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 .
Examiners Note
Claims use of the term “complex” is not clear in the claims. Looking at the published specification paragraph [0092] is defined: “determining whether a page is complex relates to whether the page can be rendered “at speed” during printing operations”. This will be the interpretation the examiner will use for the meaning of complex page.
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)(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.
Claims 1-3 and 9-10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Coulter et al. (US 2020/0285430 A1).
With respect to Claim 1, Coulter’430 shows a method for optimizing printing operations, the method comprising:
determining a page of a print job is complex (paragraph [0030] determine a page complexity value, figure 2 206 detailed in figure 4 paragraph [0042]); [ ].
applying at least one optimization operation to the page (paragraph [0042] decompressing a high-resolution image before sending to the RIP spool, figure 4 steps 226); and
generating an optimized page from the page and the at least one optimization operation (figure 4 step 228 and paragraph [0042]).
With respect to Claim 2, Coulter’430 shows the method of claim 1, further comprising evaluating whether the optimized page is complex according a complexity criterion (figure 2 step 212).
With respect to Claim 3, Coulter’430 shows the method of claim 2, further comprising, if the optimized page is still complex, processing the optimized page using a specially configured raster image processor (RIP) (figure 2 step 214 assigning logical pages in the print job to RIPS based on their complexity values).
With respect to Claim 9, Coulter’430 shows the method of claim 1, wherein the at least one optimization operation includes at least one of an application of a lower resolution, use of a pre-rendered image, flatness of a vector object or a transparency, and use of an embedded font (paragraph [0042] lowering high-resolution of the image, paragraphs [0038]-[0039] fixing embedded fonts, glyphs for the embedded subset fonts may be repeatedly rendered on each page, paragraph [0048]-[0049] transparency blending modes and flattening).
With respect to Claim 10, Coulter’430 shows the method of claim 1, wherein the optimized page has a lower processing overhead than the page (paragraph [0042] lowering high-resolution of the image).
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.
Claims 4-5, 7-8, 11-13, 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Coulter et al. (US 2020/0285430 A1) in view of Ishi (US 2020/0379690 A1).
With respect to Claim 4, Coulter’430 does not specifically show the method of claim 1, further comprising training a neural network with the page, the at least one optimization operation, and the optimized page.
Ishi’690 shows further comprising training a neural network with the page, the at least one optimization operation, and the optimized page (paragraphs [0010]-[0011] shows machine learning with a learned model obtained and conducting machine learning of the relationships among data sizes of document data, print setting conditions, RIP (Raster Image Processor) setting conditions and printing speeds of a plurality of print jobs when document data are printed by a printing unit; paragraph [0053] training machine learning optimized RIP setting conditions).
At the time of the invention, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claim invention to modify Coulter’430 to include training a neural network with the page, the at least one optimization operation, and the optimized page method taught by Ishi’690. The suggestion/motivation for doing so would have been to improve the system’s ability to be able to maximize printing speeds (paragraph [0095]).
With respect to Claim 5, the combination of Coulter’430 and Ishi’690 shows the method of claim 4, wherein the at least one optimization operation includes a plurality of optimization operations (in Coulter’430: paragraph [0042] lowering high-resolution of the image, paragraphs [0038]-[0039] fixing embedded fonts, glyphs for the embedded subset fonts may be repeatedly rendered on each page, paragraph [0048]-[0049] transparency blending modes and flattening)).
With respect to Claim 7, the combination of Coulter’430 and Ishi’690 shows the method of claim 4, further comprising applying the trained neural network to a subsequent page (in Ishi’690: paragraph [0053] training machine learning optimized RIP setting conditions).
With respect to Claim 8, the combination of Coulter’430 and Ishi’690 shows the method of claim 7, further comprising determining at least one preferred optimization operation for the subsequent page using the trained neural network (in Ishi’690: paragraph [0086] obtain optimized RIP settings).
With respect to Claim 11, Coulter’430 shows a [ ], the method comprising:
applying at least one optimization operation to a complex page of a print job (paragraph [0042] decompressing a high-resolution image before sending to the RIP spool, figure 4 steps 226);
generating an optimized page from the complex page and the at least optimization operation (figure 4 step 228 and paragraph [0042]); [ ].
Coulter’430 does not specifically show method for training a neural network to optimize printing operations; training the neural network with the complex page, the at least one optimization operation, and the optimized page, wherein the neural network is trained to apply the at least one optimization operation to a subsequent complex page.
Ishi’690 shows method for training a neural network to optimize printing operations (paragraph [0053] training machine learning optimized RIP setting conditions); training the neural network with the complex page, the at least one optimization operation, and the optimized page, wherein the neural network is trained to apply the at least one optimization operation to a subsequent complex page (paragraphs [0010]-[0011] shows machine learning with a learned model obtained and conducting machine learning of the relationships among data sizes of document data, print setting conditions, RIP (Raster Image Processor) setting conditions and printing speeds of a plurality of print jobs when document data are printed by a printing unit).
At the time of the invention, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claim invention to modify Coulter’430 to include training a neural network to optimize printing operations; training the neural network with the complex page, the at least one optimization operation, and the optimized page, wherein the neural network is trained to apply the at least one optimization operation to a subsequent complex page method taught by Ishi’690. The suggestion/motivation for doing so would have been to improve the system’s ability to be able to train a model on all the complex pages and optimization to maximize printing speeds (paragraph [0095]).
With respect to Claim 12, the combination of the combination of Coulter’430 and Ishi’690 shows the method of claim 11, wherein the at least one optimization operation includes a plurality of optimization operations (in Coulter’430: paragraph [0042] lowering high-resolution of the image, paragraphs [0038]-[0039] fixing embedded fonts, glyphs for the embedded subset fonts may be repeatedly rendered on each page, paragraph [0048]-[0049] transparency blending modes and flattening).
With respect to Claim 13, the combination of the combination of Coulter’430 and Ishi’690 shows the method of 12, further comprising applying a first optimization operation of the plurality of optimization operations to the complex page (in Coulter’430: paragraph [0042] decompressing a high-resolution image before sending to the RIP spool, figure 4 steps 226); and evaluating whether the optimized page is complex after application of the first optimization operation (in Coulter’430: figure 4 step 212).
With respect to Claim 15, the combination of the combination of Coulter’430 and Ishi’690 shows the method of claim 13, further comprising applying a second optimization operation of the plurality of optimization operations to the complex page after the application of the first optimization operation (in Coulter’430: figure 2 step 212).
With respect to Claim 16, Coulter’430 shows a method for optimizing printing operations at a printing device, the method comprising:
determining a page of a print job is complex (paragraph [0030] determine a page complexity value, figure 2 206 detailed in figure 4 paragraph [0042]);
applying at least one optimization operation to the page (paragraph [0042] decompressing a high-resolution image before sending to the RIP spool, figure 4 steps 226);
generating an optimized page from the page and the at least one optimization operation (figure 4 step 228 and paragraph [0042]); [ ] and
optimizing the subsequent page with the at least one optimization operation (figure 2 steps 208-214).
Coulter’430 does not specifically show training a neural network with the page, the at least one optimization operation, and the optimized page;
applying the trained neural network to a subsequent page to identify the at least one optimization operation for a subsequent page similar to the page.
Ishi’690 shows training a neural network with the page, the at least one optimization operation, and the optimized page (paragraph [0053] training machine learning optimized RIP setting conditions); applying the trained neural network to a subsequent page to identify the at least one optimization operation for a subsequent page similar to the page (paragraphs [0010]-[0011] shows machine learning with a learned model obtained and conducting machine learning of the relationships among data sizes of document data, print setting conditions, RIP (Raster Image Processor) setting conditions and printing speeds of a plurality of print jobs when document data are printed by a printing unit).
At the time of the invention, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claim invention to modify Coulter’430 to include training a neural network with the page, the at least one optimization operation, and the optimized page;
applying the trained neural network to a subsequent page to identify the at least one optimization operation for a subsequent page similar to the page method taught by Ishi’690. The suggestion/motivation for doing so would have been to improve the system’s ability to be able to train a model on all the complex pages and optimization to maximize printing speeds (paragraph [0095]).
With respect to Claim 17, the combination of Coulter’430 and Ishi’690 shows the method of claim 16, wherein the at least one optimization operation includes a plurality of optimization operations (in Coulter’430: paragraph [0042] lowering high-resolution of the image, paragraphs [0038]-[0039] fixing embedded fonts, glyphs for the embedded subset fonts may be repeatedly rendered on each page, paragraph [0048]-[0049] transparency blending modes and flattening).
With respect to Claim 18, the combination of Coulter’430 and Ishi’690 shows the method of claim 16, further comprising evaluating whether the optimized page is complex according a complexity criterion (in Coulter’430: figure 4 steps 212-214).
With respect to Claim 19, the combination of Coulter’430 and Ishi’690 shows the method of claim 18, further comprising, if the optimized page is still complex, processing the optimized page using a specially configured raster image processor (RIP) (in Coulter’430: figure 4 step 214).
With respect to Claim 20, the combination of Coulter’430 and Ishi’690 shows the method of claim 19, wherein training the neural network includes training the neural network with the processed optimized page and the specially configured RIP (in Ishi’690: paragraphs [0010]-[0011] shows machine learning with a learned model obtained and conducting machine learning of the relationships among data sizes of document data, print setting conditions, RIP (Raster Image Processor) setting conditions and printing speeds of a plurality of print jobs when document data are printed by a printing unit).
Allowable Subject Matter
Claims 6, 14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Plettinick et al. (US 2014/0104628 A1) shows paragraphs [0009], [0024] flattening for every page of a PDF, other optimization operations to be applied to a page.
Olie et al. (US 2026/0120500 A1) shows in paragraph [0029] Training methodology involves supervised learning on annotated webpage datasets. Each training sample comprises a rendered webpage screenshot paired with pixel-level segmentation masks indicating content region boundaries and category labels. The training process optimizes model but does not specifically show optimizing page complexity.
Lam et al. (US 10997405) shows in column 6, lines 18-20 a linear model 120 may be trained to accurately classify pages into categories reflecting a relative consistency of expected words and/or information on the page (such as a billing category), the linear model 120 may be less accurate in classifying more complex pages or sections of documents, and in column 7, lines 26-33 deep learning model 130 is particularly useful in learning relationships between the various words occurring on a page, the position and size of those words relative to other words, and the patterns thereof that are strong indicators of a particular category. In this regard, using a window size of 3 pages or more may improve the accuracy of the deep learning model 130 in classifying the more complex pages by analyzing the content before and after the subject page. But does not specifically uses that learning for optimizing usage of RIPs.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IRIANA CRUZ whose telephone number is (571)270-3246. The examiner can normally be reached 10-6.
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, Akwasi M. Sarpong can be reached at (571) 270-3438. 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.
/IRIANA CRUZ/ Primary Examiner, Art Unit 2681
Prosecution Insights