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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/01/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of Claim(s)
Claim(s) 1, 3, and 5 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by He (US 20190188524 A1).
Claim(s) 2, 4, and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over He (US 20190188524 A1) in view of Wang (US 11308675 B2).
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 3, and 5 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by He (US 20190188524 A1).
Regarding Claim 1:
He teaches: A video processing device comprising (Paragraph 2, “Computer-implemented visual object classification, also called object recognition, pertains to classifying visual representations of real-life objects found in still images or motion videos captured by a camera.”):
a foreground extraction unit, including one or more processors (Paragraph 18, “a processor communicatively coupled to the image sensor; and a memory device communicatively coupled to the processor, wherein the memory device has stored thereon computer program code that is executable by the processor and that, when executed by the processor, causes the processor to perform a method. The method may comprise receiving at an artificial neural network a sample image comprising the object-of-interest overlaying a background”), configured to classify each pixel in an input image as foreground, background or unclassifiable (Paragraph 101, “If the motion vector for an unclassified pixel (i.e., a pixel that has not been classified as a background or foreground pixel) is below a background threshold, and ideally zero, the module 224 classifies that unclassified pixel as a background pixel and averages background pixels from different frames to maintain the background model 704.”);
an error rate evaluation unit, including one or more processors, configured to obtain an error rate for unclassifiable pixels based on previous classification results to calculate an evaluated value representing difficulty of classification (Paragraph 102, “More generally, in at least some example embodiments the module 224 may determine which pixels of a frame 700 comprise background pixels using any suitable method in which the false positive rate (i.e., the rate at which foreground pixels are misclassified as being in the background) and the false negative rate (i.e., the rate at which background pixels are misclassified as being in the foreground) are sufficiently small. In some example embodiments, so long as the false negative rate is low enough that during an averaging interval of N frames a background pixel representing a particular location in the background is correctly classified as a background pixel in at least one of those N frames, the module 224 is able to represent that location in the background model 704.”); and
an output unit configured, including one or more processors, to output a subject image extracting pixels classified as foreground from the input image and the evaluated value (Paragraph 85, “Based on the determinations made, the video analytics module 224 may further output metadata providing information about the determinations. Examples of determinations made by the video analytics module 224 may include one or more of foreground/background segmentation, object detection,”).
Regarding Claim 3:
He teaches: A video processing method executed by a computer, the video processing method comprising (Abstract, “Methods, systems, and techniques for classifying an object-of-interest using an artificial neural network, such as a convolutional neural network. An artificial neural network receives a sample image including the object-of-interest overlaying a background and a sample background image excluding the object-of-interest and corresponding to the background overlaid by the object-of-interest.”):
classifying each pixel in an input image as foreground, background or unclassifiable (Paragraph 101, “If the motion vector for an unclassified pixel (i.e., a pixel that has not been classified as a background or foreground pixel) is below a background threshold, and ideally zero, the module 224 classifies that unclassified pixel as a background pixel and averages background pixels from different frames to maintain the background model 704.”);
obtaining an error rate for unclassifiable pixels based on previous classification results to calculate an evaluated value representing difficulty of classification (Paragraph 102, “More generally, in at least some example embodiments the module 224 may determine which pixels of a frame 700 comprise background pixels using any suitable method in which the false positive rate (i.e., the rate at which foreground pixels are misclassified as being in the background) and the false negative rate (i.e., the rate at which background pixels are misclassified as being in the foreground) are sufficiently small. In some example embodiments, so long as the false negative rate is low enough that during an averaging interval of N frames a background pixel representing a particular location in the background is correctly classified as a background pixel in at least one of those N frames, the module 224 is able to represent that location in the background model 704.”); and
outputting a subject image extracting pixels classified as foreground from the input image and the evaluated value (Paragraph 85, “Based on the determinations made, the video analytics module 224 may further output metadata providing information about the determinations. Examples of determinations made by the video analytics module 224 may include one or more of foreground/background segmentation, object detection,”).
Regarding Claim 5:
He teaches: A non-transitory computer-readable storage medium storing a program for causing a computer to perform operations comprising (Paragraph 40, “According to another aspect, there is provided a non-transitory computer readable medium having stored thereon computer program code that is executable by a processor and that, when executed by the processor, causes the processor to perform a method according to any of the foregoing aspects and suitable combinations thereof.”):
classifying each pixel in an input image as foreground, background or unclassifiable (Paragraph 101, “If the motion vector for an unclassified pixel (i.e., a pixel that has not been classified as a background or foreground pixel) is below a background threshold, and ideally zero, the module 224 classifies that unclassified pixel as a background pixel and averages background pixels from different frames to maintain the background model 704.”);
obtaining an error rate for unclassifiable pixels based on previous classification results to calculate an evaluated value representing difficulty of classification (Paragraph 102, “More generally, in at least some example embodiments the module 224 may determine which pixels of a frame 700 comprise background pixels using any suitable method in which the false positive rate (i.e., the rate at which foreground pixels are misclassified as being in the background) and the false negative rate (i.e., the rate at which background pixels are misclassified as being in the foreground) are sufficiently small. In some example embodiments, so long as the false negative rate is low enough that during an averaging interval of N frames a background pixel representing a particular location in the background is correctly classified as a background pixel in at least one of those N frames, the module 224 is able to represent that location in the background model 704.”); and
outputting a subject image extracting pixels classified as foreground from the input image and the evaluated value (Paragraph 85, “Based on the determinations made, the video analytics module 224 may further output metadata providing information about the determinations. Examples of determinations made by the video analytics module 224 may include one or more of foreground/background segmentation, object detection,”).
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) 2, 4, and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over He (US 20190188524 A1) in view of Wang (US 11308675 B2).
Regarding Claim 2:
He teaches the limitations of claim 1 as applied above.
He does not explicitly teach the following; however, in related art, Wang teaches:
a processing unit, including one or more processors, configured to arrange an effect to be superimposed on the subject image in accordance with the evaluated value (Wang, Col 19. Lines 58 – 66, Processing unit is used to generate effects to be implemented onto foreground/background subject of image),
wherein the output unit is configured to output an output image in which the effect is superimposed on the subject image (Wang, Col 9. Lines 6-24, Outputting different visual effects on a facial model or background model such as filmic effects, makeup effects, or VR effects for a digital avatar).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Wang’s special effect addition to subject of interest with He’s system for identifying foreground, background, and unclassified pixels which are then paired and overlaid within different foregrounds or backgrounds of interest.
Regarding Claim 4:
He teaches the limitations of claim 3 as applied above.
He does not explicitly teach the following; however, in related art, Wang teaches:
arranging an effect to be superimposed on the subject image in accordance with the evaluated value (Wang, Col 19. Lines 58 – 66, Processing unit is used to generate effects to be implemented onto foreground/background subject of image); and
outputting an output image in which the effect is superimposed on the subject image (Wang, Col 9. Lines 6-24, Outputting different visual effects on a facial model or background model such as filmic effects, makeup effects, or VR effects for a digital avatar).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Wang’s special effect addition to subject of interest with He’s system for identifying foreground, background, and unclassified pixels which are then paired and overlaid within different foregrounds or backgrounds of interest.
Regarding Claim 6:
He teaches the limitations of claim 3 as applied above.
He does not explicitly teach the following; however, in related art, Wang teaches:
arranging an effect to be superimposed on the subject image in accordance with the evaluated value (Wang, Col 19. Lines 58 – 66, Processing unit is used to generate effects to be implemented onto foreground/background subject of image); and
outputting an output image in which the effect is superimposed on the subject image (Wang, Col 9. Lines 6-24, Outputting different visual effects on a facial model or background model such as filmic effects, makeup effects, or VR effects for a digital avatar).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Wang’s special effect addition to subject of interest with He’s system for identifying foreground, background, and unclassified pixels which are then paired and overlaid within different foregrounds or backgrounds of interest.
Relevant Prior Art Directed to State of Art
Lum (US 11558209 B1)
Lum (US 20230036861 A1)
Price (US 20220237799 A1)
Paik (US 20210216822 A1)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DYLAN J SHERRILLO whose telephone number is (703)756-5605. The examiner can normally be reached 1st Week of Bi-week Monday - Thursday 10am - 7:30pm EST, 2nd Week of Bi-week Monday-Thursday 10am - 7:30pm EST Friday 10am-6:30pm EST.
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, Stephen R Koziol can be reached at (408) 918-7630. 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.
/D.J.S./Examiner, Art Unit 2665
/BOBBAK SAFAIPOUR/Primary Examiner, Art Unit 2665