DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 8/14/26 has been entered.
Response to Amendment
This is in response to the amendments filed on 8/14/26. Claims 1, 8, and 11 have been amended. Claims 1 – 20 are pending in the current application.
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.
Claims 1 – 8, 10, 11, 15 – 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Leydon et al. (U.S. 2014/0229157) in view of van Rensburg (U.S. 2019/0354879).
Regarding claims 1 and 11, Leydon discloses a method and device for dynamic chat translation, (“FIG. 7 is a diagram 700 illustrating an example multi-lingual chat session, between chat client systems”, par. 0115, “a chat dialogue box 702 configured to present the chat dialogue 712 in a first language (e.g., English) in an output area 708 and to receive chat input in the first language in a second area 710. The chat client GUI module 406-2 may comprise a chat dialogue box 714 configured to present the chat dialogue 712 in a second language (e.g., French) in an output area 720 and to receive chat input in the second language in a second area”, par. 0116 and fig. 7 parts 706 and 718, wherein the Examiner views the chat being translated from the first language, (English), to the second language, (French), shown in fig. 7 parts 706 and 718 as being equivalent to dynamic chat translation), the method comprising receiving, by a device, a communication for a first user of an electronic game, (“multi-user chat system associated with an online game “, par. 0009), the device storing a localization setting for the first user, (“a system or method may perform translation from chatspeak in a first language”, par. 0052, wherein the Examiner views the system having a first language as being equivalent to a stored localization setting), converting, by the device, the communication using the localization setting, (“a translation module 116 configured to receive and service requests for machine text translation”, par. 0063), wherein converting includes replacing at least one segment of the communication with a replacement segment, (fig. 7 parts 706 and 718), and outputting, by the device, an updated communication including the replacement segment to the user, (fig. 7 parts 706 and 718).
Leydon, however, is silent on the issue of disclosing a machine learning model and detecting a facial reaction of a user. In a related art, van Rensburg discloses a system that comprises chat conversations, (“Users of a communication service provider system can participate in communications sessions with other users. Examples of communications sessions may include voice-over-Internet-Protocol (VOIP) calls, videoconferences, chat conversations”, par. 0002), that may be implemented on a game console, (“Moreover, a computer can be embedded in another device, e.g., a mobile telephone, a smart phone, a mobile audio or video player, a game console”, par. 0074), wherein van Rensburg further discloses a machine learning model, (“one or more machine learning models”, par. 0035), that detects a facial reaction of a user and updating the machine learning model based on the detected facial reaction of the user, (“the video data analysis module 204A performs the facial expression detection in three steps: (1) locate a user's face, (2) extract facial features from the located face, and (3) classify the facial features into facial expressions and corresponding confidence scores for modality-specific reaction types” and “The video data analysis module 204A can use one or more machine learning models to locate and track key points in the face. The video data analysis module 204A then feeds the set of facial features to another machine learning model for classification into one or more of the predefined modality-specific reaction types”, par. 0035, wherein the Examiner views the machine learning model tracking key points in the face and then a second machine learning model classifying the facial features into a predefined reaction type as being equivalent detecting a facial reaction and updating a machine learning model based on the reaction”, wherein van Rensburg further teaches example reactions such as a smile indicating satisfaction or happiness, (“The video data analysis module 204A detects facial expressions or gestures in video reaction data and use the detected features to predict video-specific reaction data. For example, a smile by a user can indicate that the reaction “satisfaction” or “happiness” should have a high confidence score while a frown can indicate that the reaction “concern” or “sadness” should have a high confidence score”, par. 0033), wherein this is viewed in combination with Leydon as meeting the claim limitation of detecting a facial reaction of the user in response to outputting, by the device, an updated communication including the replacement segment to the user and updating the machine learning model for language processing based on the detected facial reaction of the user.
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to combine the machine learning model detecting facial reaction teachings of van Rensburg, including van Rensburg’s facial reaction correction, (“the video data analysis module 204A can generate video-specific reaction data that includes a high confidence score for the reaction “happiness” from the facial expression of a user, but the audio data analysis module 204B can generate a high confidence score for the reaction “surprise” based on the pitch of the user's voice”, par. 0042), into the art disclosed by Leydon in order to create dynamic gameplay by providing live reactions and true immersion and to also correct an incorrect translation by comparing the translation to a detected facial reaction.
Regarding claims 2 and 12, Leydon, as cited above, discloses a method for dynamic chat translation, which is viewed by the Examiner as being equivalent to text communication, however, Leydon is silent on the issue of disclosing voice communication of the electronic game. In a related art, van Rensburg discloses chat conversations wherein the communication is at least one of a voice or text communication, (“The communications devices can support various means of information exchange between users, namely text messages, voice messages”, par. 0015).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was to combine the voice communications of van Rensburg into the art disclosed by Leydon in order to improve teamwork and build stronger bonds between players by sharing fast updates without typing.
Regarding claims 3 and 13, Leydon discloses wherein the communication is at least one of audio output and text output of a character of an electronic game, (“FIG. 7 is a diagram 700 illustrating an example multi-lingual chat session, between chat client systems”, par. 0115, “a chat dialogue box 702 configured to present the chat dialogue 712 in a first language (e.g., English)”, par. 0116, wherein the chat session displayed in the chat dialogue box as being equivalent to a text output).
Regarding claims 4 and 14, Leydon discloses wherein converting the communication includes replacing at least one of voice, audio and text of the communication with a translated communication as the replacement segment, (“a chat dialogue box 702 configured to present the chat dialogue 712 in a first language (e.g., English) in an output area 708 and to receive chat input in the first language in a second area 710. The chat client GUI module 406-2 may comprise a chat dialogue box 714 configured to present the chat dialogue 712 in a second language (e.g., French) in an output area 720 and to receive chat input in the second language in a second area”, par. 0116 and fig. 7 parts 706 and 718, wherein the Examiner views the chat being translated from the first language, (English), to the second language, (French)).
Regarding claims 5 and 15, Leydon discloses wherein the localization setting includes at least one of a regional, location and language preference of the user, (“a system or method may perform translation from chatspeak in a first language”, par. 0052).
Regarding claims 6 and 16, Leydon, as stated above, discloses a localization dataset, (“a system or method may perform translation from chatspeak in a first language”, par. 0052), but is silent on disclosing a machine learning model. In a related art, van Rensburg, as cited above, discloses machine learning model that is configured based on a training set of data for the user, (“The video data analysis module 204A can use one or more machine learning models to locate and track key points in the face. The video data analysis module 204A then feeds the set of facial features to another machine learning model for classification into one or more of the predefined modality-specific reaction types”, par. 0035, wherein the Examiner views the machine learning model detecting a user’s facial reaction as being equivalent to a machine learning model that is configured based on a training set of data for the user).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to combine the machine learning model teachings of van Rensburg into the art disclosed by Leydon in order to create dynamic gameplay by providing live reactions and true immersion.
Regarding claims 7 and 17, Leydon discloses converting the communication includes replacing the at least one segment including offensive commentary, (“The profanity module 316 may be configured to identify one or more profane words or phrases (hereafter, referred to as a "profanity") in a chat message, and may be further configured to suggest replacement words or phrases (e.g., suitable substitute) corresponding to the profanity (e.g., a toned down euphemism)”, par. 0079).
Regarding claims 8 and 18, Leydon, as stated above, discloses chat translation and determining a replacement segment, (“The profanity module 316 may be configured to identify one or more profane words or phrases (hereafter, referred to as a "profanity") in a chat message, and may be further configured to suggest replacement words or phrases (e.g., suitable substitute) corresponding to the profanity (e.g., a toned down euphemism)”, par. 0079, wherein the Examiner views profane words as discrepancies), however, Leydon is silent on disclosing a machine learning model. In a related van Rensburg discloses updating the machine learning model based on the detected facial reaction comprises comparing the detected facial reaction against an expected user behavior model, (“the video data analysis module 204A can generate video-specific reaction data that includes a high confidence score for the reaction “happiness” from the facial expression of a user, but the audio data analysis module 204B can generate a high confidence score for the reaction “surprise” based on the pitch of the user's voice. To resolve conflicts and increase detection accuracy, the reaction detection engine 204 can combine results from different analysis modules to generate the standardized reaction data” and “the reaction detection engine 204 can use a machine learning model to generate reaction”, par. 0042, wherein the Examiner views the reaction detection engine using a machine learning model to combine reaction results to increase accuracy as being equivalent to comparing detected facial reactions against expected facial expression of a user), wherein this is viewed by the Examiner in combination with Leydon as meeting the claim limitation of updating the machine learning model based on the detected facial reaction comprises comparing the detected facial reaction against an expected user behavior model to detect a discrepancy; determining the replacement segment is an incorrect translation based on the detected discrepancy, and modifying a subsequent communication output based on the determined incorrect translation.
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to combine the machine learning model teachings of van Rensburg including van Rensburg’s facial reaction correction, (“the video data analysis module 204A can generate video-specific reaction data that includes a high confidence score for the reaction “happiness” from the facial expression of a user, but the audio data analysis module 204B can generate a high confidence score for the reaction “surprise” based on the pitch of the user's voice”, par. 0042), into the art disclosed by Leydon in order to create dynamic gameplay by providing live reactions and true immersion and to also correct an incorrect translation by comparing the translation to a detected facial reaction.
Regarding claims 10 and 20, as stated above, Leydon discloses language processing, but is silent on disclosing a machine learning model. In a related art, van Rensburg discloses updating the machine learning model for processing using the reaction of the user to the replacement segment, (“The video data analysis module 204A can use one or more machine learning models to locate and track key points in the face. The video data analysis module 204A then feeds the set of facial features to another machine learning model for classification into one or more of the predefined modality-specific reaction types”, par. 0035, wherein the Examiner views the machine learning model tracking key points in the face and then a second machine learning model classifying the facial features into a predefined reaction type as being equivalent detecting a facial reaction and updating a machine learning model based on the reaction”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to combine the machine learning model teachings of van Rensburg into the art disclosed by Leydon in order to create dynamic gameplay by providing live reactions and true immersion.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Leydon (U.S. 2014/0229157) as applied to claims 1 and 11 above, in view of van Rensburg (U.S. 2019/0354879) and in further view of Osterhout et al. (U.S. 2012/0075168).
Regarding claims 9 and 19, as cited above, Leydon discloses chat translation and van Rensburg discloses detecting facial reactions, however, both Leydon and van Rensburg are silent on disclosing eye tracking data. In a related art, Osterhout discloses language translation, (“a visual recognition language translation facility for providing translations”, par. 0357), wherein Osterhout further discloses eye tracking data for the user, wherein said data is detected during output, (“the glasses may be equipped with eye tracking devices for tracking movement of the user's eye”, par. 0339).
Therefore, it would have been obvious to one of ordinary skill to combine the eye tracking data of Osterhout into the art disclosed by Leydon and van Rensburg in order to deepen player immersion by drastically increasing game accessibility.
Response to Arguments
Applicant’s arguments with respect to claims 1 - 20 have been considered but are moot based on new grounds of rejection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC M THOMAS whose telephone number is (571)272-1699. The examiner can normally be reached 9:00am - 5:00pm.
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/E.M.T/Examiner, Art Unit 3715
/JUSTIN L MYHR/Primary Examiner, Art Unit 3715
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