DETAILED ACTION
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 .
Specification
The disclosure is objected to because of the following informalities: the last line of para [0090] refers to “in 417,” where element 417 is not found in the drawings.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
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 1-20 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. Using the subject matter eligibility test from page 74621 of the Federal Register Notice titled “2014 Interim Guidance on Patent Subject Matter Eligibility,” a two-step process is performed. Under step 1, the claims are analyzed to determine if the claim is directed to a process, machine, article of manufacture, or composition of matter. In this case, claims 1-15 are directed to a method, which is a process; claims 16-19 are directed to a system, which is a machine or an article of manufacture; claim 20 is directed to a computer program product, which is an article of manufacture. Step 2A (part 1 of the Mayo test), using the guidance from pages 50-57 of the Federal Register Vol. 84 No. 4 from Monday, January 7, 2019, requires applying a two-prong inquiry. In Prong One, examiners evaluate whether the claim recites a judicial exception, determining if the claim is directed to a law of nature, a natural phenomenon, or an abstract idea. In this case, claim 1 recites predicting a token, which is a mental process, as well as determining probability distributions, which is a mathematical calculation. In Prong Two, examiners evaluate whether the judicial exception is integrated into a practical application that imposes a meaningful limit on the judicial exception. In this case, additional elements of processor, storage media, and machine learning models are generic computing components, while inputting and receiving outputs from a model are mere extrasolution activity, none of which integrate the abstract ideas into a practical application.
Step 2B (part 2 of the Mayo test) requires analyzing the claims to determine if they recite additional elements that amount to significantly more than the judicial exception. In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea itself.
Regarding claims 1, 16, and 20, predicting a token is a mental process, while determining probability distributions is a mathematical calculation, both of which are abstract ideas. Additional elements of processor, storage media, and machine learning models are generic computing components, while inputting and receiving outputs from a model are mere extrasolution activity, none of which integrate the abstract ideas into a practical application or constitute significantly more.
Regarding claims 2 and 17, selecting values and translating to tokens are mental processes or mathematical calculations, which are abstract ideas without integration into a practical application and without significantly more.
Regarding claims 3-5, 8-12, 15, and 18-19, the limitations are further clarifications of the above abstract ideas.
Regarding claim 6, generating tokens from text is a mental process, while generating probability distributions and predictions are mathematical calculations, both of which are abstract ideas. Receiving input is mere extrasolution activity, and does not integrate the abstract idea into a practical application or constitute significantly more.
Regarding claim 7, performing embedding is a mathematical calculation, which is an abstract idea without integration into a practical application and without significantly more.
Regarding claim 13, producing output scores and probability distributions are mathematical calculations, which is an abstract idea without integration into a practical application and without significantly more.
Regarding claim 14, performing supervised learning using ground truth probability distributions is a series of mathematical calculations, which is an abstract idea without integration into a practical application and without significantly more.
The limitations of the claims, taken alone, do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Applicable case law cited in the Federal Register includes, but is not limited to: Alice Corp., 134 S. Ct. at 2355-56, Digitech Image Tech., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344 (Fed. Cir. 2014), Benson, 409 U.S. at 63.
See "Preliminary Examination Instructions in view of the Supreme Court Decision in Alice Corporation Pty. Ltd. v. CLS Bank International, et al.," dated June 25, 2014, and the Federal Register notice titled "2014 Interim Guidance on Patent Subject Matter Eligibility" (79 FR 74618).
Allowable Subject Matter
Claims 1-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter: the closest prior art of Sainath et al. (US 2022/0310062 A1) and Rabin (US 2025/0307546 A1) do not teach the limitations of the claims. Specifically, none of the cited prior art teaches feeding an output probability distribution back as an input autoregressively into a generative machine learning model to iteratively produce a next answer token represented by an output probability distribution, in combination with the other limitations. Hence, none of the cited prior art, either alone or in combination thereof, teaches the combination of limitations found in the claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 2022/0310062 A1 para [0046] teaches calculating a loss using probability distributions of speech recognition hypotheses; US 2025/0307546 A1 para [0079] teaches generating an updated input by sampling a word from a probability distribution sequence corresponding to an input sequence and performing multiple iterations.
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/BRYAN S BLANKENAGEL/Primary Examiner, Art Unit 2658
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