DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
(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, 5, 6, 9, 13, 14, and 17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tsutsumi (JP 2013022360).
Regarding claim 1, Tsutsumi discloses a method of detecting obstructive sleep apnea (OSA) by a user, the method comprising:
receiving audio data emanating from the user while the user is sleeping, the audio data comprising multiple distinct frequency bands (“FIG. 9 is a diagram showing the analysis result of a normal snoring sound. Referring to FIG. 9, a normal snoring sound is generally composed of a sound having a sound pressure of 1000 Hz or less… In contrast, OSAS snoring differs in the frequency of the sound pressure that constitutes it… Referring to FIG. 11, OSAS snoring is composed of a sound that includes a large amount of sound pressure having a frequency of a fundamental tone in the vicinity of 150 Hz and its harmonics, and a fundamental tone in the range of 1000 to 1500 Hz and its harmonics.”);
analyzing the audio data to detect patterns in frequency content that matches two or more formants of a plurality of formants, the two or more formants indicating a position of a tongue of the user (“Further, the third column and the fourth column are respectively set to a sound pressure having a frequency representing a fundamental tone in the vicinity of 150 Hz, which is a fundamental tone generated by resonance when the soft palate is closed in the audio signal, and its harmonic, and a harmonic thereof. A predetermined process is applied to the value obtained by performing the above processing, and the fundamental pressure generated by resonance when the tongue base portion is blocked and the harmonic pressure thereof, and the sound pressure at the frequency of 1000 Hz to 1500 Hz”);
based on the analyzing, determining when the tongue has moved from a forward position to a rear position in an oral cavity of the user (“The obtained value is represented, and the fifth column represents the result of the snoring determination in step S30 using this audio signal. In FIG. 19, as a result of the snoring determination, a determination result indicating that a sound corresponding to normal snoring has occurred is “0”, and a determination result indicating that a sound corresponding to OSAS snoring has been generated is “1”.”); and
generating a signal indicating an OSA episode when it is determined that the tongue has moved from a forward position to a rear position (“The obtained value is represented, and the fifth column represents the result of the snoring determination in step S30 using this audio signal. In FIG. 19, as a result of the snoring determination, a determination result indicating that a sound corresponding to normal snoring has occurred is “0”, and a determination result indicating that a sound corresponding to OSAS snoring has been generated is “1”.”).
Regarding claims 5 and 13, Tsutsumi discloses the method further comprising measuring a duration of each inspiration of the user to estimate a magnitude of an indrawn breath (“Referring to FIG. 15, the waveform represented by the sensor signal includes a waveform (hereinafter also referred to as a breathing waveform) representing body movement (chest movement) accompanying breathing of the measurement subject, and body movement other than breathing such as turning over. Is a synthesized wave including a waveform (hereinafter also referred to as a body motion waveform)”; Fig. 15).
Regarding claim 6, Tsutsumi discloses the two or more formants comprising an F1 formant and an F2 formant (“FIG. 11 is a diagram illustrating an analysis result of the OSAS snoring sound. Referring to FIG. 11, OSAS snoring is composed of a sound that includes a large amount of sound pressure having a frequency of a fundamental tone in the vicinity of 150 Hz and its harmonics, and a fundamental tone in the range of 1000 to 1500 Hz and its harmonics. The former frequency is a fundamental tone and its harmonics generated by resonance when the soft palate is closed, and the latter frequency is a fundamental tone and its harmonics generated by resonance when the tongue base is closed.”).
Regarding claim 9, Tsutsumi discloses an obstructive sleep apnea (OSA) detection system comprising:
a respiration monitoring device adapted to receiving audio data emanating from the user while the user is sleeping, the audio data comprising multiple distinct frequency bands (“FIG. 9 is a diagram showing the analysis result of a normal snoring sound. Referring to FIG. 9, a normal snoring sound is generally composed of a sound having a sound pressure of 1000 Hz or less… In contrast, OSAS snoring differs in the frequency of the sound pressure that constitutes it… Referring to FIG. 11, OSAS snoring is composed of a sound that includes a large amount of sound pressure having a frequency of a fundamental tone in the vicinity of 150 Hz and its harmonics, and a fundamental tone in the range of 1000 to 1500 Hz and its harmonics.”); and
one or more processors adapted to analyze the audio data to detect patterns in frequency content that matches two or more formants of a plurality of formants, the two or more formants indicating a position of a tongue of the user, based on the analyzing, determine when the tongue has moved from a forward position to a rear position in an oral cavity of the user, and generate a signal indicating an OSA episode when it is determined that the tongue has moved from a forward position to a rear position (“The control unit 40 includes a CPU (Central Processing Unit) 41 for performing overall control, and a memory 42 for storing programs executed by the CPU 41. In the control unit 40, the CPU 41 executes a display program stored in the memory 42, performs an operation using the input operation signal and sensor signal, detects OSAS snoring described later, An OSAS cycle to be described later is detected. Then, an index representing the OSAS level is calculated and processed for output.”).
Regarding claim 14, Tsutsumi discloses the two or more formants comprising an F1 formant and an F2 formant (“FIG. 11 is a diagram illustrating an analysis result of the OSAS snoring sound. Referring to FIG. 11, OSAS snoring is composed of a sound that includes a large amount of sound pressure having a frequency of a fundamental tone in the vicinity of 150 Hz and its harmonics, and a fundamental tone in the range of 1000 to 1500 Hz and its harmonics. The former frequency is a fundamental tone and its harmonics generated by resonance when the soft palate is closed, and the latter frequency is a fundamental tone and its harmonics generated by resonance when the tongue base is closed.”).
Regarding claim 17, Tsutsumi discloses a non-transitory computer-readable medium storing instructions which, when executed by at least one of a plurality of processors, cause the processor to detect obstructive sleep apnea (OSA) by a user, the detecting comprising:
receiving audio data emanating from the user while the user is sleeping, the audio data comprising multiple distinct frequency bands (“FIG. 9 is a diagram showing the analysis result of a normal snoring sound. Referring to FIG. 9, a normal snoring sound is generally composed of a sound having a sound pressure of 1000 Hz or less… In contrast, OSAS snoring differs in the frequency of the sound pressure that constitutes it… Referring to FIG. 11, OSAS snoring is composed of a sound that includes a large amount of sound pressure having a frequency of a fundamental tone in the vicinity of 150 Hz and its harmonics, and a fundamental tone in the range of 1000 to 1500 Hz and its harmonics.”);
analyzing the audio data to detect patterns in frequency content that matches two or more formants of a plurality of formants, the two or more formants indicating a position of a tongue of the user (“Further, the third column and the fourth column are respectively set to a sound pressure having a frequency representing a fundamental tone in the vicinity of 150 Hz, which is a fundamental tone generated by resonance when the soft palate is closed in the audio signal, and its harmonic, and a harmonic thereof. A predetermined process is applied to the value obtained by performing the above processing, and the fundamental pressure generated by resonance when the tongue base portion is blocked and the harmonic pressure thereof, and the sound pressure at the frequency of 1000 Hz to 1500 Hz”);
based on the analyzing, determining when the tongue has moved from a forward position to a rear position in an oral cavity of the user (“FIG. 11 is a diagram illustrating an analysis result of the OSAS snoring sound. Referring to FIG. 11, OSAS snoring is composed of a sound that includes a large amount of sound pressure having a frequency of a fundamental tone in the vicinity of 150 Hz and its harmonics, and a fundamental tone in the range of 1000 to 1500 Hz and its harmonics. The former frequency is a fundamental tone and its harmonics generated by resonance when the soft palate is closed, and the latter frequency is a fundamental tone and its harmonics generated by resonance when the tongue base is closed.”); and
generating a signal indicating an OSA episode when it is determined that the tongue has moved from a forward position to a rear position (“The obtained value is represented, and the fifth column represents the result of the snoring determination in step S30 using this audio signal. In FIG. 19, as a result of the snoring determination, a determination result indicating that a sound corresponding to normal snoring has occurred is “0”, and a determination result indicating that a sound corresponding to OSAS snoring has been generated is “1”.”).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 2, 10, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tsutsumi (JP 2013022360), in view of King (US PGPUB 2004/0210261).
Regarding claim 2, Tsutsumi does not disclose the method further comprising: initiating an electrical stimulation of nerves of the user, via electrodes, to cause the tongue to move and to alleviate the OSA episode. However, King, in the same field of endeavor of monitoring obstructive sleep apnea, discloses “Stimulation of these ascending pathways also increases parasympathetic outflow via cranial nerves (not shown) of patient 12, such as the glossopharyngeal nerve, which may lessen obstructive sleep apnea (OSA) by influencing coordination of upper airway muscle groups, such as the pharyngeal muscles. Increased parasympathetic outflow via cranial nerves also affects the hypoglossal nerve, which can lessen OSA by improving the position of the tongue (not shown) of patient 12.” (Par. 28). Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include electrical stimulation to nerves in order to change the position of the tongue, as taught and suggested by King, for the purpose of “open the upper airway of patient 12 to lessen the likelihood of OSA” (Par. 72).
Regarding claim 10, Tsutsumi does not disclose the system further comprising: a patch adapted to receive the signal and, in response, initiate an electrical stimulation of nerves of the user, via electrodes, to cause the tongue to move and to alleviate the OSA episode. However, King, in the same field of endeavor of monitoring obstructive sleep apnea, discloses “Stimulation of these ascending pathways also increases parasympathetic outflow via cranial nerves (not shown) of patient 12, such as the glossopharyngeal nerve, which may lessen obstructive sleep apnea (OSA) by influencing coordination of upper airway muscle groups, such as the pharyngeal muscles. Increased parasympathetic outflow via cranial nerves also affects the hypoglossal nerve, which can lessen OSA by improving the position of the tongue (not shown) of patient 12.” (Par. 28) and “Sensor 62 can also take the form of one or more additional patch electrodes to detect thoracic impedance or electrical activity of the heart of patient 12. Sensor 62 can take the form of an acoustic sensor to detect snoring, as discussed above.” (Par. 58). Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include electrical stimulation to nerves in order to change the position of the tongue, as taught and suggested by King, for the purpose of “open the upper airway of patient 12 to lessen the likelihood of OSA” (Par. 72).
Regarding claim 18, Tsutsumi does not disclose the detecting further comprising: initiating an electrical stimulation of nerves of the user, via electrodes, to cause the tongue to move and to alleviate the OSA episode. However, King, in the same field of endeavor of monitoring obstructive sleep apnea, discloses “Stimulation of these ascending pathways also increases parasympathetic outflow via cranial nerves (not shown) of patient 12, such as the glossopharyngeal nerve, which may lessen obstructive sleep apnea (OSA) by influencing coordination of upper airway muscle groups, such as the pharyngeal muscles. Increased parasympathetic outflow via cranial nerves also affects the hypoglossal nerve, which can lessen OSA by improving the position of the tongue (not shown) of patient 12.” (Par. 28). Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include electrical stimulation to nerves in order to change the position of the tongue, as taught and suggested by King, for the purpose of “open the upper airway of patient 12 to lessen the likelihood of OSA” (Par. 72).
Claim(s) 3, 4, 11, 12, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tsutsumi (JP 2013022360), in view of Bruce (WO 2011/150362).
Regarding claims 3, 11, and 19, Tsutsumi does not disclose the analyzing comprising:
converting a time domain audio signal into a frequency domain;
creating a data sequence to show, at each sampled time step, an energy content of the audio at each specified frequency band.
However, Bruce, in the same field of endeavor of monitoring sleep apnea, discloses “In terms of quantifying this, the integral of the Fourier transform 505 of an apneic snoring event 510 can be compared to the Fourier transform 515 of a simple snoring event 520, which can be used to quantify the measurements of apneic snoring as compared to simple snoring. In this example, while the loudness of sounds may change, the frequency distribution may not change. Thus, the wide range of frequencies that comprise apneic snoring can be evident in the Fourier transform even though the subject may change posture” (Pg. 16, lines 16-22) and “For example, the graphs can depict sound intensities, Fourier transforms, and the like. These graphs can be of time periods that have been determined to be indicative of snoring events, time periods containing unknown sounds, time periods indicative of apneic snoring events, all times, and the like.” (Pg. 11, lines 17-21) and “In operation 215, a bandpass filter can be applied to the recorded sound to permit only analysis of sound in the frequency range of human snores. This filter can eliminate adventitial sounds, lowering the likelihood of false positive snores by only submitting to analysis sounds in the snoring frequency. In some cases, the expected bandpass filters can be pre-specified or can be modified for clinical information (e.g., age, gender, etc.) or for verbal cues for baseline.” (Pg. 9, lines 9-17) . Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include converting time domain audio to frequency domain, as taught and suggested by Bruce, for the purpose of using the data to “quantify the measurements of apneic snoring” (Pg. 16, lines 18-19).
Regarding claims 4, 12, and 20, Bruce discloses the method further comprising adjusting the frequency bands while the user is sleeping in response to changes in breathing of the user in response to at least one of a change in a sleeping position or a congestion in a passageway (Pg. 6, lines 25-29: “comparisons of recorded data to the calibration data can be used to identify when a user emits a snore, when the user speaks (e.g., sleep talking), when a user changes position, and the like. The frequency, volume, and other details about emitted snores can be useful in determining if the user is suffering from sleep apnea”; Pg. 13, lines 1-5: “In addition to a very rapid envelope autocorrelation, a simple power spectrum may show both an increased variation in power spectrum during apneic snores compared to baseline and a high energy density in the high frequency band (e.g., in the frequency range of the vibration) during snoring versus other types of sounds (See Figure 5).”). Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include different frequency bands for changes in the user’s state, as taught and suggested by Bruce, for the purpose of “analyze these sounds (or transmit sounds for analysis) to determine whether OSA is present” (Pg. 6, lines 23-24).
Claim(s) 7 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tsutsumi (JP 2013022360), in view of Dodd (WO 2021/260656).
Regarding claims 7 and 15, Dodd discloses the method further comprising: training a machine learning model based on formant data over time; using the trained machine learning model for the analyzing (Par. 110: “In some implementations, step 503 includes identifying event(s) using a machine learning algorithm that is trained (e.g., using supervised or unsupervised learning techniques) to receive the respiration data (step 501) and/or the determined respiration signal (step 502) and output an identification of one or more events. In such implementations, the machine learning algorithm can also be trained to additionally receive as an input the audio data (step 501) and/or the determined audio signal (step 502) and output the identification of one or more events.”). Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include a machine learning model to analyze the data, as taught and suggested by Dodd, for the purpose of “determining one or more sleep-related parameters associated with the user during the sleep session based at least in part on the received data” (Par. 111).
Claim(s) 8 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tsutsumi (JP 2013022360) in view of King (2004/0210261), further in view of Martinot (WO 2022/069748).
Regarding claims 8 and 16, Martinot discloses the method further comprising using a trained machine learning model that was trained based on formant data or breathing data over time to adjust the electrical stimulation over time as the patterns of formants or breathing changes (Pg. 33, lines 7-25: “The matching may be fully or partially automated by the provision of a machine learning model, such that the data analysis unit is configured to learn a number of statistical and/or physical metrics in order to capture the characteristics of the signal in frequency and time domains and identify patterns of rotation signal to specific events, such as sleep stages, respiratory effort, muscle fatigue, and the like… In an embodiment the data analysis unit, preferably by means of recorded mandibular activity data, may be configured to determine efficacy of the stimulation without requiring subject feedback, specifically during subject sleep… a high occurrence of irregular mandibular movement may indicate that the muscles of the subject are not sufficiently responding to the applied electrical stimulation and as such one or more stimulation parameters may need to be adjusted, for example by increasing the current intensity.”). Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include a machine learning model to adjust the electrical stimulation, as taught and suggested by Martinot, for the purpose of “determine efficacy of the stimulation without requiring subject feedback, specifically during subject sleep” (Pg. 33, lines 18-20).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARELY A MENDIOLA whose telephone number is (571)272-0447. The examiner can normally be reached Monday - Friday 9am-5pm ET.
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/A.M./Examiner, Art Unit 3792
/NIKETA PATEL/Supervisory Patent Examiner, Art Unit 3792