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 .
DETAILED ACTION
Claims 1-20 are pending. Claims 1, 8, and 16 are independent.
This Application was published as US 20250006206.
Apparent priority is 1 July 2023.
The instant Application is directed to a device which detects hypophonia in a user’s speech and outputs a notification to the user.
Specification
The disclosure is objected to because of the following informalities: pg. 17, line 10, “tythe” is understood to mean “the”. Appropriate correction is required.
Claim Objections
Claim 20 objected to because of the following informalities: last line, “an amplitude” is understood to mean “or an amplitude”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 11-12 and 14 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 11 recites the limitation "the threshold associated with hypophonia" in lines 7-8. There is insufficient antecedent basis for this limitation in the claim.
Claim 12 rejected as depending on claim 11.
Claim 14 recites the limitation “providing, as an input to a machine-learned (ML) model trained to identify hypophonia data;”. It is unclear whether the model is trained to identify hypophonia data (in which case the claim does not specify what is input), or whether data is input to the model to identify hypophonia (in which case it is indefinite whether this is the same data as previously recited in claim 8). Based on the specification, and for the purposes of compact prosecution, claim 14 is interpreted to mean: “providing, as an input to a machine-learned (ML) model trained to identify hypophonia, the data;”
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 8, 10-14, 16-17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 1: The independent Claims are directed to statutory categories:
Claim 8 is a device claim and directed to the machine or manufacture category of patentable subject matter.
Claim 16 is a device claim and directed to the machine or manufacture category of patentable subject matter.
Step 2A, Prong One: Does the Claim recite a Judicially Recognized Exception? Abstract Idea? Are these Claims nevertheless considered Abstract as a Mathematical Concept (mathematical relationships, mathematical formulas or equations, mathematical calculations), Mental Process (concepts performed in the human mind (including an observation, evaluation, judgment, opinion), or Certain Methods of Organizing Human Activity (1-fundamental economic principles or practices (including hedging, insurance, mitigating risk), 2-commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations), 3- managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) and fall under the judicial exception to patentable subject matter?)
The rejected Claims recite Mental Processes because determining hypophonia from human speech can be performed in the mind.
Step 2A, Prong Two: Additional Elements that Integrate the Judicial Exception into a Practical Application? Identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. “Integration into a practical application” requires an additional element(s) or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. Uses the considerations laid out by the Supreme Court and the Federal Circuit to evaluate whether the judicial exception is integrated into a practical application.
The rejected Claims do not include additional limitations that point to integration of the abstract idea into a practical application and are therefore directed to a Mental Process.
Claim 8 is a generic automation of a mental process because a human agent can listen to a user’s speech, determine characteristics, and determine if the characteristics match hypophonia. Prong Two of step 2A in the 101 analysis asks whether the abstract idea is integrated with a practical application. The answer is no in this instance because there is no technological solution in the Claim that “integrates” the abstract idea. The Claim only suggests that the abstract idea be applied. It does not describe an application.
8. A speech therapy device comprising: one or more sensors; one or more output components;
one or more processors; and one or more non-transitory computer-readable media storing computer-executable instructions that, when executed by the one or more processors, cause the speech therapy device to perform acts comprising: [these limitations are generic computing components]
receiving, from the one or more sensors, data, [agent listens to user speaking]
determining that the data is indicative of speech of a user, [agent determines that it is speech]
determining, based at least in part on the data being indicative of the speech, one or more biomarkers associated with the speech, [agent listens to the pitch and volume of the speech]
the one or more biomarkers including at least an amplitude associated with the speech,
determining that the one or more biomarkers fail to satisfy a threshold, and [agent determines that the volume is too low to understand and therefore fails the threshold]
causing, based at least in part on the one or more biomarkers failing to satisfy the threshold, output of a notification via the one or more output components. [agent asks user to speak louder]
Step 2B: Search for Inventive Concept: Additional Element Do not amount to Significantly More: The limitations of “one or more sensors; one or more output components; one or more processors; and one or more non-transitory computer-readable media storing computer-executable instructions” are well-understood, routine, and conventional machine components that are being used for their well-understood, routine, and conventional and rather generic functions. Additionally, these limitations are expressed parenthetically and lack nexus to the Claim language and as such are a separable and divisible mention to a machine. Accordingly, they are not sufficient to cause the Claim to amount to significantly more than the underlying abstract idea.
The Dependent Claims do not add limitations that could help the Claim as a whole to amount to significantly more than the Abstract idea identified for the Independent Claim:
10. The speech therapy device of claim 8, wherein the one or more output components comprise at least one of: a lighting element; a speaker; or a haptic motor. [these are generic computing components, and agent’s voice is used the same as a speaker to output an alert]
11. The speech therapy device of claim 8, the acts further comprising: receiving, from the one or more sensors, second data; determining that the second data is indicative of second speech of the user; determining, based at least in part on the second data being indicative of the second speech, one or more second biomarkers associated with the second speech, the one or more second biomarkers including at least a second amplitude associated with the second speech; determining that the one or more second biomarkers satisfy the threshold associated with hypophonia; and
causing, based at least in part on the one or more second biomarkers satisfying the threshold, output of a second notification via the one or more output components, the second notification being different than the notification. [agent continues listening to the user, determines the volume level is good, and gives user a thumbs up]
12. The speech therapy device of claim 11, the acts further comprising: receiving, from the one or more sensors, third data; determining that the third data is indicative of third speech of the user;
determining, based at least in part on the third data being indicative of the third speech, one or more third biomarkers associated with the third speech, the one or more third biomarkers including at least a third amplitude associated with the third speech; determining that the one or more third biomarkers fail to satisfy the threshold associated with hypophonia; and causing, based at least in part on the one or more third biomarkers failing to satisfy the threshold, output of the notification via the one or more output components. [agent continues listening; user’s volume drops, and agent reminds the user to speak up again]
13. The speech therapy device of claim 8, wherein the one or more biomarkers further include at least one of a pitch of the speech, an intonation in the speech, a tone associated with the speech, a pause in the speech, or a phonation associated with the speech. [agent also analyzes the user’s pitch, intonation, tone, pauses, and phonation]
14. The speech therapy device of claim 8, wherein determining the one or more biomarkers is based at least in part on: providing, as an input to a machine-learned (ML) model trained to identify hypophonia data; and receiving, as an output from the ML model, an indication associated with the one or more biomarkers. [the ML model is described at a high level of generality and therefore amounts to a generic computing component]
The additional limitations introduced by the Dependent Claims are not sufficient as additional elements that integrate the judicial exception into a practical application or as additional elements that cause the Claim as a whole to amount to substantially more than the underlying abstract idea.
With respect to Independent Claim 16, which have limitations similar to the limitations of Claim 8, the limitations of “a first microphone” and “a second microphone” are expressed parenthetically and lack nexus to the Claim language and as such are a separable and divisible mention to a machine. Accordingly, they do not include additional limitations that cause the Claim as a whole to amount to more than the underlying abstract idea.
The Dependent Claims 17 and 19-20 are similar to claims 9, 11 and 13 and do not add limitations that could integrate the judicial exception into a practical application or help the Claim as a whole to amount to significantly more than the Abstract idea identified for the Independent Claim.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 3, 5, 7-10, 15-17, and 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Anderson et al. (US 20240379093 A1).
Regarding claim 1, Anderson discloses: 1. A wearable speech therapy device to be worn by a user, ("[0004] An exemplary system and method are disclosed for a head-worn device for augmenting speech therapy or mitigating Parkinson's effect on speech and other speech-impairing conditions by providing haptic feedback, vibratory feedback, audio feedback, or other stimulations to a wearer by isolating and analyzing vocal/speech output of the user for signal-associated assessment, including, e.g., based on timing, direction, loudness, and/or speaking rate..." )
the wearable speech therapy device comprising: a housing including a first end and a second end spaced apart from the first end; (Fig. 2C shows a housing with two ends )
a first microphone located closer to the first end than the second end; (Fig. 2C shows a first microphone 250a closer to one end )
a second microphone located closer to the second end than the first end; (Fig. 2C shows a second microphone 250b. Fig. 2B, 220a shows a configuration with the second microphone on the other side of the user's head, which corresponds to the second end of the housing. )
a speaker; ("[0140]...Output device(s) 512, such as a display, speakers, printer, etc., may also be included..." )
one or more processors; and one or more non-transitory computer-readable media storing computer-executable instructions that, when executed by the one or more processors, cause the wearable speech therapy device to perform acts comprising: ("[0139] In its most basic configuration, computing device 500 typically includes at least one processing unit 506 and system memory 504…" )
receiving, from the first microphone, first audio data associated with audio captured within an environment, receiving, from the second microphone, second audio data associated with the audio, ("[0071] At step 310, the method 300 includes receiving an audio signal, for example, via a plurality of acoustic sensors." )
determining, based at least in part on the first audio data and the second audio data, that the audio is associated with user speech of the user within the environment, ("[0102]...Two MEMS microphones and an accelerometer were used to determine if the user was speaking. This speech data was filtered to reduce environmental noise and then compared to the vocal sound pressure level thresholds." )
determining, based at least in part on the audio being associated with the user speech, one or more biomarkers associated with the user speech, ("[0074] At step 316, the method 300 includes determining whether the wearer's speech signal satisfies one or more speech parameters, including at least one of an acoustic intensity parameter or a speech rate parameter…" )
determining that the one or more biomarkers satisfy a threshold associated with the user speech containing hypophonia, and ("[0074]... In some implementations, the method 300 includes determining whether the wearer's speech signal satisfies an acoustic intensity parameter or speech rate parameter based on an amount of energy in the wearer's speech signal. By way of example, the at least one threshold for the acoustic intensity parameter can include a low acoustic intensity level and a high acoustic intensity level. Similarly, the at least one predetermined threshold for the speech rate parameter can include a low speech rate level and a high speech rate level." – the low acoustic intensity level is associated with hypophonia)
causing, based at least in part on the one or more biomarkers satisfying the threshold, output of a notification via the speaker. ("[0075] At step 318, in response to detecting that the wearer's speech signal fails to satisfy at least one of the speech parameters, the method 300 includes outputting haptic biofeedback and/or other stimulation (e.g., audio, visual) to the wearer." – failing to satisfy the speech parameters is satisfying a threshold for low acoustic intensity)
Regarding claim 3, Anderson discloses: 3. The wearable speech therapy device of claim 1, further comprising at least one of a lighting element or a haptic motor, the acts further comprising causing at least one of a second notification to be output on the at least one of the lighting element or the haptic motor. ("[0010] In some embodiments, at least one of the feedback elements comprises at least one haptic biofeedback element, wherein the processor is further configured to output haptic biofeedback via the at least one haptic biofeedback element." )
Regarding claim 5, Anderson discloses: 5. The wearable speech therapy device of claim 1, wherein when the wearable speech therapy device is worn by the user, the first microphone and the second microphone are substantially aligned with an anatomical axis of the user. (Fig. 2B shows multiple configurations which align the microphones with an axis of the user. For example, 220a shows a configuration where the microphones are aligned on the axis of the user's ears. As another example, 220g shows a configuration where the microphones are aligned to the axis of the "front" of the user. )
Regarding claim 7, Anderson discloses: 7. The wearable speech therapy device of claim 1, wherein the one or more biomarkers include at least one of a pitch of the user speech, an intonation in the user speech, a tone associated with the user speech, a pause in the user speech, a phonation associated with the user speech, or an amplitude of the user speech. ("[0061] In some implementations, as depicted in FIG. 2A, the wearable speech-assisting device 108 is configured to perform a noise reduction operation to reduce ambient noise, and determine whether the wearer's speech signal satisfies one or more speech parameters, for example, by comparing an amplitude of the wearer's speech signal to a predetermined threshold.")
Regarding claim 8, Anderson discloses: 8. A speech therapy device comprising: one or more sensors; (Fig. 2C shows two microphones and an accelerometer)
one or more output components; ("[0140]...Output device(s) 512, such as a display, speakers, printer, etc., may also be included..." )
one or more processors; and one or more non-transitory computer-readable media storing computer-executable instructions that, when executed by the one or more processors, cause the speech therapy device to perform acts comprising: ("[0139] In its most basic configuration, computing device 500 typically includes at least one processing unit 506 and system memory 504…" )
receiving, from the one or more sensors, data, ("[0071] At step 310, the method 300 includes receiving an audio signal, for example, via a plurality of acoustic sensors." )
determining that the data is indicative of speech of a user, ("[0102]...Two MEMS microphones and an accelerometer were used to determine if the user was speaking. This speech data was filtered to reduce environmental noise and then compared to the vocal sound pressure level thresholds." )
determining, based at least in part on the data being indicative of the speech, one or more biomarkers associated with the speech, ("[0074] At step 316, the method 300 includes determining whether the wearer's speech signal satisfies one or more speech parameters, including at least one of an acoustic intensity parameter or a speech rate parameter…" )
the one or more biomarkers including at least an amplitude associated with the speech, ("[0061] In some implementations, as depicted in FIG. 2A, the wearable speech-assisting device 108 is configured to perform a noise reduction operation to reduce ambient noise, and determine whether the wearer's speech signal satisfies one or more speech parameters, for example, by comparing an amplitude of the wearer's speech signal to a predetermined threshold." )
determining that the one or more biomarkers fail to satisfy a threshold, and ("[0074]... In some implementations, the method 300 includes determining whether the wearer's speech signal satisfies an acoustic intensity parameter or speech rate parameter based on an amount of energy in the wearer's speech signal. By way of example, the at least one threshold for the acoustic intensity parameter can include a low acoustic intensity level and a high acoustic intensity level. Similarly, the at least one predetermined threshold for the speech rate parameter can include a low speech rate level and a high speech rate level." )
causing, based at least in part on the one or more biomarkers failing to satisfy the threshold, output of a notification via the one or more output components. ("[0075] At step 318, in response to detecting that the wearer's speech signal fails to satisfy at least one of the speech parameters, the method 300 includes outputting haptic biofeedback and/or other stimulation (e.g., audio, visual) to the wearer." )
Regarding claim 9, Anderson discloses: 9. The speech therapy device of claim 8, wherein the one or more sensors comprise: at least one microphone; and (Fig. 2C shows a microphone 250a )
at least one of an internal measurement unit (IMU), an accelerometer, a gyroscope, or a piezoelectric sensor. ("[0059] In some embodiments, the additional sensor(s) 204 include an accelerometer that facilitates accelerometer-based voice monitoring…" )
Regarding claim 10, Anderson discloses: 10. The speech therapy device of claim 8, wherein the one or more output components comprise at least one of: a lighting element; a speaker; or a haptic motor. ("[0140]...Output device(s) 512, such as a display, speakers, printer, etc., may also be included..."; see also "[0010] In some embodiments, at least one of the feedback elements comprises at least one haptic biofeedback element, wherein the processor is further configured to output haptic biofeedback via the at least one haptic biofeedback element." )
Regarding claim 15, Anderson discloses: 15. The speech therapy device of claim 8, further comprising a housing including a first end and a second end, (Fig. 2C shows a housing with two ends.)
wherein: the one or more sensors include a first microphone and a second microphone, the first microphone being locater closer to the first end as compared to the second microphone, the second microphone being located closer to the second end as compared to the first microphone; and (Fig. 2C shows microphone 250a is closer to the first end than microphone 250b. 250b is closer to the second end than 250a.)
when the speech therapy device is worn by the user, the first microphone is located closer to a mouth of the user as compared to the second microphone. (Fig. 2A shows that the microphone at the first end is closer to the user's mouth than the microphone behind the user's ear.)
Regarding claim 16, Anderson discloses: 16. A speech therapy device configured to be worn by a user, ("[0004] An exemplary system and method are disclosed for a head-worn device for augmenting speech therapy or mitigating Parkinson's effect on speech and other speech-impairing conditions by providing haptic feedback, vibratory feedback, audio feedback, or other stimulations to a wearer by isolating and analyzing vocal/speech output of the user for signal-associated assessment, including, e.g., based on timing, direction, loudness, and/or speaking rate..." )
the speech therapy device comprising: a first microphone; (Fig. 2C shows a first microphone 250a)
a second microphone; (Fig. 2C shows a second microphone 250b)
one or more output components; ("[0140]...Output device(s) 512, such as a display, speakers, printer, etc., may also be included..." )
one or more processors; and one or more non-transitory computer-readable media storing computer-executable instructions that, when executed by the one or more processors, cause the speech therapy device to perform acts comprising: ("[0139] In its most basic configuration, computing device 500 typically includes at least one processing unit 506 and system memory 504…" )
receiving, from the first microphone, first audio data associated with a sound captured in an environment, receiving, from the second microphone, second audio data associated with the sound, ("[0071] At step 310, the method 300 includes receiving an audio signal, for example, via a plurality of acoustic sensors." )
determining, based at least in part on the first audio data and the second audio data, that the sound is associated with user speech of the user, ("[0102]...Two MEMS microphones and an accelerometer were used to determine if the user was speaking. This speech data was filtered to reduce environmental noise and then compared to the vocal sound pressure level thresholds." )
determining, based at least in part on the sound being associated with the user speech, one or more characteristics associated with the user speech, ("[0074] At step 316, the method 300 includes determining whether the wearer's speech signal satisfies one or more speech parameters, including at least one of an acoustic intensity parameter or a speech rate parameter…" )
determining that the one or more characteristics are indicative of hypophonia, and ("[0074]... In some implementations, the method 300 includes determining whether the wearer's speech signal satisfies an acoustic intensity parameter or speech rate parameter based on an amount of energy in the wearer's speech signal. By way of example, the at least one threshold for the acoustic intensity parameter can include a low acoustic intensity level and a high acoustic intensity level. Similarly, the at least one predetermined threshold for the speech rate parameter can include a low speech rate level and a high speech rate level." )
causing, based at least in part on the one or more characteristics being indicative of hypophonia, output of a notification via the one or more output components. ("[0075] At step 318, in response to detecting that the wearer's speech signal fails to satisfy at least one of the speech parameters, the method 300 includes outputting haptic biofeedback and/or other stimulation (e.g., audio, visual) to the wearer." )
Regarding claim 17, Anderson discloses: 17. The speech therapy device of claim 16, wherein the one or more output components comprise at least one of a speaker, a lighting element, or a haptic motor. ("[0140]...Output device(s) 512, such as a display, speakers, printer, etc., may also be included..."; see also "[0010] In some embodiments, at least one of the feedback elements comprises at least one haptic biofeedback element, wherein the processor is further configured to output haptic biofeedback via the at least one haptic biofeedback element." )
Regarding claim 20, Anderson discloses: 20. The speech therapy device of claim 16, wherein the one or more characteristics comprise at least one of a pitch of the user speech, an intonation in the user speech, a tone associated with the user speech, a pause in the user speech, a phonation associated with the user speech, an amplitude of the user speech. ("[0061] In some implementations, as depicted in FIG. 2A, the wearable speech-assisting device 108 is configured to perform a noise reduction operation to reduce ambient noise, and determine whether the wearer's speech signal satisfies one or more speech parameters, for example, by comparing an amplitude of the wearer's speech signal to a predetermined threshold.";)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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.
Claim(s) 2, 11-12, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Anderson in view of McNaney et al. ("LApp: A Speech Loudness Application for People with Parkinson’s on Google Glass").
Regarding claim 2, Anderson discloses: 2. The wearable speech therapy device of claim 1, the acts further comprising: receiving, from the first microphone, third audio data associated with second audio captured within the environment; receiving, from the second microphone, fourth audio data associated with the second audio; determining, based at least in part on the second audio being associated with the second user speech, one or more second biomarkers associated with the second user speech; determining that the one or more second biomarkers fail to satisfy the threshold associated with the second user speech containing hypophonia; and ("[0048]... The system 100 is configured to monitor a wearer's 111 (i.e., subject, user, patient) speech/voice as audio signals 101 via multiple acoustic sensors and continuously perform real-time analysis of the audio signals 101. " – see claim 1; [0048] discloses that the steps are performed continuously which reads on the limitations)
causing, based at least in part on the one or more second biomarkers failing to satisfy the threshold, output of a second notification via the speaker, the second notification being different than the notification. (not explicitly disclosed)
Anderson does not explicitly disclose a second notification when the threshold for hypophonia is not met.
McNaney discloses: causing, based at least in part on the one or more second biomarkers failing to satisfy the threshold, output of a second notification via the speaker, the second notification being different than the notification. ("To this end we redesigned the cue as a large ‘thumbs up’ symbol that could be more easily seen peripherally and to provide positive reinforcement that appropriate volume levels were being met. " pg. 499, first para)
Anderson and McNaney are considered analogous art to the claimed invention because they disclose devices for notifying the user of low speech volume. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Anderson with a separate notification that the user’s speech is in the target volume zone as disclosed by McNaney. Doing so would have been beneficial to provide positive reinforcement. (McNaney pg. 499, first para)
Regarding claim 11, Anderson discloses: 11. The speech therapy device of claim 8, the acts further comprising: receiving, from the one or more sensors, second data; determining that the second data is indicative of second speech of the user; determining, based at least in part on the second data being indicative of the second speech, one or more second biomarkers associated with the second speech, the one or more second biomarkers including at least a second amplitude associated with the second speech; determining that the one or more second biomarkers satisfy the threshold associated with hypophonia; and ("[0048]... The system 100 is configured to monitor a wearer's 111 (i.e., subject, user, patient) speech/voice as audio signals 101 via multiple acoustic sensors and continuously perform real-time analysis of the audio signals 101. " – see claim 1; [0048] discloses that the steps are performed continuously which reads on the limitations)
causing, based at least in part on the one or more second biomarkers satisfying the threshold, output of a second notification via the one or more output components, the second notification being different than the notification. (not explicitly disclosed)
Anderson does not explicitly disclose a second notification when the threshold associated with hypophonia is met (in this case the threshold being met indicates no hypophonia).
McNaney discloses: causing, based at least in part on the one or more second biomarkers satisfying the threshold, output of a second notification via the one or more output components, the second notification being different than the notification. ("To this end we redesigned the cue as a large ‘thumbs up’ symbol that could be more easily seen peripherally and to provide positive reinforcement that appropriate volume levels were being met. " pg. 499, first para)
Anderson and McNaney are considered analogous art to the claimed invention because they disclose devices for notifying the user of low speech volume. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Anderson with a separate notification that the user’s speech is in the target volume zone as disclosed by McNaney. Doing so would have been beneficial to provide positive reinforcement. (McNaney pg. 499, first para)
Regarding claim 12, Anderson discloses: 12. The speech therapy device of claim 11, the acts further comprising: receiving, from the one or more sensors, third data; determining that the third data is indicative of third speech of the user; determining, based at least in part on the third data being indicative of the third speech, one or more third biomarkers associated with the third speech, the one or more third biomarkers including at least a third amplitude associated with the third speech; determining that the one or more third biomarkers fail to satisfy the threshold associated with hypophonia; and causing, based at least in part on the one or more third biomarkers failing to satisfy the threshold, output of the notification via the one or more output components. ("[0048]... The system 100 is configured to monitor a wearer's 111 (i.e., subject, user, patient) speech/voice as audio signals 101 via multiple acoustic sensors and continuously perform real-time analysis of the audio signals 101. " – see claim 1; [0048] discloses that the steps are performed continuously which reads on the limitations)
Regarding claim 19, Anderson discloses: 19. The speech therapy device of claim 16, the acts further comprising: receiving, from the first microphone, third audio data associated with a second sound captured in the environment; receiving, from the second microphone, fourth audio data associated with the second sound, determining, based at least in part on the third audio data and the fourth audio data, that the second sound is associated with second user speech of the user, determining, based at least in part on the second sound being associated with the second user speech of the user, one or more second characteristics associated with the second user speech; determining that the one or more second characteristics are not indicative of hypophonia; and ("[0048]... The system 100 is configured to monitor a wearer's 111 (i.e., subject, user, patient) speech/voice as audio signals 101 via multiple acoustic sensors and continuously perform real-time analysis of the audio signals 101. " – see claim 1; [0048] discloses that the steps are performed continuously which reads on the limitations)
causing, based at least in part on the one or more second characteristics not being indicative of hypophonia, output of a second notification via the one or more output components, the second notification being different than the notification. (not explicitly disclosed)
Anderson does not explicitly disclose a second notification when hypophonia is not indicated.
McNaney discloses: causing, based at least in part on the one or more second characteristics not being indicative of hypophonia, output of a second notification via the one or more output components, the second notification being different than the notification. ("To this end we redesigned the cue as a large ‘thumbs up’ symbol that could be more easily seen peripherally and to provide positive reinforcement that appropriate volume levels were being met. " pg. 499, first para)
Anderson and McNaney are considered analogous art to the claimed invention because they disclose devices for notifying the user of low speech volume. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Anderson with a separate notification that the user’s speech is in the target volume zone as disclosed by McNaney. Doing so would have been beneficial to provide positive reinforcement. (McNaney pg. 499, first para)
Claim(s) 4, 6, 13-14, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Anderson in view of Berisha et al. (US 20230045078 A1).
Regarding claim 4, Anderson discloses: 4. The wearable speech therapy device of claim 1, further comprising a sensor, the acts further comprising receiving, from the sensor, sensor data, ("[0059] In some embodiments, the additional sensor(s) 204 include an accelerometer that facilitates accelerometer-based voice monitoring…" )
wherein: determining that the audio corresponds to the user speech is based at least in part on the sensor data; and ("[0102]...Two MEMS microphones and an accelerometer were used to determine if the user was speaking. This speech data was filtered to reduce environmental noise and then compared to the vocal sound pressure level thresholds." )
determining the one or more biomarkers is based at least in part on the sensor data. ("[0137]... VoxLog uses both an accelerometer and an Air microphone, while APM solely relies on an accelerometer [2]. These latter two devices measure both SPL and the fundamental frequency (F0) of the patient's speech to derive additional measurements relating to vocal dose [3]..." )
Anderson discloses that VoxLog can determine biomarkers based on accelerometer and microphone data, but does not explicitly disclose that they use this method for their system.
Berisha discloses: determining the one or more biomarkers is based at least in part on the sensor data. (“[0058] In certain embodiments, such machine learning algorithms (or other signal processing approaches) may compare the multi-dimensional statistical signature against one or more baseline statistical signatures of speech production and respiratory abilities by comparing each of several features (e.g., articulation precision, respiratory support, nasality, prosody, and phonatory control) to corresponding baseline speech and respiration feature of one or more baseline statistical signatures of speech production and respiration abilities. In certain embodiments, the machine learning algorithms may also take into account additional data, such as sensor data (e.g., from an accelerometer or environmental sensor), a time of day, an ambient light level, and/or a device usage pattern of the user.”)
Anderson and Berisha are considered analogous art to the claimed invention because they disclose devices for notifying the user of low speech volume. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Anderson to use a machine learning algorithm that takes into account accelerometer data as disclosed by Berisha. Doing so would have been beneficial to provide more efficient and objective results to the user. (Berisha [0005])
Regarding claim 6, Anderson does not disclose the additional limitations.
Berisha discloses: 6. The wearable speech therapy device of claim 1, wherein determining the one or more biomarkers is based at least in part on: providing, as an input to a machine-learned (ML) model ("[0017]...In some embodiments, the comparing the multi-dimensional statistical signature against the one or more baseline statistical signatures of speech production ability comprises applying a machine learning algorithm to the multi-dimensional statistical signature. In some embodiments, the machine learning algorithm is trained with past comparisons for other users. In some embodiments, extracting the multi-dimensional statistical signature of speech production abilities of the user from the input signal comprises measuring speech features across one or more of the following perceptual dimensions: articulation, prosodic variability, phonation changes, rate, and rate variation; and comparing the multi-dimensional statistical signature against the one or more baseline statistical signatures of speech production ability comprises comparing each speech feature to a corresponding baseline speech feature of the one or more baseline statistical signatures of speech production ability." )
trained to identify hypophonia, (Table 1 shows hypophonia is one of the conditions evaluated.)
the first audio data and the second audio data; and ("[0054] The audio input circuitry 108 may comprise at least one microphone. In certain embodiments, the audio input circuitry 108 may comprise a bone conduction microphone, a near field air conduction microphone array, or a combination thereof..." )
receiving, as an output from the ML model, an indication associated with the one or more biomarkers. ("[0101] In some embodiments, the systems, devices, and methods disclosed herein utilize one or algorithms or models configured to evaluate or assess speech and/or respiration, which may include generating an output indicative of a physiological state or condition or change (e.g., congestion, smoking cessation, etc.) corresponding to the speech and/or respiration evaluation." )
Anderson and Berisha are considered analogous art to the claimed invention because they disclose devices for notifying the user of low speech volume. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Anderson to use a machine learning algorithm to detect if the speech is associated with hypophonia as disclosed by Berisha. Doing so would have been beneficial to provide more efficient and objective results to the user. (Berisha [0005])
Regarding claim 13, Anderson discloses: 13. The speech therapy device of claim 8, wherein the one or more biomarkers further include at least one of a pitch of the speech, an intonation in the speech, a tone associated with the speech, a pause in the speech, or a phonation associated with the speech. ("[0009] In some embodiments, the one or more speech parameters include at least one of an acoustic intensity parameter, a speech rate parameter, pitch, speech duration, voice quality, or response time." )
Anderson discloses pitch as a biomarker; however, this does not appear to be supported in Anderson’s provisional application. The instant application’s provisional application also does not appear to support the limitations of claim 13. However, for the purposes of compact prosecution, claim 13 is rejected over Anderson in view of Berisha.
Berisha discloses: 13. The speech therapy device of claim 8, wherein the one or more biomarkers further include at least one of a pitch of the speech, an intonation in the speech, a tone associated with the speech, a pause in the speech, or a phonation associated with the speech. (“[0016]… In some embodiments, the signal processing circuitry is configured to process the input signal by measuring speech features represented in the input signal, the speech features comprising one or more of articulation rate, articulation entropy, vowel space area, energy decay slope, phonatory duration, and average pitch…”; see also “[0017]… In some embodiments, extracting the multi-dimensional statistical signature of speech production abilities of the user from the input signal comprises measuring speech features across one or more of the following perceptual dimensions: articulation, prosodic variability, phonation changes, rate, and rate variation; and comparing the multi-dimensional statistical signature against the one or more baseline statistical signatures of speech production ability comprises comparing each speech feature to a corresponding baseline speech feature of the one or more baseline statistical signatures of speech production ability.”)
Anderson and Berisha are considered analogous art to the claimed invention because they disclose devices for notifying the user of low speech volume. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Anderson to use additional biomarkers to detect if the speech is associated with hypophonia as disclosed by Berisha. Doing so would have been beneficial so that the biomarkers could be compared to baseline speech signatures. (Berisha [0016])
Regarding claim 14, Anderson does not disclose the additional limitations.
Berisha discloses: 14. The speech therapy device of claim 8, wherein determining the one or more biomarkers is based at least in part on: providing, as an input to a machine-learned (ML) model trained to identify hypophonia data; ("[0017]...In some embodiments, the comparing the multi-dimensional statistical signature against the one or more baseline statistical signatures of speech production ability comprises applying a machine learning algorithm to the multi-dimensional statistical signature. In some embodiments, the machine learning algorithm is trained with past comparisons for other users. In some embodiments, extracting the multi-dimensional statistical signature of speech production abilities of the user from the input signal comprises measuring speech features across one or more of the following perceptual dimensions: articulation, prosodic variability, phonation changes, rate, and rate variation; and comparing the multi-dimensional statistical signature against the one or more baseline statistical signatures of speech production ability comprises comparing each speech feature to a corresponding baseline speech feature of the one or more baseline statistical signatures of speech production ability." )
and receiving, as an output from the ML model, an indication associated with the one or more biomarkers. ("[0101] In some embodiments, the systems, devices, and methods disclosed herein utilize one or algorithms or models configured to evaluate or assess speech and/or respiration, which may include generating an output indicative of a physiological state or condition or change (e.g., congestion, smoking cessation, etc.) corresponding to the speech and/or respiration evaluation." )
Anderson and Berisha are considered analogous art to the claimed invention because they disclose devices for notifying the user of low speech volume. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Anderson to use a machine learning algorithm to detect if the speech is associated with hypophonia as disclosed by Berisha. Doing so would have been beneficial to provide more efficient and objective results to the user. (Berisha [0005])
Regarding claim 18, Anderson discloses: 18. The speech therapy device of claim 16, further comprising one or more sensors that include at least one of an accelerometer, a gyroscope, an internal measurement unit (IMU), or a piezoelectric sensor, ("[0059] In some embodiments, the additional sensor(s) 204 include an accelerometer that facilitates accelerometer-based voice monitoring…" )
the acts further comprising receiving, from the one or more sensors, data, ("[0059] In some embodiments, the additional sensor(s) 204 include an accelerometer that facilitates accelerometer-based voice monitoring…" )
wherein: determining that the sound is associated with the user speech is based at least in part on the data; and ("[0102]...Two MEMS microphones and an accelerometer were used to determine if the user was speaking. This speech data was filtered to reduce environmental noise and then compared to the vocal sound pressure level thresholds." )
determining the one or more characteristics associated with the user speech is based at least in part on the data. ("[0137]... VoxLog uses both an accelerometer and an Air microphone, while APM solely relies on an accelerometer [2]. These latter two devices measure both SPL and the fundamental frequency (F0) of the patient's speech to derive additional measurements relating to vocal dose [3]..." )
Anderson discloses that VoxLog can determine characteristics based on accelerometer and microphone data, but does not explicitly disclose that they use this method for their system.
Berisha discloses: determining the one or more characteristics associated with the user speech is based at least in part on the data. (“[0058] In certain embodiments, such machine learning algorithms (or other signal processing approaches) may compare the multi-dimensional statistical signature against one or more baseline statistical signatures of speech production and respiratory abilities by comparing each of several features (e.g., articulation precision, respiratory support, nasality, prosody, and phonatory control) to corresponding baseline speech and respiration feature of one or more baseline statistical signatures of speech production and respiration abilities. In certain embodiments, the machine learning algorithms may also take into account additional data, such as sensor data (e.g., from an accelerometer or environmental sensor), a time of day, an ambient light level, and/or a device usage pattern of the user.”)
Anderson and Berisha are considered analogous art to the claimed invention because they disclose devices for notifying the user of low speech volume. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Anderson to use a machine learning algorithm that takes into account accelerometer data as disclosed by Berisha. Doing so would have been beneficial to provide more efficient and objective results to the user. (Berisha [0005])
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Robertson et al. (US 9336795 B1). Robertson discloses a system for providing real-time loudness alerts to patients with hypophonia.
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/JON CHRISTOPHER MEIS/Examiner, Art Unit 2654
/HAI PHAN/Supervisory Patent Examiner, Art Unit 2654
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