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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been received.
Information Disclosure Statement
The Information Disclosure Statements (IDS’s) submitted on 08 December 2023 and 19 November 2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the Information Disclosure Statements have been considered by the Examiner.
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 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.
This application currently names joint inventors. In considering patentability of the claims the Examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the Examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 5, 6, & 10 are rejected under 35 U.S.C. 103 as being unpatentable over YAMAMOTO (US 2021/0297557 A1) in view of Wuzik et al. (US 2004/0128775 A1).
As related to independent claims 1 & 6, YAMAMOTO teaches a printing system [claim 1] and method of color-matching [claim 6] with intelligent color-matching, comprising: a storage device, storing a program, a color-matching dataset, and a color- matching model, wherein the color-matching dataset comprises multiple pieces of color-matching data, and each piece of color- matching data includes an ink formula and a corresponding printing color value for the ink formula; a printing device, controlled to produce a printed product; and a processing device (YAMAMOTO – Page 1, Paragraphs 2-3), connected to the printing device, and configured to load the program from the storage device to perform: training the color-matching model using the color-matching dataset; inputting an expected color value into the trained color-matching model, and obtaining a predicted formula output by the trained color-matching model; and based on the predicted formula, controlling the printing device to produce the printed product (YAMAMOTO – Page 2, Paragraph 15 – Page 4, Page 37 and Figures 1 & 3, shown below). YAMAMOTO directly teaches the intelligent color-matching, meanwhile, Wuzik et al. teaches ink formulas which are used to print and match the specific colors as needed (Wuzik et al. – Page 1, Paragraphs 1-3). It would have been obvious to one of ordinary skill in the art at the time of filing to use the ink formulas and colorants of Wuzik et al. in the printing device to produce a printed product which has high definition and resolution, the formulas meet stringent requirements, and the image is photographic quality with high light fastness, water fastness, and rub fastness (Wuzik et al. – Page 1, Paragraph 2).
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As related to claims 5 & 10, the combination of YAMAMOTO and Wuzik et al. remains as applied above and continues to teach the color-matching model is selected from a linear regression model, a neural network regression model, a decision tree regression model, and a random forest regression model (YAMAMOTO – Page 1, Paragraph 3 and Page 2, Paragraph 21).
Claims 2-4 & 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of YAMAMOTO (US 2021/0297557 A1) and Wuzik et al. (US 2004/0128775 A1) in further view of Zimmer et al. (US 6,843,838 B2).
As related to dependent claims 2 & 7, the combination of YAMAMOTO and Wuzik et al. remains as applied above and continues to teach the ink formula and the predicted formula are each associated with a composition of multiple chemical materials (Wuzik et al. – Page 6, Paragraph 60-64), and Zimmer et al. teaches ink formulations that are needed to provide color matched images [i.e. appropriate color characteristics and brilliant color reproduction] (Zimmer et al. – Column 1, Lines 43-66) and specifically teaches some of the chemical materials are a surface tension-adjusting additives (Zimmer et al. – Column 11, Line 65 – Column 13, Line 67). It would have been obvious to one of ordinary skill in the art at the time of filing to specify the components of the chemical materials in the ink formula of the combination of YAMAMOTO and Wuzik et al. with the components included in the formulas of Zimmer et al. in an effort to ensure the printed image is photographic quality with high light fastness, water fastness, and rub fastness (Wuzik et al. – Page 1, Paragraph 2 and Zimmer et al. – Page 1, Lines 43-66).
As related to further dependent claims 3 & 8, the combination of YAMAMOTO, Wuzik et al., and Zimmer et al. remains as applied above and continues to teach content of the surface tension-adjusting additive in the predicted formula ranges from 0.1 to 3.5 phr (parts per hundreds of resin) (Wuzik et al. – Page 6, Paragraphs 60-64 and Zimmer et al. – Column 12, Lines 44-49; Column 13, Lines 8-12 & Line 66 – Column 14, Line 3; and Table 1).
As related to further dependent claims 4 & 9, the combination of YAMAMOTO, Wuzik et al., and Zimmer et al. remains as applied above and continues to teach the surface tension- adjusting additive has a spherical polyester structure, and periphery of the polyester structure has hydroxyl groups, along with multiple alkyl extended chains (Wuzik et al. – Page 7, Paragraphs 67-69 and Zimmer et al. – Column 11, Line 65 – Column 13, Line 67).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Breton (US 5,938,827 A) teaches ink compositions and formulas using spherical polyester structures as additives. Viturro et al. (US 7,069,164 B2) teaches a printing system with intelligent color-matching to ensure color output consistency. Lutz (US 2011/0122185 A1) teaches a printing system with intelligent color-matching based on expected color density. Ernst et al. (US 8,358,440 B2) teaches a printing system and calibration system to intelligently color-match based inter alia on medium type. Soriano (US 2018/0063383 A1) teaches a printing system with intelligent color-matching using color conversion based on color profile and medium type. Ferreri et al. (US 2019/0266455 A1) teaches a printing system with intelligent color-matching and color management using look up tables. Lopez Alvarez et al. (US 11,451,686 B2) teaches a printing system and calibration thereof using color-matching of color values. MOROVIC et al. (US 2023/0396728 A1) teaches an intelligent color prediction model using neural network and other regression models.
Examiner's Note: Examiner has cited particular Figures & Reference Numbers, Columns, Paragraphs and Line Numbers in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to JOHN P ZIMMERMANN whose telephone number is (571)270-3049. The Examiner can normally be reached Monday-Thursday 0700-1730 EST.
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, Stephen Meier can be reached at (571) 272-2149. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/John P Zimmermann/Primary Examiner, Art Unit 2853