CTNF 18/530,879 CTNF 85908 DETAILED ACTION Claim Rejections - 35 USC § 112 07-30-02 AIA 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. 07-34-05 AIA Claim 14 recites the limitation " the contrastive loss " in line 1 . There is insufficient antecedent basis for this limitation in the claim. 07-34-05 AIA Claim 15 recites the limitation " the machine learning network " in line 7 . There is insufficient antecedent basis for this limitation in the claim. 07-34-05 AIA Claim 20 recites the limitation " the machine learning network " in line 6 . There is insufficient antecedent basis for this limitation in the claim. 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. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. When reviewing independent claim 1, and based upon consideration of all of the relevant factors with respect to the claim as a whole, claims 1-20 are held to claim an abstract idea without reciting elements that amount to significantly more than the abstract idea and is/are therefore rejected as ineligible subject matter under 35 U.S.C. 101. The Examiner will analyze Claim 1, and similar rationale applies to independent Claims 15 and 20. The rationale, under MPEP § 2106, for this finding is explained below. The claimed invention (1) must be directed to one of the four statutory categories, and (2) must not be wholly directed to subject matter encompassing a judicially recognized exception, as defined below. The following two step analysis is used to evaluate these criteria. Step 1: Is the claim directed to one of the four patent-eligible subject matter categories: process, machine, manufacture, or composition of matter? When examining the claim under 35 U.S.C. 101, the Examiner interprets that the claims is related to a process since the claim is directed to a method for updating a machine learning network parameter. Step 2a, Prong 1 : Does the claim wholly embrace a judicially recognized exception, which includes laws of nature, physical phenomena, and abstract ideas, or is it a particular practical application of a judicial exception? The Examiner interprets that the judicial exception applies since Claim 1 limitation of (iii) generating a visual matrix utilizing the plurality of input images; (iv) generating a text matrix utilizing a text encoder; (v) multiplying the text matrix and the visual matrix to generate an image-text similarity matrix; (vi) utilizing the numerical values assigned at the image-text similarity matrix, determining a loss function; (vii) identifying a gradient of the loss function with respect to parameters; (viii) utilizing the gradient , updating the parameters , and “ determining when a variable associated with the machine learning network meets a threshold , are directed to an abstract . The claim is related to mathematical concept. “(x) in response to when the variable does not meet the threshold, repeating steps (iii-ix)” is a repetition of the abstract ideas above. If/when the claim recites a judicial exception (i.e., an abstract idea enumerated in MPEP § 2106.04(a), a law of nature, or a natural phenomenon), the claim requires further analysis in Prong Two. Step 2a, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application? The additional claim limitations (i) receiving a plurality of input images [data gathering]; (ii) receiving a plurality of text prompts associated with the plurality of input images [data gathering] is nothing more than insignificant extra solution activity. Image encoder , text encoder , and machine learning network are generic MLs. Outputting final updated parameters is a post solution activity. Step 2b : If a judicial exception into a practical application is not recited in the claim, the Examiner must interpret if the claim recites additional elements that amount to significantly more than the judicial exception. They do not, because the claim remains focused on training computations and ends by outputting updated parameters. The additional elements do not integrate the mathematical training routine into a practical application The Examiner finds that Claims 2-14, and 16-19 do not have a limitation that amounts to significantly more. Thus, claims 1-20 recite the same abstract idea and therefore are not drawn to the eligible subject matter as they are directed to the abstract idea without significantly more. Therefore, all claims are rejected under 35 U.S.C. 101. RELEVANT PRIOR ARTS CITED BUT NOT APPLIED (This is not a rejection) Regarding claim 1: Pham et al. (Pub. No. US 2023/0154161) teaches a computer-implemented method for a pre-trained machine- learning network, the computer-implemented method comprising the following steps: (i) receiving a plurality of input images [Para. 51 “ Each training pair including an input image and an input text segment. In particular, the input text segment has been determined by the system or an external source to describe the contents of the input image or otherwise be relevant to the input image ”]; (ii) receiving a plurality of text prompts associated with the plurality of input images [Para. 51 “ Each training pair including an input image and an input text segment . In particular, the input text segment has been determined by the system or an external source to describe the contents of the input image or otherwise be relevant to the input image ”]; (vi) utilizing the numerical values assigned at the image-text similarity matrix (matrix a), determining a loss (contrastive loss) function associated with the image-text similarity matrix (matrix a) [Para. 37]; (vii) identifying a gradient of the loss function (contrastive loss function) with respect to parameters associated with the image encoder (image encoder neural network parameters) and parameters associated with the text encoder (text encoder neural network parameters) [Para. 69 and 83]; (viii) utilizing the gradient, updating the parameters associated with the image encoder (image encoder neural network parameters) and the parameters associated with the text encoder (text encoder neural network parameters) [Para. 73 and 85]; (ix) determining when a variable associated with the machine learning network meets a threshold [Para. 50]; and (x) in response to when the variable does not meet the threshold, repeating steps (iii-ix) and when the variable meets the threshold [Para. 48-50]. Willmott et al. (Pub. No. US 2025/0005916) teaches outputting final updated parameters associated with the text encoder and image encoder of the machine learning network [para. 60 “ If the threshold is met, the system may output a tuned machine learning model at step 417 . If the threshold is not met, the system may update the image encoder parameters and continue to re-run the image set again with an updated image encoder parameter and the sparse logistic regression layer with the unfrozen entries. Thus, the image encoder parameters may be updated until a threshold is met ”]. Yu et al. (US Pub. No. US 2023/0351149) Contrastive captioning neural networks . Veit et al. (Pub. No. US 2023/0111978) cross-example softmax and/or cross-example negative mining. Li et al. (Pub. No. US 2022/0391755) system and mothods for vision-and-language representation learning. NOTE: Claims 1-20 would be allowable if the rejections, above, are overcome through claim amendments or persuasive arguments. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to SOLOMON G BEZUAYEHU whose telephone number is (571)270-7452. The examiner can normally be reached on Monday-Friday 10 AM-7 PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, O’Neal Mistry can be reached on 313-446-4912 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-0101 (IN USA OR CANADA) or 571-272-1000. /SOLOMON G BEZUAYEHU/ Primary Examiner, Art Unit 2666 Application/Control Number: 18/530,879 Page 2 Art Unit: 2674 Application/Control Number: 18/530,879 Page 3 Art Unit: 2674 Application/Control Number: 18/530,879 Page 4 Art Unit: 2674 Application/Control Number: 18/530,879 Page 5 Art Unit: 2674 Application/Control Number: 18/530,879 Page 6 Art Unit: 2674 Application/Control Number: 18/530,879 Page 7 Art Unit: 2674 Application/Control Number: 18/530,879 Page 8 Art Unit: 2674