CTFR 17/538,120 CTFR 72204 Detailed Action Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 2. The amendment filed 3/30/26 has been entered. Clams 1, 11, and 20 have been amended. Claims 1-3, 5-13, and 15-20 are pending. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 3. 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 1-3, 5-13, and 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. CLAIMS 1, 11, & 20 Step 1: These respectively recite a system, method, and non-transitory computer readable medium, and pertains to one of the four categories of eligible matter. Step 2A Prong 1: generating a first training set to include a first training data associated with a first machine learning model performing a first text classification task for a first NLP application implemented in a first chatbot in a first industry, and a second training data associated with a second machine learning model performing a second text classification task for a second NLP application implemented in a second chatbot in a second industry (Mental Process of Evaluation and Judgments which can reasonably be performed in one’s mind with the aid of pencil and paper. Note that the added language of “for a first/second NLP application implemented in a first/second chatbot in a first/second industry” is all just modifying the text classification task, and thus the feature is still just generating a first/second training set); generating a second training set to include a third training data associated with a third machine learning model performing a third text classification task different than the first and second text classification tasks for a third NLP in a third industry (Mental Process of Evaluation and Judgments which can reasonably be performed in one’s mind with the aid of pencil and paper. Again, the added language “for a third NLP in a third industry” just modifies the third classification task); wherein the first machine learning model generates a first label classifying an intent of the expression for the first NLP application implemented in the first chatbot based on the text representation, and the second machine learning model generates a second label classifying an intent of the expression for the second NLP application implemented in the second chatbot based on the text representation (Mental Process of Evaluation and Judgments which can reasonably be performed in one’s mind with the aid of pencil and paper.) Step 2A Prong 2: at least one data processor; and at least one memory storing instructions which, when executed by the at least one data processor, result in operations comprising: (Mere Instructions to Apply an exception as it recites only the idea of a solution or outcome as discussed in MPEP 2106.05(f)); training, based at least on the first training set, a shared machine learning model to generate one or more text representations (Field of Use and Technological Environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP 2106.05(h)); and training the shared machine learning model by at least subjecting the shared machine learning model to a first training iteration using the first training set and a second training iteration using the second training set. (Field of Use and Technological Environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP 2106.05(h)); and deploying the trained shared machine learning model to generate a text representation of an expression received from a client device (Field of Use and Technological Environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP 2106.05(h)). wherein the first machine learning model generates a first label classifying an intent of the expression for the first NLP application implemented in the first chatbot based on the text representation, and the second machine learning model generates a second label classifying an intent of the expression for the second NLP application implemented in the second chatbot based on the text representation (Field of Use and Technological Environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP 2106.05(h)). Step 2B: training, based at least on the first training set, a shared machine learning model to generate one or more text representations; (Field of Use and Technological Environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP 2106.05(h)); and training the shared machine learning model by at least subjecting the shared machine learning model to a first training iteration using the first training set and a second training iteration using the second training set. (Field of Use and Technological Environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP 2106.05(h)); and deploying the trained shared machine learning model to generate a text representation of an expression received from a client device (Field of Use and Technological Environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP 2106.05(h)). wherein the first machine learning model generates a first label classifying an intent of the expression for the first NLP application implemented in the first chatbot based on the text representation, and the second machine learning model generates a second label classifying an intent of the expression for the second NLP application implemented in the second chatbot based on the text representation (Field of Use and Technological Environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP 2106.05(h)). The claim, when considered as a whole, does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible. CLAIM 2 & 12 incorporates the rejections of claim 1 Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated. The claim recites an abstract idea. determine a first intent of the first expression (Mental Process of Evaluation and Judgments which can reasonably be performed in one’s mind with the aid of pencil and paper.) Step 2A Prong 2: The judicial exceptions are not integrated into a practical application wherein the training of the shared machine learning model includes adjusting one or more weights applied by the shared machine learning model such that the shared machine learning model generates, for a first expression from the first training data, a first text representation that enables the first machine learning model to correctly determine a first intent of the first expression . (Field of Use and Technological Environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP 2106.05(h)); Step 2B: wherein the training of the shared machine learning model includes adjusting one or more weights applied by the shared machine learning model such that the shared machine learning model generates, for a first expression from the first training data, a first text representation that enables the first machine learning model to correctly determine a first intent of the first expression . (Field of Use and Technological Environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP 2106.05(h)); The claim, when considered as a whole, does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible. CLAIM 3 & 13 incorporates the rejections of claim 2 Step 2A Prong 1: The judicial exceptions of claim 2 are incorporated. The claim recites an abstract idea. determine a second intent of the second expression. (Mental Process of Evaluation and Judgments which can reasonably be performed in one’s mind with the aid of pencil and paper.) Step 2A Prong 2: The judicial exceptions are not integrated into a practical application wherein the training of the shared machine learning model further includes adjusting the one or more weights applied by the shared machine learning model such that the shared machine learning model generates, for a second expression from the second training data, a second text representation that enables the second machine learning model to correctly determine a second intent of the second expression . (Field of Use and Technological Environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP 2106.05(h)); Step 2B: wherein the training of the shared machine learning model further includes adjusting the one or more weights applied by the shared machine learning model such that the shared machine learning model generates, for a second expression from the second training data, a second text representation that enables the second machine learning model to correctly determine a second intent of the second expression . (Field of Use and Technological Environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP 2106.05(h)); The claim, when considered as a whole, does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible. CLAIM 5 & 15 incorporates the rejections of claim 1 Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated. The claim recites an abstract idea. Step 2A Prong 2: The judicial exceptions are not integrated into a practical application wherein the operations further comprise: tuning one or more of the shared machine learning model, the first machine learning model, or the second machine learning model on the first training data and/or the second training data by applying a regularization technique. (Field of Use and Technological Environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP 2106.05(h)); Step 2B: wherein the operations further comprise: tuning one or more of the shared machine learning model, the first machine learning model, or the second machine learning model on the first training data and/or the second training data by applying a regularization technique. (Field of Use and Technological Environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP 2106.05(h)); The claim, when considered as a whole, does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible. CLAIM 6 & 16 incorporates the rejections of claim 1 Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated. The claim recites an abstract idea. Step 2A Prong 2: The judicial exceptions are not integrated into a practical application wherein the first text classification task and the second classification task comprise natural language processing (NLP) applications associated with different industries. (Field of Use and Technological Environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP 2106.05(h)); Step 2B: wherein the first text classification task and the second classification task comprise natural language processing (NLP) applications associated with different industries. (Field of Use and Technological Environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP 2106.05(h)); The claim, when considered as a whole, does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible. CLAIM 7 & 17 incorporates the rejections of claim 1 Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated. The claim recites an abstract idea. Step 2A Prong 2: The judicial exceptions are not integrated into a practical application wherein the shared machine learning model performs the text embedding task by applying one or more of sum, average, power mean (p-mean), word piece model, skip-thoughts-vectors, quick-thoughts-vectors, InferSent, multi-tasks learning, or Google universal sentence encoder. (Field of Use and Technological Environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP 2106.05(h)); Step 2B: wherein the shared machine learning model performs the text embedding task by applying one or more of sum, average, power mean (p-mean), word piece model, skip-thoughts-vectors, quick-thoughts-vectors, InferSent, multi-tasks learning, or Google universal sentence encoder. (Field of Use and Technological Environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP 2106.05(h)); The claim, when considered as a whole, does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible. CLAIM 8 & 18 incorporates the rejections of claim 1 Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated. The claim recites an abstract idea. Step 2A Prong 2: The judicial exceptions are not integrated into a practical application wherein the shared machine learning model comprises a recurrent neural network (RNN), a convolutional neural network (CNN), and/or a transformer. (Field of Use and Technological Environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP 2106.05(h)); Step 2B: wherein the shared machine learning model comprises a recurrent neural network (RNN), a convolutional neural network (CNN), and/or a transformer. (Field of Use and Technological Environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP 2106.05(h)); The claim, when considered as a whole, does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible. CLAIM 9 & 19 incorporates the rejections of claim 1 Step 1: This recites a system, one of the four categories of eligible matter. Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated. The claim recites an abstract idea. Step 2A Prong 2: The judicial exceptions are not integrated into a practical application wherein the first machine learning model and/or the second machine learning model comprises one or more of a multilayer perceptron (MLP), a recurrent neural network (RNN), a convolutional neural network (CNN), or a transformer. (Field of Use and Technological Environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP 2106.05(h)); Step 2B: wherein the first machine learning model and/or the second machine learning model comprises one or more of a multilayer perceptron (MLP), a recurrent neural network (RNN), a convolutional neural network (CNN), or a transformer. (Field of Use and Technological Environment, it does no more than generally link a judicial exception to a particular technological environment. MPEP 2106.05(h)); The claim, when considered as a whole, does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible. CLAIM 10 incorporates the rejections of claim 1 Step 2A Prong 1: The judicial exceptions of claim 1 are incorporated. The claim recites an abstract idea. wherein the first machine learning model and/or the second machine learning model determines the intent of the expression by at least assigning, to the expression, one or more labels corresponding to an intent of the expression. (Mental Process of Evaluation and Judgments which can reasonably be performed in one’s mind with the aid of pencil and paper.) The claim, when considered as a whole, does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible. Allowable Subject Matter 4. Claims 1-3, 5-13, and 15-20 would be allowable over the prior art of record if the 101 issue were remedied. The prior art alone or in combination does not show the combined features recited in the amended independent claims, including generating a first training set to include a first training data associated with a first machine learning model performing a first text classification task for a first natural language processing (NLP) application implemented in a first chatbot in a first industry and a second training data associated with a second machine learning model performing a second text classification task different than the first text classification task for a second NLP application implemented in a second chatbot in a second industry, the first training data including a first plurality of expressions that are different than a second plurality of expressions comprising the second training data; generating a second training set to include a third training data associated with a third machine learning model performing a third text classification task different than the first and second text classification tasks for a third NLP application in a third industry; training a shared machine learning model to generate one or more text representations by at least training the shared machine learning model in a first training iteration using the first training set and a second training iteration using the second training set; and deploying the trained shared machine learning model to generate a text representation of an expression received from a client device, wherein the first machine learning model generates a first label classifying an intent of the expression for the first NLP application implemented in the first chatbot based on the text representation, and the second machine learning model generates a second label classifying the intent of the expression for the second NLP application implemented in the second chatbot based on the text representation. Response to Arguments 07-37 AIA 5. Applicant's arguments filed 3/3/26 with respect to the 35 U.S.C. 101 rejections , have been fully considered but they are not persuasive. Applicant argues that additional claim elements integrate the abstract idea into a practical application and that the claim improves the technical field of machine learning NLP particularly when the machine learning model is tasked with performing NLP across different industries. However, the claim elements applicant then lists may still be considered mental processes and do not integrate into or improve the physical device on which the machine learning model is deployed. Even if the NLP accuracy is not deficient in a particular industry, still the invention as claimed is not improving any system architecture or processing ability, and this is not integrating the abstract idea into a practical application that improves the technical field per se. Applicant argues that the independent claims do not recite any abstract idea groupings and in particular do not recite a mental process and that the claimed limitations cannot be practically performed in the human mind, nor by a human using pen and paper. The Examiner respectfully disagrees. This limitations describes generating a first training set with first and second training data associated with first and second models respectively performing different text classifications for NLP applications implemented in chatbots; generating a second training set including third training data associated with a third model performing a third text classification for an NLP application implemented in a chatbot; training a shared machine learning model to generate one or more text representations by at least training the shared machine learning model in a first training iteration using the first training set and a second training iteration using the second training set; deploying the trained shared machine learning model to generate a text representation of an expression received from a client device; wherein the first machine learning model generates a first label classifying an intent of the expression for the first NLP application implemented in the first chatbot based on the text representation"; and wherein… the second machine learning model generates a second label classifying the intent of the expression for the second NLP application implemented in the second chatbot based on the text representation. These indeed can be performed by a human and are based on mental processes. The claim language does not specify any extent of data needed to be processed within any given timeframe. Indeed, these training sets may be small, and the models may be very simple. Using results from one model to train a shared model may involve a simple algorithm. Generating text from an expression to determine an intent is very easily accomplished by a human especially with pen and paper. Even exposing a model to expressions associated with other models still may be a mental process and is not in and of itself integrating the abstract idea into a practical application that improves the technical field per se. Applicant recalls the August Memo from 8/4/25,; however, as the layout even shows, the limitations recited in Example 39 are very different with regard to the goals and processes and technological steps, than are the limitations in Applicant’s claims. The limitations of the independent claims may be mental processes performed by a human, as explained above . Conclusion 07-40 AIA 6. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL . See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a). A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. 07-96 AIA 7. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure : Perkins et al. (Patent No.: US 2022/0253647 A1) – “AUTOMATIC MACHINE LEARNING MODEL EVALUATION” relates to making first and second classifications using a first and second machine learning model. Sar Shalom et al. (Patent No.: US 2021/0192136 A1) – “MACHINE LEARNING MODELS WITH IMPROVED SEMANTIC AWARENESS” relates to using a phrase recognition model to determine a final score associated with the input of ngrams of text. Weller (Patent No.: US 2022/0012429 A1) – “MACHINE LEARNING ENABLED TEXT ANALYSIS WITH MULTI-LANGUAGE SUPPORT” relates to using a language determination model to detect whether input text should be analyzed by either the first or second machine learning model. Pentyala et al. (Patent No.: US 11,880,659 B2) – “HIERARCHICAL NATURAL LANGUAGE UNDERSTANDING SYSTEMS” relates to using a hierarchical natural language understanding system with two machine learning models. Tan et al. (CN 113780418 A) – “FILTER METHOD, SYSTEM, DEVICE, AND STORAGE MEDIUM” relates to generating a second training set to include data associated with a third machine learning model performing text classification. 8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEVEN PAUL SAX whose telephone number is (571)272-4072. The examiner can normally be reached Monday - Friday, 9:30 - 6:00 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, Usmaan Saeed can be reached at 571-272-4046. 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. /STEVEN P SAX/ Primary Examiner, Art Unit 2146 Application/Control Number: 17/538,120 Page 2 Art Unit: 2146 Application/Control Number: 17/538,120 Page 3 Art Unit: 2146 Application/Control Number: 17/538,120 Page 4 Art Unit: 2146 Application/Control Number: 17/538,120 Page 5 Art Unit: 2146 Application/Control Number: 17/538,120 Page 6 Art Unit: 2146 Application/Control Number: 17/538,120 Page 7 Art Unit: 2146 Application/Control Number: 17/538,120 Page 8 Art Unit: 2146 Application/Control Number: 17/538,120 Page 9 Art Unit: 2146 Application/Control Number: 17/538,120 Page 10 Art Unit: 2146 Application/Control Number: 17/538,120 Page 11 Art Unit: 2146 Application/Control Number: 17/538,120 Page 12 Art Unit: 2146 Application/Control Number: 17/538,120 Page 13 Art Unit: 2146 Application/Control Number: 17/538,120 Page 14 Art Unit: 2146 Application/Control Number: 17/538,120 Page 15 Art Unit: 2146 Application/Control Number: 17/538,120 Page 16 Art Unit: 2146 Application/Control Number: 17/538,120 Page 17 Art Unit: 2146 Application/Control Number: 17/538,120 Page 18 Art Unit: 2146
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