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 Objections
Claims 6-7 and 14-16 are objected to because of the following informalities:
In claim 6, line 2, “trained based to classify” should read “trained to classify”.
In claim 7, line 2, “a said text box” should read “the text box”.
In claim 14, line 3, “each said text box” should read “each text box”.
In claim 15, line 2, “each said text box” should read “each text box”.
In claim 16, line 2, “each said text box” should read “each text box”.
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.
Claim 11 is 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 11 recites “the first editable text box” (line 2). There is insufficient antecedent basis for the limitation in claims 11 and 1. For examination purpose, “the first editable text box” will be read as “a first editable text box”.
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 1-2, 7, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (CN 111860257 A, hereinafter “Li”) in view of Kouidri (Easy stable diffusion inpainting with Segment Anything Model, archive.org, https://web.archive.org/web/20231207073924/https://www.ikomia.ai/blog/stable-diffusion-inpainting-with-segment-anything-model-sam-using-the-ikomia-api, hereinafter “Kouidri”). A machine-translated English version for Li is attached.
Regarding claim 1, Li discloses A computer implemented method including: (page 1, lines 10-11, “provides a table recognition method that integrates various text features and geometric information”; page 23, lines 30-32, “device and each module provided by the present invention in the form of pure computer readable program 31 code”).
for image data defining a first image and associated text data, the associated text data including optical character recognition (OCR) data formed based on the first image: (page 17, lines 24-27, “After obtaining the picture of the table area, this design performs OCR identification (necessary data information) and line identification (optional operation and information) respectively … Through OCR recognition, the specific information of the characters can be obtained,”). Note that: (1) the picture of the table area can be regarded as a first image including characters as associated text data; and (2) OCR is performed on the first image, and the specific information of the characters (text) as OCR data can be obtained as associated text data based on the first image.
determining, based on the first image, at least one of a predicted colour and a predicted font of text defined by the OCR data; and (page 17, lines 27-29, “Through OCR recognition, the specific information of the characters can be obtained, which can be further processed into text box strings. For each text box, its text content, text font, text color, and text size can be obtained”). Note that: (1) the OCR data based on the first image can be further processed into text boxes with character or text strings; and (2) the text font and text color of the text strings can be obtained as the predicted font and color of the text.
forming, based on the OCR data and the at least one of a predicted colour and a predicted font, at least one text box, each text box comprising the text defined by the OCR data and having attributes including:
a text colour that is or is associated with the predicted colour, or
a text font that is or is associated with the predicted font, or
a text colour that is or is associated with the predicted colour and a text font that is or is associated with the predicted font; (page 17, lines 27-30, “(1) Through OCR recognition, the specific information of the characters can be obtained, which can be further processed into text box strings. For each text box, its text content, text font, text color, and text size can be obtained. The rectangular coordinates of the text box (called four-point coordinates)”). Note that: (1) the text boxes are obtained by further processing the specific information of the characters (text); (2) each text box includes the text box string or its text content with its text font and / or its text color / size; (3) the text color is the predicted color, and the text font that is the predicted font; and (4) the rectangular coordinates of the text box is the location of the text box on the first image, and can be used for later use (i.e., locating the text box).
locating the at least one text box … (page 17, 29-30, “The rectangular coordinates of the text box 29 (called four-point coordinates)”). Note that: (1) the rectangular coordinates of the text box can be obtained by calculating; and (2) using the coordinates, one text box (e.g., a text box similar to the text box) can be located or positioned on an image or a generated image (a second image) according to the coordinates.
… an area of the first image corresponding to said text defined by the OCR data … Note that: (1) the area defined can be the coordinates of the text box can be regarded as an area of the first image corresponding to said text defined by the OCR data; and (2) the area can be taken as a mask area of a mask image with same size as that of the first image.
However, Li fails to disclose, but in the same art of computer graphics, Kouidri disclose
forming a second image based on the first image by inpainting the first image, including inpainting an area of the first image corresponding to said text defined by the OCR data; and … on the second image … (Kouidri, page 6, para. 3, “Inpainting refers to the process pf restoring or repairing an image by filling in missing or damaged parts. It is a valuable technique widely used in image editing and restoration, enabling the removal of flaws and unwanted objects to achieve a seamless and nature-looking final image”; page 6, paras. 4-5 “Stable Diffusion Inpainting is a specific type of inpainting technique that leverages the properties of heat diffusion to fill in missing or damaged areas of an image. It accomplishes this by applying a heat diffusion process to the surrounding pixels. During this process, values are assigned to these pixels based on their proximity to the affected area. The heat equation is then utilized to redistribute intensity values, resulting in a seamless and natural patch. The repetition of this equation ensures the complete filling of the image patch, ultimately creating a smooth and seamless result that blends harmoniously with the rest of the image.”; page 8, “
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”). Note that: (1) for the object removal, the horse in the upper image (the first image) of the figure of page 8 I removed using the stable Diffusion inpainting v2, resulting in the lower image (a second image); (2) A bounding box defining the horse in the upper image can be determined manually or automatically using a segmentation method; (3) guided by the bounding box for the horse, the removed can be performed; (4) the bounding box for the horse can be substituted by the text box for the first image with the coordinates defined above to remove the area corresponding to text box out of the first image while the area are naturally patched with the first image, resulting in a second image after the inpainting the first image.
Li and Kouidri are in the same field of endeavor, namely computer graphics. Before the effective filing date of the claimed invention, it would have been obvious to apply inpainting an area of an image corresponding to mask to remove the area, as taught by Kouidri into Li. The motivation would have been “Stable Diffusion Inpainting sets itself apart from other inpainting techniques due to its notable stability and smoothness. Unlike slower or less reliable alternatives that can produce visible artifacts, Stable Diffusion Inpainting guarantees a stable and seamless patch. It excels particularly in handling images with complex structures, including textures, edges, and sharp transitions” (Kouidri, page 7, para. 1). The suggestion for doing so would allow to apply inpainting an area of an image and guarantee a stable and seamless patch for the area. Therefore, it would have been obvious to combine Li and Kouidri.
Regarding claim 2, Li in view of Kouidri discloses The computer implemented method of claim 1, wherein locating the at least one text box on the second image is location at or near a location of the text defined by the OCR data. (Li, page 17, 29-30, “The rectangular coordinates of the text box 29 (called four-point coordinates)”). Note that: (1) the rectangular coordinates of the text box can be obtained by calculating; and (2) using the coordinates, one text box (e.g., a text box similar to the text box) can be located or positioned on an image or a generated image (a second image) at or near the location of the text according to the coordinates.
Regarding claim 7, Li in view of Kouidri discloses The computer implemented method of claim 1, including determining a predicted font size based on the first image, wherein the text in a said text box is text with a font size matching the predicted font size. (Li, page 17, lines 27-29, “Through OCR recognition, the specific information of the characters can be obtained, which can be further processed into text box strings. For each text box, its text content, text font, text color, and text size can be obtained”). Note that: (1) the OCR data based on the first image can be further processed into text boxes with strings; (2) the text size or font size can be obtained as the predicted font size by further processing; and (3) the text in the text box is text with the predicted font size.
Regarding claim 12, Li in view of Kouidri discloses The computer implemented method of claim 1, including determining, based on the first image, both of a predicted colour and a predicted font of the text defined by the OCR data, wherein the at least one text box is formed based on both the predicted colour and the predicted font. (Li, page 17, lines 27-29, “Through OCR recognition, the specific information of the characters can be obtained, which can be further processed into text box strings. For each text box, its text content, text font, text color, and text size can be obtained”). Note that: (1) the text boxes are obtained by further processing the specific information of the characters (text); (2) the text color and text font can be obtained as the predicted color and the predicted font by further processing; and (3) the text in the text box is text with both the predicted color and the predicted font.
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Kouidri and Surryadevara (IMAGE COLOR SEGMENTATION USING K-MEANS CLUSTERING ALGORITHM, INTERNATIONAL JOURNAL OF INNOVATIONS IN ENGINEERING RESEARCH AND TECHNOLOGY, VOLUME 6, ISSUE 11, Nov. 2019, hereinafter “Surryadevara”).
Regarding claim 3, Li in view of Kouidri discloses The computer implemented method of claim 1, including determining, based on a portion of the first image including the text defined by the OCR data, a predicted colour of the text, wherein Note that: the area corresponding to the text box for the text can be regarded as a portion of the first image.
However, Li in view of Kouidri fails to disclose, but in the same art of computer graphics, Surryadevara discloses determining of the predicted colour is by k-means clustering applied to the portion of the first image. (Surryadevara, page 102, Abstract, “finding the dominant colors of an image thanks to the K-means clustering algorithm … The The K-means clustering algorithm defines a number K of clusters and the best “centroids” to cluster the data around. When applied to images, it allows extracting the k dominant colors in an image to be used for other purposes”). Note that: (1) the area of the text box of the text as a portion of the first image can be regarded as an image extracted from the first image; and (2) the k-means clustering method can be applied to the image for determining of the predicted color.
Li in view of Kouidri, and Surryadevara, are in the same field of endeavor, namely computer graphics. Before the effective filing date of the claimed invention, it would have been obvious to apply k-means clustering method to an image to determine a color, as taught by Surryadevara into Li in view of Kouidri. The motivation would have been “finding the dominant colors of an image thanks to the K-means clustering algorithm” (Surryadevara, page 102, Abstract). The suggestion for doing so would allow to apply apply k-means clustering method to an image to determine a color as a predicted color of the text. Therefore, it would have been obvious to combine Li, Kouidri, and Surryadevara.
Regarding claim 4, the combination of Li, Kouidri, and Surryadevara discloses The computer implemented method of claim 3, wherein applying the k-means clustering includes determining two dominant colours within the portion of the first image and determining one of the dominant colours as the predicted colour. (Surryadevara, page 102, Abstract, “The K-means clustering algorithm defines a number K of clusters and the best “centroids” to cluster the data around. When applied to images, it allows extracting the k dominant colors in an image to be used for other purposes”). Note that: (1) by applying the k-means method to the portion of the first image, the two colors corresponding to two clusters with k=2 can be regarded as two dominant colors; (2) it is obvious to one having skill in the art to select a first color as the predicted color after visually verifying that the first color does indeed label the text; if the first color labels the background of the text, one selects the second color (the other color) as the predicted text color.
The motivation to combine Li, Kouidri, and Surryadevara given in claim 3 is incorporated here.
Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Kouidri and Wang et al. (DeepFont: Identify Your Font from An Image, arXiv.org, arXiv:1507.03196v1 [cs.CV] 12 Jul 2015, hereinafter “Wang”).
Regarding claim 5, Li in view of Kouidri discloses The computer implemented method of claim 1, including determining, based on a portion of the first image, a predicted font of the text, wherein Note that: the area corresponding to the text box for the text can be regarded as a portion of the first image.
the determining of the predicted font is by applying a trained image classification model to the portion of the first image. (Wang, page 2, left, para., “Fig. 1 shows successful VFR examples using DeepFont. In (a)(b), given the real-world query images, top-5 font recognition results are listed, within which the ground truth font classes are marked out”;
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”). Note that: (1) DeepFont is a deep CNN network model that is trained and can take input images to predict the font classes as output; and (2) the portion of the first image corresponding to the text box can be regarded as an image for feeding the trained model to have a predicted font class selected according to the likelihood values.
Li in view of Kouidri, and Wang, are in the same field of endeavor, namely computer graphics. Before the effective filing date of the claimed invention, it would have been obvious to apply a CNN based neural network model to classified the text in an input image into font classes, as taught by Wang into Li in view of Kouidri. The motivation would have been “The DeepFont system achieves an accuracy of higher than 80% (top-5) on our collected dataset, and also produces a good font similarity measure for font selection and suggestion.” (Wang, page 1, Abstract). The suggestion for doing so would allow to apply applying a trained image classification model to the portion of the first image to increase the font classification accuracy. Therefore, it would have been obvious to combine Li, Kouidri, and Wang.
Regarding claim 6, the combination of Li, Kouidri, and Wang discloses The computer implemented method of claim 5, wherein the trained image classification model is a model trained based to classify images into classes comprising a plurality of fonts of a text editor operable to edit the at least one text box. (Wang, page 3, col. left, para. 3, “The AdobeVFR Dataset … Chen et. al. in [4] selected 2,420 font classes to work on. We remove some script classes, ending up with a total of 2,383 font classes. We collected 201,780 text images from various typography forums, where people post these images seeking help from experts to identify the fonts … Finally, we obtain 4,384 real-world test images with reliable labels, covering 617 classes (out of 2,383). Compared to the synthetic data, these images typically have much larger appearance variations caused by scaling, background clutter, lighting, noise, perspective distortions, and compression artifacts. Removing the 4,384 labeled images from the full set, we are left with 197,396 unlabeled real-world images which we denote as VFR real u … The entire AdobeVFR dataset, consisting of VFR real test, VFR real u, VFR syn train and VFR syn val, are made publicly available”). Note that: (1) the AdobeVFR dataset with tons of images with text covering 2383 font classes is use for the model training; (2) it is obvious to one having ordinary skill in the art that a number 2383 font classes significantly excel the number of fonts that a conventional text editor (e.g., Adobe Acrobat) used to edit the text in a text box may have; and (3) after the model has been trained, the trained should be able to classify images into classes.
The motivation to combine Li, Kouidri, and Wang given in claim 5 is incorporated here.
Claims 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Kouidri and Qin (CN114581926A, hereinafter “Qin”). A machine-translated English version for Qin is attached.
Regarding claim 8, Li in view of Kouidri discloses The computer implemented method of claim 1, further including determining a line length for each of a plurality of lines of a said text box based on the OCR data, wherein the text box is formed with line lengths corresponding to the determined line lengths. (Qin, page 19, lines 26-29, “(2) Since the multi-line text image is directly recognized as a whole, only one text box containing multiple text lines needs to be detected in the early stage, which can greatly simplify the text detection operation required in the early stage and improve text detection. efficiency”; page 32, lines 3-13, “The multi-line text recognition method as claimed in claim 8, wherein the multi-line text recognition model is trained and obtained according to the following steps: Obtain a text image sample; the text image sample includes a plurality of text lines and carries label information; wherein, the label information includes a plurality of character sequences, and a plurality of the character sequences are associated with a plurality of the text lines. One-to-one correspondence, a plurality of the character sequences are all of a specified length, and the value of the specified length is the maximum value among the actual lengths of the multiple character sequences; … The initial model is trained using the text image samples to obtain a multi-line text recognition model.”). Note that: (1) the text box is formed by detected multiple text lines; (2) the text length is determined for each text line. A maximum value among the actual lengths of the multiple character sequences is obtained as a specified value; and (3) the text box is related to the specified length.
Li in view of Kouidri, and Qin, are in the same field of endeavor, namely computer graphics. Before the effective filing date of the claimed invention, it would have been obvious to apply forming a text box with multiple text lines and determining text length of each text line, as taught by Qin into Li in view of Kouidri. The motivation would have been “Since the multi-line text image is directly recognized as a whole, only one text box containing multiple text lines needs to be detected in the early stage … One-to-one correspondence, a plurality of the character sequences are all of a specified length, and the value of the specified length is the maximum value among the actual lengths of the multiple character sequences” (Surryadevara, page 102, Abstract). The suggestion for doing so would allow to apply form a text box with multiple text lines and determine a maximum text line length for text box. Therefore, it would have been obvious to combine Li, Kouidri, and Qin.
Regarding claim 9, the combination of Li, Kouidri, and Qin discloses The computer implemented method of claim 8, wherein the determined line length for at least one line is a length greater than what can be accommodated within the text box. (page 32, lines 3-13, “The multi-line text recognition method as claimed in claim 8, wherein the multi-line text recognition model is trained and obtained according to the following steps: Obtain a text image sample; the text image sample includes a plurality of text lines and carries label information; wherein, the label information includes a plurality of character sequences, and a plurality of the character sequences are associated with a plurality of the text lines. One-to-one correspondence, a plurality of the character sequences are all of a specified length, and the value of the specified length is the maximum value among the actual lengths of the multiple character sequences; … The initial model is trained using the text image samples to obtain a multi-line text recognition model.”). Note that: (1) the determined actual text line length can be a large number (e.g., 256); (2) it is known that a text string with a length longer than 80 is hard to read (e.g., The Web Accessibility Initiative (WCAG) guideline 1.4.8 states that, in order to be accessible to all users, lines of text should be 80 or fewer characters (or 40 or fewer characters if the text is Chinese, Japanese, or Korean). Therefore, in this case, a maximum number specified above by obtaining a maximum among all actual text lengths may be improved to accommodate the guideline (e.g., presetting the maximum text line length as 80). In this way, the text line’s length of 256 for a long text line is greater than the specified maximum value (e.g., 80) for the text box; and (4) it is obvious to one having ordinary skill in the art that: when the text line length is greater than a specified maximum value (e.g., 8), the text line can be automatically broken into set of short text lines using a certain editing function.
The motivation to combine Li, Kouidri, and Qin given in claim 8 is incorporated here.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Kouidri and Brown (Font metrics and vertical space in CSS, archive.org, https://web.archive.org/web/20230516141928/https://blog.typekit.com/2010/07/14/font-metrics-and-vertical-space-in-css/, hereinafter “Brown”).
Regarding claim 10, Li in view of Kouidri discloses The computer implemented method of claim 1, wherein locating the at least one text box on the second image includes
However, Li in view of Kouidri fails to disclose, but in the same art of compuer graphics, Brown discloses determining a vertical position for at least one of the text boxes based on the text font for that text box. (Brown, page 2, para. 2, “Font files provide structure for glyphs by establishing the invisible bounds that will govern them. A font’s em square and baseline determine its relative size and placement when typeset. Vertical metrics influence the height of ascenders and depth of descenders”; page 2, “
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”). Note that: (1) the font files have different vertical space to reflect the vertical position when typesetting the fonts in the text box because of the vertical metrics; and (2) It is obvious to one having ordinary skill in the art that a relative vertical position needs to be considered when locating one text box on the second image at or near the coordinates of the text box in vertical direction to make sure the text fonts are not cut off.
Li in view of Kouidri, and Brown, are in the same field of endeavor, namely computer graphics. Before the effective filing date of the claimed invention, it would have been obvious to determine a vertical position for the text box based on the text font, as taught by Brown into Li in view of Kouidri. The motivation would have been “Vertical metrics influence the height of ascenders and depth of descenders” (Brown, page 2, para. 2). The suggestion for doing so would allow to determine an adjustment of vertical position for one text box avoiding the font space cut-off or font cut-off of the fonts for the text box. Therefore, it would have been obvious to combine Li, Kouidri, and Brown.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Kouidri and Wiki_Office2010 (Microsoft Office 2010, Archive.org, Font metrics and vertical space in CSS, archive.org, https://web.archive.org/web/20230609101949/https://en.wikipedia.org/wiki/Microsoft_Office_2010, hereinafter “Wiki_Office2010”).
Regarding claim 11, Li in view of Kouidri discloses The computer implemented method of claim 1, further including
However, Li in view of Kouidri fails to disclose, but in the same art of computer graphics, Wiki_Office2010 discloses providing a text editor and responsive to user input for the text editor, editing the text of the first editable text box, wherein the text editor supports a plurality of fonts and wherein the text font is supported by the text editor. (Wiki_Office2010, page 1, para. 1, “Microsoft Office 2010 (codenamed Office 14[6]) is a version of Microsoft Office for Microsoft Windows unveiled by Microsoft on May 15, 2009”; page 7, para. 1, “Excel, PowerPoint, and Word support text effects such as bevels, gradient fills, glows, reflections, and shadows. Publisher and Word support OpenType features such as kerning, ligatures, stylistic sets, and text figures with fonts such as Calibri, Cambria, Corbel, and Gabriola”). Note that: (1) it is known that Office2010’s Word and PowerPoint can be used as a text editor that can be used to edit the text of an editable text box responsive to user input, and the text editor can be provided to method; and (2) the editor has a plurality of fonts for the users to be applied to the text.
Li in view of Kouidri, and Wiki_Office2010, are in the same field of endeavor, namely computer graphics. Before the effective filing date of the claimed invention, it would have been obvious to apply a text editor supporting a plurality of predefined fonts and fonts features for users to edit text, as taught by Wiki_Office2010 into Li in view of Kouidri. The motivation would have been “Excel, PowerPoint, and Word support text effects such as bevels, gradient fills, glows, reflections, and shadows. Publisher and Word support OpenType features such as kerning, ligatures, stylistic sets, and text figures with fonts such as Calibri, Cambria, Corbel, and Gabriola” (Wiki_Office2010, page 7, para. 1). The suggestion for doing so would allow a text editor supporting a plurality of predefined fonts and fonts features for users to edit text. Therefore, it would have been obvious to combine Li, Kouidri, and Wiki_Office2010.
Claims 13 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Kouidri and Peng et al. (US 20200007914 A1, hereinafter “Peng”).
Claim 13 reciting “A computer processing system including: a processing unit; and a non-transitory computer-readable storage medium storing instructions, which when executed by the processing unit, cause the processing unit to perform a method including:” is corresponding to the method of claim 1. Therefore, claim 13 is rejected for the same rationale for claim 1.
However, Li in view of Kouidri fails to discloses, but in the same art of computer graphics, Peng discloses A computer processing system including: a processing unit; and a non-transitory computer-readable storage medium storing instructions, which when executed by the processing unit, cause the processing unit to perform a method including: (Peng, para. [0006], “The electronic device includes at least one processor and a computer readable storage. The computer readable storage is coupled to the at least one processor and stores at least one computer executable instruction thereon which, when executed by the at least one processor, causes the at least one processor to:”; para. [0007], “a non-transitory computer readable storage medium is provided. The non-transitory computer readable storage medium is configured to store a computer program which, when executed by a processor, causes the processor to carry out following actions”). Note that: (1) the electronic device can be regarded as a system; (2) the processor is a processing unit; and (3) and a non-transitory computer readable storage medium is provided.
Li in view of Kouidri, and Peng, are in the same field of endeavor, namely computer graphics. Before the effective filing date of the claimed invention, it would have been obvious to apply the electronic device includes at least one processor and a non-transitory computer-readable storage medium, as taught by Peng into Li in view of Kouidri. The motivation would have been “The electronic device includes at least one processor and a computer readable storage. The computer readable storage is coupled to the at least one processor and stores at least one computer executable instruction thereon which, when executed by the at least one processor, causes the at least one processor to:” and “a non-transitory computer readable storage medium is provided. The non-transitory computer readable storage medium is configured to store a computer program which, when executed by a processor, causes the processor to carry out following actions” (Peng, paras. [0006]-[0007]). The suggestion for doing so would allow to use an electronic data processing device to perform computer graphics operations. Therefore, it would have been obvious to combine Li, Kouidri, and Peng.
Claim 18 reciting “A non-transitory storage medium storing instructions executable by processing unit to cause the processing unit to perform a method including:” is corresponding to the method of claim 1. Therefore, claim 13 is rejected for the same rationale for claim 1.
However, Li in view of Kouidri fails to discloses, but in the same art of computer graphics, Peng discloses A non-transitory storage medium storing instructions executable by processing unit to cause the processing unit to perform a method including: (Peng, para. [0007], “a non-transitory computer readable storage medium is provided. The non-transitory computer readable storage medium is configured to store a computer program which, when executed by a processor, causes the processor to carry out following actions”). Note that: a processor is a processing unit.
The motivation to combine Li, Kouidri, and Peng given in claim 13 is incorporated here.
Claims 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Li, Kouidri, Peng, and Wiki_Offie2010.
Regarding claim 14, the combination of Li, Kouidri, and Peng discloses The computer processing system of claim 13, wherein the non-transitory computer-readable storage medium further stores instructions to implement
However, the combination of Li, Kouidri, and Peng fails to disclose, but in the same art of computer graphics, Wiki_Office2010 discloses a text editor, wherein the text editor is configured to allow a user of the computer processing system to edit each said text box. (Wiki_Office2010, page 1, para. 1, “Microsoft Office 2010 (codenamed Office 14[6]) is a version of Microsoft Office for Microsoft Windows unveiled by Microsoft on May 15, 2009”; page 7, para. 1, “Excel, PowerPoint, and Word support text effects such as bevels, gradient fills, glows, reflections, and shadows. Publisher and Word support OpenType features such as kerning, ligatures, stylistic sets, and text figures with fonts such as Calibri, Cambria, Corbel, and Gabriola”). Note that: (1) it is known that Office2010’s Word and PowerPoint can be used as a text editor that can be used to edit the text of an editable text box responsive to user input; (2) the editor has a plurality of fonts for the users to be applied to the text; and (3) the text editor can be implemented to perform the functions.
the combination of Li, Kouidri, and Peng, and Wiki_Office2010, are in the same field of endeavor, namely computer graphics. Before the effective filing date of the claimed invention, it would have been obvious to apply a text editor supporting a plurality of predefined fonts and fonts features for users to edit text, as taught by Wiki_Office2010 into the combination of Li, Kouidri, and Peng. The motivation would have been “Excel, PowerPoint, and Word support text effects such as bevels, gradient fills, glows, reflections, and shadows. Publisher and Word support OpenType features such as kerning, ligatures, stylistic sets, and text figures with fonts such as Calibri, Cambria, Corbel, and Gabriola” (Wiki_Office2010, page 7, para. 1). The suggestion for doing so would allow a text editor supporting a plurality of predefined fonts and fonts features for users to edit text. Therefore, it would have been obvious to combine Li, Kouidri, Peng, and Wiki_Office2010.
Regarding claim 15, the combination of Li, Kouidri, Peng, and Wiki_Office2010 discloses The computer processing system of claim 14, wherein the text editor has a set of predefined fonts and wherein the editing of each said text box includes changing the font of the text within the text box. Note that: It is well known by Wiki_Office2010 that the text editor (Word and PowerPoint) has a set of predefined fonts that users can click the font pull-down button to select a font and apply it to the text in the text box to change the font.
The motivation to combine Li, Kouidri, Peng, and Wiki_Office2010 given in claim 14 is incorporated here.
Regarding claim 16, the combination of Li, Kouidri, Peng, and Wiki_Office2010 discloses The computer processing system of claim 14, wherein the text editor has a set of colours for text and wherein the editing of each said text box includes changing the colour of the text within the text box. Note that: It is well known by Wiki_Office2010 that the text editor (Word and PowerPoint) has a set of colors for text that users can click the color pull-down button to select a color and apply it to the text in the text box to change the color.
The motivation to combine Li, Kouidri, Peng, and Wiki_Office2010 given in claim 14 is incorporated here.
Regarding claim 17, the combination of Li, Kouidri, Peng, and Wiki_Office2010 discloses The computer processing system of claim 14, wherein the text editor is configured to change the location of the text box. Note that: It is well known by Wiki_Office2010 that the text editor (PowerPoint) allows users to select the text box and drag it to a location and drop it to change the location of the text box.
The motivation to combine Li, Kouidri, Peng, and Wiki_Office2010 given in claim 14 is incorporated here.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wells (US20240221168A1) teaches “obtaining one or more images of the document, wherein the document in the one or more images are valid samples of the document; identify a set of document components based on document issuer provided information and a set of direct checks; deriving a set of document features based at least in part on the one or more images of the document”.
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/Biao Chen/
Patent Examiner, Art Unit 2611
/KEE M TUNG/Supervisory Patent Examiner, Art Unit 2611
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