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 the Application
Claims 97-116 have been examined in this application. Claim 116 has been added. This communication is Final Rejection in response to Applicant’s “Amendments/Remarks” filed on 02/02/2026.
Claim Objections
The claim objections made in the Non-Final Rejection on 10/01/2025 are withdrawn in light of the amendment to the claims filed on 02/02/2026.
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) 97-116 are rejected under 35 U.S.C. 103 as being unpatentable by Streeter (U.S. Pub. No. 2019/0000701) in further view of Squitieri (U.S. Pub. No. 2021/0361501).
Regarding claim 97,
Streeter teaches (Currently Amended) A cushion system comprising: a cushion component including a plurality of bladders (Streeter: [0006] pneumatic cushion include interconnected bladders); a control component including a pneumatic subsystem and an electrical subsystem (Streeter: [0198] a controller may make such determinations based on trends of the data received from a sensor 250), the pneumatic subsystem being pneumatically coupled to the plurality of bladders and including a bladder-specific pressure transducer for each bladder of the plurality of bladders (Streeter: FIG. 25 [0444] monitored pressure over time in each actuator 16, or actuator channel 520 as a proxy measurement to estimate the amount of fluid in, or the height of each actuator); and pneumatic lines including (Streeter: FIG. 14 [0444] actuator channel 520), a bladder-specific pneumatic line that pneumatically couples a bladder of the plurality of bladders to a corresponding bladder specific pressure transducer wherein the bladder-specific pressure transducers are remote from the plurality of bladders (Streeter: FIG. 34 [0203] pressure transducer may be included in the manifold 518); within the bladder from a measured pressure that is generated in response to a signal received from the bladder-specific pressure transducer that corresponds to the bladder (Streeter: FIG. 34 [0203] pressure transducer may be included in the manifold 518 in addition to a pressure transducer for each actuator channel 520).
Streeter does not teach wherein the electrical subsystem is configured to predict an actual pressure.
Squitieri teaches wherein the electrical subsystem is configured to predict an actual pressure (Squitieri: [0090] processor 702 may apply algorithms designed for temporal aligning, artifact removal, and the like).
It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify Streeter in view of Squitieri directed to including inflatable chamber machine with a machine learning algorithm to obtain pressure mitigation data. A person having ordinary skill in the art would have been motivated to make this modification in order to vary the pressure of the inflatable chambers to move the point of pressure applied to the surface to a different region (Squitieri: [0030]).
Regarding claim 98,
Streeter teaches (Previously Presented) The cushion system of claim 97, the predictor system configured to predict the actual pressure within the bladder in response to receiving 1) a measured pressure at a time a prediction request is made (Streeter: FIG. 25 [0444] monitored pressure over time in each actuator 16, or actuator channel 520 as a proxy measurement to estimate the amount of fluid in, or the height of each actuator), 2) a measured pressure prior to a start of a current pump or vent operation (Streeter: FIG. 25 [0444] monitored pressure over time in each actuator 16, or actuator channel 520 as a proxy measurement to estimate the amount of fluid in, or the height of each actuator), and 3) how much time has elapsed between the start of the current pump or vent operation and the time the prediction request was made (Streeter: FIG. 34 [0446] control logic for determining the time between pulses Δt may be a function of an error parameter E, e.g. a measurement of how far from the desired pressure set point or range the actuator 16 pressure).
Streeter does not teach wherein the electrical subsystem includes a predictor system configured within a microcontroller of the electrical subsystem.
Squitieri teaches wherein the electrical subsystem includes a predictor system configured within a microcontroller of the electrical subsystem (Squitieri: [0090] processor 702 may apply algorithms designed for temporal aligning, artifact removal, and the like).
It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify Streeter in view of Squitieri directed to including inflatable chamber machine with a machine learning algorithm to obtain pressure mitigation data. A person having ordinary skill in the art would have been motivated to make this modification in order to vary the pressure of the inflatable chambers to move the point of pressure applied to the surface to a different region (Squitieri: [0030]).
Regarding claim 99,
Streeter teaches (Previously Presented) The cushion system of claim 98.
Streeter, as modified, does not teach wherein the predictor system includes a bladder-specific set of weights for each bladder of the plurality of bladders.
Squitieri teaches wherein the predictor system includes a bladder-specific set of weights for each bladder of the plurality of bladders (Squitieri: [0110] ML algorithm may derive, compute, or otherwise obtain a heuristic that indicates how the pressure of chambers in a pressure-mitigation device relates to the weight of the individual positioned).
It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify Streeter in view of Squitieri directed to including inflatable chamber machine with a machine learning algorithm to obtain pressure mitigation data. A person having ordinary skill in the art would have been motivated to make this modification in order to vary the pressure of the inflatable chambers to move the point of pressure applied to the surface to a different region (Squitieri: [0030]).
Regarding claim 100,
Streeter teaches (Previously Presented) The cushion system of claim 99.
Streeter does not teach wherein the bladder-specific set of weights for each bladder of the plurality of bladders includes two separate sets of weights, one set of weights for use in a pump operation, and one set of weights for use in a vent operation.
Squitieri teaches wherein the bladder-specific set of weights for each bladder of the plurality of bladders includes two separate sets of weights, one set of weights for use in a pump operation, and one set of weights for use in a vent operation (Squitieri: [0461] leak compensation mode may, in some embodiments, be referred to as a closed-loop system, where monitoring, inflating and deflating may be automatic based on pre-set/pre-determined values, e.g. the baseline pressure, pressure set point or range and/or error threshold).
It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify Streeter in view of Squitieri directed to including inflatable chamber machine with a machine learning algorithm to obtain pressure mitigation data. A person having ordinary skill in the art would have been motivated to make this modification in order to vary the pressure of the inflatable chambers to move the point of pressure applied to the surface to a different region (Squitieri: [0030]).
Regarding claim 101,
Streeter teaches (Previously Presented) The cushion system of claim 97, the predictor system configured to implement a polynomial function to predict the actual pressure from the measured pressure (Streeter: [0451] it should be understood by those skilled in the art that the relationship between the time between pulses Δt and the error parameter E could take many forms including a linear function, a quadratic function, a cubic function or any other similar polynomial function).
Streeter, as modified, does not teach wherein the electrical subsystem includes a predictor system configured within a microcontroller of the electrical subsystem.
Squitieri teaches wherein the electrical subsystem includes a predictor system configured within a microcontroller of the electrical subsystem (Squitieri: [0090] processor 702 may apply algorithms designed for temporal aligning, artifact removal, and the like).
It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify Streeter in view of Squitieri directed to including inflatable chamber machine with a machine learning algorithm to obtain pressure mitigation data. A person having ordinary skill in the art would have been motivated to make this modification in order to vary the pressure of the inflatable chambers to move the point of pressure applied to the surface to a different region (Squitieri: [0030]).
Regarding claim 102,
Streeter teaches (Previously Presented) The cushion system of claim 101, wherein the predictor system is configured to determine a set of coefficients of the polynomial function in response to feedback from an external signal (Streeter: [0451] it should be understood by those skilled in the art that the relationship between the time between pulses Δt and the error parameter E could take many forms including a linear function, a quadratic function, a cubic function or any other similar polynomial function).
Regarding claim 103,
Streeter teaches (Currently Amended) A method comprising: receiving a signal from a bladder-specific pressure transducer that is pneumatically coupled to a bladder of a cushion system that includes a plurality of bladders and a plurality of bladder-specific pressure transducers (Streeter: FIG. 34 [0203] pressure transducer may be included in the manifold 518), wherein the bladder-specific pressure transducer is pneumatically coupled to the bladder via a bladder-specific pneumatic line (Streeter: FIG. 25 [0444] monitored pressure over time in each actuator 16, or actuator channel 520 as a proxy measurement to estimate the amount of fluid in, or the height of each actuator), and wherein the bladder-specific pressure transducers are remote from the plurality of bladders (Streeter: FIG. 34 [0203] pressure transducer may be included in the manifold 518);
Streeter does not teach generating a measured pressure from the received signal; and predicting an actual pressure within the bladder in response to the measured pressure.
Squitieri teaches generating a measured pressure from the received signal; and predicting an actual pressure within the bladder in response to the measured pressure (Squitieri: [0110] ML algorithm may derive, compute, or otherwise obtain a heuristic that indicates how the pressure of chambers in a pressure-mitigation device relates to the weight of the individual positioned).
It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify Streeter in view of Squitieri directed to including inflatable chamber machine with a machine learning algorithm to obtain pressure mitigation data. A person having ordinary skill in the art would have been motivated to make this modification in order to vary the pressure of the inflatable chambers to move the point of pressure applied to the surface to a different region (Squitieri: [0030]).
Regarding claim 104,
Streeter teaches (Previously Presented) The method of claim 103, the predictor system configured to predict the actual pressure within the bladder in response to receiving 1) a measured pressure at a time a prediction request is made (Streeter: FIG. 25 [0444] monitored pressure over time in each actuator 16, or actuator channel 520 as a proxy measurement to estimate the amount of fluid in, or the height of each actuator), 2) a measured pressure prior to a start of a current pump or vent operation (Streeter: FIG. 25 [0444] monitored pressure over time in each actuator 16, or actuator channel 520 as a proxy measurement to estimate the amount of fluid in, or the height of each actuator, and 3) how much time has elapsed between the start of the current pump or vent operation and the time the prediction request was made (Streeter: FIG. 34 [0446] control logic for determining the time between pulses Δt may be a function of an error parameter E, e.g. a measurement of how far from the desired pressure set point or range the actuator 16 pressure).
Streeter, as modified, does not teach wherein the electrical subsystem includes a predictor system configured within a microcontroller of the electrical subsystem.
Squitieri teaches wherein the electrical subsystem includes a predictor system configured within a microcontroller of the electrical subsystem (Squitieri: [0110] ML algorithm may derive, compute, or otherwise obtain a heuristic that indicates how the pressure of chambers in a pressure-mitigation device relates to the weight of the individual positioned).
It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify Streeter in view of Squitieri directed to including inflatable chamber machine with a machine learning algorithm to obtain pressure mitigation data. A person having ordinary skill in the art would have been motivated to make this modification in order to vary the pressure of the inflatable chambers to move the point of pressure applied to the surface to a different region (Squitieri: [0030]).
Regarding claim 105,
Streeter teaches (Previously Presented) The method of claim 104.
Streeter, as modified, does not teach wherein the predictor system includes a bladder-specific set of weights for each bladder of the plurality of bladders.
Squitieri teaches wherein the predictor system includes a bladder-specific set of weights for each bladder of the plurality of bladders (Squitieri: [0110] ML algorithm may derive, compute, or otherwise obtain a heuristic that indicates how the pressure of chambers in a pressure-mitigation device relates to the weight of the individual positioned).
It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify Streeter in view of Squitieri directed to including inflatable chamber machine with a machine learning algorithm to obtain pressure mitigation data. A person having ordinary skill in the art would have been motivated to make this modification in order to vary the pressure of the inflatable chambers to move the point of pressure applied to the surface to a different region (Squitieri: [0030]).
Regarding claim 106,
Streeter teaches (Previously Presented) The method of claim 105, wherein the bladder-specific set of weights for each bladder of the plurality of bladders includes two separate sets of weights, one set of weights for use in a pump operation, and one set of weights for use in a vent operation (Streeter: [0436] value chosen for the threshold may depend on the type of pump being used… processor may continue commanding the pump to add or remove fluid from the actuator(s) it is in communication with).
Regarding claim 107,
Streeter teaches (Previously Presented) The method of claim 103.
Streeter, as modified, does not teach wherein predicting an actual pressure within the bladder in response to the measured pressure involves applying a machine learning technique to a sequence of pressure measurements associated with the bladder.
Squitieri teaches wherein predicting an actual pressure within the bladder in response to the measured pressure involves applying a machine learning technique to a sequence of pressure measurements associated with the bladder (Squitieri: [0090] processor 702 may apply algorithms designed for temporal aligning, artifact removal, and the like. In other embodiments, the analysis module 706 is designed to analyze the pressure data in its unprocessed (i.e., raw) form. As further discussed below, the processor 702 may forward at least some of the pressure data, in either its processed or unprocessed form, to the communication module 704 for transmittal to another computing device for analysis).
It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify Streeter in view of Squitieri directed to including inflatable chamber machine with a machine learning algorithm to obtain pressure mitigation data. A person having ordinary skill in the art would have been motivated to make this modification in order to vary the pressure of the inflatable chambers to move the point of pressure applied to the surface to a different region (Squitieri: [0030]).
Regarding claim 108,
Streeter teaches (Previously Presented) The method of claim 103, wherein predicting an actual pressure within the bladder in response to the measured pressure involves implementing a polynomial function to predict the actual pressure from the measured pressure (Streeter: [0077] automatic high pressure detection algorithm was used to synthesize a list of actuators which required offloading based on a calculated threshold).
Regarding claim 109,
Streeter teaches (Previously Presented) The method of claim 108, wherein predicting an actual pressure within the bladder in response to the measured pressure involves determining a set of coefficients of the polynomial function in response to feedback from an external signal (Streeter: [0451] it should be understood by those skilled in the art that the relationship between the time between pulses Δt and the error parameter E could take many forms including a linear function, a quadratic function, a cubic function or any other similar polynomial function).
Regarding claim 110,
Streeter teaches (Currently Amended) A control component for a cushion system, the control component comprising: a pneumatic subsystem (Streeter: [0198] a controller may make such determinations based on trends of the data received from a sensor 250); and an electrical subsystem (Streeter: FIG. 25 [0444] monitored pressure over time in each actuator 16, or actuator channel 520 as a proxy measurement to estimate the amount of fluid in, or the height of each actuator); the pneumatic subsystem including a plurality of couplers (Streeter: FIG. 48 [0272] the tubing 1068) configured to provide a connection to pneumatic lines that pneumatically connect a plurality of bladders of a cushion component of the cushion system to the pneumatic subsystem (Streeter: FIG. 48 [0272] the tubing 1068 to be more easily routed for the pneumatic system. The connection ports 1066 may also be disposed in any other suitable configuration), and including a bladder-specific pressure transducer for each bladder of the plurality of bladders (Streeter: [0006] pneumatic cushion include interconnected bladders), wherein the bladder-specific pressure transducers are remote from the plurality of bladders (Streeter: FIG. 34 [0203] pressure transducer may be included in the manifold 518); within a bladder from a measured pressure that is generated in response to a signal received from a bladder-specific pressure transducer that corresponds to the bladder (Streeter: FIG. 34 [0203] pressure transducer may be included in the manifold 518 in addition to a pressure transducer for each actuator channel 520).
Streeter does not teach wherein the electrical subsystem is configured to predict an actual pressure.
Squitieri teach wherein the electrical subsystem is configured to predict an actual pressure (Squitieri: [0090] processor 702 may apply algorithms designed for temporal aligning, artifact removal, and the like).
It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify Streeter in view of Squitieri directed to including inflatable chamber machine with a machine learning algorithm to obtain pressure mitigation data. A person having ordinary skill in the art would have been motivated to make this modification in order to vary the pressure of the inflatable chambers to move the point of pressure applied to the surface to a different region (Squitieri: [0030]).
Regarding claim 111,
Streeter teaches (Previously Presented) The control component of claim 110, the predictor system configured to predict the actual pressure within the bladder in response to receiving 1) a measured pressure at a time a prediction request is made (Streeter: FIG. 25 [0444] monitored pressure over time in each actuator 16, or actuator channel 520 as a proxy measurement to estimate the amount of fluid in, or the height of each actuator), 2) a measured pressure prior to a start of a current pump or vent operation (Streeter: FIG. 25 [0444] monitored pressure over time in each actuator 16, or actuator channel 520 as a proxy measurement to estimate the amount of fluid in, or the height of each actuator), and 3) how much time has elapsed between the start of the current pump or vent operation and the time the prediction request was made (Streeter: FIG. 34 [0446] control logic for determining the time between pulses Δt may be a function of an error parameter E, e.g. a measurement of how far from the desired pressure set point or range the actuator 16 pressure).
Streeter, as modified, does not teach wherein the electrical subsystem includes a predictor system configured within a microcontroller of the electrical subsystem.
Squitieri teaches wherein the electrical subsystem includes a predictor system configured within a microcontroller of the electrical subsystem (Squitieri: [0090] processor 702 may apply algorithms designed for temporal aligning, artifact removal, and the like).
It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify Streeter in view of Squitieri directed to including inflatable chamber machine with a machine learning algorithm to obtain pressure mitigation data. A person having ordinary skill in the art would have been motivated to make this modification in order to vary the pressure of the inflatable chambers to move the point of pressure applied to the surface to a different region (Squitieri: [0030]).
Regarding claim 112,
Streeter teaches (Previously Presented) The control component of claim 111.
Streeter, as modified, does not teach wherein the predictor system includes a bladder-specific set of weights for each bladder of the plurality of bladders.
Squitieri teaches wherein the predictor system includes a bladder-specific set of weights for each bladder of the plurality of bladders (Squitieri: [0110] ML algorithm may derive, compute, or otherwise obtain a heuristic that indicates how the pressure of chambers in a pressure-mitigation device relates to the weight of the individual positioned).
It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to modify Streeter in view of Squitieri directed to including inflatable chamber machine with a machine learning algorithm to obtain pressure mitigation data. A person having ordinary skill in the art would have been motivated to make this modification in order to vary the pressure of the inflatable chambers to move the point of pressure applied to the surface to a different region (Squitieri: [0030]).
Regarding claim 113,
Streeter teaches (Previously Presented) The control component of claim 112, wherein the bladder-specific set of weights for each bladder of the plurality of bladders includes two separate sets of weights, one set of weights for use in a pump operation, and one set of weights for use in a vent operation (Streeter: [0461] leak compensation mode may, in some embodiments, be referred to as a closed-loop system, where monitoring, inflating and deflating may be automatic based on pre-set/pre-determined values, e.g. the baseline pressure, pressure set point or range and/or error threshold).
Regarding claim 114,
Streeter teaches (Previously Presented) The control component of claim 110, wherein the electrical subsystem includes a predictor system configured within a microcontroller of the electrical subsystem, the predictor system configured to implement a polynomial function to predict the actual pressure from the measured pressure (Streeter: [0077] automatic high pressure detection algorithm was used to synthesize a list of actuators which required offloading based on a calculated threshold).
Regarding claim 115,
Streeter teaches (Previously Presented) The control component of claim 114, wherein the predictor system is configured to determine a set of coefficients of the polynomial function in response to feedback from an external signal (Streeter: [0077] automatic high pressure detection algorithm was used to synthesize a list of actuators which required offloading based on a calculated threshold).
Regarding claim 116,
Streeter teaches (New) The cushion system of claim 97, wherein a quick disconnect interface is coupled between the control component and the cushion component (Streeter: [0404] an option may be included to swap channels. Such an option may be used to move programming for a channel to another channel (e.g. a channel which is inactive, spare, or not currently being used)).
Response to Arguments
Applicant’s arguments, see Page No. 6-10, filed 02/02/2026, with respect to Claims 97-100, 103, 110 under 35 U.S.C. § 102 have been fully considered and are persuasive. The rejection of Claims 97-100, 103, 110 have been withdrawn in view of the prior art of Escobedo (U.S. Pub. No. 2014/0345058). However, upon further consideration, a new ground(s) of rejection under 35 U.S.C. 103 is made in view of Streeter (U.S. Pub. No. 2019/0000701) in further view of Squitieri (U.S. Pub. No. 2021/0361501) with respect to Claim(s) 97-116.
The examiner notes that Escobedo does not teach the prediction system taught by the application. The structure of the independent claims do not provide enough structure to differentiate from Streeter (U.S. Pub. No. 2019/0000701) in further view of Squitieri (U.S. Pub. No. 2021/0361501). The prediction system is outlined to use machine learning and the monitoring of a pressure cushion. Squitieri discloses a pressure prediction model with machine learning algorithms to measure pressure mitigation. Regarding the amended claim 110, “a plurality of couplers”, the examiner notes that the coupling mechanism of the claimed invention includes a QD clamp, wherein the hose is connected to the control board via the QD clamp. Additionally, the hose includes a sheath that connects to the cushion. The examiner suggests further elaboration of the airflow mechanism between the bladder, hose, QD clamp, and then to the control board as most pneumonic cushion systems are coupled to a control board and the hoses connecting to the control board can be freely rearranged.
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 JOSEANE E. TEJADA whose telephone number is (571)272-3553. The examiner can normally be reached Monday-Friday 7:30-4:30 CT.
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, Justin Mikowski can be reached at (571) 272-8525. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOSEANE E. TEJADA/Examiner, Art Unit 3673
/DAVID R HARE/Primary Examiner, Art Unit 3673
4/20/2026
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