DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/26/2026 has been entered.
This communication is in response to the Amendments and Arguments filed on 04/24/2026.
Claims 1-11 and 13-21 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All previous objections/rejections not mentioned in this Office Action have been withdrawn by the examiner.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 is being examined under the pre-AIA first to invent provisions.
Response to Arguments
Applicant's arguments filed 04/24/2026 have been fully considered but they are not persuasive and/or are moot.
Regarding the motivation to combine, Applicant asserts on pg 12-13 that Lee and Markman do not address the same problem and do not provide a meaningful basis for proposed combination. The Examiner respectfully disagrees. Both references perform automatic translation of text, which places them in a similar field of endeavor – translation. The ultimate application of such translation does not negate the fact that both references are translating text from one language to another, making the techniques relevant to one reference also relevant to the other. Additionally, as stated in the office action, the motivation to combine these references is to enable retrieval of previously translated phrases using the semantic tags (Markman [0110]).
Regarding the 101 rejection, Applicant asserts on pgs 14-22 that the claims are not directed to a mental process due to the recitation of additional and/or existing features or functionality. The Examiner respectfully disagrees.
Regarding the newly recited language: a) downloading or extracting a corpus from remote sources reads to a human accessing written material in any kind of filing system. b) the GUI does not fully integrate a human interacting with a functional component (such as clicking a button) leading to an impact on the process being performed. For example, the selection of a concept from a list of concepts reads to a human circling an option from a list on a piece of paper. While the GUI has a drop-down menu, and the human can select the option from the GUI, nothing is recited in the claims that makes use of the selection, or that the received selection impacts the following process.
The recitation of machine translator and a DNN does not inherently mean the claims are not a mental process and are, therefore, patent eligible. Humans are capable of performing the tasks recited as being performed by the machine translator/DNN, where learned rules are utilized to perform the task, where there are no additional functions of the machine translator/DNN recited in the claims that would lead to an interpretation indicating otherwise.
A human is capable of looking at rules written in a specific format and applying them, including formalized rules for how to evaluate a specific set of characters or words. The recitation of a computer system is a generalized computer component that does not amount to significantly more than the judicial exception.
A human is able to, with the use of their mind and pen and paper, read a question from another human, and search and find documents in multiple languages that have the answers to that question, therefore, as a human can perform the functions of a cross-language semantic search engine, the claims are directed to an abstract idea. The additional recitation of using heuristics reads to a human using specific rules (including back translation) to verify that the translation is correct, such as by seeing that the back translation and the original term are the same term, and then updating their understanding and/or resources based on the results of the back translation.
The determination of whether or not a judicial exception is integrated into a practical application considers both whether any additional elements integrate the judicial exception into a practical application and the reflection of a technological improvement in the claims. Satisfying either condition is the test. The first condition – additional elements – considers the fact that the claims recite generalized computer components. This is not enough, as using generalized computer components to perform the recited limitations amounts to no more than mere instructions to apply the exception using a generic computer component. The second condition – do the claims reflect a technological improvement – has also not been met in the claims as recited, as it is not clear from what is presently recited in the claims how the recited process provides an improvement to performing translations. As neither condition has been satisfied, the claims remain ineligible.
The claims have been thoroughly considered for eligibility according to the standards set forth for 101 evaluations, and found to be ineligible.
Applicant’s arguments with respect to claim(s) 1, 11, and 13 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. Please see the updated mappings below citing Geib and/or Zillner for further detail.
Hence, Applicant’s arguments are either not persuasive and/or are moot.
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 1-11 and 13-21 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 (claim 11 and 13) providing, (claim 11 and 13) translating, obtaining, (claim 1 and 13) displaying, (claim 1 and 13) receiving, filtering, querying, tagging, (claim 1 and 13) receiving, translating, (claim 1 and 13) displaying, normalizing, (claim 13) translate, (claim 11 and 13) applying, and (claim 11 and 13) updating, 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 written reference guide from a set of files 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, writing out the translation in a specific format for easy comparison to other documents, and receiving written information and instructions from a person requesting a translation. The additional limitations 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. The GUI reads to people being able to write down information on a piece of paper that can be shown to and read by another human, as the GUI does not have any functional components recited in the claims (e.g. a human clicks the button, which causes a direct action to be performed as part of the process recited in the claims). 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 a computer system, processor, and screen, in claim 1, processor in claim 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 perform the recited limitations 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 internet (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) 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.
With respect to claim(s) 21, the claim(s) recite(s) characteristics of an extraction rule, which reads on a human using specific guidelines when evaluating text. No additional limitations are present.
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, and 21 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, and further in view of Geib et al. (U.S. PG Pub No. 2018/0203849), hereinafter Geib.
Regarding claim 1, Lee teaches
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:
obtaining, automatically by a computer system, 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 method performed by an apparatus includes a large document corpus constructed from documents written in a source language being input, i.e. obtaining automatically by a computer system 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 [0005],[0014],[0022],[0059-60],[0064-6]);
filtering, automatically by the computer system, the most frequent terms for each annotation, the filtering including, by the computer system, applying the at least one extraction rule … (a high frequency word represented as a particular POS, i.e. most frequent terms for each annotation, are identified and extracted by the apparatus, i.e. filtering automatically by the computer system, where the words are tagged using word/POS/context probability data and word probability data based on a morpheme analysis dictionary that includes words, where a word not in the dictionary is dealt with as an unknown word, and where extracting the highest frequency string of the specific corpus uses the POS data, i.e. filtering including…applying the at least one extraction rule [0005],[0014],[0067-78]);
querying, automatically by the computer system, 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], where the method is performed by an apparatus, i.e. by the computer system [0005],[0014]);
tagging, automatically by the computer system, 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], where the method is performed by an apparatus, i.e. by the computer system [0005],[0014]);
translating, automatically by the computer system, 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, where the method is performed by an apparatus, i.e. by the computer system [0005],[0014]);
normalizing, automatically by the computer system, 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], where the method is performed by an apparatus, i.e. by the computer system [0005],[0014]).
While Lee provides 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, that the translation is also tagged, and the use of a GUI, and thus does not teach
obtaining including downloading or extracting the corpus from at least one of: the computer system, a server, the internet, a memory, a distant memory, a database, or a distant database;
the taxonomy having a lexicalization in a form of extraction rules, at least one extraction rule including machine-readable operator-based expressions;
obtaining a tagged translation; and
displaying on the screen, automatically by the computer system, via the GUI, a translation window comprising the translated extracted portions in the at least one target language.
Markman, however, teaches obtaining including downloading or extracting the corpus from at least one of: the computer system, a server, the internet, a memory, a distant memory, a database, or a distant database (the computing infrastructure includes a server and client systems, where the server computer involves a database that stores generated phrases to be displayed to the user for selection, i.e. obtaining including…extracting the corpus from at least one of…a database Fig. 3A,[0035],[0042],[0048]);
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]);
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]); and
displaying on the screen, automatically by the computer system, via the GUI, a translation window comprising the translated extracted portions in the at least one target language (the translated phrase, i.e. comprising the translated extracted portions in the at least one target language, is output to the client system, i.e. by the computer system, to be shown to the user on a display, i.e. displaying on the screen, such as in a chat component within a virtual world application, i.e. via the GUI a translation window Fig. 2B,[0037],[0039-40],[0102],[0110]).
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 apply 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]).
While Lee in view of Markman provides translations of text input and output through a GUI, Lee in view of Markman does not specifically teach selecting a concept from a list of concepts displayed on a GUI, and thus does not teach
displaying on a screen, automatically by the computer system, a graphical user interface (GUI), the GUI comprising a list of concepts of the taxonomy;
receiving, by the computer system, via the GUI, one or more user inputs selecting at least one concept from the list of concepts of the taxonomy;
receiving, by the computer system, via the GUI, one or more user inputs indicating that a translation is sought in the at least one target language.
Geib, however, teaches displaying on a screen, automatically by the computer system, a graphical user interface (GUI), the GUI comprising a list of concepts of the taxonomy (the computing system displays a user interface, i.e. displaying on a screen automatically by the computer system a graphical user interface (GUI), that allows a user to input a source language expression, and further includes a drop-down menu facilitating user selection of a domain of usage, i.e. a list of concepts of the taxonomy, that is included in the translation-recommendation request [0013],[0016-7],[0040]);
receiving, by the computer system, via the GUI, one or more user inputs selecting at least one concept from the list of concepts of the taxonomy (the computing system displays a user interface, i.e. computer system via the GUI, that allows a user to input a source language expression, and further includes a drop-down menu facilitating user selection of a domain of usage, i.e. receiving…one or more user inputs selecting at least one concept from the list of concepts of the taxonomy, that is included in the translation-recommendation request [0013],[0016-7],[0040]);
receiving, by the computer system, via the GUI, one or more user inputs indicating that a translation is sought in the at least one target language (the computing system displays a user interface, i.e. computer system via the GUI, that allows a user to input a source language expression, and further includes a drop-down menu facilitating user selection of a domain of usage, that is included in the translation-recommendation request for translating a source language expression into a target language expression, i.e. receiving…one or more user inputs indicating that a translation is sought in the at least one target language [0013],[0016-7],[0040]).
Lee, Markman, and Geib, 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 translations of text input and output through a GUI teachings of Lee, as modified by Markman, with the selection of domain of usage by a user as taught by Geib. It would have been obvious to combine the references to utilize a concept database for the disambiguation of equivalent expressions for a given concept in multiple languages, such as when a source language word is a homonym with different meanings for the same word depending on domain (Geib [0003],[0010]).
Regarding claim 2, Lee in view of Markman and Geib teaches claim 1, 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 and Geib 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 claim 21, Lee in view of Markman and Geib teaches claim 1, and Lee further teaches
an extraction rule appliable to a string of characters and indicating at least one of (the system extracts a string of the specific corpus [0021-2],[0036]):
the string of characters is case-sensitive;
terms of the string of characters have to be searched in a given order (a sentence/phrase syntax pattern is determined based on a word string that is frequency repeated, such as a specific word string being the start/end node of a phrase, i.e. terms of the string of characters have to be searched in a given order [0021-2],[0036],[0090-2]);
or a predetermined number of other terms may be present between two terms of the string of characters.
Claim(s) 3 and 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee, in view of Markman, in view of Geib, and further in view of Ittycheriah et al. (U.S. PG Pub No. 2017/0300475), hereinafter Ittycheriah.
Regarding claim 3 , Lee in view of Markman and Geib teaches claim 1.
While Lee in view of Markman and Geib provides using a corpus of documents, Lee in view of Markman and Geib does not specifically teach using the translated terms to crawl a new corpus of the internet 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 internet 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, Geib, 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 and Geib, 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, Geib, 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.
Claim(s) 5 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee, in view of Markman, in view of Geib, 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 and Geib teaches claim 1.
While Lee in view of Markman and Geib provides extracting high frequency terms and their co-occurring words, Lee in view of Markman and Geib 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, Geib, 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 and Geib, 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, Geib, 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, in view of Geib, 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 and Geib 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 and Geib provides machine translation, Lee in view of Markman and Geib 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, Geib, 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 and Geib, 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, in view of Geib, 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 and Geib teaches claim 1.
While Lee in view of Markman and Geib provides identifying high frequency expressions, Lee in view of Markman and Geib 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, Geib, 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 and Geib, 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, 13, 14, 16-18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee, in view of Markman, in view of Geib, and further in view of Zillner et al. (U.S. PG Pub No. 2015/0095013), as found in the IDS, hereinafter Zillner.
Regarding claim 10, Lee in view of Markman and Geib teaches claim 9.
While Lee in view of Markman and Geib provides analyzing morphemes and recognizing the base form of a verb, Lee in view of Markman and Geib 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.
Zillner, 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 (individual words are stemmed to the smallest word unit supplying the main meaning, removing any possible affixes, i.e. a morphology comprises transforming all inflected forms of terms to their stems, where the stemming is applied in annotation tasks and is used for both the source and target language terms, i.e. normalizing the translations in the at least one target language [0041-2],[0084-7]).
Lee, Markman, Geib, and Zillner 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 and Geib, with determining the stems of terms in both the source and target languages as taught by Zillner. It would have been obvious to combine the references to enable efficient extension of concept labels by labels in a target language in order to increase concept coverage for a given corpus of text (Zillner [0010]).
Regarding claim 13, Lee teaches
A device comprising (an apparatus [0014]):
translate of a Natural Language Processing (NLP) system given in a source language into at least one target language, the NLP system being based on a lexicalized taxonomy and allowing text annotation and classification , the processor being configured to translate by being configured to (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], where the method is performed by an apparatus, i.e. processor [0005],[0014]):
obtain 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 including 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 method performed by an apparatus includes a large document corpus constructed from documents written in a source language being input, i.e. obtaining automatically by a computer system 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 [0005],[0014],[0022],[0059-60],[0064-6]);
filter the most frequent terms for each annotation, the filtering including, by the processor, applying the at least one extraction rule… (a high frequency word represented as a particular POS, i.e. most frequent terms for each annotation, are identified and extracted by the apparatus, i.e. filtering automatically by the computer system, where the words are tagged using word/POS/context probability data and word probability data based on a morpheme analysis dictionary that includes words, where a word not in the dictionary is dealt with as an unknown word, and where extracting the highest frequency string of the specific corpus uses the POS data, i.e. filtering including…applying the at least one extraction rule [0005],[0014],[0067-78]);
query 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], where the method is performed by an apparatus, i.e. processor [0005],[0014]);
tag 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], where the method is performed by an apparatus, i.e. processor [0005],[0014]);
translate 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, where the method is performed by an apparatus, i.e. processor [0005],[0014]);
normalize 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], where the method is performed by an apparatus, i.e. processor [0005],[0014]);
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]).
While Lee provides 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, that the translation is also tagged, and the use of a GUI, and thus does not teach
a non-transitory computer-readable data storage medium having recorded thereon a computer program comprising instructions causing a processor to be configured to:
obtaining including downloading or extracting the corpus from at least one of: the computer system, a server, the internet, a memory, a distant memory, a database, or a distant database;
the taxonomy having a lexicalization in a form of extraction rules, at least one extraction rule including machine-readable operator-based expressions;
obtaining a tagged translation; and
display on the screen, via the GUI, a translation window comprising the translated extracted portions in the at least one target language; and
and/or causing the processor to be configured to:
provide a cross-language semantic search engine; and
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 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]):
obtaining including downloading or extracting the corpus from at least one of: the computer system, a server, the internet, a memory, a distant memory, a database, or a distant database (the computing infrastructure includes a server and client systems, where the server computer involves a database that stores generated phrases to be displayed to the user for selection, i.e. obtaining including…extracting the corpus from at least one of…a database Fig. 3A,[0035],[0042],[0048]);
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]);
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]); and
display on the screen, via the GUI, a translation window comprising the translated extracted portions in the at least one target language (the translated phrase, i.e. comprising the translated extracted portions in the at least one target language, is output to the client system to be shown to the user on a display, i.e. displaying on the screen, such as in a chat component within a virtual world application, i.e. via the GUI a translation window Fig. 2B,[0037],[0039-40],[0102],[0110]); and
and/or causing the processor to be configured to:
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
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 apply 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]).
While Lee in view of Markman provides translations of text input and output through a GUI, Lee in view of Markman does not specifically teach selecting a concept from a list of concepts displayed on a GUI, and thus does not teach
display on a screen a graphical user interface (GUI), the GUI comprising a list of concepts of the taxonomy;
receive, via the GUI, one or more user inputs selecting at least one concept from the list of concepts of the taxonomy;
receive, via the GUI, one or more user inputs indicating that a translation is sought in the at least one target language.
Geib, however, teaches display on a screen a graphical user interface (GUI), the GUI comprising a list of concepts of the taxonomy (the computing system displays a user interface, i.e. displaying on a screen automatically by the computer system a graphical user interface (GUI), that allows a user to input a source language expression, and further includes a drop-down menu facilitating user selection of a domain of usage, i.e. a list of concepts of the taxonomy, that is included in the translation-recommendation request [0013],[0016-7],[0040]);
receive, via the GUI, one or more user inputs selecting at least one concept from the list of concepts of the taxonomy (the computing system displays a user interface, i.e. computer system via the GUI, that allows a user to input a source language expression, and further includes a drop-down menu facilitating user selection of a domain of usage, i.e. receiving…one or more user inputs selecting at least one concept from the list of concepts of the taxonomy, that is included in the translation-recommendation request [0013],[0016-7],[0040]);
receive, via the GUI, one or more user inputs indicating that a translation is sought in the at least one target language (the computing system displays a user interface, i.e. computer system via the GUI, that allows a user to input a source language expression, and further includes a drop-down menu facilitating user selection of a domain of usage, that is included in the translation-recommendation request for translating a source language expression into a target language expression, i.e. receiving…one or more user inputs indicating that a translation is sought in the at least one target language [0013],[0016-7],[0040]).
Lee, Markman, and Geib, 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 translations of text input and output through a GUI teachings of Lee, as modified by Markman, with the selection of domain of usage by a user as taught by Geib. It would have been obvious to combine the references to utilize a concept database for the disambiguation of equivalent expressions for a given concept in multiple languages, such as when a source language word is a homonym with different meanings for the same word depending on domain (Geib [0003],[0010]).
While Lee in view of Markman and Geib provides statistical POS tagging and structural analysis, Lee in view of Markman and Geib does not specifically teach heuristics include back translating a translation, and thus does not teach
the applying of the heuristics including, for each translated term, back translating the normalized tagged translation back to the source language; and
updating the lexicalized taxonomy of the search engine given in the source language by adding terms that are back translated with high frequency.
Zillner, however, teaches the applying of the heuristics including, for each translated term, back translating the normalized tagged translation back to the source language (for adding the partially missing target language label, i.e. for each translated term, the German label has to be translated back to English, i.e. back translating the normalized tagged translation back to the source language, in order to extend labels into a target language with correct mapping to an ontology concept, i.e. applying of the heuristics [0050-4],[0102]); and
updating the lexicalized taxonomy of the search engine given in the source language by adding terms that are back translated with high frequency (for adding the partially missing target language label to the ontology, i.e. updating the lexicalized taxonomy of the search engine given in the source language by adding terms that are back translated, the German label has to be translated back to English in order to extend labels into a target language with correct mapping to an ontology concept, where a filtering algorithm is applied to limit the number of terms which are to be translated into the source language, such as by a frequency of occurrence, i.e. terms that are back translated with high frequency [0050-4],[0102]).
Lee, Markman, Geib, and Zillner 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 statistical POS tagging and structural analysis teachings of Lee, as modified by Markman and Geib, with translating target language labels back into the source language to ensure correct mapping to an ontology concept as taught by Zillner. It would have been obvious to combine the references to enable efficient extension of concept labels by labels in a target language in order to increase concept coverage for a given corpus of text (Zillner [0010]).
Regarding claim 14, Lee in view of Markman, Geib, and Zillner, teaches claim 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 16, Lee in view of Markman, Geib, and Zillner, teaches claim 13, and Lee further teaches
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, Geib, and Zillner, 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) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee, in view of Markman, and further in view of Zillner.
Regarding claim 11, Lee teaches
A computer-implemented method for maintenance of a cross-language semantic search engine (an apparatus and method for automatic translation [0014]), the method for maintenance comprising:
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:
obtaining a corpus in the source language, the given lexicalized taxonomy including annotations allowing determination of the most frequent terms describing a given concept in the corpus, the taxonomy including 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]);
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]); and
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]).
While Lee provides using dictionaries for term transformation 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, and that the translation is also tagged, and thus does not teach
providing a cross-language semantic search engine; and
the taxonomy including a lexicalization in a form of extraction rules, at least one extraction rule including machine-readable operator- based expressions;
obtaining a tagged translation.
Markman, however, teaches 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
the taxonomy including 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]);
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]).
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 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]).
While Lee in view of Markman provides statistical POS tagging and structural analysis, Lee in view of Markman does not specifically teach heuristics include back translating a translation, and thus does not teach
the applying of the heuristics including, for each translated term, back translating the normalized tagged translation back to the source language; and
updating the lexicalized taxonomy of the search engine given in the source language by adding terms that are back translated with high frequency.
Zillner, however, teaches the applying of the heuristics including, for each translated term, back translating the normalized tagged translation back to the source language (for adding the partially missing target language label, i.e. for each translated term, the German label has to be translated back to English, i.e. back translating the normalized tagged translation back to the source language, in order to extend labels into a target language with correct mapping to an ontology concept, i.e. applying of the heuristics [0050-4],[0102]); and
updating the lexicalized taxonomy of the search engine given in the source language by adding terms that are back translated with high frequency (for adding the partially missing target language label to the ontology, i.e. updating the lexicalized taxonomy of the search engine given in the source language by adding terms that are back translated, the German label has to be translated back to English in order to extend labels into a target language with correct mapping to an ontology concept, where a filtering algorithm is applied to limit the number of terms which are to be translated into the source language, such as by a frequency of occurrence, i.e. terms that are back translated with high frequency [0050-4],[0102]).
Lee, Markman, and Zillner 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 statistical POS tagging and structural analysis teachings of Lee, as modified by Markman, with translating target language labels back into the source language to ensure correct mapping to an ontology concept as taught by Zillner. It would have been obvious to combine the references to enable efficient extension of concept labels by labels in a target language in order to increase concept coverage for a given corpus of text (Zillner [0010]).
Claim(s) 15 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee, in view of Markman, in view of Zillner, and further in view of Ittycheriah.
Regarding claim 15, Lee in view of Markman, Geib, and Zillner teaches claim 13.
While Lee in view of Markman, Geib, and Zillner provides using a corpus of documents, Lee in view of Markman, Geib, and Zillner does not specifically teach using the translated terms to crawl a new corpus of the internet 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 internet 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 internet 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, Geib, Zillner, 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, Geib, and Zillner, 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 19, Lee in view of Markman, Geib, Zillner, 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.
Conclusion
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/NICOLE A K SCHMIEDER/Primary Examiner, Art Unit 2659
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