DETAILED ACTION
This communication is in response to the Amendments and Arguments filed on 12/15/2025.
Claims 1-20 are pending and have been examined.
All previous objections/rejections not mentioned in this Office Action have been withdrawn by the examiner.
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 12/15/2025 have been fully considered but they are not persuasive.
Regarding the rejections under 103, Applicant asserts on pgs 9-11 that Lee does not teach a lexicalized taxonomy or machine-readable extraction rules forming part of the lexicalized taxonomy, and Markman does not cure the deficiencies of Lee. The Examiner respectfully disagrees with these assertions. Lee teaches the tagging of words using a lexicalized HMM, with word/POS/context probability data based on a morpheme dictionary that includes words, and where a word not in the dictionary is dealt with as an unknown word. The POS data is then used to extract the highest frequency string of the corpus. (see [0020-2],[0059-60],[0064-6]) The morpheme dictionary and processing by a lexicalized HMM reads on the BRI of a lexicalized taxonomy, where the word/POS/context probability data and the way that words are dealt with read to extraction rules. The use of the POS data reads to the enablement of determining the most frequent terms. Markman teaches the use of specific rules with operators to build and tag phrases, which reads on the BRI of the extraction rule including machine-readable operator-based expressions (see Table 1,[0023],[0096], [0099-108]). Therefore, the combination of Lee and Markman teaches the amended claim language. Please see the updated mappings below for further detail.
Regarding the rejections under 101, Applicant asserts on pgs 11-15 that the recitation of operator-based expressions are not written for human interpretation, but are structured to allow automated processing by machine, therefore, the claims cannot be considered as directed to a mental process. Applicant further asserts that the claims are directed to an improvement to the technology, where the system is improved by enabling automated detection and processing of concept-related terms. The Examiner respectfully disagrees with these assertions. There is nothing in the claim language that suggests the use of operator-based expressions are incomprehensible to the human mind, and the BRI of the term would include any kind of logical expression that has operators – Boolean, mathematical, etc. – which can be understood by a human. That it is able to be read by a machine does not inherently mean it cannot be read and utilized by a human. The use of a processor reads to the use of a generalized computer component, which does not impose any meaningful limits on practicing the abstract idea. Further, there is nothing in the claim language to indicate that the recited limitations provide a technological improvement to performing or utilizing machine translations, only that it is a way to obtain translations.
Hence, Applicant’s arguments are not persuasive.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim(s) 1, 11, and 13, the limitation(s) of obtaining, filtering, querying, tagging, translating, and normalizing, as well as translate, provide, and (translate - claim 13 only) additionally in claims 11 and 13, as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind and/or with pen and paper but for the recitation of generic computer components. More specifically, the mental process of a human reading a document in one language, using a reference guide to identify the most frequent terms in specific categories where the reference guide has specific features and enables specific language analysis, writing out the sentences associated with the terms on a separate sheet of paper and annotating the words in the sentences, translating the sentences into a second language using a knowledge of the relationship between the first and second language and maintaining the annotations for the words, and writing out the translation in a specific format for easy comparison to other documents. The additional limitations from claims 11 and 13 read to a human using their knowledge of first and second languages and the above steps to write out a reference that can be used for future translations, and using the reference to search for an answer to a query where the answer may be written in either of the languages. The machine translator and NLP system reads to a human being able to utilize rules for understanding and translating different human languages. The search engine reads to a human being able to look at a question, read relevant documents, and determine an answer to the question. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind and/or with pen and paper but for the recitation of generic computer components, then it falls within the --Mental Processes-- grouping of abstract ideas. Accordingly, the claim(s) recite(s) an abstract idea.
This judicial exception is not integrated into a practical application because the recitation of processor in claims 1 and 11, and a device, storage medium, and processor in claim 13, reads to generalized computer components, based upon the claim interpretation wherein the structure is interpreted using pg. 16 line 10 – pg. 18 line 8 in the specification. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim(s) is/are directed to an abstract idea.
The claim(s) do(es) not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using generalized computer components to obtain, filter, query, tag, translate, and normalize, as well as translate, provide and translate, amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) is/are not patent eligible.
With respect to claim(s) 2 and 14, the claim(s) recite(s) applying heuristics, which reads on a human using a specific set of rules to determine if the translation is correct. No additional limitations are present.
With respect to claim(s) 3, 4, and 15, the claim(s) recite(s) crawling the web (claims 3 and 15) and using the results to ensure the translation is correct (claim 4), which reads on a human searching a specific database for specific translated terms and using the result to determine if the translation is correct. No additional limitations are present.
With respect to claim(s) 5 and 6, the claim(s) recite(s) using a predetermined number of words before and after a term, which reads on a human writing out a sentence using the identified word, where the sentence has specific features. No additional limitations are present.
With respect to claim(s) 7, the claim(s) recite(s) the quality machine-translator is a DNN, which reads on a human following a specific set of rules to translate and annotate translations. No additional limitations are present.
With respect to claim(s) 8, the claim(s) recite(s) characteristics of the most frequent terms, which reads on a human selecting terms with specific characteristics. No additional limitations are present.
With respect to claim(s) 9, the claim(s) recite(s) the target language has a morphology, which reads on a human language having specific characteristics. No additional limitations are present.
With respect to claim(s) 10, the claim(s) recite(s) transforming inflected forms of terms to their stems, which reads on a human formatting the final translation in a specific manner. No additional limitations are present.
With respect to claim(s) 12 and 16, the claim(s) recite(s) updating/apply, respectively, the lexicalized taxonomy, which reads on a human updating and/or using a specific reference. No additional limitations are present.
With respect to claim(s) 17-20, the claim(s) recite(s) the processor is couple to the storage medium, which reads on a generalized computer component as per pg. 16 line 10 – pg. 18 line 8 in the specification.
These claims further do not remedy the judicial exception being integrated into a practical application and further fail to include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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, 2, 9, 11-14, 16-18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (U.S. PG Pub No. 2007/0150260), hereinafter Lee, in view of Markman et al. (U.S. PG Pub No. 2012/0191445), hereinafter Markman.
Regarding claims 1, 11, and 13, Lee teaches
(claim 1) A computer-implemented method for translating a Natural Language Processing (NLP) system given in a source language into at least one target language (an apparatus and method for automatic translation [0014]), the NLP system being based on a lexicalized taxonomy and allowing text annotation and classification (automatic translation for documents in a restrictive domain through morpheme analysis and tagging and extracting technical terms from documents using a lexicalized HMM, and classifying terms [0020-2],[0059-60],[0064-6]), the method comprising:
(claim 11) A computer-implemented method comprising (an apparatus and method for automatic translation [0014]):
(claim 11) translating at least one lexicalized taxonomy of the search engine given in a source language into at least one target language by translating a Natural Language Processing (NLP) system given in the source language into the at least one target language, the NLP system being based on a given lexicalized taxonomy and allowing text annotation and classification (automatic translation for documents in a restrictive domain in a source language to a target language through morpheme analysis and tagging and extracting technical terms from documents using a lexicalized HMM, and classifying terms [0020-2],[0059-60],[0064-66]), the translating including:
(claim 13) A device comprising (an apparatus [0014]):
obtaining a corpus in the source language, the taxonomy including annotations allowing determination of the most frequent terms describing a given concept in the corpus, the taxonomy having a lexicalization in a form of extraction rules, …the extraction rules enabling automated processing, by a processor, of the determination of the most frequent terms (a large document corpus constructed from documents written in a source language are input, i.e. obtaining a corpus in the source language, where the words are tagged using word/POS/context probability data and word probability data, i.e. the taxonomy including annotations, based on a morpheme analysis dictionary that includes words, and where a word not in the dictionary is dealt with as an unknown word, i.e. taxonomy having a lexicalization in a form of extraction rules, and extracting the highest frequency string of the specific corpus using the POS data, i.e. allowing determination of the most frequent terms describing a given concept in the corpus…the extraction rules enabling automated processing, by a processor, of the determination of the most frequent terms [0059-60],[0064-6]);
filtering the most frequent terms for each annotation (a high frequency word represented as a particular POS, i.e. most frequent terms for each annotation, are identified and extracted, i.e. filtering [0067-78]);
querying the corpus with the most frequent terms and extracting portions of sentences comprising these terms (technical terms with a high frequency are extracted from the documents, i.e. querying the corpus with the most frequent terms, and terms co-occurring with the word to result in a sentence/phrase pattern are filtered, i.e. extracting portions of sentences comprising these terms [0067-78]);
tagging the terms in each extracted portion (the corpus is divided into sentences, and the sentences into tokens, and all the tokens are tagged with all allowable POS for that token, i.e. tagging the terms in each extracted portion [0059-60],[0064-5]);
translating the extracted portions in the at least one target language using a quality machine-translator, thereby obtaining a … translation for each portion (a specific sentence translation pattern is determined for a whole sentence, where the frequently repeated word strings are determined to be the sentence pattern candidates, i.e. extracted portions, where the sentence pattern of the source language is transformed into the sentence structure of the target language sentence structure, and the term transformation is performed to select the optimal translated word, i.e. translating the extracted portions in the at least one target language, where the result is a structure and term transformation data structure, i.e. obtaining a translation for each portion [0088-90],[0134-6], using an automatic translation system, such as machine translation, that effectively performs automatic translation corresponding to a restrictive domain, i.e. a quality machine-translator [0005],[0014]); and
normalizing the translations (a final sentence in a target language is generated by the output using the transformed structure and terms, i.e. normalizing the translations [0135-7]).
While Lee provides using dictionaries for term transformation (claim 11), and applying tags to the source text before translation, Lee does not specifically teach a taxonomy having a lexicalization in a form of extraction rules with a specific form, cross-lingual search (claim 11), and that the translation is also tagged, and thus does not teach
(claim 13) a non-transitory computer-readable data storage medium having recorded thereon a computer program comprising instructions causing a processor to be configured to:
the taxonomy having a lexicalization in a form of extraction rules, at least one extraction rule including machine-readable operator-based expressions;
(claim 11) providing a cross-language semantic search engine; and
obtaining a tagged translation.
(claim 13) and/or causing the processor to be configured to:
(claim 13) provide a cross-language semantic search engine; and
(claim 13) translate at least one lexicalized taxonomy of the search engine given in a source language into at least one target language by applying the translation of the NLP system.
Markman, however, teaches
(claim 13) a non-transitory computer-readable data storage medium having recorded thereon a computer program comprising instructions causing a processor to be configured to (a CPU retrieves and executes programming instructions stored in the memory [0037-8]):
the taxonomy having a lexicalization in a form of extraction rules, at least one extraction rule including machine-readable operator-based expressions (phrases are generated from a dictionary of frequently used words and a set of language specific rules, i.e. taxonomy having a lexicalization in a form of extraction rules, where the rules have a structure including operators such as “OR”, “=”, and “AND”, that the system uses to generate and tag phrases, i.e. at least one extraction rule including machine-readable operator-based expressions, Table 1,[0023],[0096], [0099-108]);
(claim 11) providing a cross-language semantic search engine (a translated phrase can be retrieved from the translated phrases using the semantic tag when translating from a first language to the second language, i.e. cross-language semantic search engine [0103-10]); and
obtaining a tagged translation (the translated Spanish phrase and the original English phrase have matching semantic tags, i.e. obtaining a tagged translation [0103-10]).
(claim 13) and/or causing the processor to be configured to:
(claim 13) provide a cross-language semantic search engine (a translated phrase can be retrieved from the translated phrases using the semantic tag when translating from a first language to the second language, i.e. cross-language semantic search engine [0103-10]); and
(claim 13) translate at least one lexicalized taxonomy of the search engine given in a source language into at least one target language by applying the translation of the NLP system (a translated phrase can be retrieved from the translated phrases, i.e. translate at least one lexicalized taxonomy of the search engine, using matching semantic tags when translating from a first language to the second language, i.e. given in a source language into at least one target language by applying the translation of the NLP system [0103-10])).
Lee and Markman are analogous art because they are from a similar field of endeavor in performing automatic translation. Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the dictionaries for term transformation (claim 11) and applying tags to the source text before translation teachings of Lee with the use of a dictionary and language-specific rules resulting in translated phrases having matching semantic tags as taught by Markman. It would have been obvious to combine the references to enable retrieval of previously translated phrases using the semantic tags (Markman [0110]).
Regarding claims 2 and 14, Lee in view of Markman teaches claims 1 and 13, and Lee further teaches
applying heuristics using statistics to ensure that the normalized tagged translations are correct (the statistical POS tagging enables an optimized POS to be assigned to each word, where the words are extracted with co-occurring words where frequently-occurring word strings are likely to be syntax or sentence pattern candidates, and the structure analysis of the source language, i.e. heuristics using statistics, is used to make sure the structure of the translated output is customized for patent documents, i.e. applying…to ensure that the normalized tagged translations are correct [0065],[0133-7]).
Where Markman teaches that the translation is tagged [0103-10].
And where the motivation to combine is the same as previously presented.
Regarding claim 9, Lee in view of Markman teaches claim 1, and Markman further teaches
the at least one target language comprise at least one target language having a morphology (the tagged words related to the second language, i.e. at least one target language, identify by the base form of a word, such as the verb “comer”, versus the 3rd person plural of the verb “comen”, along with other characteristics of each word, such as POS, masculine/feminine, and tense, i.e. comprise at least one target language having a morphology [0101-10]).
Where the motivation to combine is the same as previously presented.
Regarding claims 12 and 16, Lee in view of Markman teaches claims 11 and 13, and Lee further teaches
((claim 12) updating/(claim 16) apply) the lexicalized taxonomy of the search engine given in the source language (constructing a specific corpus according to a restrictive domain through morpheme-analysis and tagging, extracting technical terms for documents written in a source language, i.e. given in the source language, applying a weight according to the restrictive domain and extracting a high-frequency expression by a longest-first method, filtering a sentence/phrase pattern, and constructing translated words for the constructed technical terms, constructing a syntax translation pattern and a sentence translation pattern based on the specific corpus constructed, and performing transformation of the target language structure and terms using the information, i.e. updating/apply the lexicalized taxonomy of the search engine [0021]).
Regarding claims 17, 18, and 20, Lee in view of Markman teaches claims 13, 14, and 16, and Markman further teaches
the processor coupled to the storage medium (a CPU retrieves and executes programming instructions stored in the memory [0037-8]).
Where the motivation to combine is the same as previously presented.
Claim(s) 3, 4, 15, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee, in view of Markman, and further in view of Ittycheriah et al. (U.S. PG Pub No. 2017/0300475), hereinafter Ittycheriah.
Regarding claims 3 and 15, Lee in view of Markman teaches claims 1 and 13.
While Lee in view of Markman provides using a corpus of documents, Lee in view of Markman does not specifically teach using the translated terms to crawl a new corpus of the web in the target language, and thus does not teach
using translated terms included in the translated extracted portions to crawl a new corpus of the web in the at least one target language.
Ittycheriah, however, teaches using translated terms included in the translated extracted portions to crawl a new corpus of the web in the at least one target language (a web crawler may crawl web addresses in the target language, i.e. crawl a new corpus of the web in the at least one target language, to extract content including terms similar to the extracted set of terms from the translation, such as the extracted terms “Kiryoshiti”, “space vehicle”, and “Mars”, i.e. translated terms included in the translated extracted portions, and their translation pairs “Curiosity and space vehicle” or “Curiosity and Mars”, i.e. using the translated terms included in the translated extracted portions [0038-43],[0050-2]).
Lee, Markman, and Ittycheriah, are analogous art because they are from a similar field of endeavor in performing automatic translation. Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the using a corpus of documents teachings of Lee, as modified by Markman, with the use of a web crawler to identify related translation pair terms in a target language as taught by Ittycheriah. It would have been obvious to combine the references to enable a translation system to determine a correct translation of transliterated terms using web search information (Ittycheriah [0037]).
Regarding claim 4, Lee in view of Markman and Ittycheriah teaches claim 3, and Ittycheriah further teaches
using results of a web search to ensure that the translated terms are correct (the extracted sets of terms from the web addresses in the target language are used by a comparison model, i.e. using results of a web search, to be compared with the extracted set of terms in the translation to determine a correct translation, such as “Kiryoshiti” should be “Curiosity”, i.e. ensure that the translated terms are correct [0038-43],[0050-2]).
Where the motivation to combine is the same as previously presented.
Regarding claim 19, Lee in view of Markman and Ittycheriah teaches claim 15, and Ittycheriah further teaches
the processor coupled to the storage medium (a CPU retrieves and executes programming instructions stored in the memory [0037-8]).
Where the motivation to combine is the same as previously presented.
Claim(s) 5 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee, in view of Markman, and further in view of Wang et al. (U.S. PG Pub No. 2022/0043987), hereinafter Wang.
Regarding claim 5, Lee in view of Markman teaches claim 1.
While Lee in view of Markman provides extracting high frequency terms and their co-occurring words, Lee in view of Markman does not specifically teach that there are a predetermined number words before and after the terms extracted, and thus does not teach
each portion comprises a predetermined number of words before the terms and a predetermined number of words after the terms.
Wang, however, teaches each portion comprises a predetermined number of words before the terms and a predetermined number of words after the terms (noun phrases with different levels are made from nouns, i.e. terms, such as one or more noun phrases are combined with other POS to form a level 1 noun phrase, at least one level 1 noun phrase is combined with another tag to form a level 2 noun phrase, and at least one level 2 noun phrase is combined with another tag to form a level 3 noun phrase, i.e. each portion comprises a predetermined number of words, such as the term “translation” being combined with other tagged words to generate the chunk “no one single best translation of that text to another language”, i.e. predetermined number of words before the terms and a predetermined number of words after the terms [0079],[0090-4]).
Where Lee teaches that a specific term is a high-frequency term [0067-78].
Lee, Markman, and Wang are analogous art because they are from a similar field of endeavor in performing automatic translation. Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the extracting high frequency terms and their co-occurring words teachings of Lee, as modified by Markman, with the combinations of different word tags to form levels of noun phrases as taught by Wang. It would have been obvious to combine the references to enable machine translation with syntax-based analysis, sentence structure extraction, and multi-layer translation adjustment providing an advantage in the use of semantic syntax rules of multiple languages (Wang [0062-3]).
Regarding claim 6, Lee in view of Markman and Wang teaches claim 5, and Wang further teaches
the predetermined number of words before the terms or the predetermined number of terms after the terms is larger than or equal to 3 including larger than or equal to 4 or 5 (noun phrases with different levels are made from nouns, i.e. terms, such as one or more noun phrases are combined with other POS to form a level 1 noun phrase, at least one level 1 noun phrase is combined with another tag to form a level 2 noun phrase, and at least one level 2 noun phrase is combined with another tag to form a level 3 noun phrase, i.e. each portion comprises a predetermined number of words, such as the term “translation” being combined with other tagged words to generate the chunk “no one single best translation of that text to another language”, i.e. the predetermined number of words before the terms or the predetermined number of terms after the terms is larger than or equal to 3 [0079],[0090-4]).
Where Lee teaches that a specific term is a high-frequency term [0067-78].
And where the motivation to combine is the same as previously presented.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee, in view of Markman, and further in view of Le et al. (U.S. PG Pub No. 2016/0117316), hereinafter Le.
Regarding claim 7, Lee in view of Markman teaches claim 1, and Markman further teaches
the quality machine-translator is a machine-translator … able to, when a term is tagged in a sentence of the source language to be translated, tag the term translated by the machine-translator in the sentence translated by the machine- translator in the at least one target language (the phrase generation engine generates a semantic tag for the generated phrase, i.e. when a term is tagged in a sentence of the source language to be translated, and the phrase translation engine is configured to translate a selected phrase, i.e. quality machine-translator, the translated Spanish phrase, i.e. term translated by the machine-translator in the sentence translated by the machine- translator in the at least one target language, and the original English phrase, i.e. term…in a sentence of the source language to be translated, have matching semantic tags, i.e. tag the translated term [0101-10]).
While Lee in view of Markman provides machine translation, Lee in view of Markman does not specifically teach that the translation is performed by a deep neural network, and thus does not teach
the quality machine-translator is a machine- translator based on a Deep Neural Network.
Le, however, teaches the quality machine-translator is a machine- translator based on a Deep Neural Network (the neural network translation model is a deep neural network that maps source language sentences to target language sentences [0016]).
Lee, Markman, and Le are analogous art because they are from a similar field of endeavor in performing automatic translation. Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the machine translation teachings of Lee, as modified by Markman, with the use of a DNN translation model as taught by Le. It would have been obvious to combine the references to combine NMT with alignment-based techniques to mitigate or overcome the inability of current NMT systems to translate words that are not in their vocabulary (Le [0006-7]).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee, in view of Markman, and further in view of Zhou et al. (U.S. PG Pub No. 2014/0280011), hereinafter Zhou.
Regarding claim 8, Lee in view of Markman teaches claim 1.
While Lee in view of Markman provides identifying high frequency expressions, Lee in view of Markman does not specifically teach that the high frequency terms have a specific percentage frequency and below a certain number of terms, and thus does not teach
the most frequent terms are the terms of which cumulated frequencies are greater than 90% and are below 10 terms.
Zhou, however, teaches the most frequent terms are the terms of which cumulated frequencies are greater than 90% and are below 10 terms (relative frequency measure for each n-gram is a count of pages divided by the number of pages on the site, i.e. percent, where the frequency measure of a phrase in the sites can include 90 and 100, i.e. most frequent terms are the terms of which cumulated frequencies are greater than 90%, and the number of phrases on a site to make a prediction of site quality can be a fixed number such as 3 or 5, i.e. below 10 terms [0021-3],[0035]).
Lee, Markman, and Zhou are analogous art because they are from a similar field of endeavor in processing phrases in documents. Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the identifying high frequency expressions teachings of Lee, as modified by Markman, with the identification of phrases with a frequency above 90, and that a minimum number of phrases are identified as taught by Zhou. It would have been obvious to combine the references to enable the automatic determination of a website quality for use in a search using evaluation of phrases (Zhou [0001-2],[0006]).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee, in view of Markman, and further in view of McCarley et al. (U.S. Patent No. 6,092,034), hereinafter McCarley.
Regarding claim 10, Lee in view of Markman teaches claim 9.
While Lee in view of Markman provides analyzing morphemes and recognizing the base form of a verb, Lee in view of Markman does not specifically teach the stem of all the terms, and thus does not teach
normalizing the translations in the at least one target language having a morphology comprises transforming all inflected forms of terms to their stems.
McCarley, however, teaches normalizing the translations in the at least one target language having a morphology comprises transforming all inflected forms of terms to their stems (the source language text and target language text are tokenized, have part of speech tags applied, and have the morphological root words, i.e. morphology comprises transforming all inflected forms of terms to their stems, determined, i.e. normalizing the translations in the at least one target language (6:09-33)).
Lee, Markman, and McCarley are analogous art because they are from a similar field of endeavor in performing automatic translation. Thus, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the analyzing morphemes and recognizing the base form of a verb teachings of Lee, as modified by Markman, with determining the morphological root words for the tokens of target language text as taught by McCarley. It would have been obvious to combine the references to result in a better translation of words that translate into rare words in the target language because evidence gathered from different conjugations/declensions of the same morphological root word ("morph") can be incorporated into the decision of how to translate the rare words (McCarley (6:09-33)).
Conclusion
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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/NICOLE A K SCHMIEDER/Primary Examiner, Art Unit 2659