CTNF 18/342,880 CTNF 78823 DETAILED ACTION 1. This Office Action is in response to the application filed on 06/28/2023. Claims 1-20 are pending and are examined. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Information Disclosure Statements 3. The information disclosure statements (IDS) submitted on 06/28/2023, 06/20/2024 and 10/04/2024 are being considered by the examiner. Claim Rejection - 35 USC § 212(b) 35 USC § 212(b) reads as follows: 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. 4. Claim 20 creates an ambiguity with a structure that can be interpreted as an independent claim with the limitations of Claim 13 or a dependent claim of Independent Claim 13. The structure needs to set clear limitations configured without ambiguity. Claim Rejections - 35 USC § 101 07-04 AIA 07-04-01 35 USC § 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvements thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. 5. Claim 1 is rejected under 35 U.S.C. § 101 as being directed to patent-ineligible subject matter. ANALYSIS UNDER ALICE/MAYO FRAMEWORK Step 1: Statutory Category Claim 1 recites a “data processing apparatus” and falls within the statutory category of a machine under 35 U.S.C. § 101. Step 2A, Prong One: Abstract Idea However, Claim 1 is directed to an abstract idea. Specifically, the Claim is directed to: A. Mathematical concepts – using computational algorithms to determine operation result data and using a machine learning model to predict results, which are mathematical relationships and formulas (MPEP 2106.04(a)(2)) B. Mental processes – collecting data, analyzing it using algorithms/models, and generating predictions, which are concepts that can be performed in the human mind or with pen and paper (MPEP 2106.04(a)(2)) The recitations of “computational algorithm” and “machine learning model” are high-level functional language that do not provide meaningful limitations beyond generic data processing steps. Step 2A, Prong Two: Integration into Practical Application The Claim does not integrate the abstract idea into a practical application. The additional elements recited (generic processors and memory) merely perform their basic functions of processing, storing, and executing instructions. The claim does not: A. Improve computer functionality or other technology. B. Apply the abstract idea with any particular machine or transformation C. Add meaningful limitations beyond generally linking the abstract idea to a particular technological environment The claim amounts to no more than instructions to apply the abstract idea using generic computer components. See Alice Corp. v. CLS Bank Int’l , 573 U.S. 208 (2014). Step 2B: Inventive Concept The claim does not contain an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter. The recitation of generic processors and memory performing routine, conventional functions do not provide an inventive concept. 5. Independent Claims 10, and 13 have similar limitations to that of Claim 1 and are rejected under 35 USC § 101 for similar reasons as noted above. Dependent claims 2. - 9., 11. - 12., and 14. - 20. Do not correct the related independent claim from which they depend and are in consequence rejected under 35 USC 101 for similar reasons as noted above. 6. Claim Amendment Strategies to properly respond to the above rejections are informally provided as examples for applicant to consider in concept but must be consistent within the metes and bounds of the specification . To overcome the § 101 rejection, the claims must be amended to include three essential elements that work together to transform it from an abstract concept into a patent-eligible technical solution: ELEMENT 1: NARROW TO SPECIFIC TECHNICAL FIELD WITH CONCRETE PROBLEMS What to Add: Specific technical domain (not generic “data processing”) Examples: “for reducing computational latency in real-time control systems,” “for managing processor resources in autonomous vehicle systems,” “for maintaining continuous sensor output in industrial monitoring” The specific computer functionality problem being solved Why continuous computation is problematic (resource exhaustion, thermal issues) Why computation gaps are unacceptable (safety, real-time requirements) Technical constraints that create the problem (processing time, hardware limits) Quantifiable technical parameters showing problem severity Processing time requirements (“requires at least N milliseconds”) Resource constraints (“causes processor utilization exceeding X%”) Performance requirements that conventional approaches cannot meet Why This Matters: Establishes the claim solves a computer functionality problem, not an abstract business/mental process Shows the problem is rooted in computer technology and cannot exist outside computing context Addresses Step 2A, Prong Two (practical application) ELEMENT 2: ADD IMPLEMENTATION DETAILS SHOWING UNCONVENTIONAL COMPUTER OPERATIONS What to Add: A. Machine Learning Specificity: Specific architecture type: “recurrent neural network,” “LSTM network with at least three hidden layers,” “transformer-based model” Technical configuration: “configured to extrapolate temporal patterns,” “trained using historical execution data from at least N cycles” Performance characteristics: “maintains prediction accuracy within X% over Y time steps” B. Computational Avoidance Mechanism: Explicit bypass language: “wherein predicting the second predicted result data bypasses execution of [the algorithm] during the second time period” Technical function: “wherein processor resources allocated to [the algorithm] are released during the second time period” Speed comparison: “wherein the machine learning inference requires at least X% less processing time than executing [the algorithm]” C. Feedback Integration: Comparison mechanism: “wherein the third operation result data is compared with the second predicted result data to generate error metrics” Adjustment criteria: “wherein parameters of the machine learning model are dynamically adjusted when error metrics exceed a predetermined threshold of X%” Technical purpose: “to improve prediction accuracy for subsequent predictions” Integration explanation: “wherein the temporal arrangement reduces overall computational load while maintaining accuracy through periodic recalibration” Why This Matters: Demonstrates specific technical implementation, not generic “apply it on a computer” Shows unconventional computer operations (using prediction to avoid computation) Proves ordered combination serves specific technical function Establishes operations cannot be performed mentally (requires specific computer architecture) Addresses both Step 2A, Prong Two (not merely linking to field of use) and Step 2B (inventive concept) ELEMENT 3: DEMONSTRATE MEASURABLE IMPROVEMENTS TO COMPUTER FUNCTIONALITY What to Add: Explicit improvement language: “reducing processor utilization by at least X% during the second time period” “maintaining continuous output availability while avoiding computational gaps” “decreasing system latency by at least Y milliseconds” Technical explanation of how improvement is achieved: “by bypassing execution of the computationally-intensive algorithm during the second time period” “wherein the second predicted result data maintains output continuity without processor-intensive computation” Quantifiable metrics or thresholds: Specific percentage reductions (processor usage, latency, resource consumption) Timing requirements met (millisecond-level specifications) Accuracy maintained (percentage thresholds) Connection between temporal arrangement and improvement: Show WHY the three-period structure achieves the technical benefit Explain the technical trade-off being managed (accuracy vs. efficiency) Why This Matters: Directly satisfies Enfish test (improvement to computer functionality itself) Provides objective, measurable evidence of technical advancement Distinguishes from using computers merely for speed or efficiency in abstract process Shows invention makes computers work better, not just uses computers to automate Critical for establishing practical application under Step 2A, Prong Two Conclusion 07-96 7. The prior art made of record and not relied upon is considered pertinent applicant's disclosure: See IDS 8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joseph P. Hirl whose telephone number is (571)272-3685. The examiner can normally be reached Monday - Thursday 5:30 am to 3:30 pm. 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. 9. If attempts to reach the examiner by telephone are unsuccessful, the examiner's Director, Amy C. Johnson can be reached on 571-272-2238. 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. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /JOSEPH P HIRL/Supervisory Patent Examiner, Art Unit 2435 Application/Control Number: 18/342,880 Page 2 Art Unit: 2435 Application/Control Number: 18/342,880 Page 3 Art Unit: 2435 Application/Control Number: 18/342,880 Page 4 Art Unit: 2435 Application/Control Number: 18/342,880 Page 6 Art Unit: 2435 Application/Control Number: 18/342,880 Page 7 Art Unit: 2435 Application/Control Number: 18/342,880 Page 8 Art Unit: 2435