DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed on 6/29/2026 have been fully considered but they are not persuasive.
Regarding applicant’s argument of the 35 USC 101 rejection, the claim as amended does not overcome the 101 rejection. Since no specific argument was made for the independent claims, applicants are referred to the actual rejection.
Applicant argues that claim 3 is eligible under 35 U.S.C. 101. Specifically, Applicant asserts that generating a cancellation wave does not read on a human plugging their ears because ear plugs block all sound, whereas the claimed invention selectively generates a waveform to cancel specific sounds identified as being below a threshold, allowing the user to hear other sounds. Applicant asserts that the claimed selective noise cancellation, analyzing sound inputs, determining relevance relative to a threshold, and generating a waveform to selectively cancel irrelevant noise, are not conventional active noise cancellation.
In response, analyzing sound inputs, calculating/determining a relevance value relative to a threshold, and deciding which sounds to isolate or ignore recite operations that can be performed mentally or mathematically. Determining whether a sound is “relevant” to a user’s context is an mental process that humans perform routinely when selectively focusing on a speaker in a noisy environment and filtering out background noise. While Applicant notes that ear plugs physically block all sound indiscriminately, the human mind is capable of selective listening focusing on a primary sound/conversation while mentally tuning out or ignoring specific background noises based on their perceived relevance. Therefore, the logical function of categorizing sounds into “relevant” versus “irrelevant” and acting to suppress the irrelevant sounds remains an abstract concept under the mental process grouping. Because the physical implementation relies entirely on conventional listening device functions (generating a cancellation waveform) executing an abstract filtering rule, the additional elements, considered individually and as an ordered combination, do not add significantly more to the abstract idea.
Regarding applicant’s argument of the 35 USC 103 rejection, applicant argues that Kraft fails to teach “determining, by a context identifier using natural language processing (NLP), a context, wherein the context is a topic of a conversation related to the user”; and “based on the relevance score being below a configured threshold, mute, by the listening device, comprising generating a natural waveform to each of a one or more source of irrelevant noise.”
Kraft explicitly discloses receiving and processing human spoken language/commands to establish audio processing parameters and identify the context of the environment. Kraft discloses that user input used to characterize the audio environment may be “spoken commands” (Kraft, paragraph [0050]). Identifying and processing spoken commands via a microphone inherently requires analyzing human spoken language (NLP). Kraft also discloses an “ambient sound characterization function 450” (a context identifier) that analyzes ambient audio to develop a profile identifying the environment or context (Kraft, paragraphs [0051], [0064]- [0067]). Kraft specifically identifies speech and human conversation as the target auditory context to be isolated from surrounding noise. For example, paragraph [0079] teaches isolating human speech by recognizing that “all sounds except human voices may be decreased in volume using ranges of generalized frequencies associated with human speech while those same ranges for human voices are increased in volume.” Paragraph [0117] explicitly discloses characterizing ambient sound to “perform active noise cancellation on all sources of ambient audio except for that user’s voice and an individual with whom that user is communicating. In this way, the user may be able to carry on a conversation in an otherwise loud room or space.”
Regarding the limitation “based on the relevance score being below a configured threshold, mute, by the listening device, comprising generating a natural waveform to each of a one or more source of irrelevant noise,” Kraft explicitly teaches comparing a calculated sound-matching metric against a threshold to selectively cancel non-matching/irrelevant background sound. Kraft teaches that an ambient sound profile is represented as a vector in an n-dimensional space and compared against stored profiles using a Euclidean distance metric (Kraft, paragraphs [0069]-[0070], [0620], FIG. 6). The Euclidean distance metric measures the degree of match (relevance) between the current sound feature and target profiles derived via a learning model (Kraft, paragraphs [0053]-[0055]). Kraft explicitly teaches making a determination at step 630 of FIG. 6 as to “whether the calculated Euclidean distance is less than or equal to a predetermined threshold” (Kraft, paragraph [00630], FIG. 6). Muting via Waveform Generation: When ambient sounds do not meet the desired threshold criteria (identifying them as unwanted/irrelevant noise), Kraft mutes/attenuates the noise by generating counter-sound waveforms: paragraph [0045] explicitly discloses Active Noise Cancellation (ANC) “where anti-sound is generated to counter-act undesirable sound.” paragraphs [0116]–[0117] teach generating “counter-noise to counteract audio” and performing ANC to cancel irrelevant noise sources while leaving the ongoing conversation uninhibited. Therefore, comparing Kraft’s Euclidean distance match metric against a predetermined threshold to trigger anti-sound (waveform) generation for undesirable noise meets the limitation of muting irrelevant noise by generating a waveform based on a relevance score threshold.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. This judicial exception is not integrated into a practical application because the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claims 1, 11, and 16, the limitations of “identifying one or more sources of a noise in a vicinity of a listening device, by a listening device…”, “determining, by a context identifier using natural language processing (NLP), a context, wherein the context is a topic of a conversation related to the user…”, “calculating, for each of the one or more sources of noise, a relevance score using a learning model and the identified context, wherein the relevance score is a likelihood a sound is relevant to the conversation related to the user…”, and “and based on the relevance score being below a configured threshold, muting, by the listening device, comprising generating a natural waveform to each of a one or more source of irrelevant noise”, as written,
are processes that under broadest reasonable interpretation, can be performed in a person’s mind or by using pen and paper. To be more specific, the mental process of a human hearing multiple sounds around them in a vicinity, recognizing that someone is speaking to them about a particular topic (determining a context that is a topic of a conversation related to the user), mentally judging how important or relevant each surrounding sound is relative to the conversation (calculating a relevance score indicating a likelihood a sound is relevant to the conversation), and choosing to mentally tune out or ignore sounds that are not relevant to the conversation (muting noise based on the relevance score being below a threshold) fall within the Mental Processes grouping of abstract ideas. Additionally, calculating a “relevance score” using a model and comparing it against a “configured threshold” recites operations in the Mathematical Concepts grouping of abstract ideas.
The judicial exception is not integrated into a practical application because the recitation of the processor, context identifier, learning model, computer-readable storage medium, listening device, and noise canceling function read to generalized computer components based upon the claim interpretation wherein the structure is interpreted using the specification.
The “listening device” with a “noise canceling function” that mutes noise by “generating a natural waveform” is recited at a high level of generality and amounts to no more than generally linking the abstract idea to a particular technological environment (personal audio devices and conventional active noise cancellation). Furthermore, performing natural language processing (NLP) to identify a conversational topic represents generic software functionality executing on generic processing hardware. As such, these additional elements do not integrate the abstract idea into a practical application.
Therefore, these claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using generalized computer components to identify, determine, calculate, and mute amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claims are not patent eligible.
With respect to claims 2 and 12 and 17, the claims recite “capturing, by a microphone on the listening device, each of the one or more sources of noise; and converting, in response to the capturing, each noise into a waveform,” which reads on a human hearing surrounding sounds and mentally perceiving them as distinct sound patterns. The microphone is a generic data-gathering component. No additional limitations are present.
With respect to claims 3, the claim recites “generating, by the listening device, a cancelation wave, wherein the cancelation wave is configured to cancel the waveform of the noise,” which reads on a human plugging their ears or mentally tuning out a sound they have judged to be irrelevant. Active noise cancellation is a well-understood, routine, and conventional function of listening devices. No additional limitations are present.
With respect to claims 4, 13, and 18, the claims recite “determining a location for each source of noise,” which reads on a human naturally perceiving the direction from which a sound originates using their two ears. No additional limitations are present.
With respect to claims 5, 14, and 19, the claims recite “determining a second relevance score for a second sound is above the relevance threshold; and allowing … the second sound to pass through the listening device,” which reads on a human mentally judging that a second sound is important to their conversation and choosing to actively listen to it. No additional limitations are present.
With respect to claims 6, 15, and 20, the claims recite “wherein a second source of the second sound is a second person in a conversation with the user,” which reads on a human recognizing that a person speaking to them is relevant to their ongoing conversation and choosing to listen to that person. No additional limitations are present.
With respect to claim 7, the claim recites “wherein a first source of sound is a second user,” which reads on a human recognizing that a nearby person is making noise. No additional limitations are present.
With respect to claim 8, the claim recites “wherein the second source of sound is from a third user,” which reads on a human recognizing a third person speaking and determining whether that person’s speech is relevant to the ongoing conversation. No additional limitations are present.
With respect to claim 9, the claim recites “wherein the context is at least partially based on an open application on a user device,” which reads on a human looking at their phone or computer screen and using what they see to inform what sounds they consider relevant. The open application is a generic computer component providing data. No additional limitations are present.
With respect to claim 10, the claim recites “wherein the threshold is based on a highest relevance score of the one or more relevance scores,” which reads on a human mentally setting their attention standard based on the most relevant sound they have identified. No additional limitations are present.
These claims further do not remedy the judicial exception being integrated into a practical application and further fail to include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kraft et al. (US PGPUB 2015/0373474 A1), hereinafter Kraft.
Regarding claim 1, Kraft teaches:
A computer-implemented method comprising: identifying one or more sources of a noise in a vicinity of a listening device, by a listening device, wherein the listening device is associated with a user and the listening device includes a noise canceling cancelling function (i.e., ([0026] The housing 290 may be configured to interface with a user’s ear by fitting in, on, or over the user’s ear such that ambient sound is mostly excluded from reaching the user’s ear canal and processed sound generated by the ambient audio processing system 200 is coupled into the user’s ear canal. [0045] The audio processing function 410 may also include active noise cancellation (ANC) where anti-sound is generated to counter-act undesirable sound that passively propagates through the earbud into the ear canal. [0120] the ambient audio feature is identified at 920. This may be, using the example discussed above, a gunshot, or human footsteps, human whispering, a baby crying, or any other particular feature of ambient sound that may be detected); determining, by a context identifier using natural language processing (NLP), a context, wherein the context is a topic of a conversation related to the user (i.e., ” ([0117] the processing parameters 425, retrieved at 840 and used at 850 may act on ambient audio (and the audio within a human’s body) to perform active noise cancellation on all sources of ambient audio except for that user’s voice and an individual with whom that user is communicating. In this way, the user may be able to carry on a conversation in an otherwise loud room or space. [0079] For example, all sounds except human voices may be decreased in volume using ranges of generalized frequencies associated with human speech while those same ranges for human voices are increased in volume) (Also refer to paragraphs 0050 and 0051); calculating, for each of the one or more sources of noise, a relevance score using a learning model and the identified context, wherein the relevance score is a likelihood a sound is relevant to the conversation related to the user (i.e., in paragraph [0053], Kraft discloses “The augmented reality sound system 400 may ‘listen’ to the ambient sound and learn what filter parameters the user sets in the presence of various ambient sound characteristics.”); Paragraph [0055] (“The processing parameters 425 may be saved in the augmented reality parameter memory 430 in response to a user action, or may be automatically ‘learned’ by the active augmented reality sound system 400.”…Kraft also discloses in paragraph ([0069] the ambient sound profile and each stored sound profile may be considered as vectors in an n-dimensional space. Comparison of the ambient sound profile and a stored sound profile may then be performed using similarity metrics, such as calculating the Euclidean distance between the corresponding vectors, spectral or spectrographic (spectrum versus time) comparisons, or other similarity metrics known to those of skill in the art. [0070] The Euclidean distance between the ambient sound profile and the retrieved sound profile may be calculated at 620.); and based on the relevance score being below a configured threshold, muting, by the listening device, comprising generating a natural waveform to each of a one or more source of irrelevant noise (i.e., in paragraph [00630], Kraft discloses (“At 630, a determination may be made if the calculated Euclidean distance is less than or equal to a predetermined threshold.”); Paragraph [0070] (“When the Euclidean distance is less than or equal to the predetermined threshold (‘yes’ at 630), the ambient sound profile may be considered to match the retrieved sound profile…”)…Kraft also discloses in paragraph [0045] (“The audio processing function 410 may also include active noise cancellation (ANC) where anti-sound is generated to counter-act undesirable sound that passively propagates through the earbud into the ear canal.”); Paragraph [0116] (“The processing parameters may further cause the augmented reality sound system 140 to cancel or provide counter-noise to counteract audio from the user’s own voice as he or she speaks.”); Paragraph [0117] (“…perform active noise cancellation on all sources of ambient audio except for that user’s voice and an individual with whom that user is communicating”). Also refer to paragraph [0057].
Regarding claim 2, the method of claim 1 is disclosed by Kraft. Kraft further teaches:
wherein the identifying further comprises: capturing, by a microphone on the listening device, each of the one or more sources of noise ([0028] The microphone 210 converts ambient sound 205 into an electrical signal that is amplified by preamplifier 215. [0030] The microphone 210 may be one or more transducers for converting sound into an electrical signal that is sufficiently compact for use within the housing 290. [0065] ambient sound is received at 510.); and
converting, in response to the capturing, each noise into a waveform ([0028] converted into digital ambient sound converting, in response to the capturing, each noise into a waveform ([0028] converted into digital ambient sound 222 by A/D converter 220. [0032] The A/D converter 220 may digitize the output from preamplifier 215, which is to say convert the output from preamplifier 215 into a series of digital ambient sound samples at a rate at least twice the highest frequency present in the ambient sound. For example, the A/D converter may output digital ambient sound 222 in the form of sequential sound samples at rate of 40 kHz or higher.).
Regarding claim 3, the method of claim 2 is disclosed by Kraft. Kraft further teaches:
wherein the muting further comprises: generating, by the listening device, a cancelation wave, wherein the cancelation wave is configured to cancel the waveform of the noise ([0045] The audio processing function 410 may also include active noise cancellation (ANC) where anti-sound is generated to counter-act undesirable sound that passively propagates through the earbud into the ear canal.).
Regarding claim 4, the method of claim 2 is disclosed by Kraft. Kraft further teaches:
wherein the muting further comprises: determining a location for each source of noise ([0121] the location of the isolated sound is determined at 930. Relative distance of several microphones, either on a single user’s head or on multiple user’s heads may be calculated. [0122] Triangulation may also be used if multiple microphones are available. Sound travels relatively slowly in air—approximately 1,126 ft/s (or approximately 768 mph) in typical conditions. Thus, for ambient audio features, even slight delays in receipt of sound can be used, in conjunction with locational data from a geo-location function 440, to derive the relative location of an ambient audio feature. [0122] If three or more microphones are available, the sound location may be calculated. If more than three microphones are used, the location can be better-triangulated and more-closely calculated to the actual location.).
Regarding claim 5, the method of claim 1 is disclosed by Kraft. Kraft further teaches:
determining a second relevance score for a second sound is above the relevance threshold ([0070] At 630, a determination may be made if the calculated Euclidean distance is less than or equal to a predetermined threshold. When the Euclidean distance is less than or equal to the predetermined threshold (“yes” at 630), the ambient sound profile may be considered to match the retrieved sound profile. [0057] instructions to emphasize on sounds that already stand out from the overall ambient sound environment (e.g., footsteps, dogs barking, animal sounds, human voices, whispers, weapon noises, etc.).); and
allowing, in response to the determination the second relevance score is above the relevance threshold, the second sound to pass through the listening device ([0117] perform active noise cancellation on all sources of ambient audio except for that user’s voice and an individual with whom that user is communicating. In this way, the user may be able to carry on a conversation in an otherwise loud room or space. [0007] There are many situations where a listener may desire emphasis or de-emphasis of ambient sound at certain frequencies, while allowing ambient sound at other frequencies to reach their ears. [0079] all sounds except human voices may be decreased in volume using ranges of generalized frequencies associated with human speech while those same ranges for human voices are increased in volume.).
Regarding claim 6, the method of claim 5 is disclosed by Kraft. Kraft further teaches:
wherein a second source of the second sound is a second person in a conversation with the user ([0117] perform active noise cancellation on all sources of ambient audio except for that user’s voice and an individual with whom that user is communicating.).
Regarding claim 7, the method of claim 5 is disclosed by Kraft. Kraft further teaches:
wherein a first source of sound is a second user ([0025] The augmented reality sound system may be used by multiple users in the same vicinity. [0117] perform active noise cancellation on all sources of ambient audio except for that user’s voice and an individual with whom that user is communicating.).
Regarding claim 8, the method of claim 5 is disclosed by Kraft. Kraft further teaches:
wherein the second source of sound is from a third user ([0025] The augmented reality sound system may be used by multiple users in the same vicinity. [0097] The augmented reality sound system may be able to distinguish voices from different individuals. Some individuals’ voices may be emphasized or de-emphasized relative to others.).
Regarding claim 9, the method of claim 1 is disclosed by Kraft. Kraft further teaches:
wherein the context is at least partially based on an open application on a user device ([0022] A personal computing device, such as a smart phone or tablet, may also be used in conjunction with the augmented reality sound system. [0041] The stored software programs may include an application or ‘app’ to cause the personal computing device to perform portions of the processes and functions described herein. [0055] the personal computing device may be used to select an augmented reality profile from a plurality of stored augmented reality sound profiles.).
Regarding claim 10, the method of claim 1 is disclosed by Kraft. Kraft further teaches:
wherein the threshold is based on a highest relevance score of the one or more relevance scores ([0069] the ambient sound profile and each stored sound profile may be considered as vectors in an n-dimensional space. Comparison of the ambient sound profile and a stored sound profile may then be performed using similarity metrics, such as calculating the Euclidean distance between the corresponding vectors. [0070] At 630, a determination may be made if the calculated Euclidean distance is less than or equal to a predetermined threshold. [0071] When the Euclidean distance is greater than the predetermined threshold (“no” at 630), a further determination may be made if additional stored sound profiles are available for comparison. When there are more stored sound profiles (“Yes” at 640), a next stored sound profile may be retrieved at 640 and the process 600 may repeat until a match is found at 630 or all of the stored sound profiles have been compared to the ambient sound profile.).
Regarding claim 11, Kraft teaches:
A system comprising: a processor ([0034] The processor 230 may include one or more processor devices such as a microcontroller, a microprocessor, and/or a digital signal processor. [0037] the personal computing device 300 includes a processor 310.); and
a computer-readable storage medium communicatively coupled to the processor and storing program instructions which, when executed by the processor, are configured to cause the processor to ([0034] The processor 230 can include and/or be coupled to the memory 235. The memory 235 may store software programs, which may include an operating system, for execution by the processor 230. [0041] The memory 320 may store software programs and routines for execution by the processor. [0041] The stored software programs may include an application or ‘app’ to cause the personal computing device to perform portions of the processes and functions described herein.):
identify one or more sources of a noise in a vicinity of a listening device, wherein the listening device is associated with a user and the listening device includes a noise canceling function ([0026] The housing 290 may be configured to interface with a user’s ear by fitting in, on, or over the user’s ear. [0045] The audio processing function 410 may also include active noise cancellation (ANC) where anti-sound is generated to counter-act undesirable sound that passively propagates through the earbud into the ear canal. [0120] the ambient audio feature is identified at 920. This may be, using the example discussed above, a gunshot, or human footsteps, human whispering, a baby crying, or any other particular feature of ambient sound that may be detected.);
determine, for the user, a context, wherein the context represents a subject of a conversation related to the user ([0117] the processing parameters 425, retrieved at 840 and used at 850 may act on ambient audio (and the audio within a human’s body) to perform active noise cancellation on all sources of ambient audio except for that user’s voice and an individual with whom that user is communicating. In this way, the user may be able to carry on a conversation in an otherwise loud room or space. [0079] For example, all sounds except human voices may be decreased in volume using ranges of generalized frequencies associated with human speech while those same ranges for human voices are increased in volume.);
calculate, for each of the one or more sources of noise, a relevance score ([0069] the ambient sound profile and each stored sound profile may be considered as vectors in an n-dimensional space. Comparison of the ambient sound profile and a stored sound profile may then be performed using similarity metrics, such as calculating the Euclidean distance between the corresponding vectors, spectral or spectrographic (spectrum versus time) comparisons, or other similarity metrics known to those of skill in the art. [0070] The Euclidean distance between the ambient sound profile and the retrieved sound profile may be calculated at 620.); and
mute, by the listening device, each of the sources of noise where the associated relevance score is below a relevance threshold ([0070] At 630, a determination may be made if the calculated Euclidean distance is less than or equal to a predetermined threshold. [0045] The audio processing function 410 may also include active noise cancellation (ANC) where anti-sound is generated to counter-act undesirable sound. [0117] perform active noise cancellation on all sources of ambient audio except for that user’s voice and an individual with whom that user is communicating. [0057] instructions to emphasize on sounds that already stand out from the overall ambient sound environment (e.g., footsteps, dogs barking, animal sounds, human voices, whispers, weapon noises, etc.) while deemphasizing (e.g. decreasing the overall volume) of other ambient sounds.).
Regarding claim 12, the system of claim 11 is disclosed by Kraft. Kraft further teaches:
wherein the stored program instructions for identification of the one or more sources are further configured to cause the processor to: capture, by a microphone on the listening device, each of the one or more sources of noise ([0028] The microphone 210 converts ambient sound 205 into an electrical signal that is amplified by preamplifier 215. [0030] The microphone 210 may be one or more transducers for converting sound into an electrical signal that is sufficiently compact for use within the housing 290. [0065] ambient sound is received at 510.); and
convert, in response to the capturing, each noise into a waveform ([0028] converted into digital ambient sound 222 by A/D converter 220. [0032] The A/D converter 220 may digitize the output from preamplifier 215, which is to say convert the output from preamplifier 215 into a series of digital ambient sound samples at a rate at least twice the highest frequency present in the ambient sound. For example, the A/D converter may output digital ambient sound 222 in the form of sequential sound samples at rate of 40 kHz or higher.).
Regarding claim 13, the system of claim 12 is disclosed by Kraft. Kraft further teaches:
wherein the stored program instructions for muting are further configured to cause the processor to: determine a location for each source of noise ([0121] the location of the isolated sound is determined at 930. Relative distance of several microphones, either on a single user’s head or on multiple user’s heads may be calculated. [0122] Triangulation may also be used if multiple microphones are available. Sound travels relatively slowly in air—approximately 1,126 ft/s (or approximately 768 mph) in typical conditions. Thus, for ambient audio features, even slight delays in receipt of sound can be used, in conjunction with locational data from a geo-location function 440, to derive the relative location of an ambient audio feature. [0122] If three or more microphones are available, the sound location may be calculated. If more than three microphones are used, the location can be better-triangulated and more-closely calculated to the actual location.).
Regarding claim 14, the system of claim 11 is disclosed by Kraft. Kraft further teaches:
wherein the stored program instructions are further configured to cause the processor to: determine a second relevance score for a second sound is above the relevance threshold ([0070] At 630, a determination may be made if the calculated Euclidean distance is less than or equal to a predetermined threshold. When the Euclidean distance is less than or equal to the predetermined threshold (“yes” at 630), the ambient sound profile may be considered to match the retrieved sound profile. [0057] instructions to emphasize on sounds that already stand out from the overall ambient sound environment (e.g., footsteps, dogs barking, animal sounds, human voices, whispers, weapon noises, etc.).); and
allow, in response to the determination the second relevance score is above the relevance threshold, the second sound to pass through the listening device ([0117] perform active noise cancellation on all sources of ambient audio except for that user’s voice and an individual with whom that user is communicating. In this way, the user may be able to carry on a conversation in an otherwise loud room or space. [0007] There are many situations where a listener may desire emphasis or de-emphasis of ambient sound at certain frequencies, while allowing ambient sound at other frequencies to reach their ears. [0079] all sounds except human voices may be decreased in volume using ranges of generalized frequencies associated with human speech while those same ranges for human voices are increased in volume.).
Regarding claim 15, the system of claim 14 is disclosed by Kraft. Kraft further teaches:
wherein a second source of the second sound is a second person in a conversation with the user ([0117] perform active noise cancellation on all sources of ambient audio except for that user’s voice and an individual with whom that user is communicating.).
Regarding claim 16, Kraft teaches:
A computer program product, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processing unit to cause the processing unit to ([0034] The processor 230 can include and/or be coupled to the memory 235. The memory 235 may store software programs, which may include an operating system, for execution by the processor 230. [0041] The memory 320 may store software programs and routines for execution by the processor. [0041] The stored software programs may include an application or ‘app’ to cause the personal computing device to perform portions of the processes and functions described herein. [0034] The memory 235 may include a combination of read-only memory, flash memory, and static or dynamic random access memory.):
identify one or more sources of a noise in a vicinity of a listening device, wherein the listening device is associated with a user and the listening device includes a noise canceling function ([0026] The housing 290 may be configured to interface with a user’s ear by fitting in, on, or over the user’s ear. [0045] The audio processing function 410 may also include active noise cancellation (ANC) where anti-sound is generated to counter-act undesirable sound that passively propagates through the earbud into the ear canal. [0120] the ambient audio feature is identified at 920. This may be, using the example discussed above, a gunshot, or human footsteps, human whispering, a baby crying, or any other particular feature of ambient sound that may be detected.);
determine, for the user, a context, wherein the context represents a subject of a conversation related to the user ([0117] the processing parameters 425, retrieved at 840 and used at 850 may act on ambient audio (and the audio within a human’s body) to perform active noise cancellation on all sources of ambient audio except for that user’s voice and an individual with whom that user is communicating. In this way, the user may be able to carry on a conversation in an otherwise loud room or space. [0079] For example, all sounds except human voices may be decreased in volume using ranges of generalized frequencies associated with human speech while those same ranges for human voices are increased in volume.);
calculate, for each of the one or more sources of noise, a relevance score ([0069] the ambient sound profile and each stored sound profile may be considered as vectors in an n-dimensional space. Comparison of the ambient sound profile and a stored sound profile may then be performed using similarity metrics, such as calculating the Euclidean distance between the corresponding vectors, spectral or spectrographic (spectrum versus time) comparisons, or other similarity metrics known to those of skill in the art. [0070] The Euclidean distance between the ambient sound profile and the retrieved sound profile may be calculated at 620.); and
mute, by the listening device, each of the sources of noise where the associated relevance score is below a relevance threshold ([0070] At 630, a determination may be made if the calculated Euclidean distance is less than or equal to a predetermined threshold. [0045] The audio processing function 410 may also include active noise cancellation (ANC) where anti-sound is generated to counter-act undesirable sound. [0117] perform active noise cancellation on all sources of ambient audio except for that user’s voice and an individual with whom that user is communicating. [0057] instructions to emphasize on sounds that already stand out from the overall ambient sound environment (e.g., footsteps, dogs barking, animal sounds, human voices, whispers, weapon noises, etc.) while deemphasizing (e.g. decreasing the overall volume) of other ambient sounds.).
Regarding claim 17, the computer program product of claim 16 is disclosed by Kraft. Kraft further teaches:
wherein the stored program instructions for identification of the one or more sources are further configured to cause the processing unit to: capture, by a microphone on the listening device, each of the one or more sources of noise ([0028] The microphone 210 converts ambient sound 205 into an electrical signal that is amplified by preamplifier 215. [0030] The microphone 210 may be one or more transducers for converting sound into an electrical signal that is sufficiently compact for use within the housing 290. [0065] ambient sound is received at 510.); and
convert, in response to the capturing, each noise into a waveform ([0028] converted into digital ambient sound 222 by A/D converter 220. [0032] The A/D converter 220 may digitize the output from preamplifier 215, which is to say convert the output from preamplifier 215 into a series of digital ambient sound samples at a rate at least twice the highest frequency present in the ambient sound. For example, the A/D converter may output digital ambient sound 222 in the form of sequential sound samples at rate of 40 kHz or higher.).
Regarding claim 18, the computer program product of claim 17 is disclosed by Kraft. Kraft further teaches:
wherein the stored program instructions for muting are further configured to cause the processing unit to: determine a location for each source of noise ([0121] the location of the isolated sound is determined at 930. Relative distance of several microphones, either on a single user’s head or on multiple user’s heads may be calculated. [0122] Triangulation may also be used if multiple microphones are available. Sound travels relatively slowly in air—approximately 1,126 ft/s (or approximately 768 mph) in typical conditions. Thus, for ambient audio features, even slight delays in receipt of sound can be used, in conjunction with locational data from a geo-location function 440, to derive the relative location of an ambient audio feature. [0122] If three or more microphones are available, the sound location may be calculated. If more than three microphones are used, the location can be better-triangulated and more-closely calculated to the actual location.).
Regarding claim 19, the computer program product of claim 16 is disclosed by Kraft. Kraft further teaches:
wherein the stored program instructions are further configured to cause the processing unit to: determine a second relevance score for a second sound is above the relevance threshold ([0070] At 630, a determination may be made if the calculated Euclidean distance is less than or equal to a predetermined threshold. When the Euclidean distance is less than or equal to the predetermined threshold (“yes” at 630), the ambient sound profile may be considered to match the retrieved sound profile. [0057] instructions to emphasize on sounds that already stand out from the overall ambient sound environment (e.g., footsteps, dogs barking, animal sounds, human voices, whispers, weapon noises, etc.).); and
allow, in response to the determination the second relevance score is above the relevance threshold, the second sound to pass through the listening device ([0117] perform active noise cancellation on all sources of ambient audio except for that user’s voice and an individual with whom that user is communicating. In this way, the user may be able to carry on a conversation in an otherwise loud room or space. [0007] There are many situations where a listener may desire emphasis or de-emphasis of ambient sound at certain frequencies, while allowing ambient sound at other frequencies to reach their ears. [0079] all sounds except human voices may be decreased in volume using ranges of generalized frequencies associated with human speech while those same ranges for human voices are increased in volume.).
Regarding claim 20, the computer program product of claim 19 is disclosed by Kraft. Kraft further teaches:
wherein a second source of the second sound is a second person in a conversation with the user ([0117] perform active noise cancellation on all sources of ambient audio except for that user’s voice and an individual with whom that user is communicating.).
Conclusion
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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
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/PIERRE LOUIS DESIR/Supervisory Patent Examiner, Art Unit 2659
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