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 .
Response to Amendment
The Amendment filed October 10, 2025 has been entered. Claims 1, 4, 6-8, 11, 13-15 and 18-20 remain pending in the application.
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1, 3-4, 6-8, 10-11, 13-15 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Valys et al. (US 2019/0076031 A1) ("Valys") in view of Lee et al. (US 2018/0317811 A1) ("Lee") in further view of Sobol et al. (US 2019/0209022 A1) ("Sobol") in further view of Taulu et al. (US 2019/0125268 A1) ("Taulu").
Regarding claims 1, 8 and 15, Valys discloses A system/method/computer program product for health monitoring using artificial intelligence based on sensor data, comprising (Abstract and entire document):
one or more wearable devices affixable to a user, each wearable device including one or more sensors (Para. [0023], “Many devices continuously obtain data to provide a measurement or calculation of the health-indicator data, for example and without limitation FitBit®, Apple Watch®, Polar®, smart phones, tablets among others are in the class of wearable and/or mobile devices.”);
Valys fails to disclose a first wearable device affixable to a first appendage of a user and a second wearable device affixable to a second appendage of the user, each of the first wearable device and the second wearable device including one or more sensors;
However, in the same field of endeavor, Lee teaches a first wearable device affixable to a first appendage of a user and a second wearable device affixable to a second appendage of the user, each of the first wearable device and the second wearable device including one or more sensors (FIG. 1 and FIG. 6, and [0010], “a left-side motion measurement unit wearable on a left arm or a left leg; a right-side motion measurement unit wearable on a right arm or a right leg;” and [0032], “Each of the left-side motion measurement unit 10 and the right-side motion measurement unit 20 may include an acceleration sensor (a gravity sensor) or an angular acceleration sensor (a gyro sensor) to measure a motion.”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the system/method/computer program product as taught by Valys to include a first wearable device affixable to a first appendage of a user and a second wearable device affixable to a second appendage of the user, each of the first wearable device and the second wearable device including one or more sensors as taught by Lee to compare the data across multiple body parts ([0043 – 0044]).
Valys further discloses a memory device for storing program code; and at least one processor operatively coupled to the memory device and configured to execute program code stored on the memory device to (Para. [0024], “In some embodiments of the platform, the platform includes one or more hardware components (e.g. one or more sensing devices, processing devices, or microprocessors). In some embodiments, a platform is configured to operate together with one or more devices and/or one or more systems. That is, a device as described herein, in some embodiments, is configured to run an application of a platform using a built-in processor, and in some embodiments, a platform is utilized by a system comprising one or more computing devices that interact with or run one or more applications of the platform.”):
collect sensor data from the one or more sensors on a health monitoring processing device (FIG. 4A-4C and FIG. 5A-E also see FIG. 8, see para. [0047], “The raw signal/data (electric signal from ECG, chest strap, or PPG signals) itself is a time sequence of data that can be used in accordance with some embodiments. For the purpose of clarity, and not by way of limitation, this description uses PPG to refer to the data representing the health-indicator. The skilled artisan will readily appreciate that either form of the data for the health-indicator, raw data, waveform or number derived from raw data or waveform, may be used in accordance with some embodiments described herein.” An d[0103], “As discussed herein, the low-fidelity health-indicator data may be collected and measured by a low-fidelity health-indicator data sensor (e.g., a PPG) and a corresponding mobile computing device, such as a smartwatch, other wearable (e.g., a fitness band), computer tablet, a laptop computer, etc.”);
wherein the one or more sensors monitors body movement traces (Valys, [0005], and [0035], “The low-fidelity (e.g., heartrate) data and activity data can then be input into a machine learning system to determine a prediction of a high-fidelity outcome. For example, the machine learning system may use the low-fidelity data to predict an arrhythmia or other indication of a user's cardiac health.” Discussing monitoring body movement traces such as activity data. See also [0049], “As will be appreciated by the skilled artisan, the other-data may be obtained from any of many known sources including without limitation accelerometer data, GPS data, a weight scale, user entry etc., and may include without limitation air temperature, activity (running, walking, sitting, cycling, falling, climbing stairs, steps etc.), BMI, weight, height, age etc.”);
Valys does not explicitly disclose wherein the one or more sensors include at least one gyroscope that monitors body movement traces;
However, in the same field of endeavor, Sobol teaches wherein the one or more sensors includes at least one gyroscope that monitors body movement traces ([0166], “In one form, baseline activity data such as that acquired from sensors 121 that are in the form of accelerometers, gyroscopes and the like may be created through examples that can be correlated to known movements of the individual being monitored. For example, the individual may go through various sitting, standing, walking, running (if possible) and related movements that can be labeled for each activity where classification is desired. As will be discussed in more detail later, such labeling may be useful in performing supervised machine learning, particularly as it applies to training a machine learning model.” And [0167], “As such, numerous combinations of sensors 121A, 121B, 121C and 121D may contribute to a fusion of the acquired data in order to improve the accuracy of the inferred event.”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the system/method/computer program product as taught by Valys to include wherein the one or more sensors includes at least one gyroscope that monitors body movement traces as taught by Sobol to improve accuracy ([0167], “As such, numerous combinations of sensors 121A, 121B, 121C and 121D may contribute to a fusion of the acquired data in order to improve the accuracy of the inferred event.”).
Valys further discloses process the collected sensor data of the body movement traces to remove noise (Valys, Para. [0021], “providing the primary and secondary time sequence(s) to a pre-processor, which may perform operations on the data like filtering, caching, averaging, time alignment, buffering, upsampling and downsampling;”);
process the collected sensor data with other trackable environmental data to automatically generate labels (para. [0047], “The raw signal/data (electric signal from ECG, chest strap, or PPG signals) itself is a time sequence of data that can be used in accordance with some embodiments. For the purpose of clarity, and not by way of limitation, this description uses PPG to refer to the data representing the health-indicator. The skilled artisan will readily appreciate that either form of the data for the health-indicator, raw data, waveform or number derived from raw data or waveform, may be used in accordance with some embodiments described herein.” See also [0049], “FIGS. 4A-4C show hypothetical plots against time for PPG (FIG. 4A), steps taken (FIG. 4B) and air temperature (FIG. 4C). PPG is an example of health-indicator data, where steps, activity level, and air temperature are examples other-factor data for other factors that may impact the health-indicator data.” See also [0050], “It is again emphasized that these input data (P, R and T) are merely examples of health-indicator data and other-factor data. It will also be appreciated that data for more than one health-indicator may be input and predicted, and more or less than two other-factor data may be used, where the choice depends on for what the model is being designed. It will be further appreciated by the skilled artisan that other-factor data is collected to correspond in time with the collection or measurement of the health-indicator data. In some cases, e.g. weight, other-factor data will remain relatively constant over certain periods of time.” See also at least [0037], [0099] labels are generated based on environmental data and the sensor data);
transform the processed sensor data into a first waveform and a second waveform, the first waveform and the second waveform representing regions in a two-dimensional (2D) graphical image (FIG. 4A-4C and FIG. 5A-E also see FIG. 8, see para. [0047], “The raw signal/data (electric signal from ECG, chest strap, or PPG signals) itself is a time sequence of data that can be used in accordance with some embodiments. For the purpose of clarity, and not by way of limitation, this description uses PPG to refer to the data representing the health-indicator. The skilled artisan will readily appreciate that either form of the data for the health-indicator, raw data, waveform or number derived from raw data or waveform, may be used in accordance with some embodiments described herein.” A waveform is a graphical image as shown in FIG. 4A-4C. See also FIG. 11 showing multiple regions or waveforms. The raw processed data is transformed into a graph. See also [0049], “FIGS. 4A-4C show hypothetical plots against time for PPG (FIG. 4A), steps taken (FIG. 4B) and air temperature (FIG. 4C). PPG is an example of health-indicator data, where steps, activity level, and air temperature are examples other-factor data for other factors that may impact the health-indicator data.” And ECG is a first waveform and a PPG is a second waveform that can be displayed together in different regions of a display);
train a convolutional neural network (CNN) model to analyze the 2D graphical image and detect patterns indicative of premonitory symptoms, the CNN model producing an N dimensional vector, where N is a number of diseases, and each component in the N dimensional vector represents a probability of the diseases ([0037], “ This is repeated over and over with the same n-training examples until the convolutional neural network (CNN) is trained or converges on the correct outputs for the known inputs….The skilled artisan will appreciate the CNN is applicable to data in a fixed array (e.g., a picture, character, word etc.) or a time sequence of data. For example, sequenced health-indicator data and other-factor data can be modeled using a CNN. Some embodiments utilize a feed-forward, CNN with skip connections and a Gaussian Mixture Model output to determine a probability distribution for the predicted health-indicator, e.g., heart rate, PPG, or arrhythmia.” See also [0036], “The final layer(s) of the network is a fully connected layer, which takes the output of the last convolutional layer and outputs an n-dimensional output vector representing the quantity to be predicted, e.g., probabilities of image classification 20% automobile, 75% boat 5% bus and 0% bicycle , i.e., resulting in predictive output 106…. skilled artisan in neural networks will fully understand the description above provides a somewhat simplistic view of CNNs to provide some context for the present discussion” para [0036] is a general description and simplification of how the wearable sensor data is input into a CNN to output the N dimensional vector with probabilities of diseases as claimed. See also [0057] discussing probability output vectors. See also FIG. 11 and [0098 – 0100] showing multiple regions of data each from a wearable device or other sensor. See also [0050], “It is again emphasized that these input data (P, R and T) are merely examples of health-indicator data and other-factor data. It will also be appreciated that data for more than one health-indicator may be input and predicted, and more or less than two other-factor data may be used, where the choice depends on for what the model is being designed. It will be further appreciated by the skilled artisan that other-factor data is collected to correspond in time with the collection or measurement of the health-indicator data. In some cases, e.g. weight, other-factor data will remain relatively constant over certain periods of time.” Any number of health data from any number of sensors including any number of wearable devices from any number of appendages can be input into the model. See also at least [0037], [0099]);
predict a risk of the premonitory symptoms based on the patterns detected, the analyzing the 2D graphical image by the CNN model and labels generated based on the premonitory symptoms labels ([0057 – 0063], a risk of symptoms is output using the 2D graphical image and labels based on training the CNN model, see also [0093], “In step 914, diagnosis or categorization of the high-fidelity measurement is received by a computing system, which may be in some embodiments the mobile or wearable computing system used to collect the user's heart rate data (or other health-indicator data), and in step 916 the low-fidelity health-indicator data sequence (heart rate data in this example) is labeled with the diagnosis. In step 918, the labeled user's low-fidelity data sequence is used to train a high-fidelity machine learning model, and optionally other-factor data sequence is also provided to train the model. The trained high-fidelity machine learning model, in some embodiments, has the capability to receive measured low-fidelity health-indicator data sequence (e.g., heart rate data or PPG data) and optionally other-factor data and give a probability or predict or diagnose or detect when a user is experiencing an event typically diagnosed or detected using high-fidelity data.”); and
transmit an alert to one or more entities associated with the user based on the predicted risk (Para. [0051], “If yes, step 510 notifies/alerts the user his/her health-indicator is outside the bounds/threshold predicted as normal or predicted for a healthy person. The alert/notification/detection could be, for example and not by way of limitation, a suggestion to see/consult a doctor, a simple notification like a haptic feedback,” see also [0062 - 0063] and FIG. 4A-4C and FIG. 11 and corresponding descriptions).
Valys as modified fails to disclose process the collected sensor data by performing cross-validation to remove noise,
However, in the same field of endeavor, Taulu teaches process the collected sensor data by performing cross-validation to remove noise (Para. [0029], “At block 208, the process 200 estimates time-domain amplitude components using cross-validation for each channel, and at block 210, the process 200 determines the sensor noise and/or artifacts for each channel. Once identified, the process 200 suppresses the sensor noise and/or artifacts for each channel.”), and
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the system/method/computer program product as taught by Valys as modified to include to process the collected sensor data by performing cross-validation to remove noise as taught by Taulu in order to reduce noise (Para. [0017], “Cross-validation (CV) based algorithms have recently been developed to address sensor noise.”).
Regarding claims 4, 11 and 18, Valys as modified discloses The system of claim 1, Valys further discloses wherein the at least one processor is configured to transmit the alert to the user, one or more persons associated with the user, or combinations thereof (Para. [0051], “If yes, step 510 notifies/alerts the user his/her health-indicator is outside the bounds/threshold predicted as normal or predicted for a healthy person. The alert/notification/detection could be, for example and not by way of limitation, a suggestion to see/consult a doctor, a simple notification like a haptic feedback,” see also [0062 - 0063] and FIG. 4A-4C and FIG. 11 and corresponding descriptions).
Regarding claims 6, 13 and 19, Valys as modified discloses The system of claim 1, Valys further discloses wherein the at least one processor is further configured to train the convolutional neural network model by: obtaining training sensor data (FIG. 4A-4C and FIG. 5A-E also see FIG. 8, see para. [0047], “The raw signal/data (electric signal from ECG, chest strap, or PPG signals) itself is a time sequence of data that can be used in accordance with some embodiments. For the purpose of clarity, and not by way of limitation, this description uses PPG to refer to the data representing the health-indicator. The skilled artisan will readily appreciate that either form of the data for the health-indicator, raw data, waveform or number derived from raw data or waveform, may be used in accordance with some embodiments described herein.” A waveform is a graphical image as shown in FIG. 4A-4C);
transforming the training sensor data into a training 2D graphical image (FIG. 4A-4C and FIG. 5A-E also see FIG. 8, see para. [0047], “The raw signal/data (electric signal from ECG, chest strap, or PPG signals) itself is a time sequence of data that can be used in accordance with some embodiments. For the purpose of clarity, and not by way of limitation, this description uses PPG to refer to the data representing the health-indicator. The skilled artisan will readily appreciate that either form of the data for the health-indicator, raw data, waveform or number derived from raw data or waveform, may be used in accordance with some embodiments described herein.” A waveform is a graphical image as shown in FIG. 4A-4C); and
training the convolutional neural network model, including at least one pooling layer, based on the 2D graphical image and the generated premonitory symptoms labels (para. [0036], “In some cases, a pooling layer (not shown) may be applied after the nonlinear layers, also referred to as a downsampling layer, which basically takes a filter and stride of the same length and applies it to the input, and outputs the maximum number in every sub-region the filter convolves around. Other options for pooling are average pooling and L2-norm pooling. The pooling layer reduces the spatial dimension of the input volume reducing computational costs and to control overfitting. “ and Para. [0093], “In step 918, the labeled user's low-fidelity data sequence is used to train a high-fidelity machine learning model, and optionally other-factor data sequence is also provided to train the model.”).
Regarding claims 7, 14 and 20, Valys as modified discloses The system of claim 6, Valys further discloses wherein the at least one processor is further configured to train the convolutional neural network model by removing noise from the training sensor data (Para. [0021], “providing the primary and secondary time sequence(s) to a pre-processor, which may perform operations on the data like filtering, caching, averaging, time alignment, buffering, upsampling and downsampling; providing the time sequences of data to a machine learning model, trained and/or configured to utilize the values of the primary and secondary time sequence(s) to predict next value(s) of the primary sequence at a future time;”).
Response to Arguments
Applicant’s arguments with respect to claims 1, 4, 6-8, 11, 13-15 and 18-20 have been considered but are moot because the new ground of rejection does not solely rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
With respect to the arguments regarding Valys, Valys discloses a system for artificial intelligence health monitoring using multiple sources of sensor data to be input to a CNN, Valys discloses multiple sources of data as inputs, see FIG. 11 and [0098 – 0100] showing multiple regions of data each from a wearable device or other sensor. See also [0050], “It is again emphasized that these input data (P, R and T) are merely examples of health-indicator data and other-factor data. It will also be appreciated that data for more than one health-indicator may be input and predicted, and more or less than two other-factor data may be used, where the choice depends on for what the model is being designed. It will be further appreciated by the skilled artisan that other-factor data is collected to correspond in time with the collection or measurement of the health-indicator data. Thus, while Valys fails to explicitly disclose using a wearable device on two appendages for the health monitoring, Valys is thus modified with newly cited reference Lee. Thus, teaching multiple wearable devices on multiple appendages to collect sensor data as input for the CNN.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH A TOMBERS whose telephone number is (571)272-6851. The examiner can normally be reached on M-TH 7:00-16:00, F 7:00-11:00(Eastern).
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/J.A.T./Examiner, Art Unit 3791 /TSE W CHEN/Supervisory Patent Examiner, Art Unit 3791
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