Detailed Notice
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 .
Status of Claims
Claims 1-20 are currently pending.
Claims 1-20 are rejected.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1:
In the instant case, claims 1-20 are directed toward a patient support apparatus (i.e., machine). Thus, each of the claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A—Prong 1:
Independent claims 1, 9, and 15 recites steps that, under their broadest reasonable interpretations, cover performance of the limitations of a certain method of organizing human activity but for the recitation of generic computer components.
Claim 1 recites: “A patient support apparatus configured to infer a patient's future behavior, the patient support apparatus comprising: a frame, a patient support surface positioned on the frame, communication circuitry carried by the frame and configured to receive data from a hospital information system related to a condition of the patient, and a controller carried by the frame and coupled to the communication circuitry, the controller including a processor and a non-transitory memory device, the memory device including instructions that, when executed by the processor, acquire movement data related to a patient's movement on the patient support apparatus from a sensor that tracks the movement data, the memory device further including instructions that, when executed by the processor, acquire position data related to the patient's position relative to the patient support apparatus from a camera that tracks the position data, the memory device further including instructions that, when executed by the processor, analyze the movement data and the position data to track data related to patient bed exits from the patient support apparatus to detect patterns in patient bed exit behavior, build a customized schedule of patient bed exit behavior to predict a probability of future patient bed exits, and notify a caregiver when a future patient bed exit is to occur based on the probability of the patient bed exit occurring, wherein a model for predicting the probability of the future patient bed exits is created based on a severity of the patient's condition as received from the communication circuitry, wherein the model for predicting the probability of the future patient bed exits is updated over time based on changes in the severity of the patient's condition as received from the communication circuitry”.
The limitations of acquire movement data related to a patient's movement, acquire position data related to the patient's position, analyze the movement data and the position data to track data related to patient bed exits… to detect patterns in patient bed exit behavior, build a customized schedule of patient bed exit behavior to predict a probability of future patient bed exits, and notify a caregiver when a future patient bed exit is to occur based on the probability of the patient bed exit occurring, wherein a model for predicting the probability of the future patient bed exits is created based on a severity of the patient's condition as received from the communication circuitry, wherein the model for predicting the probability of the future patient bed exits is updated over time based on changes in the severity of the patient's condition as received from the communication circuitry, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions—in this case the aforementioned steps recite a process of acquire, analyze detect, build, notify, predicting, and updated, which is properly interpreted as a “personal behavior”), but instead automates the process via a computer model), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements”, and will be discussed in further detail below.
Further, the abstract idea of claim 15 is identical as the abstract idea of claim 1. This limitation, given the broadest reasonable interpretation, also falls under the abstract idea of a certain method of organizing human activity because it recites managing personal behavior or relationships or interactions between people.
Additionally, claim 9 recites: “A patient support apparatus configured to infer a patient's future behavior, the patient support apparatus comprising: a frame, a patient support surface positioned on the frame, communication circuitry carried by the frame and configured to receive data from a hospital information system related to a condition of the patient, and a controller carried by the frame and coupled to the communication circuitry, the controller including a processor and a non-transitory memory device, the memory device including instructions that, when executed by the processor, acquire movement data related to a patient's movement on the patient support apparatus from a sensor that tracks the movement data, the memory device further including instructions that, when executed by the processor, acquire position data related to the patient's position relative to the patient support apparatus from a camera that tracks the position data, the memory device further including instructions that, when executed by the processor, analyze the movement data and the position data to track data related to patient bed exits from the patient support apparatus to predict a probability of future patient bed exits, and notify a caregiver when a future patient bed exit is to occur based on the probability of the patient bed exit occurring”.
The limitations of acquire movement data related to a patient's movement on the patient support apparatus, acquire position data related to the patient's position relative, analyze the movement data and the position data to track data related to patient bed exits from the patient support apparatus to predict a probability of future patient bed exits, and notify a caregiver when a future patient bed exit is to occur based on the probability of the patient bed exit occurring, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions—in this case the aforementioned steps recite a process of acquire, analyze, predict, and notify, which is properly interpreted as a “personal behavior”), but instead automates the process via a computer model or machine learning), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements”, and will be discussed in further detail below.
Dependent claims 2-8, 10-14, and 16-20 include other limitations, as well as specific step of data to be processed, received, and applied, but these only serve to further limit the abstract idea and do not add and additional elements, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 9, and 15. However, recitation of an abstract idea is not the end of the 35 U.S.C. 101 analysis. Each of the claims must be analyzed for additional elements that indicate the abstract idea is integrated into a practical application to determine whether the claim is considered to be “directed to” an abstract idea.
Step 2A—Prong 2:
Claims 1-20 are not integrated into a practical application because the additional elements (i.e. any limitations that are not identified as part of the abstract idea) amount to no more than limitations which:
Amount to mere instructions to apply an exception—for example, the recitation of “patient support apparatus”, “frame”, “patient support surface”, “communication circuitry”, “hospital information system”, “controller”, “processor”, “non-transitory memory device”, “memory device”, “sensor”, “camera”, and “model”, which amount to merely invoking a computer as a tool to perform the abstract idea, e.g. see FIG. 1, [0014]-[0015], and [0023], of the present specification, and see further MPEP 2106.05(f);
Generally linking the abstract idea to a particular technological environment or field of use, for example, “A patient support apparatus configured to infer a patient's future behavior, the patient support apparatus comprising: a frame, a patient support surface positioned on the frame, communication circuitry carried by the frame and configured to receive data from a hospital information system related to a condition of the patient, and a controller carried by the frame and coupled to the communication circuitry, the controller including a processor and a non-transitory memory device, the memory device including instructions that, when executed by the processor”, “on the patient support apparatus from a sensor that tracks the movement data, the memory device further including instructions that, when executed by the processor”, “relative to the patient support apparatus from a camera that tracks the position data, the memory device further including instructions that, when executed by the processor”, “from the patient support apparatus”, and “A patient support apparatus configured to infer a patient's future behavior, the patient support apparatus comprising: a frame, a patient support surface positioned on the frame, communication circuitry carried by the frame and configured to receive data from a hospital information system related to a condition of the patient, and a controller carried by the frame and coupled to the communication circuitry, the controller including a processor and a non-transitory memory device, the memory device including instructions that, when executed by the processor”, which amounts to limiting the abstract idea to the field of technology/the environment of computers, see MPEP 2106.05(h); and/or
Merely acquiring information for further analysis by the system and the particular manner of acquisition is not described or shown to be important, for example, “receive data from a hospital information system related to a condition of the patient,”, “acquire movement data related to a patient's movement on the patient support apparatus from a sensor that tracks the movement data”, and “acquire position data related to the patient's position relative to the patient support apparatus from a camera that tracks the position data”, which amounts to insignificant extra-solution activity in the form of mere data gathering because it merely functions tangentially to the main idea of the invention and serves only to bring in the data necessary for the inventions main analysis, see MPEP 2106.05(g).
Additionally, dependent claims 2-8, 10-14, and 16-20 include other limitations, but as stated above, the limitations recited by these claims do not include any additional elements beyond those already recited in independent claims 1, 9, and 15, and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B:
The claims do not include additional elements (i.e., “patient support apparatus”, “frame”, “patient support surface”, “communication circuitry”, “hospital information system”, “controller”, “processor”, “non-transitory memory device”, “memory device”, “sensor”, “camera”, and “model”) that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the elements other than the abstract idea), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, and/or generally link the abstract idea to a particular technological environment or field of use, which even when reevaluated under the considerations of Step 2B of the analysis, do not amount to “significantly more” than the abstract idea.
Dependent claims 2-8, 10-14, and 16-20 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because, as stated above, the aforementioned dependent claims do not recite any additional elements not already recited in independent claims 1, 9, and 15, and hence do not amount to “significantly more” than the abstract idea.
Additionally, the additional elements (i.e., “receive data from a hospital information system related to a condition of the patient,”, “acquire movement data related to a patient's movement on the patient support apparatus from a sensor that tracks the movement data”, and “acquire position data related to the patient's position relative to the patient support apparatus from a camera that tracks the position data”), add extra solution activity, which comprises limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in a particular field as demonstrated by:
Relevant court decisions (See MPEP 2106.05(d)(II)):
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) (“Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink.” (emphasis added)).
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Huster et al. (US20200103837A1), hereinafter Huster, in view of Derenne et al. (US 20150109442 A1), hereinafter Derenne, and DeMazumder (US 20210153814 A1), hereinafter DeMazumder.
Regarding claim 1 Huster teaches a patient support apparatus configured to infer a patient's future behavior (Huster, Abstract, FIG. 3, [0074]: “A generalized algorithm for predicting adverse events and mitigating the risk of adverse events is displayed graphically at FIG. 3”, and [0092]), the patient support apparatus comprising: a frame (Huster, [0066]: “The control system 16 of the patient support apparatus 14 includes input devices that provide information to a controller 44 of the control system 16. For example, referring to FIG. 2, frame position sensors 36, siderail position sensors 38, support surface sensors 40, a scale system 42, and caster brake sensors 46 are all in communication with the controller 44. The frame position sensors 36 provide information regarding the position of various components of the patient support apparatus 14. Information provided may include the height of the patient support apparatus 14, the inclination of a head section, the degree of tilt of an upper frame, or any other frame position data that might be available from frame position sensors 36 of the particular patient support apparatus 14” and [0068]: “Such a support surface 56 may be integrated into the frame of the patient support apparatus 14 or may be a separate structure that is operated generally independently of the patient support apparatus 14, but communicates with the controller 44 of the patient support apparatus 14”), a patient support surface positioned on the frame (Huster, [0031], [0066]: “The control system 16 of the patient support apparatus 14 includes input devices that provide information to a controller 44 of the control system 16. For example, referring to FIG. 2, frame position sensors 36, siderail position sensors 38, support surface sensors 40, a scale system 42, and caster brake sensors 46 are all in communication with the controller 44. The frame position sensors 36 provide information regarding the position of various components of the patient support apparatus 14. Information provided may include the height of the patient support apparatus 14, the inclination of a head section, the degree of tilt of an upper frame, or any other frame position data that might be available from frame position sensors 36 of the particular patient support apparatus 14” and [0068]: “Such a support surface 56 may be integrated into the frame of the patient support apparatus 14 or may be a separate structure that is operated generally independently of the patient support apparatus 14, but communicates with the controller 44 of the patient support apparatus 14”, [0068]: “The support surface sensors 40 provide information regarding the operation of a support surface 56, such as an inflatable/pneumatic mattress, of the patient support apparatus 14. Such a support surface 56 may be integrated into the frame of the patient support apparatus 14 or may be a separate structure that is operated generally independently of the patient support apparatus 14, but communicates with the controller 44 of the patient support apparatus 14. The support surface sensors 40 may include pressure sensors that identify pressures in particular inflatable structures of the support surface or they may include position sensors. For example, accelerometers positioned in particular locations within the support surface 56 may provide feedback regarding the amount of inclination of a particular section of the support surface 56 relative to gravity, independent of the frame position sensors 36. The support surface sensors 40 may also provide information regarding the degree of lateral rotation of a patient supported on the support surface 56. In addition, the control system 16 includes a support surface pressure control system 54 which is operable to control the pressure in one or more air bladders in the support surface 56 “, and [0094]: “ Similarly, the type of patient support surface is being used may also provide mitigation factors or be statistically shown to have a higher incidence of patient falls. This may be due to the structure of the patient support surface such as having soft edges or other structures which make ingress from the patient support apparatus more difficult. Still another factor which may be considered is the state in which the patient support surface is operating, such as if the patient support surface is in a rotation mode resulting in discontinuities in the top surface of the patient support surface or movement of portions of the patient support surface”), communication circuitry carried by the frame and configured to receive data from a hospital information system related to a condition of the patient (Huster, [0061]: “The relationship between a patient support apparatus 14 positioned in a room 10 of a care facility and a hospital information system 12 is shown diagrammatically in FIG. 1. In the illustrative embodiment, the hospital information system 12 includes a centralized nurse call system 18 and a centralized electronic medical record system 20. Both the nurse call system 18 and electronic medical records system 20 include information that is related to a patient support apparatus 14 and associated with the patient stored in memory as related records. The information related to the patient stored in memory in the nurse call system 18 and electronic medical records system 20 is constantly updated as information is added to the electronic medical records system 20 and the nurse call system 18 receives information related to the patient and the patient support apparatus 14” and [0064]: “In other cases, the information may be stored by the hospital information system 12… It should be understood that peripheral devices such as the peripheral devices 34, may be in direct communication with the hospital information system 12 without being connected through the patient support apparatus 14”), and a controller carried by the frame and coupled to the communication circuitry (Huster, [0009]-[0020]: “According to a first aspect of the present disclosure, a patient support apparatus comprises a plurality of sensors, a user interface including a plurality of input devices, a plurality of controllable devices, and a controller electrically coupled to the plurality of sensors and plurality of controllable devices. The controller includes a processor and a memory device electrically coupled to the processor. The memory device includes instructions that, when executed by the processor, cause the processor to process a plurality of data items related to physiological conditions of a patient associated with the patient support apparatus as detected by at least one of the plurality of sensors or the input devices. The processor analyzes the plurality of data items to determine a risk of an adverse event occurring to the patient”), the controller including a processor and a non-transitory memory device (Huster, Claim 1: “a controller electrically coupled to the plurality of sensors, the plurality of controllable devices, the controller including a processor and a non-transitory memory device electrically coupled to the processor, the non-transitory memory device including instructions that, when executed by the processor, cause the processor to”), the memory device including instructions that, when executed by the processor (Huster, [0009]-[0020]: “According to a first aspect of the present disclosure, a patient support apparatus comprises a plurality of sensors, a user interface including a plurality of input devices, a plurality of controllable devices, and a controller electrically coupled to the plurality of sensors and plurality of controllable devices. The controller includes a processor and a memory device electrically coupled to the processor. The memory device includes instructions that, when executed by the processor, cause the processor to process a plurality of data items related to physiological conditions of a patient associated with the patient support apparatus as detected by at least one of the plurality of sensors or the input devices. The processor analyzes the plurality of data items to determine a risk of an adverse event occurring to the patient… In some embodiments, the memory device may further include instructions that, when executed by the processor, cause the processor identify at least one data item indicative of an environmental factor associated with the environment in which the patient support apparatus is located, analyze the plurality of data items related to the physiological conditions of the patient and the data item indicative of an environmental factor to determine the risk of an adverse event occurring to the patient”), the memory device further including instructions that, when executed by the processor (Huster, [0009]-[0020]: “According to a first aspect of the present disclosure, a patient support apparatus comprises a plurality of sensors, a user interface including a plurality of input devices, a plurality of controllable devices, and a controller electrically coupled to the plurality of sensors and plurality of controllable devices. The controller includes a processor and a memory device electrically coupled to the processor. The memory device includes instructions that, when executed by the processor, cause the processor to process a plurality of data items related to physiological conditions of a patient associated with the patient support apparatus as detected by at least one of the plurality of sensors or the input devices. The processor analyzes the plurality of data items to determine a risk of an adverse event occurring to the patient… In some embodiments, the memory device may further include instructions that, when executed by the processor, cause the processor identify at least one data item indicative of an environmental factor associated with the environment in which the patient support apparatus is located, analyze the plurality of data items related to the physiological conditions of the patient and the data item indicative of an environmental factor to determine the risk of an adverse event occurring to the patient”), wherein a model for predicting the probability of the future patient bed exits is created based on a severity of the patient's condition as received from the communication circuitry (Huster, [0005]: “update any monitoring conditions that may need to be modified due to the change in the patient's condition”, [0006]: “patient populations may also be at risk of falls depending on other medical conditions that are normally assessed at the time of admission into a care facility. However, as with any statistic, there are exceptions that mitigate the risk even in at risk populations. As a result, applying a “one-size-fits-all” fall prevention program based on age may not provide a patient who has a low risk with the appropriate care for that particular patient. A patient who has mitigating conditions which significantly reduce the risk of fall, even though their age places them in a high-risk group, may be negatively impacted in their recovery if the highest fall prevention protocol is applied to that particular patient. Generally, a fall prevention program requires a patient to be assisted when ambulating. For a patient who feels healthy and is at low risk of falling, such a protocol may result in the patient being noncompliant to other protocols”, [0009]: “The memory device includes instructions that, when executed by the processor, cause the processor to process a plurality of data items related to physiological conditions of a patient associated with the patient support apparatus as detected by at least one of the plurality of sensors or the input devices. The processor analyzes the plurality of data items to determine a risk of an adverse event occurring to the patient”, [0010]: “analyze the plurality of data items related to the physiological conditions of the patient and the data item indicative of an environmental factor to determine the risk of an adverse event occurring to the patient”, and [0011]: “the memory device may further include instructions that, when executed by the processor, cause the processor to identify at least one data item indicative of a patient support apparatus factor associated with the patient support apparatus, analyze the plurality of data items related to the physiological conditions of the patient, the data item indicative of an environmental factor, and the data item indicative of a patient support apparatus factor to determine the risk of an adverse event occurring to the patient”), wherein the model for predicting the probability of the future patient bed exits is updated over time based on changes in the severity of the patient's condition as received from the communication circuitry (Huster, [0005]: “update any monitoring conditions that may need to be modified due to the change in the patient's condition”, [0006]: “patient populations may also be at risk of falls depending on other medical conditions that are normally assessed at the time of admission into a care facility. However, as with any statistic, there are exceptions that mitigate the risk even in at risk populations. As a result, applying a “one-size-fits-all” fall prevention program based on age may not provide a patient who has a low risk with the appropriate care for that particular patient. A patient who has mitigating conditions which significantly reduce the risk of fall, even though their age places them in a high-risk group, may be negatively impacted in their recovery if the highest fall prevention protocol is applied to that particular patient. Generally, a fall prevention program requires a patient to be assisted when ambulating. For a patient who feels healthy and is at low risk of falling, such a protocol may result in the patient being noncompliant to other protocols”, [0009]: “The memory device includes instructions that, when executed by the processor, cause the processor to process a plurality of data items related to physiological conditions of a patient associated with the patient support apparatus as detected by at least one of the plurality of sensors or the input devices. The processor analyzes the plurality of data items to determine a risk of an adverse event occurring to the patient”, [0010]: “analyze the plurality of data items related to the physiological conditions of the patient and the data item indicative of an environmental factor to determine the risk of an adverse event occurring to the patient”, and [0011]: “the memory device may further include instructions that, when executed by the processor, cause the processor to identify at least one data item indicative of a patient support apparatus factor associated with the patient support apparatus, analyze the plurality of data items related to the physiological conditions of the patient, the data item indicative of an environmental factor, and the data item indicative of a patient support apparatus factor to determine the risk of an adverse event occurring to the patient”).
Huster does not teach acquire movement data related to a patient's movement on the patient support apparatus from a sensor that tracks the movement data, acquire position data related to the patient's position relative to the patient support apparatus from a camera that tracks the position data, the memory device further including instructions that, when executed by the processor, analyze the movement data and the position data to track data related to patient bed exits from the patient support apparatus to detect patterns in patient bed exit behavior.
However, Derenne teaches acquire movement data related to a patient's movement on the patient support apparatus from a sensor that tracks the movement data (Derenne, [0091]: “Some software modules 34 are adapted to utilize such thermal images, if available, for carrying out their function. For example, at least one software module 34 that tracks the movement of a patient in a hospital bed is adapted to utilize thermal images to detect the patient's movement while covered with a sheet and/or other bedding. Another software module 34 to capture patient movement in low light conditions that is indicative of the patient's impending, or actual, departure from his or her bed. Still other software modules 34 utilize the infrared image data in other manners and for other purposes”, [0095]: “these algorithms are designed to be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce high resolution images of entire scenes, find similar images from an image database, follow eye movements, recognize scenery and establish markers to overlay scenery with augmented reality, and other tasks”, [0139]: “For example, many healthcare facilities utilize an Admission, Discharge, and Tracking (ADT) computer system for keeping track of its patients. Such systems include information that correlates a patient identification with a specific room number and/or bed bay within a multi-person room. In some embodiments, system 20 is adapted to determine the identification of a patient by querying the ADT system for the identification of the patient corresponding to a specific room and/or bay within the room”, and [0257]: “System 20 also identifies what side a patient is on (left, right, back, front) and tracks how long the patient has been on a particular side. System 20 sends an alert to a caregiver if patient has been on a particular side longer than a predetermined time. Such an alert is forwarded to the caregiver by sending a signal to caregiver alert computer device 64, which is programmed to carry out the alerting process”), acquire position data related to the patient's position relative to the patient support apparatus from a camera that tracks the position data, the memory device further including instructions that, when executed by the processor (Derenne, [0091]: “Some software modules 34 are adapted to utilize such thermal images, if available, for carrying out their function. For example, at least one software module 34 that tracks the movement of a patient in a hospital bed is adapted to utilize thermal images to detect the patient's movement while covered with a sheet and/or other bedding. Another software module 34 to capture patient movement in low light conditions that is indicative of the patient's impending, or actual, departure from his or her bed. Still other software modules 34 utilize the infrared image data in other manners and for other purposes”, [0095]: “these algorithms are designed to be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce high resolution images of entire scenes, find similar images from an image database, follow eye movements, recognize scenery and establish markers to overlay scenery with augmented reality, and other tasks”, [0139]: “For example, many healthcare facilities utilize an Admission, Discharge, and Tracking (ADT) computer system for keeping track of its patients. Such systems include information that correlates a patient identification with a specific room number and/or bed bay within a multi-person room. In some embodiments, system 20 is adapted to determine the identification of a patient by querying the ADT system for the identification of the patient corresponding to a specific room and/or bay within the room”, and [0257]: “System 20 also identifies what side a patient is on (left, right, back, front) and tracks how long the patient has been on a particular side. System 20 sends an alert to a caregiver if patient has been on a particular side longer than a predetermined time. Such an alert is forwarded to the caregiver by sending a signal to caregiver alert computer device 64, which is programmed to carry out the alerting process”), analyze the movement data and the position data to track data related to patient bed exits from the patient support apparatus to detect patterns in patient bed exit behavior (Derenne, [0178]: “Computer device 24 then seeks to match the three-dimensional pattern of the detected object to the attribute data 44 of a known object that is stored in database 50”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Huster to incorporate the teachings of Derenne and account for utilizing video cameras for monitoring patients, caregivers, equipment, and other items within a room in a caregiver setting, such as a hospital, nursing home, treatment center, or the like (Derenne, Abstract and [0002]).
Huster and Derenne do not teach build a customized schedule of patient bed exit behavior to predict a probability of future patient bed exits, and notify a caregiver when a future patient bed exit is to occur based on the probability of the patient bed exit occurring.
However, DeMazumder teaches build a customized schedule of patient bed exit behavior to predict a probability of future patient bed exits (DeMazumder, [0009]: “These signals are delivered into a computer system implementing a learning routine which constructs one or more personalized kinetic state models of positional states for the individual and transitions between the positional states, and further develops one or more customized multi-dimensional prediction models for the individual, and uses the multi-dimensional prediction models to predict behaviors, activities and/or positional changes likely to occur in the future. The system further includes a notification system initiating a notification, alert or warning upon prediction of a behavior, activity or positional change associated with an unsafe or undesired outcome, and transmitting the notification, alert or warning to a recipient associated with the individual”, [0012]: “Sensors can capture data from a plurality of locations having visibility of the individual, so that the learning routine can identify the position of key positional points for the individual in a three-dimensional Cartesian plane using a combination of video or images acquired from said plurality of locations. Further, the kinesthetic activity sensors may capture haptic, tactile, pressure, accelerometric, gyroscopic and/or temperature data from the vicinity of the individual”, [0014]-[0016], [0019]-[0022], and [0050]), and notify a caregiver when a future patient bed exit is to occur based on the probability of the patient bed exit occurring (DeMazumder, [0009]: “These signals are delivered into a computer system implementing a learning routine which constructs one or more personalized kinetic state models of positional states for the individual and transitions between the positional states, and further develops one or more customized multi-dimensional prediction models for the individual, and uses the multi-dimensional prediction models to predict behaviors, activities and/or positional changes likely to occur in the future. The system further includes a notification system initiating a notification, alert or warning upon prediction of a behavior, activity or positional change associated with an unsafe or undesired outcome, and transmitting the notification, alert or warning to a recipient associated with the individual”, [0012]: “Sensors can capture data from a plurality of locations having visibility of the individual, so that the learning routine can identify the position of key positional points for the individual in a three-dimensional Cartesian plane using a combination of video or images acquired from said plurality of locations. Further, the kinesthetic activity sensors may capture haptic, tactile, pressure, accelerometric, gyroscopic and/or temperature data from the vicinity of the individual”, [0014]-[0016], [0019]-[0022], and [0050]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Huster and Derenne to incorporate the teachings of DeMazumder and account for effective continuous monitoring of these environments can uniquely improve outcomes by predicting unsafe or undesirable situations and impending adverse and respond to them before harm to persons or property and can save many lives (DeMazumder, Abstract [0002]-[0008]).
Regarding claim 2 Huster further teaches the probability of the future patient bed exits is updated over time based on improvements or deterioration in the patient's condition (Huster, [0046]: “In some embodiments, the patient support apparatus includes a plurality of sensors, at least one of the plurality of sensors providing a signal including a second data item indicative of a second physiological condition of a patient supported on the patient support apparatus, and the controller is operable to analyze the first data item and second data item collectively to determine a risk of an adverse event occurring to the patient” and [0071]).
Regarding claim 3 Huster and Derenne do not teach the customized schedule is created based on the severity of the patient's condition.
However, DeMazumder teaches the customized schedule is created based on the severity of the patient's condition (DeMazumder, [0009]: “These signals are delivered into a computer system implementing a learning routine which constructs one or more personalized kinetic state models of positional states for the individual and transitions between the positional states, and further develops one or more customized multi-dimensional prediction models for the individual, and uses the multi-dimensional prediction models to predict behaviors, activities and/or positional changes likely to occur in the future. The system further includes a notification system initiating a notification, alert or warning upon prediction of a behavior, activity or positional change associated with an unsafe or undesired outcome, and transmitting the notification, alert or warning to a recipient associated with the individual”, [0012]: “Sensors can capture data from a plurality of locations having visibility of the individual, so that the learning routine can identify the position of key positional points for the individual in a three-dimensional Cartesian plane using a combination of video or images acquired from said plurality of locations. Further, the kinesthetic activity sensors may capture haptic, tactile, pressure, accelerometric, gyroscopic and/or temperature data from the vicinity of the individual”, [0014]-[0016], [0019]-[0022], and [0050]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Huster and Derenne to incorporate the teachings of DeMazumder and account for effective continuous monitoring of these environments can uniquely improve outcomes by predicting unsafe or undesirable situations and impending adverse and respond to them before harm to persons or property and can save many lives (DeMazumder, Abstract [0002]-[0008]).
Regarding claim 4 Huster and Derenne do not teach the customized schedule is updated based on improvements or deterioration in the patient's condition.
However, DeMazumder teaches the customized schedule is updated based on improvements or deterioration in the patient's condition (DeMazumder, [0009]: “These signals are delivered into a computer system implementing a learning routine which constructs one or more personalized kinetic state models of positional states for the individual and transitions between the positional states, and further develops one or more customized multi-dimensional prediction models for the individual, and uses the multi-dimensional prediction models to predict behaviors, activities and/or positional changes likely to occur in the future. The system further includes a notification system initiating a notification, alert or warning upon prediction of a behavior, activity or positional change associated with an unsafe or undesired outcome, and transmitting the notification, alert or warning to a recipient associated with the individual”, [0012]: “Sensors can capture data from a plurality of locations having visibility of the individual, so that the learning routine can identify the position of key positional points for the individual in a three-dimensional Cartesian plane using a combination of video or images acquired from said plurality of locations. Further, the kinesthetic activity sensors may capture haptic, tactile, pressure, accelerometric, gyroscopic and/or temperature data from the vicinity of the individual”, [0014]-[0016], [0019]-[0022], and [0050]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Huster and Derenne to incorporate the teachings of DeMazumder and account for effective continuous monitoring of these environments can uniquely improve outcomes by predicting unsafe or undesirable situations and impending adverse and respond to them before harm to persons or property and can save many lives (DeMazumder, Abstract [0002]-[0008]).
Regarding claim 5 Huster further teaches the sensor is positioned in the patient support apparatus (Huster, [0009]: “According to a first aspect of the present disclosure, a patient support apparatus comprises a plurality of sensors, a user interface including a plurality of input devices, a plurality of controllable devices, and a controller electrically coupled to the plurality of sensors and plurality of controllable devices. The controller includes a processor and a memory device electrically coupled to the processor. The memory device includes instructions that, when executed by the processor, cause the processor to process a plurality of data items related to physiological conditions of a patient associated with the patient support apparatus as detected by at least one of the plurality of sensors or the input devices. The processor analyzes the plurality of data items to determine a risk of an adverse event occurring to the patient”, [0032]: “According to a second aspect of the present disclosure, a patient support apparatus comprises at least one sensor, at least one component having multiple states, at least one actuator operable to vary the states of the at least one component, and a controller. The controller is operable to receive an input signal from the at least one sensor, the at least one signal including a first data item indicative of a physiological condition of a patient supported on the patient support apparatus and analyze the of data item to determine a risk of an adverse event occurring to the patient”, [0066]: “The control system 16 of the patient support apparatus 14 includes input devices that provide information to a controller 44 of the control system 16. For example, referring to FIG. 2, frame position sensors 36, siderail position sensors 38, support surface sensors 40, a scale system 42, and caster brake sensors 46 are all in communication with the controller 44. The frame position sensors 36 provide information regarding the position of various components of the patient support apparatus 14. Information provided may include the height of the patient support apparatus 14, the inclination of a head section, the degree of tilt of an upper frame, or any other frame position data that might be available from frame position sensors 36 of the particular patient support apparatus 14”, and [0068]: “The support surface sensors 40 provide information regarding the operation of a support surface 56, such as an inflatable/pneumatic mattress, of the patient support apparatus 14. Such a support surface 56 may be integrated into the frame of the patient support apparatus 14 or may be a separate structure that is operated generally independently of the patient support apparatus 14, but communicates with the controller 44 of the patient support apparatus 14. The support surface sensors 40 may include pressure sensors that identify pressures in particular inflatable structures of the support surface or they may include position sensors. For example, accelerometers positioned in particular locations within the support surface 56 may provide feedback regarding the amount of inclination of a particular section of the support surface 56 relative to gravity, independent of the frame position sensors 36. The support surface sensors 40 may also provide information regarding the degree of lateral rotation of a patient supported on the support surface 56. In addition, the control system 16 includes a support surface pressure control system 54 which is operable to control the pressure in one or more air bladders in the support surface 56”).
Regarding claim 6 Huster does not teach the camera is at least one of carried by the patient support apparatus or positioned in a room housing the patient support apparatus.
However, Derenne teaches the camera is at least one of carried by the patient support apparatus or positioned in a room housing the patient support apparatus (Derenne, [0003]: “According to various aspects of the systems and methods of the present invention, improved patient care is accomplished through the use of one or more video cameras positioned within a patient's room in order to provide patient care assistance in one or more of a variety of different manners. Such patient care assistance results from the analysis of video camera images that are used for any one or more of the following purposes: preventing patient falls, reducing the chances and/or spread of infection, ensuring patient care protocols are properly executed, and/or monitoring patient activity. Such analysis takes place using one or more computer devices programmed to process and analyze video images. The computer devices are either positioned inside the room of the patient, or located remotely”, [0004]: “According to one embodiment, a monitoring system is provided that includes a camera, a database, and a computer device. The camera is adapted to capture images of at least a portion of a person support apparatus and output image data representative of the images. The database contains fall prevention protocol data that defines one or more conditions that are to be met prior to an occupant exiting the person support apparatus in order to reduce a fall risk of the occupant. The computer device communicates with the camera and the database, and the computer device is adapted to analyze the image data to determine if the one or more conditions have been met”, [0015], and [0019]-[0020]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Huster to incorporate the teachings of Derenne and account for utilizing video cameras for monitoring patients, caregivers, equipment, and other items within a room in a caregiver setting, such as a hospital, nursing home, treatment center, or the like (Derenne, Abstract and [0002]).
Regarding claim 7 Huster does not teach the memory device further includes instructions that, when executed by the processor, acquire additional position data related to a patient's position relative to the patient support apparatus from a real time locating system.
However, Derenne teaches the memory device further includes instructions that, when executed by the processor, acquire additional position data related to a patient's position relative to the patient support apparatus from a real time locating system (Derenne, [0075]: “The Kinect™ motion sensing device automatically detects the position of one or more persons and outputs data indicating the locations of multiple body portions, such as various joints of the person, multiple times a second. Such information is then processed to determine any one or more of the conditions discussed herein”, [0127]: “and/or locations where the individual moves in the room (e.g. visitor who sits in a chair next to the patient's bed)”, [0164]: “the positions of the siderails of the patient's bed, the status of the bed's brakes, the height of the deck of the bed, whether the an exit detection system of the bed is armed, the weight detected by the bed's scale system, a center of gravity of the patient's current location on the bed, movement of the patient on the bed, and the outputs of any patient sensors that are incorporated into the bed (e.g. any vital sign sensors, position sensors, incontinence sensors, interface pressure sensors, etc.)”, [0165]: “In general, such uses include monitoring movement of the patient on patient support apparatus 36 and/or monitoring the status of one or more components of the patient support apparatus 36 (e.g. brakes, height, Fowler angle, etc.)”, and [0169]: “System 20 is adapted to perform a number of general functions regardless of which set of software modules 34 are loaded on computer device 24. These general functions include identifying persons, monitoring their movement, identifying their behavior, recognizing faces, blurring faces in facial data stored in database 50 as part of collected data 56, and issuing notifications, alerts, and/or reminders”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Huster to incorporate the teachings of Derenne and account for utilizing video cameras for monitoring patients, caregivers, equipment, and other items within a room in a caregiver setting, such as a hospital, nursing home, treatment center, or the like (Derenne, Abstract and [0002]).
Regarding claim 8 Huster does not teach the real time locating system is at least one of carried by the patient support apparatus or positioned in a room housing the patient support apparatus.
However, Derenne teaches the real time locating system is at least one of carried by the patient support apparatus or positioned in a room housing the patient support apparatus (Derenne, [0164]: “Such data includes a variety of different data regarding the status of the bed and/or the status of the patient, such as, but not limited to: the positions of the siderails of the patient's bed, the status of the bed's brakes, the height of the deck of the bed, whether the an exit detection system of the bed is armed, the weight detected by the bed's scale system, a center of gravity of the patient's current location on the bed, movement of the patient on the bed, and the outputs of any patient sensors that are incorporated into the bed (e.g. any vital sign sensors, position sensors, incontinence sensors, interface pressure sensors, etc.)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Huster to incorporate the teachings of Derenne and account for utilizing video cameras for monitoring patients, caregivers, equipment, and other items within a room in a caregiver setting, such as a hospital, nursing home, treatment center, or the like (Derenne, Abstract and [0002]).
Regarding claim 9 Huster teaches a patient support apparatus configured to infer a patient's future behavior (Huster, Abstract, FIG. 3, [0074]: “A generalized algorithm for predicting adverse events and mitigating the risk of adverse events is displayed graphically at FIG. 3”, and [0092]), the patient support apparatus comprising: a frame (Huster, [0066]: “The control system 16 of the patient support apparatus 14 includes input devices that provide information to a controller 44 of the control system 16. For example, referring to FIG. 2, frame position sensors 36, siderail position sensors 38, support surface sensors 40, a scale system 42, and caster brake sensors 46 are all in communication with the controller 44. The frame position sensors 36 provide information regarding the position of various components of the patient support apparatus 14. Information provided may include the height of the patient support apparatus 14, the inclination of a head section, the degree of tilt of an upper frame, or any other frame position data that might be available from frame position sensors 36 of the particular patient support apparatus 14” and [0068]: “Such a support surface 56 may be integrated into the frame of the patient support apparatus 14 or may be a separate structure that is operated generally independently of the patient support apparatus 14, but communicates with the controller 44 of the patient support apparatus 14”), a patient support surface positioned on the frame (Huster, [0031], [0066]: “The control system 16 of the patient support apparatus 14 includes input devices that provide information to a controller 44 of the control system 16. For example, referring to FIG. 2, frame position sensors 36, siderail position sensors 38, support surface sensors 40, a scale system 42, and caster brake sensors 46 are all in communication with the controller 44. The frame position sensors 36 provide information regarding the position of various components of the patient support apparatus 14. Information provided may include the height of the patient support apparatus 14, the inclination of a head section, the degree of tilt of an upper frame, or any other frame position data that might be available from frame position sensors 36 of the particular patient support apparatus 14” and [0068]: “Such a support surface 56 may be integrated into the frame of the patient support apparatus 14 or may be a separate structure that is operated generally independently of the patient support apparatus 14, but communicates with the controller 44 of the patient support apparatus 14”, [0068]: “The support surface sensors 40 provide information regarding the operation of a support surface 56, such as an inflatable/pneumatic mattress, of the patient support apparatus 14. Such a support surface 56 may be integrated into the frame of the patient support apparatus 14 or may be a separate structure that is operated generally independently of the patient support apparatus 14, but communicates with the controller 44 of the patient support apparatus 14. The support surface sensors 40 may include pressure sensors that identify pressures in particular inflatable structures of the support surface or they may include position sensors. For example, accelerometers positioned in particular locations within the support surface 56 may provide feedback regarding the amount of inclination of a particular section of the support surface 56 relative to gravity, independent of the frame position sensors 36. The support surface sensors 40 may also provide information regarding the degree of lateral rotation of a patient supported on the support surface 56. In addition, the control system 16 includes a support surface pressure control system 54 which is operable to control the pressure in one or more air bladders in the support surface 56 “, and [0094]: “ Similarly, the type of patient support surface is being used may also provide mitigation factors or be statistically shown to have a higher incidence of patient falls. This may be due to the structure of the patient support surface such as having soft edges or other structures which make ingress from the patient support apparatus more difficult. Still another factor which may be considered is the state in which the patient support surface is operating, such as if the patient support surface is in a rotation mode resulting in discontinuities in the top surface of the patient support surface or movement of portions of the patient support surface”), communication circuitry carried by the frame and configured to receive data from a hospital information system related to a condition of the patient (Huster, [0061]: “The relationship between a patient support apparatus 14 positioned in a room 10 of a care facility and a hospital information system 12 is shown diagrammatically in FIG. 1. In the illustrative embodiment, the hospital information system 12 includes a centralized nurse call system 18 and a centralized electronic medical record system 20. Both the nurse call system 18 and electronic medical records system 20 include information that is related to a patient support apparatus 14 and associated with the patient stored in memory as related records. The information related to the patient stored in memory in the nurse call system 18 and electronic medical records system 20 is constantly updated as information is added to the electronic medical records system 20 and the nurse call system 18 receives information related to the patient and the patient support apparatus 14” and [0064]: “In other cases, the information may be stored by the hospital information system 12… It should be understood that peripheral devices such as the peripheral devices 34, may be in direct communication with the hospital information system 12 without being connected through the patient support apparatus 14”), and a controller carried by the frame and coupled to the communication circuitry (Huster, [0009]-[0020]: “According to a first aspect of the present disclosure, a patient support apparatus comprises a plurality of sensors, a user interface including a plurality of input devices, a plurality of controllable devices, and a controller electrically coupled to the plurality of sensors and plurality of controllable devices. The controller includes a processor and a memory device electrically coupled to the processor. The memory device includes instructions that, when executed by the processor, cause the processor to process a plurality of data items related to physiological conditions of a patient associated with the patient support apparatus as detected by at least one of the plurality of sensors or the input devices. The processor analyzes the plurality of data items to determine a risk of an adverse event occurring to the patient”), the controller including a processor and a non-transitory memory device (Huster, Claim 1: “a controller electrically coupled to the plurality of sensors, the plurality of controllable devices, the controller including a processor and a non-transitory memory device electrically coupled to the processor, the non-transitory memory device including instructions that, when executed by the processor, cause the processor to”), the memory device including instructions that, when executed by the processor (Huster, [0009]-[0020]: “According to a first aspect of the present disclosure, a patient support apparatus comprises a plurality of sensors, a user interface including a plurality of input devices, a plurality of controllable devices, and a controller electrically coupled to the plurality of sensors and plurality of controllable devices. The controller includes a processor and a memory device electrically coupled to the processor. The memory device includes instructions that, when executed by the processor, cause the processor to process a plurality of data items related to physiological conditions of a patient associated with the patient support apparatus as detected by at least one of the plurality of sensors or the input devices. The processor analyzes the plurality of data items to determine a risk of an adverse event occurring to the patient… In some embodiments, the memory device may further include instructions that, when executed by the processor, cause the processor identify at least one data item indicative of an environmental factor associated with the environment in which the patient support apparatus is located, analyze the plurality of data items related to the physiological conditions of the patient and the data item indicative of an environmental factor to determine the risk of an adverse event occurring to the patient”), the memory device further including instructions that, when executed by the processor (Huster, [0009]-[0020]: “According to a first aspect of the present disclosure, a patient support apparatus comprises a plurality of sensors, a user interface including a plurality of input devices, a plurality of controllable devices, and a controller electrically coupled to the plurality of sensors and plurality of controllable devices. The controller includes a processor and a memory device electrically coupled to the processor. The memory device includes instructions that, when executed by the processor, cause the processor to process a plurality of data items related to physiological conditions of a patient associated with the patient support apparatus as detected by at least one of the plurality of sensors or the input devices. The processor analyzes the plurality of data items to determine a risk of an adverse event occurring to the patient… In some embodiments, the memory device may further include instructions that, when executed by the processor, cause the processor identify at least one data item indicative of an environmental factor associated with the environment in which the patient support apparatus is located, analyze the plurality of data items related to the physiological conditions of the patient and the data item indicative of an environmental factor to determine the risk of an adverse event occurring to the patient”), the memory device further including instructions that, when executed by the processor (Huster, [0009]-[0020]: “According to a first aspect of the present disclosure, a patient support apparatus comprises a plurality of sensors, a user interface including a plurality of input devices, a plurality of controllable devices, and a controller electrically coupled to the plurality of sensors and plurality of controllable devices. The controller includes a processor and a memory device electrically coupled to the processor. The memory device includes instructions that, when executed by the processor, cause the processor to process a plurality of data items related to physiological conditions of a patient associated with the patient support apparatus as detected by at least one of the plurality of sensors or the input devices. The processor analyzes the plurality of data items to determine a risk of an adverse event occurring to the patient… In some embodiments, the memory device may further include instructions that, when executed by the processor, cause the processor identify at least one data item indicative of an environmental factor associated with the environment in which the patient support apparatus is located, analyze the plurality of data items related to the physiological conditions of the patient and the data item indicative of an environmental factor to determine the risk of an adverse event occurring to the patient”),.
Huster does not teach acquire movement data related to a patient's movement on the patient support apparatus from a sensor that tracks the movement data, acquire position data related to the patient's position relative to the patient support apparatus from a camera that tracks the position data, analyze the movement data and the position data to track data related to patient bed exits from the patient support apparatus to predict a probability of future patient bed exits.
However, Derenne teaches acquire movement data related to a patient's movement on the patient support apparatus from a sensor that tracks the movement data (Derenne, [0091]: “Some software modules 34 are adapted to utilize such thermal images, if available, for carrying out their function. For example, at least one software module 34 that tracks the movement of a patient in a hospital bed is adapted to utilize thermal images to detect the patient's movement while covered with a sheet and/or other bedding. Another software module 34 to capture patient movement in low light conditions that is indicative of the patient's impending, or actual, departure from his or her bed. Still other software modules 34 utilize the infrared image data in other manners and for other purposes”, [0095]: “these algorithms are designed to be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce high resolution images of entire scenes, find similar images from an image database, follow eye movements, recognize scenery and establish markers to overlay scenery with augmented reality, and other tasks”, [0139]: “For example, many healthcare facilities utilize an Admission, Discharge, and Tracking (ADT) computer system for keeping track of its patients. Such systems include information that correlates a patient identification with a specific room number and/or bed bay within a multi-person room. In some embodiments, system 20 is adapted to determine the identification of a patient by querying the ADT system for the identification of the patient corresponding to a specific room and/or bay within the room”, and [0257]: “System 20 also identifies what side a patient is on (left, right, back, front) and tracks how long the patient has been on a particular side. System 20 sends an alert to a caregiver if patient has been on a particular side longer than a predetermined time. Such an alert is forwarded to the caregiver by sending a signal to caregiver alert computer device 64, which is programmed to carry out the alerting process”), acquire position data related to the patient's position relative to the patient support apparatus from a camera that tracks the position data (Derenne, [0091]: “Some software modules 34 are adapted to utilize such thermal images, if available, for carrying out their function. For example, at least one software module 34 that tracks the movement of a patient in a hospital bed is adapted to utilize thermal images to detect the patient's movement while covered with a sheet and/or other bedding. Another software module 34 to capture patient movement in low light conditions that is indicative of the patient's impending, or actual, departure from his or her bed. Still other software modules 34 utilize the infrared image data in other manners and for other purposes”, [0095]: “these algorithms are designed to be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce high resolution images of entire scenes, find similar images from an image database, follow eye movements, recognize scenery and establish markers to overlay scenery with augmented reality, and other tasks”, [0139]: “For example, many healthcare facilities utilize an Admission, Discharge, and Tracking (ADT) computer system for keeping track of its patients. Such systems include information that correlates a patient identification with a specific room number and/or bed bay within a multi-person room. In some embodiments, system 20 is adapted to determine the identification of a patient by querying the ADT system for the identification of the patient corresponding to a specific room and/or bay within the room”, and [0257]: “System 20 also identifies what side a patient is on (left, right, back, front) and tracks how long the patient has been on a particular side. System 20 sends an alert to a caregiver if patient has been on a particular side longer than a predetermined time. Such an alert is forwarded to the caregiver by sending a signal to caregiver alert computer device 64, which is programmed to carry out the alerting process”), analyze the movement data and the position data to track data related to patient bed exits from the patient support apparatus to predict a probability of future patient bed exits (Derenne, [0178]: “ Computer device 24 then seeks to match the three-dimensional pattern of the detected object to the attribute data 44 of a known object that is stored in database 50”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Huster to incorporate the teachings of Derenne and account for utilizing video cameras for monitoring patients, caregivers, equipment, and other items within a room in a caregiver setting, such as a hospital, nursing home, treatment center, or the like (Derenne, Abstract and [0002]).
Huster and Derenne do not teach notify a caregiver when a future patient bed exit is to occur based on the probability of the patient bed exit occurring.
However, DeMazumder teaches notify a caregiver when a future patient bed exit is to occur based on the probability of the patient bed exit occurring (DeMazumder, [0009]: “These signals are delivered into a computer system implementing a learning routine which constructs one or more personalized kinetic state models of positional states for the individual and transitions between the positional states, and further develops one or more customized multi-dimensional prediction models for the individual, and uses the multi-dimensional prediction models to predict behaviors, activities and/or positional changes likely to occur in the future. The system further includes a notification system initiating a notification, alert or warning upon prediction of a behavior, activity or positional change associated with an unsafe or undesired outcome, and transmitting the notification, alert or warning to a recipient associated with the individual”, [0012]: “Sensors can capture data from a plurality of locations having visibility of the individual, so that the learning routine can identify the position of key positional points for the individual in a three-dimensional Cartesian plane using a combination of video or images acquired from said plurality of locations. Further, the kinesthetic activity sensors may capture haptic, tactile, pressure, accelerometric, gyroscopic and/or temperature data from the vicinity of the individual”, [0014]-[0016], [0019]-[0022], and [0050]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Huster and Derenne to incorporate the teachings of DeMazumder and account for effective continuous monitoring of these environments can uniquely improve outcomes by predicting unsafe or undesirable situations and impending adverse and respond to them before harm to persons or property and can save many lives (DeMazumder, Abstract [0002]-[0008]).
Regarding claim 10 Huster further teaches a model for predicting the probability of the future patient bed exits is created based on the patient's condition as received from the communication circuitry (Huster, [0009]-[0011], [0046]: “In some embodiments, the patient support apparatus includes a plurality of sensors, at least one of the plurality of sensors providing a signal including a second data item indicative of a second physiological condition of a patient supported on the patient support apparatus, and the controller is operable to analyze the first data item and second data item collectively to determine a risk of an adverse event occurring to the patient” and [0071]).
Regarding claim 11 Huster further teaches the model for predicting the probability of the future patient bed exits is updated over time based on changes in the patient's condition as received from the communication circuitry (Huster, [0009]-[0011], [0046]: “In some embodiments, the patient support apparatus includes a plurality of sensors, at least one of the plurality of sensors providing a signal including a second data item indicative of a second physiological condition of a patient supported on the patient support apparatus, and the controller is operable to analyze the first data item and second data item collectively to determine a risk of an adverse event occurring to the patient” and [0071]).
Regarding claim 12 Huster further teaches the probability of the future patient bed exits is updated over time based on changes in the patient's condition (Huster, [0009]-[0011], [0046]: “In some embodiments, the patient support apparatus includes a plurality of sensors, at least one of the plurality of sensors providing a signal including a second data item indicative of a second physiological condition of a patient supported on the patient support apparatus, and the controller is operable to analyze the first data item and second data item collectively to determine a risk of an adverse event occurring to the patient” and [0071]).
Regarding claim 13 Huster and Derenne do not teach a customized schedule is created based on the patient's condition.
However, DeMazumder teaches a customized schedule is created based on the patient's condition (DeMazumder, [0009]: “These signals are delivered into a computer system implementing a learning routine which constructs one or more personalized kinetic state models of positional states for the individual and transitions between the positional states, and further develops one or more customized multi-dimensional prediction models for the individual, and uses the multi-dimensional prediction models to predict behaviors, activities and/or positional changes likely to occur in the future. The system further includes a notification system initiating a notification, alert or warning upon prediction of a behavior, activity or positional change associated with an unsafe or undesired outcome, and transmitting the notification, alert or warning to a recipient associated with the individual”, [0012]: “Sensors can capture data from a plurality of locations having visibility of the individual, so that the learning routine can identify the position of key positional points for the individual in a three-dimensional Cartesian plane using a combination of video or images acquired from said plurality of locations. Further, the kinesthetic activity sensors may capture haptic, tactile, pressure, accelerometric, gyroscopic and/or temperature data from the vicinity of the individual”, [0014]-[0016], [0019]-[0022], and [0050]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Huster and Derenne to incorporate the teachings of DeMazumder and account for effective continuous monitoring of these environments can uniquely improve outcomes by predicting unsafe or undesirable situations and impending adverse and respond to them before harm to persons or property and can save many lives (DeMazumder, Abstract [0002]-[0008]).
Regarding claim 14 Huster and Derenne do not teach the customized schedule is updated based on changes in the patient's condition.
However, DeMazumder teaches the customized schedule is updated based on changes in the patient's condition (DeMazumder, [0009]: “These signals are delivered into a computer system implementing a learning routine which constructs one or more personalized kinetic state models of positional states for the individual and transitions between the positional states, and further develops one or more customized multi-dimensional prediction models for the individual, and uses the multi-dimensional prediction models to predict behaviors, activities and/or positional changes likely to occur in the future. The system further includes a notification system initiating a notification, alert or warning upon prediction of a behavior, activity or positional change associated with an unsafe or undesired outcome, and transmitting the notification, alert or warning to a recipient associated with the individual”, [0012]: “Sensors can capture data from a plurality of locations having visibility of the individual, so that the learning routine can identify the position of key positional points for the individual in a three-dimensional Cartesian plane using a combination of video or images acquired from said plurality of locations. Further, the kinesthetic activity sensors may capture haptic, tactile, pressure, accelerometric, gyroscopic and/or temperature data from the vicinity of the individual”, [0014]-[0016], [0019]-[0022], and [0050]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Huster and Derenne to incorporate the teachings of DeMazumder and account for effective continuous monitoring of these environments can uniquely improve outcomes by predicting unsafe or undesirable situations and impending adverse and respond to them before harm to persons or property and can save many lives (DeMazumder, Abstract [0002]-[0008]).
Regarding claim 15 Huster teaches a patient support apparatus configured to infer a patient's future behavior, the patient support apparatus comprising: a frame (Huster, [0066]: “The control system 16 of the patient support apparatus 14 includes input devices that provide information to a controller 44 of the control system 16. For example, referring to FIG. 2, frame position sensors 36, siderail position sensors 38, support surface sensors 40, a scale system 42, and caster brake sensors 46 are all in communication with the controller 44. The frame position sensors 36 provide information regarding the position of various components of the patient support apparatus 14. Information provided may include the height of the patient support apparatus 14, the inclination of a head section, the degree of tilt of an upper frame, or any other frame position data that might be available from frame position sensors 36 of the particular patient support apparatus 14” and [0068]: “Such a support surface 56 may be integrated into the frame of the patient support apparatus 14 or may be a separate structure that is operated generally independently of the patient support apparatus 14, but communicates with the controller 44 of the patient support apparatus 14”), a patient support surface positioned on the frame (Huster, [0031], [0066]: “The control system 16 of the patient support apparatus 14 includes input devices that provide information to a controller 44 of the control system 16. For example, referring to FIG. 2, frame position sensors 36, siderail position sensors 38, support surface sensors 40, a scale system 42, and caster brake sensors 46 are all in communication with the controller 44. The frame position sensors 36 provide information regarding the position of various components of the patient support apparatus 14. Information provided may include the height of the patient support apparatus 14, the inclination of a head section, the degree of tilt of an upper frame, or any other frame position data that might be available from frame position sensors 36 of the particular patient support apparatus 14” and [0068]: “Such a support surface 56 may be integrated into the frame of the patient support apparatus 14 or may be a separate structure that is operated generally independently of the patient support apparatus 14, but communicates with the controller 44 of the patient support apparatus 14”, [0068]: “The support surface sensors 40 provide information regarding the operation of a support surface 56, such as an inflatable/pneumatic mattress, of the patient support apparatus 14. Such a support surface 56 may be integrated into the frame of the patient support apparatus 14 or may be a separate structure that is operated generally independently of the patient support apparatus 14, but communicates with the controller 44 of the patient support apparatus 14. The support surface sensors 40 may include pressure sensors that identify pressures in particular inflatable structures of the support surface or they may include position sensors. For example, accelerometers positioned in particular locations within the support surface 56 may provide feedback regarding the amount of inclination of a particular section of the support surface 56 relative to gravity, independent of the frame position sensors 36. The support surface sensors 40 may also provide information regarding the degree of lateral rotation of a patient supported on the support surface 56. In addition, the control system 16 includes a support surface pressure control system 54 which is operable to control the pressure in one or more air bladders in the support surface 56 “, and [0094]: “ Similarly, the type of patient support surface is being used may also provide mitigation factors or be statistically shown to have a higher incidence of patient falls. This may be due to the structure of the patient support surface such as having soft edges or other structures which make ingress from the patient support apparatus more difficult. Still another factor which may be considered is the state in which the patient support surface is operating, such as if the patient support surface is in a rotation mode resulting in discontinuities in the top surface of the patient support surface or movement of portions of the patient support surface”), communication circuitry carried by the frame and configured to receive data from a hospital information system related to a condition of the patient (Huster, [0061]: “The relationship between a patient support apparatus 14 positioned in a room 10 of a care facility and a hospital information system 12 is shown diagrammatically in FIG. 1. In the illustrative embodiment, the hospital information system 12 includes a centralized nurse call system 18 and a centralized electronic medical record system 20. Both the nurse call system 18 and electronic medical records system 20 include information that is related to a patient support apparatus 14 and associated with the patient stored in memory as related records. The information related to the patient stored in memory in the nurse call system 18 and electronic medical records system 20 is constantly updated as information is added to the electronic medical records system 20 and the nurse call system 18 receives information related to the patient and the patient support apparatus 14” and [0064]: “In other cases, the information may be stored by the hospital information system 12… It should be understood that peripheral devices such as the peripheral devices 34, may be in direct communication with the hospital information system 12 without being connected through the patient support apparatus 14”), and a controller carried by the frame and coupled to the communication circuitry (Huster, [0009]-[0020]: “According to a first aspect of the present disclosure, a patient support apparatus comprises a plurality of sensors, a user interface including a plurality of input devices, a plurality of controllable devices, and a controller electrically coupled to the plurality of sensors and plurality of controllable devices. The controller includes a processor and a memory device electrically coupled to the processor. The memory device includes instructions that, when executed by the processor, cause the processor to process a plurality of data items related to physiological conditions of a patient associated with the patient support apparatus as detected by at least one of the plurality of sensors or the input devices. The processor analyzes the plurality of data items to determine a risk of an adverse event occurring to the patient”), the controller including a processor and a non-transitory memory device (Huster, Claim 1: “a controller electrically coupled to the plurality of sensors, the plurality of controllable devices, the controller including a processor and a non-transitory memory device electrically coupled to the processor, the non-transitory memory device including instructions that, when executed by the processor, cause the processor to”), the memory device including instructions that, when executed by the processor (Huster, [0009]-[0020]: “According to a first aspect of the present disclosure, a patient support apparatus comprises a plurality of sensors, a user interface including a plurality of input devices, a plurality of controllable devices, and a controller electrically coupled to the plurality of sensors and plurality of controllable devices. The controller includes a processor and a memory device electrically coupled to the processor. The memory device includes instructions that, when executed by the processor, cause the processor to process a plurality of data items related to physiological conditions of a patient associated with the patient support apparatus as detected by at least one of the plurality of sensors or the input devices. The processor analyzes the plurality of data items to determine a risk of an adverse event occurring to the patient… In some embodiments, the memory device may further include instructions that, when executed by the processor, cause the processor identify at least one data item indicative of an environmental factor associated with the environment in which the patient support apparatus is located, analyze the plurality of data items related to the physiological conditions of the patient and the data item indicative of an environmental factor to determine the risk of an adverse event occurring to the patient”), the memory device further including instructions that, when executed by the processor (Huster, [0009]-[0020]: “According to a first aspect of the present disclosure, a patient support apparatus comprises a plurality of sensors, a user interface including a plurality of input devices, a plurality of controllable devices, and a controller electrically coupled to the plurality of sensors and plurality of controllable devices. The controller includes a processor and a memory device electrically coupled to the processor. The memory device includes instructions that, when executed by the processor, cause the processor to process a plurality of data items related to physiological conditions of a patient associated with the patient support apparatus as detected by at least one of the plurality of sensors or the input devices. The processor analyzes the plurality of data items to determine a risk of an adverse event occurring to the patient… In some embodiments, the memory device may further include instructions that, when executed by the processor, cause the processor identify at least one data item indicative of an environmental factor associated with the environment in which the patient support apparatus is located, analyze the plurality of data items related to the physiological conditions of the patient and the data item indicative of an environmental factor to determine the risk of an adverse event occurring to the patient”), the memory device further including instructions that, when executed by the processor (Huster, [0009]-[0020]: “According to a first aspect of the present disclosure, a patient support apparatus comprises a plurality of sensors, a user interface including a plurality of input devices, a plurality of controllable devices, and a controller electrically coupled to the plurality of sensors and plurality of controllable devices. The controller includes a processor and a memory device electrically coupled to the processor. The memory device includes instructions that, when executed by the processor, cause the processor to process a plurality of data items related to physiological conditions of a patient associated with the patient support apparatus as detected by at least one of the plurality of sensors or the input devices. The processor analyzes the plurality of data items to determine a risk of an adverse event occurring to the patient… In some embodiments, the memory device may further include instructions that, when executed by the processor, cause the processor identify at least one data item indicative of an environmental factor associated with the environment in which the patient support apparatus is located, analyze the plurality of data items related to the physiological conditions of the patient and the data item indicative of an environmental factor to determine the risk of an adverse event occurring to the patient”), wherein a model for predicting the probability of the future patient bed exits is created based on a severity of the patient's condition as received from the communication circuitry (Huster, [0005]: “update any monitoring conditions that may need to be modified due to the change in the patient's condition”, [0006]: “patient populations may also be at risk of falls depending on other medical conditions that are normally assessed at the time of admission into a care facility. However, as with any statistic, there are exceptions that mitigate the risk even in at risk populations. As a result, applying a “one-size-fits-all” fall prevention program based on age may not provide a patient who has a low risk with the appropriate care for that particular patient. A patient who has mitigating conditions which significantly reduce the risk of fall, even though their age places them in a high-risk group, may be negatively impacted in their recovery if the highest fall prevention protocol is applied to that particular patient. Generally, a fall prevention program requires a patient to be assisted when ambulating. For a patient who feels healthy and is at low risk of falling, such a protocol may result in the patient being noncompliant to other protocols”, [0009]: “The memory device includes instructions that, when executed by the processor, cause the processor to process a plurality of data items related to physiological conditions of a patient associated with the patient support apparatus as detected by at least one of the plurality of sensors or the input devices. The processor analyzes the plurality of data items to determine a risk of an adverse event occurring to the patient”, [0010]: “analyze the plurality of data items related to the physiological conditions of the patient and the data item indicative of an environmental factor to determine the risk of an adverse event occurring to the patient”, and [0011]: “the memory device may further include instructions that, when executed by the processor, cause the processor to identify at least one data item indicative of a patient support apparatus factor associated with the patient support apparatus, analyze the plurality of data items related to the physiological conditions of the patient, the data item indicative of an environmental factor, and the data item indicative of a patient support apparatus factor to determine the risk of an adverse event occurring to the patient”).
Huster does not teach acquire movement data related to a patient's movement on the patient support apparatus from a sensor that tracks the movement data, acquire position data related to the patient's position relative to the patient support apparatus from a camera that tracks the position data), analyze the movement data and the position data to track data related to patient bed exits from the patient support apparatus to predict a probability of future patient bed exits.
However, Derenne teaches acquire movement data related to a patient's movement on the patient support apparatus from a sensor that tracks the movement data (Derenne, [0091]: “Some software modules 34 are adapted to utilize such thermal images, if available, for carrying out their function. For example, at least one software module 34 that tracks the movement of a patient in a hospital bed is adapted to utilize thermal images to detect the patient's movement while covered with a sheet and/or other bedding. Another software module 34 to capture patient movement in low light conditions that is indicative of the patient's impending, or actual, departure from his or her bed. Still other software modules 34 utilize the infrared image data in other manners and for other purposes”, [0095]: “these algorithms are designed to be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce high resolution images of entire scenes, find similar images from an image database, follow eye movements, recognize scenery and establish markers to overlay scenery with augmented reality, and other tasks”, [0139]: “For example, many healthcare facilities utilize an Admission, Discharge, and Tracking (ADT) computer system for keeping track of its patients. Such systems include information that correlates a patient identification with a specific room number and/or bed bay within a multi-person room. In some embodiments, system 20 is adapted to determine the identification of a patient by querying the ADT system for the identification of the patient corresponding to a specific room and/or bay within the room”, and [0257]: “System 20 also identifies what side a patient is on (left, right, back, front) and tracks how long the patient has been on a particular side. System 20 sends an alert to a caregiver if patient has been on a particular side longer than a predetermined time. Such an alert is forwarded to the caregiver by sending a signal to caregiver alert computer device 64, which is programmed to carry out the alerting process”), acquire position data related to the patient's position relative to the patient support apparatus from a camera that tracks the position data (Derenne, [0091]: “Some software modules 34 are adapted to utilize such thermal images, if available, for carrying out their function. For example, at least one software module 34 that tracks the movement of a patient in a hospital bed is adapted to utilize thermal images to detect the patient's movement while covered with a sheet and/or other bedding. Another software module 34 to capture patient movement in low light conditions that is indicative of the patient's impending, or actual, departure from his or her bed. Still other software modules 34 utilize the infrared image data in other manners and for other purposes”, [0095]: “these algorithms are designed to be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce high resolution images of entire scenes, find similar images from an image database, follow eye movements, recognize scenery and establish markers to overlay scenery with augmented reality, and other tasks”, [0139]: “For example, many healthcare facilities utilize an Admission, Discharge, and Tracking (ADT) computer system for keeping track of its patients. Such systems include information that correlates a patient identification with a specific room number and/or bed bay within a multi-person room. In some embodiments, system 20 is adapted to determine the identification of a patient by querying the ADT system for the identification of the patient corresponding to a specific room and/or bay within the room”, and [0257]: “System 20 also identifies what side a patient is on (left, right, back, front) and tracks how long the patient has been on a particular side. System 20 sends an alert to a caregiver if patient has been on a particular side longer than a predetermined time. Such an alert is forwarded to the caregiver by sending a signal to caregiver alert computer device 64, which is programmed to carry out the alerting process”), analyze the movement data and the position data to track data related to patient bed exits from the patient support apparatus to predict a probability of future patient bed exits (Derenne, [0178]: “ Computer device 24 then seeks to match the three-dimensional pattern of the detected object to the attribute data 44 of a known object that is stored in database 50”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Huster to incorporate the teachings of Derenne and account for utilizing video cameras for monitoring patients, caregivers, equipment, and other items within a room in a caregiver setting, such as a hospital, nursing home, treatment center, or the like (Derenne, Abstract and [0002]).
Huster and Derenne do not teach notify a caregiver when a future patient bed exit is to occur based on the probability of the patient bed exit occurring.
However, DeMazumder teaches notify a caregiver when a future patient bed exit is to occur based on the probability of the patient bed exit occurring (DeMazumder, [0009]: “These signals are delivered into a computer system implementing a learning routine which constructs one or more personalized kinetic state models of positional states for the individual and transitions between the positional states, and further develops one or more customized multi-dimensional prediction models for the individual, and uses the multi-dimensional prediction models to predict behaviors, activities and/or positional changes likely to occur in the future. The system further includes a notification system initiating a notification, alert or warning upon prediction of a behavior, activity or positional change associated with an unsafe or undesired outcome, and transmitting the notification, alert or warning to a recipient associated with the individual”, [0012]: “Sensors can capture data from a plurality of locations having visibility of the individual, so that the learning routine can identify the position of key positional points for the individual in a three-dimensional Cartesian plane using a combination of video or images acquired from said plurality of locations. Further, the kinesthetic activity sensors may capture haptic, tactile, pressure, accelerometric, gyroscopic and/or temperature data from the vicinity of the individual”, [0014]-[0016], [0019]-[0022], and [0050]) .
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Huster and Derenne to incorporate the teachings of DeMazumder and account for effective continuous monitoring of these environments can uniquely improve outcomes by predicting unsafe or undesirable situations and impending adverse and respond to them before harm to persons or property and can save many lives (DeMazumder, Abstract [0002]-[0008]).
Regarding claim 16 Huster further teaches the model for predicting the probability of the future patient bed exits is updated over time based on changes in the patient's condition as received from the communication circuitry (Huster, [0009]-[0011], [0046]: “In some embodiments, the patient support apparatus includes a plurality of sensors, at least one of the plurality of sensors providing a signal including a second data item indicative of a second physiological condition of a patient supported on the patient support apparatus, and the controller is operable to analyze the first data item and second data item collectively to determine a risk of an adverse event occurring to the patient” and [0071]).
Regarding claim 17 Huster further teaches the sensor is positioned in the patient support apparatus (Huster, [0009]: “According to a first aspect of the present disclosure, a patient support apparatus comprises a plurality of sensors, a user interface including a plurality of input devices, a plurality of controllable devices, and a controller electrically coupled to the plurality of sensors and plurality of controllable devices. The controller includes a processor and a memory device electrically coupled to the processor. The memory device includes instructions that, when executed by the processor, cause the processor to process a plurality of data items related to physiological conditions of a patient associated with the patient support apparatus as detected by at least one of the plurality of sensors or the input devices. The processor analyzes the plurality of data items to determine a risk of an adverse event occurring to the patient”, [0032]: “According to a second aspect of the present disclosure, a patient support apparatus comprises at least one sensor, at least one component having multiple states, at least one actuator operable to vary the states of the at least one component, and a controller. The controller is operable to receive an input signal from the at least one sensor, the at least one signal including a first data item indicative of a physiological condition of a patient supported on the patient support apparatus and analyze the of data item to determine a risk of an adverse event occurring to the patient”, [0066]: “The control system 16 of the patient support apparatus 14 includes input devices that provide information to a controller 44 of the control system 16. For example, referring to FIG. 2, frame position sensors 36, siderail position sensors 38, support surface sensors 40, a scale system 42, and caster brake sensors 46 are all in communication with the controller 44. The frame position sensors 36 provide information regarding the position of various components of the patient support apparatus 14. Information provided may include the height of the patient support apparatus 14, the inclination of a head section, the degree of tilt of an upper frame, or any other frame position data that might be available from frame position sensors 36 of the particular patient support apparatus 14”, and [0068]: “The support surface sensors 40 provide information regarding the operation of a support surface 56, such as an inflatable/pneumatic mattress, of the patient support apparatus 14. Such a support surface 56 may be integrated into the frame of the patient support apparatus 14 or may be a separate structure that is operated generally independently of the patient support apparatus 14, but communicates with the controller 44 of the patient support apparatus 14. The support surface sensors 40 may include pressure sensors that identify pressures in particular inflatable structures of the support surface or they may include position sensors. For example, accelerometers positioned in particular locations within the support surface 56 may provide feedback regarding the amount of inclination of a particular section of the support surface 56 relative to gravity, independent of the frame position sensors 36. The support surface sensors 40 may also provide information regarding the degree of lateral rotation of a patient supported on the support surface 56. In addition, the control system 16 includes a support surface pressure control system 54 which is operable to control the pressure in one or more air bladders in the support surface 56”).
Regarding claim 18 Huster does not teaches the camera is at least one of carried by the patient support apparatus or positioned in a room housing the patient support apparatus.
However, Derenne teaches the camera is at least one of carried by the patient support apparatus or positioned in a room housing the patient support apparatus (Derenne, [0003]: “According to various aspects of the systems and methods of the present invention, improved patient care is accomplished through the use of one or more video cameras positioned within a patient's room in order to provide patient care assistance in one or more of a variety of different manners. Such patient care assistance results from the analysis of video camera images that are used for any one or more of the following purposes: preventing patient falls, reducing the chances and/or spread of infection, ensuring patient care protocols are properly executed, and/or monitoring patient activity. Such analysis takes place using one or more computer devices programmed to process and analyze video images. The computer devices are either positioned inside the room of the patient, or located remotely”, [0004]: “According to one embodiment, a monitoring system is provided that includes a camera, a database, and a computer device. The camera is adapted to capture images of at least a portion of a person support apparatus and output image data representative of the images. The database contains fall prevention protocol data that defines one or more conditions that are to be met prior to an occupant exiting the person support apparatus in order to reduce a fall risk of the occupant. The computer device communicates with the camera and the database, and the computer device is adapted to analyze the image data to determine if the one or more conditions have been met”, [0015], and [0019]-[0020]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Huster to incorporate the teachings of Derenne and account for utilizing video cameras for monitoring patients, caregivers, equipment, and other items within a room in a caregiver setting, such as a hospital, nursing home, treatment center, or the like (Derenne, Abstract and [0002]).
Regarding claim 19 Huster does not teach the memory device further includes instructions that, when executed by the processor, acquire additional position data related to a patient's position relative to the patient support apparatus from a real time locating system.
However, Derenne teaches the memory device further includes instructions that, when executed by the processor, acquire additional position data related to a patient's position relative to the patient support apparatus from a real time locating system (Derenne, [0075]: “The Kinect™ motion sensing device automatically detects the position of one or more persons and outputs data indicating the locations of multiple body portions, such as various joints of the person, multiple times a second. Such information is then processed to determine any one or more of the conditions discussed herein”, [0127]: “and/or locations where the individual moves in the room (e.g. visitor who sits in a chair next to the patient's bed)”, [0164]: “the positions of the siderails of the patient's bed, the status of the bed's brakes, the height of the deck of the bed, whether the an exit detection system of the bed is armed, the weight detected by the bed's scale system, a center of gravity of the patient's current location on the bed, movement of the patient on the bed, and the outputs of any patient sensors that are incorporated into the bed (e.g. any vital sign sensors, position sensors, incontinence sensors, interface pressure sensors, etc.)”, [0165]: “In general, such uses include monitoring movement of the patient on patient support apparatus 36 and/or monitoring the status of one or more components of the patient support apparatus 36 (e.g. brakes, height, Fowler angle, etc.)”, and [0169]: “System 20 is adapted to perform a number of general functions regardless of which set of software modules 34 are loaded on computer device 24. These general functions include identifying persons, monitoring their movement, identifying their behavior, recognizing faces, blurring faces in facial data stored in database 50 as part of collected data 56, and issuing notifications, alerts, and/or reminders”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Huster to incorporate the teachings of Derenne and account for utilizing video cameras for monitoring patients, caregivers, equipment, and other items within a room in a caregiver setting, such as a hospital, nursing home, treatment center, or the like (Derenne, Abstract and [0002]).
Regarding claim 20 Huster does not teaches the real time locating system is at least one of carried by the patient support apparatus or positioned in a room housing the patient support apparatus.
However, Derenne teach the real time locating system is at least one of carried by the patient support apparatus or positioned in a room housing the patient support apparatus (Derenne, [0164]: “Such data includes a variety of different data regarding the status of the bed and/or the status of the patient, such as, but not limited to: the positions of the siderails of the patient's bed, the status of the bed's brakes, the height of the deck of the bed, whether the an exit detection system of the bed is armed, the weight detected by the bed's scale system, a center of gravity of the patient's current location on the bed, movement of the patient on the bed, and the outputs of any patient sensors that are incorporated into the bed (e.g. any vital sign sensors, position sensors, incontinence sensors, interface pressure sensors, etc.)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Huster to incorporate the teachings of Derenne and account for utilizing video cameras for monitoring patients, caregivers, equipment, and other items within a room in a caregiver setting, such as a hospital, nursing home, treatment center, or the like (Derenne, Abstract and [0002]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RACHAEL SOJIN STONE whose telephone number is (571)272-8798. The examiner can normally be reached Monday-Friday 7 AM - 7 PM (EST).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Choi can be reached at (469) 295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/R.S.S./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
Prosecution Insights