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 .
DETAILED ACTION
Introduction
This office action is in response to Applicant’s submission filed on 1/22/2025 and 12/18/25. As such, claims 1-11, and 13-21 have been examined.
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.
Claims 1-5, and 13-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a method that, under the broadest reasonable interpretation, claims limitations that cover performance of the limitations in the human mind with the assistance of physical aids (e.g., pen and paper), but for the recitation of generic or well-known or conventional computer components. That is, other than reciting “translation model” nothing in these claim limitations precludes the steps from practically being performed in the mind. As a whole, claim 1 pertains to identifying keywords set in a chapter, masking the keyword set in the chapter and use the masked text to learn translation rules, which is a mental process that a human can do. Individually, each of the limitations also pertains to a mental process and/or insignificant extra solution activity, for example:
determining a keyword set associated with a chapter-level monolingual corpus in a target language, the keyword set comprising a plurality of entity words and a plurality of pronouns; (e.g., a human reading content and looking for keywords, such as entity and pronouns.)
masking the chapter-level monolingual corpus based on the keyword set; (e.g., the human crossing out the keyword or obscures/replaces the keywords with placeholder tags to focus on the grammatical structure and sentence flow.)
and generating a chapter-level text translation model based on the masked chapter-level monolingual corpus. (e.g., the human then either mentally and/or using pen and paper creating a rule-book after observing how sentence structure change between the source language and target language with the entities and pronoun remain intact. The human learns grammar structure rules and generates a chapter level translation model from analyzing multiple masked chapters)
The judicial exception is not integrated into a practical application. In particular, the claims only recites generic computing components. Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of receiving, determining, or outputting information) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations of using generic computer components amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Claim 1 is not patent eligible.
The examiner further notes that the use of claimed generic computer components (“translation model”) to obtain, extract, and/or generate data invokes such generic computer components “merely as a tool to perform an existing process”. MPEP 2106.05(f). MPEP 2106.05(f) further explains:
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
Claim 1 recites generic computer components (“a translation model”), with respect to performing tasks. MPEP 2106.05(d) and (f) further provides examples of court decisions where the courts found generic computing components to be mere instructions to apply a judicial exception, and further explains “increased speed” (e.g., using a computer to increase the speed of an otherwise mental process) does not provide an inventive concept. For example:
A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
A process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016) (emphasis added).
Performing repetitive calculations. Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.")
Claim 13 recites an electronic device claim that corresponds to the method of claim 1 and is therefore rejected under the same grounds as claim 1 above. While claim 13 further recites “a processor, a memory coupled to the processor”, these are merely generic computer components recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Therefore, none of these limitations (a) integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or (b) amount to significantly more than the judicial exception, because in either case the additional limitations merely utilize generic computer components that amounts to no more than mere instructions to apply the exception using generic computer function. Claim 13 is not patent eligible.
Claim 14 recites a computer-readable storage medium claim that corresponds to the method of claim 1 and is therefore rejected under the same grounds as claim 1 above. While claim 14 further recites “computer-readable storage medium, and computer-executable instructions”, these are merely generic computer components recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Therefore, none of these limitations (a) integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or (b) amount to significantly more than the judicial exception, because in either case the additional limitations merely utilize generic computer components that amounts to no more than mere instructions to apply the exception using generic computer function. Claim 14 is not patent eligible.
Claims 2-7 depend from independent claims 1, do not remedy any of the deficiencies of claim 1, and therefore are rejected on the same grounds as claim 1 from above.
Claim 2 further comprising: wherein determining the keyword set associated with the chapter-level monolingual corpus in the target language comprises: extracting the plurality of pronouns from the chapter-level monolingual corpus; (e.g., the human identifying and circling pronouns from the document.)
extracting the plurality of entity words from the chapter-level monolingual corpus, wherein a type of the plurality of entity words comprises one or more of: a person name, a place name, an institution name, or a noun phrase; and generating the keyword set based on the plurality of pronouns and the plurality of entity words. (e.g., the human identifying and circling entity words, such as name of person, character, place and etc.)
Claim 3 further recite: wherein determining the keyword set associated with the chapter-level monolingual corpus in the target language comprises: determining a word frequency corresponding to a plurality of words in the chapter-level monolingual corpus, wherein the plurality of words comprise an entity word and a pronoun; (e.g., the human determining how frequently words appear in the document.)
and generating the keyword set based on the word frequency corresponding to the plurality of words. (e.g., the human coming up with the keyword list based on how often the words appear in the document.)
Claim 4 further comprising: wherein masking the chapter-level monolingual corpus based on the keyword set comprises: dividing a first chapter in the chapter-level monolingual corpus into a plurality of sentences; (e.g., the human separating sentences in the chapter.)
determining, based on a predetermined ratio, a number of words to be masked in each of the plurality of sentences; (e.g., the human use a rule to determine how many words to be masked.)
and masking a corresponding number of words in the each of the plurality of sentences based on the keyword set. (e.g., the human mask number of words in each sentences based on the keyword set.)
Claim 5 further recites: wherein masking the corresponding number of words in the each of the plurality of sentences based on the keyword set comprises: determining, based on the keyword set, a subset in a first sentence in the plurality of sentences; (e.g., the human determining a subset of keywords in a first sentence based on the keyword set.)
and randomly selecting, from the subset, a group of words with the corresponding number for masking. (e.g., the human randomly selecting a group of word from the subset of keyword for masking.)
Claim 6 further recites: wherein masking the corresponding number of words in the each of the plurality of sentences based on the keyword set further comprises: randomly selecting, from the subset, another group of words with the corresponding number for masking at a predetermined time after masking the group of words. (e.g., the human randomly selecting another group of words from the subset of keywords to be masked at a predetermined time after masking a group of words.)
The analysis of Claims 15-18 corresponds to claims 2-5, and therefore similar rationale of rejection is applied to these claims respectively.
In sum, claims 2-5, and 15-18 depend from claims 1 and 14 respectively, and further recite mental processes as explained above. None of the additional limitations recited in claims 2-5 and 15-18 amount to anything more than the same or a similar abstract idea as recited in claims 1 and 14. Nor do any limitations in claims 2-5 and 15-18: (a) integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or (b) amount to significantly more than the judicial exception because the additional limitations of using generic computer components amounts to no more than mere instructions to apply the exception using generic computer components. Claims 2-5 and 15-18 are not patent eligible.
Claims 6 and 19 contains a specific way of implementing dynamic masking which helps training masked language model to better understand language, context and predictive pattern and therefore is patent eligible.
Claims 7-11 and 20-21 contains a specific way of training process for AI machine translation and therefore is patent eligible.
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 5-6 and 18-19 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.
In Claims 5 and 18, “a subset in a first sentence in the plurality of sentences;” it is not clear what “a subset” of what is being recited. For sake of compact prosecution, Examiner is interpreting “a subset of keywords”.
Specification
The disclosure is objected to because of the following informalities: in para 0066-0067, all instances of “encoder 512” should be change to “decoder 512” to stay consistent with the drawing of figure 5.
Appropriate correction is required.
Potentially Allowable Subject Matter
Claims 1-11, and 13-21 would be potentially allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and amended to overcome the pertinent rejections under section 35 U.S.C. 101 and 112.
The following is a statement of reasons for the indication of potentially allowable subject matter:
Zhang (CN 114065778) with machine translated English version.
Regarding Claim 1, Hou (CN 113886591) machine translated version in English discloses: A method for ([0040-0044] S100, data preprocessing, extracting pronouns in the corpus through string matching, processing tools to extract named entities, noun phrases, etc. in the corpus, as the mask candidate set in the training data generation stage, the extraction tool is the python natural language processing toolkit Spacy Entity recognition module; specifically includes: extraction of pronouns (S120), name entities (S130) from English Wikepedia data (S110), to create PronounSet and EntitySet keyword sets)
masking the chapter-level monolingual corpus based on the keyword set; ([0045-0050] mask word training data (S240) where pronouns are masked, mask phrase training data (S250) where named entities are masked)
and ([0051] S300, pre-training, adaptively switching word_learning mode or phrase_learning mode for training according to the training mode selection factor at, including: in word_learning mode, the mask_word training data is input into the BERT network to predict the masked words, and calculate the corresponding loss; In phrase_learning mode, the mask_phrase training data is input into the BERT network to predict the masked phrases and calculate the corresponding loss; specifically:)
However, Hou does not appear to explicitly disclose generating model for chapter-level text translation.
Applicant supplied reference, Zhang (CN 114065778) with machine translated English version in the related art discloses: (pg. 12, middle portion, discuss obtaining a chapter level translation model based on monolingual corpus.) However, there is silent on generating the chapter-level translation model based on “masked” monolingual corpus, and it also does not mention or suggest extracting of a keyword set involving entity and pronouns. Accordingly, the prior art of record fails to explicitly teach or fairly suggest the invention set forth in claim 1. Further dependent claims 2-11 inherit the potentially allowable subject matter from claim 1, and thus, also contain potentially allowable subject matter by virtue of their dependency. Claims 13 and 14 although in different statutory categories, but they contain similar elements as claim 1, and therefore also contains similar potentially allowable subject matter. Dependent claims 14-21 inherit the potentially allowable subject matter from claim 1, and thus, also contain potentially allowable subject matter by virtue of their dependency.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Ravid (US 20100150453 – discloses method/system for determining noisy data objects, and “translated documents are not mapped to words having a dictionary meaning (and therefore being language independent). The glossary that serves for masking noise features in the documents does not include a dictionary that pertains to a natural language in which said document is written.” See Abstract, and para 0217 for additional details.
Chen, L., Li, J., Gong, Z., Duan, X., Chen, B., Luo, W., ... & Zhou, G. (2021). Improving Context-Aware Neural Machine Translation with Source-side Monolingual Documents. In IJCAI (pp. 3794-3800). – discloses a pre-training approach with Global Context (PGC) which consist of a global context encoder, a sentence encoder and decoder, which improves translation performance of context aware NMT. See Abstract, sections 2-3 and fig. 3 for additional details.
Maruf, S., Martins, A. F., & Haffari, G. (2019, June). Selective attention for context-aware neural machine translation. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (pp. 3092-3102). – discloses: “we propose a novel and scalable top-down approach to hierarchical attention for context-aware NMT which uses
sparse attention to selectively focus on relevant sentences in the document context and then
attends to key words in those sentences.” See Abstract, and section 3 for additional details.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Philip H Lam whose telephone number is (571)272-1721. The examiner can normally be reached 9 AM-3 PM Pacific time.
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/PHILIP H LAM/ Examiner, Art Unit 2656
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