DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Claim Status: Claims 1-16 are pending.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 1 is rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 11 of U.S. Patent No. 12226223. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application claim(s) is/are broader than the corresponding claim(s) in the reference application and thus the corresponding claim(s) is/are a species of the more generic instant claim(s). It has been held that the generic invention is "anticipated" by the "species". See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993).
Re Claim 1, US 122262231 teaches a multi-modal sleep monitoring system for sleep data collection of a person's sleep for characterizing sleep and awake states, comprising:
a data processor, in a housing, the data processor configured for operating in a plurality of operating modes using a plurality of sensors, the data processor configured to:
detect at least one sensor of the plurality of sensors (claim 1, detect the one sensor providing data to the data processor at the individual female port);
determine a sensor type associated with the at least one sensor (claim 1, determine a sensor type associated with the one sensor at the individual female port) and a signal quality of a signal from the at least one sensor (claim 1, determine a quality associated with at least one signal from the one sensor at the individual female port) and select a mode of operation based on the determined sensor type of the detected at least one sensor (claim 1, select, for the sleep session, a mode of operation from the plurality of modes of operation for the determined sensor type and the detected one sensor) and the determined signal quality of the at least one sensor (claim 11, wherein the data processor is configured to invoke a process of the data processor based on the determined quality);
receive data from the at least one detected sensor (claim 1, receive data from the one detected sensor); and
process the received data according to the selected mode of operation to output a characterization for assessing the person's sleep, wherein the data processor is configured to operate in the selected mode of the plurality of operating modes (claim 1, process the received data based on the determined sensor type and according to the data processing method of the selected mode of operation for outputting a characterization of the person's sleep);
wherein the data processor is configured to receive signals from the plurality of sensors (claim 1, a data processor in the housing and configured to operate in a plurality of modes of operation and to record data of signals from the accelerometer and the set of sensors), the plurality of sensors comprising mountable sensors configured to be mounted on the person, the plurality of sensors comprising a photoplethysmography sensor and a built-in three-axis accelerometer to generate signals indicative of physical movement of a body of the person (claim 1, a set of sensors configured to detect patient parameters comprising physiological parameters, the set of sensors comprising mountable sensors configured to be mounted on the person, the mountable sensors including a photoplethysmography sensor; a built-in three-axis accelerometer in the housing to generate signals indicative of physical movement of a body of the person);
a software application configured to control (a) generation of a user interface on a computing device external to the housing, the user interface configured to enable a user to make an indication to the data processor of a selection of a sensor modality (claim 1, a software application configured to: control generation of a user interface on a computing device external to the wearable processing component, the user interface to enable a user to make an indication to the wearable processing component of a selection of a sensor modality from among a plurality of sensor modalities corresponding to the sensor type of the one sensor connected via the individual female port,); and (b) communicate with the data processor to cause configuration of the data processor to operate according to the indication of the selected sensor modality (claim 1, communicate the indication to the wearable processing component to cause configuration of at least the data processor to operate according to the selected sensor modality); and
web server apparatus connected to a network, the web server apparatus comprising a plurality of user accounts, wherein a user account of the plurality of user accounts is configured for receiving and evaluating data recorded by the data processor on a memory card (claim 1, a web server apparatus connected to a network, the web server apparatus comprising a plurality of user accounts, wherein a user account of the plurality of user accounts is configured for receiving and evaluating the recorded data from the memory card), the recorded data comprising at least a characteristic of respiration and heart rate (claim 1, the recorded data comprising at least a characteristic of respiration, and heart rate).
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 pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 9-14, and 16 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Burton (US 2004/0193068) in view of Levendowski et al. (US 8,355,769).
Re Claim 1, Burton discloses a multi-modal sleep monitoring system for sleep data collection of a person's sleep for characterizing sleep and awake states, comprising:
a data processor, in a housing (para. [0198], the apparatus may include a means to incorporate amplification, filtering, storage, and CPU either as a throwaway disposable system or with the more expensive electronics being part of a re-usable part of the apparatus), the data processor configured for operating in a plurality of operating modes using a plurality of sensors (block 7, fig. 18, para. [0571], a means for automatic analysis format linked to signals connected, such as in the case of sleep and wake analysis where the analysis parameters applied will depend on the validated signals. If, for example, only EEG outer malbar electrodes are validated, then frequency optimized EEG outer malbar signals can be used for analysis, as opposed to more complex analysis signal combinations including EMG and EOG signals; para. [0197], A specialised identification connection system may allow automatic identification and channel characterisation (system configuration to suit particular channel type) for matching between electrode application types. "Electrode application" types may include ECG, EMG, muscle activity, piezo movement detection, bi-coherence EEG, and EOG. "Characterisation" may include sample rates, data analysis types, data condensing formats, data storage rates, data storage format, optimal power management, and electrical and processing optimization. Data format may include on-board electrode data storage, versus remote patient worn data storage or remote linked data storage; para. [0198]), the data processor configured to:
detect at least one sensor of the plurality of sensors (para. [0212], wireless patient electrode identification and characterization function. This function may provide a means for the system to automatically identify the electrode type selected by the user. Automatic identification may be by way of wireless module scanning or electrically interfacing to some resident data or optical or magnetic code sequence, where a unique code is associated with each unique electrode type);
determine a sensor type associated with the at least one sensor (para. [0212], para. [0197], a specialized identification connection system allow automatic identification and channel characterization for matching between electrode application types. “Electrode application” types may include ECG, EMG, muscle activity, piezo movement detection, bi-coherence EEG, and EOG.) and a signal quality of a signal from the at least one sensor (para. [0150], signal quality measurement, para. [0195], system include a wireless electrode system with automatic quality verification, redundant electrode substitution, and minimal sensor-electrode attachment system. The system provides automatic electrode impedance measurement for detecting potential electrode quality problems, redundant electrode substitution for substituting back-up electrodes for poor quality electrode connections and dynamic signal quality for detecting current or pending electrode problems) and select a mode of operation based on the determined sensor type of the detected at least one sensor and the determined signal quality of the at least one sensor (para. [0195], [0197], A specialised identification connection system may allow automatic identification and channel characterisation (system configuration to suit particular channel type) for matching between electrode application types. “Characterisation” may include sample rates, data analysis types, data condensing formats, data storage rates, data storage format, optimal power management, and electrical and processing optimisation.; para. [0571], in the case of sleep and wake analysis where the analysis parameters applied will depend on the validated signals. If, for example, only EEG outer malbar electrodes are validated, then frequency optimised EEG outer malbar signals can be utilised for analysis, as opposed to more complex analysis signal combinations including EMG and EOG signals.);
receive data from the at least one detected sensor (para. [0197] and [0571] disclose automatic identification and channel characterization for matching between electrode application types, where the electrode application types may include ECG, EMG, muscle activity, piezo movement detection, bi-coherence EEG, and EOG.); and
process the received data according to the selected mode of operation to output a characterization for assessing the person's sleep, wherein the data processor is configured to operate in the selected mode of the plurality of operating modes (para. [0571], a means for automatic analysis format linked to signals connected, such as in the case of sleep and wake analysis where the analysis parameters applied will depend on the validated signals. If, for example, only EEG outer malbar electrodes are validated, then frequency optimized EEG outer malbar signals can be used for analysis, as opposed to more complex analysis signal combinations including EMG and EOG signals. Para. [0571] discloses a plurality of modes of operation, where each mode of operation is based on different analysis parameters, which are dependent on identified and validated sensor types.),
wherein the data processor is configured to receive signals from the plurality of sensors, the plurality of sensors comprising mountable sensors configured to be mounted on the person (fig. 36, para. [0398]-[0408], electrodes and sensors; para. [0375], [0376], a single self-adhesive forehead electrode system or a single sensor device extending over the patient’s forehead and chin, containing forehead EOG, EEG, and EMG; para. [0174], biological blanket sensor (BBS), which may enable a wired or wireless interface providing a range of measurements for assistance in determining arousal movements, body movement, breathing sounds, heart sounds, respiration, heart rate, PTT, blood pressure, and temperature; para. [0142], EAS includes EEG, EOG, EMG; para. [0143], a combination system may provide R&K and/or bicoherence signal attachment in one wireless hybrid device),
the plurality of sensors comprising a photoplethysmography sensor (para. [0176], [0177], [0180], pulse oximeter);
a software application configured to control (a) generation of a user interface on a computing device external to the housing (Fig. 17 discloses that user interface is connected to central processing unit, which is external to wearable units. The user interface includes display (touch control – optional), keypad, and status LEDs; para. [0165], a user programmable device with real-time display of integrated analysis index), the user interface configured to enable a user to make an indication to the data processor of a selection of a sensor modality; and (b) communicate with the data processor to cause configuration of the data processor to operate according to the indication of the selected sensor modality (para. [0587], user select on/off. User can configure which input channels are selected, para. [0581]-[0584] shows different channels corresponding to different sensor signal type; para. [0532], apparatus may be configured by the user for different modes of operation; para. [0397] discloses that the ADMS system is capable of providing user adjustable modes, para. [0398]-[0408], table 1 discloses different modes; and para. [0348], monitoring system allows the user to select or change the sensors or electrode status; para. [0171], The user of the HCM system can select a desired function (for example depth of anaesthesia monitoring, vigilance monitoring, sedation monitoring); para. [0038], The combinations of multiple sensory monitoring and analysis may include a provision for a user to configure, select or operate the system with one or more channels of input data from a subject together with a range of system set-ups or montages); and
web server apparatus connected to a network (para. [158], the IMES device may be wirelessly linked to close proximity or distance monitoring systems equipped with a wireless data interface capability to IMES. WEM may also be wirelessly linked to mobile phones or wireless modems or a network interface including an internet connect; fig. 17, computer network interface), the web server apparatus is configured for receiving and evaluating data recorded by the data processor on a memory card (para. [0198], transmitting data to a “remote” device. The “remote” device may provide a means of transmitting and storing less condensed and more comprehensive data, as may be required for clinical or research diagnosis or validation of diagnosis; para. [0158], The WEM may be wirelessly linked to remote computer devices wherein WEM data may be stored, displayed and/or analysed.), the recorded data comprising at least a characteristic of respiration and heart rate (para. [0174], [0220], measurements for breathing sounds, heart sounds, respiration, heart rate, Pulse Transient Time, Blood pressure).
Burton is silent regarding a built-in three-axis accelerometer to generate signals indicative of physical movement of a body of the person.
Burton is silent regarding the web server apparatus comprising a plurality of user accounts, wherein a user account of the plurality of user accounts is configured for receiving and evaluating data recorded by the data processor on a memory card.
However, Levendowski discloses a system for the assessment of sleep quality in adults and children (abstract) and discloses a data acquisition system including, data acquisition unit with sensor strip (col. 5, col. 6, figs. 2A and 2B). Levendowski teaches the data acquisition unit including an accelerometer 317 that can measure full range of head positions, including both sleep and wake conditions, as well as behavioral arousals defined by subtle head movement (col. 8), which reads on “a built-in three-axis accelerometer to generate signals indicative of physical movement of body of the person”.
Levendowski also teaches acquiring physiological signals and analyzing signals to assess sleep stage, wherein the system is configured to output characterizations of sleep comprising sleep and awake states based on the physical movement of the person’s body as obtained from the three-axis accelerometer (step 100, step 110 in fig. 1, col. 14, line 65- col. 16, line 30, the data acquisition system performs concurrent measurements of two categories of signal data: (1) signal data related to sleep states, and (2) signal data related to the type of sleep disruption. Physiological signals are acquired using electrodes and sensors, the physiological signals including EEG, EMG, EOG, ECG; col. 9 discloses DAU including peripheral sensors including sensors measuring leg movements and discloses that data acquired from these sensors can be used to determine the user’s sleep architecture and/or identify sleep disruptions that can negatively impact sleep quality; col. 12 discloses electro-neuro-cardio-respiratory sensors used to assess sleep quality; col. 18, lines 47-64, The duration and frequency of head or body movements are also useful in differentiating behavioral sleep from wake; col. 19, the actigraphy signals can be used independently to distinguish sleep from wake and/or combined with changes in snoring and/or airflow to further improve the accuracy of sleep/wake detection.).
Levendowski further teaches the web server apparatus comprising a plurality of user accounts, wherein a user account of the plurality of user accounts is configured for receiving and evaluating data recorded by the data processor on a memory card (col. 11, external computer system can be a user’s home computer system and the DAU can include software for downloading data captured by the DAU and/or sensors interfaced with the DAU to a remote computer system via a network. The remote computer system can be a web portal comprising one more remote servers that can collect and analyze data received from DAU units. For example, a doctor treating a patient can create an account on the web portal for that patient and associate the account with a particular DAU.).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made, to modify Burton, by adding a built-in three-axis accelerometer to generate signals indicative of physical movement of a body of the person, wherein the system is configured to output characterizations of sleep comprising sleep and awake states based on the body movement data of the recorded data, as taught by Levendowski, for the purpose of improving the accuracy of sleep/wake detection (col. 19); and by configuring the web server apparatus to comprise a plurality of user accounts, wherein a user account of the plurality of user accounts is configured for receiving and evaluating data recorded by the data processor on a memory card, as taught by Levendowski, for the purpose of remote monitoring of patient’s physiological data and thereby his condition by a clinician (col. 11).
Re Claim 2, Burton discloses that the data processor is configured to receive a raw electrical signal from electrodes and movement data (para. [0009], movement state, para. [0020], HCM system may provide means to position electrodes and sensors for monitoring arousal and body movements from any location on the patient's body).
Re Claim 3, Burton discloses that the raw electrical signal includes information indicative of one or more of EEG (para. [0035], EEG analysis; para. [0484], sleep stage estimation of a subjects (derived) EEG, EOG and EMG data (in the form of sleep stage as derived from spectral analysis of EEG and correlation of EMG and EOG signals), muscle tone, eye movement (para. [0043], Eye movement sensors (such as piezo or PVD movement sensors) and electrodes (such as EOG) have been used in the past for detecting eye movement or eye-lid movement respectively) and galvanic skin response (para. [0786], GSR (galvanic skin response) or EDA (electro dermal activity) or SCR (skin conductivity response)).
Re Claim 4, Burton discloses that the system is further configured to generate a sleep quality estimate based on movement data tracked with the data processor (para. [0200], quality of sleep; para. [0079], patient stats of sleep, wake, depth of consciousness are derived from arousal analysis and body movement analysis).
Re Claim 5, Burton discloses that the system is configured to depict physical movements experienced by a user from stored movement data (para. [0220], [0354], body movement analysis).
Re Claim 9, Burton discloses a base station configured to communicate with the data processor, wherein the base station is configured to carry out at least a portion of an analysis of an electrical signal from electrodes of the system (para. [0158], [0159], The WEM may be wirelessly linked to remote computer devices wherein WEM data may be stored, displayed and/or analysed. The remote WEM device may also provide a controlled interface to the WEM module for calibration and impedance testing.; para. [0144]-[0153] discloses that WEM data includes electrical signal from electrodes of the system).
Re Claim 10, Burton discloses that base station is configured to present to a user, determined one or more sleep stages and/or duration of determined one or more sleep stages (para. [0200], This display may be in a form where the index can represent an amount of time detected in a sleep or wake state (could be any stage or combinations of state including REM, non-REM, stage 1, stage 2, stage 3, stage 4, wake) by means of say a pair of bi-coherence electrodes).
Re Claim 11, Burton discloses that the data processor is configured with an LED/photodiode pair for generating a photoplethysmographic signal (para. [0177], [0220], [0791], pulse oximetry).
Re Claim 12, Burton discloses that the system is configured to determine heart rate, heart rate variability and/or respiration rate from the photoplethysmographic signal, and determine one or more sleep stages based on one or more of the heart rate, heart rate variability and/or respiration rate (para. [0079], [0177], patient states of sleep, wake, depth of consciousness, depth of anaesthesia and vigilance in accordance with analysis states derived from a combination of analysis types including Pulse Transient Time (PTT) based arousal detection (31), PTT measure and PTT based blood-pressure reference measure, PTT based heart rate and blood pressure with simple non-invasive oximeter (31, 32) and bio-blanket-heart-temperature-PTT blood-pressure-respiration-breathing sound (49)).
Re Claim 13, Burton discloses that the data processor is further configured to forward received data to the web server apparatus via a wired or wireless communications connection (para. [0158], The WEM may be wirelessly linked to remote computer devices wherein WEM data may be stored, displayed and/or analysed. The remote WEM device may also provide a controlled interface to the WEM module for calibration and impedance testing.).
Re Claim 14, Burton discloses that the data processor is configured with separate processing modules for each of (a) an electrical signal from electrodes, and (b) movement data output (abstract, one or more analysis algorithms including combinations of simultaneous, real-time R&K analysis, AEP spectral analysis-SEF-MF, Bi-coherence analysis, initial wave analysis, auditory response, arousal analysis, and body movement analysis.).
Burton is silent regarding the data processor configured with separate processing module for movement data output by the accelerometer.
Levendowski further teaches a separate processing module for movement data output by the accelerometer (col. 8, lines 30-33, The DAU 210 includes an accelerometer 317 that can measure a full range of head positions, including both sleep and wake conditions, as well as behavioral arousals defined by subtle head movements; col. 18, lines 47-56, The duration and frequency of head or body movements are also useful in differentiating behavioral sleep from wake. FIG. 9 includes example data that displays periods with substantial head movement (HMOV) indicates the patient is awake).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Burton as modified by Levendowski, by adding a separate processing module for movement data output by the accelerometer, as taught by Levendowski, for the purpose of identifying sleep and wake conditions and behavioral arousals (col. 8, lines 30-33).
Re Claim 16, Burton as modified by Levendowski discloses the claimed invention substantially as set forth in claim 1.
Burton further discloses that a mode of the plurality of operating modes operates to process data output by EMG to weigh a sleep condition analysis otherwise executed on data output by a sensor of different type from EMG (abstract, the systems weigh the outputs of one or more analysis algorithms including combinations of simultaneous, real-time R&K analysis, AEP spectral analysis-SEF-MF, Bi-coherence analysis, initial wave analysis, auditory response, arousal analysis, and body movement analysis.).
Burton is silent regarding a mode of the plurality of operating modes operating to process data output by the accelerometer to weight a sleep condition analysis otherwise executed on data output by a sensor of a different type from the accelerometer.
However, Levendowski further discloses that a mode of the plurality of operating modes operates to process data output by the accelerometer to weight a sleep condition analysis otherwise executed on data output by a sensor of a different type from the accelerometer (col. 19, line 52 – col. 20, line 3, As described previously, the actigraphy signals can be used independently to distinguish sleep from wake, and/or combined with changes in snoring and/or airflow to further improve the accuracy of sleep/wake detection. FIG. 9 highlights temporal periods of snoring between periods of gross head movement, with snoring indicating the patient is asleep and the head movement indicating the patient is awake.).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Burton as modified by Levendowski, by configuring a mode of the plurality of operating modes to operate to process data output by the accelerometer to weight a sleep condition analysis otherwise executed on data output by a sensor of a different type from the accelerometer, as taught by Levendowski, for the purpose of improving the accuracy of sleep/wake detection (col. 19, line 52 – col. 20, line 3).
Claims 6-8 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Burton (US 2004/0193068) as modified by Levendowski et al. (US 8,355,769) and further in view of Dziubinski (US 2009/0171227A1).
Re Claim 6, Burton as modified by Levendowski discloses the claimed invention substantially as set forth in claim 1.
Burton discloses that a remote computer device configures to carry out at least a portion of an analysis of an electrical signal from electrodes of the system (para. [0158], [0159], The WEM may be wirelessly linked to remote computer devices wherein WEM data may be stored, displayed and/or analysed. The remote WEM device may also provide a controlled interface to the WEM module for calibration and impedance testing. WEM may also be wirelessly linked to mobile phones; para. [0154], data from EEG, EOG, EMG, EEG or other).
Burton is silent regarding an application for a mobile device configures the mobile device to carry out at least a portion of an analysis of an electrical signal from electrodes of the system.
However, Dziubinski discloses safe and remote outpatient ECG monitoring system (abstract) and teaches that an application for a mobile device configures the mobile device to carry out at least a portion of an analysis of an electrical signal from electrodes of the system (fig. 1, para. [0070], The communication procedure, i.e., communication between the PDA microcomputer 121 application, responsible for constant monitoring and analysis of the patient's ECG and the desktop visualization/editing application operated by the physician on the desktop computer 142, has been designed to allow the doctor to efficiently monitor a large group of patients at the same time. This effect is achieved by pre-selection (filtering) and auto-analysis of the information before it is presented to the specialist. The filtering and analysis is performed using ECG analysis algorithms operating on the patient's PDA 120 or microcomputer 121; para. [0063], a personal digital assistant (PDA) or SmartPhone).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Burton as modified by Levendowski, by adding an application for a mobile device that configures the mobile device to carry out at least a portion of an analysis of an electrical signal from electrodes of the system, as taught by Dziubinski, for the purpose of constant monitoring and analysis of the patient's ECG (para. [0070]).
Re Claim 7, Burton discloses that the data processor is further configured to wirelessly communicate with the mobile device (para. [0158], WEM may also be wirelessly linked to mobile phones; para. [0139], [0140]).
Re Claim 8, Burton discloses that mobile device is configured to present to a user with determined one or more sleep stages and/or duration of determined one or more sleep stages (para. [0158], WEM may also be wirelessly linked to mobile phones; para. [0200], This display may be in a form where the index can represent an amount of time detected in a sleep or wake state (could be any stage or combinations of state including REM, non-REM, stage 1, stage 2, stage 3, stage 4, wake) by means of say a pair of bi-coherence electrodes).
Claim 15 is rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Burton (US 2004/0193068) as modified by Levendowski et al. (US 8,355,769) and further in view of Keenan (US 2005/0240087 A1).
Re Claim 15, Burton as modified by Levendowski discloses the claimed invention substantially as set forth in claim 1.
Burton and Levendowski are silent regarding the data processor configured with an intertwined processing module for (a) a first data set from an electrical signal from electrodes, and (b) a second data set of movement data output by the accelerometer, wherein the intertwined processing module uses one of the first data set and the second data set to adjust the other one of the first data set and the second data set.
However, Keenan discloses a multi-modal sleep monitoring system (para. [0010], systems and methods for improved robust and reliable extraction of physiological information from signals gathered during concurrent monitoring of multiple physiological (MPM) parameters of a subject, especially MPM monitoring when the subject is carrying out normal waking and sleeping activities) and teaches a data processor configured with an intertwined processing module for (a) a first data set from an electrical signal from electrodes (para. [0101], EEG signals), and (b) a second data set of movement data output by the accelerometer (para. [0096], accelerometer 81 signal), wherein the intertwined processing module uses one of the first data set and the second data set to adjust the other one of the first data set and the second data set (para. [0096] Continuing now with the details of motion artifact removal from respiratory signals, respiratory signal 75 including motion artifact resulting from the subject's motion is the primary input signal, and accelerometer 81 signal (motion signal) is the reference signal. The reference signal is filtered by adaptive filter 83, and then filtered reference signal 85 is combined 77 with primary signal 75 resulting in error signal 87. The filter weights are adapted so that the error signal is minimized, in other words, so that as much as possible of the motion artifact is subtracted from the primary signal. The error signal with enhanced respiratory components is as "RC out"; para. [0101] Motion artifacts are removed from other signals, in particular the thorax signal in step 55 and the EEG signal in step 57, with techniques substantially similar to those described above for motion artifact removal from respiratory signals.).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time of filing, to modify Burton as modified by Levendowski, by configuring the data processor with an intertwined processing module for (a) a first data set from an electrical signal from electrodes, and (b) a second data set of movement data output by the accelerometer, wherein the intertwined processing module uses one of the first data set and the second data set to adjust the other one of the first data set and the second data set, as taught by Keenan, for the purpose of removing motion artifacts (para. [0101]).
Conclusion
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/JONATHAN T KUO/Primary Examiner, Art Unit 3792
/V.V.H./
Vynn Huh, May 30, 2026Examiner, Art Unit 3792
Prosecution Insights