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 .
Response to Arguments
In response to Argument(s), Applicant's arguments, see Remark, filed 03/25/2026, with respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C. 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn.
However, upon further consideration, a new ground(s) of rejection is made in view of Khan et al (US 2018/0268238 A1) in view of Nerayoff et al (US 2018/0137356 A1).
Office Action Summary
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khan et al (US 2018/0268238 A1) in view of Nerayoff et al (US 2018/0137356 A1).
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khan et al (US 2018/0268238 A1) in view of Nerayoff et al (US 2018/0137356 A1).
Regarding claim(s) 1 and 11, Khan teaches a traffic monitoring system, comprising:
a traffic sensor (Figure 1; and Paragraph [0042]: “one or a plurality of cameras 100”) including:
an imaging unit configured to generate a recognition record by image recognition processing a captured image of a vehicle, wherein the recognition record includes primary data and additional data (Paragraph [0042]: “one or a plurality of cameras 100 to capture image(s) of incoming, outgoing or passing vehicle(s) and/or their license plate(s)”; and Paragraph [0014]: “the plate records stored in the database consist of one or a plurality of textual data items including plate number, capture time, capture date, camera/system name, state/province, felony […] The plate record also includes plate and vehicle images, and possibly a short video clip of the vehicle. Each plate record further includes a plurality of image signatures/features of the license plate and/or vehicle”).
Khan fails to teach a transceiver configured to transmit the primary data and the additional data to a server system, wherein the additional data is transmitted in response to a request from the server system received by the traffic sensor after the primary data is transmitted; and the server system configured to transmit the request to the traffic sensor based on the primary data received from the traffic sensor.
However, Nerayoff teaches a transceiver configured to transmit the primary data and the additional data to a server system, wherein the additional data is transmitted in response to a request from the server system received by the traffic sensor after the primary data is transmitted (Figure 1; Paragraph [0019]: “Identification camera 120 and destination camera 125 may be configured to each directly communicate with network 110 via respective communication links 127 and 128 […]”; Paragraph [0020]: “Server system 140 comprises one or more computer systems which provide central data storage, data retrieval, and processing services”; Paragraph [0034]: the amount of data sent from identification camera 120 to server system 140 may be significantly reduced, as a vehicle identifier can be sent in place of identification images. However, one or more identification images may still be transmitted to server system 140 for storage and/or determination of vehicle characteristics by server system 140”; and Paragraph [0028]: “one or more of the identification and destination cameras capture images at a higher frame rate and/or resolution than the images transmitted from the cameras to server system 140 […] Additionally, such cameras are configured to respond to requests for such additional image data, including, […] images which may be useful for license plate recognition. By this mechanism, server system 140 may automatically determine that additional images may be useful for identifying and tracking vehicles […]”); and
the server system configured to transmit the request to the traffic sensor based on the primary data received from the traffic sensor (Paragraph [0034]: the amount of data sent from identification camera 120 to server system 140 may be significantly reduced, as a vehicle identifier can be sent in place of identification images. However, one or more identification images may still be transmitted to server system 140 for storage and/or determination of vehicle characteristics by server system 140”; and Paragraph [0028]: “one or more of the identification and destination cameras capture images at a higher frame rate and/or resolution than the images transmitted from the cameras to server system 140 […] Additionally, such cameras are configured to respond to requests for such additional image data, including, […] images which may be useful for license plate recognition. By this mechanism, server system 140 may automatically determine that additional images may be useful for identifying and tracking vehicles […]”).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time of the invention to incorporate Nerayoff’s server-controlled additional-image request architecture into Khan’s structured recognition/plate-record system so that a server system could selectively request and retrieve additional vehicle image information after receiving initial vehicle-identifying information from a traffic sensor. The motivation for this combination of references would have been to significantly reduce the amount of data sent from identification camera to server system, while allowing server system to automatically determine that additional images may be useful for identifying and tracking vehicles, wherein such cameras are configured to respond to requests for such additional image data, including, for example, portions of higher resolution images which may be useful for license plate recognition. This motivation for the combination of Khan and Nerayoff is/are supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Regarding claim(s) 2 and 12, Khan as modified by Nerayoff teaches the traffic monitoring system of claim 1, where Khan teaches wherein the recognition record is a data set of image recognition processing recognized values for each of a set of vehicle characteristics, wherein the primary data (read as “plate number”) corresponds to at least a first vehicle characteristic of the set of vehicle characteristics, and wherein the additional data (read as “vehicle image information and/or image signatures/features of the vehicle”) corresponds to at least a second vehicle characteristic of the set of vehicle characteristics (Paragraph [0014]: “the plate records stored in the database consist of one or a plurality of textual data items including plate number, capture time, capture date, camera/system name, state/province, felony […] The plate record also includes plate and vehicle images, and possibly a short video clip of the vehicle. Each plate record further includes a plurality of image signatures/features of the license plate and/or vehicle”; and Paragraph [0043]: “vehicle images from the camera signals, and processes these images using OCR and image feature detection techniques to read the license plate numbers”).
Regarding claim(s) 3 and 13, Khan as modified by Nerayoff teaches the traffic monitoring system of claim 1, where Khan teaches wherein the set of vehicle characteristics includes one or more of:
vehicle type, class, make, model, color, year, drive type, registration, trajectory, speed, location and license plate number (Paragraph [0014]: “the plate records stored in the database consist of one or a plurality of textual data items including plate number, capture time, capture date, camera/system name, state/province, felony […] The plate record also includes plate and vehicle images, and possibly a short video clip of the vehicle. Each plate record further includes a plurality of image signatures/features of the license plate and/or vehicle”).
Regarding claim(s) 4 and 14, Khan as modified by Nerayoff teaches the traffic monitoring system of claim 3, where Khan teaches wherein the first vehicle characteristic is the vehicle license plate number (Paragraph [0014]: “the plate records stored in the database consist of one or a plurality of textual data items including plate number […]”).
Regarding claim(s) 5 and 15, Khan as modified by Nerayoff teaches the traffic monitoring system of claim 3, wherein the primary data is only the vehicle license plate number (where Khan teaches in Paragraph [0014]: “the plate records stored in the database consist of one or a plurality of textual data items including plate number, capture time, capture date, camera/system name, state/province, felony […] The plate record also includes plate and vehicle images, and possibly a short video clip of the vehicle. Each plate record further includes a plurality of image signatures/features of the license plate and/or vehicle”; and where Nerayoff teaches in Paragraph [0034]: “the amount of data sent from identification camera 120 to server system 140 may be significantly reduced, as a vehicle identifier can be sent in place of identification images”; and Paragraph [0028]: “such cameras are configured to respond to requests for such additional image data, including, […] images which may be useful for license plate recognition. By this mechanism, server system 140 may automatically determine that additional images may be useful for identifying and tracking vehicles […]”).
Regarding claim(s) 6 and 16, Khan as modified by Nerayoff teaches the traffic monitoring system of claim 3, where Khan teaches wherein the additional data is at least one of:
vehicle type, class, make, model, color, year, drive type hybrid, registration, trajectory, speed, and location (Paragraph [0014]: “the plate records stored in the database consist of one or a plurality of textual data items including plate number, capture time, capture date, camera/system name, state/province, felony […] The plate record also includes plate and vehicle images, and possibly a short video clip of the vehicle. Each plate record further includes a plurality of image signatures/features of the license plate and/or vehicle”).
Regarding claim(s) 7 and 17, Khan as modified by Nerayoff teaches the traffic monitoring system of claim 1, wherein the primary data is only the vehicle license plate number (where Khan teaches in Paragraph [0014]: “the plate records stored in the database consist of one or a plurality of textual data items including plate number, capture time, capture date, camera/system name, state/province, felony […] The plate record also includes plate and vehicle images, and possibly a short video clip of the vehicle. Each plate record further includes a plurality of image signatures/features of the license plate and/or vehicle”; and where Nerayoff teaches in Paragraph [0034]: “the amount of data sent from identification camera 120 to server system 140 may be significantly reduced, as a vehicle identifier can be sent in place of identification images”; and Paragraph [0028]: “such cameras are configured to respond to requests for such additional image data, including, […] images which may be useful for license plate recognition. By this mechanism, server system 140 may automatically determine that additional images may be useful for identifying and tracking vehicles […]”).
Regarding claim(s) 8 and 18, Khan as modified by Nerayoff teaches the traffic monitoring system of claim 1, where Khan teaches wherein the primary data consists of the vehicle license plate number and at least one of:
a timestamp (read as “capture time/date”) of the captured image, an image of the license plate, and a limited image of the vehicle (Paragraph [0014]: “the plate records stored in the database consist of one or a plurality of textual data items including plate number, capture time, capture date, camera/system name, state/province, felony […] The plate record also includes plate and vehicle images, and possibly a short video clip of the vehicle. Each plate record further includes a plurality of image signatures/features of the license plate and/or vehicle”).
Regarding claim(s) 9 and 19, Khan as modified by Nerayoff teaches the traffic monitoring system of claim 1, where Khan teaches wherein the server system is further configured to compare the primary data to a stored hit list identifying one or more vehicles-of-interest (Paragraph [0014]: “plate records stored in the database consist of one or a plurality of textual data items including plate number, capture time, capture date, camera/system name, state/province, felony (in the case where the captured license plate matches with a number in a hot license plate list of a law enforcement agency)”), and where Nerayoff teaches wherein the request is transmitted in response to the comparison indicating that the vehicle is one of the vehicles-of-interest (Paragraph [0034]: the amount of data sent from identification camera 120 to server system 140 may be significantly reduced, as a vehicle identifier can be sent in place of identification images. However, one or more identification images may still be transmitted to server system 140 for storage and/or determination of vehicle characteristics by server system 140”; and Paragraph [0028]: “one or more of the identification and destination cameras capture images at a higher frame rate and/or resolution than the images transmitted from the cameras to server system 140 […] Additionally, such cameras are configured to respond to requests for such additional image data, including, […] images which may be useful for license plate recognition. By this mechanism, server system 140 may automatically determine that additional images may be useful for identifying and tracking vehicles […]”).
Regarding claim(s) 10 and 20, Khan as modified by Nerayoff teaches the traffic monitoring system of claim 1, where Nerayoff teaches wherein the server system is further configured to ad hoc transmit the request to the traffic sensor (Paragraph [0034]: the amount of data sent from identification camera 120 to server system 140 may be significantly reduced, as a vehicle identifier can be sent in place of identification images. However, one or more identification images may still be transmitted to server system 140 for storage and/or determination of vehicle characteristics by server system 140”; and Paragraph [0028]: “one or more of the identification and destination cameras capture images at a higher frame rate and/or resolution than the images transmitted from the cameras to server system 140 […] Additionally, such cameras are configured to respond to requests for such additional image data, including, […] images which may be useful for license plate recognition. By this mechanism, server system 140 may automatically determine that additional images may be useful for identifying and tracking vehicles […]”).
Relevant Prior Art Directed to State of Art
Schmer (US 2021/0097300 A1) are relevant prior art not applied in the rejection(s) above. He discloses a computer program product for determining a likelihood that an automatic license plate recognition system has correctly identified license plate data that is associated with a vehicle, the computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to: receive, from a digital camera system, one or more images associated with a vehicle, the one or more images including a digital representation of at least one of a license plate identifier (ID) associated the vehicle, and a digital representation of the vehicle; send the one or more received images to an automatic license plate recognition (ALPR) system; receive a license plate ID from the ALPR system; obtain, from a first database, a vehicle identification number associated with the license plate ID that is received from the ALPR system; obtain, from a second database, at least one of vehicle make and model information associated with the obtained vehicle identification number; apply machine learning and prediction application to the one or more received images that are associated with the vehicle; predict, based on the applied machine learning and prediction application, at least one of make and model information of the vehicle that is represented in the one or more received images; compare the make and model information that is obtained from the second database to the make and model information that is predicted based on the applied machine learning and prediction application; and compute a comparison score based on the comparison between the make and model information that is obtained from the second database and the predicted make and model information, the comparison score indicting a likelihood that the license plate ID obtained from the ALPR system is the same the license plate ID that is represented in the one or more received images.
Wu et al (U.S. 2013/0088600 A1) are relevant prior art not applied in the rejection(s) above. Wu discloses a video-based analysis system comprising: an image capturing device that captures video stream data having video data frames at a first high resolution; a vehicle detection module that detects at least one vehicle within the video data frames; a vehicle analysis module that is configured to analyze the video data frames containing the detected vehicle and to extract or tag one or more key vehicle features from the video data frames to enable identification of a vehicle of interest (VOI) according to a set of predetermined criteria; and a subsampling module that creates a reduced resolution video stream in a second subsampled resolution that is lower than the first high resolution while maintaining the one or more extracted key features within the reduced resolution video stream in the first high resolution, and archives the reduced resolution video stream into a video database. Furthermore, Wu discloses the key vehicle features include one or more of vehicle trajectory, measured vehicle speed, vehicle color, vehicle make, vehicle model, vehicle type, license plate, observable defects, geometric measures, and facial data of driver and/or passengers, and are extracted or tagged by the vehicle analysis module based on portions of data frames within the first high resolution video stream data having the vehicle, the vehicle having been identified as a vehicle of interest according to the predetermined criteria, and vehicle analytic data provided by the vehicle detection module located communicatively upstream.
Conclusion
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/JONGBONG NAH/Examiner, Art Unit 2674
/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674
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