CTNF 18/746,171 CTNF 87828 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. DETAILED ACTION This communication is responsive to the application filed on 06/18/2024. Claims 1-9 are pending in this application. This action is made non-final . 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 1-9 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. Step 1 — Statutory Category The claim is directed to: “A learning system comprising … memory … processor …” This is a machine/system and therefore falls within a statutory category under §101. Conclusion: Step 1 is satisfied. Step 2A, Prong One — Whether the Claim Recites a Judicial Exception The claim recites: collecting information, analyzing information, and making a prediction. In particular: training machine learning models, computing performance scores, and selecting a model and that could be characterized as: mathematical concepts, statistical analysis, or mental processes performed using generic computing components. Machine learning model training and scoring are often treated by examiners as mathematical calculations or data analysis operations. Conclusion: The claim recites abstract idea. Step 2A, Prong Two — Whether the Claim Integrates the Exception into a Practical Application The claim recites: receiving medical data of patients, training models using resource constraints, and predicting sickness. These limitations tie the claim to: processing real-world sensor data, computer-implemented machine learning operations, and predictive modeling. The “in consideration of available computational resources and time budget” limitation is particularly helpful because it suggests: adaptive resource-aware model training, system-level optimization, and practical engineering constraints. The claim lacks some specifics: how models are trained, what technological improvement is achieved. Conclusion: The claim does not integrate the exception into a practical application. Step 2B — Whether the Claim Includes Significantly More The claim does not recite additional elements sufficient to amount to significantly more than the judicial exception. The additional elements, including the memory, processor, camera, plurality of machine learning models, performance scores, and model selection operations, are generic computer components performing well-understood, routine, and conventional computer functions such as: receiving data, processing information, performing mathematical calculations, evaluating results, and selecting an outcome based on the evaluation. The claim further recites the training of machine learning models and computation of performance scores at a high level of abstraction. It did not recite: a specific machine learning technique, a specialized model-training architecture, or any technological improvement to computer functionality. The claim elements merely apply the abstract idea using generic computing technology and do not provide an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter. Accordingly, the claim does not amount to significantly more than the recited abstract idea and is directed to patent-ineligible subject matter under 35 U.S.C. §101. Claim Rejections - 35 USC § 103 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 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 . 07-20-aia AIA 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 of this title, 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. 07-21-aia AIA Claim s 1 and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Grady et al. (US 2026/0120273; Hereinafter Grady) in view of Bridgewater et al. (US 2020/0383648; Hereinafter Bridgewater) . Re claims 1 and 8-9, Grady teaches a learning system comprising: at least one memory configured to store instructions (claim 9, memory storing instructions); and at least one processor configured to execute the instructions (claim 9, one processor operatively connected to the at least one memory and configured to execute the instructions) to: receive medical data of patients in a hospital ([0023], the method may include initiating a process for determining patient anatomy from raw medical acquisition data by starting with model parameters of an average individual, or model parameters based on some input associated with a patient (e.g., patient weight)); train a plurality of machine learning models based on the medical data in consideration of available computational resources and time budget ([0027], one patient may have a machine learning system trained on his/her image/acquisition data. That machine learning system may be used to produce patient-specific model parameter values for the patient); select a machine learning model using for a prediction of classifications of sickness among the plurality of trained machine learning models based on the computed performance scores ([0027], predict patient-specific model parameter values. Other embodiments include using both patient data and individual data to train the machine learning system. For example, after an application phase, the patient image data of the application phase may be used as input to the training phase, to supplement the training of the machine learning system. That machine learning system may then be applied to predict patient-specific model parameters for another patient, or for the same patient at a different point in time). Grady does not explicitly teach: compute performance scores of the plurality of trained machine learning models. However, it is taught by Bridgewater ([0182], the MLE may have been previously initialized and trained as described in more detail elsewhere herein, which may have involved using the health data model 304 . The step 1202 may include applying the MLE after the training to produce a list of one or more actions. For each determined action, the MLE also may have calculated a value (e.g., a weight or score) indicative of the effectiveness of the action for the health condition being serviced, and the one or more actions may be ranked according to an effectiveness value). In addition, Bridgewater also teach: receive medical data of patients in a hospital ([0012], providing the health service based on information about the person, including the monitored values of the health metric of the person); train a plurality of machine learning models based on the medical data in consideration of available computational resources and time budget ([0152], the MLE may be trained by running several data sets (i.e., input vectors) of health metric values (e.g., test data) through the MLE. Also see [0150], the value of analyzing a feature may be outweighed by the benefits of removing the feature from the analysis to save resources (e.g., computational resources and memory)); select a machine learning model using for a prediction of classifications of sickness among the plurality of trained machine learning models based on the computed performance scores ([0187], the actions may be ranked based on a determined effectiveness of the actions (e.g., based on a determined effectiveness value). One or more top ranked action may be selected. Also see [0026], determining, based on the initial values, baseline values of the health metrics for the person, receiving, from the health sensor at a time after the initial period, an additional value corresponding to at least one of the health metrics, and determining whether the additional value represents an irregularity with respect to the baseline values for the person). Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to add the teaching as seen in Bridgewater’s content into Grady’s invention because it would evaluate and compare the predictive performance of the model and select an optimal model for deployment. Allowable Subject Matter Claims 2-7 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims because the additional limitations integrate the abstract idea into a practical application and therefore overcome the rejection under 35 USC § 101 . Conclusion The prior art made of record on form PTO-892 and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111 ( c ) to consider these references fully when responding to this action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to TOAN H VU whose telephone number is (571)270-3482. The examiner can normally be reached on PHP 9-5:30 PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen Hong can be reached on 571-274124. 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 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /TOAN H VU/Primary Examiner, Art Unit 2178 Application/Control Number: 18/746,171 Page 2 Art Unit: 2178 Application/Control Number: 18/746,171 Page 3 Art Unit: 2178 Application/Control Number: 18/746,171 Page 4 Art Unit: 2178 Application/Control Number: 18/746,171 Page 5 Art Unit: 2178 Application/Control Number: 18/746,171 Page 6 Art Unit: 2178 Application/Control Number: 18/746,171 Page 7 Art Unit: 2178 Application/Control Number: 18/746,171 Page 8 Art Unit: 2178 Application/Control Number: 18/746,171 Page 9 Art Unit: 2178 Application/Control Number: 18/746,171 Page 10 Art Unit: 2178
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