DETAILED ACTION
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.
Claim(s) 1-4 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xiong (US PGPub 2022/0414382)
Regarding claim 1, Xiong teaches an information processing device that detects an object in an image, the information processing device comprising: (Xiong teaches a method for video surveillance object detection in which an initial object detection algorithm model is checked by a more computationally intensive object verification detection model.)
one or more processors; and (¶ 0024)
a memory storing one or more instructions, wherein the one or more instructions, when executed by the one or more processors, cause the information processing device to: (¶ 0024)
output a first detection result indicating a region where an object has been detected in a region in an image by using a first machine learning model that detects the object in the image; (See ¶ 0008 and 0049 teaching the field object detector as well as the machine learning architectures for the object detection at ¶ 0061.)
output a second detection result indicating a region where an object has been detected in a region in the image by using a second machine learning model having a different predetermined characteristic related to detection of the object from that of the first machine learning model; and (See verification object detector at Figs. 5 and 6 as well as ¶ 0060 and 0093 which teach that the verification object detector is a different heavier weight algorithm that is more computationally intensive.)
make a notification of presence of erroneous detection regarding detection of an object by the first machine learning model when a difference between the first detection result and the second detection result satisfies a predetermined condition. (¶ 0093 teaches a comparator to test the match of the bounding boxes between the two object detections and determine a false detection result. Also see Fig. 5, numeral 526 and ¶ 0124 for notification of presence of erroneous detection.)
Xiong does not expressly disclose that all of its above-cited teachings on object detection with a first and second model are expressly disclosed as occurring in the same embodiment. That is, despite the reference being clear that these functions are disclosed, there is no express disclosure that the details are all found in the same embodiment. Instead, the reference presents some of the individual detailed disclosures as ‘according to some embodiments.’ It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the various teachings to provide a single system capable of the variety of tasks which are disclosed. In view of these teachings, this cannot be considered a non-obvious improvement over the prior art. Using known engineering design, no “fundamental” operating principle of the teachings are changed; they continue to perform the same functions as originally taught prior to being combined.
Regarding claim 2, the above combination teaches the information processing device according to claim 1, wherein the second machine learning model has a robust predetermined characteristic related to detection of an object compared to the first machine learning model. (As above, see verification object detector at Figs. 5 and 6 as well as ¶ 0060 and 0093 which teach that the verification object detector is a different more robust heavier weight algorithm that is more computationally intensive.)
Regarding claim 3, the above combination teaches the information processing device according to claim 2, wherein the making a notification includes determining that the predetermined condition is satisfied in a case where an object in a region having a predetermined size or more is detected in the second detection result, in a region where no object is detected in the first detection result. (As above, see Fig. 5, numeral 526 and ¶ 0124 for notification of presence of erroneous detection due to a conflict between the field object detector and the verification object detector. That a test for predetermined size or more of an object is used is taught at ¶ 0086-0087.)
Regarding claim 4, the above combination teaches the information processing device according to claim 1, wherein the one or more instructions further cause the information processing device to acquire images of a plurality of consecutive frames, the first machine learning model outputs the first detection result to a first number of images per second among the images of the plurality of frames, and the second machine learning model outputs the second detection result to a second number of images per second that is smaller than the first number, among the images of the plurality of frames. (Fig. 5, numeral 510, a video stream of consecutive frames is input, all of which are processed by the field object detection. Fig. 6 and ¶ 130-0135 show selective verification sending frames segments only when accuracy is below a threshold confidence for example, leading to a lower fraction of the total video stream frames sent to verification (lower frames per second of the total stream frames.)
Regarding claim 7, the above combination teaches the information processing device according to claim 1, wherein the second machine learning model has a characteristic with which a specific type of an object is capable of being robustly detected compared to the first machine learning model. (As above, see verification object detector at Figs. 5 and 6 as well as ¶ 0060 and 0093 which teach that the verification object detector is a different more robust heavier weight algorithm that is more computationally intensive. See ¶ 0094 regarding specific face object types.)
Claim(s) 5-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xiong (US PGPub 2022/0414382) in view of Casado-Garcia (“Ensemble Methods for Object Detection”)
Regarding claim 5, the above combination teaches the information processing device according to claim 3, wherein the one or more instructions further cause the information processing device to output a detection result indicating a region in which an object is detected in a region in the image by using a machine learning model having a robust predetermined characteristic related to detection of the object compared to the first machine learning model, and the making a notification includes making a notification of presence of erroneous detection for detection of an object by the first machine learning model in a case where an object in a region of the predetermined size or more is detected in at least one of the second detection result. (See detailed analysis above.)
In the field of object detection Casado-Garcia teaches using a third detection result from a third machine learning model with a second predetermined characteristic and in a region where no object is detected in the first detection result. (Casado-Garcia teaches a technique for using multiple ensemble objectors in order to generate a more robust overall object detection. A few algorithmic strategies are disclosed involving three object detectors. Section 3.1 on pg. 2-3 teach the different strategies for multiple object detection models for ensemble detection such as the consensus/majority and the unanimous strategy. These algorithms meet the claim requirements. Table 1 teaches an example of applying 5 models and then takes top 3 and applying 3 best consensus/ unanimous models.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Xiong’s dual object detector strategy with Casado-Garcia’s triple object detector strategy. Xiong teaches a simple ensemble detection strategy with a primary detector and a verification detector. Casado-Garcia teaches a technique for using a triple ensemble object detector in order to generate a more robust overall object detection. Three object detectors are used. The combination constitutes the repeatable and predictable result of simply applying Casado-Garcia’s teaching here and cannot be considered a non-obvious improvement in view of the relevant prior art here. Using known engineering design, no “fundamental” operating principle of the teachings are changed; they continue to perform the same functions as originally taught prior to being combined.
Regarding claim 6, the above combination teaches the information processing device according to claim 5, wherein the making a notification includes making a notification of information regarding an object related to erroneous detection. (Xiong Fig. 5, numeral 526 and ¶ 0124 teaches notification of presence of erroneous detection.)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Raphael Schwartz whose telephone number is (571)270-3822. The examiner can normally be reached Monday to Friday 9am-5pm CT.
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, Vincent Rudolph can be reached at (571) 272-8243. 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.
/RAPHAEL SCHWARTZ/ Examiner, Art Unit 2671