Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed 19 December 2025 has been entered. Claim(s) 1, 8-12, and 17-18 are pending in the application; claims 6 and 15 are newly cancelled. Applicant’s amendments to the claims have overcome each and every objection to the claims as well as each and every rejection under 35 U.S.C. 112(a) previously applied in the office action mailed 20 August 2025.
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.
Utilizing the two step process adopted by the Supreme Court (Alice Corp vs CLS Bank Int'l, US
Supreme Court, 110 USPQ2d 1976 (2014) and the recent 101 guideline Federal Register Vol. 84, No., Jan
2019)), determination of the subject matter eligibility under the 35 U.S.C. 101 is as follows: Specifically, the Step 1 requires claim belongs to one of the four statutory categories (process, machine, manufacture, or composition of matter). If Step 1 is satisfied, then in the first part of Step 2A (Prong One), identification of any judicial recognized exceptions in the claim is made. If any limitation in the claim is identified as judicial recognized exception, then in the second part of Step 2A (Prong Two), determination is made whether the identified judicial exception is being integrated into practical application. If the identified judicial exception is not integrated into a practical application, then in Step 2B, the claim is further evaluated to see if the additional elements, individually and in combination provide "inventive concept" that would amount to significantly more than the judicial exception. If the element and combination of elements do not amount to significantly more than the judicial recognized exception itself, then the claim is ineligible under the 35 U.S.C. 101.
Claims 1, 8-12, and 17-18 are rejected under 35 U.S.C. 101.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, in this case an abstract idea, without significantly more. The claim recite(s) "calculate a scalar value based on each component of an n-th-order time derivative vector acquired from an acceleration vector in a part of a body of the user, the n-th-order time derivative vector being a vector in which the acceleration vector is differentiated by n-th-order time derivative, and n being a natural number,, the scalar value being an L1 norm; wherein the feature value is a histogram generated by dividing the scalar value or logarithm thereof into classes with a plurality of threshold values; perform machine learning by using the correlation, as training data, of the feature value of the desired epoch, the feature value of each of the plurality of peripheral epochs, and the sleep and wakefulness of the user, the machine learning including: preparing the training data and a machine learning algorithm; applying the training data to the machine learning algorithm to construct the machine learning model trained to learn the correlation; and storing the machine learning model into the memory; update the machine learning model, which has been trained to learn the correlation, based on the performed machine learning; apply the feature value of the desired epoch and the feature value of each of the plurality of peripheral epochs to the machine learning model; determine the sleep and wakefulness of the user based on the machine learning model; and diagnose a sleep-related health condition of the user based on the determination of the sleep and wakefulness". This judicial exception is not integrated into a practical application and the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim 1 satisfies Step 1, namely the claim is directed to one of the four statutory classes, machine. Following Step 2A Prong one, any judicial exceptions are identified in the claims. In claim 1, the limitations "calculate a scalar value based on each component of an n-th-order time derivative vector acquired from an acceleration vector in a part of a body of the user, the n-th-order time derivative vector being a vector in which the acceleration vector is differentiated by n-th-order time derivative, and n being a natural number,, the scalar value being an L1 norm; wherein the feature value is a histogram generated by dividing the scalar value or logarithm thereof into classes with a plurality of threshold values; perform machine learning by using the correlation, as training data, of the feature value of the desired epoch, the feature value of each of the plurality of peripheral epochs, and the sleep and wakefulness of the user, the machine learning including: preparing the training data and a machine learning algorithm; applying the training data to the machine learning algorithm to construct the machine learning model trained to learn the correlation; and storing the machine learning model into the memory; update the machine learning model, which has been trained to learn the correlation, based on the performed machine learning; apply the feature value of the desired epoch and the feature value of each of the plurality of peripheral epochs to the machine learning model; determine the sleep and wakefulness of the user based on the machine learning model; and diagnose a sleep-related health condition of the user based on the determination of the sleep and wakefulness" are abstract ideas as they are directed to a mental process. With the identification of an abstract idea, the next phase is to proceed Step 2A, Prong Two, wherewith additional elements and taken as a whole, evaluation occurs of whether the identified abstract idea is integrated into a practical application.
In Step 2A, Prong Two, the claim does not recite any additional elements or evidence that amounts to significantly more than the judicial exception. Besides the abstract idea, the claim recites the additional elements “a sleep-wakefulness determination apparatus configured to determine sleep and wakefulness of a user, comprising: a memory configured to store a program and a machine learning model, the machine learning model being configured to lean a correlation among a features value of a desired epoch, the features value of each of a plurality of peripheral epochs that are included before and after the desired epoch in a time series, and the sleep and wakefulness of the user; and a processor configured to execute the program”. Regarding “a memory” and “a processor”, the limitation amounts to nothing more than an instruction to apply the abstract idea using a generic computer, which does not render an abstract idea eligible. The steps performed by the memory and processor are, as claimed, capable of being performed in the human mind similar to the examples given in MPEP 2106.04(a)(2)(III)(A)-(C), wherein it is described that “a claim to ‘collecting information, analyzing it, and displaying certain results of the collection and analysis’ where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind” recites a mental process and that claims which merely use a computer as a tool to perform a mental process are not eligible when “there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper” such as “mental processes of parsing and comparing data” when the steps are recited at a high level of generality and a computer is used merely as a tool to perform the processes. The steps of “calculat[ing]” a scalar value and a feature value may be seen as mental processes, and additionally may be seen as the use of well-understood, routine, or conventional elements to perform a non-mental process in order to gather data for a mental process step, much like the example given in MPEP 2106.04(d)(2)(c), such that these limitations are extra-solution activity and thus do not integrate the judicial exception into a practical application. The calculating steps lead to the limitation of “determin[ing]” and “diagnos[ing]” such that the end result of use of the system is only the generic diagnosis which may be any generic output relating to a possible health condition, or no output at all. As this determination is not defined as requiring any further action, such as a form of prophylaxis or treatment or an improvement to a computer or other technology, the claim limitations constitute mere generation of data, in this case the calculation of scalar and feature values, such that the claim does not integrate the judicial exception into any practical application. Under the broadest reasonable interpretation, the claim elements are recited with a high level of generality (as written, each claimed step performed by the processor may be performed by a person in an undefined manner alone or with the aid of pen and paper) that there are no meaningful limitations to the abstract idea. Consequently, with the identified abstract idea not being integrated into a practical application, the next step is Step 2B, evaluating whether the additional elements provide "inventive concept" that would amount to significantly more than the abstract idea.
In Step 2B, claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitation of “a sleep-wakefulness determination apparatus configured to determine sleep and wakefulness of a user, comprising: a memory configured to store a program and a machine learning model, the machine learning model being configured to lean a correlation among a features value of a desired epoch, the features value of each of a plurality of peripheral epochs that are included before and after the desired epoch in a time series, and the sleep and wakefulness of the user; and a processor configured to execute the program” constitutes extra-solution activity to the judicial exception, which does not amount to an inventive concept when the activity is well-understood, routine, or conventional, and are thus not indicative of integration into a practical application. The claim limitation constitutes adding a generic memory and processor, which Slonneger (US 20140364770 A1) describes as well-understood, routine, or conventional in its description of a commonly available processor and memory devices (Paragraph 0023-0024). As discussed above with respect to integration of the abstract idea into a practical application, the present elements amount to no more than mere indications to apply the exception.
In Summary, claim 1 recites abstract idea without being integrated into a practical application, and does not provide additional elements that would amount to significantly more. As such, taken as a whole, the claim and is ineligible under the 35 U.S.C. 101.
Claims 8-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, in this case an abstract idea, without significantly more. As each of these claims depends from claim 1, which was rejected under 35 U.S.C. 101 in paragraph 5 of this action, these claims must be evaluated on whether they sufficiently add to the practical application of claim 1, or comprise significantly more than the limitations of claim 1.
Besides the abstract idea of claim 1, dependent claims 8-11 merely describe additional elements of the abstract idea, which are themselves mental processes and thus do not provide a practical application or meaningfully limit the abstract idea; claims 8-10 recite additional elements of a storage unit, a storage media reading unit, a communication unit, an acceleration sensor, and a wearable device. However, these components may be seen as the use of well-understood, routine, or conventional elements to perform a non-mental process in order to gather data for the mental process step, much like the example given in MPEP 2106.04(d)(2)(c), such that these limitations are extra-solution activity and thus do not integrate the judicial exception into a practical application. The steps performed by these additional elements lead to the limitation of “determin[ing]” and “diagnos[ing]” based on the determination such that the end result of use of the system is only the generic determined indicator which may be any generic output, or no output at all. As this diagnosis is not defined as requiring any further action, such as a form of prophylaxis or treatment or an improvement to a computer or other technology, the claim limitations constitute mere generation of data, in this case the measurement of data relating to first and second physiological information, such that the claim does not integrate the judicial exception into any practical application. The limitations constitute extra-solution activity to the judicial exception, which does not amount to an inventive concept when the activity is well-understood, routine, or conventional, and are thus not indicative of integration into a practical application. The claim limitations constitute adding a generic memory, processor, transmitter, sensor, and wearable device, respectively, which Slonneger (US 20140364770 A1) describes as well-understood, routine, or conventional in its description of commonly available processor and memory devices (Paragraph 0023-0024), transmitters (Paragraph 0030-0031), sensors and wearables (Paragraph 0005, 0023, 0034-0035). The limitations of the dependent claims provides no practical application, nor does it provide meaningful limitations to the abstract idea.
Claim 12 is rejected under 35 U.S.C. 101 for the same reasons as claim 1.
Claims 17-18 are rejected under 35 U.S.C. 101 for the same reasons as claims 6 and 8-11.
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.
Claim(s) 1, 8-10, 12, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Slonneger (US 20140364770 A1—previously cited) in view of Proud (US 20160317781 A1—previously cited), further in view of Suzuki (JP 2006271894 A—previously cited), further in view of Bandyopadhyay (WO 2017040331 A1), henceforth referred to as Awarables.
Regarding claim 1 and 12, Slonneger teaches a sleep-wakefulness determination apparatus configured to determine sleep and wakefulness of a user (Abstract, paragraph 0020, 0040), comprising:
a memory configured to store a program (Memory 140, Fig. 1; paragraph 0023-0025, 0027—memory 140 can include one or more operating systems or applications…executable instructions stored in a non-transitory computer-readable medium)
a processor configured to execute the program (processor 130, Fig. 1; paragraph 0023-0024, 0029—processor 130 may include or implement various modules and execute programs…) so as to:
calculate a scalar value based on each component of an acceleration vector in a part of a body of the user (Paragraph 0032-0033, 0055-- accelerometer 115 measures acceleration along coordinate axes (i.e., acceleration along the x, y, and z axes). The measured acceleration is used to determine motion of the user device 110, which may then be used to calculate a level of the user's motion… mag=|x,y,z|= {square root over (x.sup.2+y.sup.2+z.sup.2)});
calculate the feature value for each epoch defined by a predetermined time based on the scalar value (Paragraph 0043-0044—raw output may then be processed by the dynamic calibrator…; paragraph 0046-0047-- The sleep-state determiner 440 determines an average normalized activity level over one second using a number of accelerator-magnitude samples (N samples). The average normalized activity level is used to determine a sample measure over one second for the accelerometer 115. Consecutive sample measures, each taken over a one second interval, may be used to determine a sample activity count using set high and low thresholds. 60 consecutive sample activity counts may be used to determine an epoch activity count, the epoch activity count being a sample metric for a one-minute epoch); and
determine the sleep and wakefulness of the user based on the feature value of a desired epoch and the feature value of peripheral epochs included in a plurality of epochs before and after the desired epoch in a time series, in the epoch (paragraph 0047-0048-- The epoch activity rate may be compared to thresholds for sleep states to determine a sleep-state epoch decision… A series of epoch decisions is used to determine a sleep decision, the sleep decision being the sleep state of the user during a sleep-decision window. For example, a sleep-decision window of five minutes may combine five consecutive one-minute epoch decisions. In an embodiment, in order to determine the sleep state, the sleep-state determiner 440 chooses the maximum value from the consecutive epochs within the sleep-state window); and
diagnose a sleep-related health condition of the user based on the determination of the sleep and wakefulness (Paragraph 0020-- In some disclosed sleep-analysis methods, the methods discern between three sleep states: deep sleep, light sleep, and awake. Data indicating such states, based on predetermined metrics, are processed to ascertain health of a sleep cycle, sleep quality, or other sleep-related conditions or measurements).
Slonneger additionally teaches the scalar value is a norm (paragraph 0033, 0055-- The 3-D acceleration data may be read as, or converted to, a magnitude value ("mag") defined as: mag=|x,y,z|= {square root over (x.sup.2+y.sup.2+z.sup.2)} which is an L2 norm).
Slonneger additionally discloses using thresholds to divide counts of a feature into different classes (Paragraph 0047, 0055-0056-- Consecutive sample measures, each taken over a one second interval, may be used to determine a sample activity count using set high and low thresholds. 60 consecutive sample activity counts may be used to determine an epoch activity count, the epoch activity count being a sample metric for a one-minute epoch. The epoch activity rate may be compared to thresholds for sleep states to determine a sleep-state epoch decision).
However, Slonnegar fails to explicitly disclose the scalar value is an L1 norm. Proud, in the same field of endeavor of sleep monitoring (Abstract, paragraph 0003-0012, 0023-0028, 0100, 0125-- (xiii) sleep, including but not limited to: sleep patterns, type of sleep, sleep disorders, movement during sleep, waking up, falling asleep, problems with sleep, habits during, before and after sleep, time of sleep, length sleep in terms of the amount of time for each sleep, body activities during sleep, brain patterns during sleep and the like), discloses the use of an L1 norm of an acceleration vector in a part of a body of the user (Paragraph 0179, 0199-- The vector distances can include at least one of a Euclidean distance between a motion pattern vector and a motion reading vector or a Manhattan distance between a motion pattern vector and a motion reading vector) to be used in determining the sleep and wakefulness of the user which is used to diagnose a sleep-related health condition of the user based on the determination of the sleep and wakefulness (Abstract-- The movement detection device and the monitoring system assist to determine user sleep information and sleep behavior information; Paragraph 0180-0188, 0200-0201, 0353-0372, 0394-0402). It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Slonnegar to utilize an L1 norm as taught by Proud as a matter of simple substitution of elements known in the art, in this case an L1 norm in place of an L2 norm, where both norms are used to enable calculations based on an acceleration vector of a body part of a user. It would additionally have been obvious to one having ordinary skill in the art at the time of filing to utilize the system of Slonnegar, including the determination of sleep-related health conditions based on the determination of the sleep and wakefulness (see paragraph 0020 of Slonnegar), to determine specific sleep-related health conditions such as mental disorders producing insomnia, depression, anxiety, and sleep apnea as disclosed by Proud (see paragraphs 0353-0372, 0394-0402 of Proud) in order to predictably improve the ability of the system distinguish between various specific disorders and allow for their diagnosis.
However, Slonneger does not explicitly disclose wherein the processor is configured to execute the program so as to calculate the scalar value based on each component of an n-th- order time derivative vector, the n-th-order time derivative vector being a vector in which the acceleration vector is differentiated by n-th-order time derivative, and n being a natural number. Suzuki, in the same field of endeavor of a device which determines a degree of arousal (or sleep/wakefulness) based on acceleration signals (Tech solution-- a wake-up device, wherein the subject is recommended to wake up based on the sleep state measurement means for measuring the sleep state of the subject and the sleep state of the subject measured by the sleep state measurement means. It is determined that the recommended wake-up state determination means for determining whether or not the subject is in motion, the body motion detection means for detecting the presence or absence of body movement of the subject, the recommended wake-up state determination means is in the recommended wake-up state, and a wake-up control means for driving a wake-up function when the body movement detecting means detects the body movement), wherein the processor is configured to execute the program so as to calculate the scalar value based on each component of an n-th- order time derivative vector, the n-th-order time derivative vector being a vector in which the acceleration vector is differentiated by n-th-order time derivative, and n being a natural number (The body motion determination unit 33 obtains a differential coefficient of acceleration in the triaxial direction by time differentiation of the acceleration data in the triaxial direction acquired from the acceleration measurement unit 21, and calculates a sum of squares of the differential coefficients of the triaxial accelerations… The awakening determination unit 34 determines that the state is awakening when the frequency of occurrence of body movement determined by the body movement determination unit 33 is equal to or greater than a predetermined threshold, and the frequency of occurrence of body movement is less than the predetermined threshold. Is determined to be in a sleep state). It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Slonneger to utilize the nth order differentiation of Suzuki as a matter of simple substitution of elements known in the art, as a time differential is a simple and well-known mathematical operation which may replace the particular scalar value calculation of Slonneger, an L2 norm also well known in the art (see Paragraph 0032-0033, 0055 of Slonnegar). It would have alternatively been obvious to one having ordinary skill in the art at the time of filing to modify the system of Slonneger to additionally include the known mathematical processing technique of an nth order differentiation which would predictably improve the ability of the system to accurately determine sleep and wakefulness of a user by providing additional scalar values which may be indicators of sleep or wakefulness.
However, Slonneger fails to specifically disclose the memory stores a machine learning model, wherein the feature value is a histogram generated by dividing the scalar value or logarithm thereof into classes with a plurality of threshold values; perform machine learning by using the correlation, as training data, of the feature value of the desired epoch, the feature value of each of the plurality of peripheral epochs, and the sleep and wakefulness of the user; the machine learning including: preparing the training data and a machine learning algorithm; applying the training data to the machine learning algorithm to construct the machine learning model trained to learn the correlation; and storing the machine learning model into the memory; update the machine learning model, which has been trained to learn the correlation; apply the feature value of the desired epoch and the feature value of each of the plurality of peripheral epochs to the machine learning model; and determine the sleep and wakefulness of the user based on the machine learning model.
Awarables, in the same field of endeavor of a sleep-wakefulness determination apparatus configurated to determine sleep and wakefulness of a user (Paragraph 0003, 0006-0008), discloses an apparatus comprising a memory configured to store a program (Paragraph 0171-0173--Embodiments of the invention may be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a computer-readable medium for execution by, or to control the operation of, data processing apparatus. The computer readable medium may be a non-transitory computer readable storage medium, a machine-readable storage device, a machine- readable storage substrate, a memory device, a composition of matter effecting a machine-readable propagated signal, or a combination of one or more of them) and a machine learning model (Paragraph 0008--sleep stages including REM, slow wave sleep or deep sleep (N3), light sleep (N1 , N2) and Wake are estimated using processes including but not limited to sensor signal pattern matching, learning, HRV frequency analysis, sensor fusion, approximate entropy detection, and/or rule-based decision making; paragraph 0083, 0123--other data can be provided to a trained classifier that provides outputs indicative of likelihoods that the input signals correspond to different sleep stages…signals, such as motion data, sound data, breathing rate data, and so on can also be used as signals to the classifier… the stage prediction is made by a stage classifier trained on truth data, e.g., examples of inputs having actual corresponding sleep stages labelled. Examples of such classifiers include, for example, neural networks, maximum entropy classifiers, support vector machines, and decision trees.), the machine learning model being configured to learn a correlation among a features value of a desired epoch, the features value of each of a plurality of peripheral epochs that are included before and after the desired epoch in a time series (Paragraph 0049-0050, 0096, 0122, 0154--In some implementations, dividing the time period into a series of intervals includes diving the time period into adjacent periods each having a same duration…time series can be divided into epochs to analyze the numerical trends in the data…aggregating motion sensor data, accelerometer and gyroscope data in epochs over the sleep session. In some implementations, this epoch size is 30 seconds. The variances, maximums, minimums, averages are computed for each epoch and then aggregated for bigger windows), and the sleep and wakefulness of the user (Paragraph 0042-- the sleep stage classifier is configured to generate the sleep stage probability distribution and the sleep stage label for a particular interval by: for each of the signals provided as input to the sleep stage classifier, accessing a histogram for the signal and determining a count indicated by the histogram for each sleep stage label corresponding to a value of the signal during the particular interval; and computing, for each of the sleep stages, a total count across all signals; paragraph 0123-0124-- Methods and systems are described for classifying segments of a set of signals to a set of discrete decisions about said signals in the form of sleep stages. The method includes a procedure for training the system to predict sleep stages based on a set of input signals. The trained classifier can then be used to evaluate a set of input signals using the trained system to predict sleep stage); and a processor configured to execute the program (Paragraph 0171-0173-- one or more modules of computer program instructions encoded on a computer-readable medium for execution by, or to control the operation of, data processing apparatus),
Where the signal is an acceleration signal (Paragraph 0123-- The input signals may include sensor data or other parameters calculated from sensor data, e.g., values indicating a subject's movement; Paragraph 0144--other sensor data can be used as input. This includes but is not limited to: audio, inertial measurements, EEG, temperature, and any mathematical transformations of these measurements…Inertial measurements could be used directly, as there is likely to be more movement during the wake phases, which makes the signal directly useful)
Calculate the feature value for each epoch defined by a predetermined time based on the scalar value, the feature value being a histogram generated by dividing the scalar value or a logarithm of the scalar value into classes with a plurality of thresholds (Paragraph 0124-0133--the system described will build a set of histograms over this data which will be used during evaluation to compute a probability distribution describing sleep stage at some point in time… Let Fj.sub.j(t) be a set of N.sub.f functions for each individual i which correlates with each Sj(t)in time. These functions are arbitrary, but could represent directly, or a variation on, sensor data taken during the sleep study… we define N.sub.f x 4 histograms H.sub.jswith N bins where each bin value is equal to the sum of number of samples of j for all individuals i in which Fj.sub.j(t) falls into the bin for a given sleep stage s); It is noted that the feature value is the histogram, which is generated by dividing a scalar value (function Fj.sub.j(t), which may represent directly, or a variation on, sensor data) into classes with a plurality of thresholds (the bins, which each correspond to a given sleep stage 0, 1, 3, and potentially additional stages such that a threshold may be seen to separate each bin).
Perform machine learning by using the correlation, as training data, of the feature value of the desired epoch, the feature value of each of the plurality of peripheral epochs, and the sleep and wakefulness of the user (Paragraph 0038—using the signal range histograms to train a sleep stage classifier; paragraph 0041-- training or using a sleep stage classifier configured to receive, as input, signals indicating measurements during a sleep session of a person; paragraph 0123-0124-- Examples of these sets of signals that have been labeled with a truth signal, e.g., data that indicates correct stages corresponding to the examples, can be used to train the classifier…the purpose of the training step is to supply a set of ground truth data for some number of subjects, along with arbitrary data that corresponds with the ground truth data in time);
the machine learning including:
preparing the training data and a machine learning algorithm (Paragraph 0123-0134--Examples of such classifiers include, for example, neural networks, maximum entropy classifiers, support vector machines, and decision trees… a set of input signals…Examples of these sets of signals that have been labeled with a truth signal, e.g., data that indicates correct stages corresponding to the examples, can be used to train the classifier…);
applying the training data to the machine learning algorithm to construct the machine learning model trained to learn the correlation (Paragraph 0083, 0123-0134--The method includes a procedure for training the system to predict sleep stages based on a set of input signals…Examples of these sets of signals that have been labeled with a truth signal, e.g., data that indicates correct stages corresponding to the examples, can be used to train the classifier…); and
storing the machine learning model into the memory (Paragraph 0094, 0172-0175);
update the machine learning model, which has been trained to learn the correlation (Paragraph 0123-0134--The trained classifier can then be used to evaluate a set of input signals using the trained system to predict sleep stage…Signals can be passed along with ground truth to the training system to generate a sleep stage evaluation engine, which can evaluate new input signals to produce a prediction of sleep stage…The system described will build a set of histograms over this data which will be used during evaluation to compute a probability distribution describing sleep stage at some point in time… New training data can be added to an already trained system, so long as the values of the new training data fall within Fmin.sub.jand FmaX.sub.jOf the already trained system. If they do not, the system can be retrained using the old and new training data);
apply the feature value of the desired epoch and the feature value of each of the plurality of peripheral epochs to the machine learning model (Paragraph 0042-- the sleep stage classifier is configured to generate the sleep stage probability distribution and the sleep stage label for a particular interval by: for each of the signals provided as input to the sleep stage classifier, accessing a histogram for the signal and determining a count indicated by the histogram for each sleep stage label corresponding to a value of the signal during the particular interval; and computing, for each of the sleep stages, a total count across all signals; paragraph 0123-- The trained classifier can then be used to evaluate a set of input signals using the trained system to predict sleep stage);
determine the sleep and wakefulness of the user based on the machine learning model (Paragraph 0041-- wherein the sleep stage classifier is configured to generate, for each interval of the sleep session, (i) a sleep stage probability distribution that indicates a likelihood for each of multiple different sleep stages, and (ii) a sleep stage label; paragraph 0123-- The trained classifier can then be used to evaluate a set of input signals using the trained system to predict sleep stage);
It would have been obvious to one having ordinary skill in the art at the time of filing to modify the system of Slonneger, which discloses the use of thresholds to classify epochs into sleep states, to utilize the feature value determinations of Awarables using a histogram as a matter of simple substitution of elements known in the art, as a histograms and metrics are simple and well-known mathematical operations which may replace the particular feature value calculation of Slonneger, an activity count also known in the art (see Paragraph 0043-0044, 0046-0047 of Slonnegar). It would have alternatively been obvious to one having ordinary skill in the art at the time of filing to modify the system of Slonneger to additionally include the known mathematical processing technique of histograms and metrics and to use these features in the training and application of a machine learning model as described in Awarables which would predictably improve the ability of the system to accurately determine sleep and wakefulness of a user by providing additional feature values which may be indicators of sleep or wakefulness.
Regarding claim 8, the combination of Slonneger, Proud, Suzuki, and Awarables teaches the sleep-wakefulness determination apparatus according to claim 1. Slonneger additionally teaches further comprising a communication interface configured to read the acceleration vector stored in the memory (paragraph 0023-0025, 0027, 0029, 0041, 0049).
Regarding claim 9 and 17, the combination of Slonneger, Proud, Suzuki, and Awarables teaches the sleep-wakefulness determination apparatus according to claim 1. Slonneger additionally teaches further comprising a communication interface, the communication interface being configured to: communicate with an acceleration sensor, the acceleration sensor being configured to be worn on the part of the body of the user, and to receive the acceleration vector measured by the acceleration sensor (Paragraph 0023--a user device may be associated with the user in the form of a wrist-watch…may include wireless transceivers 120, a processor 130, a memory 140, one or more output components 150, one or more input components 160, an accelerometer 115; paragraph 0038-0039--the user device 110 transmits accelerometer data or processed sleep-analysis data to a server 220 via a network 230. Such data may be transferred from the user device 110 using the wireless transceivers 120, which can include, as shown in FIG. 1, a cellular transceiver 124 or a WLAN transceiver 126; Fig. 1).
Regarding claim 10, the combination of Slonneger, Proud, Suzuki, and Awarables teaches the sleep-wakefulness determination apparatus according to claim 1. Slonneger additionally teaches the apparatus further comprising an acceleration sensor configured to measure the acceleration vector, wherein the sleep-wakefulness determination apparatus is configured to be wearable on the part of the body of the user (Paragraph 0023--a user device may be associated with the user in the form of a wrist-watch…may include wireless transceivers 120, a processor 130, a memory 140, one or more output components 150, one or more input components 160, an accelerometer 115; paragraph 0038-0039--the user device 110 transmits accelerometer data or processed sleep-analysis data to a server 220 via a network 230. Such data may be transferred from the user device 110 using the wireless transceivers 120, which can include, as shown in FIG. 1, a cellular transceiver 124 or a WLAN transceiver 126; Fig. 1).
Claim(s) 11 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Slonneger in view of Proud, further in view of Suzuki, further in view of Awarables, further in view of Kaji (US 20170273617 A1).
Regarding claim 11 and 18, the combination of Slonneger, Proud, Suzuki, and Awarables teaches the sleep-wakefulness determination apparatus according to claim 1. Slonneger additionally teaches wherein the processor is configured to execute the program so as to convert a result of the determination of the sleep-wakefulness (paragraph 0046-0049-- the data may be compiled into a chart or graph showing sleep states at each sleep-decision window over a given period of time for display to the user via an output component 150 of the user device (i.e., a visual display), or the data may be stored on the server 220 or memory 140 for later viewing on the user device 110 or on another computing device 240). However, Slonnegar fails to explicitly teach the result is converted into: a sleep-wakefulness period of the sleep and wakefulness using a Chi-square periodogram method; or an amplitude of the sleep and wakefulness based on a variation coefficient of the result. Kaji, in the same field of endeavor of a sleep state determination device which utilizes acceleration measurements (Abstract, paragraph 0003, 0009) teaches the processor is configured to convert a result of the determination of the sleep-wakefulness into: a sleep-wakefulness period of the sleep and wakefulness using a Chi-square periodogram method; or an amplitude of the sleep and wakefulness based on a variation coefficient of the result (Paragraph 0011, 0050, 0055—amplitude coefficient of variation…). It would have alternatively been obvious to one having ordinary skill in the art at the time of filing to modify the system of Slonneger to convert the result as disclosed by Kaji which would predictably improve the ability of the system to provide additional means of monitoring qualities of sleep and wakefulness of a user.
Response to Arguments
Applicant's arguments filed 19 December 2025 regarding the rejection of the claims under 35 U.S.C. 101 have been fully considered but they are not persuasive.
In particular, the applicant argues that the amended claim 1 recites limitations which amount to significantly more than the judicial exception and integrates the abstract idea into a practical application, specifically citing Example 39 of the Subject Matter Eligibility Guidance.
However, the limitations of the instant claims are more akin to example 47 of the July 2024 Subject Matter Eligibility Memo, wherein the example claim 2 which recites in steps (a) through (d) “(a) receiving, at a computer, continuous training data; (b) discretizing, by the computer, the continuous training data to generate input data; (c) training, by the computer, the ANN based on the input data and a selected training algorithm to generate a trained ANN, wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm; (d) detecting one or more anomalies in a data set using the trained ANN”.
Per the example: Steps (a), (b), and (c) are all recited as being performed by a computer. The recited computer is recited at a high level of generality, i.e., as a generic computer performing generic computer functions. Step (d) recites detecting one or more anomalies in a data set using the trained ANN. The claim does not provide any details about how the trained ANN operates or how the detection is made, and the plain meaning of “detecting” encompasses mental observations or evaluations, e.g., a computer programmer’s mental identification of an anomaly in a data set.
Similarly, the instant claim limitations of “preparing” the data and model and “storing” the model do not provide any details that would preclude this from being performed by a generic computer or even in the human mind via simple observation as “preparing” has been recited at a high level of generality which thus falls within the mental process grouping of abstract ideas.
The instant claim limitations of “applying the training data…” and “updating the model…” are similar to example claim step (c) but fails to include any specific selected algorithm for training the model. While the example step (c) recites selected algorithms for training a neural network, these amount to mathematical calculations; the “applying the training data” of the instant claim is broadly claimed so as to encompass both a mathematical calculation and a mental process of comparison and judgment which may allow one to learn a connection between a particular classification and respective data via the labeled training data and thus similarly to the example claim falls within an abstract idea, in this case either of mental process or mathematical calculation groupings.
The newly amended limitations fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III.
As a result, these newly amended features cannot amount to significantly more than the judicial exception nor integrate the abstract idea into a practical application.
The claims remain rejected under 35 U.S.C. 101.
Applicant's arguments filed 19 December 2025 with respect to the prior art rejections in view of Awarables have been fully considered but they are not persuasive.
The applicant argues that while Awarables describes calculating values across multiple epochs, it is silent about a correlation among a histogram of a desired epoch, the histograms of plurality of epochs, and sleep and wakefulness.
However, Slonnegar has been relied upon to disclose a correlation among a feature value of a desired epoch, feature values of a plurality of peripheral epochs before and after the desired epoch, and the sleep and wakefulness of a user. Awarables has merely been used to motivate the use of a histogram as a feature value in the system of Slonnegar.
Applicant has provided no argument against this combination to teach the limitations.
The claims remain rejected under 35 U.S.C. 103, including newly cited limitations of Awarables to reject the newly amended limitations of the claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ANNA ROBERTS/Examiner, Art Unit 3791 /ALEX M VALVIS/Supervisory Patent Examiner, Art Unit 3791