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 filed November 6, 2025 fails to comply with 37 CFR 1.98(a)(3)(i) because it does not include a concise explanation of the relevance, as it is presently understood by the individual designated in 37 CFR 1.56(c) most knowledgeable about the content of the information, of each reference listed that is not in the English language. It has been placed in the application file, but the information referred to therein has not been considered.
Specifically, the Japanese Office Action is untranslated.
Response to Arguments
Applicant’s arguments and amendment have persuasively overcome the claim objection, many of the 112 rejections
The remaining issues are addressed below.
103
Applicant argues:
The secondary reference ("Wikipedia") is a general background description of error diffusion.
Examiner responds:
Applicant only discloses the standard error diffusion technique.
Applicant argues:
However, Wikipedia does not teach or suggest the claimed area gradation feature, nor the specific use of binary images to represent color tone characteristics.
Examiner responds:
Wikipedia teaches:
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The text on the sign has a lower density of black pixels because the color version is lighter than the shadow on the tree in the background. This is an example of both “area gradation” and representing color tone characteristics.
Wikipedia also teaches “Shades of gray were rendered by intermittently raising and lowering the pen, depending upon the luminance of the gray desired.” (see the “early history” section) Wikipedia’s “luminance of the gray desired” teaches the claimed representing color tone characteristics. Additionally, the section titled “Digital era” teaches “Victor Ostromoukhov introduced a variable error diffusion method, which enhances dithering quality by dynamically adjusting the diffusion coefficients based on the color values of the pixel’s RGB channels.”
Applicant argues:
There is no teaching in Wikipedia regarding generating a binary image by error diffusion as preprocessing for inputting the generated image to a neural network model.
Examiner responds:
Wikipedia teaches “Error diffusion has the tendency to enhance edges in an image. This can make text in images more readable than in other halftoning techniques.” Thus, it is obvious to use error diffusion before performing optical character recognition because optical character recognition works better with enhanced edges. Further, this appears to be the same reason that Applicant is using error diffusion.
Applicant argues:
The cited Wikipedia passage relates to edge enhancement for text readability. The claim, however, requires generating a binary image having area gradation which represents characteristics in color tone of the multi-level image. Enhancing edges to make text more readable is fundamentally different from preserving gradation in a binary image.
Examiner responds:
Applicant admits that “Kriegman is focused on generating computer-searchable text” and the “Wikipedia passage relates to edge enhancement for text readability.” These references fit hand in glove – that they don’t use Applicant’s claim language is not an issue.
Claim Objections
Claims 1, 7, and 8 are objected to because of the following informalities:
Claims 1, 7, and 8 recite “ready” instead of “read.”
Claims 1, 7, and 8 recite “preprocessing to the multi-level image,” but the “to” should be “on.”
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 1-5, 7 and 8 (all claims) 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 1, 7 and 8 recite “area gradation,” but this is new terminology. MPEP 2173.05(a). Note that reciting “error diffusion” instead of “area gradation” is expected to overcome the rejection.
Claims 1, 7 and 8 recite “binary image.” The specification (see specification amendment of July 3, 2025) states that this is “8bit/pixel,” but also “two colors: black and white.” The issue is that 8 bits is 2^8 colors (256 colors). (The examiner notes that the specification now states that this has a gradation with a plurality of levels, but it is not clear to the examiner what that means.) Reciting either “8 bits per pixel” or “black and white” is expected to overcome this rejection.
Claims 1, 7 and 8 recite “area gradation which represents characteristics in color tone of the multi-level image using black and white,” but this lacks a plain meaning (see, e.g., the response to arguments above). It is not clear to the examiner what is intended with this language beyond the already recited “error diffusion.”
Claims 1, 7 and 8 recite “in a case in which the binary image does not have a preset size,” but this is subjective because the preset size lacks an objective standard. MPEP 2173.05(b)(IV). Reciting that the preset size is retrieved from memory is expected to clarify this because that is an objective standard for what the value is.
Claim 2 recites “generate the binary image … with reference to peripheral pixels,” but this is new terminology. MPEP 2173.05(a). Note that reciting “error diffusion” instead of “with reference to peripheral pixels” is expected to overcome the rejection.
Dependent claims are likewise rejected.
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.
Claims 1-5, 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over US20200410291A1 (“Kriegman”) in view of Wikipedia’s “Error Diffusion” article as of September 21, 2021 (retrieved from https://en.m.wikipedia.org/w/index.php?oldid=1045663157&title=Error_diffusion) (“Wikipedia”)
1. (Currently Amended) An image forming apparatus comprising: circuitry configured to:
obtain a multi-level image read by a scanner; (Kriegman, abstract, “The present disclosure relates to generating computer searchable text from digital images that depict documents.” See also, Kriegman, [0196] “I/O interface may include … an optical scanner.” See also, Fig. 2A. Fig. 2A shows the claimed “multi-level” images.)
perform pre-processing to the multi-level image ready by the scanner, the preprocessing including (Kriegman, [0031] “the digital image character recognition system can modify the digital images (e.g., to prepare the digital image for transformation into searchable text)”)
generating a scaled image having the area gradation from the binary image in a case in which the binary image does not have a preset size; (Kriegman, [0118] “In some embodiments, digital image character recognition system 112 can resize the individual word boxes of single words into the appropriate input size for text prediction neural network 500.”)
input the scaled image having the area gradation generated from the binary image to a neural network model; and (Kriegman, [0118] “… the appropriate input size for text prediction neural network 500.”)
cause the neural network model to infer a top-bottom identification of the scaled image input to the neural network model, and (Kriegman, abstract, “identify the orientation of the depicted documents”)
output the top-bottom identification inferred by the neural network model. (Kriegman, abstract, “identify the orientation of the depicted documents”)
Kriegman is not relied on for the below claim language.
However, Wikipedia teaches generating a binary image, from the multi-level image, having area gradation which represents characteristics in color tone of the multi-level image using black and white, according to an error diffusion method; (Wikipedia, “Error diffusion is a type of halftoning in which the quantization residual is distributed to neighboring pixels that have not yet been processed. Its main use is to convert a multi-level image into a binary image.” See also the image on page 1 captioned “An error-diffused image”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Wikipedia to the teachings of Kriegman such that Kriegman performs error diffusion for the purpose of improving Kriegman’s generation of computer searchable text. See, e.g., Wikipedia “Error diffusion has the tendency to enhance edges in an image. This can make text in images more readable than in other halftoning techniques.”
Based on the above, this is an example of “combining prior art elements according to known methods to yield predictable results.” MPEP 2143.
2. (Previously Presented) The image forming apparatus according to claim 1,wherein the circuitry is configured to generate the binary image with reference to peripheral pixels in the multi-level image read by the scanner according to the error diffusion method. (Wikipedia, “Error diffusion is a type of halftoning in which the quantization residual is distributed to neighboring pixels that have not yet been processed. Its main use is to convert a multi-level image into a binary image”)
3. (Previously Presented) The image forming apparatus according to claim 1, wherein the circuitry is configured to scale the binary image after converting a bit depth of the binary image into a predetermined value, to generate the scaled image that maintains the area gradation. (Wikipedia, “Error diffusion is a type of halftoning in which the quantization residual is distributed to neighboring pixels that have not yet been processed. Its main use is to convert a multi-level image into a binary image.” Wikipedia’s “that have not yet been processed” teaches the claimed “after a converting a bit depth.” (Note also that binarization teaches the claimed predetermined value of bit depth.))
4. (Original) The image forming apparatus according to claim 1,wherein the neural network model has a plurality of filters in layers. (Kriegman, [0108] “Specifically, the orientation neural network 304 comprises a deep CNN that includes convolutional layers, pooling layers, fully connected layers, ReLu layers, and normalization layers, that feed to an output layer that produces a predicted orientation.”)
5. (Currently Amended) The image forming apparatus according to claim 1, wherein a grayscale image is used as an input for the neural network model to learn atop-bottom identification. (Kriegman, Fig. 6)
6. (Canceled)
Claims 7 and 8 are rejected as per claim 1. As to claim 8’s preamble, see Kriegman, claim 15 “A non-transitory computer readable storage medium comprising instructions that … .”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 10805481 B2 – discusses settings for scanning, and para 0024 discusses error diffusion and para 0025 discusses using optical character recognition to determine orientation.
US 11087428 B2 – para 0130 discusses error diffusion for processing, and para 0131 says that the processing can prepare the document for optical character recognition.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID ORANGE whose telephone number is (571)270-1799. The examiner can normally be reached Mon-Fri, 9-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gregory Morse can be reached at 571-272-3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DAVID ORANGE/Primary Examiner, Art Unit 2663
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