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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. The effective filing date of the claimed invention is recognized as June 23, 2023, in continuity with JP2023-103563.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on June 4, 2024, November 27, 2024, and March 9, 2026, are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Response to Amendment
In response to Applicant’s amendments date June 4, 2024, Examiner withdraws the claim objection, withdraws the interpretation under 35 U.S.C. 112(f), withdraws the rejections under 35 U.S.C. 101, 35 U.S.C. 102(a)(2), and 35 U.S.C. 103, and issues new grounds of rejection under 35 U.S.C. 112(b), 35 U.S.C. 101, 35 U.S.C. 103, issues a new claim objection, and identifies allowable subject matter.
Response to Arguments
Applicant’s arguments, filed February 19, 2025, with respect to the rejections of Claims 1-8 under 35 U.S.C. 102(a)(2) and 35 U.S.C. 103, 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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 8 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Regarding Claim 8,
Claim 8 recites, on Line 3, “a program”, however, a program is already recited in antecedent Claims 1 and 6. Thus, it is unclear if the limitation refers to this program, or is another program. For the purpose of compact prosecution, Examiner is interpreting the limitation as referring to further program instructions within a single program stored in the memory. Appropriate correction is required.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2 are rejected under 35 U.S.C. 103 as being unpatentable over Nerayoff (US 20140039987 A1), newly of record, in view of Tang (US 20250136153 A1), newly of record, herein after referred to simply as Nerayoff and Tang, respectively.
Regarding Claim 1,
Nerayoff discloses the following limitations,
A system comprising: a moving object that moves … (Fig. 2A, depicting a vehicle)
a memory storing a program; and (Paragraph [0017], “Server system 140 comprises one or more computer systems which provide central data storage, data retrieval, and processing services. The computer systems included in server system 140 will generally each include a random access memory 142 and a processor 143. Server system 140 includes database 141, which is used to record information such as usage of destination locations, such as parking spaces, by vehicles; vehicle account information; identifications of vehicles obtained via identification camera images;”)
a processor, wherein the processor executes the program stored in the memory to: (Paragraph [0017], “The computer systems included in server system 140 will generally each include a random access memory 142 and a processor 143”)
acquire environmental information indicating an environment in which the moving object moves … the environment having a potential to impact on the image for detection to be acquired; (Paragraph [0092], “In an embodiment, locations and directions of travel may be specified for lanes/paths of traffic in each of the image feeds,” A vehicle operational area is identified, and image overlap is identified, Paragraph [0057], “FIG. 4 illustrates how images captured by cameras with overlapping fields of view may be used to identify a vehicle, track the movement of the vehicle to a destination location, and identify use of the destination location by the vehicle.”)
determine at least one of a part of the moving object to be used as the detection point and an image capturing direction of capturing the image for detection in response to the acquired environmental information; (Paragraph [0092], “In an embodiment, locations and directions of travel may be specified for lanes/paths of traffic in each of the image feeds,” A vehicle operational area is identified, and image overlap is identified, Paragraph [0057], “FIG. 4 illustrates how images captured by cameras with overlapping fields of view may be used to identify a vehicle, track the movement of the vehicle to a destination location, and identify use of the destination location by the vehicle.” The vehicle features are identified in response to determinations of overlapping. Further, at least one of a part of a moving object is used as the detection point, Paragraph [0039], “if pixels associated with one or more license plates and/or additional identifications are at new locations, the one or more license plates and/or additional identifications are determined to have moved.”)
acquire the image for detection in response to the determined at least one of the part and the image capturing direction; (Paragraph [0033], “In such an embodiment, server system 140 may be configured to perform a coordinate transformation in order to accurately determine vehicle characteristics while varying the field of view of identification camera 120.” The vehicle image acquiring is in response to tracked vehicle features, as the camera is dynamically controlled in order to more clearly identify a feature.)
However, Nerayoff does not disclose the following limitations,
a moving object that moves by unmanned driving using a detection point included in an image for detection resulting from image capturing of the moving object ...
the moving object moves by the unmanned driving
and generate a control command for the unmanned driving using the detection point included in the acquired image for detection.
However, this is taught by Tang, in the same field of endeavor, which teaches that an area monitoring system may remote control vehicles within its observation (Paragraph [0041], “Moreover, since the initial vehicle pose is calculated if a match is established, it is possible to track the vehicle with extremely high accuracy in both position and direction after a match is established. Therefore, the edge server 9 can control vehicle behavior within the ODD with extremely high accuracy using only the sensor information received from the existing infrastructure sensor 5.”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the area monitoring system of Nerayoff with vehicle control of Tang, as this creates a cheap solution to expand autonomous driving, (Paragraph [0008], “The present invention allows even vehicles, the functions of which are restricted to Level 2 autonomous driving, to operate fully autonomously within the ODD, i.e., in a state of Level 4 autonomous driving, without the need to install expensive vehicle-mounted sensors in each vehicle, through a mechanism based on predetermined rules. Thus, the present invention makes it possible to provide a less expensive alternative for achieving Level 4 autonomous driving.”). Further, the combination is a simple substitution of elements yielding results which are predictable to one of ordinary skill in the art.
Regarding Claim 2,
The combination of Nerayoff and Tang, as shown, teaches all the limitations of Claim 1. Nerayoff further discloses the following limitations,
wherein the environmental information includes at least one of information indicating timing of moving of the moving object … and information indicating a place where the moving object moves … (Paragraph [0061], “In some embodiments, the first destination camera and the second destination camera are synchronized, such that images 420b' and 420c are both captured at essentially the same time t3.” The vehicle is tracked in time and space. A timing is thus obtained.)
Tang further teaches the following limitations,
moving of the moving object by the unmanned driving; the moving object moves by unmanned driving (Paragraph [0041], “the edge server 9 can control vehicle behavior within the ODD with extremely high accuracy using only the sensor information received from the existing infrastructure sensor 5.”)
Claims 3 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Nerayoff and Tang as applied to Claim 1 above, further in view of Carranza (US 20190043207 A1), previously of record, herein after referred to simply as Carranza.
Regarding Claim 3,
Nerayoff, as shown, discloses all the limitations of Claim 1. Nerayoff further discloses the following limitation,
further comprising: a plurality of cameras for image capturing of the moving object (Fig. 1, showing cameras 120, 125 capturing the vehicle 130)
However, the combination does not teach the following limitations,
wherein the process executes the program stored in the memory to: that determine a camera of the plurality of cameras as a responsible camera to capture the image for detection in response to the environmental information, the responsible camera for capturing the image for detection is determined based on a condition relating to the at least one of the determined part of the image and the determined image capturing direction, and acquire the image for detection from the determined camera.
However, Carranza, in the same field of endeavor, teaches that an external observation of a vehicle (Paragraph [0069], “In the example of FIG. 6A, a car entering the parking lot can be captured, along with its license plate, in the video stream of a camera positioned near the entrance of the parking lot. The car can then be identified from the video stream using license plate recognition algorithms, and the video stream can be further processed to generate metadata describing the car and its current state (e.g., physical characteristics, position, direction of travel, speed).”) can identify a next camera to be used for viewing the vehicle (Paragraph [0079], “For example, the object's predicted future state can be used to identify the best camera(s) to use for capturing the object in optimal conditions at certain moments in the future. Accordingly, a second camera may be identified as having the best perspective or view for capturing the object in optimal conditions at a predicted future location and point in time.”). The identification of a best camera is the manner of fulfilling a condition.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention and with a reasonable likelihood of success, to have modified the vehicle tracking of Nerayoff, as previously modified by Tang, with the camera prediction of Carranza, as this optimizes the observation of a tracked vehicle (Paragraph [0021], “This metadata can then be used to predict a future state of the object, and the predicted future state can be used to proactively configure other cameras in the surveillance system to subsequently capture, detect, and/or identify the object under optimal conditions.”). Further, the combination is a simple substitution of elements yielding results which are predictable to one of ordinary skill in the art.
Claims 4-6 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Nerayoff and Tang as applied to Claim 1 above, further in view of Ferencz (US 20220136853 A1), previously of record, herein after referred to simply as Ferencz.
Regarding Claim 4,
The combination of Nerayoff and Tang, as shown, teaches all the limitations of Claim 1. However, the combination does not teach the following limitations,
wherein the processor executes the program stored in the memory to: determine the at least one of the part to be a left side part or a right side part of the moving object alternatively in response to the environmental information
However, this is taught by Ferencz, which teaches that a camera can track a left or right side of a vehicle using a symmetric processing method (Paragraph [0348], “Leveraging these symmetries may allow a neural network to greatly reduce the memory load used to operate the vehicle and thus may allow for increased accuracy of object detection. For example, a vehicle in an image is generally symmetrical in appearance. Therefore, a kernel channel used to detect the left edge of a vehicle (or diagonal left, etc.) should similarly detect the right edge of a vehicle when flipped left-right (i.e. about a vertical axis of the matrix). Accordingly, left-right symmetrical pairs of kernels used in a given convolutional layer may be just as effective for detecting objects or features in a road environment compared to the same number of kernels without this constraint” and Paragraph [0144], “For instance, a camera included in image acquisition unit 120 (such as image capture device 122 having field of view 202) may capture a plurality of images of an area forward of vehicle 200 (or to the sides or rear of a vehicle, for example)”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the vehicle tracking of Nerayoff, as previously modified by Tang, with the left or right edge vehicle detection of Ferencz, as this allows vehicles in an environment without fiducial markers to be tracked, and further, the symmetric method reduces processing load (Paragraph [0348],“Leveraging these symmetries may allow a neural network to greatly reduce the memory load used to operate the vehicle”). Further, the combination is a simple substitution of elements, yielding results which are predictable to one of ordinary skill in the art.
Regarding Claim 5,
The combination of Nerayoff, Tang, and Ferencz, as shown, teaches all the limitations of Claim 4. Ferencz further already teaches the following limitations,
wherein the processor executes the program stored in the memory to determine the at least one of the part to be a left rear corner or a right rear corner of the moving object alternatively in response to the environmental information. (Paragraph [0348], “Therefore, a kernel channel used to detect the left edge of a vehicle (or diagonal left, etc.) should similarly detect the right edge of a vehicle when flipped left-right (i.e. about a vertical axis of the matrix).” and Paragraph [0144], “For instance, a camera included in image acquisition unit 120 (such as image capture device 122 having field of view 202) may capture a plurality of images of an area forward of vehicle 200 (or to the sides or rear of a vehicle, for example)” – edges can be detected, which can be from a rear of the vehicle, which would observe rear corners.)
Regarding Claim 6,
The combination of Nerayoff and Tang, as shown, teaches all the limitations of Claim 1. However, the combination does not teach the following limitation,
wherein the processor executes the program stored in the memory to determine details of an estimating process in response to the at least one of the determined part and the determined image capturing direction, the estimating process being a process of estimating a location of the moving object to be used for generating the control command for the unmanned driving using the detection point in the image for detection
However, this is taught by Ferencz, which teaches that a camera can track a left or right side of a vehicle using a symmetric processing method (Paragraph [0348], “Leveraging these symmetries may allow a neural network to greatly reduce the memory load used to operate the vehicle and thus may allow for increased accuracy of object detection. For example, a vehicle in an image is generally symmetrical in appearance. Therefore, a kernel channel used to detect the left edge of a vehicle (or diagonal left, etc.) should similarly detect the right edge of a vehicle when flipped left-right (i.e. about a vertical axis of the matrix). Accordingly, left-right symmetrical pairs of kernels used in a given convolutional layer may be just as effective for detecting objects or features in a road environment compared to the same number of kernels without this constraint” and Paragraph [0144], “For instance, a camera included in image acquisition unit 120 (such as image capture device 122 having field of view 202) may capture a plurality of images of an area forward of vehicle 200 (or to the sides or rear of a vehicle, for example)”). The observation of the vehicle includes an estimating process to determine that vehicle’s location (Paragraph [0174], “For example, processing unit 110 may combine the processed information derived from each of image capture devices 122, 124, and 126 (whether by monocular analysis, stereo analysis, or any combination of the two) and determine visual indicators (e.g., lane markings, a detected vehicle and its location and/or path,”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the vehicle tracking of Nerayoff, as previously modified by Tang, with the left or right edge vehicle detection of Ferencz, as this allows vehicles in an environment without fiducial markers to be tracked, and further, the symmetric method reduces processing load (Paragraph [0348],“Leveraging these symmetries may allow a neural network to greatly reduce the memory load used to operate the vehicle”). Further, the combination is a simple substitution of elements, yielding results which are predictable to one of ordinary skill in the art.
Regarding Claim 8,
The combination of Nerayoff, Tang, and, Ferencz, as shown, teaches all the limitations of Claim 6. Ferencz further already teaches the following limitations,
wherein the processor executes the program stored in the memory to determine the details of the estimating process by determining a program to be used in the estimating process in response to the at least one of the determined part and the determined image capturing direction (Paragraph [0348], “Leveraging these symmetries may allow a neural network to greatly reduce the memory load used to operate the vehicle and thus may allow for increased accuracy of object detection. For example, a vehicle in an image is generally symmetrical in appearance. Therefore, a kernel channel used to detect the left edge of a vehicle (or diagonal left, etc.) should similarly detect the right edge of a vehicle when flipped left-right (i.e. about a vertical axis of the matrix). Accordingly, left-right symmetrical pairs of kernels used in a given convolutional layer may be just as effective for detecting objects or features in a road environment compared to the same number of kernels without this constraint” – the use of a left or right channel is the determination of a process to be used in the estimating process)
Allowable Subject Matter
Claim 7 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: Ferencz (US 20220136853 A1) merely teaches to use a left or right pair of evaluations which are symmetrical to one another (Paragraph [0348], “Leveraging these symmetries may allow a neural network to greatly reduce the memory load used to operate the vehicle and thus may allow for increased accuracy of object detection. For example, a vehicle in an image is generally symmetrical in appearance. Therefore, a kernel channel used to detect the left edge of a vehicle (or diagonal left, etc.) should similarly detect the right edge of a vehicle when flipped left-right (i.e. about a vertical axis of the matrix).”), however, it does not teach to conditionally generate a reversed image based on determining if a feature is on one or another side of an axis of symmetry, as is recited by Claim 7. A complete search has been conducted on the claim language, and an interference search revealed no sufficient art.
As allowable subject matter has been indicated, applicant's reply must either comply with all formal requirements or specifically traverse each requirement not complied with. See 37 CFR 1.111(b) and MPEP § 707.07(a).
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 KAREN LYNELLE FURGASON whose telephone number is (571)272-5619. The examiner can normally be reached Monday - Friday, 7:30 AM - 6 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Erin Bishop can be reached at 571-270-3713. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/K.L.F./Examiner, Art Unit 3666
/Erin D Bishop/Supervisory Patent Examiner, Art Unit 3665
Prosecution Insights