Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. The United States Patent & Trademark Office appreciates the application that is by the
inventor/assignee. The United States Patent & Trademark Office reviewed the following
application and has made the following comments below.
Information Disclosure Statement
3. The information disclosure statement (IDS) submitted on 4/8/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Objections
4. The abstract of the disclosure is objected to because it is 197 words, exceeding the word limit of 150 words. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Rejections - 35 USC § 103
5. 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.
6. Claims 1-9 and 12-20 are rejected under 35 U.S.C. 103 as being unpatentable over Arnold (US Patent Pub No. 9589176 B1, hereafter referred to as Arnold) in view of Duan et al. (US Patent Pub No. 2006/0013495 A1, hereafter referred to as Duan).
7. Regarding Claim 1, Arnold teaches a system comprising: a first non-transitory memory (col 21 lines 23-30, Arnold teaches a tangible non-transitory computer storage media as a computer-readable media within the configuration of the portable electronic device of a system that analyses integral images with respect to HAAR features.), one or more first hardware processors coupled to the first non-transitory memory and
configured to read first instructions from the first non-transitory memory to cause the one or more first hardware processors to: receive first image data in a raw form from an image data source (col 2 lines 56-60, Arnold teaches a network-on-chip, NoC that includes an image node that received images from four corner cameras, where the data coming directly from an image node within a NoC system is raw image data. The Examiner interprets the image data received by the NoC as raw image data received by hardware processors.), and a central computing system configured to identify one or more elements of interest in the compressed first image data (Fig. 15, col 19 lines 18-26 and col 21 lines 5-19, Arnold teaches the use of a central processing unit within a system for identifying objects within an image using image data such as face position with respect to HAAR features.).
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Arnold does not teach transform the first image data into first domain-transformed image data based on a first domain transform, detect one or more potential regions of interest (ROIs) in the first image data based on the first domain-transformed image data, compress the first image data in each of the one or more potential ROIs at a first compression level, and refrain from compressing, at the first compression level, a remainder portion of the first image data that lies outside the one or more potential ROIs.
Duan is in the same field of art of processing and analyzing image data for recognition of ROIs. Further, Duan teaches transform the first image data into first domain-transformed image data based on a first domain transform (Fig. 2, paragraph 36, Duan teaches the application of a Discrete Cosine Transform, DCT, to each 8x8 block of pixels, the image data, where the blocks are transformed into new transformed image data, frequency domain coefficients, a new feature space. The Examiner interprets this transformation as the first image data transformed into first domain-transformed image data based on a first domain transform, where the first domain transform is the DCT.), detect one or more potential regions of interest (ROIs) in the first image data based on the first domain-transformed image data (Fig. 3-6, paragraphs 17, 38-39, Duan teaches detecting regions of movement and foreground regions using transformed DCT coefficients in the form of motion analysis and foreground-background separation. The Examiner interprets regions of movement and foreground regions as potential regions of interest.), compress the first image data in each of the one or more potential ROIs at a first compression level (paragraphs 17, 19, 21, 35, 37, and 39, Duan teaches applying segmentation and morphological techniques such as compression in the form of a compression unit at various levels in the detection of a regions of movements/foreground regions, where if found, blocks of the detected regions are extracted at different bit rates using a traffic detection unit responsible for setting conversion rates and a quantization step for varying the amount of compression.), and refrain from compressing, at the first compression level, a remainder portion of the first image data that lies outside the one or more potential ROIs (paragraphs 35 and 39, Duan teaches that only the data or blocks of the detected regions of movement/foreground regions with motion, known as the regions of interest, are extracted for the transformations and compressions. The Examiner interprets the fact that the data that lies outside of these regions are being ignored as refraining from compressing a remainder portion that lies outside the one or more potential ROIs.).
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Therefore it would have been obvious to one having ordinary skill in the art before the
effective filing date of the claimed invention to modify the invention of Arnold by adding
domain transformations such DCT to analog signals for compression and communication that is taught by Duan to enable higher efficient and lower latent ROI identification without comprehension image decoding; thus one of ordinary skill in the art would be motivated to combine the references since they are both in the same field of art of processing and analyzing image data for recognition of ROIs (Fig. 2-6 and paragraphs 17, 19, 21, 35-39, Duan).
Thus, the claimed subject matter would have been obvious to a person having ordinary
skill in the art before the effective filing date.
8. In regards to Claim 2, Arnold in view of Duan teaches wherein the compressing of the first image data in each of the one or more potential ROIs comprises: transforming at least a portion of the first image data in the one or more potential ROIs into second domain-transformed image data based on a second domain transform associated with the first compression level (paragraph 7, 17, 36-37, and 39, Duan teaches the transformation of a first JPEG image data to a frequency domain coefficients via a second domain, the frequency domain by applying DCT to each 8x8 block pixels, where the quantization step is used to vary the amount of compression from the first compression levels to identify and extract distinct regions such as moving regions to form a foreground frame.).
9. In regards to Claim 3, Arnold in view of Duan teaches wherein the transforming of at least the portion of the first image data in the one or more potential ROIs into the second domain-transformed image data comprises: selecting the second domain transform from a plurality of domain transforms based on content of the first image data (Fig. 2-3 and paragraphs 36-39, Duan teaches a plurality of domain transforms being performed to identify and transform potential ROIs from the first JPEG image data to a different domain based on the content of the first image data such as DCT, a vector domain transform known as Zig-Zag coding, and transformation to the bitstream domain in the form of Huffman Coding.).
10. In regards to Claim 4, Arnold in view of Duan teaches wherein the refraining of the compressing of the remainder portion of the first image data comprises: discarding the remainder portion of the first image data (paragraph 35, Duan teaches only the data of the foreground of where moving regions would be present being saved into the data storage unit and the remainder portion of the first image data not being saved. The Examiner interprets this as discarding the remainder portion of the first image data.).
11. In regards to Claim 5, Arnold in view of Duan teaches wherein the refraining of the compressing of the remainder portion of the first image data comprises: compressing the remainder portion of the first image data at a second compression level different than the first compression level (paragraph 35, Duan teaches the remainder portion of the first image data, which is the background, being handled by adjusting frequency of the transmission frame rate and bit rate, both based on network traffic, where changing the bit rate directly changes the level of compression.).
12. In regards to Claim 6, Arnold in view of Duan teaches wherein the compressing of the remainder portion of the first image data at the second compression level comprises:
transforming the remainder portion of the first image data into third domain-transformed image data based on a third domain transform associated with the second compression level (Fig. 2-3 and paragraphs 36-39, Duan teaches a plurality of domain transforms being performed to identify and transform potential ROIs from the first JPEG image data to a different domain based on the content of the first image data such as DCT, a vector domain transform known as Zig-Zag coding, and transformation to the bitstream domain in the form of Huffman Coding.).
13. In regards to Claim 7, Arnold in view of Duan teaches wherein the compressing of the first image data in each of the one or more potential ROIs comprises: transforming a first portion of the first image data in a first potential ROI of the one or more potential ROIs into second domain-transformed image data based on a second domain transform associated with the first compression level (col 4 lines 24-26, col 6 lines 16-42, and col 13 lines 33-38, Arnold teaches the identification of ROIs using a camera and various domain transformations in the form of HAAR features, where five types are used to transform image data from one domain with first compression levels to a second and subsequent domains.); and transforming a second portion of the first image data in a second potential ROI of the one or more potential ROIs into third domain-transformed image data based on a third domain transform associated with the first compression level (col 4 lines 24-26, col 6 lines 16-42, and col 13 lines 33-38, Arnold teaches the identification of ROIs using a camera and various domain transformations in the form of HAAR features, where five types are used for to transform image data from one domain with first compression levels to a second domain, third, fourth, and fifth domain.).
14. In regards to Claim 8, Arnold in view of Duan teaches wherein the first image data represents a first image in a stream of images, and execution of the first instructions further causes the one or more first hardware processors to: receive second image data representing a second image that follows the first image in the stream of images (col 3 lines 14-19 and col 5 lines 14-20, Arnold teaches the use of a second image and its data searched by the image search node, which was done with the first image data, after recording the image data.); transform the second image data into second domain-transformed image data based on a second domain transform (Fig. 12 and 13, col 5 lines 51-63, col 7 lines 1-24 and col 7 lines 39-47, Arnold teaches the transform of the second image data through the generation of an integral image of the image and HAAR features, specifically features type IV and V, which performs domain transformation.); and compress the second image data based on the second domain-transformed image data (Fig. 7A, col 9 lines 10-18, and col 9 lines 52-59, Arnold teaches the HAAR feature transformations being associated with internal compression levels, where compression modules such as 702a, 702b, and 702c, are within the elevation circuit that processes the transformed data, including data of the second image at specific ratios from a 4:1 to a 4:2.).
15. In regards to Claim 9, Arnold in view of Duan teaches wherein the transforming of the second image data into the second domain-transformed image data comprises: selecting the second domain transform from a plurality of domain transforms based on content of the first image data (Fig. 2 and 3, paragraphs 36-39, Duan teaches a plurality of domain transforms being performed to identify and transform potential ROIs from the first JPEG image data to a different domain based on the content of the first image data such as DCT, a vector domain transform known as Zig-Zag coding, and transformation to the bitstream domain in the form of Huffman Coding.).
16. Regarding Claim 12, Arnold teaches one or more first hardware processors coupled to the first non-transitory memory and configured to read first instructions from the first non-transitory memory to cause the one or more first hardware processors to: receive first image data in a raw form from an image data source (col 2 lines 56-60, Arnold teaches a network-on-chip, NoC that includes an image node that received images from four corner cameras, where the data coming directly from an image node within a NoC system is raw image data. The Examiner interprets the image data received by the NoC as raw image data received by hardware processors.) and a central computing system configured to identify one or more elements of interest in the compressed first image data (Fig. 15, col 19 lines 18-26 and col 21 lines 5-19, Arnold teaches the use of a central processing unit within a system for identifying objects within an image using image data such as face position with respect to HAAR features.).
Arnold does not teach transform the first image data into first domain-transformed image data based on a first domain transform, detect one or more potential regions of interest (ROIs) in the first image data based on the first domain-transformed image data, compress the first image data in each of the one or more potential ROIs at a first compression level, and refrain from compressing, at the first compression level, a remainder portion of the first image data that lies outside the one or more potential ROIs.
Duan is in the same field of art of processing and analyzing image data for recognition of ROIs. Further, Duan teaches transform the first image data into first domain-transformed image data based on a first domain transform (Fig. 2, paragraph 36, Duan teaches the application of a Discrete Cosine Transform, DCT, to each 8x8 block of pixels, the image data, where the blocks are transformed into new transformed image data, frequency domain coefficients, a new feature space. The Examiner interprets this transformation as the first image data transformed into first domain-transformed image data based on a first domain transform, where the first domain transform is the DCT.), detect one or more potential regions of interest (ROIs) in the first image data based on the first domain-transformed image data (Fig. 3-6, paragraphs 17, 38-39, Duan teaches detecting regions of movement and foreground regions using transformed DCT coefficients in the form of motion analysis and foreground-background separation. The Examiner interprets regions of movement and foreground regions as potential regions of interest.), compress the first image data in each of the one or more potential ROIs at a first compression level (paragraphs 17, 19, 21, 35, 37, and 39, Duan teaches applying segmentation and morphological techniques such as compression in the form of a compression unit at various levels in the detection of a regions of movements/foreground regions, where if found, blocks of the detected regions are extracted at different bit rates using a traffic detection unit responsible for setting conversion rates and a quantization step for varying the amount of compression.), and refrain from compressing, at the first compression level, a remainder portion of the first image data that lies outside the one or more potential ROIs (paragraphs 35 and 39, Duan teaches that only the data or blocks of the detected regions of movement/foreground regions with motion, known as the regions of interest, are extracted for the transformations and compressions. The Examiner interprets the fact that the data that lies outside of these regions are being ignored as refraining from compressing a remainder portion that lies outside the one or more potential ROIs.).
Therefore it would have been obvious to one having ordinary skill in the art before the
effective filing date of the claimed invention to modify the invention of Arnold by adding
domain transformations such DCT to analog signals for compression and communication that is taught by Duan to enable higher efficient and lower latent ROI identification without comprehension image decoding; thus one of ordinary skill in the art would be motivated to combine the references since they are both in the same field of art of processing and analyzing image data for recognition of ROIs (Fig. 2-6 and paragraphs 17, 19, 21, 35-39, Duan).
Thus, the claimed subject matter would have been obvious to a person having ordinary
skill in the art before the effective filing date.
17. In regards to Claim 13, Arnold in view of Duan teaches wherein the compressing of the first image data in each of the one or more potential ROIs comprises: transforming at least a portion of the first image data in the one or more potential ROIs into second domain-transformed image data based on a second domain transform associated with the first compression level (paragraph 7, 17, 36-37, and 39, Duan teaches the transformation of a first JPEG image data to a frequency domain coefficients via a second domain, the frequency domain by applying DCT to each 8x8 block pixels, where the quantization step is used to vary the amount of compression from the first compression levels to identify and extract distinct regions such as moving regions to form a foreground frame.).
18. In regards to Claim 14, Arnold in view of Duan teaches wherein the transforming of at least the portion of the first image data in the one or more potential ROIs into the second domain-transformed image data comprises: selecting the second domain transform from a plurality of domain transforms based on content of the first image data (Fig. 2-3 and paragraphs 36-39, Duan teaches a plurality of domain transforms being performed to identify and transform potential ROIs from the first JPEG image data to a different domain based on the content of the first image data such as DCT, a vector domain transform known as Zig-Zag coding, and transformation to the bitstream domain in the form of Huffman Coding.).
19. In regards to Claim 15, Arnold in view of Duan teaches wherein the refraining of the compressing of the remainder portion of the first image data comprises: discarding the remainder portion of the first image data (paragraph 35, Duan teaches only the data of the foreground of where moving regions would be present being saved into the data storage unit and the remainder portion of the first image data not being saved. The Examiner interprets this as discarding the remainder portion of the first image data.).
20. In regards to Claim 16, Arnold in view of Duan teaches wherein the refraining of the compressing of the remainder portion of the first image data comprises: compressing the remainder portion of the first image data at a second compression level different than the first compression level (paragraph 35, Duan teaches the remainder portion of the first image data, which is the background, being handled by adjusting frequency of the transmission frame rate and bit rate, both based on network traffic, where changing the bit rate directly changes the level of compression.).
21. In regards to Claim 17, Arnold in view of Duan teaches wherein the compressing of the remainder portion of the first image data at the second compression level comprises: transforming the remainder portion of the first image data into third domain-transformed image data based on a third domain transform associated with the second compression level (Fig. 2-3 and paragraphs 36-39, Duan teaches a plurality of domain transforms being performed to identify and transform potential ROIs from the first JPEG image data to a different domain based on the content of the first image data such as DCT, a vector domain transform known as Zig-Zag coding, and transformation to the bitstream domain in the form of Huffman Coding.).
22. In regards to Claim 18, Arnold in view of Duan teaches wherein the compressing of the first image data in the one or more potential ROIs comprises: transforming a first portion of the first image data in a first potential ROI of the one or more potential ROIs into second domain-transformed image data based on a second domain transform associated with the first compression level (col 4 lines 24-26, col 6 lines 16-42, and col 13 lines 33-38, Arnold teaches the identification of ROIs using a camera and various domain transformations in the form of HAAR features, where five types are used to transform image data from one domain with first compression levels to a second and subsequent domains.); and transforming a second portion of the first image data in a second potential ROI of the one or more potential ROIs into third domain-transformed image data based on a third domain transform associated with the first compression level (col 4 lines 24-26, col 6 lines 16-42, and col 13 lines 33-38, Arnold teaches the identification of ROIs using a camera and various domain transformations in the form of HAAR features, where five types are used for to transform image data from one domain with first compression levels to a second domain, third, fourth, and fifth domain.).
23. In regards to Claim 19, Arnold in view of Duan teaches wherein the first image data represents a first image in a stream of images, the method further comprising: receiving second image data representing a second image that follows the first image in the stream of images (col 3 lines 14-19 and col 5 lines 14-20, Arnold teaches the use of a second image and its data searched by the image search node, which was done with the first image data, after recording the image data.); transforming the second image data into second domain-transformed image data based on a second domain transform (Fig. 12 and 13, col 5 lines 51-63, col 7 lines 1-24 and col 7 lines 39-47, Arnold teaches the transform of the second image data through the generation of an integral image of the image and HAAR features, specifically features type IV and V, which performs domain transformation.); and compressing the second image data based on the second domain-transformed image data (Fig. 7A, col 9 lines 10-18, and col 9 lines 52-59, Arnold teaches the HAAR feature transformations being associated with internal compression levels, where compression modules such as 702a, 702b, and 702c, are within the elevation circuit that processes the transformed data, including data of the second image at specific ratios from a 4:1 to a 4:2.).
24. In regards to Claim 20, Arnold in view of Duan teaches wherein the transforming of the second image data into the second domain-transformed image data comprises:
selecting the second domain transform from a plurality of domain transforms based on content of the first image data (Fig. 2 and 3, paragraphs 36-39, Duan teaches a plurality of domain transforms being performed to identify and transform potential ROIs from the first JPEG image data to a different domain based on the content of the first image data such as DCT, a vector domain transform known as Zig-Zag coding, and transformation to the bitstream domain in the form of Huffman Coding.).
25. Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Arnold (US Patent Pub No. 9589176 B1, hereafter referred to as Arnold) in view of Duan et al. (US Patent Pub No. 2006/0013495 A1, hereafter referred to as Duan) in further view of Ahonen et al. (US Patent Pub No. 9202108 B2, hereafter referred to as Ahonen).
26. Regarding Claim 10, Arnold in view of Duan teaches the system of Claim 1 that transforms image data and detects potential ROIs.
Arnold in view of Duan does not teach wherein the central computing system comprises: a second non-transitory memory and one or more second hardware processors coupled to the second non-transitory memory and configured to read second instructions from the second non-transitory memory to cause the one or more second hardware processors to: receive at least the compressed first image data from the one or more first hardware processors, decompress at least the compressed first image data, analyze the one or more potential ROIs in the decompressed first image data to determine whether the one or more potential ROIs include the one or more elements of interest, and identify at least one of the one or more elements of interest.
Ahonen is in the same field of art of processing and analyzing image data for recognition of ROIs. Further, Ahonen teaches wherein the central computing system comprises: a second non-transitory memory (col 8 lines 21-29, Ahonen teaches a memory that comprises a non-transitory computer-readable storage medium, which may include a plurality of memories, including a second non-transitory memory, distributed across a plurality of computing devices configured to function as the image analysis apparatus.) and one or more second hardware processors coupled to the second non-transitory memory and configured to read second instructions from the second non-transitory memory to cause the one or more second hardware processors to: receive at least the compressed first image data from the one or more first hardware processors (col 6 lines 44-48, col 8 line 64-col 9 line 9, Ahonen teaches processor elements in a communication interface system that includes supporting hardware such as a camera, which is an image data source, for receiving image data that is to be read, transformed, compressed, and decompressed. The Examiner interprets the receiving of image data straight from supporting camera hardware as receiving compressed image data from one or more hardware processors.), decompress at least the compressed first image data (Fig. 2 and 3, col 10 lines 40-66, col 11 lines 34-56, Ahonen teaches a system for computing, compressing, analyzing, and decompressing elements such as face descriptors from compressed first image data for face image matching.), analyze the one or more potential ROIs in the decompressed first image data to determine whether the one or more potential ROIs include the one or more elements of interest (Fig. 4, col 6 lines 40-51, col 9 lines 52-60, col 12 lines 6-22. Ahonen teaches the analysis of a plurality of face descriptors, which are regions of interests within a face image as well as region-based processing, compressing, and decompressing to facilitate face image analysis.), and identify at least one of the one or more elements of interest (Fig. 4, col 10 lines 1-5, and col 1 lines 11-31, Ahonen teaches face descriptors as elements needed to complete stage by stage facial analysis for determining the region of a face image, which would require identifying at least one face descriptor. The examiner interprets face descriptors as elements of interests.).
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Therefore it would have been obvious to one having ordinary skill in the art before the
effective filing date of the claimed invention to modify the invention of Arnold by adding histogram-based face descriptor determination techniques that use a plurality of hardware processes that is taught by Ahonen to enable higher efficient and lower latent ROI identification without comprehension image decoding; thus one of ordinary skill in the art would be motivated to combine the references since they are both in the same field of art of processing and analyzing image data for recognition of ROIs (Fig. 2-4 and col 6 lines 40-51, col 8 line 64 – col 9 line 9, col 9 lines 52-60, col 10 lines 11-31 & 40-66, col 11 lines 34-56, col 12 lines 6-22, Ahonen).
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date.
27. In regards to Claim 11, Arnold in view of Duan in further view of Ahonen teaches wherein the one or more potential ROI are one or more potential facial ROIs that may include one or more faces (Fig. 14, col 15 lines 29-38, and col 20 lines 37-57, Arnold teaches the one or more ROIs within one or more images to confirm the presence of objects such as a primary face and additional faces within the frame.), the one or more elements of interest includes one or more faces (Fig. 14, col 20 lines 55-59, Arnold teaches additional features being extracted from the image and subsequently, the face after the identification of the primary face in the image. The Examiner interprets this as one or more elements of interests including one or more faces.), and wherein execution of the second instructions further causes the one or more second hardware processors to perform full facial recognition to identify the one or more faces of the one or more elements of interest (Fig. 1-2, col 3 lines 50-65, col 4 lines 11-18, col 11 lines 10-22, Arnold teaches one or more hardware processors that follow multiple sets of instructions to execute more than one sets of algorithms and processes for the compression, decompression, and analysis of regions of interests and objects within an image such as face positions.).
Conclusion
28. Any inquiry concerning this communication or earlier communications from the
examiner should be directed to LOUIS NWUHA whose telephone number is (571)272 -0219.
The examiner can normally be reached Monday to Friday 8 am to 5 pm.
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30. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s
supervisor, Oneal Mistry can be reached at 3134464912. The fax phone number for the
organization where this application or proceeding is assigned is 571-273-8300.
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/LOUIS NWUHA/Examiner, Art Unit 2674
/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674