DETAILED ACTION
Claims 31, 33-34, 36-38, 40-41, 43-45, 47, 49, 51 and 53-60 are pending. Claims 1-30, 32, 35, 39, 42, 46, 48, and 50 are cancelled and claims 53-60 are new.
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 Arguments
Applicant’s arguments, filed 6/12/26, have been fully considered but are not persuasive, except where noted below.
Applicant’s arguments regarding 35 U.S.C. § 101 (page 9) are persuasive and the claims are no longer rejected under that statute.
Applicant’s arguments regarding 35 U.S.C. § 103 (pages 9-12) are moot in view of the new combination of references used in the rejection of the independent claims, i.e. Turner and Schultz. Hood is now merely cited for teaching specific features of dependent claims 34 and 41.
With regard to claim 49, Applicant states ‘Turner does not cure the deficiencies of Schultz and Hood’ (page 12) and had previously stated that ‘Schultz in view of Hood does not teach or suggest at least: "determining a target position of the first zone based on a user associated with the first zone" and "adjusting a position of an adjustable section of an adjustable bed frame associated with the first zone to the target position, wherein the adjustable section is configured to adjust an angular position of the first zone relative to a rest position."… Schultz does not disclose any particular "position" to which the adjustments are made, let alone disclose the user-zone-specific features recited in claim 31 of first "determining a target position of the first zone based on a user associated with the first zone" and then "adjusting a position of an adjustable section of an adjustable bed frame associated with the first zone to the target position’ (page 10).
It is respectfully submitted that Turner teaches these features because Turner describes that if the sensing units 44 and or the microphones detect a snoring event, the head of the bedding unit 30 on which the person is sleeping can be raised slightly and controlled by the computing device 60. As an example, the head of the bed could be raised by seven degrees (target position)... This monitoring and raising can be programmed to occur automatically and can continue up to some predetermined maximum raised position, such as thirty five degrees. Once the snoring has stopped for a set period of time, such as five minutes, the bed 10 can react by lowering the head of the bed to the horizontal, standard, sleeping position. It should be understood that amount of each head raise, and the length of time between each raise, can be customized to best accommodate each individual user (based on a user associated with the first zone) [0053]; that the bed 10 has a frame 12 designed to provide a structural base to the bed… a pair of adjustable bed units 30 are coupled to the frame 12 [0020-0025, Fig. 1]; and that the adjustable bedding unit includes an articulating frame supporting a mattress [claim 13], as detailed below in the current rejection under 35 U.S.C. § 103. Applicant’s argument is therefore not persuasive.
For at least these reasons, the rejection of the claims is maintained.
Claim Objections
The claims are objected to because of the following informalities:
‘predicting an onset of a disease associated based on the biological data should read ‘predicting an onset of a disease
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 of this title, 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.
Claim(s) 31, 33, 36, 38, 40, 45, 47, 49, 51 and 53-60 is/are rejected under 35 U.S.C. 103 as being unpatentable over Turner et al. U.S. Patent Publication No. 20090177327 (hereinafter Turner) in view of Schultz et al. U.S. Patent Publication No. 20130245389 (hereinafter Schultz).
Regarding claim 31, Turner teaches a method for adjusting a position of a bed device to address a sleeping disorder [0053 — if the sensing units 44 and or the microphones detect a snoring event, the head of the bedding unit 30 on which the person is sleeping can be raised slightly and controlled by the computing device 60. As an example, the head of the bed could be raised by seven degrees. The system continues to monitor for snoring, and if the snoring continues, the head of the bed can be raised further. This monitoring and raising can be programmed to occur automatically and can continue up to some predetermined maximum raised position, such as thirty five degrees. Once the snoring has stopped for a set period of time, such as five minutes, the bed 10 can react by lowering the head of the bed to the horizontal, standard, sleeping position; claim 15 — computer executed method], the method comprising:
receiving, by one or more processors, biological data from at least one sensor disposed within the bed device in a first zone of the bed device [0020-0025, Fig. 1 - a pair of adjustable bed units 30 are coupled to the frame 12… Each bed unit 30 is individually adjustable, to provide a "his" and "hers" style; 0026-0028, Fig. 14 — Each bed unit 30 is also provided with a sensor unit 44 (FIG. 14)… Another channel can be "listening" for frequencies between 0.5 Hz to 2 Hz (Heartbeat)… The end effect is that both signals will be fed into a microprocessor… The signals from the sensor units 44 and the microphones are used to detect the respiration, motion, pulse and snoring of a person laying on the bed unit 30.; 0031 — the signals from the sensing units 44 and microphone are passed to the computing device 60];
identifying the sleeping disorder in the first zone to detect the sleeping disorder [0053 — if the sensing units 44 and or the microphones detect a snoring event, the head of the bedding unit 30 on which the person is sleeping can be raised slightly and controlled by the computing device 60. As an example, the head of the bed could be raised by seven degrees... This monitoring and raising can be programmed to occur automatically and can continue up to some predetermined maximum raised position, such as thirty five degrees. Once the snoring has stopped for a set period of time, such as five minutes, the bed 10 can react by lowering the head of the bed to the horizontal, standard, sleeping position. It should be understood that amount of each head raise, and the length of time between each raise, can be customized to best accommodate each individual user];
determining a target position of the first zone based on a user associated with the first zone [0053 — if the sensing units 44 and or the microphones detect a snoring event, the head of the bedding unit 30 on which the person is sleeping can be raised slightly and controlled by the computing device 60. As an example, the head of the bed could be raised by seven degrees (target position)... This monitoring and raising can be programmed to occur automatically and can continue up to some predetermined maximum raised position, such as thirty five degrees. Once the snoring has stopped for a set period of time, such as five minutes, the bed 10 can react by lowering the head of the bed to the horizontal, standard, sleeping position. It should be understood that amount of each head raise, and the length of time between each raise, can be customized to best accommodate each individual user (based on a user associated with the first zone)]; and
adjusting a position of an adjustable section of an adjustable bed frame associated with the first zone to the target position, wherein the adjustable section is configured to adjust an angular position of the first zone relative to a rest position [0053 — if the sensing units 44 and or the microphones detect a snoring event, the head of the bedding unit 30 on which the person is sleeping can be raised slightly and controlled by the computing device 60. As an example, the head of the bed could be raised by seven degrees (target position)... This monitoring and raising can be programmed to occur automatically and can continue up to some predetermined maximum raised position, such as thirty five degrees. Once the snoring has stopped for a set period of time, such as five minutes, the bed 10 can react by lowering the head of the bed to the horizontal, standard, sleeping position. It should be understood that amount of each head raise, and the length of time between each raise, can be customized to best accommodate each individual user (based on a user associated with the first zone); 0020-0025, Fig. 1 - The bed 10 has a frame 12 designed to provide a structural base to the bed… a pair of adjustable bed units 30 are coupled to the frame 12; claim 13 — the adjustable bedding unit includes an articulating frame supporting a mattress].
But Turner fails to clearly specify identifying the sleeping disorder by inputting the biological data into a machine learning model that is trained to detect the sleeping disorder.
However, Schultz teaches identifying the sleeping disorder by inputting the biological data into a machine learning model that is trained to detect the sleeping disorder [0015 — Actuators automatically directed by unit 107 (or worker interaction) control patient bed position (to reduce consequential damage of a heart attack or shock, to prevent breathing and snoring problems); 0024-0025 — as long as learning processor 25 does not have sufficient amounts of data (less than a predetermined threshold amount), the One Class SVM machine learning method is used for classifying patient parameters acquired from a selected set of sensors; 0027 — the system monitors and improves measured sensor data such as blood pressure, blood oxygen saturation SPO2 and heart rate, of Emergency Room (ER) patients by actively controlling an environment of each bed and accelerating recovery time… the system automatically turns patients during sleep when snoring is detected. This is used to prevent obstructive sleep apnea (OSA); 0038 — Learning processor 25 processes patient data training data sets to learn a model of statistical knowledge; 0040-0042, Fig. 6 — Data processor 15 in step 613 determines the set of different received patient parameters exceeds the determined normal range and in response to this determination and in response to the type of parameters in the set and medical record information of the patient and the criticality of the different received patient parameters, adaptively selects an action to be performed. The multiple predetermined actions include, initiating adjusting a patient bed].
Turner and Schultz are analogous art. They relate to user monitoring and related bed control systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by Tuner, by incorporating the above limitations, as taught by Schutz.
One of ordinary skill in the art would have been motivated to do this modification in order to provide automated intervention care for the patients/users, including learning/bed adjustment, as taught by Schutz [0003, 0016-0020].
Regarding claim 33, the combination of Turner and Schultz teaches all the limitations of the base claims as outlined above.
Further, Turner teaches changing an angle of the first zone of the bed device relative to a control position [0053 — if the sensing units 44 and or the microphones detect a snoring event, the head of the bedding unit 30 on which the person is sleeping can be raised slightly and controlled by the computing device 60. As an example, the head of the bed could be raised by seven degrees (target position)... This monitoring and raising can be programmed to occur automatically and can continue up to some predetermined maximum raised position, such as thirty five degrees. Once the snoring has stopped for a set period of time, such as five minutes, the bed 10 can react by lowering the head of the bed to the horizontal, standard, sleeping position. It should be understood that amount of each head raise, and the length of time between each raise, can be customized to best accommodate each individual user (based on a user associated with the first zone); 0020-0025, Fig. 1 - The bed 10 has a frame 12 designed to provide a structural base to the bed… a pair of adjustable bed units 30 are coupled to the frame 12; claim 13 — the adjustable bedding unit includes an articulating frame supporting a mattress].
Regarding claim 36, the combination of Turner and Schultz teaches all the limitations of the base claims as outlined above.
Further, Turner teaches the adjustable bed comprises a plurality of adjustable sections configured to be adjusted independently from one another, wherein the plurality of adjustable sections comprises two or more members selected from the group consisting of a head section, a back section, a legs section, and a feet section [0023, Figs. 1-2 — bed units 30 are adjustable to a number of different positions. For example, the head of the bed can be raised, as can the area of the bed adjacent the knee area of the user — head and feet sections are shown raised in the figures; 0053 — if the sensing units 44 and or the microphones detect a snoring event, the head of the bedding unit 30 on which the person is sleeping can be raised slightly and controlled by the computing device 60. As an example, the head of the bed could be raised by seven degrees... This monitoring and raising can be programmed to occur automatically and can continue up to some predetermined maximum raised position, such as thirty five degrees. Once the snoring has stopped for a set period of time, such as five minutes, the bed 10 can react by lowering the head of the bed to the horizontal, standard, sleeping position. It should be understood that amount of each head raise, and the length of time between each raise, can be customized to best accommodate each individual user].
Further, Schultz teaches the adjustable bed comprises a plurality of adjustable sections configured to be adjusted independently from one another, wherein the plurality of adjustable sections comprises two or more members selected from the group consisting of a head section, a back section, a legs section, and a feet section [0031 — John's smart-bed is also a motorized bed with functionality for adjusting the position of the bed head and foot automatically].
Regarding claim 38, the combination of Turner and Schultz teaches all the limitations of the base claims as outlined above.
Further, Turner teaches the biological data comprises at least one of heart signal data, breathing signal data, or temperature data [0026-0028, Fig. 14 — Each bed unit 30 is also provided with a sensor unit 44 (FIG. 14)… One channel can be used to "listen" for frequencies below 0.5 Hz (Breathing) with a low-level electrical gain. Another channel can be "listening" for frequencies between 0.5 Hz to 2 Hz (Heartbeat)… The end effect is that both signals will be fed into a microprocessor… The signals from the sensor units 44 and the microphones are used to detect the respiration, motion, pulse and snoring of a person laying on the bed unit 30].
Regarding claim 40, the combination of Turner and Schultz teaches all the limitations of the base claims as outlined above.
Further, Schultz determining a trend associated with an increased risk of a disease based on the biological data measured over a period of time [0038 — Learning processor 25 processes patient data training data sets to learn a model of statistical knowledge comprising complex, multidimensional, patient specific data about the status quo (normality) of a patient across time and space and monitors events that may indicate "abnormal" conditions and require medical decisions to be taken in response… The system employs a network of intelligent assistants (units 107) for advanced processing and cloud storage, search, query, pattern-based discovery of unusual conditions and trends; 0025 — If there exists enough data to give sufficient confidence (e.g., there is a 98% probability) that the patient is showing abnormal behavior and in response to the medical condition of the patient and the criticality of the sensor parameters, data processor 15 notifies the primary physician of the patient. For example, if the patient has a heart condition and the blood pressure rises to an abnormal level (increased risk of a disease) immediate action is initiated by processor 15].
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Tuner and Schultz, by incorporating the above limitations, as taught by Schutz.
One of ordinary skill in the art would have been motivated to do this modification in order to provide intervention care for the patients/users in a timely fashion, e.g. based on a trend rather than after a serious event, as suggested by Schutz [0025, 0038].
Regarding claim 45, the combination of Turner and Schultz teaches all the limitations of the base claims as outlined above.
Further, Turner teaches sending, to a user device, a notification indicative of the sleeping disorder [0040-0042, Fig. 11— The bed 10, using the computing device 60, can be used to provide the sleep data to the user in the morning to provide a quick "sleep summary" to the user…. data can be used to calculate the quality of the sleep achieved during any sleep session. This calculation can factor in the total time a person is in bed, the number of major movements during the sleep session, the number of times a user left the bed, any respiratory interruptions and any snoring activity. Basically, all or part of the data collected during a sleep session can be used to calculate the quality of sleep, or "rest factor" for any given sleep session — the ‘rest factor’ is shown on a display in Fig. 11].
Regarding claim 47, the combination of Turner and Schultz teaches all the limitations of the base claims as outlined above.
Further, Turner teaches the sleeping disorder comprises snoring or sleep apnea [0053-0054 — the sensing units 44 and or the microphones detect a snoring event… sleep apnea].
Regarding claim 49, the combination of Tuner and Schultz teaches all the limitations of the base claims as outlined above.
Further, Turner teaches the user sensor comprises a piezo sensor or a microphone [0026-0028, 0053 — sensor unit 44 uses piezo-electric strain gauges 50… the sensor unit 44, each bed is also preferably provided with a microphone].
Regarding claim 51, the combination of Tuner and Schultz teaches all the limitations of the base claims as outlined above.
Further, Turner teaches the bed device comprises a mattress, a pillow, and/or a mattress cover [0024-0026, Fig. 14 — The mattress of each bed unit 30 is preferable made up of three layers].
Further, Schultz teaches the bed device comprises a mattress, a pillow, and/or a mattress cover [0014 — The ranges of sensors integrated with unit 107 include, wired sensors woven into an intelligent bed or an intelligent room (mattress, pillow, bars, walls).; 0040 — sensors also include a sensor located in at least one of, (a) a mattress, (b) a pillow].
Regarding claim 53, the combination of Tuner and Schultz teaches all the limitations of the base claims as outlined above.
Further, Turner teaches receiving additional biological data from at least one second sensor disposed within the bed device in a second zone [0020-0025, Fig. 1 - a pair of adjustable bed units 30 are coupled to the frame 12 (first/second zones)… Each bed unit 30 is individually adjustable, to provide a "his" and "hers" style; 0026-0028, Fig. 14 — Each bed unit 30 is also provided with a sensor unit 44 (FIG. 14)… Another channel can be "listening" for frequencies between 0.5 Hz to 2 Hz (Heartbeat)… The end effect is that both signals will be fed into a microprocessor… The signals from the sensor units 44 and the microphones are used to detect the respiration, motion, pulse and snoring of a person laying on the bed unit 30.; 0031 — the signals from the sensing units 44 and microphone are passed to the computing device 60];
identifying a sleeping disorder in the second zone [0053 — if the sensing units 44 and or the microphones detect a snoring event, the head of the bedding unit 30 on which the person is sleeping can be raised slightly and controlled by the computing device 60. As an example, the head of the bed could be raised by seven degrees... This monitoring and raising can be programmed to occur automatically and can continue up to some predetermined maximum raised position, such as thirty five degrees. Once the snoring has stopped for a set period of time, such as five minutes, the bed 10 can react by lowering the head of the bed to the horizontal, standard, sleeping position. It should be understood that amount of each head raise, and the length of time between each raise, can be customized to best accommodate each individual user]; and
in response to identifying the sleeping disorder in the second zone, adjusting a position of the second zone of the adjustable bed frame to a second target position [0053 — if the sensing units 44 and or the microphones detect a snoring event, the head of the bedding unit 30 on which the person is sleeping can be raised slightly and controlled by the computing device 60. As an example, the head of the bed could be raised by seven degrees (target position)... This monitoring and raising can be programmed to occur automatically and can continue up to some predetermined maximum raised position, such as thirty five degrees. Once the snoring has stopped for a set period of time, such as five minutes, the bed 10 can react by lowering the head of the bed to the horizontal, standard, sleeping position. It should be understood that amount of each head raise, and the length of time between each raise, can be customized to best accommodate each individual user (based on a user associated with the first/second zone); 0020-0025, Fig. 1 - The bed 10 has a frame 12 designed to provide a structural base to the bed… a pair of adjustable bed units 30 are coupled to the frame 12 (first/second zones); claim 13 — the adjustable bedding unit includes an articulating frame supporting a mattress].
Further, Schultz teaches by inputting the additional biological data into the machine learning model [0015 — Actuators automatically directed by unit 107 (or worker interaction) control patient bed position (to reduce consequential damage of a heart attack or shock, to prevent breathing and snoring problems); 0024-0025 — as long as learning processor 25 does not have sufficient amounts of data (less than a predetermined threshold amount), the One Class SVM machine learning method is used for classifying patient parameters acquired from a selected set of sensors; 0027 — the system monitors and improves measured sensor data such as blood pressure, blood oxygen saturation SPO2 and heart rate, of Emergency Room (ER) patients by actively controlling an environment of each bed and accelerating recovery time… the system automatically turns patients during sleep when snoring is detected. This is used to prevent obstructive sleep apnea (OSA); 0038 — Learning processor 25 processes patient data training data sets to learn a model of statistical knowledge; 0040-0042, Fig. 6 — Data processor 15 in step 613 determines the set of different received patient parameters exceeds the determined normal range and in response to this determination and in response to the type of parameters in the set and medical record information of the patient and the criticality of the different received patient parameters, adaptively selects an action to be performed. The multiple predetermined actions include, initiating adjusting a patient bed].
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by Tuner, by incorporating the above limitations, as taught by Schutz.
One of ordinary skill in the art would have been motivated to do this modification in order to provide automated intervention care for the patients/users, including learning/bed adjustment, as taught by Schutz [0003, 0016-0020].
Regarding claim 54, Turner teaches a system [0020-0025, Figs. 1-6 and 14 - a pair of adjustable bed units 30 are coupled to the frame 12… Each bed unit 30 is individually adjustable, to provide a "his" and "hers" style], comprising:
a bed device comprising an adjustable bed frame [0020-0025, Fig. 1 - a pair of adjustable bed units 30 are coupled to the frame 12… Each bed unit 30 is individually adjustable, to provide a "his" and "hers" style];
at least one sensor disposed within the bed device [0026-0028, Fig. 14 — Each bed unit 30 is also provided with a sensor unit 44 (FIG. 14)… Another channel can be "listening" for frequencies between 0.5 Hz to 2 Hz (Heartbeat)… The end effect is that both signals will be fed into a microprocessor… The signals from the sensor units 44 and the microphones are used to detect the respiration, motion, pulse and snoring of a person laying on the bed unit 30];
at least one processor [0026-0028, Fig. 14 — Each bed unit 30 is also provided with a sensor unit 44 (FIG. 14)… Another channel can be "listening" for frequencies between 0.5 Hz to 2 Hz (Heartbeat)… The end effect is that both signals will be fed into a microprocessor… The signals from the sensor units 44 and the microphones are used to detect the respiration, motion, pulse and snoring of a person laying on the bed unit 30.; 0031 — the signals from the sensing units 44 and microphone are passed to the computing device 60] and configured to:
receive biological data from at least one sensor disposed within the bed device in a first zone of the bed device [0020-0025, Fig. 1 - a pair of adjustable bed units 30 are coupled to the frame 12… Each bed unit 30 is individually adjustable, to provide a "his" and "hers" style; 0026-0028, Fig. 14 — Each bed unit 30 is also provided with a sensor unit 44 (FIG. 14)… Another channel can be "listening" for frequencies between 0.5 Hz to 2 Hz (Heartbeat)… The end effect is that both signals will be fed into a microprocessor… The signals from the sensor units 44 and the microphones are used to detect the respiration, motion, pulse and snoring of a person laying on the bed unit 30.; 0031 — the signals from the sensing units 44 and microphone are passed to the computing device 60];
identify the sleeping disorder in the first zone to detect a sleeping disorder [0053 — if the sensing units 44 and or the microphones detect a snoring event, the head of the bedding unit 30 on which the person is sleeping can be raised slightly and controlled by the computing device 60. As an example, the head of the bed could be raised by seven degrees... This monitoring and raising can be programmed to occur automatically and can continue up to some predetermined maximum raised position, such as thirty five degrees. Once the snoring has stopped for a set period of time, such as five minutes, the bed 10 can react by lowering the head of the bed to the horizontal, standard, sleeping position. It should be understood that amount of each head raise, and the length of time between each raise, can be customized to best accommodate each individual user];
determine a target position of the first zone based on a user associated with the first zone [0053 — if the sensing units 44 and or the microphones detect a snoring event, the head of the bedding unit 30 on which the person is sleeping can be raised slightly and controlled by the computing device 60. As an example, the head of the bed could be raised by seven degrees (target position)... This monitoring and raising can be programmed to occur automatically and can continue up to some predetermined maximum raised position, such as thirty five degrees. Once the snoring has stopped for a set period of time, such as five minutes, the bed 10 can react by lowering the head of the bed to the horizontal, standard, sleeping position. It should be understood that amount of each head raise, and the length of time between each raise, can be customized to best accommodate each individual user (based on a user associated with the first zone)]; and
adjust a position of an adjustable section of an adjustable bed frame associated with the first zone to the target position, wherein the adjustable section is configured to adjust an angular position of the first zone relative to a rest position [0053 — if the sensing units 44 and or the microphones detect a snoring event, the head of the bedding unit 30 on which the person is sleeping can be raised slightly and controlled by the computing device 60. As an example, the head of the bed could be raised by seven degrees (target position)... This monitoring and raising can be programmed to occur automatically and can continue up to some predetermined maximum raised position, such as thirty five degrees. Once the snoring has stopped for a set period of time, such as five minutes, the bed 10 can react by lowering the head of the bed to the horizontal, standard, sleeping position. It should be understood that amount of each head raise, and the length of time between each raise, can be customized to best accommodate each individual user (based on a user associated with the first zone); 0020-0025, Fig. 1 - The bed 10 has a frame 12 designed to provide a structural base to the bed… a pair of adjustable bed units 30 are coupled to the frame 12; claim 13 — the adjustable bedding unit includes an articulating frame supporting a mattress].
But Turner fails to clearly specify a memory; at least one processor coupled to the memory and identifying the sleeping disorder by inputting the biological data into a machine learning model that is trained to detect the sleeping disorder.
However, Schultz teaches a memory; at least one processor coupled to the memory [0043 — A processor as used herein is a device for executing machine-readable instructions stored on a computer readable medium… A processor may also comprise memory storing machine-readable instructions executable for performing tasks] and identifying the sleeping disorder by inputting the biological data into a machine learning model that is trained to detect the sleeping disorder [0015 — Actuators automatically directed by unit 107 (or worker interaction) control patient bed position (to reduce consequential damage of a heart attack or shock, to prevent breathing and snoring problems); 0024-0025 — as long as learning processor 25 does not have sufficient amounts of data (less than a predetermined threshold amount), the One Class SVM machine learning method is used for classifying patient parameters acquired from a selected set of sensors; 0027 — the system monitors and improves measured sensor data such as blood pressure, blood oxygen saturation SPO2 and heart rate, of Emergency Room (ER) patients by actively controlling an environment of each bed and accelerating recovery time… the system automatically turns patients during sleep when snoring is detected. This is used to prevent obstructive sleep apnea (OSA); 0038 — Learning processor 25 processes patient data training data sets to learn a model of statistical knowledge; 0040-0042, Fig. 6 — Data processor 15 in step 613 determines the set of different received patient parameters exceeds the determined normal range and in response to this determination and in response to the type of parameters in the set and medical record information of the patient and the criticality of the different received patient parameters, adaptively selects an action to be performed. The multiple predetermined actions include, initiating adjusting a patient bed].
Turner and Schultz are analogous art. They relate to user monitoring and related bed control systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by Tuner, by incorporating the above limitations, as taught by Schutz.
One of ordinary skill in the art would have been motivated to do this modification in order to provide specific computer control functions when required, as suggested by Schultz [0043] and to provide automated intervention care for the patients/users, including learning/bed adjustment, as taught by Schultz [0003, 0016-0020].
Regarding claim 55, the combination of Tuner and Schultz teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected based on the same rationale as claim 33.
Regarding claim 56, the combination of Tuner and Schultz teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected based on the same rationale as claim 38.
Regarding claim 57, the combination of Tuner and Schultz teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected based on the same rationale as claim 47.
Regarding claim 58, the combination of Tuner and Schultz teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected based on the same rationale as claim 49.
Regarding claim 59, the combination of Tuner and Schultz teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected based on the same rationale as claim 51.
Regarding claim 60, the combination of Tuner and Schultz teaches all the limitations of the base claims as outlined above and this claim is otherwise rejected based on the same rationale as claim 53.
Claim(s) 34 and 41 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Turner and Schutz in view of Hood et al. U.S. Patent Publication No. 20150136146 (hereinafter Hood).
Regarding claim 34, the combination of Turner and Schultz teaches all the limitations of the base claims as outlined above.
Further, Schultz teaches the machine learning model [0024-0025 — as long as learning processor 25 does not have sufficient amounts of data (less than a predetermined threshold amount), the One Class SVM machine learning method is used for classifying patient parameters acquired from a selected set of sensors; 0038 — Learning processor 25 processes patient data training data sets to learn a model of statistical knowledge; 0040-0042, Fig. 6 — Learning processor 25 in step 607 adaptively selects from multiple different functions, a function employed by the learning processor for determining at least one of, (a) a normal range and (b) an abnormal range, for the set of multiple different received patient parameters in response to at least one of, (i) the amount of recorded patient data available from sensors and (ii) the type of recorded patient data available from sensors. The normal range is derived from a patient population having similar demographics including age, weight, height, gender, pregnancy status as the patient and similar medical conditions. Training data may come both from the patient (to capture a particular condition) and from other patient].
But the combination of Turner and Schultz fails to clearly specify that a machine learning model utilizes a neural network algorithm.
However, Hood teaches a machine learning model utilizes a neural network algorithm [0085 — an apnea event can be accomplished using a Bayesian "belief network" model. In some contemplated embodiments, prediction of an apnea event can be accomplished using large memory storage and retrieval (LAMSTAR) artificial neural networks to analyze signals].
Turner, Schultz and Hood are analogous art. They relate to user monitoring and related bed control systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to simply substitute the known neural network of Hood for the known machine learning model of Turner and Schultz for the predictable result of a method utilizing a neural network algorithm.
Regarding claim 41, the combination of Turner and Schultz teaches all the limitations of the base claims as outlined above.
Further, Schultz an onset of a disease associated based on the biological data measured over the period of time [0038 — Learning processor 25 processes patient data training data sets to learn a model of statistical knowledge comprising complex, multidimensional, patient specific data about the status quo (normality) of a patient across time and space and monitors events that may indicate "abnormal" conditions and require medical decisions to be taken in response… The system employs a network of intelligent assistants (units 107) for advanced processing and cloud storage, search, query, pattern-based discovery of unusual conditions and trends; 0025 — If there exists enough data to give sufficient confidence (e.g., there is a 98% probability) that the patient is showing abnormal behavior and in response to the medical condition of the patient and the criticality of the sensor parameters, data processor 15 notifies the primary physician of the patient. For example, if the patient has a heart condition and the blood pressure rises to an abnormal level (onset of a disease) immediate action is initiated by processor 15].
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Tuner and Schultz, by incorporating the above limitations, as taught by Schutz.
One of ordinary skill in the art would have been motivated to do this modification in order to provide intervention care for the patients/users in a timely fashion, e.g. based on a trend rather than after a serious event, as suggested by Schutz [0025, 0038].
However, Hood teaches predicting an onset of a disease associated based on the biological data measured over the period of time [0082-0085 — the instruction set causes the processor 100 to carry out a proactive procedure 136 that configures the person support apparatus 12 and/or the person support surface 14 when the processor 100 predicts the onset of an adverse event. Procedure 136 begins with step 138 where the system for mitigating adverse conditions is aimed by the caregiver or the bed or EMR based on the occupant's risk profile… the processor 100 stores the signal values in the memory 104 and determines an amount and/or a magnitude of change in the values for a predetermined time period… prediction of an apnea event can be accomplished by analyzing tracheal breath sounds].
Turner, Schultz and Hood are analogous art. They relate to user monitoring and related bed control systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Turner and Schultz, by incorporating the above limitations, as taught by Hood.
One of ordinary skill in the art would have been motivated to do this modification to enable a caregiver to mitigate a disease event, as taught by Hood [0082-0085].
Claim(s) 37 and 43-44 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Turner and Schultz in view of Pinhas et al. U.S. Patent Publication No. 20070118054 (hereinafter Pinhas).
Regarding claim 37, the combination of Turner and Schultz teaches all the limitations of the base claims as outlined above.
But the combination of Turner and Schultz fails to clearly specify analyzing at least one peak frequency of the biological data indicative of the sleeping disorder.
However, Pinhas teaches analyzing at least one peak frequency of the biological data indicative of the sleeping disorder [0249 — Pattern analysis modules 22 and 23 typically analyze changes in breathing rate patterns, breathing rate variability patterns, heart rate patterns, and/or heart rate variability patterns in combination to predict the onset of an asthma attack. For some applications, breathing and/or heart rates are extracted from the signal by computing the Fourier transform of the filtered signal, and finding the frequency corresponding to the highest spectral peak value within allowed ranges corresponding to breathing and heart rate; 0300 — system 10 monitors and analyzes episodes of nocturnal restlessness and/or awakening, which are symptoms of several chronic conditions, such as asthma and CHF. Typically, system 10 quantifies these episodes to provide an objective measure of nocturnal restlessness and/or awakening. As described hereinabove, system 10 analyzes a cyclical motion signal of the subject in the frequency domain, and identifies peaks in the frequency domain signal corresponding to respiration rate and heart rate; 0334-0335 — system 10 monitors breathing patterns through the mechanical channel and the acoustic or audio signals, for example, snoring, through the audio channel. Snoring is identified as a significant acoustic signal that is time correlated with the breathing pattern. The system recognizes epochs, that is, time periods, that include loud snoring. The system marks events as partial OSA; 0428 — the system uses breathing patterns and accompanying acoustic sounds to identify snoring. In another embodiment, the system causes a change in the body posture in order to eliminate or reduce snoring, e.g., by changing bed or mattress angle, or increasing or decreasing head elevation by inflating or deflating a pillow].
Turner, Schultz, and Pinhas are analogous art. They relate to patient/user monitoring and related bed control systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Turner and Schultz, by incorporating the above limitations, as taught by Pinhas.
One of ordinary skill in the art would have been motivated to do this modification in order to identify specific biological functions, as taught by Pinhas [0300].
Regarding claim 43, the combination of Turner and Schultz teaches all the limitations of the base claims as outlined above.
Further, Turner teaches causing a display to display sleeping information [0040-0041 — computing device 60, can be used to provide the sleep data to the user in the morning to provide a quick "sleep summary" to the user. This can be provided through the display… the summary data, a real time display of the data being gathered can be seen on the user interface, if the user so desires].
But the combination of Turner and Schultz fails to clearly specify determining a number of episodes of the sleeping disorder during a use of the bed device and causing a display to display the number of sleeping disorder episodes.
However, Pinhas teaches determining a number of episodes of the sleeping disorder during a use of the bed device [0202 — STD of the signal during consecutive minutes is expected to be quite similar during sleep unless the subject changes sleeping positions. A criterion for the extent of change in STD between consecutive minutes is defined, typically 10%-50%, for example, 25%. Each time a change of larger magnitude than the criterion is identified, an event is defined and counted. The total number of such events and their distribution during the sleeping period is logged as an indication of body position change… The number and distribution of body posture changes during sleep is an indication to the level of restlessness in sleep which is a clinical parameter used to identify clinical conditions; 0302 — system 10 monitors and analyzes events of augmented breaths (also known as `sighs`) and deep inspirations. Typically, system 10 quantifies these events and measures their number and rate at different segments of the night and in some cases in different sleep stages. This serves as an additional clinical parameter for the evaluation of the patient's clinical status].
Turner, Schultz, and Pinhas are analogous art. They relate to patient/user monitoring and related bed control systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Turner and Schultz, by determining a number of episodes of the sleeping disorder during a use of the bed device, as taught by Pinhas, and causing a display to display the number of sleeping disorder episodes, as suggested by the teachings of Turner.
One of ordinary skill in the art would have been motivated to do this modification in order to identify and evaluate a patient/user clinical status, as taught by Pinhas [0202, 0302], and directly apprise the patient/user of the desired information, as suggested by Turner [0040-0041].
Regarding claim 44, the combination of Turner and Schultz teaches all the limitations of the base claims as outlined above.
But the combination of Turner and Schultz fails to clearly specify determining a duration of the sleeping disorder.
However, Pinhas teaches determining a duration of the sleeping disorder [0383-0388 — system 10 additionally detects arousal events according to the duration of each restless event. For example, a restless event that lasts longer than 15 seconds is defined as an arousal].
Turner, Schultz, and Pinhas are analogous art. They relate to patient/user monitoring and related bed control systems.
Therefore at the time the invention was made, it would have been obvious to a person of ordinary skill in the art to modify the above method, as taught by the combination of Turner and Schultz, by incorporating the above limitations, as taught by Pinhas.
One of ordinary skill in the art would have been motivated to do this modification in order to provide a quantitative criterion for identifying a sleeping disorder event, as taught by Pinhas [0383-0388]. In addition, it would be obvious to one having ordinary skill in the art to determine the length of a sleeping disorder to quantify how significant the disorder is or the impact on the user/patient.
Note that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BERNARD G. LINDSAY whose telephone number is (571)270-0665. The examiner can normally be reached Monday through Friday from 8:30 AM to 5:30 PM EST.
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/BERNARD G LINDSAY/
Primary Examiner, Art Unit 2119
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