DETAILED ACTION
Applicant’s arguments, filed on 09/15/2025, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed on 09/15/2025, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1,7,10-15, and 18-20 are the current claims hereby under examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 10 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 10, applicant has added the limitation “multiple load cell sensors”, which is not described in the originally filed claims, specification, or drawings to support this newly added limitation. Thus, the newly added limitation is deemed to be new matter. Therefore, the claim fails the new matter requirement and is rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 10 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 10, the claim recites the limitation “multiple load cell sensors” in line 2. It is unclear if this is intended to refer to the load cell from claim 1, or different load cell sensors, as no other load cell sensors have been introduced in the claims nor appear in the specification. If it is referring to the load cell from claim 1, it needs to refer back to it. If it is referring to different load cell sensors, it needs to be distinguished from the load cell from claim 1. For purposes of examination, it is being interpreted as referring to the load cell from claim 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1,7,10-15, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Pearlman (US 20180008168) in further view of Labrosse (US 10827829).
Regarding independent claim 1, Pearlman teaches a system for measuring data specific to a subject using gravity ([0002]: “The present invention pertains to systems for physical and health related parameters, and in particular, to a monitoring system, such as a weight management system, that may be integrated within a piece of furniture such as a bed.”), the system comprising:
a substrate on which the subject lies ([0010]: “the present invention provides a load cell apparatus for use with a bed”), the substrate having i) multiple legs extending from the substrate to a floor to support the substrate ([0010]: “a bed having a plurality of legs”).
However, Pearlman does not teach the system comprising a bay having a supporting surface configured to receive a substrate on which the subject lies and ii) multiple wheels shaped to allow the substrate to be wheeled, wherein each of the legs is attached to one of the wheels.
Labrosse discloses a system for monitoring user presence at a work station and monitor user wellness. Specifically, Labrosse teaches a bay having a supporting surface configured to receive a substrate on which the subject lies (Fig. 30; Column 51, lines 44-45: “Chair 450 is shown positioned on a floor mat 462”, and ii) multiple wheels shaped to allow the substrate to be wheeled, wherein each of the legs is attached to one of the wheels (Fig. 30; Column 51, lines 33-35: “Attached to the remote end of each leg 466 is a caster wheel 470, collectively allowing the chair to selectively roll along floor mat 462”). Pearlman and Labrosse are analogous arts as they are both related to devices that use pressure sensors to measure health parameters of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the bay and the legs being on wheels from Labrosse into the system from Pearlman as it allows the substrate to be mobile, which allows it to be used in multiple locations. Additionally, the bay allows for a specific location for the substrate to be placed, which ensures it is in the same location every time and includes the load cells for proper measurement.
The Pearlman/Labrosse combination teaches wherein the supporting surface comprises load sensor assemblies, each of the load sensor assemblies positioned in the supporting surface to simultaneously support one of the wheels (Pearlman, [0010]: “a plurality of legs that includes a housing having a top portion and a bottom portion and a load cell device held by the bottom portion of the housing”; Labrosse, Fig. 30; Column 18, lines 1-8: “work station 10 is shown according to another embodiment of the invention wherein a pressure-sensitive mat 36 is provided, positioned on the floor in front of work station 10 and between bases 20a, 20b. Mat 36 includes appropriate circuitry (or other internal structure) to detect and measure isolated pressure within predetermined zones, preferably a right pressure zone 38 and a left pressure zone 40”; Column 40, lines 51-52: “A preferred type of pressure sensor is a load cell”), and comprising:
a cap configured to receive a load from the substrate (Pearlman, [0010]: “a housing having a top portion and a bottom portion and a load cell device held by the bottom portion of the housing”);
a base configured to provide contact with the floor (Pearlman, [0050]: “bottom housing portion 32 includes a base member 34 having an outer wall 36 extending upwardly therefrom”), the base and cap configured to fit together to maintain alignment of the cap to the base while allowing vertical movement of the cap (Pearlman, [0067]: “bottom housing portion 104 includes a plurality of channel members 109 that are each structured to receive and hold a respective pin member 106 in a manner which holds the pin member 106 in place horizontally but allows for vertical movement.”);
a load cell between the base and the cap, one of the base and cap configured to translate the load to the load cell (Pearlman, [0067]: “Load cell assembly 108 may be substituted for load cell assembly 8 in the various embodiments described herein. Load cell assembly 108 includes a disk-shaped housing that includes a top housing portion 102 that is similar in structure to top housing portion 32 that is coupled to a bottom housing portion 104. Top housing portion 102 and bottom housing portion 104 of the present alternative embodiment are structured to house and support the various components of load cell assembly 108, which include a load cell 44”); and
a printed circuit board that processes and outputs data from the load cell (Pearlman, [0051]: “load cell 44 includes a load cell cantilever piece 48 as shown in FIG. 8, which may be made of steel or any other suitable material. Load cell cantilever piece 48 includes an outer support frame portion 50 having a cantilever portion 52 extending therefrom and into an interior thereof. Cantilever portion 52 includes a proximal end 54 and a distal end 56. As seen in FIG. 3, load cell 44 further includes a number of strain gauges 58 that are provided on the surface of proximal end 54 of cantilever portion 52. In one particular exemplary embodiment, strain gauges 58 are provided on both the top and the bottom surfaces of proximal end 54. Strain gauges 58 are electrically connected to the electronic components provided on printed circuit board 46 such that measurements made by strain gauges 58 are communicated to printed circuit board 46 for further processing and/or transmission thereof as described herein.”), wherein a combination of all of the load sensor assemblies receive an entire the load to which the substrate is subjected (Pearlman, [0011]: “The load cell apparatus may include a support mechanism, such as a flexible member provided between the top portion of the housing and the bottom portion of the housing or a series of flexible diaphragm or bushings held by the housing, that is meant to eliminate off-axis forces being transferred through the body of the housing. That is, this design is tailored to ensure all of the force transferred from the bed leg passes directly into the tab load-cell”).
Regarding claim 7, the Pearlman/Labrosse combination teaches the system of claim 1, wherein the cap has a single sidewall and the base has a double sidewall configured to receive the single sidewall of the cap, the double sidewall configured to restrain the cap from lateral movement while allowing movement in a vertical direction (Pearlman, [0050]: “bottom housing portion 32 includes a base member 34 having an outer wall 36 extending upwardly therefrom. Base member 34 includes a recessed pocket 38, and outer wall 36 includes a ledge portion 40 adjacent recessed pocket 38. In the exemplary embodiment, recessed pocket 38 is structured to receive and securely hold a mounting tray 42 as shown in FIG. 1. Mounting tray 42 is, in turn, structured to receive and hold a load cell 44 as seen in FIGS. 3 and 4. Furthermore, ledge portion 40 is structured to receive and hold a printed circuit board 46 (that includes thereon appropriate measurement, control and communications electronics) as shown in FIGS. 3 and 4. Load cell 44 and printed circuit board 46 are structured to, in cooperation with other parts of load cell assembly 8 described herein, generate the force indicative signals that are described elsewhere herein.”; [0068]: “Top housing portion 112 and bottom housing portion 114 are structured such that outer wall 120 of bottom housing portion 114 engages the flange member 116 but allows relative vertical movement between the 2 components”; Fig. 21).
Regarding claim 10, the Pearlman/Labrosse combination teaches the system of claim 1, wherein each of the load sensor assemblies comprises multiple load cell sensors positioned in the base (Pearlman, [0047]: “Monitoring system 2 includes a plurality of (e.g., four) load cell assemblies 8 that are operatively coupled to a control unit 10”), the cap configured with a circuit contact surface configured to translate the load equally to each of the multiple load cell sensors (Pearlman, [0047]: “Each load cell assembly 8 is structured to measure the magnitude of a force that is being applied thereto by the respective leg 12 and to generate a signal indicative of that force. In addition, each load cell assembly 8 is in electronic communication with control unit 10”; [0060]: “processor apparatus 14 includes one or more routines that are structured to receive the signals from each of the load cell assemblies 8 that are proportional to the weight on the leg 12 that is associated with the load cell assembly 8 and, from those signals, monitor the weight distribution among the load cell assemblies”).
Regarding claim 11, the Pearlman/Labrosse combination teaches the system of claim 1, further comprising a controller in communication with each of the load sensor assemblies (Pearlman, [0048]: “processor apparatus 14 comprises a processor 26 and a memory 28. Processor 26 may be, for example and without limitation, a microprocessor (μP), a microcontroller, an application specific integrated circuit (ASIC), or some other suitable processing device, that interfaces with memory 28”), the controller configured to collect signals from each of the load sensor assemblies and determine a center of mass of the subject on the substrate (Pearlman, [0059]: “center of pressure could be determined by identifying the average location of the weight and monitoring whether that average location moves by a certain percentage or distance (assuming the bed size is known)”; [0060]: “changes in such weight distribution are monitored for conditions that indicate that a fall out of bed 4 is imminent, such as the center of pressure of an occupant of bed 12 approaching the edge of bed 12”).
Regarding claim 12, the Pearlman/Labrosse combination teaches the system of claim 1, further comprising a controller in communication with each of the load sensor assemblies (Pearlman, [0048]: “processor apparatus 14 comprises a processor 26 and a memory 28. Processor 26 may be, for example and without limitation, a microprocessor (μP), a microcontroller, an application specific integrated circuit (ASIC), or some other suitable processing device, that interfaces with memory 28”) and at least one external device in communication with the controller (Pearlman, [0066]: “Patient monitoring system 100 further includes a remote computing device in the form of central control and monitoring unit 104, which may be located at, for example without limitation, a nurse's station”), the controller configured to:
collect signals from each of the load sensor assemblies (Pearlman, [0066]: “the control unit 10 of each bed monitor 102 is structured to receive the signal generated by each of the load cell assemblies 8 associated therewith”);
determine if the subject is asleep or awake (Pearlman, [0016]: “The data could be used by the person who uses the bed or be passed to other family members (for example, to monitor whether grandma is sleeping, etc.) or clinicians to monitor behavior”); and
control the at least one external device based on whether the subject is asleep or awake (Pearlman, [0063]: “FIG. 25 is a top-level schematic illustrating an exemplary machine learning algorithm as just described implemented in monitoring system 2 according to another particular exemplary embodiment wherein weight, sleep quality, fall risk and pressure sore risk information, among others, may be monitored for two users. FIG. 25 illustrates the data variables, classifier, events, and alarm/outcome that may be implemented in such an embodiment. In addition, FIG. 26 is a flowchart 300 illustrating operation of such a machine learning algorithm according to one particular implementation. As seen in FIG. 26, operation of the machine learning algorithm includes a first branch 302 that is executed when one of the users enters or exits bed 4, and a second branch 304 that is executed when, instead, it is determined that a user of bed 4 has moved.”).
Regarding claim 13, the Pearlman/Labrosse combination teaches the system of claim 1, further comprising a controller in communication with each of the load sensor assemblies (Pearlman, [0048]: “processor apparatus 14 comprises a processor 26 and a memory 28. Processor 26 may be, for example and without limitation, a microprocessor (μP), a microcontroller, an application specific integrated circuit (ASIC), or some other suitable processing device, that interfaces with memory 28”) and at least one external device in communication with the controller (Pearlman, [0066]: “Patient monitoring system 100 further includes a remote computing device in the form of central control and monitoring unit 104, which may be located at, for example without limitation, a nurse's station”), the controller configured to:
collect signals from each of the load sensor assemblies (Pearlman, [0066]: “the control unit 10 of each bed monitor 102 is structured to receive the signal generated by each of the load cell assemblies 8 associated therewith”);
determine that the subject previously on the substrate has exited the substrate (Pearlman, [0063]: “operation of the machine learning algorithm includes a first branch 302 that is executed when one of the users enters or exits bed 4, and a second branch 304 that is executed when, instead, it is determined that a user of bed 4 has moved.”); and
change a status of the at least one external device in response to the determination (Pearlman, [0063]: “FIG. 25 is a top-level schematic illustrating an exemplary machine learning algorithm as just described implemented in monitoring system 2 according to another particular exemplary embodiment wherein weight, sleep quality, fall risk and pressure sore risk information, among others, may be monitored for two users. FIG. 25 illustrates the data variables, classifier, events, and alarm/outcome that may be implemented in such an embodiment. In addition, FIG. 26 is a flowchart 300 illustrating operation of such a machine learning algorithm according to one particular implementation. As seen in FIG. 26, operation of the machine learning algorithm includes a first branch 302 that is executed when one of the users enters or exits bed 4, and a second branch 304 that is executed when, instead, it is determined that a user of bed 4 has moved.”).
Regarding claim 14, the Pearlman/Labrosse combination teaches the system of claim 1, further comprising a controller in communication with each of the load sensor assemblies (Pearlman, [0048]: “processor apparatus 14 comprises a processor 26 and a memory 28. Processor 26 may be, for example and without limitation, a microprocessor (μP), a microcontroller, an application specific integrated circuit (ASIC), or some other suitable processing device, that interfaces with memory 28”) and at least one external device in communication with the controller (Pearlman, [0066]: “Patient monitoring system 100 further includes a remote computing device in the form of central control and monitoring unit 104, which may be located at, for example without limitation, a nurse's station”), the controller configured to:
collect signals from each of the load sensor assemblies (Pearlman, [0066]: “the control unit 10 of each bed monitor 102 is structured to receive the signal generated by each of the load cell assemblies 8 associated therewith”);
determine that the subject has laid down on the substrate (Pearlman, [0062]: “during a setup stage, each user (user 1 and user 2 in the present example) will set up a profile in processor apparatus 14 and then sit/rest on their side of the bed one at a time so that readings can be taken from each of the load cell assemblies 8. Next, during an operational stage, processing apparatus 14 will periodically receive and record weight data from each of the load cell assemblies 8 and determine the times at which the readings from the load cell assemblies 8 change. Processing apparatus 14 will then use the trained Naïve Bayes classifier to analyze the recorded data so that it will be able to segregate the data for any particular time into one of the four categories identified above. In addition, based on the categorization, processing apparatus 14 is able to determine and record individual weights for each of the users. In addition to recording weight information for each of the users individually, this classification mechanism may also be used to determine and store other parameters for each of the users individually), such as, without limitation, sleep quality and motion related data such as periods oi quiescence as described herein. In the example embodiment sleep quality is determined through activity, which is essentially the ratio of the amount of motion (in time) in bed normalized by the total time in bed.”); and
change a status of the at least one external device in response to the determination (Pearlman, [0063]: “FIG. 25 is a top-level schematic illustrating an exemplary machine learning algorithm as just described implemented in monitoring system 2 according to another particular exemplary embodiment wherein weight, sleep quality, fall risk and pressure sore risk information, among others, may be monitored for two users. FIG. 25 illustrates the data variables, classifier, events, and alarm/outcome that may be implemented in such an embodiment. In addition, FIG. 26 is a flowchart 300 illustrating operation of such a machine learning algorithm according to one particular implementation. As seen in FIG. 26, operation of the machine learning algorithm includes a first branch 302 that is executed when one of the users enters or exits bed 4, and a second branch 304 that is executed when, instead, it is determined that a user of bed 4 has moved.”).
Regarding independent claim 15, Pearlman teaches a system for measuring data specific to a subject using gravity ([0002]: “The present invention pertains to systems for physical and health related parameters, and in particular, to a monitoring system, such as a weight management system, that may be integrated within a piece of furniture such as a bed.”), the system comprising:
a substrate on which the subject lies ([0010]: “the present invention provides a load cell apparatus for use with a bed”), the substrate having i) multiple legs extending from the substrate to a floor to support the substrate ([0010]: “a bed having a plurality of legs”).
However, Pearlman does not teach the system comprising a bay having a supporting surface configured to receive a substrate on which the subject lies and ii) multiple wheels shaped to allow the substrate to be wheeled, wherein each of the legs is attached to one of the wheels.
Labrosse discloses a system for monitoring user presence at a work station and monitor user wellness. Specifically, Labrosse teaches a bay having a supporting surface configured to receive a substrate on which the subject lies (Fig. 30; Column 51, lines 44-45: “Chair 450 is shown positioned on a floor mat 462”, and ii) multiple wheels shaped to allow the substrate to be wheeled, wherein each of the legs is attached to one of the wheels (Fig. 30; Column 51, lines 33-35: “Attached to the remote end of each leg 466 is a caster wheel 470, collectively allowing the chair to selectively roll along floor mat 462”). Pearlman and Labrosse are analogous arts as they are both related to devices that use pressure sensors to measure health parameters of a user.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to include the bay and the legs being on wheels from Labrosse into the system from Pearlman as it allows the substrate to be mobile, which allows it to be used in multiple locations. Additionally, the bay allows for a specific location for the substrate to be placed, which ensures it is in the same location every time and includes the load cells for proper measurement.
The Pearlman/Labrosse combination teaches wherein the supporting surface comprises at least two load sensor assemblies, each of the load sensor assemblies positioned in the supporting surface to simultaneously support one of the wheels (Pearlman, [0010]: “a plurality of legs that includes a housing having a top portion and a bottom portion and a load cell device held by the bottom portion of the housing”; Labrosse, Fig. 30; Column 18, lines 1-8: “work station 10 is shown according to another embodiment of the invention wherein a pressure-sensitive mat 36 is provided, positioned on the floor in front of work station 10 and between bases 20a, 20b. Mat 36 includes appropriate circuitry (or other internal structure) to detect and measure isolated pressure within predetermined zones, preferably a right pressure zone 38 and a left pressure zone 40”; Column 40, lines 51-52: “A preferred type of pressure sensor is a load cell”);
a controller (Pearlman, [0048]: “processor apparatus 14 comprises a processor 26 and a memory 28. Processor 26 may be, for example and without limitation, a microprocessor (μP), a microcontroller, an application specific integrated circuit (ASIC), or some other suitable processing device, that interfaces with memory 28”); and
communication means from each of the at least two load sensor assemblies to the controller (Pearlman, [0066]: “the control unit 10 of each bed monitor 102 is structured to receive the signal generated by each of the load cell assemblies 8 associated therewith”), wherein the controller processes output from each of the at least two load sensor assemblies (Pearlman, [0012]: “The system further includes a processing apparatus coupled to each of the load cell apparatuses that is structured to: (i) receive the signal generated by each of the load cell apparatuses, (ii) determine periods of quiescence based on the received signals, and (iii) determine a risk factor for pressure sores based on the periods of quiescence.”).
Regarding claim 18, the Pearlman/Labrosse combination teaches the system of claim 15. further comprising at least one external device in communication with the controller (Pearlman, [0066]: “Patient monitoring system 100 further includes a remote computing device in the form of central control and monitoring unit 104, which may be located at, for example without limitation, a nurse's station”), the controller configured to:
collect signals from each of the load sensor assemblies (Pearlman, [0066]: “the control unit 10 of each bed monitor 102 is structured to receive the signal generated by each of the load cell assemblies 8 associated therewith”);
determine if the subject is asleep or awake (Pearlman, [0016]: “The data could be used by the person who uses the bed or be passed to other family members (for example, to monitor whether grandma is sleeping, etc.) or clinicians to monitor behavior”); and
control the at least one external device based on whether the subject is asleep or awake (Pearlman, [0063]: “FIG. 25 is a top-level schematic illustrating an exemplary machine learning algorithm as just described implemented in monitoring system 2 according to another particular exemplary embodiment wherein weight, sleep quality, fall risk and pressure sore risk information, among others, may be monitored for two users. FIG. 25 illustrates the data variables, classifier, events, and alarm/outcome that may be implemented in such an embodiment. In addition, FIG. 26 is a flowchart 300 illustrating operation of such a machine learning algorithm according to one particular implementation. As seen in FIG. 26, operation of the machine learning algorithm includes a first branch 302 that is executed when one of the users enters or exits bed 4, and a second branch 304 that is executed when, instead, it is determined that a user of bed 4 has moved.”).
Regarding claim 19, the Pearlman/Labrosse combination teaches the system of claim 15, further comprising at least one external device in communication with the controller (Pearlman, [0066]: “Patient monitoring system 100 further includes a remote computing device in the form of central control and monitoring unit 104, which may be located at, for example without limitation, a nurse's station”), the controller configured to:
collect signals from each of the load sensor assemblies (Pearlman, [0066]: “the control unit 10 of each bed monitor 102 is structured to receive the signal generated by each of the load cell assemblies 8 associated therewith”);
determine that the subject previously on the substrate has exited the substrate (Pearlman, [0063]: “operation of the machine learning algorithm includes a first branch 302 that is executed when one of the users enters or exits bed 4, and a second branch 304 that is executed when, instead, it is determined that a user of bed 4 has moved.”); and
change a status of the at least one external device in response to the determination (Pearlman, [0063]: “FIG. 25 is a top-level schematic illustrating an exemplary machine learning algorithm as just described implemented in monitoring system 2 according to another particular exemplary embodiment wherein weight, sleep quality, fall risk and pressure sore risk information, among others, may be monitored for two users. FIG. 25 illustrates the data variables, classifier, events, and alarm/outcome that may be implemented in such an embodiment. In addition, FIG. 26 is a flowchart 300 illustrating operation of such a machine learning algorithm according to one particular implementation. As seen in FIG. 26, operation of the machine learning algorithm includes a first branch 302 that is executed when one of the users enters or exits bed 4, and a second branch 304 that is executed when, instead, it is determined that a user of bed 4 has moved.”).
Regarding claim 20, the Pearlman/Labrosse combination teaches the system of claim 15, further comprising at least one external device in communication with the controller (Pearlman, [0066]: “Patient monitoring system 100 further includes a remote computing device in the form of central control and monitoring unit 104, which may be located at, for example without limitation, a nurse's station”), the controller configured to:
collect signals from each of the load sensor assemblies (Pearlman, [0066]: “the control unit 10 of each bed monitor 102 is structured to receive the signal generated by each of the load cell assemblies 8 associated therewith”);
determine that the subject has laid down on the substrate (Pearlman, [0062]: “during a setup stage, each user (user 1 and user 2 in the present example) will set up a profile in processor apparatus 14 and then sit/rest on their side of the bed one at a time so that readings can be taken from each of the load cell assemblies 8. Next, during an operational stage, processing apparatus 14 will periodically receive and record weight data from each of the load cell assemblies 8 and determine the times at which the readings from the load cell assemblies 8 change. Processing apparatus 14 will then use the trained Naïve Bayes classifier to analyze the recorded data so that it will be able to segregate the data for any particular time into one of the four categories identified above. In addition, based on the categorization, processing apparatus 14 is able to determine and record individual weights for each of the users. In addition to recording weight information for each of the users individually, this classification mechanism may also be used to determine and store other parameters for each of the users individually), such as, without limitation, sleep quality and motion related data such as periods oi quiescence as described herein. In the example embodiment sleep quality is determined through activity, which is essentially the ratio of the amount of motion (in time) in bed normalized by the total time in bed.”); and
change a status of the at least one external device in response to the determination (Pearlman, [0063]: “FIG. 25 is a top-level schematic illustrating an exemplary machine learning algorithm as just described implemented in monitoring system 2 according to another particular exemplary embodiment wherein weight, sleep quality, fall risk and pressure sore risk information, among others, may be monitored for two users. FIG. 25 illustrates the data variables, classifier, events, and alarm/outcome that may be implemented in such an embodiment. In addition, FIG. 26 is a flowchart 300 illustrating operation of such a machine learning algorithm according to one particular implementation. As seen in FIG. 26, operation of the machine learning algorithm includes a first branch 302 that is executed when one of the users enters or exits bed 4, and a second branch 304 that is executed when, instead, it is determined that a user of bed 4 has moved.”).
Response to Arguments
All of applicant’s argument regarding the rejections and objections previously set forth have been fully considered and are persuasive unless directly addressed subsequently.
Applicant's arguments filed 09/15/2025 have been fully considered but they are not persuasive. Applicant argues that neither Pearlman nor Labrosse describes a bay having a supporting surface configured to receive a substrate on which the subject lies or load sensor assemblies positioned in the supporting surface to simultaneously support one of the wheels, however as stated in the rejection above, the Pearlman/Labrosse combination teaches on both of these limitations, with the mats from Labrosse acting as the bay with the load cells in the mat, and Pearlman stating that the load cells are underneath the legs (Labrosse, Fig. 30; Column 51, lines 44-45: “Chair 450 is shown positioned on a floor mat 462; Column 51, lines 33-35: “Attached to the remote end of each leg 466 is a caster wheel 470, collectively allowing the chair to selectively roll along floor mat 462”; Pearlman, [0010]: “a plurality of legs that includes a housing having a top portion and a bottom portion and a load cell device held by the bottom portion of the housing”; Labrosse, Fig. 30; Column 18, lines 1-8: “work station 10 is shown according to another embodiment of the invention wherein a pressure-sensitive mat 36 is provided, positioned on the floor in front of work station 10 and between bases 20a, 20b. Mat 36 includes appropriate circuitry (or other internal structure) to detect and measure isolated pressure within predetermined zones, preferably a right pressure zone 38 and a left pressure zone 40”; Column 40, lines 51-52: “A preferred type of pressure sensor is a load cell”).
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 ERIN K MCCORMACK whose telephone number is (703)756-1886. The examiner can normally be reached Mon-Fri 7:30-5.
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, Jason Sims can be reached at 5712727540. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/E.K.M./Examiner, Art Unit 3791
/RENE T TOWA/Primary Examiner, Art Unit 3791