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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d).
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.
Claim(s) 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Independent claims 1, 9, and 17 recite a method, a computer device, and a non-transitory computer-readable storage medium, respectively. These claims, therefore, invoke a statutory category (process and machine, respectively) in Step 1 of the Subject Matter Eligibility Test.
Step 2A, Prong One: Independent claims 1, 9, and 17, under their broadest reasonable interpretation, recite a method of processing, analyzing, and comparing language data to generate a translation text. This is an abstract idea in the form of certain methods of organizing human activity (i.e. mental processes such as observation, evaluation, judgement, and opinion) and mathematical concepts (i.e. mathematical relationships, formulas, and calculations). The steps of determining text features, target data-pairs, probabilities, confidences, matching degrees, and determining a translation text are all mental processes that could be performed by a human using pen and paper or by purely mental reasoning, and these steps involve mathematical calculations based on linguistic features.
Step 2A, Prong Two: The claims do not integrate the judicial exception into a practical application. The recitation of “applied to a computer device” is a generic instruction to perform the abstract idea on a computer and does not impose a meaningful limit on the judicial exception. The computer device is recited at a high-level of generality and is used merely as a tool to perform the abstract idea faster or more efficiently. There is no improvement to the functioning of the computer itself or to any other technology or technical field.
Step 2B: The claims do not include any additional elements that amount to significantly more than the judicial exception. The only additional element beyond the abstract idea is the generic computer device, which performs generic computer functions such as receiving, analyzing, and outputting data. Such elements are well-understood, routine, and conventional in the field.
Accordingly, claims 1, 9, and 17 are directed to an abstract idea and do not include significantly more than the abstract idea itself.
With respect to claims 2, 10, and 18, the claims relate to determining more probabilities and confidence values. These are mental processes that could be performed by a human using pen and paper or by purely mental reasoning, and these steps involve mathematical calculations based on linguistic features. No additional elements are present.
With respect to claims 3, 11, and 19, the claims relate to determining more probabilities and confidence values. These are mental processes that could be performed by a human using pen and paper or by purely mental reasoning, and these steps involve mathematical calculations based on linguistic features. No additional elements are present.
With respect to claims 4, 12, and 20, the claims relate to the normalization of a matching degree, calibrating the normalized matching degree, and determining another probability. These are mental processes that could be performed by a human using pen and paper or by purely mental reasoning, and these steps involve mathematical calculations based on linguistic features. No additional elements are present.
With respect to claims 5 and 13, the claims relate to determining a hyperparameter based on the target data-pairs. This is a mental process that could be performed by a human using pen and paper or by purely mental reasoning, and these steps involve mathematical calculations based on linguistic features. No additional elements are present.
With respect to claims 6 and 14, the claims relate to determining probability distributions and fusing the determined distributions together. This is a mental process that could be performed by a human using pen and paper or by purely mental reasoning, and these steps involve mathematical calculations based on linguistic features. No additional elements are present.
With respect to claims 7 and 15, the claims relate to determining importance degrees, a target parameter, converting the importance degrees into weights, and fusing the probability distributions based on the determined weights. This is a mental process that could be performed by a human using pen and paper or by purely mental reasoning, and these steps involve mathematical calculations based on linguistic features. No additional elements are present.
With respect to claims 8 and 16, the claims relate to using a target text translation model to translate a text per the method of claim 1. The recitation of “a target text translation model” is a generic instruction to perform the abstract idea on a computer and does not impose a meaningful limit on the judicial exception.
Accordingly, claims 1-20 are directed to abstract ideas and do not include significantly more than the abstract idea itself.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Zheng et al. (“Adaptive Nearest Neighbor Machine Translation”, 08/06/2021; see Abstract, Sections 2-3, and Figure 1), hereinafter referred to as Zheng, describes an improved version of k-nearest-neighbor retrieval for machine translation. Zheng discloses determining at least one first probability based on a first text feature, the first text feature being a text feature of a first text (Zheng Figure 1 x), the first text being a text in a first language (Zheng Figure 1 x), the at least one first probability indicating probabilities that the first text is translated into various candidate texts of at least one candidate text ((
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, showing the vanilla NMT prediction, which is the probability of the translation based on the first text feature), and the at least one candidate text being a text in a second language (Zheng Figure 1
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distance, i.e. the matching degrees); determining at least one second probability based on the confidences and the matching degrees of the at least one target data-pair, the at least one second probability indicating probabilities that the first text is translated into various standard translation texts in the at least one target data-pair (
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represents the prediction of the k-nearest-neighbors search); and determining, based on the at least one first probability and the at least one second probability, a translation text corresponding to the first text (Zheng Figure 1 shows the Final Prediction, and
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represents this final, combined probability). Zheng fails to disclose determining confidences of the at least one target data-pair, a confidence of the target data-pair indicating reliability of the target data-pair.
Jiang et al. (“Learning Kernel-Smoothed Machine Translation with Retrieved Examples”, 11/11/2021; see Abstract and Figure 2), hereinafter referred to as Jiang, describes an improved version of neural machine translation models. Jiang Figure 2 describes the source text and associated generated tokens, as well as a database, that is then input into an NMT model to generate multiple probability distributions that are combined to a final distribution to better fit translation predictions.
He et al. (US Patent Application Publication No. 2018/0165278; see Abstract and Fig. 2), hereinafter referred to as He, describes a method and system for translating text based on artificial intelligence. He Fig. 2 shows a text to be translated along with a confidence value corresponding to the translation confidence of candidate terms.
The prior art of record, including the above cited references, alone or combined, neither teaches nor renders obvious at least the limitations comprising, as a whole, “a text translation method, applied to a computer device, the method comprising: determining at least one first probability based on a first text feature, the first text feature being a text feature of a first text, the first text being a text in a first language, the at least one first probability indicating probabilities that the first text is translated into various candidate texts of at least one candidate text, and the at least one candidate text being a text in a second language; obtaining at least one target data-pair matching the first text feature, a target data-pair comprising one second text feature and one standard translation text of a second text, the second text feature being a text feature of the second text, the second text being the text in the first language, and the standard translation text being the text in the second language; determining confidences and matching degrees of the at least one target data-pair, a confidence of the target data-pair indicating reliability of the target data-pair, and a matching degree of the target data-pair indicating similarity of the second text feature in the target data-pair with the first text feature; determining at least one second probability based on the confidences and the matching degrees of the at least one target data-pair, the at least one second probability indicating probabilities that the first text is translated into various standard translation texts in the at least one target data-pair; and determining, based on the at least one first probability and the at least one second probability, a translation text corresponding to the first text”, in combination with the rest of the limitations recited in the independent claims 1, 9, and 17. Claims 2-8 depend from claim 1, claims 10-16 depend from claim 9, and claims 18-20 depend from claim 17.
Note: If the independent claims 1, 9, and 17 are amended to overcome the rejection, the Application can be placed in condition for allowance.
Conclusion
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/ADAM MICHAEL WEAVER/Examiner, Art Unit 2658
/RICHEMOND DORVIL/Supervisory Patent Examiner, Art Unit 2658
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