CTNF 18/749,824 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 06/21/2024. Claims 1-8 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 1-8 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 using mathematical techniques, and making a prediction/selection. In particular: training machine learning models, computing performance scores, and selecting a model and that could be characterized as: mathematical concepts, or mental processes performed using generic computing components. Machine learning model training and scoring are 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 pedestrian traffic from a camera, training models using resource constraints, and predicting future pedestrian traffic demographics. These limitations tie the claim to: processing data, computer-implemented machine learning operations, and predictive modeling in a surveillance/traffic-monitoring context. The “in consideration of available computational resources and time budget” limitation particularly suggests: adaptive resource-aware model training, 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 and processing data, 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, 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, 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Vallespi-Gonzalez et al. (US 2019/0042865; Hereinafter Gonzales) in view of Bhattacharyya et al. (US 2020/0090070; Hereinafter Bhattacharyya) . Re claims 1 and 7-8, Vallespi-Gonzalez teaches a learning system (fig. 3, autonomous vehicle) comprising: at least one memory configured to store instructions (fig. 7, memory); and at least one processor configured to execute the instructions (fig. 7, processor) to: receive pedestrian traffic from a camera monitoring the pedestrian traffic (fig. 3 and [0109], the segmentation component 306 can process the received sensor data 302 and map data 304 to determine potential objects within the surrounding environment, for example using one or more object detection systems); train a plurality of machine learning models based on the pedestrian traffic in consideration of available computational resources and time budget (fig. 6 and [0120]-[0121], a training system 450 can be used to help train a classification model such as classification model 118 of FIG. 1 or classification model 160 of FIG. 2. Also see [0060], By providing an image processing system that includes an FPGA device configured to implement image transformation and object detection, valuable computing resources within a vehicle control system that would have otherwise been needed for such tasks can be reserved for other tasks such as object prediction, route determination, autonomous vehicle control, and the like); compute performance scores of the plurality of trained machine learning models ([0070], the classification output from classification model 118 can also include a probability score associated with the classification. For example, the probability score can be indicative of a probability or likelihood of accuracy for the classification (e.g., a likelihood that an object is or is not detected, or a likelihood that a classification for an object (e.g., as a pedestrian, bicycle, vehicle, etc.) is correct); and select a machine learning model used for a prediction of future pedestrian traffic demographics ([0053]-[0056], the prediction system can predict where each object will be located within the next 5 seconds, 10 seconds, 20 seconds, etc. As one example, an object can be predicted to adhere to its current trajectory according to its current speed. Also see [0133], classification model with confidence score) but Gonzalez does not explicitly teach: select a machine learning model among the plurality of trained machine learning models based on the computed performance scores. However, it is taught by Bhattacharyya ([0038], the model having highest lift and gain values in top deciles (groups) of the data set based on predictive probability score may be selected as the best machine-learning predictive model. Also, the selected machine learning predictive model may have maximum KS value compared to other models. Upon comparing different machine learning predictive models and selection of the best machine learning predictive model, the selected model may be enabled to predict future events). 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 Bhattacharyya’s content into Vallespi-Gonzalez’s invention because it would improve prediction accuracy and computational efficiency by dynamically utilizing the best performing model for a given prediction task. Allowable Subject Matter Claims 2-6 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 571-272-4124. 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: 18/749,824 Page 2 Art Unit: 2178 Application/Control Number: 18/749,824 Page 3 Art Unit: 2178 Application/Control Number: 18/749,824 Page 4 Art Unit: 2178 Application/Control Number: 18/749,824 Page 5 Art Unit: 2178 Application/Control Number: 18/749,824 Page 7 Art Unit: 2178 Application/Control Number: 18/749,824 Page 8 Art Unit: 2178
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