DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Priority
This application is a continuation of US Application no. 16/846,959, now US Patent no. 11,801,009, filed 13 April 2020, which is a continuation of US Application no. 14/429,598, now US Patent no. 10,660,563, filed 23 June 2015 which is a national stage entry under 35 USC 371 of PCT/US2013/060652 filed 19 September 2013.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., abstract idea) without significantly more.
Step 1
The claims recite a method (claims 1-9) and system (claims 10-16) which are directed to eligible statutory categories. Claims 17-20 are directed to a computer readable medium are directed to a signal per se which have been held to be non-statutory subject matter and thus as ineligible under 35 USC 101 (MPEP 2106.03 II) (An amendment to include “non-transitory” would cure the deficiency and place claims 17-20 into the article of manufacture category and therefore pass step 1).
Step 2, Prong One
Though the claims 1-16 are eligible under Step 1, they have been found to be directed to a judicial exception. The claims recite:
Receiving physiological data (respiration) from a therapy apparatus;
Deriving data features, including respiration rate, amplitude, and measures of variability;
Determining sleep stages on an epoch-by-epoch basis in a classifier, based on those features;
Generating output indicating the sleep stages.
These steps amount to data collection, processing, analysis, and displaying of certain results of the collection and analysis to data as taught in Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016). This is a judicial exception related to mental processes.
Step 2, Prong Two
The judicial exception is not integrated into a practical application. The claims do not recite any additional element or combination of elements that integrate the selected exception into a practical application, nor do they recite any additional element or combination of elements that amounts to significantly more than the selected exception.
Additional elements recited include: a respiratory therapy apparatus comprising a flow generator (e.g., a PAP device), a sensor configured to monitor the subject, a local external device comprising a mobile phone or tablet communicating via Bluetooth, and an output to a display showing a series of sleep stage indications (wake/light/deep/REM).
It is considered that the therapy apparatus and sensor are used merely as sources of data and collection means; the claims don’t recite a technological improvement to the apparatus or sensor, nor do they change how PAP therapy is delivered based on the sleep stages. They simply use the device as an input channel.
The classifier is recited at a high level, with no specific training method, architecture, or implementation detail that changes computer functionality itself (e.g., no improvement to memory, processor, or network operation).
The step of generating output for a display is just displaying results of the classification, a conventional post-solution activity.
It is also considered that the Bluetooth communication between a PAP device and a mobile phone or tablet is a conventional way to transfer data, not a specific non-conventional use.
In view of this, the additional elements amount to generic computing components and conventional medical hardware used in their ordinary roles, and do not integrate the abstract idea into a practical application in the sense of improving computer technology or the PAP device itself, nor for effecting a particular treatment or prophylaxis.
Step 2B
The additional elements when considered individually and in combination are not enough to qualify as significantly more than the abstract idea. The additional elements recite generic processors, conventional communication techniques (Bluetooth), common medical hardware (PAP device with a sensor monitoring respiration), and algorithmic steps (feature extraction, variability, approximate entropy, classification) that when viewed individually and in combination are well-understood, routine, and conventional.
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, 4, 5, 7, 9, 10, 13, 16, 17, and 20 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Ramanan et al. (US Publication no. 2012/0179061) in view of Heneghan et al. (US Publication no. 2009/0203972).
In regard to claim 1, Ramanan et al. describes a sleep condition detection device 102 or apparatus having a controller 104 that may have one or more processors to implement particular sleep state and/or sleep arousal detection methodologies and algorithms. The device 102 of Ramanan et al. may have access to data from a respiratory flow signal or may otherwise include an optional flow measurement module 105. the detection may be performed during a sleep session contemporaneously with the measuring of a respiratory flow signal using a present measuring of flow data with a flow sensor. However, the methodologies and algorithms do not includes the steps of receiving, deriving, determining, and generating as claimed.
Heneghan et al. discloses a computer processing method for classifying sleep stages of a subject based on respiration data, the method comprising:
receiving, by one or more processors, data concerning respiration of the subject (para 49, describes the block diagram of figure 13, with processor 1340; para 89, a processor is arranged to analyze the signals to produce measurements of one or more of a respiration parameter, cardiac activity, and a bodily movement or function), wherein the data concerning respiration of the subject is determined with a sensor of the respiratory therapy apparatus, wherein the sensor is configured to monitor the subject (para 50; a sensor unit can be placed relatively close to where the subject is sleeping; it is noted that Heneghan et al. obtains a signal reflected from the subject, it is considered that the technique of data processing described therein is applicable to data acquired from any type of respiration sensor/monitor such as the detection device of Ramanan et al.);
deriving, by the one or more processors, one or more data features based on the received data, the one or more data features comprising at least one of a respiration rate and a respiration amplitude, the deriving comprising calculating, based on at least one of the respiration rate and the respiration amplitude, one or more measures of variability (para 67, respiration frequency (i.e., rate) is a useful means of characterizing breathing patterns as faster breathing is associated with respiratory distress, the respiration frequency is calculated for each epoch, and variation is observed to differentiate REM from non-REM; para 68, the amplitude of the respiration signal is also of importance and amplitude variation is an identifying feature of a sleep disordered breathing called Cheyne-Stokes respiration and provides for tracking the variation of amplitudes over epochs of time; para 69, the periodic nature of the patterns in the respiratory signal are also important as it can indicate the presence of sleep disorder breathing);
determining, on an epoch-by-epoch basis, in a classifier of the one or more processors, the sleep stages based on at least one or both of the respiration rate and the respiration amplitude and the one or more measures of variability (para 44, information from the derived from respiratory signals is extracted into meaningful physiological classifications, by using a classifier model, each stream of data is segmented into time epochs, and statistical features are generated for each epoch, the classification from epochs can be further combined with classification from other epochs to form higher level decisions (such as whether the person is in REM, non-REM, or WAKE states); para 80 also supports this limitation, as selected features may be fed into a classifier model (such as a conventional linear discriminant analysis classifier) which will then provide the probability for that epoch to belong to a certain class of interest. As a specific example, three classes are known and defined in the art for sleep state: AWAKE, NON-REM SLEEP, REM SLEEP); and
generating, by the one or more processors, output for a display, the output comprising a series of indications of the sleep stages (para 80, these epoch classifications can then be combined over an entire night's recording to provide a so-called hypnogram, which maps the time period into different sleep stages; para 50, the system of Heneghan et al. includes a display unit through which results can be analyzed, visualized and communicated to the user).
In view of this, the modification of Ramanan et al. to implement the data processing technique of Heneghan et al. is considered to have been obvious to one of ordinary skill in the art since the modification would comprise the application of a known technique to a known device to yield the predictable result of processing respiratory data into sleep state information.
In regard to claim 4, in Heneghan et al., the generated output indicates the determined sleep stages, wherein the generated output comprises indications of any one or more of: wake, light sleep, deep sleep and REM sleep (para 80, these epoch classifications can then be combined over an entire night's recording to provide a so-called hypnogram, which maps the time period into different sleep stages; para 50, the system of Heneghan et al. includes a display unit through which results can be analyzed, visualized and communicated to the user; para 80 also teaches that the epochs are classified into AWAKE, NON-REM SLEEP, REM SLEEP states).
In regard to claim 5, the technique of Heneghan et al. includes identifying an epoch of deep sleep to distinguish it from other epochs of sleep or wake (para 80 and 82, it appears that Heneghan et al. distinguishes between REM sleep and non-REM sleep (i.e., between deep and other epochs of sleep) and awake. Additionally, this step is demonstrated as being known to one of ordinary skill in the art by Ramanan et al. (para 147, the method of the sleep detector may also involve detecting a state from potential sleep states such one or more of a Non-REM sleep state, a REM sleep state, Phasic REM state, Tonic REM state, a Deep REM sleep state and/or a Light REM sleep state).
In regard to claim 7, Heneghan et al. teaches determining a respiration rate variability signal (para 67, variability in respiratory frequency (i.e., rate) is useful indicator of sleep state).
In regard to claim 9, the device of Ramanan et al. for obtaining respiratory data is a positive airway pressure device (para 386).
In regard to claim 10, Ramanan et al. describes a sleep condition detection device 102 or apparatus having a controller 104 that may have one or more processors to implement particular sleep state and/or sleep arousal detection methodologies and algorithms. The device 102 of Ramanan et al. may have access to data from a respiratory flow signal or may otherwise include an optional flow measurement module 105. the detection may be performed during a sleep session contemporaneously with the measuring of a respiratory flow signal using a present measuring of flow data with a flow sensor. However, the methodologies and algorithms do not includes the steps of receiving, deriving, determining, and generating as claimed.
Heneghan et al. discloses a computer processing system for classifying sleep stages of a subject based on respiration data, the method comprising:
receiving, by one or more processors, data concerning respiration of the subject (para 49, describes the block diagram of figure 13, with processor 1340; para 89, a processor is arranged to analyze the signals to produce measurements of one or more of a respiration parameter, cardiac activity, and a bodily movement or function), wherein the data concerning respiration of the subject is determined with a sensor of the respiratory therapy apparatus, wherein the sensor is configured to monitor the subject (para 50; a sensor unit can be placed relatively close to where the subject is sleeping; it is noted that Heneghan et al. obtains a signal reflected from the subject, it is considered that the technique of data processing described therein is applicable to data acquired from any type of respiration sensor/monitor such as the detection device of Ramanan et al.);
deriving, by the one or more processors, one or more data features based on the received data, the one or more data features comprising at least one of a respiration rate and a respiration amplitude, the deriving comprising calculating, based on at least one of the respiration rate and the respiration amplitude, one or more measures of variability (para 67, respiration frequency (i.e., rate) is a useful means of characterizing breathing patterns as faster breathing is associated with respiratory distress, the respiration frequency is calculated for each epoch, and variation is observed to differentiate REM from non-REM; para 68, the amplitude of the respiration signal is also of importance and amplitude variation is an identifying feature of a sleep disordered breathing called Cheyne-Stokes respiration and provides for tracking the variation of amplitudes over epochs of time; para 69, the periodic nature of the patterns in the respiratory signal are also important as it can indicate the presence of sleep disorder breathing);
determining, on an epoch-by-epoch basis, in a classifier of the one or more processors, the sleep stages based on at least one or both of the respiration rate and the respiration amplitude and the one or more measures of variability (para 44, information from the derived from respiratory signals is extracted into meaningful physiological classifications, by using a classifier model, each stream of data is segmented into time epochs, and statistical features are generated for each epoch, the classification from epochs can be further combined with classification from other epochs to form higher level decisions (such as whether the person is in REM, non-REM, or WAKE states); para 80 also supports this limitation, as selected features may be fed into a classifier model (such as a conventional linear discriminant analysis classifier) which will then provide the probability for that epoch to belong to a certain class of interest. As a specific example, three classes are known and defined in the art for sleep state: AWAKE, NON-REM SLEEP, REM SLEEP); and
generating, by the one or more processors, output for a display, the output comprising a series of indications of the sleep stages (para 80, these epoch classifications can then be combined over an entire night's recording to provide a so-called hypnogram, which maps the time period into different sleep stages; para 50, the system of Heneghan et al. includes a display unit through which results can be analyzed, visualized and communicated to the user).
In view of this, the modification of Ramanan et al. to implement the data processing technique of Heneghan et al. is considered to have been obvious to one of ordinary skill in the art since the modification would comprise the application of a known technique to a known device to yield the predictable result of processing respiratory data into sleep state information.
In regard to claim 13, the technique of Heneghan et al. includes identifying an epoch of deep sleep to distinguish it from other epochs of sleep or wake (para 80 and 82, it appears that Heneghan et al. distinguishes between REM sleep and non-REM sleep (i.e., between deep and other epochs of sleep) and awake. Additionally, this step is demonstrated as being known to one of ordinary skill in the art by Ramanan et al. (para 147, the method of the sleep detector may also involve detecting a state from potential sleep states such one or more of a Non-REM sleep state, a REM sleep state, Phasic REM state, Tonic REM state, a Deep REM sleep state and/or a Light REM sleep state).
In regard to claim 16, the device of Ramanan et al. for obtaining respiratory data is a positive airway pressure device which is a therapy apparatus (para 386).
In regard to claim 17, Ramanan et al. describes a sleep condition detection device 102 or apparatus having a controller 104 that may have one or more processors to implement particular sleep state and/or sleep arousal detection methodologies and algorithms. The device 102 of Ramanan et al. may have access to data from a respiratory flow signal or may otherwise include an optional flow measurement module 105. the detection may be performed during a sleep session contemporaneously with the measuring of a respiratory flow signal using a present measuring of flow data with a flow sensor. However, the methodologies and algorithms do not includes the steps of receiving, deriving, determining, and generating as claimed.
Heneghan et al. discloses a computer processing system with a computer readable medium comprising instructions (para 91) for classifying sleep stages of a subject based on respiration data, the method comprising:
instructions to receive data, the data concerning respiration of the subject, (para 49, describes the block diagram of figure 13, with processor 1340; para 89, a processor is arranged to analyze the signals to produce measurements of one or more of a respiration parameter, cardiac activity, and a bodily movement or function), wherein the data concerning respiration of the subject is determined with a sensor of the respiratory therapy apparatus, wherein the sensor is configured to monitor the subject (para 50; a sensor unit can be placed relatively close to where the subject is sleeping; it is noted that Heneghan et al. obtains a signal reflected from the subject, it is considered that the technique of data processing described therein is applicable to data acquired from any type of respiration sensor/monitor such as the detection device of Ramanan et al.);
instructions to derive one or more data features based on the received data, the one or more data features comprising at least one of a respiration rate and a respiration amplitude, the deriving comprising calculating, based on at least one of the respiration rate and the respiration amplitude, one or more measures of variability (para 67, respiration frequency (i.e., rate) is a useful means of characterizing breathing patterns as faster breathing is associated with respiratory distress, the respiration frequency is calculated for each epoch, and variation is observed to differentiate REM from non-REM; para 68, the amplitude of the respiration signal is also of importance and amplitude variation is an identifying feature of a sleep disordered breathing called Cheyne-Stokes respiration and provides for tracking the variation of amplitudes over epochs of time; para 69, the periodic nature of the patterns in the respiratory signal are also important as it can indicate the presence of sleep disorder breathing);
instructions to determine on an epoch-by-epoch basis, in a classifier, the sleep stages based on at least one or both of the respiration rate and the respiration amplitude and the one or more measures of variability (para 44, information from the derived from respiratory signals is extracted into meaningful physiological classifications, by using a classifier model, each stream of data is segmented into time epochs, and statistical features are generated for each epoch, the classification from epochs can be further combined with classification from other epochs to form higher level decisions (such as whether the person is in REM, non-REM, or WAKE states); para 80 also supports this limitation, as selected features may be fed into a classifier model (such as a conventional linear discriminant analysis classifier) which will then provide the probability for that epoch to belong to a certain class of interest. As a specific example, three classes are known and defined in the art for sleep state: AWAKE, NON-REM SLEEP, REM SLEEP); and
instructions to generate output for a display, the output comprising a series of indications of the sleep stages (para 80, these epoch classifications can then be combined over an entire night's recording to provide a so-called hypnogram, which maps the time period into different sleep stages; para 50, the system of Heneghan et al. includes a display unit through which results can be analyzed, visualized and communicated to the user).
In view of this, the modification of Ramanan et al. to implement the instructions of Heneghan et al. is considered to have been obvious to one of ordinary skill in the art since the modification would comprise the application of a known technique to a known device to yield the predictable result of processing respiratory data into sleep state information.
In regard to claim 20, in Heneghan et al., the instructions to determine in the classifier the sleep stages comprise instructions to identify an epoch of deep sleep to distinguish it from other epochs of sleep or wake (para 80 and 82, it appears that Heneghan et al. distinguishes between REM sleep and non-REM sleep (i.e., between deep and other epochs of sleep) and awake. Additionally, this step is demonstrated as being known to one of ordinary skill in the art by Ramanan et al. (para 147, the method of the sleep detector may also involve detecting a state from potential sleep states such one or more of a Non-REM sleep state, a REM sleep state, Phasic REM state, Tonic REM state, a Deep REM sleep state and/or a Light REM sleep state).
Claims 2, 3, 11, 12, 18, and 19 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Ramanan et al. (US Publication no. 2012/0179061) in view of Heneghan et al. (US Publication no. 2009/0203972), further in view of Eyal et al. (US Publication no. 2013/0006124).
In regard to claims 2, 11, and 18, Ramanan et al. in view of Heneghan et al. suggest the invention as claimed, however neither reference teaches that the one or more processors includes a processor in a local external device that comprises a mobile phone or tablet, and wherein the processor in the local external device is configured to perform at least one or more of the receiving, the deriving, the determining, and the generating. Eyal et al. describe a sleep analysis based system that includes a data processor suitable for processing data that can include electronic computing devices such as laptop computer, notebook computer, or a hand held system such as a cellular telephone or table (para 57). The modification of the data processing technique described by Ramanan et al. modified by Heneghan et al. to be processed by an external device such as a mobile phone or tablet is considered to have been obvious to one of ordinary skill in the art since Eyal et al. explicitly teach that an external device is suitable for processing signals for sleep analysis.
In regard to claims 3, 12, and 19, Ramanan et al. in view of Heneghan et al. suggest the invention as claimed, however neither reference teaches that the local external device and the respiratory therapy apparatus are configured to communicate wirelessly via Bluetooth protocol. Eyal et al. teaches that the data processor can receive the data from sensing device 14 using a wired communication line or via wireless communication (e.g., Bluetooth.RTM. communication, WiFi.RTM. communication, Infrared Data Association communication, home radio frequency communication etc.) (para 58). The modification of the data processing technique described by Ramanan et al. modified by Heneghan et al. to be enabled communication between a data sensor and external processing device via Bluetooth is considered to have been obvious to one of ordinary skill in the art since Eyal et al. explicitly teach that the data processor and sensing device communicate data over Bluetooth.
Claims 6 and 14 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Ramanan et al. (US Publication no. 2012/0179061) in view of Heneghan et al. (US Publication no. 2009/0203972), further in view of Dothie et al. (US Patent no. 8,398,538).
In regard to claims 6 and 14, Ramanan et al. in view of Heneghan et al. suggest the invention as claimed, however neither reference teaches determining using the one or more processors, an absent state corresponding to an absence of the subject. Dothie et al. is directed to a sleep management method which includes calculating sleep metrics (i.e., sleep stages and REM sleep) for a subject (e.g., a child) (col 12 lines 23-44). Dothie et al. also teaches that a physiological sensor to record behavior of the subject in bed and may be used to determine the absence of data from the physiological sensors can be used to indicate that the child is not present in the bed (col 10 lines 48-49). The modification of the data processing technique described by Ramanan et al. modified by Heneghan et al. to detect the presence or absence of the subject is considered to have been obvious to one of ordinary skill in the art since it is explicitly taught by Dothie et al. to determine when the subject has gotten up from bed as this would prevent corruption of data analysis.
Claims 8 and 15 is/are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over Ramanan et al. (US Publication no. 2012/0179061) in view of Heneghan et al. (US Publication no. 2009/0203972), further in view of Wetmore et al. (US Publication no. 2012/0251989).
In regard to claims 8 and 15, Ramanan et al. in view of Heneghan et al. suggest the invention as claimed, however neither reference teaches processing the respiration rate to produce an approximate entropy value, and applying the approximate entropy value to the classifier for determining the sleep stages. Wetmore et al. teaches a sleep monitor to determine a user’s sleep state. Various parameters are provided that are useful for determining sleep state includes measuring respiratory rate or entropy or other features of breathing (para 126). The modification of the data processing technique described by Ramanan et al. modified by Heneghan et al. to process an entropy value of respiration is considered to have been obvious to one of ordinary skill in the art since it is explicitly taught by Wetmore et al. as being a useful parameter to measure for determining the sleep stages.
Conclusion
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/BRIAN T GEDEON/Primary Examiner, Art Unit 3796 8 December 2025