DETAILED ACTION
Status of the Application
Claims 1-20 are pending and currently under consideration for patentability under 37 CFR 1.104.
Priority
The instant application has a filing date of April 1, 2024 and does not claim for the benefit of a prior-filed application.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on June 16, 2025 has been considered by the examiner.
Claim Objections
The claims are objected because of the following informalities: there are numerous locations throughout the claims where Applicant refers to “at least one (X)” as well as “the (X)” (e.g., “at least one signal” and then “the signal”, “at least one predicted audio object” and “the audio object”, etc.,). The claims should be amended to maintain consistency of terminology throughout the claims. Appropriate correction is required.
Subject Matter Eligibility
The Examiner is persuaded that the claims do not recite any of the judicial exceptions. For Example, the at least the steps of “alter the predicted audio object received from ML model to render an altered audio object; and replace an audio object from the computer game that the signal represents with the altered audio object such that at least one speaker plays the altered audio object in lieu of the audio object from the computer game that the signal represents” (claim 1) and “enhancing the predicted audio object received from the ML model to render an enhanced audio object; and playing, on at least one speaker, audio from the computer simulation using the enhanced audio object” (claim 10) and “using output of the ML model to render an altered audio object; and playing the altered audio object during game play instead of an audio object in the audio from the computer game” (claim 17) cannot reasonably be performed mentally and/or with pen and paper. Furthermore, the claims do not recite subject matter failing within any of the Certain Methods of Organizing Human Activity subgroupings. Finally, although some of the limitations may be based on mathematical concepts, the mathematical concepts themselves are not recited in the claims. Thus, the claims do not recite a judicial exception.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4 and 14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
v Claim 4 requires “wherein the altered audio object has greater acoustic clarity than the audio object from the computer game that the signal represents” and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Whether or not an audio object has “greater acoustic clarity” is a subjective matter. Furthermore, although the specification suggests clarity may generally be associated with waveform “smoothness”, the specification does not supply an objective standard for measuring the scope of the term (e.g., what objective standard is required for something to have “greater acoustic clarity”). Some objective standard must be provided in order to allow the public to determine the scope of the claim. A claim term that requires the exercise of subjective judgment without restriction may render the claim indefinite. In re Musgrave, 431 F.2d 882, 893, 167 USPQ 280, 289 (CCPA 1970). Claim scope cannot depend solely on the unrestrained, subjective opinion of a particular individual purported to be practicing the invention. Datamize LLC v. Plumtree Software, Inc., 417 F.3d 1342, 1350, 75 USPQ2d 1801, 1807 (Fed. Cir. 2005)); Therefore, the claim is indefinite for failing to particularly and distinctly claim the subject matter which the application regards as the invention.
Claim 14 recites nearly identical limitation, and is indefinite for these same reasons
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 of this title, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
v Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Cheatham III. et al. (U.S. PG Pub No. 2017/0372697, December 28, 2017 - hereinafter "Cheatham”) in view of Mahlmeister et al. (U.S. PG Pub No. 2023/0364508, November 16, 2016 - hereinafter "Mahlmeister”)
With respect to claim 1, Cheatham teaches an apparatus comprising;
at least one processor assembly configured to: (Fig 1 tag 102 “sound processing controller”, Fig 2 tag 110 “processor” & [0020] “Sound processing controller 102 includes processing electronics having a processor 110 and a memory 112. Processor 110 may be or include one or more microprocessors, an application specific integrated circuit (ASIC), a circuit containing one or more processing components, a group of distributed processing components, circuitry for supporting a microprocessor, or other hardware configured for processing…configured to execute computer code stored in memory 112 to complete and facilitate the activities described herein”
receive at least one signal from at least one microphone during presentation of a computer game; ([0016] “The sound input may come from a variety of sources. In some embodiments the sound input is provided by a media device…computers, televisions, video game systems…In some embodiments, the sound input is acquired from the ambient environment, for example from one or more microphones 104. Directional microphones may also be used to detect sounds emanating from particular locations” – therefore the sound input signal being processed may come from one or more microphones during presentation of a computer game (e.g., the sound signal is from a video/computer game the person is playing per [0036]-[0037] “Rule-based user control of audio rendering as described herein may be implemented in many…applications….video games…For example, when playing a first person shooter or other action type video game the user may be part of a team with each team member having different tasks. Accordingly, the user may want to focus on particular sounds to better accomplish his tasks…volume level of team members…volume level for opponents…the volume of specific background sounds 406 including the sound of an alarm, the sound of gun fire or the sound of air support approaching” & [0024] “target sound virtual reality or video game environment…”), [0031] “one or more traits of the sound input…traits may indicate a particular sound source (e.g., a sound received by a particular microphone”), - Examiner notes than an alternative embodiment taught by Cheatham is that sound input received directly from a video game system or computer may comprise team member voices (e.g., they are speaking while playing a cooperative/multiplayer game) and this/these voice signals are “from at least one microphone” (because the teammates voices are captured via microphones before being transmitted) and the sound input signal received from the video game system or computer directly is therefore “at least one signal from at least one microphone during presentation of a computer game)
input the signal to at least one model; ([0028]-[0030] “Sound analysis module… receive a sound input, analyze the sound input for the target sound input(s)…cocktail party processing…processing approaches can be found in…Cocktail Party Processing via Structured Prediction…which are incorporated by reference herein. In some embodiments, sound analysis module 116 uses speech detection, speech recognition…Suitable techniques can be found in Smart Headphones: Enhancing Auditory Awareness Through Robust Speech Detection and Source Localization”…in In some embodiments, sound analysis module 116 makes use of specific tracks, inputs, metadata, or other identifying characteristics to analyze the sound input for the target sound input(s).” – therefore the signal is input to at least one model, [0025] “receive…one or more quantifiable properties of the target sound input (e.g., probability of presence of the target sound…” – the model may output a probability of presence of one or more target sounds)
receive from the model at least one predicted audio object; ([0028]-[0030] “Sound analysis module… receive a sound input, analyze the sound input for the target sound input(s)…cocktail party processing…processing approaches can be found in…Cocktail Party Processing via Structured Prediction…which are incorporated by reference herein. In some embodiments, sound analysis module 116 uses speech detection, speech recognition…Suitable techniques can be found in Smart Headphones: Enhancing Auditory Awareness Through Robust Speech Detection and Source Localization”…in In some embodiments, sound analysis module 116 makes use of specific tracks, inputs, metadata, or other identifying characteristics to analyze the sound input for the target sound input(s).” –there the at least one model analyzes the signal to identify/predict at least one target sound (i.e., “at least one predicted audio object”), [0023]-[0025] “receives a target sound input identifying one or more target sounds…may indicate a type of sound…a voice…a manmade sound…an alarm, a mechanical noise…voice of a specific person…one or more quantifiable properties of the target sound input (e.g., probability of presence of the target sound…” – the received indication of at least one target sound may be a predicted target sound (i.e., “at least one predicted audio object”), [0037] “when playing a first person shooter or other action type video game…particular sounds….specific background sounds 406 including the sound of an alarm, the sound of gun fire or the sound of air support approaching”)
alter the predicted audio object received from the model to render an altered audio object; and ([0035]-[0037] “establishing a sound processing rule…receiving a user input of a target sound…receiving a rule input (step 306), and receiving a sound processing input indicating the sound processing to be performed (step 308) to establish a sound processing rule (step 310) in which the target sound(s) are evaluated according to the rule and the sound processing will be performed in response to that evaluation…the target sound may be selected from a list of possible target sounds…the sound processing input may be selected by the user similar to the selection of the target sound….(e.g., increase volume, decrease volume…Rule-based user control of audio rendering…allows the user to modify the sound track for virtual applications according to the user's selected sound processing rules…when playing a first person shooter or other action type video game…the user may want to focus on particular sounds to better accomplish his tasks…the user can control the volume level of team members 402….specific background sounds 406 including the sound of an alarm, the sound of gun fire or the sound of air support approaching…Adjusting…the volume for each of these target sounds increases or decreases the volume of the target sound from its original volume” – therefore the system alters the detected target sound(s) (i.e., the predicted audio object received from the model) such as by adjusting the volume of these sounds from their original volume (i.e., altering the target sound to render an altered target sound), [0023]-[0026] “one or more sound processing rules that each use at least one target sound as an input… receives a target sound input identifying one or more target sounds and a rule input to define a sound processing rule…define the sound processing to be applied…increase volume, change apparent position…mute…sound processing applied by the sound processing rule may control various audio aspects of the sound….Audio aspects include volume, equalization spectrum, time delay, pitch, apparent source location, tone, frequency, etc. The sound processing may be applied to one or more sounds in the sound input (e.g., the target sound… The sound processing may make no change to the sound input when the results of the rule analysis indicate no sound processing is to be performed”)
replace an audio object from the computer game that the signal represents with the altered audio object such that at least one speaker plays the altered audio object in lieu of the audio object from the computer game that the signal represents ([0035]-[0037] “sound processing will be performed in response to that evaluation ….(e.g., increase volume, decrease volume…Rule-based user control of audio rendering…allows the user to modify the sound track for virtual applications according to the user's selected sound processing rules… the user can control the volume level of team members 402….specific background sounds 406 including the sound of an alarm, the sound of gun fire or the sound of air support approaching…Adjusting…the volume for each of these target sounds increases or decreases the volume of the target sound from its original volume” – therefore the system replaces the original target sound from the computer game that the signal represents (i.e., replaces the original target sound) with the altered target sound (i.e., a new version of the target sound having a different volume/amplitude, pitch, tone, frequency, EQ spectrum, apparent location, etc.) such that the user hears (i.e., at least one speaker plays) the altered target sound instead of the original target sound from the game that the signal represents, [0025]-[0026] “… receives a target sound input identifying one or more target sounds and a rule input to define a sound processing rule…define the sound processing to be applied…increase volume, change apparent position…mute…sound processing applied by the sound processing rule may control various audio aspects of the sound….Audio aspects include volume, equalization spectrum, time delay, pitch, apparent source location, tone, frequency, etc. The sound processing may be applied to one or more sounds in the sound input (e.g., the target sound…”[0017] “The processed sound output may directly or indirectly drive one or more speakers 106”- replaces such that at least one speaker plays the altered sound)
Cheatham’s specification refers to, and incorporates by reference, multiple possible processing/analysis models that may be used to detect/predict target sounds from the input signal(s). For example, Cheatham refers to, and incorporates by reference, cocktail party processing approaches such as those disclosed in “Cocktail Party Processing via Structured Prediction” and/or other detection techniques such as those found in “Smart Headphones: Enhancing Auditory Awareness Through Robust Speech Detection and Source Localization”. Both of these two incorporated references/techniques use machine learning (ML) models to detect/predict audio objects (e.g., “Cocktail Party Processing via Structured Prediction” uses functions learned by deep neural networks, “Smart Headphones: Enhancing Auditory Awareness Through Robust Speech Detection and Source Localization” uses learning algorithms and also references neural network learning). However, these essential details are incorporated by reference. Cheatham does not explicitly disclose,
at least one machine learning (ML) model…receive from the ML model at least one predicted audio object… predicted audio object received from ML model
Mahlmeister discloses
at least one machine learning (ML) model…receive from the ML model at least one predicted audio object… predicted audio object received from ML model ([0162]-[0163] “the audio processing system 7600 may automatically characterize audio events occurring in the game audio stream, the chat audio stream, the microphone audio stream or some combination of these…machine learning techniques may be used to characterize or profile audio events in the audio stream to identify a predetermined audio event such as a footstep or a gunshot…may be provided to a neural network. The neural network may be trained using any suitable data such audio from other games or conversations. The training data may be tagged, for example, to identify predetermined audio events such as a gunshot or a footstep. Different characteristics may be further trained and identified, such as a footstep on wet pavement or a gunshot with ricochet. Once trained and provided with the live audio, the neural network or other processing module may produce an indication when the predetermined audio event has occurred. In some embodiments, the indication is a value corresponding to the probability that the predetermined audio event has occurred. If the probability exceeds a threshold, such as 75 percent or 95 percent, the audio processing system may conclude that the predetermined event has been detected…if a footstep is detected, the spectrum may be adjusted in the parametric equalizer to emphasize to the listener, the player, the sound of the footstep. Further, if the player is using a headset or other audio equipment that provides directionality or other surround sound effect, the audio may be automatically adjusted to emphasize the direction of origin of the predetermined event….Other sounds from that area may be suppressed to emphasize the footprint”, see also [0150]-[0153])
Mahlmeister suggests it is advantageous to include “at least one machine learning (ML) model…receive from the ML model at least one predicted audio object… predicted audio object received from ML model”, because machine learning models such as neural networks provide efficient and effective analysis mechanisms for detecting one or more target sounds from input audio, can be trained to detect specific desired target sounds, and are capable of near real-time detection ([0153]-[0154], [0162], [0272]-0276] ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of Cheatham to include “at least one machine learning (ML) model…receive from the ML model at least one predicted audio object… predicted audio object received from ML model”, as taught by Mahlmeister, because machine learning models such as neural networks provide efficient and effective analysis mechanisms for detecting one or more target sounds from input audio, can be trained to detect specific desired target sounds, and are capable of near real-time detection.
Furthermore, since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. That is in the substitution of the sound analysis model of Mahlmeister (i.e., at least one machine learning (ML) model) for the that of Cheatham. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
With respect to claim 2, Cheatham teaches the apparatus of claim 1;
wherein the altered audio object has a greater amplitude than the audio object from the computer game that the signal represents ([0025]-[0026] “… receives a target sound input identifying one or more target sounds and a rule input to define a sound processing rule…define the sound processing to be applied…increase volume, change apparent position…mute…sound processing applied by the sound processing rule may control various audio aspects of the sound….Audio aspects include volume, equalization spectrum, time delay, pitch, apparent source location, tone, frequency, etc. The sound processing may be applied to one or more sounds in the sound input (e.g., the target sound…” – therefore the altered audio object has a greater amplitude than the audio object from the computer game that the signal represents)
Examiner notes Mahlmeister also discloses this limitation.
With respect to claim 3, Cheatham teaches the apparatus of claim 1;
wherein the altered audio object has a different frequency than the audio object from the computer game that the signal represents ([0025]-[0026] “… receives a target sound input identifying one or more target sounds and a rule input to define a sound processing rule…define the sound processing to be applied…increase volume, change apparent position…mute…sound processing applied by the sound processing rule may control various audio aspects of the sound….Audio aspects include volume, equalization spectrum, time delay, pitch, apparent source location, tone, frequency, etc. The sound processing may be applied to one or more sounds in the sound input (e.g., the target sound...” – therefore the altered audio object has a different frequency than the audio object from the computer game that the signal represents)
Examiner notes Mahlmeister also discloses this limitation.
With respect to claim 4, Cheatham teaches the apparatus of claim 1;
wherein the altered audio object has greater acoustic clarity than the audio object from the computer game that the signal represents ([0025]-[0026] “… receives a target sound input identifying one or more target sounds and a rule input to define a sound processing rule…define the sound processing to be applied…increase volume, change apparent position…mute…In this way, the user would be better able to hear the voice of the designated speaker even when an alarm is sounding sound processing applied by the sound processing rule… may control various audio aspects of the sound….Audio aspects include volume, equalization spectrum, time delay, pitch, apparent source location, tone, frequency, etc. The sound processing may be applied to one or more sounds in the sound input (e.g., the target sound...” – therefore the altered audio object has greater acoustic clarity (i.e., the user can better hear it) than the audio object from the computer game that the signal represents)
Examiner notes Mahlmeister also discloses this limitation ([0199] “for the gaming audio stream, the user has selected a configuration titled “Fortnite footsteps” which may include data defining a set of audio settings for the parametric equalizer that improve the clarity of footsteps heard by the user in the game play. Adjusting the frequency, gain and Q factor of a set of filters may greatly improve the user's ability to discern footsteps during gameplay”)
With respect to claim 5, Cheatham teaches the apparatus of claim 1;
wherein the processor assembly is configured to receive from the ML model, in response to input of the signal, the predicted audio object and no other audio objects ([0028]-[0030] “Sound analysis module… receive a sound input, analyze the sound input for the target sound input(s)…cocktail party processing…processing approaches can be found in…Cocktail Party Processing via Structured Prediction…which are incorporated by reference herein. In some embodiments, sound analysis module 116 uses speech detection, speech recognition…Suitable techniques can be found in Smart Headphones: Enhancing Auditory Awareness Through Robust Speech Detection and Source Localization”…in In some embodiments, sound analysis module 116 makes use of specific tracks, inputs, metadata, or other identifying characteristics to analyze the sound input for the target sound input(s).” –there the at least one model analyzes the signal to identify/predict at least one target sound (i.e., “at least one predicted audio object”) and the model outputs any detected/predicted target sounds and no other audio objects, [0023]-[0025] “receives a target sound input identifying one or more target sounds…may indicate a type of sound…a voice…a manmade sound…an alarm, a mechanical noise…voice of a specific person…one or more quantifiable properties of the target sound input (e.g., probability of presence of the target sound…”), [0037] “when playing a first person shooter or other action type video game…particular sounds….specific background sounds 406 including the sound of an alarm, the sound of gun fire or the sound of air support approaching”)
Examiner notes Mahlmeister also discloses this limitation ([0162] “machine learning techniques may be used to characterize or profile audio events in the audio stream to identify a predetermined audio event such as a footstep or a gunshot associated with another player”)
With respect to claim 6, Cheatham and Mahlmeister teach the apparatus of claim 1. Cheatham does not explicitly disclose,
wherein the altered audio object comprises a footstep object
However, Mahlmeister discloses
wherein the altered audio object comprises a footstep object ([0162]-[0163] “the audio processing system 7600 may automatically characterize audio events…machine learning techniques may be used to characterize or profile audio events in the audio stream to identify a predetermined audio event such as a footstep or a gunshot…may be provided to a neural network. The neural network may be trained using any suitable data such audio from other games or conversations. The training data may be tagged, for example, to identify predetermined audio events such as a gunshot or a footstep. Different characteristics may be further trained and identified, such as a footstep on wet pavement or a gunshot with ricochet. Once trained and provided with the live audio, the neural network or other processing module may produce an indication when the predetermined audio event has occurred. In some embodiments, the indication is a value corresponding to the probability that the predetermined audio event has occurred. If the probability exceeds a threshold, such as 75 percent or 95 percent, the audio processing system may conclude that the predetermined event has been detected…if a footstep is detected, the spectrum may be adjusted in the parametric equalizer to emphasize to the listener, the player, the sound of the footstep. Further, if the player is using a headset or other audio equipment that provides directionality or other surround sound effect, the audio may be automatically adjusted to emphasize the direction of origin of the predetermined event….Other sounds from that area may be suppressed to emphasize the footprint”, see also [0150]-[0153])
Mahlmeister suggests it is advantageous to include wherein the altered audio object comprises a footstep object, because these types of target sounds may be of particular interest to a user playing a computer game/simulation, which may increase user satisfaction with the system if/when they are playing a computer game/simulation ([0153]-[0154], [0162], [0272]-0276] ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of Cheatham to include wherein the altered audio object comprises a footstep object, as taught by Mahlmeister, because these types of target sounds may be of particular interest to a user playing a computer game/simulation, which may increase user satisfaction with the system if/when they are playing a computer game/simulation.
With respect to claim 7, Cheatham teaches the apparatus of claim 1;
wherein the altered audio object comprises a weapon noise object ([0035]-[0037] “sound processing will be performed in response to that evaluation ….(e.g., increase volume, decrease volume…Rule-based user control of audio rendering…allows the user to modify the sound track for virtual applications according to the user's selected sound processing rules… the user can control the volume level of team members 402….specific background sounds 406 including the sound of an alarm, the sound of gun fire or the sound of air support approaching…Adjusting…the volume for each of these target sounds increases or decreases the volume of the target sound from its original volume” – sounds of gun fire or air support are weapon noise objects)
Examiner notes Mahlmeister also discloses this limitation.
With respect to claim 8, Cheatham teaches the apparatus of claim 1;
wherein the altered audio object comprises a voice ([0023]-[0025] “receives a target sound input identifying one or more target sounds…may indicate a type of sound…a voice…a manmade sound…an alarm, a mechanical noise…voice of a specific person…”, [0037] controlling volume of voices of different team members)
Examiner notes Mahlmeister also discloses this limitation.
With respect to claim 9, Cheatham teaches the apparatus of claim 1;
wherein the signal represents only an initial portion of the audio object from the computer game that the signal represents ([0015] “The sound input may be…audio information that is sampled by the sound processing control 102 at an appropriate sampling rate (e.g., 1 kHz or more). The samples of the sound input can then be analyzed and processed.” – therefore the signal is a sampled portion (i.e., only an “initial portion”, such as a first millisecond or even smaller sample) of an audio object from the computer game that the signal represents rather than a whole portion, [0039])
With respect to claim 10, Cheatham teaches a method, comprising;
sending microphone signals ([0016] “The sound input may come from a variety of sources. In some embodiments the sound input is provided by a media device…computers, televisions, video game systems…In some embodiments, the sound input is acquired from the ambient environment, for example from one or more microphones 104. Directional microphones may also be used to detect sounds emanating from particular locations” – therefore the sound input signal being processed may come from one or more microphones during presentation of a computer game (e.g., the sound signal is from a video/computer game the person is playing per [0036]-[0037] “Rule-based user control of audio rendering as described herein may be implemented in many…applications….video games…For example, when playing a first person shooter or other action type video game the user may be part of a team with each team member having different tasks. Accordingly, the user may want to focus on particular sounds to better accomplish his tasks…volume level of team members…volume level for opponents…the volume of specific background sounds 406 including the sound of an alarm, the sound of gun fire or the sound of air support approaching” & [0024] “target sound virtual reality or video game environment…”), [0031] “one or more traits of the sound input…traits may indicate a particular sound source (e.g., a sound received by a particular microphone”), - Examiner notes than an alternative embodiment taught by Cheatham is that sound input received directly from a video game system or computer may comprise team member voices (e.g., they are speaking while playing a cooperative/multiplayer game) and this/these voice signals are “from at least one microphone” (because the teammates voices are captured via microphones before being transmitted) and the sound input signal received from the video game system or computer directly is therefore “at least one signal from at least one microphone during presentation of a computer game)
to at least one model during presentation of at least one computer simulation; ([0028]-[0030] “Sound analysis module… receive a sound input, analyze the sound input for the target sound input(s)…cocktail party processing…processing approaches can be found in…Cocktail Party Processing via Structured Prediction…which are incorporated by reference herein. In some embodiments, sound analysis module 116 uses speech detection, speech recognition…Suitable techniques can be found in Smart Headphones: Enhancing Auditory Awareness Through Robust Speech Detection and Source Localization”…in In some embodiments, sound analysis module 116 makes use of specific tracks, inputs, metadata, or other identifying characteristics to analyze the sound input for the target sound input(s).” – therefore the signal is input to at least one model, [0025] “receive…one or more quantifiable properties of the target sound input (e.g., probability of presence of the target sound…” – the model may output a probability of presence of one or more target sounds)
receiving, in response to the sending, a predicted audio object; ([0028]-[0030] “Sound analysis module… receive a sound input, analyze the sound input for the target sound input(s)…cocktail party processing…processing approaches can be found in…Cocktail Party Processing via Structured Prediction…which are incorporated by reference herein. In some embodiments, sound analysis module 116 uses speech detection, speech recognition…Suitable techniques can be found in Smart Headphones: Enhancing Auditory Awareness Through Robust Speech Detection and Source Localization”…in In some embodiments, sound analysis module 116 makes use of specific tracks, inputs, metadata, or other identifying characteristics to analyze the sound input for the target sound input(s).” –there the at least one model analyzes the signal to identify/predict at least one target sound (i.e., “at least one predicted audio object”), [0023]-[0025] “receives a target sound input identifying one or more target sounds…may indicate a type of sound…a voice…a manmade sound…an alarm, a mechanical noise…voice of a specific person…one or more quantifiable properties of the target sound input (e.g., probability of presence of the target sound…” – the received indication of at least one target sound may be a predicted target sound (i.e., “at least one predicted audio object”), [0037] “when playing a first person shooter or other action type video game…particular sounds….specific background sounds 406 including the sound of an alarm, the sound of gun fire or the sound of air support approaching”)
enhancing the predicted audio object received from the model to render an enhanced audio object; and ([0035]-[0037] “establishing a sound processing rule…receiving a user input of a target sound…receiving a rule input (step 306), and receiving a sound processing input indicating the sound processing to be performed (step 308) to establish a sound processing rule (step 310) in which the target sound(s) are evaluated according to the rule and the sound processing will be performed in response to that evaluation…the target sound may be selected from a list of possible target sounds…the sound processing input may be selected by the user similar to the selection of the target sound….(e.g., increase volume, decrease volume…Rule-based user control of audio rendering…allows the user to modify the sound track for virtual applications according to the user's selected sound processing rules…when playing a first person shooter or other action type video game…the user may want to focus on particular sounds to better accomplish his tasks…the user can control the volume level of team members 402….specific background sounds 406 including the sound of an alarm, the sound of gun fire or the sound of air support approaching…Adjusting…the volume for each of these target sounds increases or decreases the volume of the target sound from its original volume” – therefore the system alters the detected target sound(s) (i.e., the predicted audio object received from the model) such as by adjusting the volume of these sounds from their original volume (i.e., enhacning the target sound to render an enhanced target sound), [0023]-[0026] “one or more sound processing rules that each use at least one target sound as an input… receives a target sound input identifying one or more target sounds and a rule input to define a sound processing rule…define the sound processing to be applied…increase volume, change apparent position…mute…sound processing applied by the sound processing rule may control various audio aspects of the sound….Audio aspects include volume, equalization spectrum, time delay, pitch, apparent source location, tone, frequency, etc. The sound processing may be applied to one or more sounds in the sound input (e.g., the target sound… The sound processing may make no change to the sound input when the results of the rule analysis indicate no sound processing is to be performed”)
playing, on at least one speaker, audio from the computer simulation using the enhanced audio object [0017] “The processed sound output may directly or indirectly drive one or more speakers 106”- playing on at least one speaker the enhances target sound, [0035]-[0037] “sound processing will be performed in response to that evaluation ….(e.g., increase volume, decrease volume…Rule-based user control of audio rendering…allows the user to modify the sound track for virtual applications according to the user's selected sound processing rules… the user can control the volume level of team members 402….specific background sounds 406 including the sound of an alarm, the sound of gun fire or the sound of air support approaching…Adjusting…the volume for each of these target sounds increases or decreases the volume of the target sound from its original volume” – therefore the system replaces the original target sound from the computer game that the signal represents (i.e., replaces the original target sound) with the altered target sound (i.e., a new version of the target sound having a different volume/amplitude, pitch, tone, frequency, EQ spectrum, apparent location, etc.) such that the user hears (i.e., at least one speaker plays) the altered target sound instead of the original target sound from the game that the signal represents, [0025]-[0026] “… receives a target sound input identifying one or more target sounds and a rule input to define a sound processing rule…define the sound processing to be applied…increase volume, change apparent position…mute…sound processing applied by the sound processing rule may control various audio aspects of the sound….Audio aspects include volume, equalization spectrum, time delay, pitch, apparent source location, tone, frequency, etc. The sound processing may be applied to one or more sounds in the sound input (e.g., the target sound…”)
Cheatham’s specification refers to, and incorporates by reference, multiple possible processing/analysis models that may be used to detect/predict target sounds from the input signal(s). For example, Cheatham refers to, and incorporates by reference, cocktail party processing approaches such as those disclosed in “Cocktail Party Processing via Structured Prediction” and/or other detection techniques such as those found in “Smart Headphones: Enhancing Auditory Awareness Through Robust Speech Detection and Source Localization”. Both of these two incorporated references/techniques use machine learning (ML) models to detect/predict audio objects (e.g., “Cocktail Party Processing via Structured Prediction” uses functions learned by deep neural networks, “Smart Headphones: Enhancing Auditory Awareness Through Robust Speech Detection and Source Localization” uses learning algorithms and also references neural network learning). However, these essential details are incorporated by reference. Cheatham does not explicitly disclose,
at least one machine learning (ML) model…received from the ML model
Mahlmeister discloses
at least one machine learning (ML) model…received from the ML model ([0162]-[0163] “the audio processing system 7600 may automatically characterize audio events occurring in the game audio stream, the chat audio stream, the microphone audio stream or some combination of these…machine learning techniques may be used to characterize or profile audio events in the audio stream to identify a predetermined audio event such as a footstep or a gunshot…may be provided to a neural network. The neural network may be trained using any suitable data such audio from other games or conversations. The training data may be tagged, for example, to identify predetermined audio events such as a gunshot or a footstep. Different characteristics may be further trained and identified, such as a footstep on wet pavement or a gunshot with ricochet. Once trained and provided with the live audio, the neural network or other processing module may produce an indication when the predetermined audio event has occurred. In some embodiments, the indication is a value corresponding to the probability that the predetermined audio event has occurred. If the probability exceeds a threshold, such as 75 percent or 95 percent, the audio processing system may conclude that the predetermined event has been detected…if a footstep is detected, the spectrum may be adjusted in the parametric equalizer to emphasize to the listener, the player, the sound of the footstep. Further, if the player is using a headset or other audio equipment that provides directionality or other surround sound effect, the audio may be automatically adjusted to emphasize the direction of origin of the predetermined event….Other sounds from that area may be suppressed to emphasize the footprint”, see also [0150]-[0153])
Mahlmeister suggests it is advantageous to include “at least one machine learning (ML) model…received from the ML model ”, because machine learning models such as neural networks provide efficient and effective analysis mechanisms for detecting one or more target sounds from input audio, can be trained to detect specific desired target sounds, and are capable of near real-time detection ([0153]-[0154], [0162], [0272]-0276] ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Cheatham to include “at least one machine learning (ML) model…received from the ML model”, as taught by Mahlmeister, because machine learning models such as neural networks provide efficient and effective analysis mechanisms for detecting one or more target sounds from input audio, can be trained to detect specific desired target sounds, and are capable of near real-time detection.
Furthermore, since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. That is in the substitution of the sound analysis model of Mahlmeister (i.e., at least one machine learning (ML) model) for the that of Cheatham. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
With respect to claim 11, Cheatham teaches the method of claim 10;
comprising: playing the audio from the computer simulation using the enhanced audio object in lieu of an audio object that caused the microphone to generate to generate the microphone signals ([0035]-[0037] “sound processing will be performed in response to that evaluation ….(e.g., increase volume, decrease volume…Rule-based user control of audio rendering…allows the user to modify the sound track for virtual applications according to the user's selected sound processing rules… the user can control the volume level of team members 402….specific background sounds 406 including the sound of an alarm, the sound of gun fire or the sound of air support approaching…Adjusting…the volume for each of these target sounds increases or decreases the volume of the target sound from its original volume” – therefore the system replaces the original target sound from the computer game that the signal represents (i.e., replaces the original target sound) with the altered target sound (i.e., a new version of the target sound having a different volume/amplitude, pitch, tone, frequency, EQ spectrum, apparent location, etc.) such that the user hears (i.e., at least one speaker plays) the altered target sound in leu of the original target sound from the game that the signal represents that caused the microphone to generate the microphone to generate the microphone signal, [0025]-[0026] “… receives a target sound input identifying one or more target sounds and a rule input to define a sound processing rule…define the sound processing to be applied…increase volume, change apparent position…mute…sound processing applied by the sound processing rule may control various audio aspects of the sound….Audio aspects include volume, equalization spectrum, time delay, pitch, apparent source location, tone, frequency, etc. The sound processing may be applied to one or more sounds in the sound input (e.g., the target sound…”[0017] “The processed sound output may directly or indirectly drive one or more speakers 106”- replaces such that at least one speaker plays the altered sound)
With respect to claim 12, Cheatham teaches the method of claim 10;
comprising: generating the enhanced audio object at least in part by increasing an amplitude relative to an amplitude of an audio object represented by the microphone signals ([0025]-[0026] “… receives a target sound input identifying one or more target sounds and a rule input to define a sound processing rule…define the sound processing to be applied…increase volume, change apparent position…mute…sound processing applied by the sound processing rule may control various audio aspects of the sound….Audio aspects include volume, equalization spectrum, time delay, pitch, apparent source location, tone, frequency, etc. The sound processing may be applied to one or more sounds in the sound input (e.g., the target sound…” – therefore the altered audio object has a greater amplitude than the audio object from the computer game that the signal represents)
Examiner notes Mahlmeister also discloses this limitation.
With respect to claim 13, Cheatham teaches the method of claim 10;
comprising: generating the enhanced audio object at least in part by changing a frequency relative to a frequency of an audio object represented by the microphone signals ([0025]-[0026] “… receives a target sound input identifying one or more target sounds and a rule input to define a sound processing rule…define the sound processing to be applied…increase volume, change apparent position…mute…sound processing applied by the sound processing rule may control various audio aspects of the sound….Audio aspects include volume, equalization spectrum, time delay, pitch, apparent source location, tone, frequency, etc. The sound processing may be applied to one or more sounds in the sound input (e.g., the target sound...” – therefore the altered audio object has a different frequency than the audio object from the computer game that the signal represents)
Examiner notes Mahlmeister also discloses this limitation.
With respect to claim 14, Cheatham teaches the method of claim 10;
comprising: generating the enhanced audio object at least in part by increasing clarity relative to clarity of an audio object represented by the microphone signals ([0025]-[0026] “… receives a target sound input identifying one or more target sounds and a rule input to define a sound processing rule…define the sound processing to be applied…increase volume, change apparent position…mute…In this way, the user would be better able to hear the voice of the designated speaker even when an alarm is sounding sound processing applied by the sound processing rule… may control various audio aspects of the sound….Audio aspects include volume, equalization spectrum, time delay, pitch, apparent source location, tone, frequency, etc. The sound processing may be applied to one or more sounds in the sound input (e.g., the target sound...” – therefore the altered audio object has greater acoustic clarity (i.e., the user can better hear it) than the audio object from the computer game that the signal represents)
Examiner notes Mahlmeister also discloses this limitation ([0199] “for the gaming audio stream, the user has selected a configuration titled “Fortnite footsteps” which may include data defining a set of audio settings for the parametric equalizer that improve the clarity of footsteps heard by the user in the game play. Adjusting the frequency, gain and Q factor of a set of filters may greatly improve the user's ability to discern footsteps during gameplay”)
With respect to claim 15, Cheatham teaches the method of claim 10;
comprising: presenting at least one user interface configured to receive input indicating a desired enhancement to implement on an audio object (Fig 5 shows a GUI where a user can input a desired audio object to enhance (e.g., gunfire, alarm, various team member voices, etc.) and a desired enhancement to implement on this object (e.g., increase/decrease volume), see also [0037]-[0038])
Examiner notes Mahlmeister also discloses this limitation.
With respect to claim 16, Cheatham teaches the method of claim 10;
comprising: presenting at least one user interface configured to receive input indicating a desired audio object to enhance (Fig 5 shows a GUI where a user can input a desired audio object to enhance (e.g., gunfire, alarm, various team member voices, etc.) and a desired enhancement to implement on this object (e.g., increase/decrease volume), see also [0037]-[0038])
Examiner notes Mahlmeister also discloses this limitation.
With respect to claim 17, Cheatham teaches a device comprising;
at least one computer storage that is not a transitory signal and that comprises instructions executable by at least one processor assembly for:
receiving audio from a computer game during game play; ([0016] “The sound input may come from a variety of sources. In some embodiments the sound input is provided by a media device…computers, televisions, video game systems…In some embodiments, the sound input is acquired from the ambient environment, for example from one or more microphones 104. Directional microphones may also be used to detect sounds emanating from particular locations” – therefore the sound input signal being processed may come from one or more microphones during presentation of a computer game (e.g., the sound signal is from a video/computer game the person is playing per [0036]-[0037] “Rule-based user control of audio rendering as described herein may be implemented in many…applications….video games…For example, when playing a first person shooter or other action type video game the user may be part of a team with each team member having different tasks. Accordingly, the user may want to focus on particular sounds to better accomplish his tasks…volume level of team members…volume level for opponents…the volume of specific background sounds 406 including the sound of an alarm, the sound of gun fire or the sound of air support approaching” & [0024] “target sound virtual reality or video game environment…”), [0031] “one or more traits of the sound input…traits may indicate a particular sound source (e.g., a sound received by a particular microphone”), - Examiner notes than an alternative embodiment taught by Cheatham is that sound input received directly from a video game system or computer may comprise team member voices (e.g., they are speaking while playing a cooperative/multiplayer game) and this/these voice signals are “from at least one microphone” (because the teammates voices are captured via microphones before being transmitted) and the sound input signal received from the video game system or computer directly is therefore “at least one signal from at least one microphone during presentation of a computer game)
sending the audio to at least one model; ([0028]-[0030] “Sound analysis module… receive a sound input, analyze the sound input for the target sound input(s)…cocktail party processing…processing approaches can be found in…Cocktail Party Processing via Structured Prediction…which are incorporated by reference herein. In some embodiments, sound analysis module 116 uses speech detection, speech recognition…Suitable techniques can be found in Smart Headphones: Enhancing Auditory Awareness Through Robust Speech Detection and Source Localization”…in In some embodiments, sound analysis module 116 makes use of specific tracks, inputs, metadata, or other identifying characteristics to analyze the sound input for the target sound input(s).” – therefore the signal is input to at least one model, [0025] “receive…one or more quantifiable properties of the target sound input (e.g., probability of presence of the target sound…” – the model may output a probability of presence of one or more target sounds)
using output of the model ([0028]-[0030] “Sound analysis module… receive a sound input, analyze the sound input for the target sound input(s)…cocktail party processing…processing approaches can be found in…Cocktail Party Processing via Structured Prediction…which are incorporated by reference herein. In some embodiments, sound analysis module 116 uses speech detection, speech recognition…Suitable techniques can be found in Smart Headphones: Enhancing Auditory Awareness Through Robust Speech Detection and Source Localization”…in In some embodiments, sound analysis module 116 makes use of specific tracks, inputs, metadata, or other identifying characteristics to analyze the sound input for the target sound input(s).” –there the at least one model analyzes the signal to identify/predict at least one target sound (i.e., “at least one predicted audio object”), [0023]-[0025] “receives a target sound input identifying one or more target sounds…may indicate a type of sound…a voice…a manmade sound…an alarm, a mechanical noise…voice of a specific person…one or more quantifiable properties of the target sound input (e.g., probability of presence of the target sound…” – the received indication of at least one target sound may be a predicted target sound (i.e., “at least one predicted audio object”), [0037] “when playing a first person shooter or other action type video game…particular sounds….specific background sounds 406 including the sound of an alarm, the sound of gun fire or the sound of air support approaching”)
to render an altered audio object; and ([0035]-[0037] “establishing a sound processing rule…receiving a user input of a target sound…receiving a rule input (step 306), and receiving a sound processing input indicating the sound processing to be performed (step 308) to establish a sound processing rule (step 310) in which the target sound(s) are evaluated according to the rule and the sound processing will be performed in response to that evaluation…the target sound may be selected from a list of possible target sounds…the sound processing input may be selected by the user similar to the selection of the target sound….(e.g., increase volume, decrease volume…Rule-based user control of audio rendering…allows the user to modify the sound track for virtual applications according to the user's selected sound processing rules…when playing a first person shooter or other action type video game…the user may want to focus on particular sounds to better accomplish his tasks…the user can control the volume level of team members 402….specific background sounds 406 including the sound of an alarm, the sound of gun fire or the sound of air support approaching…Adjusting…the volume for each of these target sounds increases or decreases the volume of the target sound from its original volume” – therefore the system alters the detected target sound(s) (i.e., the predicted audio object received from the model) such as by adjusting the volume of these sounds from their original volume (i.e., altering the target sound to render an altered target sound), [0023]-[0026] “one or more sound processing rules that each use at least one target sound as an input… receives a target sound input identifying one or more target sounds and a rule input to define a sound processing rule…define the sound processing to be applied…increase volume, change apparent position…mute…sound processing applied by the sound processing rule may control various audio aspects of the sound….Audio aspects include volume, equalization spectrum, time delay, pitch, apparent source location, tone, frequency, etc. The sound processing may be applied to one or more sounds in the sound input (e.g., the target sound… The sound processing may make no change to the sound input when the results of the rule analysis indicate no sound processing is to be performed”)
playing the altered audio object during game play instead of an audio object in the audio from the computer game ([0035]-[0037] “sound processing will be performed in response to that evaluation ….(e.g., increase volume, decrease volume…Rule-based user control of audio rendering…allows the user to modify the sound track for virtual applications according to the user's selected sound processing rules… the user can control the volume level of team members 402….specific background sounds 406 including the sound of an alarm, the sound of gun fire or the sound of air support approaching…Adjusting…the volume for each of these target sounds increases or decreases the volume of the target sound from its original volume” – therefore the system replaces the original target sound from the computer game that the signal represents (i.e., replaces the original target sound) with the altered target sound (i.e., a new version of the target sound having a different volume/amplitude, pitch, tone, frequency, EQ spectrum, apparent location, etc.) such that the user hears (i.e., at least one speaker plays) the altered target sound instead of the original target sound from the game that the signal represents, [0025]-[0026] “… receives a target sound input identifying one or more target sounds and a rule input to define a sound processing rule…define the sound processing to be applied…increase volume, change apparent position…mute…sound processing applied by the sound processing rule may control various audio aspects of the sound….Audio aspects include volume, equalization spectrum, time delay, pitch, apparent source location, tone, frequency, etc. The sound processing may be applied to one or more sounds in the sound input (e.g., the target sound…”[0017] “The processed sound output may directly or indirectly drive one or more speakers 106”- replaces such that at least one speaker plays the altered sound)
Cheatham’s specification refers to, and incorporates by reference, multiple possible processing/analysis models that may be used to detect/predict target sounds from the input signal(s). For example, Cheatham refers to, and incorporates by reference, cocktail party processing approaches such as those disclosed in “Cocktail Party Processing via Structured Prediction” and/or other detection techniques such as those found in “Smart Headphones: Enhancing Auditory Awareness Through Robust Speech Detection and Source Localization”. Both of these two incorporated references/techniques use machine learning (ML) models to detect/predict audio objects (e.g., “Cocktail Party Processing via Structured Prediction” uses functions learned by deep neural networks, “Smart Headphones: Enhancing Auditory Awareness Through Robust Speech Detection and Source Localization” uses learning algorithms and also references neural network learning). However, these essential details are incorporated by reference. Cheatham does not explicitly disclose,
at least one machine learning (ML) model… using output of the ML model
Mahlmeister discloses
at least one machine learning (ML) model… using output of the ML model ([0162]-[0163] “the audio processing system 7600 may automatically characterize audio events occurring in the game audio stream, the chat audio stream, the microphone audio stream or some combination of these…machine learning techniques may be used to characterize or profile audio events in the audio stream to identify a predetermined audio event such as a footstep or a gunshot…may be provided to a neural network. The neural network may be trained using any suitable data such audio from other games or conversations. The training data may be tagged, for example, to identify predetermined audio events such as a gunshot or a footstep. Different characteristics may be further trained and identified, such as a footstep on wet pavement or a gunshot with ricochet. Once trained and provided with the live audio, the neural network or other processing module may produce an indication when the predetermined audio event has occurred. In some embodiments, the indication is a value corresponding to the probability that the predetermined audio event has occurred. If the probability exceeds a threshold, such as 75 percent or 95 percent, the audio processing system may conclude that the predetermined event has been detected…if a footstep is detected, the spectrum may be adjusted in the parametric equalizer to emphasize to the listener, the player, the sound of the footstep. Further, if the player is using a headset or other audio equipment that provides directionality or other surround sound effect, the audio may be automatically adjusted to emphasize the direction of origin of the predetermined event….Other sounds from that area may be suppressed to emphasize the footprint”, see also [0150]-[0153])
Mahlmeister suggests it is advantageous to include “at least one machine learning (ML) model… using output of the ML model”, because machine learning models such as neural networks provide efficient and effective analysis mechanisms for detecting one or more target sounds from input audio, can be trained to detect specific desired target sounds, and are capable of near real-time detection ([0153]-[0154], [0162], [0272]-0276] ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device of Cheatham to include “at least one machine learning (ML) model… using output of the ML model”, as taught by Mahlmeister, because machine learning models such as neural networks provide efficient and effective analysis mechanisms for detecting one or more target sounds from input audio, can be trained to detect specific desired target sounds, and are capable of near real-time detection.
Furthermore, since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. That is in the substitution of the sound analysis model of Mahlmeister (i.e., at least one machine learning (ML) model) for the that of Cheatham. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
With respect to claim 18, Cheatham teaches the device of claim 17;
wherein the instructions are executable for: presenting at least one user interface configured to receive input indicating a desired enhancement to implement on an audio object; and generating the altered audio object based at least in part on the input (Fig 5 shows a GUI where a user can input a desired audio object to enhance (e.g., gunfire, alarm, various team member voices, etc.) and a desired enhancement to implement on this object (e.g., increase/decrease volume), see also [0035]-[0038] & [0025]-[0027] generate based on the input)
Examiner notes Mahlmeister also discloses this limitation.
With respect to claim 19, Cheatham teaches the device of claim 17;
wherein the instructions are executable for: presenting at least one user interface configured to receive input indicating a desired audio object to enhance; and rendering the altered audio object based at least in part on the input (Fig 5 shows a GUI where a user can input a desired audio object to enhance (e.g., gunfire, alarm, various team member voices, etc.) and a desired enhancement to implement on this object (e.g., increase/decrease volume), see also [0035]-[0038] & [0025]-[0027] render based on the input)
Examiner notes Mahlmeister also discloses this limitation.
With respect to claim 20, Cheatham teaches the device of claim 17;
wherein the instructions are executable for: rendering the altered audio object using audio from the computer game representing only an initial portion of an audio object ([0015] “The sound input may be…audio information that is sampled by the sound processing control 102 at an appropriate sampling rate (e.g., 1 kHz or more). The samples of the sound input can then be analyzed and processed.” – therefore the signal is a sampled portion (i.e., only an “initial portion”, such as a first millisecond or even smaller sample) of an audio object from the computer game that the signal represents rather than a whole portion and the system renders the altered audio object using this, [0039])
Prior Art of Record
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
Peeler et al. (U.S. PG Pub No. 2023/0015199, January 19, 2023) teaches a system that detects specific sounds from computer game/simulation audio and alters these specific sounds as desired by a player.
Lim (U.S. PG Pub No. 2014/0194205 July 10, 2024) teaches a system that detects specific sounds from computer game/simulation audio and alters these specific sounds as desired by a player.
Freund et al. (U.S. PG Pub No. 2013/0150162, June 13, 2013) teaches a system that detects specific sounds from computer game/simulation audio and alters these specific sounds as desired by a player.
Wolff-Petersen et al. (U.S. PG Pub No. 2011/0065507, March 17, 2011) teaches a system that detects specific sounds from computer game/simulation audio and alters these specific sounds as desired by a player.
Conclusion
No claim is allowed
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES M DETWEILER whose telephone number is (571)272-4704. The examiner can normally be reached on Monday-Friday from 8 AM to 5 PM ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Waseem Ashraf can be reached at telephone number (571)-270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAMES M DETWEILER/Primary Examiner, Art Unit 3621