DETAILED ACTION
This office action is in response to Applicant’s Amendment/Request for Reconsideration, received on 12/23/2025. Claims 1, 6, 9, 16, 17, 19 have been amended. Claims 1-20 are pending and have been considered.
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, see pg. 8, filed 12/23/2025, with respect to claim 19 have been fully considered and are persuasive. The objection of claim 19 has been withdrawn.
The examiner would like to note that Applicant’s remarks regarding interpretation of claim terms under 35 U.S.C. 112(f) (see pg. 8 of remarks, filed 12/23/2025) have been considered and are persuasive. The interpretation of terms under 112(f) have been removed.
Applicant's arguments filed 12/23/2025, see pgs. 8-11, with respect to “Claim Rejections - 35 U.S.C. 101” have been fully considered but they are not persuasive.
Applicant’s representative asserts, “Claims 1-20 were rejected under 35 U.S.C. 101 because the claimed invention is allegedly directed to an abstract idea without significantly more. Office Action at page 5.
Applicant respectfully disagrees.
For Step 2A, Prong I, the Office Action asserts that the elements of claims 1 recite ‘the abstract idea of a mental process.’ Office Action at page 8.
For Step 2A, Prong II, the Office Action asserts that the elements of claims 1 are ‘not integrated into a practical application.’ Id.
For Step 2B, the Office Action asserts that the ‘instructions to apply the exception using a generic computer component cannot provide an inventive concept.’ Id.
Applicant respectfully disagrees. Nevertheless, without acquiescing to the propriety of the rejections and solely to expedite prosecution, claims 1, 9, and 16 have been amended. Applicant respectfully submits that the claims, as amended, are not directed towards any abstract ideas or any other judicial exceptions because the amended claims include additional elements and as a whole integrate the alleged abstract ideas into a practical application.
For example, amended claim 1 recites, inter alia, the following:
1. An apparatus comprising:
at least one processor assembly configured to:
receive, from a sender input device, chat in a first language;
under at least a first condition, responsive to a first term in the chat being in
a glossary associated with a computer game, look up a term in a second language
from the glossary corresponding to the first term;
responsive to a second term in the chat not being in the glossary, provide
the second term to a translation engine comprising at least one neural network configured to output a term in the second language corresponding to the second
term; and
send the term in the second language from the glossary corresponding to the
first term and the term in the second language corresponding to the second term to
a recipient output device for presentation thereof.
Applicant respectfully submits that claim 1, as amended, includes additional elements that demonstrate that claim 1, as a whole, improves upon computational efficiency in computing systems with respect to reducing latency of computing system translations. Specifically, the claimed techniques leverage techniques in machine learning to translate chat during play of a computer game while maintaining context relative to game play. Specification at pages 3 and 17.
The Specification describes that the ‘translation of chat may introduce latency into
communication between two players, which is particularly nettlesome if the players happen to be communicating about cooperation as a team in the game.’ Specification at page 1.
In contrast, the claimed techniques recite the use of a neural network to predict a failure probability for an update to a particular section of code. Use of the neural network ‘training set of chat sentences input by users of plural computer games in general with indications of ground truth chat type, i.e., whether each item in the training set is related to a computer game or is not related to a computer game (in other words, is social chat that does not involve game context).’ Specification at page 14. Additionally, selection model of the neural network ‘further may be trained to recognize game context to classify chat. For example, chat input during high activity game segments may be more likely to be game chat than social chat, whereas chat input during lull times in the game may be more likely to be social chat. Game context may be inferred by motion vectors from the game engine, direct input from the game engine indicating context, and scene recognition applied to video of the game.’ Specification at page 15. Thus, the claimed techniques do not recite purely mental processes. Rather, the claims recite concrete enhancements to computer functionality, enabling faster data processing, lower latency, and improved scalability in real-world applications and it is well-settled that claims which clearly improve computer functionality or another technical field are patent eligible. See, e.g., Enfsh, LLC v. Microsoft Corp., 822 F.3d 1327. See also McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299.
In addition, the claimed techniques cannot practically be performed in the human mind. The process incorporating ‘a translation engine comprising at least one neural network configured to output a term in the second language corresponding to the second term’ involves a series of computationally intensive and highly abstract operations that cannot practically be performed in the human mind. As will be appreciated by one of ordinary skill, such a process involves integration and normalization of disparate data formats and sources, application of sophisticated mathematical transformations (such as dimensionality reduction, encoding, and scaling), and the precise assembly of these processed data points into a unified, structured representation suitable for machine learning analysis.
For at least the foregoing reasons, it is clear that the claims include additional elements and, as a whole, integrates the alleged abstract ideas into a practical application by clearly improving a technology and a technical field. The present claims, as amended, recite a specific technological improvement related to multi-player game play. Thus, Applicant respectfully submits that the claims are not directed towards an abstract idea or any other judicial exception and amount to significantly more than just an abstract idea and any other judicial exception and thus are patent eligible.
Accordingly, Applicant respectfully requests withdrawal of the rejection under 35 U.S.C.§ 101.”
In response, the examiner respectfully disagrees. Regarding Applicant’s assertion that “claim 1, as amended, includes additional elements that demonstrate that claim 1, as a whole, improves upon computational efficiency in computing systems with respect to reducing latency of computing system translations. Specifically, the claimed techniques leverage techniques in machine learning to translate chat during play of a computer game while maintaining context relative to game play…the claimed techniques recite the use of a neural network to predict a failure probability for an update to a particular section of code” (see Applicant remarks, pg. 9), the examiner would like to refer to the current claim language of the amended claims. Specifically, independent claim 1 has been amended to recite “…provide the second term to a translation engine comprising at least one neural network configured to output a term in the second language corresponding to the second term…”. The examiner respects the plurality of cited improvements on pgs. 14-15 of the instant application, though they are not introduced into the claims with the neural network in a way to incorporate the improvements into the claims. The mere act of translating a term using a neural network (all that is claimed under BRI) is applying a well-known computing component, i.e. neural network, for performance of a mental process, i.e. translation.
Continuing down this path, with regard to Applicant’s assertion that the claimed techniques cannot be performed in the mind, the examiner respectfully asserts that translation is a well-known mental process for anyone having the knowledge of multiple languages. Incorporating a neural network to result in a “translation engine comprising at least one neural network configured to output a term in the second language corresponding to the second term” is 1. not a limiting structure, i.e. the neural network does not necessarily have to be responsible for the translation given the current claim language, and 2. not recited with any specificity indicating why the neural network must be “involving a series of computationally intensive and highly abstract operations that cannot be performed in the human mind”; therefore, it is unclear to the examiner why this operation cannot be performed mentally when no highly abstract operation or computationally expensive operation is being performed. The examiner respectfully asserts that translation of one term is not abstract, nor expensive. All claim rejections under 35 U.S.C. 101 have been maintained and updated below.
Applicant's arguments filed 12/23/2025, see pg. 11, with respect to “Claim Rejections - 35 U.S.C. 103” have been fully considered but they are not persuasive.
Applicant’s representative asserts, “Claims 1-4, 9-11,16-18 were rejected under 35 U.S.C. 103 as allegedly being unpatentable over Travieso, et al. (U.S. Pub. No. 20210209185) (hereinafter ‘Travieso’) in view of Wang, et al. (U.S. Pub. No. 20190087417) (hereinafter ‘Wang’). Claims 5-6, 12-13, 19 were rejected under 35 U.S.C. 103 as allegedly being unpatentable over Travieso in view of Wang, further in view of Leydon, et al. (U.S. Pub. No. 20140303961) (hereinafter ‘Leydon’). Claims 7- 8, 14-15, 20 were rejected under 35 U.S.C. 103 as allegedly being unpatentable over Travieso in view of Wang, further in view of Aronzon (U.S. Pub. No. 20190151755) (hereinafter ‘Aronzon’).
Applicant respectfully disagrees. Nevertheless, without conceding to the correctness of the rejections and solely to advance prosecution, Applicant has amended the claims herein. The cited portions of Travieso do not teach or suggest a neural network or modern machine learning- based translation engine, nor use of game or user context as input to the translation process. Wang similarly does not teach or suggest context-aware or receiving auxiliary information such as game context at translation time. In contrast, amended claim 1 recites ‘responsive to a second term in the chat not being in the glossary, provid[ing] the second term to a translation engine comprising at least one neural network configured to output a term in the second language corresponding to the second term.’ The Office has not shown that the cited portions of Wang or Travieso provide a structure of translation including a translation engine comprising a neural network, such as would enable context-aware translation that adapts to the nuances of in-game communication.
Accordingly, Applicant respectfully requests withdrawal of the rejection under 35 U.S.C. § 103 and allowance of the pending claims.”
In response, the examiner respectfully disagrees with Applicant’s assertion that “The Office has not shown that the cited portions of Wang or Travieso provide a structure of translation including a translation engine comprising a neural network, such as would enable context-aware translation that adapts to the nuances of in-game communication”. The examiner would like to make reference to Wang, which specifically cites chat messaging in multiplayer games ([0004]) and providing translation for these in-game chats using neural machine translation (NMT) ([0049], [0074]), wherein the neural machine translation is performed using a neural network ([0077]-[0081]). Performing translation using NMT given Wang’s definition of NMT indicates the NMT to be a translation engine comprising at least one neural network. See updated rejections below.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claim 1 recites:
receive, from a sender input device, chat in a first language;
under at least a first condition, responsive to a first term in the chat being in a glossary associated with a computer game, look up a term in a second language from the glossary corresponding to the first term;
responsive to a second term in the chat not being in the glossary, provide the second term to a translation engine comprising at least one neural network configured to output a term in the second language corresponding to the second term; and
send the term in the second language from the glossary corresponding to the first term and the term in the second language corresponding to the second term to a recipient output device for presentation thereof.
Independent claim 9 recites:
translate first terms in chat in a first language and input during play of a computer game to second terms in a second language using a translation engine comprising at least one neural network trained to output the second terms;
look up third terms in the chat in the first language using a glossary to correlate the third terms to fourth terms in the second language; and
send the second and fourth terms on an output device for presentation thereof.
Independent claim 16 recites:
receiving chat during play of a computer game and input by means of at least one input device, the chat being in a first language;
translating first terms in the chat to terms in a second language using a translation engine comprising at least one neural network configured to output the terms in a second language; using second terms in the chat to look up terms in the second language using at least one glossary; and
sending the terms in the second language obtained from the translation engine and the glossary to at least one output device.
These limitations, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, the claim(s) read(s) on receiving input text, checking if the text has a previous translation (claim 1, 16), translating the received text into a second language, determining a correlation between the translation and original language (claim 9), and sending the translation back to an output device. That is, other than reciting “translation engine comprising at least one neural network” nothing in the claim element precludes the step from practically being performed in the mind. All of these steps can be performed in the mind and/or using pen and paper.
For example, consider being the translator between two foreign parties in a business meeting or other communication arrangement. The translator will receive a document in one language to be communicated to the second party in a second language. Based on the received text, the translator will mentally read the document and be able to determine which sections they know translations for, i.e. the mind represents the claimed glossary and the associated translations are known in the mind as would be determined through reading. Continuing, based on what translation they are aware of, the translator can write a partially translated document. With sections that might contain language unfamiliar to the translator, they can use a generic translation service, i.e. a language conversion dictionary, to learn the missing translations based on terms received in a first language. Based on the translator “learning” the translation from the dictionary, they will effectively be added into the translator’s “glossary” as a memory to be used for future translations to correlate the two texts in the two languages. When the translator has completed writing of the translated document, the physical translation can be presented to the second party speaking the second language on paper. Incorporating a neural network to perform term translation is applying the mental process to a specific computing architecture. The examiner respectfully asserts that this does not provide an inventive step.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea (Step 2A, Prong one, Yes).
This judicial exception is not integrated into a practical application because the addition of generically recited computer elements does not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. The claims are directed to an abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception ( Step 2A, Prong two, No). As discussed above, with respect to integration of an abstract idea into a practical application, the additional element of “receiv[ing]”, “look[ing] up”, “translat[ing]”, “send[ing]” are merely for the purpose of data gathering, storing, processing, and/or insignificant extra-solution activity that amount to no more than mere instructions to apply the exception using a generic computer component. Paragraph(s) 1 on pg. 5 of the instant application disclose(s) applying the method to a generic computing device such as a PC (portable computers such as laptops and tablet computers). Mere instructions to apply the exception using a generic computer component cannot provide an inventive concept. Therefore, the claims are not patent eligible (Step 2B, No).
Similarly, dependent claim(s) 2-8, 10-15, 17-20 include additional steps that are considered “insignificant extra-solution activity to judicial exception” because they fail to provide meaningful significance that goes beyond generally linking the use of an abstract idea to a particular technological environment.
For example, claim 2 reads on not looking up the translation for a term in a glossary if it doesn’t pertain to a computer game associated with a glossary, rather, directly sending the language to a translation engine. Reading text, one will be able to determine if it is in the context of a video game. Further, based on this determination a user can know to look up a definition using a language dictionary or other generic translation resource as opposed to a direct translation as would be determined mentally. Therefore, these steps constitute a mental process.
Claim 3 reads on, upon the determination that a third term is a non-language term or proper noun, to not translate the third term. Determining not to translate someone’s name or other well-known proper noun is a mental process associated with reading comprehension, Similarly, if the term is non-language, i.e. boom, crash, etc. (words representing sounds), a reader will be able to understand this and know not to translate.
Claim 4 reads on the recipient output device and sender input device communicating over a wide-area computer network. A wide-area computer network is a conventional communication method well-known in the art before the effective filing date of the claimed invention. Consider the internet. Therefore, there is no inventive step in applying the mental process to a generic communication network.
Also consider:
Zhou (US-20020152258-A1) which discloses the internet as a well-known WAN, [0002].
Dupre et al. (US-20210203768-A1) which discloses a network interface including WANs as well known, [0094].
Danielyan (US-20130132065-A1) which discloses networks including WANs to be well-known in the art, [0025].
Claim 5 reads on providing a term in the second language from the glossary corresponding to the first term to the translation engine, or provide an indication as to part-of-speech of the term of the second language, or provide both the term and an indication of part-of-speech. Given a word, a user will mentally be able to determine a translation, recover a translation from an engine, and/or determine a part-of-speech of the term. The step of providing a term to a translation engine could be the step of writing a foreign word on a piece of paper to be taken to and matched in a translation dictionary. This can be performed mentally with the aid of pen and paper and the associates physically written translation dictionary.
Claim 6 reads on the maintaining of context of the term in the second language and/or an indication as to part-of-speech. Determining the context of a term, given the larger text, is a mental process. Similarly, the context of a translation can be written next to the translated term, i.e. context including tense, usage situations, etc. all of which would be determined mentally in view of the surrounding words of the input text. As previously discussed, determining a part-of-speech of a term can be performed mentally, and recorded physically with pen and paper. Therefore, this steps are also able to be mentally performed.
Claim 7 reads on identifying the first and second languages being received. Determination as to the type of language being received can be performed mentally and/or with the aid of generic translation dictionaries or equivalent well-known, public translation resources. Therefore, this step is also a mental process assisted with pen and paper.
Claim 8 reads on having glossaries specific to the scenes of the computer game they are a part of. Specifically, with first and second glossaries corresponding to first and second scenes. The step of segmenting a glossary into “scene” based glossaries is an equivalent step to segmenting a larger glossary into timing based sections. The determination as to when to decide scenes for a computer game is a mental process, i.e. on a level, minute, session basis. Similarly, based on the mentally determined scenes, a translator can create separately written glossaries based on received texts and translation requests. Therefore, all these steps can be performed mentally with the aid of pen and paper.
Claim 10 reads on only looking up the translation for a term if it pertains to a computer game. Reading text, one will be able to determine if it is in the context of a video game. Further, based on this determination a user can know to look up a definition using a language dictionary or other generic translation resource as opposed to a direct translation as would be determined mentally. Therefore, these steps constitute a mental process.
Claim 11 reads on, upon the determination that a fifth term is a non-language term or proper noun, to not translate the fifth term. Determining not to translate someone’s name or other well-known proper noun is a mental process associated with reading comprehension, Similarly, if the term is non-language, i.e. boom, crash, etc. (words representing sounds), a reader will be able to understand this and know not to translate.
Claim 12 reads on providing a second term in the second language to the translation engine, or provide an indication as to part-of-speech of the term of the second language, or provide both the term and an indication of part-of-speech. Given a word, a user will mentally be able to determine a translation, recover a translation from an engine, and/or determine a part-of-speech of the term. The step of providing a term to a translation engine could be the step of writing a foreign word on a piece of paper to be taken to and matched in a translation dictionary. This can be performed mentally with the aid of pen and paper and the associates physically written translation dictionary.
Claim 13 reads on the maintaining of context of the term in the second language and/or an indication as to part-of-speech. Determining the context of a term, given the larger text, is a mental process. Similarly, the context of a translation can be written next to the translated term, i.e. context including tense, usage situations, etc. all of which would be determined mentally in view of the surrounding words of the input text. As previously discussed, determining a part-of-speech of a term can be performed mentally, and recorded physically with pen and paper. Therefore, this steps are also able to be mentally performed.
Claim 14 reads on identifying the first and second languages being received. Determination as to the type of language being received can be performed mentally and/or with the aid of generic translation dictionaries or equivalent well-known, public translation resources. Therefore, this step is also a mental process assisted with pen and paper.
Claim 15 reads on having glossaries specific to the scenes of the computer game they are a part of. Specifically, with first and second glossaries corresponding to first and second scenes. The step of segmenting a glossary into “scene” based glossaries is an equivalent step to segmenting a larger glossary into timing based sections. The determination as to when to decide scenes for a computer game is a mental process, i.e. on a level, minute, session basis. Similarly, based on the mentally determined scenes, a translator can create separately written glossaries based on received texts and translation requests. Therefore, all these steps can be performed mentally with the aid of pen and paper.
Claim 17 reads on the neural network being trained to identify whether the chat is/isn’t related to a computer game and only looking up the translation for a term if it pertains to a computer game. Reading text, one will be able to determine if it is in the context of a video game. Further, based on this determination a user can know to look up a definition using a language dictionary or other generic translation resource as opposed to a direct translation as would be determined mentally. Therefore, these steps constitute a mental process. Applying these steps with a neural network is applying a mental process to a generic computing architecture. No inventive step is provided.
Claim 18 reads on, upon the determination that a fifth term is a non-language term or proper noun, to not translate the fifth term. Determining not to translate someone’s name or other well-known proper noun is a mental process associated with reading comprehension, Similarly, if the term is non-language, i.e. boom, crash, etc. (words representing sounds), a reader will be able to understand this and know not to translate.
Claim 19 reads on the maintaining of context of the term in the second language and/or an indication as to part-of-speech. Determining the context of a term, given the larger text, is a mental process. Similarly, the context of a translation can be written next to the translated term, i.e. context including tense, usage situations, etc. all of which would be determined mentally in view of the surrounding words of the input text. As previously discussed, determining a part-of-speech of a term can be performed mentally, and recorded physically with pen and paper. Therefore, this steps are also able to be mentally performed.
Claim 20 reads on having glossaries specific to the scenes of the computer game they are a part of. Specifically, with first and second glossaries corresponding to first and second scenes. The step of segmenting a glossary into “scene” based glossaries is an equivalent step to segmenting a larger glossary into timing based sections. The determination as to when to decide scenes for a computer game is a mental process, i.e. on a level, minute, session basis. Similarly, based on the mentally determined scenes, a translator can create separately written glossaries based on received texts and translation requests. Therefore, all these steps can be performed mentally with the aid of pen and paper.
Therefore, these claims are also not patent eligible.
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) 1-4, 9-11, 16, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Travieso et al. (US-20210209185-A1), hereinafter Travieso, in view of Wang et al. (US-20190087417-A1), hereinafter Wang.
Regarding claim 1, Travieso discloses: an apparatus ([0376] apparatus adapted for carrying out the methods described herein) comprising:
at least one processor assembly ([0379] one or more processors, such as processor 1604) configured to:
receive, from a sender input device ([Fig. 4, 400, 402, 412, 414], [0058] the translation server 400 may receive a request from a user 416 on a web site 414, the web site 414 having a first web content in a first language [Indicating the computer the user is using to be a sender input device to the translation server]), chat in a first language ([0052] The translation server 400 may parse each incoming HTML page into translatable components, [0053] A translatable component may include any one of a text segment… A text segment may be a single word, a short phrase, a sentence, a paragraph or multiple paragraphs, or any other suitable segment. [Wherein chat tracks to a form of text, the language of the incoming text tracks to a first language]).
Travieso does not disclose:
under at least a first condition, responsive to a first term in the chat being in a glossary associated with a computer game.
Wang discloses:
under at least a first condition, responsive to a first term in the chat being in a glossary associated with a computer game ([0049] Words or phrases that are tagged can be translated using a rule-based translator, while other words or phrases that are not tagged can be translated using a separate translator, such as a statistical machine translator or a neural machine translator, as described herein. Such tagging can be particularly useful in chat domains, including chat for gaming, for example, because chat domains can involve many informal named entities that are found in games (e.g., player name, alliance name, kingdom name, etc.) [Determining a tagging and associated location for translation for words, i.e. terms, in a gaming chat, i.e. computer game, domain indicates a determination that words from the chat are contained within the specific domains, wherein a chat messaging domain with specific terms/rules for translating those terms indicates the domain also tracks to a glossary, i.e. knowing where to send terms to be translated indicates a required comparison to previous rules, in the form of a glossary, to know whether or not they apply to the current text]).
Travieso and Wang are considered analogous art within translation. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Travieso to incorporate the teachings of Wang, because of the novel way to tag received text based on whether or not the words can be translated through previously determined rule-based translations, allowing for a translation system to achieve greater translation quality in real-time for the chat messaging domain (Wang, [0004]-[0006]).
Travieso further discloses:
look up a term in a second language from the glossary corresponding to the first term ([0063] In step 606, a matching translated text segment may be looked up in a cache. In step 607, it may be determined whether the matching translated text segment is found in the cache [A cache tracks to a glossary, wherein the computer game domain-specific glossary as disclosed in Wang could be used as the cache of Travieso without a change in functionality to Travieso]);
responsive to a second term in the chat not being in the glossary ([Fig. 6, 607, 618], [0063] In step 607, it may be determined whether the matching translated text segment is found in the cache, [0068] In step 618, a matching translated text segment may be looked up in the translation database 406 [Wherein the “No” options in response to the “Found?” operations indicates a determination that a second term is not within the cache and/or database, either of which could be substituted for the gaming-chat-specific domain of Wang without a change in functionality to Travieso]), provide the second term to a translation engine to output a term in the second language corresponding to the second term ([Fig. 6, 639], [0069] In step 639, the machine translation may be set as the target segment [Wherein pg. 16, par. 5 of the instant app discloses a “ML model-implemented translation engine”, indicating a machine translation applies]); and,
send the term in the second language from the glossary corresponding to the first term and the term in the second language corresponding to the second term ([Fig. 6, 640], [0064] In step 640, the target segment may be added to the output web content, or second web content (i.e., the translated HTML page or the output HTML page). In step 623, the second web content may be output for provision to the user requesting the web page [Adding translated target segments to a webpage, wherein those target segments can be translated through rule-based or machine-based translation as previously disclosed, in view of the segments consisting of words, sentences, etc. as previously disclosed, indicating the input text to be parsed could be translated using both methods, added as two distinct target segments to the translated HTML page (consider the “Incomplete Translation?” step 612 indicating multiple translation rounds). Further, output of the translation for provision indicates a required output device to present the translation for provision (see communication between user 416 and translation server over internet 412 of Fig. 4 indicating the user has an output device for presentation)]).
Wang further discloses:
a translation engine comprising at least one neural network configured to output a term in the second language corresponding to the second term ([0074] the machine translator 304 can be or include a neural machine translator (NMT) for translating the pre-processed message 212 in the translation module 154, [Wherein neural machine translation consists of at least one neural network (see [0076]-[0081]), further wherein messages will necessarily be consisting of terms]); and,
send to a recipient output device for presentation thereof ([Fig. 1, Devices 128-134], [0074] an initial message from a first user can be provided to the NMT (e.g., with little or no pre-processing) and the translation generated by the NMT can be provided to a second user [In view of the plurality of devices of Wang (any of which could be substituted for that used as the sender device of Travieso without a change in functionality to Wang), indicating each user will be requesting/receiving translations on their own device, consisting of sender and recipient devices depending on the user’s action. In view of the sending HTML output presentation of translations of Travieso which could be used as the method of presentation to the two users on two devices using the network 124 without a change in functionality to Wang. Also, consider the user interface 150 of Wang, indicating a presentation medium]).
Regarding claim 2, Travieso in view of Wang discloses: the apparatus of claim 1.
Wang further discloses:
wherein the first condition comprises the chat pertaining to the computer game associated with the glossary ([0021] the server system 112 can include one or more databases (not shown) that can store data used or generated by the pre-processing module 152, the translation module 154, and/or the post-processing module 156. Such data can be or include, for example, training data (e.g., parallel corpora) for a machine translator, training data for domain adaptation, a record of messages and corresponding translations, [0049] tagging can be particularly useful in chat domains, including chat for gaming, for example, because chat domains can involve many informal named entities that are found in games (e.g., player name, alliance name, kingdom name, etc.). Examples of markers include number tag(s) (e.g., “N U M”), player name tag(s), alliance tag(s), and/or kingdom name tag(s) [A database consisting of previous translations indicates that database is a glossary, wherein the context of translations is gaming chat, indicating the tagging operation is checking the first condition as claimed]), and the processor assembly ([In view of the previously disclosed processor assembly]) is configured to:
not look up the first term in the glossary responsive to the chat not pertaining to the computer game associated with the glossary and provide the first term to the translation engine ([0049] Words or phrases that are tagged can be translated using a rule-based translator, while other words or phrases that are not tagged can be translated using a separate translator, such as a statistical machine translator or a neural machine translator [Words not tagged, i.e. not recognized as relating to gaming chat domain, are translated using machine translations (reasonably understood to be a translation engines), which are separate from the rule-based translations used for known terms in the glossary]).
Regarding claim 3, Travieso in view of Wang discloses: the apparatus of claim 1.
Wang further discloses:
wherein the processor assembly ([In view of the previously disclosed processor assembly]) is configured to:
under at least the first condition, responsive to a third term in the chat being in the glossary and being a non-language term or proper noun associated with the computer game, send the third term unchanged to the recipient output device ([0049] The use of tags can allow these names to be retained without translation (as in player name “Assibal” in the example described herein), thereby aiding different players in recognizing the same player names… [0108] client device (e.g., for purposes of displaying data to and receiving user input from a user interacting with the client device) [Wherein tagging the player name indicates the first condition being met, further wherein a player name represents a proper noun, in view of the client device for displaying data, i.e. translations, reasonably understood to represent a recipient output device. The examiner would like to note that due to the disjunctive nature of the claim, a ”non-language term” does not require a mapping; however, a player name that is not a known word in the language, i.e. “Assibal”, could be reasonably construed to also be a non-language term]).
Regarding claim 4, Travieso in view of Wang discloses: the apparatus of claim 1.
Wang further discloses:
wherein the recipient output device and sender input device communicate with each other over a wide area computer network ([Fig. 1, Network 124, Devices 128-130], [In view of the previous definitions of recipient output devices and sender input devices representing the devices of Wang, having these devices communicate over a network 124, wherein that network is defined to be the internet (see [0022]), indicating communication of the devices over a wide area computer network, i.e. the internet]).
Regarding claim 9, Travieso discloses: an apparatus ([0376] apparatus adapted for carrying out the methods described herein) comprising:
at least one computer medium that is not a transitory signal ([0388] Tangible non-transitory “storage” type media include any or all of the memory of the computers) and that comprises instructions executable by at least one processor assembly ([0384] processor 1604) to:
translate first terms in chat in a first language to second terms in a second language using a translation engine ([0052] translation server 400 may parse each incoming HTML page into translatable components, substitute each incoming translatable component with an appropriate translated component, [0053] A translatable component may include any one of a text segment… A text segment may be a single word, a short phrase, a sentence, a paragraph or multiple paragraphs); and,
look up third terms in the chat in the first language using a glossary to correlate the third terms to fourth terms in the second language ([Fig. 6, 606, 618], [0063] a matching translated text segment may be looked up in a cache [In view of the text segments being defined to be one or multiple words, indicating at least third terms in a first language with fourth terms matched, i.e. correlated, in a second language]).
Travieso does not disclose:
a translation engine comprising at least one neural network trained to output the second terms;
first terms in chat in a first language input during play of a computer game; and,
send the second and fourth terms on an output device for presentation thereof.
Wang discloses:
a translation engine comprising at least one neural network trained to output the second terms ([0074] the machine translator 304 can be or include a neural machine translator (NMT) for translating the pre-processed message 212 in the translation module 154, [Wherein neural machine translation consists of at least one neural network (see [0076]-[0081]), further wherein messages will necessarily be consisting of terms, wherein the operation translation will inherently result in second terms in a second language corresponding to the original first terms in a first language]);
first terms in chat in a first language input during play of a computer game ([0049] Words or phrases that are tagged can be translated using a rule-based translator, while other words or phrases that are not tagged can be translated using a separate translator, such as a statistical machine translator or a neural machine translator, as described herein. Such tagging can be particularly useful in chat domains, including chat for gaming, for example, because chat domains can involve many informal named entities that are found in games (e.g., player name, alliance name, kingdom name, etc.) [Determining a tagging and associated location for translation for words, i.e. terms, in a gaming chat, i.e. computer game, domain indicates a determination that words from the chat are contained within the specific domain of a computer game]); and,
send the second and fourth terms on an output device for presentation thereof ([Fig. 1, Devices 128-134], [0074] an initial message from a first user can be provided to the NMT (e.g., with little or no pre-processing) and the translation generated by the NMT can be provided to a second user [In view of the plurality of devices of Wang (any of which could be substituted for that used as the sender device of Travieso without a change in functionality to Wang), indicating each user will be requesting/receiving translations on their own device, consisting of sender and recipient devices depending on the user’s action. In view of the sending HTML output presentation of translations of Travieso which could be used as the method of presentation to the two users on two devices using the network 124 without a change in functionality to Wang. Also, consider the user interface 150 of Wang, indicating a presentation medium. Further, in view of the previously disclosed second and fourth terms of Travieso which could be generated using the translation system 151 of Wang without a change in functionality to Wang]).
Travieso and Wang are considered analogous art within translation. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Travieso to incorporate the teachings of Wang, because of the novel way to tag received text based on whether or not the words can be translated through previously determined rule-based translations, allowing for a translation system to achieve greater translation quality in real-time for the chat messaging domain (Wang, [0004]-[0006]).
Regarding claim 10, Travieso in view of Wang discloses: the apparatus of claim 9.
Wang further discloses:
wherein the instructions are executable to:
wherein the instructions are executable to look up the third terms only responsive to determining that the chat pertains to the computer game ([0049] Words or phrases that are tagged can be translated using a rule-based translator, while other words or phrases that are not tagged can be translated using a separate translator… tagging can be particularly useful in chat domains, including chat for gaming, for example, because chat domains can involve many informal named entities that are found in games (e.g., player name, alliance name, kingdom name, etc.). Examples of markers include number tag(s) (e.g., “N U M”), player name tag(s), alliance tag(s), and/or kingdom name tag(s)… Words or phrases that are tagged can be translated using a rule-based translator, while other words or phrases that are not tagged can be translated using a separate translator [Wherein the context of translations is gaming chat, indicating the tagging operation is checking whether the chat pertains to a computer game as claimed to know where to find a translated third term, i.e. rule-based or machine translation]).
Regarding claim 11, Travieso in view of Wang discloses: the apparatus of claim 9.
Wang further discloses:
wherein the instructions are executable to:
responsive to a fifth term in the chat being in the glossary and being a non-language term or proper noun associated with the computer game, send the fifth term unchanged to the recipient output device ([0049] The use of tags can allow these names to be retained without translation (as in player name “Assibal” in the example described herein), thereby aiding different players in recognizing the same player names… [0108] client device (e.g., for purposes of displaying data to and receiving user input from a user interacting with the client device) [Wherein tagging the player name indicates the first condition being met, further wherein a player name represents a proper noun, in view of the client device for displaying data, i.e. translations, reasonably understood to represent a recipient output device. In view of the previously disclosed input consisting of multiple words, indicating a fifth term. The examiner would like to note that due to the disjunctive nature of the claim, a ”non-language term” does not require a mapping; however, a player name that is not a known word in the language, i.e. “Assibal”, could be reasonably construed to also be a non-language term]).
Regarding claim 16, Travieso discloses: a method comprising:
receiving chat during play of a computer game and input by means of at least one input device, the chat being in a first language ([Fig. 4, 400, 402, 412, 414], [0058] the translation server 400 may receive a request from a user 416 on a web site 414, the web site 414 having a first web content in a first language [Indicating the computer the user is using to be a sender input device to the translation server. A request to be translated will inherently be in a first language]);
translating first terms in the chat to terms in a second language using a translation engine ([Fig. 6, 639], [0069] In step 639, the machine translation may be set as the target segment [Wherein pg. 16, par. 5 of the instant app discloses a “ML model-implemented translation engine”, indicating a machine translation applies]);
using second terms in the chat to look up terms in the second language using at least one glossary ([Fig. 6, 607, 618], [0063] In step 607, it may be determined whether the matching translated text segment is found in the cache, [0068] In step 618, a matching translated text segment may be looked up in the translation database 406 [Wherein the “No” options in response to the “Found?” operations indicates a determination that a second term is not within the cache and/or database, i.e. glossary. Further, wherein the input text of Travieso, i.e. web pages, will generally consist of multiple words, indicating at least a second term]).
Travieso does not disclose:
a translation engine comprising at least one neural network configured to output the terms in the second language; and,
sending the terms in the second language obtained from the translation engine and the glossary to at least one output device.
Wang discloses:
a translation engine comprising at least one neural network configured to output the terms in the second language ([0074] the machine translator 304 can be or include a neural machine translator (NMT) for translating the pre-processed message 212 in the translation module 154, [Wherein neural machine translation consists of at least one neural network (see [0076]-[0081]), further wherein messages will necessarily be consisting of terms, wherein the operation translation will inherently result in second terms in a second language corresponding to the original first terms in a first language]); and,
sending the terms in the second language obtained from the translation engine and the glossary to at least one output device ([Fig. 1, Devices 128-134], [0074] an initial message from a first user can be provided to the NMT (e.g., with little or no pre-processing) and the translation generated by the NMT can be provided to a second user [In view of the plurality of devices of Wang (any of which could be substituted for that used as the sender device of Travieso without a change in functionality to Wang), indicating each user will be requesting/receiving translations on their own device, consisting of sender and recipient devices depending on the user’s action. In view of the sending HTML output presentation of translations of Travieso which could be used as the method of presentation to the two users on two devices using the network 124 without a change in functionality to Wang]).
Travieso and Wang are considered analogous art within translation. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Travieso to incorporate the teachings of Wang, because of the novel way to tag received text based on whether or not the words can be translated through previously determined rule-based translations, allowing for a translation system to achieve greater translation quality in real-time for the chat messaging domain (Wang, [0004]-[0006]).
Regarding claim 18, Travieso in view of Wang discloses: the method of claim 16.
Wang further discloses:
wherein the instructions are executable to:
responsive to a third term in the chat being in the glossary and being a non-language term or proper noun associated with the computer game, send the third term unchanged to the recipient output device ([0049] The use of tags can allow these names to be retained without translation (as in player name “Assibal” in the example described herein), thereby aiding different players in recognizing the same player names… [0108] client device (e.g., for purposes of displaying data to and receiving user input from a user interacting with the client device) [Wherein tagging the player name indicates the first condition being met, further wherein a player name represents a proper noun, in view of the client device for displaying data, i.e. translations, reasonably understood to represent a recipient output device. In view of the previously disclosed input consisting of multiple words, indicating a third term. The examiner would like to note that due to the disjunctive nature of the claim, a ”non-language term” does not require a mapping; however, a player name that is not a known word in the language, i.e. “Assibal”, could be reasonably construed to also be a non-language term]).
Claim(s) 5-6, 12-13, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Travieso in view of Wang, further in view of Leydon et al. (US-20140303961-A1), hereinafter Leydon.
Regarding claim 5, Travieso in view of Wang discloses: the apparatus of claim 1.
Travieso further discloses:
wherein the processor assembly is configured to:
provide the term in the second language from the glossary corresponding to the first term to the translation engine ([Fig. 6, 607, 610], [Wherein the Translation Server of Fig. 6 represents a translation engine, indicating a provision of the matched translation, i.e. from the “Found?” operations associated with cache 607 and/or database 622 (glossaries), in order to replace a target segment with a translated segment as seen in 610]).
Travieso in view of Wang does not disclose:
wherein the processor assembly is configured to:
or provide an indication of the part of speech of the term in the second language from the glossary corresponding to the first term to the translation engine;
or both provide the term in the second language from the glossary corresponding to the first term to the translation engine and provide an indication of the part of speech of the term in the second language from the glossary corresponding to the first term to the translation engine.
Leydon discloses:
wherein the processor assembly is configured to:
or provide an indication of the part of speech of the term in the second language from the glossary corresponding to the first term to the translation engine ([0373] the words present in the original message and in the translation are tagged (e.g., using open source POS tagger) to identify parts of speech (POS) (e.g., verbs, nouns, adjectives, etc.) in the two messages [Tagging a translated word indicates the translation engine is provided the tags. Wherein a translated message could be determined using the glossaries of Travieso and/or Wang without a change in functionality to this operation of Leydon, also see translated data store 210 of Leydon which could be reasonably construed to represent a glossary, indicating previous tagged translations which would be provided for later translations with pre-attached parts of speech tags/provisions]);
or both provide the term in the second language from the glossary corresponding to the first term to the translation engine and provide an indication of the part of speech of the term in the second language from the glossary corresponding to the first term to the translation engine ([Table 2, Shown Translation “sss ddd fff”, Description], [0364] A part of speech (POS) based language model may be used to check sentences for grammatical correctness. Additionally, some users may submit translation corrections that are grammatically correct but have nothing to do with the original message. For such cases, a word alignment match analysis feature may be useful and may be run as periodic process to approve and/or reject user submissions [A user submitting a translation correction, wherein that correction is deemed to be grammatically correct but contain different words (see description of this row), indicating a term and part of speech level analysis by the translation engine. Further, consider the repository archiving translated chats of [0342] and chat history module 3300 for performing real-time translation of historical chats, indicating these retrieved chats, i.e. from a glossary (repository), have had part of speech tagging performed from a previous translation, further indicating a term and part of speech provided to the translation engine from the glossary for an updated translation]).
Travieso, Wang, and Leydon are considered analogous art within text translation. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Travieso in view of Wang to incorporate the teachings of Leydon, because of the novel way to implement incentivized feedback in language translation through user feedback, decreasing likelihood of a failed translation (Leydon, [0024]).
Regarding claim 6, Travieso in view of Wang, further in view of Leydon discloses: the apparatus of claim 5.
Leydon further discloses:
wherein the processor assembly is configured to execute the translation engine to maintain context of the chat relative to the computer game using the term in the second language from the glossary corresponding to the first term to the translation engine ([Fig. 36D, “This is what a real player wrote in the game. It did not get translated properly because it is not written in proper {language}”], [Retrieving a previously performed translation, wherein any of the words can be reasonably understood to be a term, indicating a contextual maintenance, i.e. keeping all terms together associated with the incorrect translation and original input to be translated, wherein a translation clearly represents a first term in a second language, i.e. any word in the incorrect English translation]) and/or the indication of the part of speech of the term in the second language from the glossary corresponding to the first term ([0373] For example, in one embodiment, the words present in the original message and in the translation are tagged (e.g., using open source POS tagger) to identify parts of speech (POS)… [0375] After simplifying the POS tags, the number of verb tags VB may be counted in both the original message and in the translation [Tagging parts of speech of text and then later comparing the parts of speech between an original and translation indicates a maintained part of speech tag for a first term in a second language, i.e. any words of the translated message]).
Regarding claim 12, Travieso in view of Wang discloses: the apparatus of claim 9.
Travieso further discloses:
wherein the instructions are executable to:
provide the second term ([Fig. 6, 607, 610], [Wherein the Translation Server of Fig. 6 represents a translation engine, indicating a provision of the matched translation, i.e. from the “Found?” operations associated with cache 607 and/or database 622 (glossaries), in order to replace a target segment with a translated segment as seen in 610]).
Travieso in view of Wang does not disclose:
wherein the instructions are executable to:
or provide an indication of the part of speech of the second term;
or provide both the second term and the indication of the part of speech of the second term in the to the translation engine.
Leydon discloses:
wherein the processor assembly is configured to:
or provide an indication of the part of speech of the second term ([0373] the words present in the original message and in the translation are tagged (e.g., using open source POS tagger) to identify parts of speech (POS) (e.g., verbs, nouns, adjectives, etc.) in the two messages [Tagging a translated word with a part of speech is equivalent to tagging a second term. Further, tagging for later processing indicates the translation system is provided the tag]);
or provide both the second term and the indication of the part of speech of the second term in the to the translation engine ([Table 2, Shown Translation “sss ddd fff”, Description], [0364] A part of speech (POS) based language model may be used to check sentences for grammatical correctness. Additionally, some users may submit translation corrections that are grammatically correct but have nothing to do with the original message. For such cases, a word alignment match analysis feature may be useful and may be run as periodic process to approve and/or reject user submissions [A user submitting a translation correction, wherein that correction is deemed to be grammatically correct but contain different words (see description of this row), indicating a term and part of speech level analysis by the translation engine on the user submitted correction, i.e. received second terms]).
Travieso, Wang, and Leydon are considered analogous art within text translation. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Travieso in view of Wang to incorporate the teachings of Leydon, because of the novel way to implement incentivized feedback in language translation through user feedback, decreasing likelihood of a failed translation (Leydon, [0024]).
Regarding claim 13, Travieso in view of Wang, further in view of Leydon discloses: the apparatus of claim 12.
Leydon further discloses:
wherein the instructions are executable to execute the translation engine to maintain context using the second term ([Fig. 36D, “This is what a real player wrote in the game. It did not get translated properly because it is not written in proper {language}”], [Retrieving a previously performed translation, wherein any of the words can be reasonably understood to be a term, indicating a contextual maintenance, i.e. keeping all terms together associated with the incorrect translation and original input to be translated, wherein a translation clearly represents a first term in a second language, i.e. any word in the incorrect English translation]) and/or the indication of the part of speech of the second term ([0373] For example, in one embodiment, the words present in the original message and in the translation are tagged (e.g., using open source POS tagger) to identify parts of speech (POS)… [0375] After simplifying the POS tags, the number of verb tags VB may be counted in both the original message and in the translation [Tagging parts of speech of text and then later comparing the parts of speech between an original and translation indicates a maintained part of speech tag for a first term in a second language, i.e. any words of the translated message]).
Regarding claim 19, Travieso in view of Wang discloses: the method of claim 16.
Travieso further discloses:
providing the terms looked up using the glossary ([Fig. 6, 607, 610], [Wherein the Translation Server of Fig. 6 represents a translation engine, indicating a provision of the matched translation, i.e. from the “Found?” operations associated with cache 607 and/or database 622 (glossaries), in order to replace a target segment with a translated segment as seen in 610]).
Travieso in view of Wang does not disclose:
or indications of the parts of speech of the terms looked up using the glossary or both the terms looked up using the glossary and indications of the parts of speech of the terms looked up using the glossary to the translation engine to enable the translation engine to maintain context of the chat relative to the computer game.
Leydon discloses:
or indications of the parts of speech of the terms looked up using the glossary ([0373] the words present in the original message and in the translation are tagged (e.g., using open source POS tagger) to identify parts of speech (POS) (e.g., verbs, nouns, adjectives, etc.) in the two messages ([Tagging a translated word indicates the translation engine is provided the tags. Wherein a translated message could be determined using the glossaries of Travieso and/or Wang without a change in functionality to this operation of Leydon, also see translated data store 210 of Leydon which could be reasonably construed to represent a glossary, indicating previous tagged translations which would be provided for later translations with pre-attached parts of speech tags/provisions]);
or both the terms looked up using the glossary and indications of the parts of speech of the terms looked up using the glossary to the translation engine ([Table 2, Shown Translation “sss ddd fff”, Description], [0364] A part of speech (POS) based language model may be used to check sentences for grammatical correctness. Additionally, some users may submit translation corrections that are grammatically correct but have nothing to do with the original message. For such cases, a word alignment match analysis feature may be useful and may be run as periodic process to approve and/or reject user submissions [A user submitting a translation correction, wherein that correction is deemed to be grammatically correct but contain different words (see description of this row), indicating a term and part of speech level analysis by the translation engine. Further, consider the repository archiving translated chats of [0342] and chat history module 3300 for performing real-time translation of historical chats, indicating these retrieved chats, i.e. from a glossary (repository), have had part of speech tagging performed from a previous translation, further indicating a term and part of speech provided to the translation engine from the glossary for an updated translation]) to execute the translation engine to maintain context of the chat relative to the computer game ([Fig. 36D, “This is what a real player wrote in the game. It did not get translated properly because it is not written in proper {language}”], [Retrieving a previously performed translation, wherein any of the words can be reasonably understood to be a term, indicating a contextual maintenance, i.e. keeping all terms together associated with the incorrect translation and original input to be translated, wherein a translation clearly represents a first term in a second language, i.e. any word in the incorrect English translation]).
Travieso, Wang, and Leydon are considered analogous art within text translation. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Travieso in view of Wang to incorporate the teachings of Leydon, because of the novel way to implement incentivized feedback in language translation through user feedback, decreasing likelihood of a failed translation (Leydon, [0024]).
Claim(s) 7-8, 14-15, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Travieso in view of Wang, further in view of Aronzon (US-20190151755-A1).
Regarding claim 7, Travieso in view of Wang discloses: the apparatus of claim 1.
Travieso in view of Wang does not disclose:
wherein the processor assembly is configured to identify the first and second languages.
Aronzon discloses:
wherein the processor assembly is configured to identify the first and second languages ([Fig. 1, Gaming Device 120, 130], [0029] For example, languages can be designated in gamer profiles that are accessible by each of the gaming devices. Other techniques can be utilized to determine the language of the gamer, including transmitting requests between gaming devices [In view of the plurality of gaming devices 120/130 of Fig. 1 indicating at least a determination of first and second languages according to first and second profiles of the gaming devices in view of the at least two user communication as defined in Wang which could be used in Aronzon with each user corresponding to a gaming device without a change in functionality to Aronzon]).
Travieso, Wang, and Aronzon are considered analogous art within translation. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Travieso in view of Wang to incorporate the teachings of Aronzon, because of the novel way to determine languages of gamers who may not know each other based on profiles, removing the need for a user entry as to languages, so translations can be provided to the gamers in real-time (Aronzon, [0013]).
Regarding claim 8, Travieso in view of Wang discloses: the apparatus of claim 1.
Travieso in view of Wang does not disclose:
wherein the glossary is a first glossary and the chat is received during a first scene of the computer game; and,
the processor assembly is configured to use a second glossary responsive to the computer game presenting a second scene, the first glossary being associated with terms in the first scene and the second glossary being associated with terms in the second scene.
Aronzon discloses:
wherein the glossary is a first glossary ([0030] the translator 190 can access a multi-lingual library to perform the translation of the user input… [0021] the library can be a distributed database [Distributing the library indicates each distribution represents its own glossary, i.e. dictionary]) and the chat is received during a first scene of the computer game ([0031] a user may be moving along a beach as part of a number of different environments in a particular video game. If the user transmits a voice message stating “swim”, the translator 190 can determine the context (e.g., beach with water) and can apply voice recognition in combination with the context to distinguish between “swim” and other words, such as “win.” [Wherein the beach represents a first scene with an associated library distribution]); and,
the processor assembly is configured to use a second glossary responsive to the computer game presenting a second scene ([In view of the previously disclosed multi-lingual library being distributed, indicating at least a second glossary. Further, wherein the application of Aronzon is in video games, indicating the context, i.e. scene, based operation as applied to a beach scene could be applied to different scene, video games are generally indicated to be consisting of multiple scenes, combined with the dictionary divisions resulting in a second glossary and second associated scene. The operation as applied to the beach scene could be applied to a second scene without a change in functionality to Aronzon]), the first glossary being associated with terms in the first scene and the second glossary being associated with terms in the second scene ([In view of the previously disclosed distributed library of Aronzon, in view of the video game context of Aronzon, indicating distinctly divided glossaries based on scene. The division of the library used to determine a beach scene could be used with a different division of the larger library with a different scene without a change in functionality to Aronzon]).
Travieso, Wang, and Aronzon are considered analogous art within translation. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Travieso in view of Wang to incorporate the teachings of Aronzon, because of the novel way to determine languages of gamers who may not know each other based on profiles, removing the need for a user entry as to languages, so translations can be provided to the gamers in real-time (Aronzon, [0013]).
Regarding claim 14, Travieso in view of Wang discloses: the apparatus of claim 9.
Travieso in view of Wang does not disclose:
wherein the instructions are executable to identify the first and second languages.
Aronzon discloses:
wherein the instructions are executable to identify the first and second languages
([Fig. 1, Gaming Device 120, 130], [0029] For example, languages can be designated in gamer profiles that are accessible by each of the gaming devices. Other techniques can be utilized to determine the language of the gamer, including transmitting requests between gaming devices [In view of the plurality of gaming devices 120/130 of Fig. 1 indicating at least a determination of first and second languages according to first and second profiles of the gaming devices in view of the at least two user communication as defined in Wang which could be used in Aronzon with each user corresponding to a gaming device without a change in functionality to Aronzon]).
Travieso, Wang, and Aronzon are considered analogous art within translation. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Travieso in view of Wang to incorporate the teachings of Aronzon, because of the novel way to determine languages of gamers who may not know each other based on profiles, removing the need for a user entry as to languages, so translations can be provided to the gamers in real-time (Aronzon, [0013]).
Regarding claim 15, Travieso in view of Wang discloses: the apparatus of claim 9.
Travieso in view of Wang does not disclose:
wherein the glossary is a first glossary and the chat is received during a first scene of the computer game; and,
the instructions are executable to use a second glossary responsive to the computer game presenting a second scene, the first glossary being associated with terms in the first scene and the second glossary being associated with terms in the second scene.
Aronzon discloses:
wherein the glossary is a first glossary ([0030] the translator 190 can access a multi-lingual library to perform the translation of the user input… [0021] the library can be a distributed database [Distributing the library indicates each distribution represents its own glossary, i.e. dictionary]) and the chat is received during a first scene of the computer game ([0031] a user may be moving along a beach as part of a number of different environments in a particular video game. If the user transmits a voice message stating “swim”, the translator 190 can determine the context (e.g., beach with water) and can apply voice recognition in combination with the context to distinguish between “swim” and other words, such as “win.” [Wherein the beach represents a first scene with an associated library distribution]); and,
the instructions are executable to use a second glossary responsive to the computer game presenting a second scene ([In view of the previously disclosed multi-lingual library being distributed, indicating at least a second glossary. Further, wherein the application of Aronzon is in video games, indicating the context, i.e. scene, based operation as applied to a beach scene could be applied to different scene, video games are generally indicated to be consisting of multiple scenes, combined with the dictionary divisions resulting in a second glossary and second associated scene. The operation as applied to the beach scene could be applied to a second scene without a change in functionality to Aronzon]), the first glossary being associated with terms in the first scene and the second glossary being associated with terms in the second scene ([In view of the previously disclosed distributed library of Aronzon, in view of the video game context of Aronzon, indicating distinctly divided glossaries based on scene. The division of the library used to determine a beach scene could be used with a different division of the larger library with a different scene without a change in functionality to Aronzon]).
Travieso, Wang, and Aronzon are considered analogous art within translation. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Travieso in view of Wang to incorporate the teachings of Aronzon, because of the novel way to determine languages of gamers who may not know each other based on profiles, removing the need for a user entry as to languages, so translations can be provided to the gamers in real-time (Aronzon, [0013]).
Regarding claim 20, Travieso in view of Wang discloses: the method of claim 16.
Travieso in view of Wang does not disclose:
using a first glossary to look up terms during a first scene of the computer game and using a second glossary to look up terms during a second scene of the computer game.
Aronzon discloses:
using a first glossary to look up terms during a first scene of the computer game and using a second glossary to look up terms during a second scene of the computer game ([0030] the translator 190 can access a multi-lingual library to perform the translation of the user input… [0021] the library can be a distributed database [Distributing the library indicates each distribution represents its own glossary, i.e. dictionary], [0031] a user may be moving along a beach as part of a number of different environments in a particular video game. If the user transmits a voice message stating “swim”, the translator 190 can determine the context (e.g., beach with water) and can apply voice recognition in combination with the context to distinguish between “swim” and other words, such as “win.” [Wherein the beach represents a first scene with an associated library distribution. In view of the previously disclosed multi-lingual library being distributed, indicating at least a second glossary. Further, wherein the application of Aronzon is in video games, indicating the context, i.e. scene, based operation as applied to a beach scene could be applied to different scenes, video games are generally indicated to be consisting of multiple scenes, combined with the library divisions resulting in a second glossary and second associated scene. The operation as applied to the beach scene could be applied to a second scene using a different library division without a change in functionality to Aronzon]).
Travieso, Wang, and Aronzon are considered analogous art within translation. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Travieso in view of Wang to incorporate the teachings of Aronzon, because of the novel way to determine languages of gamers who may not know each other based on profiles, removing the need for a user entry as to languages, so translations can be provided to the gamers in real-time (Aronzon, [0013]).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Travieso in view of Wang, further in view of Garg et al. (US-20230325611-A1), hereinafter Garg.
Regarding claim 17, Travieso in view of Wang discloses: the method of claim 16.
Wang further discloses:
looking up terms using the glossary only responsive to determining that the chat pertains to the computer game ([0021] the server system 112 can include one or more databases (not shown) that can store data used or generated by the pre-processing module 152, the translation module 154, and/or the post-processing module 156. Such data can be or include, for example, training data (e.g., parallel corpora) for a machine translator, training data for domain adaptation, a record of messages and corresponding translations, [0049] tagging can be particularly useful in chat domains, including chat for gaming, for example, because chat domains can involve many informal named entities that are found in games (e.g., player name, alliance name, kingdom name, etc.). Examples of markers include number tag(s) (e.g., “N U M”), player name tag(s), alliance tag(s), and/or kingdom name tag(s)… Words or phrases that are tagged can be translated using a rule-based translator, while other words or phrases that are not tagged can be translated using a separate translator [A database consisting of previous translations indicates that database is a glossary, indicating the tagging operation is checking whether the chat pertains to a computer game as claimed to know where to find a translated term, i.e. rule-based or machine translation]).
Travieso in view of Wang does not disclose:
wherein the at least one neural network is trained to identify whether the chat is related to the computer game or is not related to the computer game.
Garg discloses:
wherein the at least one neural network is trained to identify whether the chat is related to the computer game or is not related to the computer game ([0053] Recognition or classification of the textual content 362 can be executed by a recurrent neural network (RNN) trained to classify text based on context, [In view of the domain specific NMTs of Wang, indicating that the classification could be for a computer game chat as disclosed in Wang. Further, consider the “Found?” translation operation while searching specific translation databases/caches of Travieso indicating a classification of Found/Not Found directly relates to specific domains as disclosed in Wang, i.e. computer game, using the neural network classification of Garg]).
Travieso, Wang, and Garg are considered analogous art within text translation. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Travieso in view of Wang to incorporate the teachings of Garg, because of the novel way to consider domain, source language, and target language when considering translations, resulting in a solution path with the best translation engine, further resulting in highest accuracy translations from among available options (Garg, [0024]).
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Canberk et al. (US-20240202470-A1) discloses “An augmented reality (AR) translation system is provided. The AR translation system may analyze camera data to determine objects included in a field of view of a camera of a user device. Augmented reality content may be provided that includes a visual translation of an object included in the field of view from a primary language of the user to an additional language. An audible version of the translation may also be provided as part of the augmented reality content. Users may also add an object in the field of view to a listing of translated objects associated with the user based on at least one of touch input, audio input, or gesture input” (abstract). Specifically, Canberk discloses using neural networks for translations in a specific “playing games” context ([0126]). See entire document.
Kovacs et al. (US-20230070302-A1) discloses “A computer-implemented method is programmed to support efficient and rapid generation of machine translation suggestions on client devices. Network latency is substantially reduced or eliminated by separating certain aspects of the translation workload across multiple classes of tasks, including final neural network output, between a client device and server device. The client device and server device may be connected such that a decoder portion of a machine translation system may be downloaded onto the client device, along with an initial translation suggestion and encoder outputs associated with a document, which document is in a source language to be translated into a target language. The initial translation suggestion may be replaced by an updated machine translation suggestion as a user inputs text in the target language called a prefix. This updated machine translation is generated on the client-side decoder using the previously-downloaded encoder outputs as input and the prefix as constraint” (abstract). See entire document.
Wu et al. (US-20230267285-A1) discloses “Apparatuses, systems, and techniques to translate a text string. In at least one embodiment, a text string is translated by at least, for example, using one or more neural networks to determine a length of a translated text string before a text string is to be translated” (abstract). See entire document.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THEODORE JOHN WITHEY whose telephone number is (703)756-1754. The examiner can normally be reached Monday - Friday, 8am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Flanders can be reached at (571) 272-7516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/THEODORE WITHEY/Examiner, Art Unit 2655
/ANDREW C FLANDERS/Supervisory Patent Examiner, Art Unit 2655