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 .
Claim Rejections - 35 USC § 103
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, 5 – 12, 14 and 16- 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khov et al (US 2024/0430526) in view of Mahlmeister et al (US 2023/0405458).
As per claim 1, Khov discloses:
receiving, at a gaming device, a user request to transform a sound class with a transformation type, wherein the user request includes the transformation type, (Khov discloses a system that operated on a gaming device (Khov 0056, 0069, 0183), wherein a user makes a request (Khov 0100) to transform audio content that may be undesirable such as types relating to “The defined data can be associated with, e.g., offensive, inappropriate (e.g., curse words, offensive vocalizations, explicit visual material, etc.), and/or undesirable content. “(Khov 0064) and wherein the identified content it filtered out according to a desired restriction level that is mapped to a content filter (Khov 0100)
and at least one sample instance of the sound class, and wherein the user request applies to all gaming … instantiated by the gaming device; (Khov discloses the type corresponding to a sample class such as curses or offensive vocalization applied to all media instantiations) (Khov 0064)
training a generative artificial intelligence (AI) model to identify instances of the sound class using the at least one sample instance of the sound class; (Khov discloses training of the generative AI to identify the offensive content such as curses, gunshots etc.) (Khov 0124, 0127, 0129, 0130)
instantiating, by the gaming device, comprises a dynamic audio stream; (Khov discloses a dynamic audio stream) (Khov
generating, based on the user request and in response to the instantiating, a prompt for the generative AI model, wherein the prompt is designed to instruct the generative AI model to process the dynamic audio stream to transform the instances of the sound class in the dynamic audio stream with the transformation type; and (Khov discloses the use selecting a desired request for filtering out undesirable audio and utilizing a AI model to process the dynamic audio stream to transform the undesirable audio into desired audio) (Khov 0103, 0129 – 0131, 0136, 0149, 0157,
serving, by the gaming device, an output stream from the generative AI model as the audio for the … instance. (Khov discloses the serving of the output stream to a gaming device) (Khov 0056, 0069,0104-0107, 0183)
Khov fails to specifically disclose the instantiation of a gaming instance or a gaming instance of a digital game, wherein the gaming instance.
However in a similar field of endeavor, Mahlmeister teaches detection of undesirable audio within generated gaming instances according to a user’s selection of undesirable events and filtering them out and generated new audio stream that the users hears (Mahlmeister 0162, 0163, 0164, 0201, 0241)
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Khov in view of Mahlmeister to utilize a known technique implement audio filtering by utilizing AI to detect matching undesirable game sounds and dynamically modify the audio stream to in response to users desired preferences. This would be beneficial as it would enable a game player selectively customize the game according to their unique preferences.
As per claim 3, wherein the transformation type comprises one of: a replacement transformation such that the generative AI model replaces the instances of the sound class in the dynamic audio stream with a different sound; an enhancement transformation such that the generative AI model increases a strength of the instances of the sound class in the dynamic audio stream; an isolation transformation such that the generative AI model isolates the instances of the sound class in the dynamic audio stream from other sounds in the dynamic audio stream; and a removal transformation such that the generative AI model removes the instances of the sound class from the dynamic audio stream. (Khov discloses the transformation being a muting or removal of the sound content) (Khov 0107)
As per claim 5,
digitizing the dynamic audio stream into digitized segments; and providing the digitized segments to the generative AI model for processing based on the prompt. (Khov disclose the digitizing of the segments and providing the segments to the AI) (Khov 0116, 0127)
As per claim 6,
receiving an indication of feedback from a user during the gaming instance, wherein the feedback indicates an issue in the output stream associated with at least one digitized segment of the digitized segments; and generating, based on the feedback, a second prompt for the generative AI model, wherein the second prompt is designed to instruct the generative AI model to adjust the output stream based on the feedback, and wherein the prompt identifies the at least one digitized segment. (Combination of Khov in view of Vinay as applied above, Vinay teaches a system that provides a user with AI generated results and allows a user to provide feedback on any missed results or outcomes, wherein the feedback Is further used to improve the AI detection.) (Vinay 0044)
As per claim 7,
receiving a second user selection of a thoroughness value, wherein a size of the digitized segments is selected based at least in part on the thoroughness value. (Khov discloses the user being able to make multiple selections of types of content to filter, and can select all if desired thus effectively increasing the size of the total segments that the system will process and remove content from ) (Khov 0130).
As per claim 8,
wherein the generative AI model is trained to deliver the output stream at a consistent rate in relation to the dynamic audio stream. (Khov discloses the content filtering and producing an output stream of content in real time (i.e. at a consistent rate)) (Khov 0149-0152)
As per claim 9, postprocessing the output stream to smooth a transmission rate of the output stream to match a transmission rate of the dynamic audio stream. (Khov, discloses the post processing or output to compensate for latency or transmission rate of the stream) (Khov 0149).
As per claim 10, the generative AI model is a first generative AI model of a plurality of generative AI models; each generative AI model of the plurality of generative AI models is trained to identify a unique sound class of a plurality of sound classes; and the method further comprising: receiving, at the gaming device, a second user selection of a second sound class and a second transformation type, generating, based on the second user selection and in response to the instantiating, a second prompt for a second generative AI model of the plurality of generative AI models, wherein the prompt is designed to instruct the second generative AI model to process the dynamic audio stream to transform instances of the second sound class in the dynamic audio stream with the second transformation type, and wherein the second generative AI model is pretrained to identify the instances of the second sound class, and providing a second output stream from the second generative AI model to the first generative AI model as the dynamic audio stream. (Khov discloses the usage of multiple different generative models that are each used to filter out a specific type of undesirable content of the original audio stream and transform process the identified undesirable content to thereby generate a dynamic audio stream) (Khov 0134-0140)
As per claim 11, receiving, at the gaming device, a second user request to transform a second sound class with a second transformation type, wherein the second user request includes the second transformation type, and at least one sample instance of the second sound class; training a second generative AI model to identify instances of the second sound class using the at least one sample instance of the second sound class; instantiating, by the gaming device, a second gaming instance of a digital game, wherein the second gaming instance comprises a second dynamic audio stream; generating, based on the user request and in response to the instantiating, a second prompt for the generative AI model, wherein the second prompt is designed to instruct the generative AI model to process the second dynamic audio stream to transform the instances of the sound class in the dynamic audio stream with the transformation type; generating, based on the second user request and in response to the instantiating, a third prompt for the second generative AI model, wherein the third prompt is designed to instruct the second generative AI model to process an output of the generative AI model to transform the instances of the second sound class in the output of the generative AI model with the second transformation type; and serving, by the gaming device, an output stream from the second generative AI model as the audio for the second gaming instance. (Combination of Khov and Mahlmeister as applied to claim 1, wherein further, Khov discloses a number of selectable AI models that are used to filter media streams, that are selectable by a user, wherein each filter can be used to filter selected content that a user may deem undesirable, thus each selection will be a first, second, third selection (i.e. user request), each enabling the training of each neural network model (i.e. first, second, third filters) to process individual undesirable content as applied to claim 1) (Khov 0134 – 0136)
Independent claim(s) 12 is/are made obvious by the combination of Khov and Mahlmeister based on the same analysis set forth for claim(s) 1, which are similar in claim scope.
Dependent claim(s) 14 and 16- 18 is/are made obvious by the combination of Khov, Mahlmeister based on the same analysis set forth for claim(s) 3 and 5-7, which are similar in claim scope.
Dependent claim(s) 19 and 20 is/are made obvious by the combination of Khov, Mahlmeister based on the same analysis set forth for claim(s) 9 and 10, which are similar in claim scope.
Claim(s) 2 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khov et al (US 2024/0430526) in view of Mahlmeister et al (US 2023/0405458) in view of Vinay et al (US 2022/0148308).
As per claim 2, Khov fails to disclose:
receiving indications of missed instances of the sound class in the audio from a user during the gaming instance; and fine-tuning the generative AI model based on the indications.
However, Vinay teaches a system that provides a user with AI generated results and allows a user to provide feedback on any missed results or outcomes, wherein the feedback Is further used to improve the AI detection (Vinay 0044).
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Khov in view of Vinay to utilize a mechanism that allows a user to provide feedback on the results of AI generated results and train the AI with that feedback. This would be beneficial as the next time the AI is used it would be more accurate in its detection capabilities.
Dependent claim(s) 13 is/are made obvious by the combination of Khov, Mahlmeister and Vinay based on the same analysis set forth for claim(s) 2, which are similar in claim scope.
Claim(s) 4 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khov et al (US 2024/0430526) in view of Mahlmeister et al (US 2023/0405458) in view of Mishra (2023/0368773.
As per claim 4, Khov discloses:
instantiating, by a second gaming device, a second gaming instance of a second digital game, wherein the second gaming instance comprises a second dynamic audio stream and…; generating, based on the user request and in response to the instantiating the second gaming instance, a second prompt for the generative AI model, wherein the second prompt is designed to instruct the generative AI model to process the second dynamic audio stream to transform instances of the sound class in the second dynamic audio stream with the transformation type; and serving, by the second gaming device, a second output stream from the generative AI model as the audio for the second gaming instance. (Combination of Khov and Mahlmeister wherein Khov discloses the saving of content filters that govern the AI detection of undesirable content to a user account and utilizing that save content filter for further media playing instances) (Khov 0110)
Khov fails to specifically disclose:
saving the generative AI model to a globally accessible user account;
the globally accessible user account is logged in to the second gaming device
However, in a similar field of endeavor, Mishra teaches: “In some instances, a client device (e.g., such as client device 336 or other device, etc.) or user can request generation of a personal virtual agent associated with one or more domains without using historical data of the domain. For example, client device 336 may request a personal virtual agent before communicating with a particular domain or if there are insufficient interactions between client device 336 and the domain to train a personal virtual agent. Client device 336 may register with automated communication server 208 to establish a user profile. Automated communication server 208 may then request generation of one or more personal virtual agents for one or more domains using information provided by client device 336 and/or from information sources identified by client device 336. The information may be used by automated service generator 304 to train personal virtual agents for client device 336. The generated personal virtual agents may be stored in associated with the user profile so that client device 336 can access the personal virtual agents during subsequent communication sessions with automated communication server 208 and/or domains may access the generated personal virtual agents when client device 336 or the user thereof establishes a communication with a corresponding domain.” (Mishra 0068 – 0069).
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Khov and Mahlmeister in view of Mishra to use a known technique to improve similar devices in the same way by associating an AI agent or model that is trained for a specific user to the users profile or user account. This would be beneficial as they can access the AI model at later times and able to utilize many different devices when doing so.
Dependent claim(s) 15 is/are made obvious by the combination of Khov, Mahlmeister and Mishra based on the same analysis set forth for claim(s) 4, which are similar in claim scope.
Conclusion
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/RAW/Examiner, Art Unit 3715 8/5/2026
/KANG HU/Supervisory Patent Examiner, Art Unit 3715
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