METHOD FOR USING AI TO CUSTOMIZE IN GAME AUDIO

Application No. 18/623,930

• Filed: Apr 01, 2024
• Examiner: DETWEILER, JAMES M
• Art Unit: 3621
• Tech Center: 3600 — Transportation & Electronic Commerce
• Assignee: Sony Group Corporation
• OA Round: 2 (Non-Final)

Office Action

DETAILED ACTION

Status of the Application
In response filed on March 13, 2026, the Applicant amended claims 1-8, 10, and 12-19; added claims 21-23; and cancelled claims 9, 11, and 20. Claims 1-8, 10, 12-19, and 21-23 are pending and currently under consideration for patentability.

Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .

Response to Amendments and Arguments
v Applicant has amended the claims to correct informalities identified in the previous action. These objections have been withdrawn accordingly. Examiner notes that the amended claim language has introduced new claim objections (please see blow in the “Claim Objections” section of this action).

v With respect to the rejection of claims 4 and 14 under 35 U.S.C. §112 (b), Applicant has appropriately amended the claims. The claims have been amended such that they no longer recite that which was identified as being indefinite. These rejections of claims 4 and 14 under 35 U.S.C. §112 (b) have been withdrawn.

v Applicant’s arguments, with respect to the rejection of amended claim 1 under 35 U.S.C. §103 have been considered, but are not persuasive. Applicant argues “The relied on portions of Cheatham (U.S. PG Pub No. 2017/0372697) and Mahlmeister (U.S. PG Pub No. 2023/0364508) do not teach or suggest at least these amended portions. For example, the Office notes that "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)" (Action, page 6). But such audio adjustment prior to playing on a speaker of a receiving party does not teach or suggest at least the amended claim features”. Examiner respectfully disagrees. Applicant’s argument refers to an alternative embodiment of Cheatham that was indicated as reading on an alternative interpretation of the previously-recited claim language. However, Applicant has ignored the other embodiments of Cheatham cited by the Examiner (both before, and herein as necessitated by the amendment). Cheatham does in fact disclose "receiv[ing] a signal from a microphone representing a first part of a video game sound played by a speaker controlled by a computer game" and "after the playing of the first part of the video game sound generated by the speaker controlled by the computer game, controlling the speaker to play the altered audio object instead of a second part of the video game sound configured by the computer game to play after the first part of the video game sound." For example, Cheatham discloses that the sound emanating from the speaker may be video game sounds controlled by a computer game ([0036]-[0037] “audio rendering…may be implemented in many virtual and real world applications…video games…when playing a first person shooter or other action type video game…user may want to focus on particular sounds…team members…opponents…background sounds…sounds of gun fire…”, [0031] “media being played by the media device could include…video game setting…different team members, different types of sounds… in a video game setting, a user on an espionage mission may need to eavesdrop on various conversations to identify a particular plan. The analysis module 116 could identify words spoken by a specific speaker or group of speakers ( e.g., the enemy boss and the enemies, in general), identify specific keywords”). Cheatham suggests that the speaker playing this sound could be a speaker in a television or other media device ([0016]-[0017] “sound input may come from a variety of sources…televisions…and other devices used to play audio media or audio-visual media…acquired from the ambient environment…from one or more microphones…Speakers may be…components of a larger device (e.g., televisions…”) –the sound input may be captured using at least one microphone from the ambient environment, and television speakers may output sounds associated with the audio-visual media they play (e.g., video games per [0031] & [0036]-[0037]) to the ambient environment, and therefore the speaker playing the sound that is captured by the microphone may be part of a television or other media device). Cheatham discloses a microphone capturing the sound played by the speaker and generating a sound input that is received from the microphone (([0016]-[0017] “sound input…acquired from the ambient environment…from one or more microphones…”, [0034] “Receiving the sound input (step 204) may be performed by sound processing controller 102 as described herein. For example, sound processing controller 102 may receive the sound input from one or more microphones 104”). The received sound input from the microphone represents a first part of a video game sound played by the speaker as any data in the received sound input may be considered a “first part of a video game sound played by the speaker” (i.e., the part of the video game sound that has been played and received by the mic so far). As such, Cheatham suggests "receiving a signal from a microphone representing a first part of a video game sound played by a speaker controlled by a computer game”. Cheatham further discloses inputting the sound input to a model to identify predicted one or more target sounds within the sound input([0028]-[0030] “Sound analysis module… receive a sound input, analyze the sound input…sound analysis module 116 makes use of…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). The target sounds in the video game sounds may be certain team members or background sounds (e.g., gun fire) ([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…these target sounds”). Cheatham then discloses altering the identified target sound (the predicted audio object received from the model) to alter that specific target sound (i.e., render an altered audio object – e.g., increasing the volume) (([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”). Cheatham then discloses (necessarily after the speaker(s) played the first part of the video game sound, because the first part of the sound was used to identify the target sound so as to generate altered target sound) playing the altered target sound instead of a second part of the video game sound configured by the computer game to play after the first part of the video game sound (e.g., louder version of the gun fire noise, or “altered audio object”, is played instead of the original quieter gun fire noise in a time subsequent to the initial capture/analysis/altering of the first part of the noise) ([0035]-[0037] “sound processing will be performed in response to that evaluation ….(e.g., increase volume, decrease volume…real-time sounds…are manipulated according to the 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…”, [0033] “The amount of time used by the sound processing controller 102 to carry out the processing (i.e., analyzing the sound input, processing the sound input according to the appropriate rule(s), and providing a processed sound output) can vary in different embodiments. In a first embodiment, the sound processing controller 102 carries out the processing substantially in real time with a negligible delay between receiving the sound input and providing the processed sound output”).
Finally, Cheatham suggests that the processing controller can control the speaker of the television to play this altered audio object ([0017] “The processed sound output may directly or indirectly drive one or more speakers 106. Speakers 106 may be…components of…televisions”). Therefore, the system may control the speaker controlled by the computer game (the television speaker on which the video game is being played) to play the video game soundtrack with the altered audio object. Therefore, Cheatham discloses/suggests "after the playing of the first part of the video game sound generated by the speaker controlled by the computer game, controlling the speaker to play the altered audio object instead of a second part of the video game sound configured by the computer game to play after the first part of the video game sound."

Claim Objections
v Claim 1 is objected to because of the following informalities: --the-- should be inserted preceding “ML model” in the phrase “altering the predicted audio object received from ML model” to ensure the claim language conforms with standard grammatical construction. Appropriate correction is required.

v Claim 1 is objected to because of the following informalities: --played-- should be inserted to replace “generated” in the phrase “after the playing of the first part of the video game sound generated by the speaker controlled by the computer game” to maintain consistency of terminology throughout the claims. Appropriate correction is required.

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 1-8, 10, and 12-19 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 perform operations comprising: (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”
receiving a signal from a microphone representing a first part of a video game sound played by a speaker controlled by a computer game; (Cheatham discloses that the sound emanating from the speaker may be video game sounds controlled by a computer game ([0036]-[0037] “audio rendering…may be implemented in many virtual and real world applications…video games…when playing a first person shooter or other action type video game…user may want to focus on particular sounds…team members…opponents…background sounds…sounds of gun fire…”, [0031] “media being played by the media device could include…video game setting…different team members, different types of sounds… in a video game setting, a user on an espionage mission may need to eavesdrop on various conversations to identify a particular plan. The analysis module 116 could identify words spoken by a specific speaker or group of speakers ( e.g., the enemy boss and the enemies, in general), identify specific keywords”). Cheatham suggests that the speaker playing this sound could be a speaker in a television or other media device ([0016]-[0017] “sound input may come from a variety of sources…televisions…and other devices used to play audio media or audio-visual media…acquired from the ambient environment…from one or more microphones…Speakers may be…components of a larger device (e.g., televisions…”) –the sound input may be captured using at least one microphone from the ambient environment, and television speakers may output sounds associated with the audio-visual media they play (e.g., video games per [0031] & [0036]-[0037]) to the ambient environment, and therefore the speaker playing the sound that is captured by the microphone may be part of a television or other media device). Cheatham discloses a microphone capturing the sound played by the speaker and generating a sound input that is received from the microphone (([0016]-[0017] “sound input…acquired from the ambient environment…from one or more microphones…”, [0034] “Receiving the sound input (step 204) may be performed by sound processing controller 102 as described herein. For example, sound processing controller 102 may receive the sound input from one or more microphones 104”). The received sound input from the microphone represents a first part of a video game sound played by the speaker as any data in the received sound input may be considered a “first part of a video game sound played by the speaker” (i.e., the part of the video game sound that has been played and received by the mic so far). See also ([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]). As such, Cheatham suggests "receiving a signal from a microphone representing a first part of a video game sound played by a speaker controlled by a computer game”.)
inputting the signal to a 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)
receiving from the model a predicted audio object; ([0028]-[0030] “Sound analysis module… receive a sound input, analyze the sound input for the target sound input(s)… sound analysis module 116 makes use of…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” -the target sounds in the video game sounds may be certain team members or background sounds (e.g., gun fire))
altering 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”)
after the playing of the first part of the video game sound generated by the speaker controlled by the computer game, controlling the speaker to play the altered audio object instead of a second part of the video game sound configured by the computer game to play after the first part of the video game sound (Cheatham discloses (necessarily after the speaker(s) played the first part of the video game sound, because the first part of the sound was used to identify the target sound so as to generate altered target sound) playing the altered target sound instead of a second part of the video game sound configured by the computer game to play after the first part of the video game sound (e.g., louder version of the gun fire noise, or “altered audio object”, is played instead of the original quieter gun fire noise in a time subsequent to the initial capture/analysis/altering of the first part of the noise) ([0035]-[0037] “sound processing will be performed in response to that evaluation ….(e.g., increase volume, decrease volume…real-time sounds…are manipulated according to the 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…”, [0033] “The amount of time used by the sound processing controller 102 to carry out the processing (i.e., analyzing the sound input, processing the sound input according to the appropriate rule(s), and providing a processed sound output) can vary in different embodiments. In a first embodiment, the sound processing controller 102 carries out the processing substantially in real time with a negligible delay between receiving the sound input and providing the processed sound output”). Finally, Cheatham suggests that the processing controller can control the speaker of the television to play this altered audio object ([0017] “The processed sound output may directly or indirectly drive one or more speakers 106. Speakers 106 may be…components of…televisions”). Therefore, the system may control the speaker controlled by the computer game (the television speaker on which the video game is being played) to play the video game soundtrack with the altered audio object. Therefore, Cheatham discloses/suggests "after the playing of the first part of the video game sound generated by the speaker controlled by the computer game, controlling the speaker to play the altered audio object instead of a second part of the video game sound configured by the computer game to play after the first part of the video game 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 an 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 an 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 and Mahlmeister teach the apparatus of claim 1. Cheatham does not explicitly disclose,
wherein the altering comprises smoothing a waveform of the predicted audio object and including the smoothed waveform in the altered audio object
However, Mahlmeister discloses
wherein the altering comprises smoothing a waveform of the predicted audio object and including the smoothed waveform in the altered audio object ([0244] “For a detected sound of a footstep, then, the predetermined audio profile may include filter settings to enhance the gain or volume of frequencies in which a substantial part of the energy of the footstep spectrum exists. Other frequencies may be suppressed by filtering or other suitable method. Also, in the example, the predetermined audio profile for a footstep sound may include filtering in a lowpass filter with a particular roll off frequency and a particular Q value or quality factor.” – low pass filters smooth waveforms of the predicted object by reducing high-frequency components of the noise, [0191] “curve smoothing…smooth frequency curve”, [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”)
Mahlmeister suggests it is advantageous to include wherein the altering comprises smoothing a waveform of the predicted audio object and including the smoothed waveform in the altered audio object, because doing so can increase the sound quality/clarity of the predicted audio object which may increase user satisfaction ([0244] & [0199] & [0191] ).
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 altering comprises smoothing a waveform of the predicted audio object and including the smoothed waveform in the altered audio object, as taught by Mahlmeister, because doing so can increase the sound quality/clarity of the predicted audio object which may increase user satisfaction.

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 10, Cheatham teaches a method, comprising;
after a speaker plays a first part of sound of a computer simulation, sending microphone signals ([0036]-[0037] “audio rendering…may be implemented in many virtual and real world applications…video games…when playing a first person shooter or other action type video game…user may want to focus on particular sounds…team members…opponents…background sounds…sounds of gun fire…”, [0031] “media being played by the media device could include…video game setting…different team members, different types of sounds… in a video game setting, a user on an espionage mission may need to eavesdrop on various conversations to identify a particular plan. The analysis module 116 could identify words spoken by a specific speaker or group of speakers ( e.g., the enemy boss and the enemies, in general), identify specific keywords”). Cheatham suggests that the speaker playing this sound could be a speaker in a television or other media device ([0016]-[0017] “sound input may come from a variety of sources…televisions…and other devices used to play audio media or audio-visual media…acquired from the ambient environment…from one or more microphones…Speakers may be…components of a larger device (e.g., televisions…”) –the sound input may be captured using at least one microphone from the ambient environment, and television speakers may output sounds associated with the audio-visual media they play (e.g., video games per [0031] & [0036]-[0037]) to the ambient environment, and therefore the speaker playing the sound that is captured by the microphone may be part of a television or other media device). Cheatham discloses a microphone capturing the sound played by the speaker and generating a sound input that is received from the microphone (([0016]-[0017] “sound input…acquired from the ambient environment…from one or more microphones…”, [0034] “Receiving the sound input (step 204) may be performed by sound processing controller 102 as described herein. For example, sound processing controller 102 may receive the sound input from one or more microphones 104”). The received sound input from the microphone represents a first part of a video game sound played by the speaker as any data in the received sound input may be considered a “first part of a video game sound played by the speaker” (i.e., the part of the video game sound that has been played and received by the mic so far). See also ([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]). As such, Cheatham suggests " after a speaker plays a first part of sound of a computer simulation, sending microphone signals”.)
to a 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 the speaker, the enhanced audio object instead of a second part of the sound of the computer simulation configured by the computer simulation to play after the first part of the sound (Cheatham discloses (necessarily after the speaker(s) played the first part of the video game sound, because the first part of the sound was used to identify the target sound so as to generate altered target sound) playing the altered target sound instead of a second part of the video game sound configured by the computer game to play after the first part of the video game sound (e.g., louder version of the gun fire noise, or “altered audio object”, is played instead of the original quieter gun fire noise in a time subsequent to the initial capture/analysis/altering of the first part of the noise) ([0035]-[0037] “sound processing will be performed in response to that evaluation ….(e.g., increase volume, decrease volume…real-time sounds…are manipulated according to the 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…”, [0033] “The amount of time used by the sound processing controller 102 to carry out the processing (i.e., analyzing the sound input, processing the sound input according to the appropriate rule(s), and providing a processed sound output) can vary in different embodiments. In a first embodiment, the sound processing controller 102 carries out the processing substantially in real time with a negligible delay between receiving the sound input and providing the processed sound output”). Finally, Cheatham suggests that the processing controller can control the speaker of the television to play this altered audio object ([0017] “The processed sound output may directly or indirectly drive one or more speakers 106. Speakers 106 may be…components of…televisions”). Therefore, the system may control the speaker controlled by the computer game (the television speaker on which the video game is being played) to play the video game soundtrack with the altered audio object. Therefore, Cheatham discloses/suggests " playing, on the speaker, the enhanced audio object instead of a second part of the sound of the computer simulation configured by the computer simulation to play after the first part of the 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 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 and Mahlmeister teach the method of claim 10. Cheatham does not explicitly disclose,
wherein the altering comprises smoothing a waveform of the predicted audio object and including the smoothed waveform in the altered audio object
However, Mahlmeister discloses
wherein the altering comprises smoothing a waveform of the predicted audio object and including the smoothed waveform in the altered audio object ([0244] “For a detected sound of a footstep, then, the predetermined audio profile may include filter settings to enhance the gain or volume of frequencies in which a substantial part of the energy of the footstep spectrum exists. Other frequencies may be suppressed by filtering or other suitable method. Also, in the example, the predetermined audio profile for a footstep sound may include filtering in a lowpass filter with a particular roll off frequency and a particular Q value or quality factor.” – low pass filters smooth waveforms of the predicted object by reducing high-frequency components of the noise, [0191] “curve smoothing…smooth frequency curve”, [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”)
Mahlmeister suggests it is advantageous to include wherein the altering comprises smoothing a waveform of the predicted audio object and including the smoothed waveform in the altered audio object, because doing so can increase the sound quality/clarity of the predicted audio object which may increase user satisfaction ([0244] & [0199] & [0191] ).
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 altering comprises smoothing a waveform of the predicted audio object and including the smoothed waveform in the altered audio object, as taught by Mahlmeister, because doing so can increase the sound quality/clarity of the predicted audio object which may increase user satisfaction.

With respect to claim 15, Cheatham teaches the method of claim 10;
comprising: presenting a 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 a 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 to cause the at least one processor assembly to perform operations comprising:
receiving a first part of a sound played on a speaker from a computer game during game play; ([0036]-[0037] “audio rendering…may be implemented in many virtual and real world applications…video games…when playing a first person shooter or other action type video game…user may want to focus on particular sounds…team members…opponents…background sounds…sounds of gun fire…”, [0031] “media being played by the media device could include…video game setting…different team members, different types of sounds… in a video game setting, a user on an espionage mission may need to eavesdrop on various conversations to identify a particular plan. The analysis module 116 could identify words spoken by a specific speaker or group of speakers ( e.g., the enemy boss and the enemies, in general), identify specific keywords”). Cheatham suggests that the speaker playing this sound could be a speaker in a television or other media device ([0016]-[0017] “sound input may come from a variety of sources…televisions…and other devices used to play audio media or audio-visual media…acquired from the ambient environment…from one or more microphones…Speakers may be…components of a larger device (e.g., televisions…”) –the sound input may be captured using at least one microphone from the ambient environment, and television speakers may output sounds associated with the audio-visual media they play (e.g., video games per [0031] & [0036]-[0037]) to the ambient environment, and therefore the speaker playing the sound that is captured by the microphone may be part of a television or other media device). Cheatham discloses a microphone capturing the sound played by the speaker and generating a sound input that is received from the microphone (([0016]-[0017] “sound input…acquired from the ambient environment…from one or more microphones…”, [0034] “Receiving the sound input (step 204) may be performed by sound processing controller 102 as described herein. For example, sound processing controller 102 may receive the sound input from one or more microphones 104”). The received sound input from the microphone represents a first part of a video game sound played by the speaker as any data in the received sound input may be considered a “first part of a video game sound played by the speaker” (i.e., the part of the video game sound that has been played and received by the mic so far). See also ([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]). As such, Cheatham suggests "receiving a first part of a sound played on a speaker from a computer game during game play”.)
sending the first part of the sound to a 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 a second part of the sound configured by the computer game to play after the first part of the sound (Cheatham discloses (necessarily after the speaker(s) played the first part of the video game sound, because the first part of the sound was used to identify the target sound so as to generate altered target sound) playing the altered target sound instead of a second part of the video game sound configured by the computer game to play after the first part of the video game sound (e.g., louder version of the gun fire noise, or “altered audio object”, is played instead of the original quieter gun fire noise in a time subsequent to the initial capture/analysis/altering of the first part of the noise) ([0035]-[0037] “sound processing will be performed in response to that evaluation ….(e.g., increase volume, decrease volume…real-time sounds…are manipulated according to the 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…”, [0033] “The amount of time used by the sound processing controller 102 to carry out the processing (i.e., analyzing the sound input, processing the sound input according to the appropriate rule(s), and providing a processed sound output) can vary in different embodiments. In a first embodiment, the sound processing controller 102 carries out the processing substantially in real time with a negligible delay between receiving the sound input and providing the processed sound output”). Finally, Cheatham suggests that the processing controller can control the speaker of the television to play this altered audio object ([0017] “The processed sound output may directly or indirectly drive one or more speakers 106. Speakers 106 may be…components of…televisions”). Therefore, the system may control the speaker controlled by the computer game (the television speaker on which the video game is being played) to play the video game soundtrack with the altered audio object. Therefore, Cheatham discloses/suggests " playing the altered audio object during game play instead a second part of the sound configured by the computer game to play after the first part of the 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 operations comprise: presenting a 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 operations comprise: presenting a 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.

v Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Cheatham III. in view of Mahlmeister, as applied to claim 17 above, and further in view of Amin (U.S. PG Pub No. 2012/0087516 , April 12, 2012- hereinafter "Amin”)

With respect to claim 23, Cheatham teach the device of claim 10;
comprising the speaker ([0016]-[0017] “sound input may come from a variety of sources…televisions…and other devices used to play audio media or audio-visual media…Speakers may be…components of a larger device (e.g., televisions…”) –the speaker playing the sound that is captured by the microphone may be part of a television or other media device).
Although Cheatham discloses a microphone configured to detect the first part of the sound played on the speaker ([0016]-[0017] “sound input may come from a variety of sources…televisions…and other devices used to play audio media or audio-visual media…acquired from the ambient environment…from one or more microphones…Speakers may be…components of a larger device (e.g., televisions…smartphones…”) –the sound input may be captured using at least one microphone from the ambient environment), the location of this microphone is unspecified. It is not explicit that the microphone is part of the device (e.g., television, smart phone). Although Examiner could take official notice that smart phones conventionally have microphones that capture ambient sound, art will be relied on to teach this limitation. Cheatham does not appear to disclose,
the device comprising a microphone configured to detect
However, Amin discloses
the device comprising a microphone configured to detect ([0025]-[0026] “at least one of a microphone of the media systems, a mobile phone microphone, MP-3 player microphone, MP-4 player microphone, a TV Microphone, a wireless remote microphone, an I-Pad, a tablet PC, a car media system sensors, a game console sensor, a home theatre sensor, a set-top box sensor, other noise measuring devices or any combination thereof may be adapted to measure the instantaneous ambient noise…frequencies, tones, sounds, waves,” – therefore the television and/or smartphone (i.e., the device that has the speaker and processor) comprises a microphone configured to detect the first part of the sound played on the speaker – Examiner notes that microphones are generally “configured to detect” sound (e.g., the first part of the sound), and the claim language does not even require that the first part of sound played on the speaker be captured by the mic and received from the mic)
Therefore, it was old and well known before the effective filing date of the claimed invention for televisions and smartphones to have microphones, and microphones are configured to detect ambient sounds (e.g., sounds played on the speaker of the device, including the first part of the sound).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the device comprising a microphone, as taught by Amin, in the device of Cheatham in view of Mahlmeister, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. One of ordinary skill in the art would have recognized that doing so would provide an element configured to capture ambient sound on the device, which is advantageous for numerous applications and increases the devices functionality and user satisfaction with the device.
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 microphone of Amin (a mic in the device, e.g., the tv or smartphone – as these devices usually have microphones) for the unspecified microphone of Cheatham in view of Mahlmeister. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Furthermore, it would have been obvious to try, by one of ordinary skill in the art at the time of the invention, to include the device comprising a microphone configured to detect and to incorporate it into the device of Cheatham in view of Mahlmeister, since there are a finite number of identified, predictable potential solutions (i.e., potential microphones to use) to the recognized need (use a microphone) and one of ordinary skill in the art would have pursued the known potential solutions with a reasonable expectation of success (the costs and benefits of each mic were known).

Indication of Allowable Subject Matter
Dependent claims 21 and 22 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.

The following is an examiner’s statement of reasons for indication of potentially allowable subject matter:

The closest prior art of record is Cheatham III. et al. (U.S. PG Pub No. 2017/0372697, December 28, 2017 - hereinafter "Cheatham”); Mahlmeister et al. (U.S. PG Pub No. 2023/0364508, November 16, 2016 - hereinafter "Mahlmeister”); Peeler et al. (U.S. PG Pub No. 2023/0015199, January 19, 2023); Lim (U.S. PG Pub No. 2014/0194205 July 10, 2024); Freund et al. (U.S. PG Pub No. 2013/0150162, June 13, 2013); Wolff-Petersen et al. (U.S. PG Pub No. 2011/0065507, March 17, 2011); Bonanno (U.S. PG Pub No. 2022/0086557 March 17, 2022); Peng (U.S. PG Pub No. 2014/0031122 January 30, 2014); Meneses et al. (U.S. PG Pub No. 2014/0073429 March 13, 2014); Robertson et al. (U.S. PG Pub No. 2017/0006400 January 5, 2017); Campbell et al. (U.S. PG Pub No. 2018/0324516 November 8, 2018); Pedersen et al. (U.S. PG Pub No. 2024/0205587 June 20, 2024); Zhang et al. (U.S. PG Pub No. 2021/0183353 June 17, 2021); Kim et al. (U.S. PG Pub No. 2017/0339491 November 23, 2017); Seo et al. (U.S. PG Pub No. 2019/0394339 December 26, 2019); Amin (U.S. PG Pub No. 2012/0087516 , April 12, 2012- hereinafter "Amin”); “Razer Opus review” (webpage published on January 27, 2023 at Razer Opus review: Razer's first lifestyle wireless headset features ANC and THX audio as captured using Internet Archive Wayback machine, retrieved on April 10, 2026)

Cheatham discloses a system that detects game audio output from a speaker from the ambient environment (e.g., a signal representing a first part of a video game sound played by a speaker), inputting this signal to a model, receiving a predicted target audio objects from the model, altering the predicted audio objects to render an altered audio object, and playing this altered audio object instead of a second part of the video game sound configured by the computer game to play after the first part of the video game sound using the speaker. However, does not disclose an embodiment where the microphone is part of a headset that captures the ambient audio from speakers that are also part of the headset.
Mahlmeister discloses a system that detects specific sounds from computer game/simulation audio using ML models (e.g., neural networks) and alters these specific sounds as desired by a player.
Peeler teaches a system that detects specific sounds from computer game/simulation audio and alters these specific sounds as desired by a player.
Lim teaches a system that detects specific sounds from computer game/simulation audio and alters these specific sounds as desired by a player.
Freund teaches a system that detects specific sounds from computer game/simulation audio and alters these specific sounds as desired by a player.
Wolff-Petersen a system that detects specific sounds from computer game/simulation audio and alters these specific sounds as desired by a player.
Bonanno discloses a gaming headset with internal processing that receives video game sounds and applies user-selectable sound options/modifications to enhance the sound of certain gaming elements.
Peng discloses a gaming headset with internal processing that receives video game sounds and internally applies user-selectable sound options/modifications using stored algorithms to enhance the sound of certain gaming elements, and outputting this processed audio via the headset’s speakers. Also discloses boom mic that picks up ambient sounds (e.g., the players chat).
Meneses discloses a gaming headset with internal processing that receives video game sounds and internally applies user-selectable sound options/modifications using stored algorithms to enhance the sound of certain gaming elements, and outputting this processed audio via the headset’s speakers. Also discloses boom mic that picks up ambient sounds (e.g., the players chat).
Roberstson discloses a gaming headset with internal processing that receives video game sounds and internally applies user-selectable sound options/modifications using stored algorithms to enhance the sound of certain gaming elements, and outputting this processed audio via the headset’s speakers. Also discloses boom mic that picks up ambient sounds (e.g., the players chat). Discloses that the mic audio may also be enhanced using signal processing.
Campbell discloses a headset with internal logic to enhance/modify input audio data according to equalization preferences of the wearer. Discloses use of internal mics (internal to earpiece) that that pick up the sounds played by the headphone speakers and uses this signal to calibrate/adjust the enhanced audio. Does not disclose identification of certain sounds within the audio, video game audio, or use of the feedback signal to identify specific target/predicted audio objects for enhancement (i.e., an altered audio object that is played instead of a second part of the video game sound configured by the computer game).
Pederson discloses a gaming headset with internal speakers that play video game sounds, a boom mic that picks up ambient sounds (e.g., the players chat), and internal in-ear microphones that pick up the sounds (including video game sounds) played by the headphone speakers. Discloses using the feedback in-ear mics for active noise cancelling of certain sounds, but the feedback signal is not used to identify specific target/predicted audio objects for enhancement (i.e., an altered audio object that is played instead of a second part of the video game sound configured by the computer game).
Zhang discloses a headset with internal logic to adaptively enhance/modify input audio data according to preferences of the wearer. Discloses use of internal mics (internal to earpiece) that that pick up the sounds played by the headphone speakers and uses this signal as an error signal to determine a correction signal to further apply to the speaker output so that the desired enhancement (e.g., noise cancelling, smoothing, leveling) is achieved. Does not disclose that the error/feedback signal is used as part of a process of identifying specific target/predicted audio objects for enhancement (i.e., an altered audio object that is played instead of a second part of the video game sound configured by the computer game). Instead, a separate feedback noise correction signal is determined using the feedback/error mic.
Kim discloses a headset with a plurality of external and internal (feedback/error) microphones. Discloses internal adaptive filtering of audio data to personalize/enhance the audio output by the headset speakers. Discloses use of the feedback mics to adjust the adaptive filter to compensate for unwanted effects found within the audio output by the headset speakers. However, the audio from the feedback mics is not used as part of a process of identifying specific target/predicted audio objects for enhancement and/or subsequent alteration of audio object that is played instead of a second part of the video game sound configured by the computer game).
Seo discloses a gaming headset with internal feedback mics that use the feedback signal from the audio played within the earcups to adjust coefficient values of an internal adaptive filter (ML model) so that certain objects (specifically, chat objects) from within subsequently output audio are properly eliminated/attenuated. The adaptive filters are not used to identify sounds controlled by the computer game (game sounds, as the chat is not controlled by the game), nor are the sounds identified by the adaptive filters enhanced/altered directly such that an altered object is played instead of a second part of the video game sound configured by the computer game.
Amin discloses televisions and other devices comprising a microphone configured to detect ambient sound for sound enhancement.
“Razer Opus review” discloses gaming headphones with feedback mics used for active noise cancelling.

As per claim 21, the closest prior art of record taken either individually or in combination with other prior art of record fails to teach or suggest a headset that comprises the speaker and the microphone in combination with the requirements of claim 1. Claim 21, which includes the limitations of claim 1, requires that the microphone in the headset receives a signal representing a first part of a video game sound played by a speaker of the headphone controlled by the computer game (unenhanced/altered video game sound), inputting this signal to the ML model, receiving a predicted audio object from the ML model, altering the predicted audio object to render an altered audio object, and playing this altered audio object instead of a second part of the video game sound configured by the computer game to play after the first part of the video game sound. Although there are several examples in the prior art of systems/headsets that capture video game audio signals (e.g., a first part of a video game sound), input of the sound into an ML model to identify/predict specific sounds/objects within the audio (e.g., footsteps, gunfire, chat from remote players), enhancing these certain sounds such that the altered sounds/objects are subsequently played instead of the originally-intended second part(s) of the game audio, including wherein these sounds are played via the speakers of a headset, the prior art does not disclose that the initially received signal that is processed to identify/predict specific sounds/objects for enhancement of these sounds/objects is from a microphone on a headset that captures the initial audio signal from a speaker on the headset. Furthermore, although there are numerous examples of headsets with microphones that capture audio output (e.g., including video game audio) from speakers on the headset (e.g., feedback mics, error mics – e.g., as part of performing active noise cancelling or calibrating/correcting enhanced sound outputs such that desired target sounds are cancelled or so that they sound is as desired), such uses of the audio captured by the feedback/error mics is not the same thing as using the video game audio signals (e.g., a first part of unenhanced/altered video game sound ) captured by the feedback/error mics as input into a ML model to identify/predict specific sounds/objects within the audio (e.g., footsteps, gunfire, chat from remote players), and enhancing/altering these identified/predicted sounds/objects such that the enhanced/altered identified/predicted sounds/objects are subsequently played instead of the originally-intended second part(s) of the game audio, including wherein these sounds are played via the speakers of a headset.
As per claim 22, the language of claim 22 requires that the microphone signal sent to the ML model is from a microphone of a headset, that the audio processing is part of the headset, and that the playing the enhanced audio object on the speaker comprises playing the enhanced audio object on a speaker of the headset. Based on the limitations of claim 10, the speaker of the headset is required to play the first part of the sound as well as the enhanced audio object instead of a second part of the sound of the computer simulation configured by the computer simulation to play after the first part of the sound. Therefore, for the reasons discussed above with respect to claim 21, claim 22 recites allowable subject matter.

Conclusion
Claims 1-8, 10, 12-19, and 23 are rejected.
Claims 21 and 21 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims

THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.

Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES M DETWEILER whose telephone number is [phone]. 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

Prosecution Timeline

• Apr 01, 2024: Application Filed
• Dec 30, 2025: Non-Final Rejection mailed — §103
• Mar 13, 2026: Response Filed
• Apr 01, 2026: Examiner Interview (Telephonic)
• Apr 15, 2026: Final Rejection mailed — §103
• Jun 01, 2026: Response after Non-Final Action
• Jul 07, 2026: Request for Continued Examination
• Jul 14, 2026: Response after Non-Final Action