CTNF 17/982,234 CTNF 83558 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claim 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without “significantly more”. Claim(s) 1-24 is/are directed to Abstract Idea such as an idea standing alone such as an instantiated concept, pan or scheme, as well as a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper for example using measurement received from a mobile device, transmitting from the source relay node to a donor access node. The apparatus and the method claim 1, 9 and 17 recites limitation, “generate one or more variations of an image based, at least in part, on one or more locations of textual information in the image and one or more template images that have been altered to remove original textual information”. Since the claim is directed to a process and a machine, which is one of the statutory categories of the invention (Step 1: YES). The claim is then analyzed to determine whether it is directed to any judicial exception. The claim recites generate one or more variations of an image. The generation step recited in the claim is no more than an abstract idea performed by human mind/pen and paper for example erase a writing on the image and write new number where fundamental abstract concept of applying algorithms to generate image variation. See MPEP 2106.04 (a) III claim recite a mental process when they contain limitation that can practically be performed in the human mind. For example "comparing BRCA sequences and determining the existence of alterations," where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind, University of Utah Research Foundation v. Ambry Genetics, 774 F.3d 755, 763, 113 USPQ2d 1241, 1246 (Fed. Cir. 2014); (Step 2A: Prong One Abstract Idea=Yes). The claim is then analyzed if it requires an additional elements or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception – i.e., limitation that are indicative of integration into a practical application: improving to the functioning of a computer or to any other technology or technical field. In the current claims, there is no additional elements that would integrate the abstract idea into a practical application (Step 2A: Prong Two Abstract Idea=Yes). Next the claim as a whole is analyzed to determine if there are additional limitation recited in the claim such that the claim amount to significantly more than an abstract idea. The claim requires the additional limitation of a computer with the central processing unit, memory, a printer, an input and output terminal and a program. These generic computer components are claimed to perform the basic functions of storing, retrieving and processing data through the program that enables. In the current scenario, there are no additional elements that would amount to significantly more than the abstract idea. Therefore, the claim does not amount to significantly more than the abstract idea itself (Step 2B: No). Accordingly, the claim is not patent eligible. Further, dependent claims do not add any positive limitation or step that recite within the scope of the claim and does not carry patentable weight they are also rejected for the same reasons as independent claims. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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. 07-07-aia AIA 07-07 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 – 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-03-aia AIA Claim(s) 1-24 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Gopalkrishna et al. Pub. No. US 20230005108 A1 . Regarding Claim 1, Gopalkrishna teaches a processor (Fig. 5 Unit 505, processor), comprising: one or more circuits (Fig. 5 and Para 55, An electrical bus 500 serves as an information highway interconnecting the other illustrated components of the hardware. Processor 505 is a central processing device of the system) to use one or more neural networks (Para 59, The object may be detected by any classifier (i.e., a machine learning model such as an artificial neural network that has been trained on a set of training images which have been labeled with objects that appear in the images) to generate one or more variations of an image based (Para 58 and FIG. 1 and 6 illustrates an example comparison of an original image sequence 601 that includes scene text (the word “coffee”) and a modified image sequence 602 in which the scene text has been replaced (with the word “robert”) ) at least in part, on one or more locations of textual information in the image ( Para 58, Fig. 1 orange or Fig. 6 Unit 601 coffee) and one or more template images (Fig. 6 Unit 601 inherently has a template image and Para 28, 103 the system will identify and extract a region of interest (ROI) from each of the image frames. The ROI in each frame will include text that is to be replaced) that have been altered to remove original textual information (Fig 1 and Fig. 6 Step 602 and Para 58, original image sequence 601 that includes scene text (the word “coffee”) and a modified image sequence 602 in which the scene text has been replaced (with the word “robert” i.e, altered to remove original textual information). Regarding Claim 2, Gopalkrishna teaches wherein the one or more circuits are to train a second one or more neural networks to identify text in one or more images based, at least in part, on the one or more variations of the image (Para 34 and 59). Regarding Claim 3, Gopalkrishna teaches wherein the one or more circuits are to replace text in one or more training images by generating one or more random textual features (Para 54 and 55). Regarding Claim 4, Gopalkrishna teaches wherein the one or more template images are generated from one or more training images (Para 28 and 29). Regarding Claim 5, Gopalkrishna teaches wherein a second one or more neural networks are trained, using one or more training images, to detect a location of text in the image and recognize the text at the detected location (Para 42 and 43). Regarding Claim 6, Gopalkrishna teaches wherein the one or more circuits are to train a second one or more neural networks using the one or more training images, such that the second one or more neural networks identify text from one or more images and convert the identified text into a machine-readable form (Para 42 and 43). Regarding Claim 7, Gopalkrishna teaches wherein one or more images are to be generated using one or more rectangle forms of text and a perspective transformation from a polygon (Para 65). Regarding Claim 8, Gopalkrishna teaches wherein one or more variations of the image comprise one or more variations of text at the one or more locations (Para 42). Regarding Claim 9, it has been rejected for the same reasons as claim 1 and further teaches a system (Fig. 5 and Para 56, communication system) comprising: one or more processors (Fig. 5 Unit 505) to use one or more neural networks (Para 61, An “electronic device” or a “computing device” refers to a device or system that includes a processor and memory. Each device may have its own processor and/or memory, or the processor and/or memory may be shared with other devices as in a virtual machine or container arrangement). Regarding Claim 10, Gopalkrishna teaches wherein the one or more processors are to train a second one or more neural networks to identify text in one or more images based, at least in part, on the one or more neural networks generating the one or more variations of the image based, at least in part, on one or more locations of textual information in the image (Para 42). Regarding Claim 11, it has been rejected for the same reasons as claim 3. Regarding Claim 12, Gopalkrishna teaches wherein the one or more processors are to generate the one or more variations of the image by replacement of text in one or more training images (Para 43). Regarding Claim 13, it has been rejected for the same reasons as claim 5. Regarding Claim 14, it has been rejected for the same reasons as claim 6. Regarding Claim 15, it has been rejected for the same reasons as claim 7. Regarding Claim 16, Gopalkrishna teaches wherein the variations of the image are based, at least in part, on one or more synthetic data generators to create one or more augmented images (Para 44). Regarding Claim 17, it has been rejected for the same reasons as claim 1 and further teaches a method (Fig. 2 and Para 27, FIG. 1 . FIG. 2 illustrates the steps of FIG. 1 in flowchart format ) comprising: one or more processors (Para27, processor). Regarding Claim 18, it has been rejected for the same reasons as claim 2. Regarding Claim 19, it has been rejected for the same reasons as claim 3. Regarding Claim 20, Gopalkrishna teaches further comprising: replacing text in one or more training images by inputting one or more selected textual features to generate one or more augmented images(Para 44). Regarding Claim 21, Gopalkrishna teaches further comprising: training a second one or more neural networks to detect and recognize text in images, based at least in part on the one or more variations of the image (Para 28). Regarding Claim 22 , Gopalkrishna teaches further comprising: training the one or more neural networks to replace add text in one or more locations in the one or more template images corresponding to the removed original textual information one or more training images, based at least in part on generating a template image and adding alternative text at the location of the template (Para 30). Regarding Claim 23, Gopalkrishna teaches further comprising: generating the variations of the image based, at least in part, on a perspective transformation (Para 31). Regarding Claim 24, Gopalkrishna teaches further comprising: using the one or more neural networks to create one or more augmented images for training at least one of OCDNet or OCRNet (Para 28) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Stonehouse Patent No. US 12424004 B2 - Artificial intelligence based steganographic systems and methods for analyzing pixel data of a product to detect product counterfeiting Vogler et al. Pub. No. US 20240212811 A1 - METHOD AND APPARATUS FOR PROCESSING OF MULTI-MODAL DATA Peng et al. Pub. No. US 20230177821 A1 - DOCUMENT IMAGE UNDERSTANDING Tichenor et al. Patent. No. US 11227445 B1 - Artificial reality augments and surfaces Shanmugam et al. Patent No. US 10990755 B2 - Altering text of an image in augmented or virtual reality Cooper et al. Pub. No. US 20200117953 A1 - SYSTEMS AND METHODS FOR TRAINING MACHINE MODELS WITH AUGMENTED DATA Hall Pub. No. US 20140351687 A1 - Contextual Alternate Text for Images Shelton et al. Patent No. US 8457448 B2 - Removing inserted text from an image using extrapolation for replacement pixels after optical character recognition Sonoda Patent No. US 8400675 B2 - Image forming apparatus, image forming method, computer readable medium storing image forming program and recording medium for performing control to change the number of color materials used for at least the rim portion of the recording medium Sayers et al. Pub. No. US 20120207390 A1 - SYSTEMS AND METHODS FOR REPLACING NON-IMAGE TEXT Any inquiry concerning this communication or earlier communications from the examiner should be directed to NIZAR N SIVJI whose telephone number is (571)270-7462. The examiner can normally be reached Monday-Friday 7-4. 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, Alison Slater can be reached at (571) 270-0375. 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. NIZAR N. SIVJI Primary Examiner Art Unit 2647 /NIZAR N SIVJI/Primary Examiner, Art Unit 2647 Application/Control Number: 17/982,234 Page 2 Art Unit: 2647 Application/Control Number: 17/982,234 Page 3 Art Unit: 2647 Application/Control Number: 17/982,234 Page 4 Art Unit: 2647 Application/Control Number: 17/982,234 Page 5 Art Unit: 2647 Application/Control Number: 17/982,234 Page 6 Art Unit: 2647 Application/Control Number: 17/982,234 Page 7 Art Unit: 2647 Application/Control Number: 17/982,234 Page 8 Art Unit: 2647 Application/Control Number: 17/982,234 Page 9 Art Unit: 2647 Application/Control Number: 17/982,234 Page 10 Art Unit: 2647
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