaka S12 Fig. 10, S22 Fig. 12).
As to claim 6, Tanaka and Lin teach the information processing device according to claim 1, wherein the instructions when executed by at least one processor caused the information processing device to: generates a bounding box, and performs the determination based on an amount of change in a size of the bounding box (the cut out area may be changed whenever the object information map data is updated; Tanaka [0075] after the cut out area is updated then erroneous detection is performed; Tanaka 35 Fig. 11).
As to claim 8, it differs from claim 1 in that it is the method claim. Please see claim 1 for detail mapping.
As to claim 9, it differ from claim 1 in that it requires a non-transitory computer-readable storage medium configured to store a computer program comprising instructions for executing the method. Tanaka and Lin teach storage apparatuses 91, various kinds of storage apparatus, such as RAM (Random Access Memory), ROM (Read Only Memory), a flash memory, EEPROM (Electrically Erasable Programmable Read Only Memory), and a hard disk, are used (Tanaka [0033]).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Tanaka and Lin as applied to claims 1-6 and 8-9 above, and further in view of Yang et al. (US 20190311614 A1 hereinafter “Yang”).
As to claim 7, Tanaka and Lin teach the information processing device according to claim 1, wherein the instructions when executed by the at least one processor cause the information processing device to: generate a bounding box, and perform the determination based on an amount of change within the bounding box in the image (the cut out area may be changed whenever the object information map data is updated; Tanaka [0075] after the cut out area is updated then erroneous detection is performed; Tanaka 35 Fig. 11). However, Tanaka and Lin do not explicitly teach detecting the object using color region. Yang teaches a real-time traffic monitoring with connected cars (Title) wherein detected object’s feature such as colors is extracted therefore reaching on the claimed color region. It would have been obvious for one ordinary skilled in the art before the effective filing date to modified the object detection of Tanaka and Lin in order to help distinguish objects with similar appearance (The extracted context features may describe background environment around the detected objects. The context features are useful in similarity processing especially when the captured images include objects with identical appearance. As an example, a first image may include a first car having the same appearance as a second car in a second image. However, the extracted context features may indicate that the first car is driving on the road while the second car is parked in front of a building based on their surrounding environments as indicated by the extracted context features. [0075]).
Conclusion
THIS ACTION IS MADE FINAL. 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 CLAIRE X WANG whose telephone number is (571)270-1051. The examiner can normally be reached M-F 9am-5pm.
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CLAIRE X. WANG
Supervisory Patent Examiner
Art Unit 1774
/CLAIRE X WANG/Supervisory Patent Examiner, Art Unit 1774