DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments, see pgs. 11-13, filed 09/08/2025, with respect to the rejection(s) of claim(s) 1,22, and 31 under Zambotti have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Kahn et al (US10568565B1); hereinafter Kahn.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
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 1,22, and 31 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The “specificity ratio” and the “sensitivity ratio” are not clearly defined in the claims or the specification. In fact, there is no mention of such a ratio in the specification. The applicant remarks, filed 09/08/2025 on page 13 attempts to clarify the meaning of these limitations successfully, however this clarification is not present in the claims or the specification. Taking this clarification as the broadest reasonable interpretation, the 35 USC 112(a) rejection is still applicable because the specification or the drawings provide no basis for having two separate sensors (or groups of sensors) for sleeping and waking states.
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) 1,3,4,7-11,21, 22, and 24-31 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zambotti et al (US 20200013511 A1); hereinafter Zambotti (cited previously) in view of Kahn et al (US 10568565 B1); hereinafter Kahn.
Regarding claims 1,22, and 31, Zambotti teaches a method performed by an electronic device, an electronic device, and a non-transitory computer readable storage medium comprising:
extracting features from a plurality of sensor signals ([0007] sensor circuitry) obtained, via the communication circuitry, from a plurality of sensor circuits, the plurality of sensor circuits being associated with a user and connected to the electronic device ([0066] feature extraction), wherein the plurality of sensor circuits comprise at least one of a photoplethysmogram (PPG) sensor, a skin conductance (SC) sensor, a temperature (T), or a motion (M) sensor ([0071] With regard to physiologic sensors, the sensors may include but are not limited to skin conductance sensors, skin temperature sensors, blood pressure sensors, pulse rate sensors, photoplethysmogram sensors, electrocardiogram sensors, and electroencephalogram sensors);
aligning the features to a common time point ([0092] features could also include parameters of a model best representing the data in a specific time window);
identifying, based on aligning the features, whether the user is in a sleep state or an awake state ([0128] fig 12 illustrates an example graphical representation of the amount of wake time associated with hot flashes);
based on identifying that the user is in the sleep state:
identify, based on the first sensor combination, from the features, at least one first feature extracted from one or more sensor circuits included in the first sensor combination ([0092] as shown in FIG. 5, an example first input data category for sub-model 1 543-1 can include raw physiological signals or extracted features of them. Extracted features are temporal and/or spectral features representing the physiological signals and their specific time pattern, variabilities and frequency content), and
identify, based on the at least one first feature applied with a first set of weights, a time at which a hot flash (HF) event is predicted to occur for the user in the sleep state, using a predictive data model configured to identify a probability of the HF event;
based on identifying that the user is in the awake state:
identifying, based on the at least one second feature applied with a second set of weights distinct from the first set of weights, a time at which the hot flash (HF) event is predicted to occur for the user in the awake state, using a predictive data model; and
providing a message indicative of the HF event to the user ([0079] for condition 1, mitigating action1 seems to be effective while mitigating action 2 did not appear effective in mitigating a hot flash…the data collected for a particular user can include the identification of mitigating actions applied after a particular condition is detected and whether that mitigating action was effective in preventing a hot flash, which can better enable the system 430 to predict and mitigate future hot flashes – the system being better able to predict hot flashes based on the effectiveness of different treatments in different conditions is a more generic version of adjusting weights in a predictive model).
Zambotti fails to teach differentiating between asleep and awake states and determining hot flashes accordingly. Kahn teaches determine, based on the first information on the specificity ratio of the predictive data model according to the plurality of sensor combinations and the second information on the sensitivity ratio of the predictive data model according to the plurality of sensor combinations, a second sensor combination for the awake state which provides the specificity ratio being greater than the first ratio and the sensitivity ratio being greater than a third ratio distinct from the first ratio and the second ratio (the sleep tracking system may additionally include a wristband or similar body-worn device including one or more sensors. These sensors may be used to track the user's movements directly, using an accelerometer, gyroscope, or similar sensor. In one embodiment, the body-worn device may include sensors such as thermometers, to enable measurement of the user's body temperature. This can be useful for example, if the body-worn sensor detects that a user is experiencing a hot flash, the sleep tracking system can reduce the temperature of the room or the sleeping surface to improve the user's sleep cycle, keeping the user from waking up from the hot flash – col 6 lines 4-15),
identify, based on the second sensor combination, from the features, at least one second feature extracted from one or more sensor circuits included in the second sensor combination.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of this invention to modify Zambotti with Kahn because there is some teaching, suggestion, or motivation to do so. Kahn discusses how using these sensors to detect and predict hot flashes can help maintain sleep quality. The device of Zambotti does not specify whether it meant to be used in an asleep or awake state but Kahn specifies that the system is to be used for sleep monitoring. Therefore, it would be obvious to add a system or subsystem to track only in an awake state.
Regarding claim 2, Zambotti further teaches the plurality of sensor circuits comprise two or more sensor circuits selected from a photoplethysmogram (PPG) sensor ([0071] photoplethysmogram), a skin conductance (SC) sensor ([0071] skin conductance sensor), a temperature (T) sensor ([0071] temperature sensor), and a motion (M) sensor ([0052] motion data can be collected so there must be a motion sensor).
Regarding claims 3 and 24, Zambotti further teaches the instructions, when executed by the processor circuitry, cause the electronic device to characterize a level or presence of the HF event ([0119] output layer 1090 provides a continuous hot flash probability value of 0 to 1) based on the extracted features ([0092] the first input data category can include extracted features of raw physiological signals, [0042] the input data is used to detect/predict a HF event).
Regarding claims 4 and 25, Zambotti further teaches the message includes information on at least one of the identification of the HF event ([0007] communicating a message to the user to take the action to mitigate a hot flash) and an intervention action for the HF event ([0069] actions module can perform spot cooling), and the processor circuitry is configured to identify the HF event in real-time ([0075] real-time probability of occurrence of a hot flash).
Regarding claims 7 and 26, Zambotti teaches the instructions, when executed by the processor circuitry, cause the electronic device to:
align the extracted features to the common time point based on a plurality of different time windows associated with the plurality of sensor circuits ([0092] features could also include parameters of a model best representing the data in a specific time window); and
weigh each of the extracted features based on an impact of the extracted features on the probability of the HF event occurring ([0043] weight may be based on of indicative of how predictive the respective input parameter is for the user to have a hot flash).
Regarding claims 8 and 27, Zambotti teaches the predictive data model includes a plurality of sub- models ([0008] predictive model includes a plurality of sub-models), and each of the plurality of sub-models is associated with a respective sensor circuit of the plurality ([0008] each the plurality of sub-models being associated with a particular input parameter - input parameters are the data associated with the sensor circuitry) and provides an output score indicative of the probability of the HF event occurring for the user based on the extracted features from the respective sensor signal obtained by the respective sensor circuit ([0043] predictive model 109 includes a probability that the user is going to have a hot flash); wherein the instructions, when executed by the processor circuitry cause the electronic device to combine the output scores from the plurality of sub-models to identify the HF event ([0110]The output of the sub-models 543-1, 543-2, 543-4, 543-4 (e.g., P1, P2, P3, P4) designed for each category of inputs are fused to form a final probability P of a hot flash occurrence).
Regarding claims 9 and 28, Zambotti further teaches the instructions, when executed by the processor circuitry cause the electronic device to: based on identifying that the user is in the sleep state: combine the at least one feature into a first vector and input the first vector to the predictive data model to produce an output score indicative of the probability ([0102] fig 6, feature vector).
Kahn further teaches based on identifying that the user is in the awake state: combine the at least one second feature into a second vector, and input the second vector to the predictive data model to produce the output score indicative of the probability (In one embodiment, the system turns off the light 374, when it determines, the user is starting to fall asleep – col 9, lines 33-35).
Regarding claims 10 and 29, Zambotti further teaches the instructions, when executed by the processor circuitry, cause the electronic device to: generate a decision tree structure to combine the extracted features, to produce the output score based on the combined extracted features ([0108] probabilistic decision tree), and to:
identify whether the HF event is occurring or not at a plurality of time points ([0094] table 3 - occurrence of an event at a specific time interval is marked by 1 and the rest of the elements are marked by Os);
detect consecutive identified HF events ([0095] any relationship between the occurrence of a hot flash and a pattern of different events in time may be extracted - identifying events as consecutive constitutes a pattern of different events); and
convert the consecutive identified HF events into a HF region ([0121] off-line group evidence).
Regarding claims 11 and 30, Zambotti further teaches the instructions, when executed by the processor circuitry, cause the electronic device to receive feedback data in response to the provided message, the feedback data being indicative of at least one of a user confirmation of the HF event, a user denial of the HF event, ([0008] verification of a hot flash occurring) and a severity of the HF event ([0008] indicative of a severity or impact of a hot flash).
Regarding claim 21, Zambotti further teaches the instructions, when executed by the processor circuitry, cause the electronic device to provide an intervention action to induce psychophysiological relief for the user ([0007] indicative of the action including communicating a message to the user to take the action to mitigate a hot flash), and wherein the intervention action is changed based on a state of the user identified by using the extracted features ([0010] the action being based on prior user response).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Dhrasti SNEHAL Dalal whose telephone number is (571)272-0780. The examiner can normally be reached Monday - Thursday 8:30 am - 6:00 pm, Alternate Friday off, 8:30 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Carl Layno can be reached at (571) 272-4949. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/D.S.D./Examiner, Art Unit 3796
/CARL H LAYNO/Supervisory Patent Examiner, Art Unit 3796