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 .
Election/Restrictions
Applicant’s election without traverse of Invention I (claims 1-9 and 16-20) in the reply filed on March 12, 2026 is acknowledged.
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) 1-9 and 16-26 are is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by M. He, Y. Zhu et al., CN 116797868 A (hereinafter He).
Independent claim 1, He discloses a method comprising: obtaining a text content image and a text style image; encoding, using a text content adapter of an image generation model, the text content image to obtain content guidance information (i.e. According to a second aspect of embodiments of the present specification, there is provided a handwritten text image generation method, comprising:
receiving a handwritten text image generation request sent by a user, wherein the handwritten text image generation request carries an initial image and initial style information of the initial image – Disclosure of Invention, p. 3, Para 7-8);
encoding, using a text style adapter of the image generation model, the text style image to obtain style guidance information (i.e. inputting the initial image and the initial style information into a condition encoder to obtain visual features (e.g. guidance information), semantic features (e.g. guidance information) and style features of the initial image - Disclosure of Invention, p. 3, Para 9); and
generating, using the image generation model, a synthesized image based on the content guidance information and the style guidance information, wherein the synthesized image includes text from the text content image having a text style from the text style image (i.e. inputting an initial image, visual features, semantic features and style features into a diffusion generation model to obtain noise data corresponding to the initial image, wherein the diffusion generation model is obtained based on a sample text image, sample image features of the sample text image and noise sample image training, and the noise sample image is obtained by adding sample noise to the sample text image, and the sample image features comprise sample visual features, sample semantic features and sample style features;
Generating a target handwritten text image corresponding to the initial image according to the initial image and the noise data – Disclosure of Invention, p. 4, Para 1-2).
Claim 2, He discloses the method of claim 1, further comprising: encoding, using a background adapter of the image generation model, a background image to obtain background guidance information, wherein the synthesized image is generated based on the background guidance information (i.e. the server 200 is configured to input an initial image into the condition encoder, and obtain image features of the initial image; inputting the initial image and the image characteristics into a diffusion generation model to obtain noise data corresponding to the initial image – p. 6, Para 11; In an alternative embodiment of the present disclosure, the visual information rich in text images is concentrated on text, unlike the pattern of natural images. Thus, embodiments of the present description propose a text recognition encoder using a text recognition model that obtains visual features that can better express general information (e.g., texture and color) of a text image rather than noise information (e.g., background) – p. 8, Para 4).
Claim 3, He discloses the method of claim 2, wherein: the background image indicates a location of the text (i.e. The initial image may be an image of a different scene, - p. 7, Para 6).
Claim 4, He discloses the method of claim 1, further comprising: encoding, using a text encoder of the image generation model, a text prompt to obtain text guidance information (i.e. receiving a handwritten text image generation request sent by a user, wherein the handwritten text image generation request carries an initial image and initial style information of the initial image – Disclosure of Invention, p. 3, Para 7-8; inputting the initial image and the initial style information into a condition encoder - Disclosure of Invention, p. 3, Para 9), wherein the synthesized image is generated based on the text guidance information (i.e. training a text recognition model requires expanding training samples, such as data synthesis and data augmentation – p. 10, Para 15).
Claim 5, He discloses the method of claim 1, further comprising: determining, using a character recognition component, a character location of each character in the text content image, wherein the content guidance information is based on the character location (i.e. Optical character recognition technology is one of the most successful applications in pattern recognition, and has extremely high research value, while text recognition is a key link in optical character recognition. Because complexity and diversity in the real world are difficult to achieve by collecting and annotating limited real text image data, training a text recognition model requires expanding training samples, such as data synthesis and data augmentation – p. 10, Para 15).
Claim 6, He discloses the method of claim 1, further comprising: generating a style vector map that indicates a location of the text style in the text style image, wherein the style guidance information is based on the style vector map (i.e. an image block index of the initial image based on the initial visual characteristics – p. 8, Para 5; It should be noted that, assuming that the initial image is input to the text recognition encoder, the initial visual feature is obtained with a size of h×w×c, where H and W are the height and width of the feature sequence. Since the text recognition encoder processes text lines, H is 1 and c is the number of encoded feature channels. The block index of the initial image is P i ， P i ∈ [1 ， W]For identifying the location of the image block in the initial image. – p. 8, Para 8).
Claim 7, He discloses the method of claim 1, wherein generating the synthesized image comprises: performing a reverse diffusion process (i.e. In practical application, a model can be generated by training diffusion, and noise is gradually removed based on random Gaussian noise in reasoning, so that a natural image is generated. – p. 11, Para 1).
Claim 8, He discloses the method of claim 1, wherein generating the synthesized image comprises: providing the content guidance information and the style guidance information to an up- sampling layer of the image generation model (i.e. Diffusion generation model: and gradually denoising the initial image through random Gaussian noise to generate a target text image… different generation conditions and denoised text image I n' Is combined… , and the combining process employs a cross-attention mechanism to facilitate information interaction and learning with each other – p. 13, Para 6).
Claim 9, He discloses the method of claim 1, wherein: the text content adapter is trained using a character recognition loss (i.e. adjusting parameters of the text recognition model according to the recognition loss value, - p, 11, Para 6).
Independent claim 16, the claim is similar in scope to claim 1. Therefore, the rationale as applied in the rejection of claim 1 applies herein.
Claim 18, He discloses the apparatus of claim 16, wherein: the text content adapter and the text style adapter comprise a control network that is initialized using parameters from the image generator (i.e. By applying the scheme of the embodiment of the specification, a text recognition training set is constructed according to a plurality of initial images and target text images corresponding to the initial images; training the text recognition model by using the text recognition training set to obtain a target text recognition model. – p. 11, Para 7).
Claim 19, He discloses the apparatus of claim 16, wherein: the image generator comprises a diffusion model (i.e. According to a second aspect of embodiments of the present specification, there is provided a handwritten text image generation method, comprising:… inputting an initial image, visual features, semantic features and style features into a diffusion generation model - Disclosure of Invention, pg. 4, Para 1).
Claim 20, He discloses the apparatus of claim 16, further comprising: a multi-modal encoder configured to encode a text prompt to obtain text guidance information, wherein the synthesized image is generated based on the text guidance information (i.e. receiving a handwritten text image generation request sent by a user, wherein the handwritten text image generation request carries an initial image and initial style information of the initial image – Disclosure of Invention, p. 3, Para 7-8).
Independent claim 21, the claim is similar in scope to claim 1. Therefore, the rationale as applied in the rejection of claim 1 applies herein.
Claims 22-26, the corresponding rationale as applied in the rejection of claims 2, 4-6 and 8 apply herein.
Conclusion
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/CHANTE E HARRISON/Primary Examiner, Art Unit 2615
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