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) 1,2, 7, 8, 14, 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2020/0337625 hereinafter Aimone.
In regards to Claim 1: Aimone teaches a sleep assessment system comprising:
a sensor assembly comprising:
a plurality of sensors configured to capture measurements that include indications of both brain and eye activity (Aimone, Paragraphs 0065-0066, Item 112), the plurality of sensors being configured to be directed to a forehead of a subject; (Aimone, Paragraph 0059)
processing circuitry operably coupled to the sensor assembly (Aimone, Paragraph 0060, Item 120),
wherein the processing circuitry is configured to:
receive sensor signals based on the measurements made by the plurality of sensors (Aimone, Paragraph 0060);
process the sensor signals to generate sleep data (Aimone, Paragraph 0055);
apply a sleep state convolutional neural network to the sleep data to determine a current sleep state of a subject (Aimone, Paragraph 0343) (Aimone, Figure 5B);
identify, based on the sleep data and the current sleep state, a sleep state-based data feature (Aimone, Paragraph 0226);
and output a stimulus to the subject based on sleep state-based data feature (Aimone, Paragraph 0359); and a housing configured to be secured to the forehead of the subject, wherein the sensor assembly and processing circuitry are disposed on or within the housing (Aimone, Paragraph 0059, Item 110).
In regards to Claim 2: Aimone teaches the sleep assessment system of claim 1, further comprising a sounder, wherein the processing circuitry is further configured to output the stimulus as an audible output from the sounder. (Aimone, Paragraph 0061, “speaker”)
In regards to Claim 7: Aimone teaches the sleep assessment system of claim 1, wherein the sleep state convolutional neural network is based on a sleep state transitional pattern of sleep states (Aimone, Paragraph 0325) (Aimone, Figure 19).
In regards to Claim 14: Aimone teaches a sleep assessment system comprising:
a sensor assembly comprising:
a plurality of sensors configured to capture measurements that include indications of both brain and eye activity (Aimone, Paragraphs 0065-0066, Item 112), the plurality of sensors being configured to be directed at a forehead of a subject (Aimone, Paragraph 0059);
a sounder configured to output an audible sound (Aimone, Paragraph 0061, “speaker”); and
processing circuitry operably coupled to the sensor assembly, wherein the processing circuitry is configured to:
receive sensor signals based on the measurements made by the plurality of sensors (Aimone, Paragraph 0060, Item 120);
process the sensor signals to generate sleep data (Aimone, Paragraph 0055);
apply a sleep state convolutional neural network to the sleep data to determine a current sleep state of a subject (Aimone, Paragraph 0343) (Aimone, Figure 5B);
identify, based on the sleep data and the current sleep state, a sleep state-based data feature (Aimone, Paragraph 0226); and
output a stimulus in the form of the audible sound via the sounder to the subject based on sleep state-based data feature (Aimone, Paragraph 0359)
In regards to Claim 15: Aimone teaches the sleep assessment system of claim 14, wherein the sleep state convolutional neural network is based on a sleep state transitional pattern of sleep states. (Aimone, Paragraph 0325) (Aimone, Figure 19).
In regards to Claim 20: Aimone teaches a method for performing a sleep assessment of a subject, the method comprising:
receiving sensor signals based on measurements made by a plurality of sensors (Aimone, Paragraphs 0065 & 0066, Item 112), the plurality of sensors being configured to capture measurements that include indications of both brain and eye activity and to be directed at a forehead of the subject (Aimone, Paragraph 0059);
processing the sensor signals to generate sleep data (Aimone, Paragraph 0055);
applying, via processing circuitry, a sleep state convolutional neural network to the sleep data to determine a current sleep state of a subject (Aimone, Paragraph 0060),
identifying, based on the sleep data and the current sleep state, a sleep state-based data feature (Aimone, Paragraph 0226); and
outputting a stimulus in the form of an audible sound via a sounder based on the sleep state-based data feature (Aimone, Paragraph 0359).
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) 3 & 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aimone in view of US 2022/0386947 hereinafter Garcia Molina.
In regards to Claim 3: Aimone teaches all of claim 1, but does not teach of a processing circuitry further configured to output the stimulus by communicating instructions to a temperature-controlled mattress to control a temperature based on the sleep state-based data feature.
Garcia Molina teaches the processing circuitry is further configured to output the stimulus by communicating instructions to a temperature-controlled mattress to control a temperature based on the sleep state-based data feature (Garcia Molina, Paragraph 0057).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to add the temperature control output to the processing circuitry described in Aimone, the motivation being to provide a better sleep experience for the user by creating a more optimal environment to facilitate sleep.
In regards to Claim 4: Aimone teaches all of claim 1, applying an exponential weighting to the buffered sleep data to generate weighted sleep data (Aimone, Paragraph 237), wherein the processing circuitry is further configured to apply the sleep state convolutional neural network to the weighted sleep data to determine the current sleep state of the subject (Aimone, Paragraph 240), but does not teach of a processing circuitry further configured to generate the sleep data by; capturing and buffering the sensor signals for a buffer duration to assemble buffered sleep data; and wherein the buffer duration is a thirty second epoch.
Garcia Molina teaches capturing and buffering the sensor signals for a buffer duration to assemble buffered sleep data and wherein the buffer duration is a thirty second epoch (Garcia Molina, Paragraph 0209).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the buffer duration for buffering the sensor signals taught by Garcia Molina to the processing circuitry in Aimone, the motivation being to provide greater processing speed by the device to improve responsiveness.
Claim(s) 5 & 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aimone in view of US 2020/0215299 hereinafter Myllymaki.
In regards to Claim 5: Aimone teaches all of claim 1, subject based training for the sleep state neural network (Aimone, Paragraph 0219), but does not teach of short-term training periods for the sleep state neural network.
Myllymaki teaches of short-term training for a training period of time (Myllymaki, Paragraph 0011).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to add time requirement for training taught in Myllymaki to the processing circuitry of Aimone, the motivation being to provide an appropriate length of training time for a neural network used by an individual.
In regards to Claim 6: Aimone teaches all of claim 1, non-subject based training using prior captured, historical sleep-related data (Aimone, Paragraph 0123), but does not teach of long-term training of the neural network.
Myllymaki teaches of long-term training of the neural network (Myllymaki, Paragraph 0011).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to add long-term training timetable taught in Myllymaki with the processing circuitry of Aimone, the motivation being to create a more stable brain model by letting it absorb as much information as possible from the databases described.
Claim(s) 8 & 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aimone in view of US 2016/006716 hereinafter Rao.
In regards to claim 8: Aimone teaches all of claim 1, but does not teach of the processing circuitry is further configured to apply the sleep state convolutional neural network to determine the current sleep state of the subject by: determining a confidence estimate of the current sleep state; and determining the current sleep state based on the confidence estimate exceeding a sleep state confidence threshold, wherein the processing circuitry is further configured to identify the sleep state-based data feature in response to the confidence estimate exceeding a sleep state confidence threshold.
Rao teaches the processing circuitry is further configured to apply the sleep state convolutional neural network to determine the current sleep state of the subject by:
determining a confidence estimate of the current sleep state (Rao, Paragraph 0120); and
determining the current sleep state based on the confidence estimate exceeding a sleep state confidence threshold (Rao, Paragraph 0120),
wherein the processing circuitry is further configured to identify the sleep state-based data feature in response to the confidence estimate exceeding a sleep state confidence threshold (Rao, Paragraph 0126, Item 2828).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to add the confidence estimate with the identification feature taught by Rao to the processing circuitry of Aimone, the motivation being to ensure the accuracy of the data and identifying the sleep state with the data accuracy in mind.
In regards to Claim 16: Aimone teaches all of claim 1, but does not teach of the processing circuitry is further configured to apply the sleep state convolutional neural network to determine the current sleep state of the subject by: determining a confidence estimate of the current sleep state; and determining the current sleep state based on the confidence estimate exceeding a sleep state confidence threshold, wherein the processing circuitry is further configured to identify the sleep state-based data feature in response to the confidence estimate exceeding a sleep state confidence threshold.
Rao teaches the processing circuitry is further configured to apply the sleep state convolutional neural network to determine the current sleep state of the subject by:
determining a confidence estimate of the current sleep state; and
determining the current sleep state based on the confidence estimate exceeding a sleep state confidence threshold (Rao, Paragraph 0120),
wherein the processing circuitry is further configured to identify the sleep state-based data feature in response to the confidence estimate exceeding a sleep state confidence threshold (Rao, Paragraph 0126, Item 2828).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to add the confidence estimate with the identification feature taught by Rao to the processing circuitry of Aimone, the motivation being to ensure the accuracy of the data and identifying the sleep state with the data accuracy in mind.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aimone in view of US 2012/0125337 hereinafter Asanoi.
In regards to Claim 9: Aimone teaches all of claim 1, the output of a stimulus to the subject (Aimone, Paragraph 0146) but does not teach of the processing circuitry further configured to output the stimulus to the subject in a repeating pattern based on repeated determinations that the current sleep state is a slow wave sleep state and a delay duration of time.
Asanoi teaches of the processing circuitry is further configured to output the stimulus to the subject in a repeating pattern based on repeated determinations that the current sleep state is a slow wave sleep state and a delay duration of time (Asanoi, Paragraph 0111 & 0106).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to add the repeated determinations of sleep as taught by Asanoi to the processing circuitry of Aimone, the motivation being to provide constant feedback to the user to keep them appraised of the test results in real time.
Claim(s) 10 11, & 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aimone in view of US 2015/0105687 hereinafter Abreu.
In regards to Claim 10: Aimone teaches all of claim 1, but does not teach of the processing circuitry further configured to output a stimulus within four seconds of identifying the sleep state-based data feature.
Abreu teaches the processing circuitry further configured to output the stimulus within four seconds of identifying the sleep state-based data feature. (Abreu, Paragraph 0499)
It would have been obvious to one of ordinary skill in the art at the filing date of the invention to add the frequency of repetition or measurements taught by Abreu to the processing circuitry of Aimone, since it has been held that where the general conditions of a claim are disclosed in the prior art, discovering the optimum or working ranges involves only routine skill in the art. In re Aller, 105 USPQ 233. See MPEP 2144.05.II. The Examiner notes that a particular parameter must be recognized as a result effective variable, in this case, that parameter is time which achieves the recognized result of keeping the user up to date on the test being performed therefore, one of ordinary skill in the art at the filing date of the invention would have found the claimed range through routine experimentation. In re Antonie, 559 F.2d 618, 195 USPQ 6 (CCPA 1977). See also In re Boesch, 617 F.2d 272, USPQ 215 (CCPA 1980).
In regards to Claim 11: Aimone teaches all of claim 1, but does not teach the processing circuitry is further configured to identify the sleep state-based data feature based on less than four seconds of sleep data.
Abreu teaches the processing circuitry is further configured to identify the sleep state-based data feature based on less than four seconds of sleep data. (Abreu, Paragraph 0499)
It would have been obvious to one of ordinary skill in the art at the filing date of the invention to add the frequency of repetition or measurements taught by Abreu to the processing circuitry of Aimone, since it has been held that where the general conditions of a claim are disclosed in the prior art, discovering the optimum or working ranges involves only routine skill in the art. In re Aller, 105 USPQ 233. See MPEP 2144.05.II. The Examiner notes that a particular parameter must be recognized as a result effective variable, in this case, that parameter is time which achieves the recognized result of keeping the user up to date on the test being performed therefore, one of ordinary skill in the art at the filing date of the invention would have found the claimed range through routine experimentation. In re Antonie, 559 F.2d 618, 195 USPQ 6 (CCPA 1977). See also In re Boesch, 617 F.2d 272, USPQ 215 (CCPA 1980).
In regards to Claim 17: Aimone teaches all of claim 1, but does not teach of the processing circuitry further configured to output a stimulus within four seconds of identifying the sleep state-based data feature.
Abreu teaches the processing circuitry further configured to output the stimulus within four seconds of identifying the sleep state-based data feature. (Abreu, Paragraph 0499)
It would have been obvious to one of ordinary skill in the art at the filing date of the invention to add the frequency of repetition or measurements taught by Abreu to the processing circuitry of Aimone, since it has been held that where the general conditions of a claim are disclosed in the prior art, discovering the optimum or working ranges involves only routine skill in the art. In re Aller, 105 USPQ 233. See MPEP 2144.05.II. The Examiner notes that a particular parameter must be recognized as a result effective variable, in this case, that parameter is time which achieves the recognized result of keeping the user up to date on the test being performed therefore, one of ordinary skill in the art at the filing date of the invention would have found the claimed range through routine experimentation. In re Antonie, 559 F.2d 618, 195 USPQ 6 (CCPA 1977). See also In re Boesch, 617 F.2d 272, USPQ 215 (CCPA 1980).
Claim(s) 12 & 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aimone in view of US 2020/0306494 hereinafter Molina.
In regards to Claim 12: Aimone teaches all of Claim 1, but does not teach of processing circuitry further configured to: output the stimulus as a first stimulus at a first time and a second stimulus at a second time; implement a refractory period after the second stimulus before reconfirming the current sleep state.
Molina teaches of processing circuitry further configured to: output the stimulus as a first stimulus at a first time and a second stimulus at a second time; implement a refractory period after the second stimulus before reconfirming the current sleep state. (Molina, Paragraph 0068 & 0069, The art teaches of stimulation being administered to the user with sensors in place to sense for changes in status of the user. It further teaches that the stimulation can be paused for a predefined period before resuming stimulation and the monitoring of the user. The predetermined pause could be set to the noted refractory period described in paragraph 0069.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to add the configuration of pausing stimulation after arousal detection taught by Molina with the processing circuitry of Aimone, the motivation being to allow for a baseline to be re-established before stimulating the user again.
In regards to Claim 18: Aimone teaches all of Claim 1, but does not teach of processing circuitry further configured to: output the stimulus as a first stimulus at a first time and a second stimulus at a second time; implement a refractory period after the second stimulus before reconfirming the current sleep state.
Molina teaches of processing circuitry further configured to: output the stimulus as a first stimulus at a first time and a second stimulus at a second time; implement a refractory period after the second stimulus before reconfirming the current sleep state. (Molina, Paragraph 0068 & 0069)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to add the configuration of pausing stimulation after arousal detection taught by Molina with the processing circuitry of Aimone, the motivation being to allow for a baseline to be re-established before stimulating the user again.
Claim(s) 13 & 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Aimone in view of Molina as applied to claim 12 above, and further in view of Rao.
In regards to Claim 13: A modified Aimone teaches all of claim 12, but does not teach of a duration of the refractory period is proportional to a confidence estimate of the current sleep state that is output from application of the sleep state convolutional neural network.
A combination of Molina and Rao teaches a duration of the refractory period (Molina, Paragraph 0068) is proportional to a confidence estimate of the current sleep state that is output from application of the sleep state convolutional neural network (Rao, Paragraph 0120).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to add the refractory period taught in Molina to the confidence estimate taught in Rao with the processing circuitry of Aimone, the motivation being to ensure that the refractory period duration is always applicable to user.
In regards to Claim 19: A modified Aimone teaches all of claim 12, but does not teach of a duration of the refractory period is proportional to a confidence estimate of the current sleep state that is output from application of the sleep state convolutional neural network.
A combination of Molina and Rao teaches a duration of the refractory period (Molina, Paragraph 0068) is proportional to a confidence estimate of the current sleep state that is output from application of the sleep state convolutional neural network (Rao, Paragraph 0120).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to add the refractory period taught in Molina to the confidence estimate taught in Rao with the processing circuitry of Aimone, the motivation being to ensure that the refractory period duration is always applicable to user.
Conclusion
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/N.R.D./Patent Examiner, Art Unit 3791 /ALEX M VALVIS/Supervisory Patent Examiner, Art Unit 3791