DETAILED ACTION
This office action is in response to Applicant’s Request for Continued Examination (RCE), received on 05/11/2026. Claims 1-2, 9, and 16 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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/11/2026 has been entered.
Information Disclosure Statement
The information disclosure statement(s) submitted on 05/11/2026 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
Response to Arguments
Applicant’s arguments, see pgs. 8-10, filed 05/11/2026, with respect to “101 Rejections” have been fully considered and are persuasive. The rejections of claims 1-20 under 35 U.S.C. 101 have been withdrawn. The examiner would like to note that Applicant has amended steps into the claims which incorporate an improvement to a computing system, namely, bypassing a translation engine in response to a term of a chat being located in a glossary associated with a computer game and not bypassing the translation engine when a term is not provided in said glossary, see “responsive…” claim elements (Step 2B, Prong 2, YES). Choosing to only perform neural network-based machine translation on terms which are not designated to be related to a computer game (not found within the glossary), effectively selectively performing two distinct translation operations based on the input term type, is an element which lends to the reduced latency of translated chats between users of a computer game as disclosed on pg. 1, Summary, para. 2. The neural-based translation no longer has to struggle to find accurate/appropriate translations for jargon terms related to the games being played.
Applicant’s arguments, see pgs. 10-11, filed 05/11/2026, with respect to the rejection(s) of claim(s) 1 under 35 U.S.C. 103 (Travieso in view of Wang) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Aronzon (US-20190151755-A1). Aronzon discloses “A system that incorporates teachings of the present disclosure may include, for example, a computing device having a controller to obtain a user input that was inputted into a first accessory operably coupled with the computing device where the first accessory provides a user interface for user interaction with a video game, determine a language of an intended recipient of the user input based on an identity of the intended recipient, access a multi-lingual library comprising a plurality of words associated with the video game, match the user input to one or more words of the plurality of words of the multi-lingual library to generate a translated message in the determined language of the intended recipient, and provide the translated message to a second accessory for presentation to the intended recipient in real-time” (abstract). Aronzon was previously used for the rejections of claims 7-8, 14-15, 20. Aronzon will be replacing Travieso. See updated rejections below.
Applicant’s arguments, see 11-12, filed 05/11/2026, with respect to the rejection(s) of claim(s) 2 under 35 U.S.C. 103 (Travieso in view of Wang) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Yoon (US-20200364413-A1). Yoon discloses “Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that facilitate generating stable real-time textual translations in a target language of an input audio data stream that is recorded in a source language. An audio stream that is recorded in a first language is obtained. A partial transcription of the audio can be generated at each time interval in a plurality of successive time intervals. Each partial transcription can be translated into a second language that is different from the first language. Each translated partial transcription can be input to a model that determines whether a portion of an input translated partial transcription is stable. Based on the input translated partial transcription, the model identifies a portion of the translated partial transcription that is predicted to be stable. This stable portion of the translated partial transcription is provided for display on a user device.” (abstract). See updated rejections below.
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, 3-4, 7-11, 14-16, 18, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aronzon (US-20190151755-A1) in view of Wang et al. (US-20190087417-A1), hereinafter Wang.
Regarding claim 1, Aronzon discloses: an apparatus ([0002] an apparatus) comprising:
at least one processor assembly ([0081] The computer system 900 may include a processor 902) configured to:
receive, from a sender input device ([Fig. 1, Gaming Device(s) 120, 130 sending information to Network 150]), chat in a first language ([Fig. 2, S202 Obtain user input], [0028] The user input can be of various formats, including a voice message, a text message and/or a macro generated by an accessory device where the macro represents a message. The message can be associated with a video game. For example, messages related to game play can be communicated between gamers using method 200) and a game context at a translation engine ([Fig. 2, S208 Determine context], [0031] the translator 190 can utilize a context of the video game to assist in translation), the translation engine trained to identify chats related to a computer game ([0031] The context of the video game can also be applied to facilitate the translation other than when using voice recognition. For example, a user input may be a text message that is a misspelling, such as “wim.” Based on the context of the video game at the time of the message being a beach with water, the translator can again distinguish between “swim” and “win.”, [A translator which uses video game context to distinguish between words indicates the correct word chosen based on context to be identified as related to the context, i.e. computer game]).
Aronzon does not disclose:
responsive to a first term in the chat being in a glossary associated with the computer game, identify a term in a second language from the glossary corresponding to the first term, wherein identifying the term in the second language from the glossary bypasses the translation engine.
Wang discloses:
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]),
identify a term in a second language from the glossary corresponding to the first term ([0049] The rule-based system can include predetermined rules for translating the phrase from the first language to the second language. For example, certain slang words such as “lol” in English can be precisely translated to “mdr” in French, [0051] sentences can be tokenized by the tokenization module 208 into discrete words while preserving any markers or tags from previous step(s), [0058] The rule-based system 302 can be used, for example, to translate any portions of messages that have been tagged by the tagging module, [Wherein the rules necessarily have to be stored in order to be remembered by the translation module. The examiner asserts that a database of rules is a glossary. Further, tokens, i.e. terms, of messages which have been tagged indicates they are identified to be translated by the rule-based translator (as opposed to the neural machine translator)]),
wherein identifying the term in the second language from the glossary bypasses the translation engine ([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, [In view of the previously disclosed rules for translation, indicating a rule based around not translating player names and/or predefined rules which eliminate a need for live translation of player names]).
Aronzon 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 Aronzon 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]).
Aronzon further discloses:
responsive to a second term in the chat not being in the glossary ([Fig. 2, S210 Match?, No], [Wherein the library accessed in S206 tracks to a glossary, in view of the previously disclosed rule-based translation glossary of Wang]), provide the second term and the game context to the translation engine ([0033] if a match is not determined then the translator 190 can request a search of other multi-lingual libraries. For example, if the first gaming device 190 cannot find a match for a voice message generated by a first gamer then the first gaming device can access or otherwise request a search of the multi-lingual library of the second gaming device 130, [In view of the previously disclosed context data clearly provided to the translation engine regardless of state of the term with respect to glossary, i.e. present/not present, requesting to search additional multi-lingual libraries indicates a lack of existence in a first glossary, i.e. library, wherein the term to be translated is gathered from the other multi-lingual library, necessarily providing it to the translation engine in order to output a translation as seen with S212. This is taken in view of the neural machine translator (NMT) of Wang, cited below, which is a secondary translation process compared to the previously discussed rule-based translation responsive to translating words which were not tagged by Wang. The examiner asserts that the tags of Wang could be applied to the “Match?” of Aronzon]); and,
send the term in the second language from the glossary corresponding to the first term ([Fig. 2, S214 Present user input at second accessory], [0033] If on the other hand in step 210 the translator 190 cannot identify a match for the message in the multi-lingual library then method 200 can proceed to step 214 where the untranslated message is presented to the intended recipient, [Wherein the first term is a term which should “bypass[ing] the translation engine”. The examiner asserts that a term that is not translated bypasses a translation engine]) and the term in the second language corresponding to the second term ([Fig. 2, S214 Present translation at second accessory], [0032] the translator 190 identifies a match for the message in the multi-lingual library then method 200 proceeds to step 212 where the translation of the message is presented to the intended recipient, [Wherein the second term is a translated term in view of the translation parameters, i.e. term, context, as disclosed in Wang]).
Wang further discloses:
a translation engine comprising at least one neural network configured to output, based at least in part of the game context, 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 Aronzon 120/130 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 presentation steps in Fig. 2 of Aronzon. Also, consider the user interface 150 of Wang, indicating a presentation medium]).
Regarding claim 3, Aronzon 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, Aronzon 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 7, Aronzon in view of Wang discloses: the apparatus of claim 1.
Aronzon further 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]).
Regarding claim 8, Aronzon in view of Wang discloses: the apparatus of claim 1.
Aronzon further 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]).
Regarding claim 9, Aronzon discloses: an apparatus ([0002] an apparatus and method for managing user inputs in video games) comprising:
at least one computer medium that is not a transitory signal ([0011] a non-transitory computer-readable storage medium that includes computer instructions) and that comprises instructions executable by at least one processor assembly ([0082] instructions 924 may also reside, completely or at least partially, within the main memory 904, the static memory 906, and/or within the processor 902 during execution thereof by the computer system 900) to:
translate first terms in chat in a first language to second terms in a second language using a translation engine ([0030] the translator 190 can access a multi-lingual library to perform the translation of the user input);
the translation engine trained to identify chats related to the computer game ([0031] The context of the video game can also be applied to facilitate the translation other than when using voice recognition. For example, a user input may be a text message that is a misspelling, such as “wim.” Based on the context of the video game at the time of the message being a beach with water, the translator can again distinguish between “swim” and “win.”, [A translator which uses video game context to distinguish between words indicates the correct word chosen based on context to be identified as related to the context, i.e. computer game]); 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. 2, S210 “Match?”], [The examiner asserts that matching terms to be used for translation (as seen in S212) indicates a correlation, i.e. match, between third and fourth terms, wherein translating indicates two languages, further wherein input messages suggests at least a third term to be translated into a fourth term in view of the “melee attack” input disclosed in [0043]]).
Aronzon does not disclose:
a translation engine comprising at least one neural network trained to output the second terms based at least in part on a game context received at the translation engine;
first terms in chat in a first language input during play of a computer game;
wherein the third terms in the chat being in the glossary bypasses the translation engine; 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]);
wherein the third terms in the chat being in the glossary bypasses the translation engine ([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, [In view of the previously disclosed rules for translation, indicating a rule based around not translating player names and/or predefined rules which eliminate a need for live translation of player names]); 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 Aronzon 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. Also, consider the user interface 150 of Wang, indicating a presentation medium. Further, in view of the previously disclosed second and fourth terms of Aronzon which could be generated using the translation system 151 of Wang without a change in functionality to Wang]).
Aronzon 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 Aronzon 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, Aronzon 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, Aronzon 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 14, Aronzon in view of Wang discloses: the apparatus of claim 9.
Aronzon further 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]).
Regarding claim 15, Aronzon in view of Wang discloses: the apparatus of claim 9.
Aronzon further 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]).
Regarding claim 16, Aronzon discloses: a method comprising:
receiving chat during play of a computer game and input by means of at least one input device ([Fig. 2, S202 “Obtain user input”], [0028] a user input of a message is obtained, [Wherein the context of Aronzon is “managing user inputs in video games”, title]), the chat being in a first language and a game context at a translation engine ([0030] the translator 190 can access a multi-lingual library to perform the translation of the user input, [0031] In one embodiment in step 208, the translator 190 can utilize a context of the video game to assist in translation), the translation engine trained to identify chats related to a computer game ([0031] The context of the video game can also be applied to facilitate the translation other than when using voice recognition. For example, a user input may be a text message that is a misspelling, such as “wim.” Based on the context of the video game at the time of the message being a beach with water, the translator can again distinguish between “swim” and “win.”, [A translator which uses video game context to distinguish between words indicates the correct word chosen based on context to be identified as related to the context, i.e. computer game]);
translating first terms in the chat to terms in a second language using the translation engine ([Fig. 2, S212 “Present translation…”], [The examiner asserts that a translation must be performed in order to be presented]);
using second terms in the chat to look up terms in the second language using at least one glossary ([Fig. 2, S206 Access library for use in matching operation S210], [Determining if words in a user input match words from a multi-lingual library indicates at least a second term being looked up in at least a second language, i.e. the library]).
Aronzon does not disclose:
a translation engine comprising at least one neural network configured to output the terms in the second language;
wherein the second terms in the chat being in the glossary bypasses the translation engine; 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]);
wherein the second terms in the chat being in the glossary bypasses the translation engine ([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, [In view of the previously disclosed rules for translation, indicating a rule based around not translating player names and/or predefined rules which eliminate a need for live translation of player names]); 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 Aronzon 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]).
Aronzon 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 Aronzon 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, Aronzon 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]).
Regarding claim 20, Aronzon in view of Wang discloses: the method of claim 16.
Aronzon further 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]).
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aronzon in view of Wang, further in view of Yoon (US-20200364413-A1).
Regarding claim 2, Aronzon in view of Wang discloses: the apparatus of claim 1.
Aronzon in view of Wang does not disclose:
wherein the at least one processor assembly is further configured to:
identify that the chat includes an incomplete sentence; and
use the at least one neural network of the translation engine to translate a predicted completion of the incomplete sentence based at least in part on the game context.
Yoon discloses:
wherein the at least one processor assembly is further configured to:
identify that the chat includes an incomplete sentence ([0110] For example, when the text input to the first UI is “custom-character (tomorrow meeting)”, the electronic device 100 may determine that “custom-character” is an incomplete sentence, [Wherein the “custom-characters” appear to be Hangul, i.e. Korean chat, see any of figures 4-10]); and
use the at least one neural network of the translation engine to translate a predicted completion of the incomplete sentence based at least in part on the game context ([Fig. 5c, Autocomplete 511], [0110] …may recommend a completed sentence, [Wherein the autocompleted sentences have clearly been translated into English, further in view of the previously disclosed neural machine translations of Wang which could be used to translate the autocompleted inputs without a change in functionality to Yoon as Yoon discloses using a neural network for object recognition, wherein text to be translated is necessarily a data object]).
Aronzon, Wang, and Yoon are considered analogous art within chat 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 Aronzon in view of Wang to incorporate the teachings of Yoon, because of the novel way to combine language translation with artificial intelligence through the use of recognition rates of artificial intelligence systems, improving the quality of machine-generated translations (Yoon, [0002]).
Claim(s) 5-6, 12-13, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aronzon in view of Wang, further in view of Leydon et al. (US-20140303961-A1), hereinafter Leydon.
Regarding claim 5, Aronzon in view of Wang discloses: the apparatus of claim 1.
Aronzon 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. 2, S210 “Match?, Yes”, S212 “Present translation…”], [Presenting a translation indicates necessarily providing the term in the second language from the glossary, i.e. multi-lingual library, to the engine performing translations]).
Aronzon 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 Aronzon 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]).
Aronzon, 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 Aronzon 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, Aronzon 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, Aronzon in view of Wang discloses: the apparatus of claim 9.
Aronzon further discloses:
wherein the instructions are executable to:
provide the second term ([Fig. 2, S210 “Match?, Yes”, S212 “Present translation…”], [Presenting a translation indicates necessarily providing the term in the second language from the glossary, i.e. multi-lingual library, to the engine performing translations]).
Aronzon 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 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 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]).
Aronzon, 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 Aronzon 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, Aronzon 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, Aronzon in view of Wang discloses: the method of claim 16.
Aronzon further discloses:
providing the terms looked up using the glossary ([Fig. 2, S210 “Match?, Yes”, S212 “Present translation…”], [Presenting a translation indicates necessarily providing the term in the second language from the glossary, i.e. multi-lingual library, to the engine performing translations]).
Aronzon 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 Aronzon 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]).
Aronzon, 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 Aronzon 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) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aronzon in view of Wang, further in view of Garg et al. (US-20230325611-A1), hereinafter Garg.
Regarding claim 17, Aronzon 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]).
Aronzon 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 “Match?” translation operation while searching specific translation libraries of Aronzon 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]).
Aronzon, 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 Aronzon 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Padfield (US-20220121827-A1) discloses “Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that facilitate generating stable real-time textual translations in a target language of an input audio data stream that is recorded in a source language. An audio stream that is recorded in a first language is obtained. A partial transcription of the audio can be generated at each time interval in a plurality of successive time intervals. Each partial transcription can be translated into a second language that is different from the first language. Each translated partial transcription can be input to a model that determines whether a portion of an input translated partial transcription is stable. Based on the input translated partial transcription, the model identifies a portion of the translated partial transcription that is predicted to be stable. This stable portion of the translated partial transcription is provided for display on a user device” (abstract). See entire document.
Kurabayashi (US-20200026763-A1) discloses “a translation support system for supporting machine translation from a source-language sentence into a target-language sentence, the translation support system including an input unit that accepts an input of a source-language sentence to be translated; an error database that at least stores words or combinations of words included in a plurality of source-language sentences for which machine translation from the source-language sentences into target-language sentences is not performed correctly; a controlled-source-language-sentence database that stores a plurality of source-language sentences as well as controlled source-language sentences, which are source-language sentences that are controlled, corresponding to the plurality of source-language sentences and expressed in a format satisfying predetermined conditions; a control unit that classifies whether or not the input source-language sentence is machine-translatable; and an output unit that is capable of outputting the input source-language sentence classified as being non-machine-translatable” (abstract). See entire document.
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/THEODORE WITHEY/Examiner, Art Unit 2655
/ANDREW C FLANDERS/Supervisory Patent Examiner, Art Unit 2655
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