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 .
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 4-23 are 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.
Claims 4, 11 and 18 recite “obtaining, as output from a machine learning model, second image data of a set of character glyphs associated with a font, wherein the second image data was generated from first image data”, however Applicant’s claims are indefinite in that the claim does not define any first image data received, retrieve, obtained or ascertained.
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.
Claim(s) 4, 5, 8-12, 15-19, 22 and 23 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Singh et al., U.S. Patent Publication Number 2020/0151442 A1.
Regarding claim 4, Singh discloses a method comprising: obtaining, as output from a machine learning model, second image data of a set of character glyphs associated with a font, wherein the second image data was generated from first image data (paragraph 0021, a glyph-based machine learning model to generate, provide, and apply matching fonts; paragraph 0064, generates matching fonts for (e.g., digital text with a different type of digital content item such as a digital image); paragraph 0068, arranges a subset of glyphs of the font; paragraph 0069, as a result of arranging the glyphs, the font matching system generates a glyph image); determining the second image data comprises one or more style inaccuracies (paragraph 0070, font matching system performs an act to filter out fonts of the plurality of stored fonts that do not include the identified glyphs); in response to determining the second image data comprises the one or more style inaccuracies, providing, as input to an adaptive refiner model, the first image data and the obtained second image data (paragraph 0091, applies the glyph-based machine learning model to the new training glyph to generate a predicted matching glyph, compare the predicted matching glyph with the ground truth matching glyph, and modify model parameters/weights to minimize the error resultant from the comparison; by repeating this process, the font matching system trains the glyph-based machine learning model to accurately generate predicted matching glyphs ); and obtaining, from the adaptive refiner model, third image data comprising modifications to the one or more style inaccuracies found in the second image data (paragraph 0030, modifying parameters of one or more layers within the glyph-based machine learning model to improve the accuracy of the predicted matching glyphs; paragraph 0053, modify its appearance, including changing font types, sizes, and/or styles).
Regarding claim 5, Singh discloses further comprising: obtaining, as output from the machine learning model, the second image data of the set of character glyphs with the font includes: receiving data representing a character glyph associated with the font (paragraph 0054, receive user input to select one or more glyphs); generating first image data from the character glyph (paragraph 0069, generates a glyph image); and providing, as input to the machine learning model, the generated first image data (paragraph 0077, applies a glyph-based machine learning model to identified glyphs within the glyph image ).
Regarding claim 8, Singh discloses providing, as input to the adaptive refiner model, the generated first image data and the obtained second image data comprises: generating input data that includes a concatenation of the generated first image data and the obtained second image data (paragraph 0079, concatenates two or more of the glyph feature vectors); and providing, as input to the adaptive refiner model, the generated input data that comprises the concatenation (paragraph 0082, applies the glyph-based machine learning model with respect to the generated target glyph features vectors).
Regarding claim 9, Singh discloses wherein the first image data, the second image data, and the third image data comprise rasterized images (paragraph 0038, font matching system arranges glyphs of a font; glyph image can refer to a digital image of multiple glyphs arranged together; a glyph image can include digital files encoding a visual representation of glyphs with the following file extensions: JPG, TIFF, BMP, PNG, RAW, or PDF).
Regarding claim 10, Singh discloses further comprising: generating a vector format of the obtained third image data of the set of character glyphs (paragraph 0078, produces a computer-based vector of features (e.g., deep features) that describes or defines the respective glyph; paragraph 0081, generating target glyph feature vectors); scaling the generated vector format to match to a form of the data representing the character glyph (paragraph 0079, generate a feature vector that is the size of the first and second glyph feature vectors); and providing the scaled vector of the set of character glyphs for output, wherein the scaled vector comprises a set of character glyphs associated with the font (paragraph 0080, generates a glyph image feature vector that includes features of each glyph feature inside it).
Regarding claims 11, 12 and 15-17, they are rejected based upon similar rational as above claims 4, 5 and 8-10. Singh further discloses a system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations (paragraph 0128).
Regarding claims 18, 19, 22 and 23, they are rejected based upon similar rational as above claims 4, 5 and 8-9. Singh further discloses a non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations (paragraph 0129).
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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) 6, 7, 13, 14, 20 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Singh et al., U.S. Patent Publication Number 2020/0151442 A1, in view of Bhakthavatsalam 12,639,866
Regarding claims 6, 13 and 20, Singh discloses paragraph 0006, utilizing a glyph-based machine learning model.
It is noted that Singh fails to disclose wherein providing, as input to the machine learning model, the generated first image data comprises providing, as input to a stable diffusion model, the generated first image data.
Bhakthavatsalam discloses providing, as input to the machine learning model, the generated first image data comprises providing, as input to a stable diffusion model, the generated first image data (col. 5, lines 62-64, diffusion model operates by iteratively improving a random noise image to align it with the given text description; col. 7, lines 59-61, text will have an aesthetically pleasing arrangement with graphic design from the diffusion mode).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include in the machine learning as disclosed by Singh, the input to a diffusion model as disclosed by Bhakthavatsalam, to improve a random noise image to align with a given text description, to create text with aesthetically pleasing arrangement.
Regarding claims 7, 14 and 21, it is noted that Singh fails to disclose wherein determining the second image data comprises the one or more style inaccuracies comprises determining one or more inaccuracies comprising a slant, a thickness, a length, and local style features of the font.
Bhakthavatsalam discloses wherein determining the second image data comprises the one or more style inaccuracies comprises determining one or more inaccuracies comprising a slant, a thickness, a length, and local style features of the font (col. 9, lines 10-16, placing the text with the appropriate attributes in the designated location on the design image; text placement model may indicate all the parameters of the added text including font, size, color, location, etc.; text parameters are then provided to the user terminal where the application will add the text to the proposed design).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the font matching system as disclosed by Singh, the inaccuracies and font style features as disclosed by Bhakthavatsalam, to provide parameters to the user terminal where the application will add the text to a proposed design.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Reddy et al., U.S. Patent Publication Number 2023/0110114 A1
Reddy discloses paragraph 0052, generate outputs (e.g., generated digital images) based on a plurality of inputs provided to the neural network;
paragraph 0171, reconstructed glyphs for Image VAE show inaccurate and blurry shapes; paragraph 0045, receives and transmits electronic data, such as indication of client device interactions, fonts, glyphs, glyph modifications, images of glyphs, font style codes, and glyph labels.
Fisher et al., U.S. Patent Number 10,621,760 B2
Fisher discloses col. 3, lines 59-61, a subset of glyphs from a particular font is received; based upon this input, all the glyphs from the alphabet corresponding to the input font are jointly generated; col. 5, lines 25-26, input glyph shape may be derived from color image data; col. 5, line 65 – col. 6, line 7, GlyphNet pre-training set over a set of fonts is generated; col. 6, lines 63-67, for example, if the glyphs corresponding to “T”, “O”, “W”, “E”, and “R” were selected; placeholders for the remaining characters in the alphabet (i.e., ‘A’-‘D’, ‘F’-‘N’, ‘P’-‘Q’ and ‘S’-‘Z’ ) are generated; col. 9, lines 15-17, OrnaNet may receive a set of synthesized glyphs from GlyphNet as input and synthesize a full set; col. 10, lines 36-40, GlyphNet may learn correlations automatically in order to generate an entire set of stylistically similar glyphs from partial observations as previously defined comprising a subset of glyphs for an entire alphabet; col. 8, lines 63-67, MC-GAN may receive a subset (partial observation) of glyph shapes from an alphabet corresponding to a font and jointly synthesize a full set of ornamented glyphs for the alphabet; col. 9, lines 2-3, MC-GAN may further comprise GlyphNet and OrnaNet; col. 10, lines 41-43, due to a style similarity among all content images, one input channel is added for each individual glyph.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Motilewa Good-Johnson whose telephone number is (571)272-7658. The examiner can normally be reached Monday - Friday 6am-2:30pm.
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MOTILEWA . GOOD JOHNSON
Primary Examiner
Art Unit 2616
/MOTILEWA GOOD-JOHNSON/Primary Examiner, Art Unit 2619
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