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 .
Election/Restrictions
Applicant’s election without traverse of Species 1, Claims 1-10, in the reply filed on 05/11/2026 is acknowledged.
Claim Objections
Claim 4 line 2 is objected to because of the following informalities: the claim recites “the neural network in reference to “the same neural network” of claim 1. Appropriate correction is required.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-10 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 line 4 recites “different ones of the texture”. It is unclear from the term how “different one” is differentiated from “a group of plural different textures” thus leading the claim to be indefinite.
Claim 6 line 4 “different ones of the texture”. It is unclear from the term how “different one” is differentiated from “a group of plural different textures” thus leading the claim to be indefinite.
Claims 2-5 and 7-10 are rejected based on the dependency of the base claim.
Claim Rejections - 35 USC § 102
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)(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 1 is rejected under 35 U.S.C. 102(a)(2) as being anticipated by Vaidyanathan US20240257405 (hereinafter “V1”).
Regarding claim 1, V1 teaches a method of compressing graphics texture data, the method comprising (see paragraph 0022-0024, a method for compression of a texture set, where texture refers to data representation of an object surface and may be represented as an image):
selecting a group of plural different textures that are to be compressed using a same neural network (see paragraphs 0025 and 0043, compression of a plurality of textures in a set of textures, where each texture in the textures set may represent a different property of a particular material. The textures are set or combined to represent a specific material [the combining of different textures to create set representing a material is interpreted as selecting a group of plural different textures]. See paragraph 0107, a neural network may be used for texture compression); and
using the same neural network to compress multiple, different ones of the textures in the selected group of plural different textures into a first, compressed format (see paragraph 0027 and 0029, the textures in the texture set [the texture set includes plural textures with different properties, 0025] may be compressed together using non-linear function and quantization, where the non-linear function is a neural network, into a compressed representation [the compressed representation of the texture set is interpreted as a first compressed format as the compressed representation is a singular representation and thus the same for all textures of the texture set]).
Claim 6, 8, and 10 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Vaidyanathan US20240378759 (hereinafter “V3”) which incorporates V1 by reference.
Regarding claim 6, V3 teaches a method of decompressing graphics texture data, the method comprising (see paragraphs 0019-0021, a method of decompressions of texture representation of a set of textures which may represent an image):
for a group of plural different textures stored in a first, compressed format, where the different textures in the group of plural different textures are selected such that a same neural network can be used to decompress different ones of the textures in the group of plural different textures (see paragraph 0022 and 0024, the texture representation may be a compressed representation [first, compressed format] of a plurality of textures in a set of textures, the textures in the texture set may be combined to represent a specific material [the combining of textures is interpreted as selecting a group of textures]. V1 teaches in paragraph 0056 the decoder for decompression is modeled as a non-linear transform utilizing a MLP [neural network]):
using the same neural network to decompress multiple, different ones of the textures in the group of plural different textures (see paragraph 0028, at least a portion of the plurality of textures in the set of textures are decompressed. V1 teaches in paragraph 0056 the decoder for decompression is modeled as a non-linear transform utilizing a MLP [neural network]).
Regarding claim 8, V3 and V1 teaches the method of claim 6, wherein the first, compressed format stores multiple levels of detail of graphics texture data (see paragraph 0040-0042, the compressed representation may be a pyramid with a plurality of feature levels (with a plurality of grids) that represent a plurality of mip levels of the original texture set at different resolution levels. The inclusion of multiple mip levels within the compressed representation is interpreted as inclusion of multiple levels of detail in the compressed representation as each mip level is a different level of detail of the texture so the use of multiple mip levels is multiple levels of detail), and wherein the neural network when decompressing one of the textures in the group of plural different textures is operable to output the texture data at a requested level of detail (see paragraph 0043, the grids used to represent the plurality of mip levels can be used during the decompression meth to unpack a portion of a texture. V1 teaches selected the feature level based on the desired level of detail during the first stage of decompression, 0059).
Regarding claim 10, V3 and V1 teach the method of claim 6, wherein the decompression is performed local to and on chip with a graphics processor in response to the graphics processor requesting graphics texture data (see paragraph 0036, on-demand (at request) decompression of the portion of texture can occur on the GPU. see paragraph 0069, any portion of the data storage [which the neural network may be stored, 0092] may be included with on-chip storage [the inclusion of being on-chip is interpreted as additionally be local to chip). V1 teaches in paragraph 0056 the decoder for decompression is modeled as a non-linear transform utilizing a MLP [neural network]).
Claims 6 and 9 is rejected under 35 U.S.C. 102(a)(2) as being anticipated by Vaidyanathan US20240378792 (hereinafter “V4”) which incorporates V1 by reference.
Regarding claim 6, V4 teaches a method of decompressing graphics texture data, the method comprising (see paragraphs 0002, texture decompression for computer graphics):
for a group of plural different textures stored in a first, compressed format, where the different textures in the group of plural different textures are selected such that a same neural network can be used to decompress different ones of the textures in the group of plural different textures (see paragraph 0022 and 0024, the texture representation may be a compressed representation [first, compressed format] of a plurality of textures in a set of textures, the textures in the texture set may be combined to represent a specific material [the combining of textures is interpreted as selecting a group of textures]. V1 teaches in paragraph 0056 the decoder for decompression is modeled as a non-linear transform utilizing a MLP [neural network]),
using the same neural network to decompress multiple, different ones of the textures in the group of plural different textures (see paragraph 0045, the decompression of the plurality of textures [0020 and 0022, the plurality of textures is a set of textures, where each texture may represent different properties] V1 teaches in paragraph 0056 the decoder for decompression is modeled as a non-linear transform utilizing a MLP [neural network]).
Regarding claim 9, V4 and V1 teaches the method of claim 6, wherein the first, compressed format stores graphics texture data having a number of channels of texture data (see paragraph 0023-0024, at least one of the textures in the texture set may include a plurality of channels. The compressed representation is of the plurality of textures in the set of textures that at least one texture includes a plurality of channels), and wherein when a request is made for a greater number of channels of texture data than are stored for the first, compressed format, the or a neural network is operable and configured to generate any additional channels of texture data that have been requested (see paragraph 0049, outputting 3N instead of N from the decoder, where N represent the number of channels and the use of a multiplier “3” increases the number of channels from the compressed format to the decompressed. This complete while transcoding from the texture representation to the texture set as part of the requested process [0051]).
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.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over V1 in view of Chen et al “High-quality texture compression using adaptive color grouping and selection algorithm” (hereinafter “Chen”).
Regarding claim 2, V1 teaches the method of claim 1, wherein the group of plural different textures that are to be compressed using the same neural network is selected (see paragraphs 0025 and 0043, compression of a plurality of textures in a set of textures, where each texture in the textures set may represent a different property of a particular material. The textures are set or combined to represent a specific material [the combining of different textures to create set representing a material is interpreted as selecting a group of plural different textures]. See paragraph 0107, a neural network may be used for texture compression).
V1 does not teach selected such that a desired compression quality threshold and/or level of compression is met for each of the different textures within the group.
Chen teaches select such that a desired compression quality threshold and/or level of compression is met for each of the different textures within the group (see section 3, setting group boundaries using a threshold to ensure proper compression quality)
Chen and Vaidyanathan are analogous art because they are from the same field of endeavor of texture compression with groups of textures based on similarities.
Before the effective filling date of the invention, it would have been obvious to one of ordinary skill in the art to modify V1 select a group of texture based on compression quality as taught by Chen. The motivation for doing so would have been to achieve high compression and to maintain texture details (Chen, section 1).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over V1 in view of Liu et al “Dep convolutional neural networks for regular texture recognition” (hereinafter “Liu”).
Regarding claim 3, V1 teaches the method of claim 1, wherein selecting the group of plural different textures that are to be compressed using the same neural network is performed (see paragraphs 0025 and 0043, compression of a plurality of textures in a set of textures, where each texture in the textures set may represent a different property of a particular material. The textures are set or combined to represent a specific material [the combining of different textures to create set representing a material is interpreted as selecting a group of plural different textures]. See paragraph 0107, a neural network may be used for texture compression).
V1 does not teach executing another neural network that is configured to identify groups of plural different textures.
Liu teaches executing another neural network that is configured to identify groups of plural different textures (see abstract and introduction, classifying textures into classes [groups] using convolutional neural networks. Identifying similar repeating elements and grouping the elements together).
V2 and Liu are analogous art because they are from the same field of endeavor of processing textures using neural networks for the use of computer graphics with the with the setting of groups of textures.
Before the effective filling date of the invention, it would have been obvious to one of ordinary skill in the art to modify V1 to use a neural network to select groups of textures as taught by Liu. The motivation for doing so would have been to recognize texture regularity and further on be used in computer graphics for texture processes (Liu, Introduction).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over V1 in view of Vaidyanathan et al. “Random-Access Neural Compression of Material Textures” (included in IDS, additional copy attached to included Appendix)(hereinafter “V2”).
Regarding claim 4, V1 teaches the method of claim 1, (The input texture set will have different level of details as it includes multiple textures which have individual corresponding levels of detail. To show obviousness we are relying on V2 for this rejection) the neural network when compressing a texture in the selected group of plural different textures is operable and configured to compress multiple (see paragraphs 0025 and 0043, compression of a plurality of textures in a set of textures, where each texture in the textures set may represent a different property of a particular material. The textures are set or combined to represent a specific material [the combining of different textures to create set representing a material is interpreted as selecting a group of plural different textures]. See paragraph 0107, a neural network may be used for texture compression), the first, compressed format thus storing multiple different graphics textures at multiple levels of detail (see paragraph 0045-0047, the compressed representation may be a pyramid with a plurality of feature levels (with a plurality of grids) that represent a plurality of mip levels of the original texture set at different resolution levels. The inclusion of multiple mip levels within the compressed representation is interpreted as inclusion of multiple levels of detail in the compressed representation as each mip level is a different level of detail of the texture so the use of multiple mip levels is multiple levels of detail).
V1 does not teaches teach the graphics texture data is provided at multiple different levels of detail, and compress multiple different levels of detail of the texture.
V2 teaches the graphics texture data is provided at multiple different levels of detail, and compress multiple different levels of detail of the texture (see abstract, the approach includes compressing multiple material textures and their mipmap chains [interpreted as multiple levels of details] using a neural network).
V2 and V1 are analogous art because they are from the same field of endeavor of texture compression of a set of textures with plural different textures using a neural network.
Before the effective filling date of the invention, it would have been obvious to one of ordinary skill in the art to modify V1 use graphics texture data at multiple levels of detail as taught by V2. The motivation for doing so would have been to providing a highly compressed representation of multiple mips at different resolutions (V2, section 4.1).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over V1 in view of Floral US10642343 (hereinafter “Floral”).
Regarding claim 5, V1 teaches the method of claim 1, wherein the graphics texture data comprises plural channels of texture data, (see paragraph 0038-0039, the textures include an arbitrary number of channels and ordering of channels [at least one texture in the texture set may include a plurality of channels, 0026]. The non-linear function may compress textures with an arbitrary number of channels into compressed representation of the texture set [0041, 0029]. Thus, as the texture set with textures with a plurality of channels are compressed into a compressed representation thus compressed representation stores the channels of the textures).
V1 does not teach the plural channels including a set of colour and optionally transparency channels, and one or more additional channels.
Floral teaches the plural channels including a set of colour and optionally transparency channels, and one or more additional channels (see col 4 lines 11-20, the texture data elements each represent a texture element which can comprise a set of color values, transparency values, or luminance and chrominance values. The component values represent the value of the channel thus, specifying the data elements include channels and the type of channels mirror the values of those channels).
Floral and V1 are analogous art because they are from the same field of endeavor of compressing graphics texture data with a plurality of channels to later be used for rendering an image.
Before the effective filling date of the invention, it would have been obvious to one of ordinary skill in the art to modify V1 use a set of colour and optionally transparency channels, and one or more additional channels as taught by Floral. The motivation for doing so would have been to represent the colour values, transparency values, and luminance and chrominance values for a texture element and upon decompression generate a corresponding texture (Floral, col 4 lines 11-20).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over V3 with V1 in view of Fukuhara US6373989 (hereinafter “Fukuhara”).
Regarding claim 7, V3 and V1 teaches the method of claim 6.
V3 teaches the first, compressed format stores graphics texture data with a first aspect ratio, and wherein the neural network when decompressing one of the textures in the group of plural different textures is operable to output the texture data (see paragraph 0022 and 0024, the texture representation may be a compressed representation of a plurality of textures in a set of textures, the textures in the texture set may be combined to represent a specific material [the combining of textures is interpreted as selecting a group of textures]. See paragraph 0028, at least a portion of the plurality of textures in the set of textures are decompressed. V1 teaches in paragraph 0056 the decoder for decompression is modeled as a non-linear transform utilizing a MLP [neural network]).
V3 nor V1 teach the first, compressed format stores graphics texture data with a first aspect ratio and output the texture data at an aspect ratio other than the aspect ratio with which the graphics texture data is stored in the first, compressed format.
Fukuhara teaches the first, compressed format stores graphics texture data with a first aspect ratio and output the texture data at an aspect ratio other than the aspect ratio with which the graphics texture data is stored in the first, compressed format (see col 8 lines 17-22 and 29-40, varying the aspect ratio during the decoding process [decompression process] for the use with textures in graphics processing).
Fukuhara, V3, and V1 are analogous art because they are from the same field of endeavor of decoding with the use of textures in the field of graphics.
Before the effective filling date of the invention, it would have been obvious to one of ordinary skill in the art to modify V3 and V1 alter the aspect ratio from the compressed representation to the decompressed representation as taught by Fukuhara. The motivation for doing so would have been to output a decoded image having the desired aspect ratio (Fukuhara, col 8 lines 17-22).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see the attached 892 notice of Reference cited.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EMILY R. HAUK whose telephone number is (571)272-5966. The examiner can normally be reached M-F 8:00-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chan Park can be reached at 571-272-7409. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/EMILY ROSE HAUK/Examiner, Art Unit 2669 /CHAN S PARK/Supervisory Patent Examiner, Art Unit 2669
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