DETAILED ACTION
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 .
Election/Restrictions
Applicant’s election without traverse of Group II (Claims 11-12, 18-19, 21-22, 24-30, and 32) in the reply filed on 8/26/2025 is acknowledged.
Claims 1-6 withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 8/26/2025.
Status of the Application
Claims 11-12, 18-19, 21-22, 24-30, 32, and 37-38 have been examined in this application. Claims 37-38 are newly added. Claims 1-10 have been canceled. This communication is a Final Rejection in response to Applicant’s “Amendment/Remarks” filed 12/17/2025.
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.
Claim(s) 11-12, 18-19, 21, 24-30, 32, 37, and 38 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 2016/0128610 to Kostic et al. (hereinafter Kostic) in view of U.S. Patent 2016/0140307 to Brosnan et al. (hereinafter Brosnan) in further view of European Patent Application EP 2701131 A2 to Meger et al. (hereinafter Meger).
Regarding claim 11, Kostic teaches: A patient support apparatus system comprising: a patient support apparatus (see Fig. 1, person support apparatus 20) comprising:
(a) a frame (see Fig. 1, litter frame 28);
(b) a support surface (see Fig. 1, support deck 30) adapted to a support a patient thereon;
(c) a sensor (see Fig. 4, exit detection system 56 with force sensors 60);
(d) a controller (see Fig. 4, controller 58) adapted to take multiple sets of readings from the sensor and record the sets of readings, each set of the multiple sets of readings including readings taken both before and after an occurrence of an event associated with the patient support apparatus (see paras [0065-0067]).
Kostic, however, does not explicitly teach the following: (e) a transceiver; and a computing device positioned remotely from the patient support apparatus and in communication with the transceiver, the computing device adapted to receive the sets of readings from the patient support apparatus and to analyze the sets of readings to determine an algorithm for predicting a future occurrence of the event using future readings from the sensor to determine a reliability of the algorithm for predicting future occurrences of the event and, if the reliability exceeds a threshold, to use the algorithm to predict the future occurrence of the event.
Brosnan teaches: (e) a transceiver (see Fig. 2, transceiver 60); and a computing device (see Fig. 5, healthcare facility network 72 and server 90) positioned remotely from the patient support apparatus and in communication with the transceiver (see Fig. 5), the computing device adapted to receive the sets of readings from the patient support apparatus and to analyze the sets of readings to determine an algorithm for predicting a future occurrence of the event using future readings from the sensor (see para [0009-0013, 0021-0022, 0116, 0131]: discussion of sending sensor data/signals to a network service to determine information about a patient’s condition as well as sending alarm conditions to health care facility network).
Kostic and Brosnan are both considered to be analogous to the claimed invention because they are the same field of medical devices/beds systems utilizing remote care systems. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified the teachings of Kostic with these aforementioned teachings of Brosnan in order to add a transceiver to remotely communicate data obtained from bed sensors to a remote computing device as taught by Borsnan on the existing device of Kostic (which does contemplate sending out an alert over wireless networks) with a reasonable expectation of success so that the network service may provide information, algorithms, data processing, and/or other features for the medical apparatus that relate to such features as: monitoring patient movement (including turns), providing patient care assessments, implementing a patient care protocol, monitoring maintenance needs, analyzing sensor data, and implementing an exit alert system (see Brosnan, para [0021-0022]).
Kostic and Brosnan fail to teach: to determine a reliability of the algorithm for predicting future occurrences of the event and, if the reliability exceeds a threshold, to use the algorithm to predict the future occurrence of the event.
Meger, however, teaches: to determine a reliability of the algorithm for predicting future occurrences of the event and, if the reliability exceeds a threshold, to use the algorithm to predict the future occurrence of the event (see Meger, paras [0165, 0167, 0169-0172]: describes a system that assigns a “confidence level” to a given clinical parameter [e.g. bed exit / respiratory rate | etc.] and limiting the generation of an alarm to only if the average confidence level is over a given threshold).
Kostic, Brosnan, and Meger are all considered to be analogous to the claimed invention because they are the same field of medical devices/beds systems utilizing remote care systems. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified the teachings of Kostic and Brosnan with these aforementioned teachings of Meger to provide added capabilities to the computing device such as algorithm reliability determination on the existing devices of Kostic and Brosnan with a reasonable expectation of success so to reduce the occurrence of false alarms being generated by the algorithm (see Meger, para [0165, 0167-0172]).
Regarding claim 12, Kostic as modified teaches all the limitations as described in the rejection of claim 11, and additionally Kostic teaches: at least one of the following is true:
(a) the event is the patient exiting from the support surface and the sensor comprises a plurality of force sensors (see Fig. 4, force sensors 60) adapted to detect downward forces exerted on the support surface (see para [0054]);
(b) the event is the patient contracting ventilator associated pneumonia, the support surface includes a backrest section adapted to pivot over a range of orientations, and the sensor is adapted to measure an angular orientation of the backrest section; or
(c) the event is the patient contracting a bed sore, patient support apparatus further comprises an inflatable bladder in communication with the controller and adapted to administer a therapy function to the patient, and the sensor is adapted to measure at least one of a duration, a frequency, or a rotational angle related to the therapy function (see Fig. 9A and para [0059, 0101]: use of turn sensor 66d to detect a turn angle 74 based on inflation pressures used to mitigate bed sore development).
Regarding claim 18, Kostic as modified teaches all the limitations as described in the rejection of claim 11, and additionally Kostic teaches: wherein the controller is further adapted to execute a function using the sensor and a second algorithm (see para [0067] and Fig. 5, exit detection algorithm 68, function may be issuing an alert 96 or adjusting pivot/tilt or siderail positions).
Regarding claim 19, Kostic as modified teaches all the limitations as described in the rejection of claim 18, and additionally Kostic teaches: further comprising a plurality of additional sensors (see Fig. 4, pivot sensor 66a, side rail sensor 66b, tilt sensor 66c, turn sensor 66d, height sensor 66e, position sensor 66f), wherein the controller is adapted to perform the following: receive an additional set of readings from the additional sensors, record the additional set of readings, and transmit the additional set of readings to the computing device (see para [0078]), receive an improved second algorithm back from the computing device (see para [0078]), and use the improved second algorithm when performing the function, wherein the improved second algorithm uses readings from at least one of the plurality of additional sensors (see para [0068, 0075, 0100], algorithm 68 can be adjusted based on information from the additional sensors 66a-f, see also “compensation factor”).
Regarding claim 21, Kostic as modified teaches all the limitations as described in the rejection of claim 19, and additionally Kostic teaches: wherein the plurality of additional sensors includes at least two of the following: a siderail sensor adapted to detect a position of a siderail of the patient support apparatus (see Fig. 4, side rail sensor 66b, see para [0056]), a first angle sensor adapted to detect an angle of a pivotable backrest section of the patient support apparatus (see Fig. 4, pivot sensor 66a, see para [0055]), a second angle sensor adapted to detect an angle of a litter frame on which the support surface is supported (see Fig. 4, tilt sensor 66c, see para [0057]), a light sensor adapted to detect an amount of ambient light in a room in which the patient support apparatus is positioned, a sound sensor adapted to detect an amount of sound in the room in which the patient support apparatus is positioned, or a clock adapted to detect a current time.
Regarding claim 24, Kostic as modified teaches all the limitations as described in the rejection of claim 11, and additionally Kostic teaches: wherein the controller is further adapted to perform a function of the patient support apparatus when a control is activated by a user (see para [0064]: user interface 62 can turn exit detection system 56 on and off), the function being performed in a plurality of different manners based upon a setting selectable by the user (see para [0064]: “use can select different sensitivity levels or zones” or “different types of alerts”), wherein the event is the selection of the setting by the user, and wherein the algorithm is adapted to predict a future setting in response to the user activating the control (see para [0063-0064 and 0117]: discussing “compensation factors…without having to reset the exit detection system”).
Regarding claim 25, Kostic teaches: A patient support apparatus system comprising: [a plurality of patient support apparatuses] each one of the plurality of patient support apparatuses (see Fig. 1, person support apparatus 20) comprising:
(a) a frame (see Fig. 1, litter frame 28);
(b) a support surface (see Fig. 1, support deck 30) adapted to a support a patient thereon;
(c) a plurality of sensors (see Fig. 4, exit detection system 56 with multiple force sensors 60);
(d) a controller (see Fig. 4, controller 58) adapted to take a set of readings from the plurality of sensors (see Fig. 4, force sensors 60) and use a first subset of the set of readings in an algorithm for performing a function of the patient support apparatus, the first subset excluding readings from at least one sensor in the plurality of sensors (see para [0064]: different zones or sensitively levels of the exit detection system 56 can be set via user interface 62, thus certain sensors may be ignored), wherein the controller is further adapted to record the set of readings (see paras [0065-0067]).
Kostic, however, does not explicitly teach the following: a plurality of patient support apparatuses, AND (e) a transceiver; and a computing device positioned remotely from the patient support apparatus and in communication with the transceiver, the computing device adapted to receive the sets of readings from the plurality of patient support apparatuses and to analyze the sets of readings to determine an improved algorithm for use by each of the plurality of patient support apparatuses when performing the function the computing device further adapted to determine a reliability of the improved algorithm and, if the reliability exceeds a threshold, to transmit the improved algorithm to the plurality of patient support apparatuses for use when subsequently performing the function.
Brosnan teaches: a plurality of patient support apparatuses (see Brosnan, para [0130]: “multiple patient support apparatuses” are contemplated), (e) a transceiver (see Fig. 2, transceiver 60); and a computing device (see Fig. 5, healthcare facility network 72 and server 90) positioned remotely from the patient support apparatus and in communication with the transceiver (see Fig. 5), the computing device adapted to receive the sets of readings from the plurality of patient support apparatuses and to analyze the sets of readings to determine an improved algorithm for use by each of the plurality of patient support apparatuses when performing the function (see para [0009-0013, 0021-0022, 0116, 0131]: discussion of sending sensor data/signals to a network service to determine information about a patient’s condition as well as sending alarm conditions to health care facility network).
Kostic and Brosnan are both considered to be analogous to the claimed invention because they are the same field of medical devices/beds systems utilizing remote care systems. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified the teachings of Kostic with these aforementioned teachings of Brosnan in order to add a transceiver to remotely communicate data obtained from bed sensors to a remote computing device as taught by Borsnan for multiple patient support apparatuses on the existing device of Kostic (which does contemplate sending out an alert over wireless networks) with a reasonable expectation of success so that the network service may provide information, algorithms, data processing, and/or other features for the medical apparatus that relate to such features as: monitoring patient movement (including turns), providing patient care assessments, implementing a patient care protocol, monitoring maintenance needs, analyzing sensor data, and implementing an exit alert system (see Brosnan, para [0021-0022]).
Kostic and Brosnan fail to teach: the computing device further adapted to determine a reliability of the improved algorithm and, if the reliability exceeds a threshold, to transmit the improved algorithm to the plurality of patient support apparatuses for use when subsequently performing the function.
Meger, however, teaches: the computing device further adapted to determine a reliability of the improved algorithm and, if the reliability exceeds a threshold, to transmit the improved algorithm to the plurality of patient support apparatuses for use when subsequently performing the function (see Meger, paras [0165, 0167, 0169-0172]: describes a system that assigns a “confidence level” to a given clinical parameter [e.g. bed exit / respiratory rate | etc.] and limiting the generation of an alarm to only if the average confidence level is over a given threshold).
Kostic, Brosnan, and Meger are all considered to be analogous to the claimed invention because they are the same field of medical devices/beds systems utilizing remote care systems. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified the teachings of Kostic and Brosnan with these aforementioned teachings of Meger to provide added capabilities to the computing device such as algorithm reliability determination on the existing devices of Kostic and Brosnan with a reasonable expectation of success so to reduce the occurrence of false alarms being generated by the algorithm (see Meger, para [0165, 0167-0172]).
Regarding claim 26, Kostic as modified teaches all the limitations as described in the rejection of claim 25, however, Kostic does not explicitly teach: wherein the controller is further adapted to receive the improved algorithm back from the computing device and to use the improved algorithm when performing the function, wherein the improved algorithm uses a second subset of the readings from the plurality of sensors, the second subset being different from the first subset.
Brosnan teaches: wherein the controller is further adapted to receive the improved algorithm back from the computing device and to use the improved algorithm when performing the function, wherein the improved algorithm uses a second subset of the readings from the plurality of sensors, the second subset being different from the first subset (see Brosnan, para [0021-0022]).
Regarding claim 27, Kostic as modified teaches all the limitations as described in the rejection of claim 26 however, Kostic does not explicitly teach: wherein the second subset of the readings does not exclude readings from the at least one sensor in the plurality of sensors.
Brosnan teaches: wherein the second subset of the readings does not exclude readings from the at least one sensor in the plurality of sensors (Brosnan doesn’t specifically note any exclusions made from any sensors in the sent data to the network service).
Regarding claim 28, Kostic as modified teaches all the limitations as described in the rejection of claim 26, and Kostic also teaches: wherein the first subset includes at least one reading from a sensor not included in the second subset (see Fig. 4, other sensors 66a-e, para [0055-0062], may provide data not included in the first subset, e.g. force sensors 60).
Regarding claim 29, Kostic as modified teaches all the limitations as described in the rejection of claim 26, and Kostic also teaches: wherein the support surface includes a backrest section (see Fig. 1, head section 42) adapted to pivot over a range of orientations (see para [0049]: “pivotable about a generally horizontal pivot axis between a generally horizontal orientation (not shown in FIG. 1) and a plurality of raised positions”), the function is a lockout function adapted to prevent pivoting of the backrest section below a specific angle (see para [0081, 0086]), and the first subset includes readings from an angle sensor (see Fig. 4, 66a pivot sensor) adapted to detect an angular orientation of the backrest section (see para [0055]).
Regarding claim 30, Kostic as modified teaches all the limitations as described in the rejection of claim 26, and Kostic also teaches: wherein each of the plurality of patient support apparatuses further comprises an inflatable bladder (see Fig. 9/9a, air bladders within mattress 76) in communication with the controller (connected via turn sensor 66d, see Fig. 4), and wherein the function is a therapy function administered by the inflatable bladder (see para [0101]), the first subset includes readings from an angle sensor (see Fig. 4, turn sensor 66d) adapted to detect an rotational angle (see Fig. 9A, turn angle 74) related to the therapy function, and the set of readings includes readings taken from at least one of a scale system adapted to detect a weight of the patient (see Fig. 4, force sensors 60, see para [0054]) or a timer adapted to measure an amount of time since the therapy function was previously performed.
Regarding claim 32, Kostic as modified teaches all the limitations as described in the rejection of claim 26, and Kostic also teaches: wherein the function is an exit alert issued when the patient moves toward exiting from the support surface (see para [0053]: “exit detection system 56 adapted to determine when a patient is likely to exit the person support apparatus”), the first subset includes reading from a plurality of force sensors (see Fig. 4, force sensors 60) adapted to detect downward forces exerted on the support surface (see para [0054]), and the plurality of sensors includes at least two of the following: a siderail sensor adapted to detect a position of a siderail of a respective patient support apparatus (see Fig. 4, side rail sensor 66b, see para [0056]), an angle sensor adapted to detect an angle of a pivotable backrest section of the respective patient support apparatus (see Fig. 4, pivot sensor 66a, see para [0055]), an angle sensor adapted to detect an angle of a litter frame on which the support surface is supported (see Fig. 4, tilt sensor 66c, see para [0057]), a light sensor adapted to detect an amount of ambient light in a room in which the respective patient support apparatus is positioned, a sound sensor adapted to detect an amount of sound in the room in which the respective patient support apparatus is positioned, or a clock adapted to detect a current time.
Regarding claim 37, Kostic as modified teaches all the limitations as described in the rejection of claim 11, however, Kostic does not teach: wherein the threshold is user-adjustable.
Meger, however, teaches: wherein the threshold is user-adjustable (see at least para [0046]: clinician can input information through a user interface 24 regarding whether the subject is treated with beta blocker medication thereby adjusting a baseline heart rate threshold sensed vs. provision of alerts).
Kostic, Brosnan, and Meger are all considered to be analogous to the claimed invention because they are the same field of medical devices/beds systems utilizing remote care systems. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified the teachings of Kostic and Brosnan with these aforementioned teachings of Meger to provide added user-adjustable threshold changeability to computing device such as algorithm reliability determination on the existing devices of Kostic and Brosnan with a reasonable expectation of success so to reduce the occurrence of false alarms being generated by the algorithm in certain patient populations on medications, e.g. beta blockers (see Meger, para [0046]).
Regarding claim 38, Kostic as modified teaches all the limitations as described in the rejection of claim 25, however, Kostic does not teach: wherein the threshold is user-adjustable.
Meger, however, teaches: wherein the threshold is user-adjustable (see at least para [0046]: clinician can input information through a user interface 24 regarding whether the subject is treated with beta blocker medication thereby adjusting a baseline heart rate threshold sensed vs. provision of alerts).
Kostic, Brosnan, and Meger are all considered to be analogous to the claimed invention because they are the same field of medical devices/beds systems utilizing remote care systems. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified the teachings of Kostic and Brosnan with these aforementioned teachings of Meger to provide added user-adjustable threshold changeability to computing device such as algorithm reliability determination on the existing devices of Kostic and Brosnan with a reasonable expectation of success so to reduce the occurrence of false alarms being generated by the algorithm in certain patient populations on medications, e.g. beta blockers (see Meger, para [0046]).
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 2016/0128610 to Kostic in view of U.S. Patent 2016/0140307 to Brosnan in view of European Patent Application EP 2701131 A2 to Meger in further view of U.S. Patent Application Publication 2021/0202091 to Receveur et al. (hereinafter Receveur).
Regarding claim 22, Kostic as modified teaches all the limitations as described in the rejection of claim 19, however does not explicitly teach the following: wherein the computing device is adapted to use a neural network to generate the improved second algorithm, and the computing device is further adapted to use at least two of the following as inputs into the neural network: a patient fall history, a patient weight, a patient age, a patient fall risk assessment, a time of day, an amount of time since the patient last exited from the patient support apparatus, and a calendar date.
Receveur teaches: wherein the computing device is adapted to use a neural network to generate the improved second algorithm (see paras [0002, 0061] and Fig. 8), and the computing device is further adapted to use at least two of the following as inputs into the neural network (see para [0061]: using “response variable data”): a patient fall history (see para [0060]: “fall score”), a patient weight (see para [0049, 0056]), a patient age, a patient fall risk assessment (see para [0003]: one or more of these claimed inputs fall under “condition of the patient” which is contemplated as a data point), a time of day, an amount of time since the patient last exited from the patient support apparatus (see para [0060]: bed exist determination), and a calendar date.
Kostic, Brosnan, Meger, and Receveur are both considered to be analogous to the claimed invention because they are the same field of medical devices/beds systems utilizing remote care systems. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified the teachings of Kostic, Brosnan, and Meger with these aforementioned teachings of Receveur in order utilize a neural network to compare actual patient data with a candidate inference data with a reasonable expectation of success so that the actual condition of the patient can be determined with more accuracy to provide improved care (see Receveur, para [0002-0003]).
Response to Arguments
Applicant’s arguments, filed 12/17/2025, with respect to the rejection(s) of claim(s) 11-12, 18-19, 21-22, 24-30, 32 rejected under 35 U.S.C 103 have been fully considered and are persuasive. Therefore, the previous rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of European Patent Application EP 2701131 A2 to Meger to teach the newly introduced amendments/limitations. See above rejections for details.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID R HARE whose telephone number is (571)272-4420. The examiner can normally be reached MON-FRI 8:00 AM-5:00 PM EST.
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Sincerely,
/DAVID R HARE/
Primary Examiner, Art Unit 3673
1/9/2026
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