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 .
Response to Arguments
Applicant's arguments filed 07/22/2026 have been fully considered but they are not persuasive. See new grounds of rejection for claims 16-17.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 16, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chang U.S. PAP 2022/0078207 A1 in view of Zhang2 U.S. PAP 2023/0367975 A1 further n view of Park KR 2023/0045688 A.
Regarding claim 16 Chang teaches a method performed by an electronic device ( domain processing system is enhanced with a first-pass domain filter configured for loading character strings representing a pair of domains consisting of a seed domain and a candidate domain in a computer memory, see abstract), comprising:
displaying a list of translation domains, the list of translation domains comprising identification information of at least one candidate translation domain of a plurality of candidate translation domains that form a machine translation model (Candidate domains that do pass the social engineering rules can be presented on a UI , see par. [0070]);
However Chang does not teach acquiring a first input of a user from a user terminal, the first input for selecting a translation domain from the list of translation domains;
in response to the first input, downloading a domain adapter of the corresponding domain from the electronic device to the user terminal, the domain adapter including at least one neural network configured to provide a translation result based on a translation request.
In the same field of endeavor Zhang2 teaches a user provides one or more of the source text, the translation text, and the context identifier to the translation evaluation apparatus by an uploading process, a device-to-device file transfer process. In some embodiments, the translation evaluation apparatus retrieves one or more of the source text, the translation text, and the context identifier from a database such as the database described with reference to FIG. 1, or from a data source such as a website, online archive, etc. In some embodiments, the translation evaluation apparatus retrieves one or more of the source text, the translation text, and the context identifier in response to a prompt received from a user via a graphical user interface, see par. [0091]. Zhang2 also teaches in some examples, encoder 225 selects a source language model based on the source language and a translation language model based on the translation language, where the source text embedding and the translation text embedding are based on the source language model and the translation language model, respectively (this means only one embedding and one model is selected therefor the other ones are not), see par. [0097].
It would have been obvious to one of ordinary skill in the art to combine the Chang invention with the teachings of Zhang2 for the benefit of reducing human involvement in the evaluation process, thereby reducing associated time and costs, see par. [0002].
However Chang in view of Zhang2 does not teach wherein domain adapters of each non-selected translation domain of the plurality of candidate translation domains that form the machine translation model are not downloaded from the electronic device to the user terminal.
In the same field of endeavor Park teaches a corpus processing device, and a machine translation device and method, the corpus processing device comprising: a storage unit which stores at least one corpus; and a processor which performs filtering on the at least one corpus to remove at least one sentence of the at least one corpus or remove at least one of the at least one corpus to obtain a filtered corpus, and classifies the filtered corpus into at least one domain, see abstract. The filtering unit 121 obtains an optimal corpus for training the machine translation model by selecting appropriate sentences or words from a given corpus, removing unnecessary sentences or words, or removing some corpus from a given corpus. Accordingly, a high-quality corpus can be built to train and obtain a high-quality artificial neural network machine translation model, see description of embodiments. The domain adaptation unit 234 may obtain a domain-specific trained machine translation model (hereinafter, a domain adaptation model) by performing domain adaptation on the machine translation model trained by the training unit 233 . For example, the domain adaptation unit 234 acquires the trained machine translation model from the training unit 233, and uses at least one pre-learned learning algorithm for domain adaptation to adapt the domain corresponding to the trained machine translation model, see description. The evaluation unit 235 evaluates the machine translation model, and according to the evaluation result, it may be delivered to the distribution processing unit 236 or to the training unit 233 or the domain adaptation unit 234 . Here, the model to be evaluated may include at least one of a machine translation model trained by the training unit 233 and a domain adaptation model obtained by the domain adaptation unit 234.
It would have been obvious to one of ordinary skill in the art to combine the Chang in view of Zhang2 invention with the teachings of Park for the benefit of obtaining an optimal corpus for training the machine translation model by selecting appropriate sentences or words from a given corpus, removing unnecessary sentences or words, or removing some corpus from a given corpus. Accordingly, a high-quality corpus can be built to train and obtain a high-quality artificial neural network machine translation model, see description of embodiments.
Regarding claim 17 Chang teaches the method of claim 16, further comprising:
displaying update prompt information, the update prompt information for prompting an update to the selected translation domain corresponding to translation(Domain processing system 180 may include a data processor 120 that is configured for pulling or requesting data provider 110 on a configurable time interval. In response, data provider 110 may return domain registration information 125 containing key-value pairs, see par. [0035]); and in response to the acquired update indication, updating the domain adapter of the respective domain (“standardRegUpdatedDateOriginal”, see table 1).
Allowable Subject Matter
Claims 1-1, 19-20 are allowed.
The following is a statement of reasons for the indication of allowable subject matter:
Claim 1 describes classifying an input text element as being in-domain for a selected machine model. However, Zhang fails to disclose or suggest that the machine learning classifier provides a likelihood that each machine translation model is a target domain adapter. That is, Zhang fails to disclose or suggest "determining, according to the first encoded feature, first indication information of the information to be translated, wherein the first indication information provides a likelihood that each candidate domain adapter is the target domain adapter," as recited in claim 1. The remaining references do not cure the deficiencies of Zhang.
Claim 18 describes “the training the data distribution prediction module comprising acquiring a prediction result of the data distribution prediction module on the target dataset and training the data distribution prediction module based on the dataset tag and the prediction result; and based on the trained data distribution prediction module, acquiring a prediction result of the trained data distribution prediction module on a training dataset, determining, based on the prediction result, a target domain adapter corresponding to each data in the training dataset, training each candidate domain adapter based on the target domain adapter corresponding to each data to obtain a machine translation model, and wherein each candidate domain adapter corresponds to at least one domain. A new search was made and no art was found which teaches the amended claim elements.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/MICHAEL ORTIZ-SANCHEZ/ Primary Examiner, Art Unit 2656
