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 .
Claim Interpretation
As per Claim 10 (and similarly Claim 20):
“from which the machine translated segments were obtained” is interpreted as referring to “a document”, and not to “metadata” and not to “other contextual information”, because interpreting the metadata or other contextual information as information/data that was used to obtain the machine translated segments would raise a written description/new matter issue because the original Specification (i.e. the Specification of Parent Application 18/373,938) does not appear to describe where metadata or contextual information was used to obtain the machine translated segments (i.e. the ones obtained by raw/traditional machine translation), and only appears to describe where the metadata or contextual information is used in the post-editing of the raw/traditional-MT-generated machine translated segments.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-4, 7, 10, 12-14, 17, and 20, are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per Claim 2 (and similarly Claim 12):
It is not clear if “with the generative model” refers to “iterations” (i.e. the log maintains a log of iterations, where the iterations are performed with the generative AI model) or “maintaining a log” (i.e. the log maintains iterations that are not necessarily iterations performed with the generative AI model, but the log is maintained by/with the generative AI model).
As per Claim 7 (and similarly Claim 17):
“calculating before and after machine translation quality estimation (MTQE) scores for a paragraph of the document or the entire document” is fairly clearly meant to refer to where before MTQE score[s] and after MTQE score[s] are computed (see Claim 13 of US Patent, 11,995,414 and Claim 13 of US Patent 12,242,819) but as claimed it is not clear if one before MTQE score and one after MTQE score, which collectively form a set of two scores, is sufficient to qualify as the “before and after machine translation quality estimation (MTQE) scores” in claim 7, or if “before and after machine translation quality estimation (MTQE) scores” requires multiple before MTQE scores and multiple after MTQE scores.
As per Claim 10 (and similarly claim 20):
“the machine translated segments” in line 2 of claim 10 is ambiguous (line 3 of Claim 1 recites “machine translated segments of a document” and lines 5-6 of claim 1 recites “machine translated segments with unsatisfactory quality estimation scores”, and it is not clear which set of “machine translated segments” is the one that “the machine translated segments” in line 2 of claim 10 is supposed to refer to).
The dependent claims include the issues of their respective parent claims.
Allowable Subject Matter
Claims 5-6, 8-9, 15-16, and 18-19, are 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.
Claims 2-4, 7, 10, 12-14, 17, and 20, would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
As per Claim(s) 1 (and similarly claim 11, and consequently claim[s] 2-10 and 12-20 which depend on claim[s] 1 and 11), the prior art of record does not teach or suggest the combination of all limitations in claim(s) 1, including (i.e. in combination with the remaining limitations in claim[s] 1) A method for automated post-editing of machine translated content, the method comprising: presenting machine translated segments of a document and associated quality estimation scores for each of the machine translated segments; invoking an automated post-editing system for machine translated segments with unsatisfactory quality estimation scores; inputting the machine translated segments with unsatisfactory quality estimation scores into a generative Al model, the generative Al model using contextual information for the document, the contextual information comprising a variable window of text adjacent to each of the machine translated segments with unsatisfactory quality estimation scores; producing a revised translation of a machine translated segment using the generative Al model; and iterating with the generative Al model with varying input until a final translation is achieved or a predetermined number of attempts are reached
2023/0252245 teaches “systems and methods that leverage machine learning to perform a post-editing of sentence-level translations, where the post-editing takes into account contextual information from the language source. As an example, the proposed post-editing system can run as a second pass to a sentence-level translation system and the goal of the post-editing system may be to refine translations which are affected by the larger context” (paragraph 22) and “example systems to perform contextual post-editing of sentence-level translations according to example embodiments of the present disclosure. The example systems can leverage a machine-learned contextual post-editing model configured to refine a preliminary translation based on source context” (paragraph 36). Figure 1A depicts where a machine-learned contextual post-editing model produces/”generates” a “refined translated sentence”. Paragraph 24 describes “source context” as “one or more other source sentences that do not correspond to the preliminary translation being refined”. This reference does not appear to use the generative model on translations with only unsatisfactory quality estimation scores.
2014/0358519 teaches “A confidence estimator 66 estimates a translation confidence for each sentence in the source text by computing a translation confidence measure c(S1), c(S2), c(S3), etc. The translation confidence may be based on one or more of the respective translated sentence, the source sentence, and features of the translation process. For a source sentence which has a low computed translation confidence, e.g., which is below the confidence threshold .gamma., the source sentence is input to a rewriting component 68, which generates one or more rewritten (alternative) source sentences. If the translation confidence measure (computed in the same manner as for the original source sentence) for the rewritten sentence is higher than for the original source sentence, the rewritten sentence may be proposed to the user as a candidate for replacement of the original source sentence” (paragraph 32) and “At S226, provision is made for selecting (automatically or manually) at least one of the optionally ranked alternative strings s.sub.i based on the computed confidence. For example, one or more alternative strings may be displayed to the user on the GUI 24 by the GUI generator 80 as candidate(s) for replacing the original string s. The displaying of the alternative source string(s) may be performed automatically for all original source sentences that are determined to be below the threshold confidence and where there is an alternative string with a confidence which exceeds that of the source string. Alternatively, the alternatives may be displayed only when a user selects a sentence that is indicated as having a low translation confidence. Provision is made for the user to select and/or edit one of the alternative source strings or to retain and/or edit the original sentence, via the GUI. Appropriate instructions for assisting the user in performing this operation and assessing the confidence in translation are displayed. For example, a translation confidence measure associated with each source sentence and each alternative source sentence is displayed” (paragraph 59). This reference does not appear to use a generative AI model and contextual information to produce improved translations.
11551013 teaches “automated quality assessment of translations. In some embodiments, quality of a translation can be assessed by generating a machine-learning (ML) model that classifies the translation as pertaining to one of three quality categories. A first quality category can include, for example, translations that are deemed satisfactory. A second quality category can include, for example, translations that are deemed subject to edition prior to being deemed satisfactory. A third quality category can include, for example, translations that are deemed unsatisfactory. The generated ML model can then be applied to the translation and a corresponding sentence in a source language in order to classify the translation as pertaining to one of the three categories” (Abstract). This reference does not appear to use a generative AI model and contextual information to produce improved translations.
2002/0040292 teaches “The MT decoding method may start with an approximate target language translation and iteratively improve the translation with each successive iteration. The approximate target language translation may be, for example, a word-for-word or phrase-for-phrase gloss, or the approximate target language translation may be a predetermined translation selected from among a plurality of predetermined translations” (paragraph 14) and “Iteratively modifying the translation may include incrementally improving the translation with each iteration, for example, by applying one or more modification operations on the translation” (paragraph 15) and “receiving as input a text segment in a source language to be translated into a target language, generating an initial translation as a current target language translation, applying one or more modification operators to the current target language translation to generate one or more modified target language translations, determining whether one or more of the modified target language translations represents an improved translation in comparison with the current target language translation, setting a modified target language translation as the current target language translation, and repeating these steps until occurrence of a termination condition” (Abstract) and “The termination condition may include a determination that a probability of correctness of a modified target language translation is no greater than a probability of correctness of the current target language translation. The termination condition may be the occurrence of a completion of a predetermined number of iterations and/or the lapse of a predetermined amount of time” (paragraph 12).
2021/0141867 teaches “the contextual translation system 112 implements a feedback loop iteratively improve contextual translations. In accordance with one or more embodiments, FIG. 7 illustrates the contextual translation system 112 implementing a feedback loop based on affinity scores for translations. When implementing a feedback loop, in some embodiments, the contextual translation system 112 assigns weights to contextual identifiers and adjusts those weights based on changes to affinity scores for translations of different term sequences over multiple iterations. Based on one or more feedback thresholds, the contextual translation system 112 identifies changes to affinity scores corresponding to different contextual translations. When affinity scores satisfy one such threshold, the contextual translation system 112 adjusts the assigned weights and (in some cases) changes contextual translations to reflect the adjusted weights” (paragraph 121).
2020/0363865 teaches “wherein at least a portion of the text is emphasized on the display where the portion is associated with a low confidence that a translation from the spoken input to the corresponding portion of the text is correct” (paragraph 413). This reference does not appear to use a generative AI model and contextual information to produce improved translations.
Zhen Yang, Wei Chen, Bo Xu, “Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets”, Institute of Automation, Chinese Academey of Sciences, public on p. 8 Nov. 2017, pp. 1-10. cited by examiner. teaches “A generator generates the target-language sentence based on the input source-language sentence” (Section 1, Introduction; Section 2.2; Section 3.2). This reference appears to use the generator to produce an initial translation (and not to produce an improved translation from an initial translation).
2017/0091177 teaches “The translation editor 107 receives the translation result from the translation generator 106 and generates a new translation result by post-editing a part of the machine translation result by utilizing the post editing model 108 that includes editing rule sets of the second language. Moreover, the translation editor 107 may utilize different kinds of post editing models, and generates one translation result with post editing for one post editing model. As for the post editing models and the post editing process, the translation editor 106 can apply statistical post editing that performs statistical translation by utilizing, for example, the original language as machine-translated sentence and the target language as reference translation” (paragraph 26). This reference does not appear to use a generator to produce the post-edited translations.
2021/0019373 teaches “The automatic post-editing model can be trained using automatically generated training instances. A training instance is automatically generated by processing text in a first language using a neural machine translation model to generate text in a second language” and “processing a first instance of text in a target language using a multilingual automatic post-editing model to generate first edited text”. This reference does not appear to use a generator to produce the post-edited translations
Upon further search (in response to the amendment filed 1/4/2024 for Application 18/373,938):
2005/0055217 teaches “Modification and evaluation of a translation are repeated until the evaluation is no longer improved” (paragraph 28) and “Following the instruction from repetition control unit 74, translation selecting unit 70 selects one of the modified translations applied from translation storing unit 73, and applies the selected one to translation modifying unit 71. Translation modifying unit 71 applies a number of modifications similar to those described above, on the applied translation. Modified translation evaluating unit 72 again evaluates each of the translations resulting from the modifications and computes the scores, and repetition control unit 74 determines whether the scores are improved. Translation modifying unit 71, modified translation evaluating unit 72, translation storing unit 73 and repetition control unit 74 repeatedly execute the process until the scores of the translations no longer improve” (paragraph 123).
Upon further search (in response to the filing of Application 18/634,731):
10248651 teaches “the machine translation system might discard the machine translated target language string, re-translate the source language string using different parameters, improve the translation pipeline and/or models utilized by the machine translation system”
10902218 teaches “providing adaptive quality estimation for machine translation during post-editing of a translated document.”
10108599 teaches “determining, based on the individual combined scores for the plurality of expressions, that a part of the machine-translated content fails to satisfy a quality threshold;”
Upon further search (in response to the filing of Application 19/039,621):
2026/0050753 (LATE filing date) teaches “In one or more implementations, the translation system controls output of the translated text based on the translation scores. For example, the translation system is configured to determine whether the translation scores meet a translation quality threshold. If one or more translation scores fall below the translation quality threshold, the translation system employs a pre-trained LLM to generate instructions for correcting the translated text with respect to the one or more translation facets that failed to meet the translation quality threshold. Next, the translation system employs the translation model to generate an updated translated text based on the generated instructions, e.g., by conditioning the translation model on a prompt that includes the generated instructions, the source text, the guidelines assigned to the respective translation facets, and/or the original translated text. This process is repeated until a translated text is generated having translation scores that satisfy the translation quality threshold. After this, the translation system presents the translated text that satisfies the translation quality threshold in a user interface along with the translation facets and associated translation scores” (paragraph 22). This reference does not qualify as prior art.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1 and 11 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 4 of U.S. Patent No. 11,995,414, hereafter Parent Patent 1. Although the claims at issue are not identical, they are not patentably distinct from each other because claims of this application are rendered obvious by the claims of Parent Patent 1.
As per Claim 1 (and similarly Claim 11, where teaching a method also suggests its corresponding system equivalent):
Claim 4 of Parent Patent 1 (interpreted as incorporating the limitations of Claim 1 of Parent Patent 1) teaches A method for automated post-editing of machine translated content, the method comprising: (“A method for automated post-editing of machine translated content, the method comprising:” in Claim 1 of Parent Patent 1)
presenting machine translated segments of a document and associated quality estimation scores for each of the machine translated segments; (“presenting a user with machine translated segments of a document and associated quality estimation scores for each of the machine translated segments;” in Claim 1 of Parent Patent 1)
invoking an automated post-editing system for machine translated segments with unsatisfactory quality estimation scores; (“allowing the user to invoke an automated post-editing system for machine translated segments with unsatisfactory quality estimation scores;” in Claim 1 of Parent Patent 1, where allowing a user to invoke automated post-editing suggests where the user requests the invocation and where the system actually invokes the post-editing system in response to the user’s request)
inputting the machine translated segments with unsatisfactory quality estimation scores into a generative Al model, the generative Al model using contextual information for the document, (“inputting the machine translated segments with unsatisfactory quality estimation scores into a generative AI model, the generative AI model using contextual information for the document” in Claim 1 of Parent Patent 1)
the contextual information comprising a variable window of text adjacent to each of the machine translated segments with unsatisfactory quality estimation scores; (“the contextual information comprising a variable window of text adjacent to each of the machine translated segments with unsatisfactory quality estimation scores,” in Claim 4 of Parent Patent 1)
producing a revised translation of a machine translated segment using the generative Al model; and iterating with the generative Al model with varying input until a final translation is achieved or a predetermined number of attempts are reached (last 2 limitations of Claim 1 of Parent Patent 1).
Claims 1 and 11 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 4 of U.S. Patent No. 12,242,819, hereafter Parent Patent 2. Although the claims at issue are not identical, they are not patentably distinct from each other because claims of this application are rendered obvious by the claims of Parent Patent 2.
As per Claim 1 (and similarly Claim 11, where teaching a method also suggests its corresponding system equivalent):
Claim 4 of Parent Patent 2 (interpreted as incorporating the limitations of Claim 1 of Parent Patent 2) teaches A method for automated post-editing of machine translated content, the method comprising: presenting machine translated segments of a document and associated quality estimation scores for each of the machine translated segments; invoking an automated post-editing system for machine translated segments with unsatisfactory quality estimation scores; inputting the machine translated segments with unsatisfactory quality estimation scores into a generative Al model, the generative Al model using contextual information for the document, (lines 1-12 of Claim 1 of Parent Patent 2)
the contextual information comprising a variable window of text adjacent to each of the machine translated segments with unsatisfactory quality estimation scores; (“the contextual information comprising a variable window of text adjacent to each of the machine translated segments with unsatisfactory quality estimation scores,” in Claim 4 of Parent Patent 2)
producing a revised translation of a machine translated segment using the generative Al model; and iterating with the generative Al model with varying input until a final translation is achieved or a predetermined number of attempts are reached (last 8 lines of Claim 1 of Parent Patent 2, where condition [b] reads on the “until… a predetermined number of attempts are reached” embodiment of the last limitation of Claim 1 of this application).
Conclusion
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EY 7/23/2026
/ERIC YEN/ Primary Examiner, Art Unit 2658
Prosecution Insights