The present application, filed on or after 16 March 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This office action is in response to Applicant’s submission filed on 14 March 2024. THIS ACTION IS NON-FINAL.
Status of Claims
Claims 1-20 are pending.
Claims 1-20 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement and/or lack of enablement.
Claims 1-20 are rejected under 35 U.S.C. 112(b) as indefinite.
There is no art rejection for claims 1-20.
Claim Rejections - 35 USC § 112
112(b) Rejection
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.
A claim is indefinite if, when read in light of the specification, it fails to inform, with reasonable certainty, those skilled in the art about the scope of the invention. Nautilus, Inc. v. Biosig Instruments, Inc., 110 USPQ.2d 1688, U.S. Supreme Court (2014).
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 pre-AIA the applicant regards as the invention.
Regarding claims 1 and 11, “a best performing ML model”, relative term without clear boundary, the claim is therefore indefinite.
Regarding claims 1 and 11, “creating a pool of ML models by sampling solutions from one or more quantum annealers, and each ML model in the pool comprises a respective one of the solutions”, the phrase "each ML model in the pool comprises a respective one of the solutions" is indefinite because it fails to inform a POSITA with reasonable certainty how a raw annealer output constitutes, corresponds to, or is transformed into a deployable machine learning model. There is no sufficient description in the specification to bridge the gap. The claim is therefore indefinite.
Regarding claims 2-10 / 11-20, which depend on above rejected claim 1 / 11, are rejected for the same reason.
35 U.S.C. 112(a) Rejections
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claims 1 and 11, “creating a pool of ML models by sampling solutions from one or more quantum annealers, and each ML model in the pool comprises a respective one of the solutions”, the specification does not provide specific details on how to create ML models from quantum annealers. The claims are therefore rejected for failing to comply with the written description requirement.
For the same reasons set forth above with respect to written description, independent claims 1 and 11 are not enabled. The Wands factors weigh heavily in favor of a finding of undue experimentation:
1)Quantity of Experimentation Required: A POSITA seeking to practice the claimed method would need to independently: (a) determine a suitable QUBO formulation for the ML model of choice; (b) address the minor embedding problem to map that QUBO onto the physical qubit topology of a specific annealer hardware (see [0021]); (c) select annealing parameters (schedule, temperature, number of reads); (d) develop a decoding mechanism to convert annealer outputs into deployable ML models; and (e) validate that the resulting models are functional. Each of these is a non-trivial research task, and together they constitute substantial independent research beyond routine experimentation.
2) Amount of Direction Provided by the Specification: the specification provides no worked example, no encoding formula, no decoding algorithm, and no experimental result demonstrating that the claimed method has been successfully practiced on any platform. The specification provides only motivational and background discussion. The amount of direction is minimal.
3) Presence of Working Examples: The specification contains no working examples of the claimed method. There is no description of an experiment, prototype, simulation, or computational result demonstrating that a pool of ML models was generated by sampling from any quantum annealer. The complete absence of working examples weighs heavily toward a finding of non-enablement.
4) Nature of the Invention: Quantum annealing for ML model generation is an active and unsettled research area. As of the filing date, encoding arbitrary ML models as QUBO problems suitable for quantum annealer hardware remained an open research problem, as evidenced by the growing body of academic literature the specification implicitly relies upon without citing. An invention in an unpredictable, nascent art requires more, not less, disclosure to satisfy enablement.
5) Breadth of the Claims: The claims recite "one or more quantum annealers" without limitation to any specific platform, architecture, qubit topology, or annealing schedule. This broad scope, across all current and future quantum annealing platforms, vastly exceeds any disclosure in the specification, which does not describe a working implementation on even a single platform.
6) Skill Level in the Art: While a POSITA in quantum computing and ML is highly skilled, the level of skill does not substitute for missing disclosure when the claimed technology requires invention, not merely application of known techniques.
Accordingly, the full scope of the claims directed to quantum-annealer-based ML model pool generation cannot be practiced without undue experimentation, and these claims are not enabled under § 112(a).
Regarding claims 2-10 / 11-20, which depend on above rejected claim 1 / 11, are rejected for the same reason.
Allowable Subject Matter
Claims 1-20 include allowable subject matter since when reading the claims in light of the specification, as per, MPEP §2111.01 or Toro Co. v. White Consolidated Industries Inc., 199F.3d 1295, 1301, 53 USPQ2d 1065, 1069, 1069 (Fed.Cir. 1999), none of the references of record alone or in combination disclose or suggest the combination of limitations specified in claims 1-20.
In interpreting the claims, in light of the specification filed on 14 March 2024, the Examiner finds the claimed invention to be patentably distinct from the prior arts of record.
Regarding the amended independent claims, the primary reason for the allowance is the inclusion of the specific process / structure of generating ML model with quantum annealers based on tested data for ML on edge devices for certain measure of interest.
None of the cited prior art references, singly or in combination, fully teaches all limitations of independent claims 1 and 11.
Regarding the dependent claims, which include all the limitations of the independent claims, are also allowed.
The followings are references close to the invention claimed:
Pojman et al., US-PGPUB NO.20200167689A1 [hereafter Pojman] teaches selecting ML models based on sampling testing. However Pojman does not teach the specific process / structure of generating ML model with quantum annealers based on tested data for ML on edge devices for certain measure of interest.
Mhatre et al., US-PGPUB NO.20240220579A1 [hereafter Mhatre] teaches selecting training network of ML models. However Mhatre does not teach the specific process / structure of generating ML model with quantum annealers based on tested data for ML on edge devices for certain measure of interest.
Ding et al., US-PGPUB NO.20260017984A1 [hereafter Ding] teaches sampling for training ML models. However Ding does not teach the specific process / structure of generating ML model with quantum annealers based on tested data for ML on edge devices for certain measure of interest.
Yang et al., “Training multilayer perceptrons by sampling with quantum annealers”, arXiv:2303.12352v1 [cs.LG] 22 Mar 2023 [hereafter Yang] teaches ML model training with sampling quantum annealers. However Yang does not teach the specific process / structure of generating ML model with quantum annealers based on tested data for ML on edge devices for certain measure of interest.
Biamonte et al. “Quantum machine learning”, Nature, 14 Sept. 2017 [hereafter Biamonte] shows machine learning with quantum computing. However Biamonte does not teach the specific process / structure of generating ML model with quantum annealers based on tested data for ML on edge devices for certain measure of interest.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TSU-CHANG LEE whose telephone number is 571-272-3567. The fax number is 571-273-3567.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez Rivas, can be reached 571-272-2589.
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/TSU-CHANG LEE/
Primary Examiner, Art Unit 2128
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