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 with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicants amendment overcomes 101 rejection of claim 20.
A new search was made and art was found to Zhang2 which teaches an encoder applies software-localization preprocessing to source text and translation text and then converts source text and translation text 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 include semantic information of each word in source text and translation text, as well as the contextual information of the words, see par. [0060].
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) 1-5, 7-8, 12-13 and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang U.S. PAP 2021/0042475 A1 in view of Zhang U.S. PAP 2023/0367975 A1 hereinafter Zhang2.
Regarding claim 1 Zhang teaches a method performed by an electronic device, the method (computer implemented systems and methods for providing improved language translation, see abstract) comprising:
acquiring information to be translated ( receiving, at a machine selector module, an input text element in a first language, see par. [0007]);
determining, based on the information to be translated, a target domain adapter from a plurality of candidate domain adapters, the target domain adapter corresponding to the information to be translated (selecting, at the machine selector module, a selected machine translator model in the plurality of the machine translation models based on a machine selector model of the machine selector module, see par. [0007]), each candidate domain adapter from the plurality of candidate domain adapters corresponding to at least one domain (selecting the selected machine translator model by classifying the input text element as in-domain for the selected machine translator model, see par. [0008]);
and obtaining, based on the target domain adapter corresponding to the information to be translated, a translation result corresponding to the information to be translated (translating, at the selected machine translator model, a first translated text element, the first translated text element resulting from a translation of the input text element in the first language into a second language based on the selected machine translation model, see par. [0007]).
However Zhang does not teach acquiring, by inputting the information to be translated into an encoder, a first encoded feature of the information to be translated, the first encoded feature including a vector that characterizes one or more semantic features of information to be translated.
In the same field of endeavor Zhang2 teaches Machine learning-based NLP techniques may be applied in a translation evaluation context. Machine learning benefits translation evaluation processes by reducing human involvement in the evaluation process, thereby reducing associated time and costs, see par. [0002]. Zhang2 teaches according to some aspects, 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, see par. [0060].
It would have been obvious to one of ordinary skill in the art to combine the Zhang 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 2 Zhang teaches the method of claim 1, wherein the determining the target domain adapter from the plurality of candidate domain adapters comprises:
determining, according to the first encoded feature, first indication information of the information to be translated, wherein the first indication information characterizes a likelihood that each candidate domain adapter is the target domain adapter (e. The machine selector module may have a machine selector model for selecting the machine translator model. The machine selector model may be a machine learning classifier that may classify the input text element as in-domain for the selected machine translated model in the plurality of machine translation models, see par. [0201]);
and determining, according to the first indication information, the target domain adapter corresponding to the information to be translated from the plurality of candidate domain adapters (classify the input text element as in-domain for the selected machine translated model , see par. [0201]).
Regarding claim 3 Zhang teaches the method of claim 1, wherein the determining the target domain adapter from the plurality of candidate domain adapters comprises:
obtaining, based on the first encoded feature, a segment decoded feature of each target segment corresponding to the information to be translated (The second RNN is for generating output text elements based on the context vector (a decoder), see par. [0108]);
obtaining, based on the segment decoded feature of each target segment, second indication information of the target segment (During language translation, the probability of the next symbol in the output text element Y is determined given the input text element and the decoded output text element translated so far, see par. [0108]);
and determining the target domain adapter of the target segment based on the second indication information of the target segment, wherein the second indication information of each target segment characterizes the likelihood that each candidate domain adapter is the target domain adapter of the target segment, wherein the obtaining the translation result corresponding to the information to be translated (At act 606, a plurality of machine classification data are provided, each of the plurality of machine classification data comprising an input classification text element corresponding to a classification value., see par. [0209]), comprises:
for each target segment, outputting, based on the segment decoded feature of the target segment and by the target domain adapter corresponding to the target segment, a respective translation result of the target segment (The decoder accepts the vector representation and generates a corresponding sequence of words as an output text element, see par.[0112]).
Regarding claim 4 Zhang teaches the method of claim 3, wherein the obtaining the second indication information of the target segment, and the determining the target domain adapter of the target segment based on the second indication information of the target segment, comprises:
for each target segment, determining, based on a segment decoded feature of a respective target segment at a first decoding level, second indication information of the respective target segment (each of the plurality of machine classification data comprising an input classification text element corresponding to a classification value, see par. [0209]);
and determining, based on the second indication information corresponding to the respective target segment at the first decoding level, a target domain adapter corresponding to the respective target segment at each decoding level (a machine selection model is determined at a machine selection model generator, based on the plurality of machine classification data, the machine selection model for determining a predicted in-domain language translation model in the plurality of language translations models for the input text element, see par. [0210]).
Regarding claim 5 Zhang teaches the method of claim 3, wherein the obtaining, based on the segment decoded feature of each target segment, the second indication information of the target segment, and determining the target domain adapter of the target segment based on the second indication information of the target segment, comprise:
for each target segment, determining, according to a segment decoded feature of the target segment at each decoding level, a second indication information corresponding to a respective target segment at a respective decoding level, and determining, according to the second indication information corresponding to the respective target segment at the respective decoding level (a machine selection model is determined at a machine selection model generator, based on the plurality of machine classification data, the machine selection model for determining a predicted in-domain language translation model in the plurality of language translations models for the input text element, see par. [0210]), a target domain adapter corresponding to the respective target segment at the respective decoding level, wherein the second indication information corresponding to the respective target segment at the respective decoding level characterizes a likelihood that each candidate domain adapter is the target domain adapter corresponding to the respective target segment at the respective decoding level (a plurality of post-edited text element pairs are provided, each of the post-edited text element pairs comprising an input pre-edited text element and a corresponding output post-edited text element, see par. [0211]).
Regarding claim 7 Zhang teaches the method of claim 3, wherein the outputting, based on the segment decoded feature of the target segment and by the target domain adapter corresponding to the target segment, a translation result of each target segment comprises: for each decoding level, converting, according to the segment decoded feature of the respective target segment at the respective decoding level and via the target domain adapter corresponding to the respective target segment at the respective decoding level to obtain the converted segment decoded feature, and outputting the converted segment decoded feature (The decoder accepts the vector representation and generates a corresponding sequence of words as an output text element, see par. [0112]);
and outputting the translation result of the respective target segment according to the converted segment decoded feature output by the last decoding level (multi-head attention in the decoder may be performed over tokens decoded as an output text element corresponding to a translation in a target language, see par. [0120]).
Regarding claim 8 Zhang teaches the method of claim 3, further comprising:
for each target segment, acquiring the decoded feature of the respective target segment at each decoding level by:
for a first decoding level, obtaining a segment decoded feature of the target segment at the first decoding level, based on the first encoded feature and a second encoded feature of a translated segment prior to the target segment (he post-editor module 227 functions to predict post-edits to the first translated text element. The predicted post-edits may be applied to the translated text element in a second text element, see par. [0134]);
and for a second decoding level, obtaining a segment decoded feature of the target segment at the second decoding level, based on the first encoded feature and a converted segment decoded feature outputted by the target segment at the previous decoding level, and wherein the first decoding level is a first decoding level of at least two decoding levels, and the second decoding level is any decoding level other than the first decoding level (he post-editor module 227 may send the generated second text element to the quality evaluation module 228. The generated post-edits, including the first translated text element and the second translated text element may be associated with each other and stored in a database , see par. [0134]).
Regarding claim 12 Zhang teaches the method of claim 3, wherein the obtaining the second indication information of the target segment based on the segment decoded feature of each target segment comprises:
for each target segment, obtaining the second indication information of the target segment based on a similarity between the segment decoded feature of the target segment and a domain feature vector of each candidate domain adapter (the machine selector module 226 is a machine learning classifier that selects one model from the plurality of machine translation models for the translation of the text element. The machine selector module 226 may use the doc2vec algorithm and logistic regression based machine learning algorithm to classify sentences as either in-domain or out-of-domain for each of a plurality of machine translation models, see par. [0129]).
Regarding claim 13 Zhang teaches the method of claim 3, wherein the obtaining the second indication information of the target segment based on the segment decoded features of each target segment comprises:
for each target segment, determining second indication information of the respective target segment, based on the segment decoded feature of the respective target segment, and a segment decoded feature of the translated segment prior to the respective target segment (the machine selector module 226 may use a classifier machine learning model that is determined based on a training set of previously classified sentences. The previously classified sentences may be a set of previously human-classified sentences, see par. [0127]).
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]);
and 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 (The training data table 720 may be used by machine classification training method (see FIG. 7D) for generating a machine classification model, see par. [0225]).
Regarding claim 19 Zhang teaches an electronic device, comprising: one or more processors; a memory; one or more computer programs, wherein the one or more computer programs are stored in the memory and configured to be executed by the one or more processors, the one or more computer programs configured to: perform the method of claim 1 (a memory, the memory comprising: a plurality of the machine translation models; a machine selector module; a post-editing module; a quality evaluation module; a processor in communication with the memory, the processor configured to: receive an input text element in a first language, see par. [0019]).
Regarding claim 20 Zhang teaches a non-transitory computer-readable storage medium for storing computer instructions that, when executed on a computer, enable a computer to perform the method of claim 1 ( Embodiments of the system may also be considered to be implemented as a non-transitory computer-readable storage medium, configured with a computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner to perform the functions described herein, see par. [0069]).
Claim(s) 9-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang U.S. PAP 2021/0042475 A1 in view of Zhang U.S. PAP 2023/0367975 A1 hereinafter Zhang2, further in view of Lee U.S. PAP 2019/011454 A1.
Regarding claim 9 Zhang teaches the method of claim 3, further comprising:
determining the first indication information of the information to be translated according to the first encoded feature of the information to be translated (an input text element in a first language is received at a machine selector module, see par. [0200]);
However Zhang in view of Zhang2 does not teach wherein the determining the target domain adapter of the target segment based on the second indication information of the target segment comprises:
for each target segment, determining the target domain adapter of the respective target segment according to the second indication information of the respective target segment and the first indication information.
In the same field of endeavor Lee teaches an apparatus and method of constructing a neural network translation model, which enhance the translation performance of a translation model in a target domain having a relatively small amount of data by using a translation result of a translation model in a source domain without a reduction in translation performance of the translation model in the source domain having a sufficient amount of data, see abstract.
Lee teaches for each target segment, determining the target domain adapter of the respective target segment according to the second indication information of the respective target segment and the first indication information (generating a first neural network translation model which includes a neural network having an encoder-decoder structure and learns a feature of source domain data used in an unspecific field; generating a second neural network translation model which includes a neural network having the encoder-decoder structure and learns a feature of target domain data used in a specific field, see par. [0007].
It would have been obvious to one of ordinary skill in the art to combine the Zhang in view of Zhang2invention with the teachings of Lee in order to enhance the translation performance of a translation model in a target domain having a relatively small amount of data, see par. [0006].
Regarding claim 10 Lee teaches the method of claim 9, wherein the determining the target domain adapter of the target segment according to the second indication information of the target segment and the first indication information comprises:
acquiring a first weight corresponding to the first indication information and a second weight corresponding to the second indication information (A neural network may be a recognition model implemented as software or hardware which emulates the calculation ability of humans. The neural network may include neuron-type nodes connected to one another. Each of the nodes may be referred to as an artificial neuron. The nodes may be classified into an input layer, an output layer, and a plurality of hidden layers disposed therebetween. Nodes of one layer may be connected to nodes of another layer through a connection line. The connection line may have a connection weigh, see par. [0035]; first neural network with source domain data, second neural network with target domain data, see par. [0098]);
weighting the first indication information and the second indication information based on the first weight and the second weight, respectively, to obtain third indication information (in step S330, a process of generating a third neural network translation model 250 which includes a neural network having the encoder-decoder structure and learns a common feature of the source domain data 21 and the target domain data 23, see par. [0099]);
and determining the target domain adapter of the target segment based on the third indication information ( generating a combiner 270 which combines translation results of the first to third neural network translation models 210, 230, and 250 may be performed, see par. [0100]).
Regarding claim 11 Lee teaches the method of claim 10, wherein the acquiring the first weight corresponding to the first indication information and the second weight corresponding to the second indication information comprises:
for each target segment, determining the second weight based on a bit-order of the respective target segment, and obtaining the first weight based on the second weight (encoders 212 and 232 specialized for the respective domains and the encoder 252 specialized for the common feature may perform learning so as to minimize the loss function of the domain classifier 254, see par. [0095]); wherein a second weight corresponding to one target segment is positively correlated to the bit-order (and by learning encoders by domains by using a new loss function which is defined in order for feature vectors by domains to have a vertical relationship therebetween, see par. [0095]).
Claim(s) 14-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang U.S. PAP 2021/0042475 A1 in view of Zhang U.S. PAP 2023/0367975 A1 hereinafter Zhang2, further in view of Chang U.S. PAP 2022/0078207 A1.
Regarding claim 14 Zhang teach the method of claim 1, comprising:
acquiring a first input of a user, the first input for selecting a domain corresponding to translation from the list of translation domains (Referring to FIG. 9A, there is shown an example of a user interface for quality evaluation 900. The quality evaluation user interface 900 shows user device 902 having a display 914 that shows the user interface. The display 914 shows a source (also referred to herein as ‘input’) text element field 912, a translation field 904, a quality score 906, optionally a slider 908, and a submit button 910, see par. [0267]);
and in response to the first input, downloading a domain adapter of the corresponding domain (The quality score may be provided by the user using a slider, or by direct entry of a number. Instead of a number, a plurality of categories may be displayed and the user may select from them. For example, the categories may be letter grades, including ‘A’, ‘B’, ‘C’, and ‘D’. The user submitted quality score may be stored in a database in association with the input text element and the translated text element upon submission using submit button 910, and may form the quality evaluation document corpus in FIG. 9B, see par. [0269]).
However Zhang in view of Zhang2does not teach 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.
In a similar field of endeavor Chang teaches high-efficiency domain name processing systems and methods useful for quickly and efficiently identifying domains for digital risk analysis and detection, see par. [0003].
Chang teaches 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 (Candidate domains that do pass the social engineering rules can be presented on a UI, see par. [0070]).
It would have been obvious to one of ordinary skill in the art to combine the Zhang in view of Zhang2 invention with the teachings of Chang for the benefit of quickly and efficiently identifying domains for digital risk analysis and detection, see par. [0003].
Regarding claim 15 Chang teaches the method of claim 14, further comprising: displaying update prompt information, the update prompt information for prompting an update to the 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).
Regarding claim 16 Zhang teach a method performed by an electronic device, comprising:
acquiring a first input of a user, the first input for selecting a domain corresponding to translation from the list of translation domains (Referring to FIG. 9A, there is shown an example of a user interface for quality evaluation 900. The quality evaluation user interface 900 shows user device 902 having a display 914 that shows the user interface. The display 914 shows a source (also referred to herein as ‘input’) text element field 912, a translation field 904, a quality score 906, optionally a slider 908, and a submit button 910, see par. [0267]);
and in response to the first input, downloading a domain adapter of the corresponding domain (The quality score may be provided by the user using a slider, or by direct entry of a number. Instead of a number, a plurality of categories may be displayed and the user may select from them. For example, the categories may be letter grades, including ‘A’, ‘B’, ‘C’, and ‘D’. The user submitted quality score may be stored in a database in association with the input text element and the translated text element upon submission using submit button 910, and may form the quality evaluation document corpus in FIG. 9B, see par. [0269]).
Zhang2 teaches the domain adapter including at least one neural network configured to provide a translation result based on a translation request (he encoder includes a recurrent neural network and a cross-attention module, see par. [0024]).
However Zhang in view of Zhang2does not teach 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.
In a similar field of endeavor Chang teaches high-efficiency domain name processing systems and methods useful for quickly and efficiently identifying domains for digital risk analysis and detection, see par. [0003].
Chang teaches 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 (Candidate domains that do pass the social engineering rules can be presented on a UI, see par. [0070]).
It would have been obvious to one of ordinary skill in the art to combine the Zhang in view of Zhang2invention with the teachings of Chang for the benefit of quickly and efficiently identifying domains for digital risk analysis and detection, see par. [0003].
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 6 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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.
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