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, see remarks, filed 04/10/2026, with respect to 103 rejection have been fully considered and are persuasive. The 103 rejection of claims 1-15, 19-20 has been withdrawn.
The applicants amendment overcomes the Zhang in view of Zhang2 rejection, a search was made and no art was found which teaches the claimed invention.
Applicant's arguments filed 04/10/2026 have been fully considered but they are not persuasive. See new grounds of rejection for claims 16-18.
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.
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, 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, the domain adapter including at least one neural network configured to provide a translation result based on a translation request, wherein domain adaptors of each non-selected translation domain of the plurality of candidate translation domains that form the machine translation model are not downloaded.
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].
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).
Claim(s)18is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang U.S. PAP 2021/0042475 in view of Zhang2 U.S. PAP 2023/0367975 A1.
Regarding claim 18 Zhang teaches a method performed by an electronic device, comprising:
acquiring a dataset tag of a target dataset, the dataset tag characterizing a data distribution category of each data in the target dataset (The metadata may be associated with the creation of the classification label itself, for example, the metadata may reference a user who performed the classification, the time the classification was made, a model identifier used to generate a classification for the input text element, etc. The classification label is associated with a machine translation model for translating the input text element, see par. [0224]);
training a data distribution prediction module based on the target dataset and the dataset tag, the data distribution prediction module for predicting a probability that each data in the target dataset belongs to respective data distribution categories, wherein each data distribution category corresponds to at least one domain (Each row in the training data table may represent historical classification data submitted for translation, and may include an input text element, one or more input text metadata, and a classification label, see par. [0220]).
However Zhang does not teach based on the trained data distribution prediction module, training each candidate domain adapter to obtain a machine translation model, wherein each candidate domain adapter corresponds to at least one domain.
IN the same field of endeavor Zhang2 teaches encoder 310 applies software-localization preprocessing to source text 350 and translation text 355 and then converts source text 350 and translation text 355 into distributional vector representations (e.g., a source text representation and a translation text representation). In some embodiments, the distributional vector representations generated by encoder 310 include semantic information of each word in source text 350 and translation text 355, as well as the contextual information of the words. According to some aspects, encoder 310 generates the distributional vector representations based on source language model 360 and/or translation language model 365, see par. [0060]. named-entity-labeled noun chunks are noun chunks in which named entities are replaced by their entity category labels. In some embodiments, the CLTE component firstly converts noun-chunks into noun-chunk vectors by looking up word vectors in the source and translation word embedding dictionaries and then averaging all word vectors for a same chunk to generate the noun-chunk vectors. The CLTE component computes similarity scores between noun-chunk vectors using cosine similarity or other similarity determination techniques, see par. [0113].
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].
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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Reavely ‘117 teaches an application-domain selector determines a certain number of highest scoring applications that can handle the intents of the intent categories included in the N-best list . Thus, a particular intent category in the N-best list may be associated with one or more intents. The supplemental application-domain selector may then identify one or more supplemental applications capable of handling those intents and may provide a score for each of those applications, see par. [0092].
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael Ortiz-Sanchez whose telephone number is (571)270-3711. The examiner can normally be reached Monday- Friday 9AM-6PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bhavesh Mehta can be reached at 571-272-7453. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL ORTIZ-SANCHEZ/ Primary Examiner, Art Unit 2656
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