Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
The drawings are objected to because:
Figure 3 and Figure 4, the text is fuzzy and not legible in the Figures.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112
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.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "the neural network model…the historical grades". There is insufficient antecedent basis for this limitation in the claim. It is also not clear if the neural network model the claims is the same and/or different from a regression artificial-intelligence (AI) model in the claim.
Claim 15 recites the limitation "the neural network model…the historical grades". There is insufficient antecedent basis for this limitation in the claim. It is also not clear if the neural network model the claims is the same and/or different from a machine-learning model in the claim.
Claim 18 recites the limitation "the neural network model…the historical grades". There is insufficient antecedent basis for this limitation in the claim. It is also not clear if the neural network model the claims is the same and/or different from a regression artificial-intelligence model in the claim.
As per claims 2-14, 16, 17, 19, and 20 are rejected to for incorporating the above limitations into the claims by dependency.
Allowable Subject Matter
Claims 1-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter:
With respect to claims 1-14, the closest prior art Krneta et al. (U.S. Pub. No.
2023/0153219 A1), discloses: An automated, hybrid-computing, quantum-infused, optimization process for monitoring, grading, and enhancing software (See Figure 2) comprising the steps of: detecting, by a quantum computing engine (QCE), non-optimal software (See Para [0019], i.e. metrics fail to satisfy the threshold, the quantum computing monitoring system may consider that algorithm including the quantum computing portion, has failed to make the desired progress); identifying, by the QCE, program metrics to analyze the non-optimal software (See Para [0029], i.e. determination of the metrics may be performed at classical computing resources).
The prior art does not teach the limitations: prioritizing, by the QCE, the program metrics into prioritized metrics; determining, by the quantum engine, industry standard values for the program metrics; executing, by the QCE, a regression artificial-intelligence (AI) model on the industry standard values to identify thresholds; encoding, by the QCE, the prioritized metrics, the industry standard values, and the thresholds into threshold qubits; capturing, by the QCE, runtime statistics for the non-optimal software; encoding, by the QCE, the runtime statistics into runtime qubits; generating, by a quantum grading circuit using quantum superposition and interference, a linear score for the non-optimal software based on the runtime qubits; comparing, with a quantum comparison circuit, the linear score and the threshold qubits in order to generate output qubits; decoding, by the QCE, the output qubits into graded metrics; receiving, by an optimization engine from the QCE, the graded metrics; analyzing, by the optimization engine, granular deviation of the graded metrics by a comparison with the historical grades and the thresholds in order to identify a problem metric; identifying, by the neural network model based on existing solutions, possible code-fix solutions for improving the non-optimal software to solve the problem metric; performing, by the optimization engine, node structure analysis based on the possible code-fix solutions, existing code patterns, and code scanned from the non-optimal software, in order to identify proposed code fixes; calculating, by the optimization engine using a quantum approximation algorithm, code weights for the proposed code fixes based on optimization improvement and cost efficiency; and recommending, by the optimization engine based on the code weights, one or more best code fixes to reduce the granular deviation to zero, as recited in independent claim 1, wherein claims 2-14 depend directly and/or indirectly from independent claim 1.
With respect to claims 15-17, the closest prior art Krneta et al. (U.S. Pub. No.
2023/0153219 A1), discloses: An automated, hybrid-computing, quantum-infused, optimization process for monitoring, grading, and enhancing software comprising the steps (See Figure 2) of: detecting, by a quantum computing engine (QCE), non-optimal software (See Para [0019], i.e. metrics fail to satisfy the threshold, the quantum computing monitoring system may consider that algorithm including the quantum computing portion, has failed to make the desired progress); identifying, by the QCE, program metrics to analyze the non-optimal software, said program metrics including at least response time, memory usage, and CPU usage (See Para [0029], i.e. determination of the metrics may be performed at classical computing resources).
The prior art does not teach the limitations: prioritizing, by the QCE, the program metrics into prioritized metrics; determining, by the QCE based on execution of a machine-learning model against historical industry data, industry standard values for the program metrics; executing, by the QCE, a regression artificial-intelligence model on the industry standard values to identify thresholds; encoding, by the QCE, the prioritized metrics, the industry standard values, and the thresholds into threshold qubits; capturing, by the QCE, runtime statistics for the non-optimal software; encoding, by the QCE, the runtime statistics into runtime qubits; generating, by a quantum grading circuit using quantum superposition and interference, a linear score for the non-optimal software based on the runtime qubits such that a unique number is assigned to each aspect of the non-optimal software to help identify degradation of performance and security risks; comparing, with a quantum comparison circuit, the linear score and the threshold qubits in order to generate output qubits; decoding, by the QCE, the output qubits into graded metrics; receiving, by an optimization engine from the QCE, the graded metrics; analyzing, by the optimization engine, granular deviation of the graded metrics by a comparison with the historical grades and the thresholds in order to identify a problem metric; identifying, by the neural network model based on existing solutions, possible code-fix solutions for improving the non-optimal software to solve the problem metric; identifying, by the neural network model, possible infrastructure solutions for improving the non-optimal software to solve the problem metric; performing, by the optimization engine, node structure analysis based on the possible code-fix solutions, existing code patterns, and code scanned from the non-optimal software, in order to identify proposed code fixes; calculating, by the optimization engine using a quantum approximation algorithm, weights for the proposed code fixes and the possible infrastructure solutions based on optimization improvement and cost efficiency; selecting, by the optimization engine based on the weights, one or more of said proposed code fixes and/or said possible infrastructure solutions to reduce the granular deviation to zero; generating, by the optimization engine, a visualization of the non-optimal software that needs optimization along with identification of said proposed code fixes and said possible infrastructure solutions that were selected; generating, by the optimization engine based on the one or more of said proposed code fixes that were selected, an optimized software version; validating, by the optimization engine, compatibility and security of the optimized integrating, by the optimization engine, the optimized software version into a runtime environment in place of the non-optimal software; and continuously monitoring and evaluating, by the QCE, the optimized software version in the runtime environment, as recited in independent claim 15, wherein claims 16 and 17 depend directly and/or indirectly from independent claim 15.
With respect to claims 18-20, the closest prior art Krneta et al. (U.S. Pub. No.
2023/0153219 A1), discloses: An automated, hybrid-computing, quantum-infused, optimization process for monitoring, grading, and enhancing software comprising the steps of (See Figure 2): detecting, by a quantum computing engine (QCE), non-optimal software (See Para [0019], i.e. metrics fail to satisfy the threshold, the quantum computing monitoring system may consider that algorithm including the quantum computing portion, has failed to make the desired progress); identifying, by the QCE, program metrics to analyze the non-optimal software, said program metrics including at least response time, memory usage, and CPU usage (See Para [0029], i.e. determination of the metrics may be performed at classical computing resources).
The prior art does not teach the limitations: prioritizing, by the QCE, the program metrics into prioritized metrics; determining, by the QCE based on execution of a machine-learning model against historical industry data, industry standard values for the program metrics; executing, by the QCE, a regression artificial-intelligence model on the industry standard values to identify thresholds; encoding, by the QCE, the prioritized metrics, the industry standard values, and the thresholds into threshold qubits; capturing, by the QCE, runtime statistics for the non-optimal software; encoding, by the QCE, the runtime statistics into runtime qubits; generating, by a quantum grading circuit using quantum superposition and interference, a linear score for the non-optimal software based on the runtime qubits such that a unique number is assigned to each aspect of the non-optimal software to help identify degradation of performance and security risks; comparing, with a quantum comparison circuit, the linear score and the threshold qubits in order to generate output qubits; decoding, by the QCE, the output qubits into graded metrics; receiving, by an optimization engine from the QCE, the graded metrics; analyzing, by the optimization engine, granular deviation of the graded metrics by a comparison with the historical grades and the thresholds in order to identify a problem metric; identifying, by the neural network model based on existing solutions, possible code-fix solutions for improving the non-optimal software to solve the problem metric; identifying, by the neural network model, possible infrastructure solutions for improving the non-optimal software to solve the problem metric; performing, by the optimization engine, node structure analysis based on the possible code-fix solutions, existing code patterns, and code scanned from the non-optimal software, in order to identify proposed code fixes; calculating, by the optimization engine using a quantum approximation algorithm, weights for the proposed code fixes and the possible infrastructure solutions based on optimization improvement and cost efficiency; selecting, by the optimization engine based on the weights, one or more of said proposed code fixes and/or said possible infrastructure solutions to reduce the granular deviation to zero; generating, by the optimization engine, a visualization of the non-optimal software that needs optimization along with identification of said proposed code fixes and said possible infrastructure solutions that were selected; generating, by the optimization engine based on the one or more of said proposed code fixes that were selected, an optimized software version; validating, by the optimization engine, compatibility and security of the optimized integrating, by the optimization engine, the optimized software version into a runtime environment in place of the non-optimal software; and continuously monitoring and evaluating, by the QCE, the optimized software version in the runtime environment, wherein the quantum computing engine is remote from the optimization engine that is local, as recited in independent claim 18, wherein claims 19 and 20 depend directly and/or indirectly from independent claim 18.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NHA T NGUYEN whose telephone number is (571)270-1405. The examiner can normally be reached M-F 8:00AM-5:00PM.
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/NHA T NGUYEN/Primary Examiner, Art Unit 2851
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