DETAILED ACTION
Claims 31-52 are pending. Claims 1-30 are cancelled.
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 .
Claim Objections
The claims are objected to because of the following informalities:
‘receiving a user sensing data’ should read ‘receiving [[a]] user sensing data’ (claim 1).
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.
Claim(s) 31-52 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to the abstract idea (mental process) of determining from data that a user is experiencing a sleeping disorder and generating a corresponding instruction.
Claim 31 recites computer-implemented method, i.e. a process, which is a statutory category of invention.
The claim recites:
(b) applying the user sensing data as an input to a machine learning model that is trained to detect when the user is experiencing a sleeping disorder;
(c) determining, using the machine learning model, when the user is experiencing the sleeping disorder from the user sensing data; and
(d) generating, based at least in part on the determining in (c), an instruction for changing a position of at least a portion of an adjustable bed frame associated with the bed device to a target position that may be performed in the human mind, or by a human using a pen and paper. Thus the claim recites an abstract idea (mental processes), see MPEP 2106.04(a).
This judicial exception is not integrated into a practical application because the additional elements, i.e. a bed device with sensors (generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)), a computer-implemented method and a machine learning model trained on data (applying the exception with generic computer technology, see MPEP 2106.04(a)(2) III C, using a known algorithm), and receiving a user sensing data associated with a user of a bed device, wherein the user sensing data is generated by a user sensor while the user is using the bed device (insignificant extra-solution elements – mere data gathering, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d))) and thereby reducing the sleeping disorder (intended use) do not impose any meaningful limits on practicing the abstract idea. The claim is therefore directed to an abstract idea.
Note that a adjustable bed devices with sensors, mattresses and pillows are well-understood, routine and conventional, see for example Schultz, Hood, and Pinhas cited below and Chacon et al. U.S. Patent Publication No. 20100302044 or Koughan et al. U.S. Patent Publication No. 20080052830. And piezo and microphone sensors are well-understood, routine and conventional, see for example Pena U.S. Patent Publication No. 20020175448 [0025]. Also note that machine learning and neural networks are well-understood, routine and conventional, see for example Turner et al. U.S. Patent Publication No. 20020072828 [0003-0006].
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, a bed device with sensors (generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)), a computer-implemented method and a machine learning model trained on data (applying the exception with generic computer technology, see MPEP 2106.04(a)(2) III C, using a known algorithm), and receiving a user sensing data associated with a user of a bed device, wherein the user sensing data is generated by a user sensor while the user is using the bed device (insignificant extra-solution elements – mere data gathering, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d))) and thereby reducing the sleeping disorder (intended use) are not considered significantly more. Considering the additionally elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Thus the claim is not patent eligible.
Claim 32 recites ‘the user sensor is integrated within the bed device’ (generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)). Thus this claim recites an abstract idea.
Claim 33 recites details of the abstract instruction generated. Thus this claim recites an abstract idea.
Claim 34 recites a neural network algorithm (applying the exception with generic computer technology, see MPEP 2106.04(a)(2) III C, using a known algorithm). Thus this claim recites an abstract idea.
Claim 35 recites retrieving the target position from a database associated with the user (insignificant extra-solution elements – mere data gathering, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d))). Thus this claim recites an abstract idea.
Claim 36 recites the adjustable bed frame 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 (generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)). Thus this claim recites an abstract idea.
Claim 37 recites analyzing at least one peak frequency of the user sensing data indicative of the sleep disorder (mental process). Thus this claim recites an abstract idea.
Claim 38 recites various types of abstract data. Thus this claim recites an abstract idea.
Claim 39 recites measuring the biological signal data associated with the user of the bed device over a period of time (insignificant extra-solution elements – mere data gathering, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d))). Thus this claim recites an abstract idea.
Claim 40 recites determining a trend associated with an increased risk of a disease associated with the user based on the biological signal data measured over the period of time (mental process). Thus this claim recites an abstract idea.
Claim 41 recites predicting an onset of a disease associated with the user based on the biological signal data measured over the period of time (mental process). Thus this claim recites an abstract idea.
Claim 42 recites that training data comprises abstract breathing data. Thus this claim recites an abstract idea.
Claim 43 recites determining a number of episodes of the sleeping disorder during a use of the bed device (either insignificant extra-solution elements – mere data gathering, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d) or a mental process depending on interpretation). Thus this claim recites an abstract idea.
Claim 44 recites determining a duration of the sleeping disorder (either insignificant extra-solution elements – mere data gathering, see MPEP 2106.05 I A, MPEP 2106.05(g) MPEP 2106.05(d) or a mental process depending on interpretation). Thus this claim recites an abstract idea.
Claim 45 recites sending, to a user device, a notification indicative of detection of the sleeping disorder of the user (insignificantly extra-solution activity — see MPEP 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d)). Thus this claim recites an abstract idea.
Claim 46 recites displaying, to a display of the user device, a graph indicative of the detection of the sleeping disorder of the user (insignificantly extra-solution activity — see MPEP 2106.04(a)(2) III A regarding displaying information and MPEP 2106.05(d)). Thus this claim recites an abstract idea.
Claim 47 recites a type of the sleeping disorder, i.e. snoring. Thus this claim recites an abstract idea.
Claim 48 recites a type of the sleeping disorder, i.e. sleep apnea. Thus this claim recites an abstract idea.
Claim 49 recites a piezo sensor, i.e. a conventional source of the abstract data. Thus this claim recites an abstract idea.
Claim 50 recites a microphone sensor, i.e. a conventional source of the abstract data. Thus this claim recites an abstract idea.
Claim 51 recites the bed device comprises a mattress or a mattress cover (generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)). Thus this claim recites an abstract idea.
Claim 52 recites the bed device comprises a pillow (generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)). Thus this claim recites an abstract idea.
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) 31-36, 38-42, 45, 47-48 and 50-52 and is/are rejected under 35 U.S.C. 103 as being unpatentable over Schultz et al. U.S. Patent Publication No. 20130245389 (hereinafter Schultz) in view of Hood et al. U.S. Patent Publication No. 20150136146 (hereinafter Hood).
Regarding claim 31, Schultz discloses a computer-implemented method [0040-0042, Fig. 6 — a flowchart of a process performed by a patient monitoring and intervention system; 0012, Fig. 2 — Server 30 includes data processor 15, learning processor 25, repository 17, interface 27 and display 19… processor 15 initiates adjusting a patient bed] comprising:
(a) receiving a user sensing data associated with a user of a bed device, wherein the user sensing data is generated by a user sensor while the user is using the bed device [0030 — John's bed is a smart-bed, with several sensors embedded in the bed structure including chemical sensors which can detect urine, sweating and saliva and also pressure sensors which detect movement and high pressure on body parts.; 0040-0042, Fig. 6 — In step 605, interface 27 receives data representing multiple different parameters from multiple different sensors, comprising sensors in a patient bed and attached to a patient including, a heart rate sensor, a respiration sensor and a pressure sensor indicating bed pressure points.];
(b) applying the user sensing data as an input to a machine learning model that is trained to detect when the user is experiencing a 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); 0027 — 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, changing medication administered to a patient, alerting a worker of the patient parameter change, labeling a parameter from a sensor as indicated by a clinician and labeling parameters from sensors for use in training. Action also include automatically turning a patient to support respiration, raising the back or feet.], wherein the machine learning model is trained based on a training data set associated with a plurality of individuals, the training data set comprising (i) a first plurality of training data indicative of a normal condition of the plurality of individuals, (ii) a second plurality of training data associated with the sleeping disorder of the plurality of individuals, or both (i) and (ii) [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 patients];
(c) determining, using the machine learning model, when the user is experiencing the sleeping disorder from the user sensing data [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); 0027 — the system automatically turns patients during sleep when snoring is detected. This is used to prevent obstructive sleep apnea (OSA); 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, changing medication administered to a patient, alerting a worker of the patient parameter change, labeling a parameter from a sensor as indicated by a clinician and labeling parameters from sensors for use in training. Action also include automatically turning a patient to support respiration, raising the back or feet.]; and
(d) generating, based at least in part on the determining in (c), an instruction for changing a position of at least a portion of an adjustable bed associated with the bed device to a target position, thereby reducing 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); 0017 — medical intervention actions (e.g. basic first aid, feet raising via bed command (instruction)…); 0027 — the system automatically turns patients during sleep when snoring is detected. This is used to prevent obstructive sleep apnea (OSA); 0031-0034 — Processor 15 in step 547 further determines whether first aid intervention is needed and if so initiates the action (e.g. raising feet with respect to head by bed adjustment); 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, changing medication administered to a patient, alerting a worker of the patient parameter change, labeling a parameter from a sensor as indicated by a clinician and labeling parameters from sensors for use in training. Action also include automatically turning a patient to support respiration, raising the back or feet.].
But Schultz fails to clearly specify an adjustable bed frame.
However, Hood teaches an instruction for changing a position of at least a portion of an adjustable bed frame [0056-0057, Figs. 2-3 — lift mechanisms 18 are configured to raise and lower the upper frame 20 with respect to the lower frame 17 and move the upper frame 20 between various orientations… At least the calf section 28, the thigh section 30, and the head and torso section 34 are movable with respect to one another and/or the upper frame base 24. In some contemplated embodiments, the calf section 28, the thigh section 30, the seat section and the head and torso section 34 cooperate to move the person support apparatus 12 between an substantially planar or lying down configuration and a chair configuration; 0079 — In step 130, if the processor determines an apnea event is in progress, the processor 100 configures the person support surface 14 and/or the person support apparatus 12 to intervene and help stop the apnea event… processor 100 increases the head of bed angle to about 15 degree; 0142 — the controller is configured to generate command signals and transmit the command signals to the circuit board contained within support system 1100 or support system 1304 to control operation of support system 1100 or support system 1304 and/or adjust parameters and/or limits].
Schultz and Hood are analogous art. They relate to patient 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 adjustable bed frame mechanism of Hood for the known adjust bed mechanism of Schultz for the predictable result of a method utilizing an adjustable bed frame.
Regarding claim 32, the combination of Schultz and Hood teaches all the limitations of the base claims as outlined above.
Further, Schultz teaches the user sensor is integrated within the bed device [0030 — John's bed is a smart-bed, with several sensors embedded in the bed structure].
Regarding claim 33, the combination of Schultz and Hood teaches all the limitations of the base claims as outlined above.
Further, Hood teaches changing the position of the at least the portion of the adjustable bed frame comprises changing an angle of the at least the portion of the adjustable bed frame relative to a control position of the adjustable bed frame [0079 — In step 130, if the processor determines an apnea event is in progress, the processor 100 configures the person support surface 14 and/or the person support apparatus 12 to intervene and help stop the apnea event… processor 100 increases the head of bed angle to about 15 degree; 0057-0058 — the deck sections help move and/or maintain the various portions of the person support surface 14 at angles .alpha., .beta. and .gamma. with respect to the reference plane RP1].
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 bed angle adjustment of Hood for the known bed adjustment of Schultz for the predictable result of a method utilizing a bed angle adjustment of an adjustable bed frame.
Regarding claim 34, the combination of Schultz and Hood teaches all the limitations of the base claims as outlined above.
Further, Schultz teaches the machine learning model [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].
Further, 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].
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 Schultz for the predictable result of a method utilizing a neural network.
Regarding claim 35, the combination of Schultz and Hood teaches all the limitations of the base claims as outlined above.
Further, Hood teaches the generating comprises retrieving the target position from a database associated with the user [0127 — Prior to sleep, the user is able to input 1208 to control system 1190 sleep data 1210 including without limitation, preferred sleeping sides and positions, the user's measurements including, for example, the user's height, weight and inseam and torso measurements, preferred lateral rotational angles and/or longitudinal rotational angles of one or more support planes defining sleep surface 1114. Based at least in part on the user's input data control system 1190 is configured to activate support system 1100 to adjust a direction and/or a level of rotation of one or more support planes defining sleep surface 1114; 0056-0057, Figs. 2-3 — lift mechanisms 18 are configured to raise and lower the upper frame 20 with respect to the lower frame 17 and move the upper frame 20 between various orientations… At least the calf section 28, the thigh section 30, and the head and torso section 34 are movable with respect to one another and/or the upper frame base 24. In some contemplated embodiments, the calf section 28, the thigh section 30, the seat section and the head and torso section 34 cooperate to move the person support apparatus 12 between an substantially planar or lying down configuration and a chair configuration; 0079 — In step 130, if the processor determines an apnea event is in progress, the processor 100 configures the person support surface 14 and/or the person support apparatus 12 to intervene and help stop the apnea event… processor 100 increases the head of bed angle to about 15 degree; 0142 — the controller is configured to generate command signals and transmit the command signals to the circuit board contained within support system 1100 or support system 1304 to control operation of support system 1100 or support system 1304 and/or adjust parameters and/or limits].
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 Schultz and Hood, by incorporating the above limitations, as taught by Hood.
One of ordinary skill in the art would have been motivated to do this modification so that the user/patient is positioned as they prefer, as taught by Hood [0127].
Regarding claim 36, the combination of Schultz and Hood teaches all the limitations of the base claims as outlined above.
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].
Further, Hood teaches the adjustable bed frame 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 [0056-0057, Figs. 2-3 — lift mechanisms 18 are configured to raise and lower the upper frame 20 with respect to the lower frame 17 and move the upper frame 20 between various orientations… At least the calf section 28, the thigh section 30, and the head and torso section 34 are movable with respect to one another and/or the upper frame base 24. In some contemplated embodiments, the calf section 28, the thigh section 30, the seat section and the head and torso section 34 cooperate to move the person support apparatus 12 between an substantially planar or lying down configuration and a chair configuration (at least, head, back, leg/foot sections are shown in Fig. 3); 0079 — In step 130, if the processor determines an apnea event is in progress, the processor 100 configures the person support surface 14 and/or the person support apparatus 12 to intervene and help stop the apnea event… processor 100 increases the head of bed angle to about 15 degree].
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 adjustable bed frame mechanism of Hood for the known adjust bed mechanism of Schultz for the predictable result of a method utilizing an adjustable bed frame.
Regarding claim 38, the combination of Schultz and Hood teaches all the limitations of the base claims as outlined above.
Further, Schultz teaches user sensing data comprises a biological signal data selected from the group consisting of heart signal data, breathing signal data, and temperature data [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). The sensors include, microphones (for breath, heartbeat, gastric sounds), vibration (for pulse, shivering, seizure, muscle cramps and twitching), pressure (for tension, body position, local high-pressure points where tissues may be compressed, determining if a patients leaves a bed), temperature (for fever, blood circulation, comfort)].
Regarding claim 39, the combination of Schultz and Hood teaches all the limitations of the base claims as outlined above.
Further, Schultz teaches measuring the biological signal data associated with the user of the bed device 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; 0042 — learning processor 25 determines a normal range for a set of multiple different received patient parameters for the patient by recording the patient parameter values over a time period].
Regarding claim 40, the combination of Schultz and Hood teaches all the limitations of the base claims as outlined above.
Further, Schultz determining a trend associated with an increased risk of a disease associated with the user based on the biological signal 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 (increased risk of a disease) immediate action is initiated by processor 15].
Regarding claim 41, the combination of Schultz and Hood teaches all the limitations of the base claims as outlined above.
Further, Schultz an onset of a disease associated with the user based on the biological signal 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].
Further, Hood teaches predicting an onset of a disease associated with the user based on the biological signal 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].
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 Schultz and Hood, 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].
Regarding claim 42, the combination of Schultz and Hood teaches all the limitations of the base claims as outlined above.
Further, Schultz teaches each of the first plurality of training data and the second plurality of training data comprises breathing data [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). The sensors include, microphones (for breath, heartbeat, gastric sounds); 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, changing medication administered to a patient, alerting a worker of the patient parameter change, labeling a parameter from a sensor as indicated by a clinician and labeling parameters from sensors for use in training.].
Regarding claim 45, the combination of Schultz and Hood teaches all the limitations of the base claims as outlined above.
Further, Schultz teaches sending, to a user device, a notification indicative of detection of the sleeping disorder of the user [0016 , Fig. 3 — If processor 15 in step 323 identifies features in the acquired sensor data are abnormal and indicate occurrence of an event, a worker is notified of the data in step 326 by audio, visual or tactile alert or communicated message — a cellphone is indicated in Fig. 3].
Regarding claim 47, the combination of Schultz and Hood teaches all the limitations of the base claims as outlined above.
Further, Schultz teaches the sleeping disorder comprises snoring [0015 — control patient bed position (to reduce consequential damage of a heart attack or shock, to prevent breathing and snoring problems); 0027 — the system automatically turns patients during sleep when snoring is detected].
Regarding claim 48, the combination of Schultz and Hood teaches all the limitations of the base claims as outlined above.
Further, Schultz teaches the sleeping disorder comprises sleep apnea [0027 — the system automatically turns patients during sleep when snoring is detected. This is used to prevent obstructive sleep apnea (OSA)].
Regarding claim 50, the combination of Schultz and Hood teaches all the limitations of the base claims as outlined above.
Further, Schultz teaches the user sensor comprises a microphone [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). The sensors include, microphones].
Regarding claim 51, the combination of Schultz and Hood teaches all the limitations of the base claims as outlined above.
Further, Schultz teaches the bed device comprises a mattress 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 52, the combination of Schultz and Hood teaches all the limitations of the base claims as outlined above.
Further, Schultz teaches the bed device comprises a pillow [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].
Claim(s) 37 and 43-44 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Schultz and Hood in view of Pinhas et al. U.S. Patent Publication No. 20070118054 (hereinafter Pinhas).
Regarding claim 37, the combination of Schultz and Hood teaches all the limitations of the base claims as outlined above.
But the combination of Schultz and Hood fails to clearly specify analyzing at least one peak frequency of the user sensing data indicative of the sleep disorder.
However, Pinhas teaches analyzing at least one peak frequency of the user sensing data indicative of the sleep 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].
Schultz, Hood 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 Schultz and Hood, 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 Schultz and Hood teaches all the limitations of the base claims as outlined above.
But the combination of Schultz and Hood fails to clearly specify determining a number of episodes of the sleeping disorder during a use of the bed device.
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].
Schultz, Hood 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 Schultz and Hood, 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 and evaluate a patient/user clinical status, as taught by Pinhas [0202, 0302].
Regarding claim 44, the combination of Schultz and Hood teaches all the limitations of the base claims as outlined above.
But the combination of Schultz and Hood 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].
Schultz, Hood 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 Schultz and Hood, 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.
Claim(s) 46 and is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Schultz and Hood in view of Auphan et al. U.S. Patent Publication No. 20160015315 (hereinafter Auphan).
Regarding claim 46, the combination of Schultz and Hood teaches all the limitations of the base claims as outlined above.
But the combination of Schultz and Hood fails to clearly specify displaying, to a display of the user device, a graph indicative of the detection of the sleeping disorder of the user.
However, Auphan teaches displaying, to a display of the user device, a graph indicative of the detection of the sleeping disorder of the user [0064-0066 — a mobile terminal 7, such as a cell phone; 0112 — The microphone 11 together with the sleep sensing unit 2 and optionally the additional sensor device 8 may be used to detect the user's movements during the night, and more particularly, it may be used in assessment of the sleep movement disorders, such as periodic limb movement disorder, or the restless leg syndrome, or other such conditions; 0125-0132, 0096, Figs. 2-4 — application, provided in the mobile terminal 7, may be adapted to: display various information about the user's sleep, e.g. graphs of sleep quality, sleep stage, sleep hypnogram and the like].
Schultz, Hood and Auphan are analogous art. They relate to patient/user monitoring and related bed monitoring or 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 Schultz and Hood, by incorporating the above limitations, as taught by Auphan.
One of ordinary skill in the art would have been motivated to do this modification so that a user to receive may conveniently access sleep data, as suggested by Auphan [0125-0132], particularly data involving time/trending.
Claim(s) 49 and is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Schultz and Hood in view of Turner et al. U.S. Patent Publication No. 20090177327 (hereinafter Turner).
Regarding claim 49, the combination of Schultz and Hood teaches all the limitations of the base claims as outlined above.
But the combination of Schultz and Hood fails to clearly specify that the user sensor comprises a piezo sensor.
However, Turner teaches the user sensor comprises a piezo sensor [0026, Fig. 14 — sensor unit 44 uses piezo-electric strain gauges 50… The placement of the sensor unit allows for body exertions (respiration, pulse, motion, and presence) to cause the semi-rigid plate (and thus the piezo-electric strain gauge) to distress, and produce a voltage].
Schultz, Hood and Turner 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 simply substitute the known a piezo sensor of Turner for the known sensor of Schultz for the predictable result of a method utilizing a piezo sensor.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zigel et al. U.S. Patent Publication No. 20150119741 discloses a system and method for diagnosing sleep quality.
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
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/BERNARD G LINDSAY/
Primary Examiner, Art Unit 2119