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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/15/2026 has been entered.
Response to Arguments
Applicant’s arguments, filed 01/15/2026, with respect to claims 1, 4-6, 8-13, and 15-21 under 35 U.S.C. § 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant contends that it would not have been obvious based on Wu to modify Addison to output a control signal to a stimulation device, where the prior art references do not teach outputting a control signal based on data from a depth camera. Applicant further contends that the programming sessions in Wu identify tremors and movement disorders for selecting therapy, but this does not render it obvious to use real-time respiratory monitoring from a depth camera to identify apnea and output a control signal as neither Wu nor Addison discuss apnea and Lee is not directed to depth data and does not remedy this deficiency. Examiner respectfully disagrees.
Obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007).
In this case, Addison and Wu are in similar fields of endeavor, where Addison teaches non-contact video monitoring (See Abstract) and Wu teaches analysis of obtained video information of patient motion during a period of time (See Abstract), where said motion data includes patient breathing patterns and sleep indications (Wu ¶[0161]). Examiner interprets that patient breathing patterns are related to apnea as apnea is an abnormal breathing pattern. Furthermore, there is motivation to combine Addison with Wu since identifying patient behaviors may be used to control or improve the delivery of therapy to patient, where identified patient behavior may be used as direct feedback used to control therapy delivery or to calibrate other sensors that provide sensed patient parameter values used as feedback in controlling therapy (Wu ¶[0137]), and in order to improve the precision with which therapy is directed to patient behavior (Wu ¶[0162]). Regarding Lee, Lee teaches an electronic device with a processor controlled by memory-stored instructions (See Abstract) to provide information on apnea states (Lee ¶[0190]). Examiner takes the position that the processor of Lee is usable with the modified invention of Addison in combination with Wu since, if a duration of the apnea state is greater than a certain time, the apnea state may detrimentally influence the health of the user (Lee ¶[0194]).
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.
Claims 1, 4-5, 8-11, 13, and 15-21 are rejected under 35 U.S.C. 103 as being unpatentable over Addison et al. (hereinafter “Addison”) (U.S. Pub. No. 2019/0209046 A1) in view of Wu et al. (hereinafter “Wu”) (U.S. Pub. No. 2014/0371599 A1) and Lee et al. (hereinafter “Lee”) (U.S. Pub. No. 2020/0268263 A1).
Regarding claim 1, Addison teaches a system (Abstract, where “The present invention relates to the field of medical monitoring, and in particular non-contact video monitoring to measure tidal volume of a patient. Systems, methods, and computer readable media are described for determining a region of interest of a patient and monitoring that region of interest to determine tidal volume of the patient”) comprising:
a processor (¶[0002], where “The monitor includes a processor that processes the signal”); and
a memory storing instructions thereon (¶[0088], where “The computing device includes a processor 218, a display 222, and hardware memory 226 for storing software and computer instructions”) that, when executed by the processor (¶[0089], where “The computing device 300 includes a processor 315 that is coupled to a memory 305. The processor 315 can store and recall data and applications in the memory 305, including applications that process information and send commands/signals”), cause the processor to:
receive depth data from a depth camera (¶[0089], where “the computing device 300 may send to the server 325 information determined about a patient from images captured by the image capture device 385 (such as a camera), such as depth information of a patient in an image or tidal volume information determined about the patient,” ¶[0095], where “the image includes depth data, such as from a depth sensing camera”) having a target subject in a field of view (¶[0090], where “image capture device 385 is a remote sensing device such as a video camera … image capture device 385 can be described as local because it is relatively close in proximity to a patient so that at least a part of a patient is within the field of view of the image capture device 385. In some embodiments, the image capture device 385 can be adjustable to ensure that the patient is captured in the field of view”);
process the received depth data to produce motion data of the target subject (¶[0095], where “The image includes a patient 390 and a region of interest (ROI) 395. The ROI 395 can be used to determine a volume measurement from the chest of the patient 390. … Because the image includes depth data, such as from a depth sensing camera, information on the spatial location of the patient 390, and therefore the patient's chest and the ROI 395, can also be determined ... As the patient 390 breathes, the patient's chest moves toward and away from the camera, changing the depth information associated with the images over time. As a result, the location information associated with the ROI 395 changes over time. The position of individual points within the ROI 395 may be integrated across the area of the ROI 395 to provide a change in volume over time as shown in FIGS. 4 and 5. FIG. 4 is a graph showing a tidal volume calculation over time according to various embodiments described herein.” Examiner interprets that information on the spatial location of the patient and the location information of the ROI that changes over time is motion data derived from the depth data. Changes in the depth information correlate to changes in location information over time, where changes in spatial location are motion.);
generate, from the motion data, a waveform signal indicative of a biometric parameter of the target subject (Figure 4, which shows a volume signal, Figure 5, which shows a tidal volume associated with a region of interest (ROI), where a volume signal can be generated by integrating the ROI, ¶[0089], where “processor 315 may also display objects, applications, data, etc. on an interface/display 310. The processor 315 may also receive inputs through the interface/display 310. The processor 315 is also coupled to a transceiver 320. With this configuration, the processor 315, and subsequently the computing device 300, can communicate with other devices, such as the server 325 through a connection 370 and the image capture device 385 through a connection 380. For example, the computing device 300 may send to the server 325 information determined about a patient from images captured by the image capture device 385 (such as a camera), such as depth information of a patient in an image or tidal volume information determined about the patient,” ¶[0095], where “The image includes a patient 390 and a region of interest (ROI) 395. The ROI 395 can be used to determine a volume measurement from the chest of the patient 390. … Because the image includes depth data, such as from a depth sensing camera, information on the spatial location of the patient 390, and therefore the patient's chest and the ROI 395, can also be determined ... As the patient 390 breathes, the patient's chest moves toward and away from the camera, changing the depth information associated with the images over time. As a result, the location information associated with the ROI 395 changes over time. The position of individual points within the ROI 395 may be integrated across the area of the ROI 395 to provide a change in volume over time as shown in FIGS. 4 and 5. FIG. 4 is a graph showing a tidal volume calculation over time according to various embodiments described herein,” ¶[0096], where “Vectors associated with points within the ROI 395 are depicted in FIG. 5, where a schematic of the box values are shown to change over time. For example, these vectors represent movement of a patient's chest toward a camera as the patient's chest expands forward with inhalation. Similarly, the vectors will then move backward, away from the camera, when the patient's chest contrasts with exhalation. This movement forward and backward can be tracked to determine a respiration rate. Furthermore, this movement forward and backward can be integrated to determine a tidal volume”); and
set a threshold condition (¶[0122], where “A threshold minute volume may also be determined as shown in FIG. 24. … a threshold minute volume may be determined that indicates a patient may be in the hypoventilation region. In some embodiments, a moving average may be used since some of the data points in the normoventilation region fall below the threshold minute volume”).
Although Addison teaches the collection of depth data from a depth sensing camera and processing of the depth data over time, Addison does not explicitly teach processing the received data in real-time. Furthermore, Addison does not teach identifying a quantity of apnea events, a quantity of hypopnea events, or both in association with a temporal duration, based at least in part on the waveform signal; setting a threshold condition based at least in part on the quantity of apnea events, the quantity of hypopnea events, or both in association with the temporal duration; outputting a control signal to a stimulation device based at least in part on the waveform signal satisfying the threshold condition, the control signal comprising instructions for delivery of a therapy treatment to the patient; nor modifying the control signal based at least in part on subsequent depth data from the imaging device.
Wu teaches a system for analyzing video information to objectively identify patient behavior (Abstract), and further teaches processing the data in real-time (¶[0175], where “Memory 360 may store computer-readable instructions that, when executed by processor 350, cause IMD 324 to perform various functions,” ¶[0178], where “Feedback control 364 may include instructions that determine what feedback to use when controlling therapy delivery such as which therapy programs, therapy parameter sets, or individual therapy parameter values to select ... feedback control 364 may include associations of values for one or more sensed patient parameters (e.g., LFP signals or patient accelerations) to respective therapy parameter sets. The values of the sensed patient parameters may be calibrated or correlated with identified patient behaviors from captured video information. In any case, IMD 324 may use the instructions within feedback control 364 to adjust the therapy delivered to patient 12A.” Examiner interprets that feedback control is real-time data processing.);
outputting a control signal to a stimulation device (¶[0175], where “IMD 324 includes processor 350, memory 360, stimulation generator 354, sensing module 356, switch module 352, telemetry module 358, sensor 359, and power source 370 … Memory 360 may store computer-readable instructions that, when executed by processor 350, cause IMD 324 to perform various functions,” ¶[0179], where “Stimulation generator 354, under the control of processor 350, generates stimulation signals for delivery to patient 12A”) based at least in part on the waveform signal satisfying the threshold condition (¶[0161], where “A variety of different patient parameters may be monitored and used to provide feedback to control stimulation therapy. For example, a patient parameter may be a … relative motion between two locations of the patient, blood pressure, heart rate, patient speech pattern, patient breathing pattern, sleep indication, or a chemical indication,” ¶[0178], where “Feedback control 364 may include instructions that determine what feedback to use when controlling therapy delivery such as which therapy programs, therapy parameter sets, or individual therapy parameter values to select ... feedback control 364 may include associations of values for one or more sensed patient parameters (e.g., LFP signals or patient accelerations) to respective therapy parameter sets. The values of the sensed patient parameters may be calibrated or correlated with identified patient behaviors from captured video information. In any case, IMD 324 may use the instructions within feedback control 364 to adjust the therapy delivered to patient 12A.” Examiner takes the position that, since the patient parameters are being monitored and utilized to provide feedback which controls the stimulation therapy and selects specific therapy, there is an inherent threshold being met in order to apply stimulation to a patient.), the control signal comprising instructions for delivery of a therapy treatment to the patient (¶[0178], where “Feedback control 364 may include instructions that determine what feedback to use when controlling therapy delivery such as which therapy programs, therapy parameter sets, or individual therapy parameter values to select ... feedback control 364 may include associations of values for one or more sensed patient parameters (e.g., LFP signals or patient accelerations) to respective therapy parameter sets. The values of the sensed patient parameters may be calibrated or correlated with identified patient behaviors from captured video information. In any case, IMD 324 may use the instructions within feedback control 364 to adjust the therapy delivered to patient 12A”); and
modifying the control signal based at least in part on subsequent depth data from the imaging device (¶[0162], where “networked server 44 may be configured to request capture of supplemental video information of patient motion during a second period of time different than the first period of the previous video information 50. Based on the supplemental video information, networked server 44 may identify any patient behavior within the supplemental video information. Networked server 44 may receive an indication of the patient behavior during the second period and use the patient behavior to determine a different therapy to be delivered to patient 12A based on the indication of the patient behavior during the second period of time … networked server 44 may update the correlations or calibrations of the patient parameter values obtained during the second period of time to the newly identified patient behaviors”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Wu, which teaches processing the data in real-time, outputting a control signal to a stimulation device based at least in part on the waveform signal satisfying the threshold condition, the control signal comprising instructions for delivery of a therapy treatment to the patient, and modifying the control signal based at least in part on subsequent depth data from the imaging device, with the invention of Addison since identifying patient behaviors may be used to control or improve the delivery of therapy to patient, where identified patient behavior may be used as direct feedback used to control therapy delivery or to calibrate other sensors that provide sensed patient parameter values used as feedback in controlling therapy (Wu ¶[0137]), and in order to improve the precision with which therapy is directed to patient behavior (Wu ¶[0162]).
Although Addison teaches a waveform based on tidal volume of an individual as well as respiration rate, where breathing may be monitored for slow, shallow, or stopped breathing (Addison ¶[0078], where “By monitoring breathing as disclosed herein, patients who have slow, shallow, or stopped breathing can be attended to more quickly, potentially saving lives and leading to better treatment.” Examiner interprets that patient breathing patterns are related to apnea as apnea is an abnormal breathing pattern where a patient stops breathing, however, identification of apnea is not explicitly mentioned in Addison.), neither Addison nor Wu teach identifying a quantity of apnea events, a quantity of hypopnea events, or both in association with a temporal duration, based at least in part on the waveform signal; nor setting a threshold condition based at least in part on the quantity of apnea events, the quantity of hypopnea events, or both in association with the temporal duration.
Lee teaches an electronic device with a processor controlled by memory-stored instructions (Abstract), and further teaches identifying a quantity of apnea events, a quantity of hypopnea events, or both in association with a temporal duration, based at least in part on the waveform signal (¶[0037], where “an electronic device providing information on an apnea state according to distortion of a PPG signal,” and where a PPG signal is a type of waveform signal, ¶[0087], where “the electronic device 301 may include … a processor 320,” ¶[0190], where “the electronic device 301 may provide information on an apnea state during sleeping. For example, the electronic device 301 may provide and store information on a number and/or a duration of apnea states during sleeping”); and
setting a threshold condition based at least in part on the quantity of apnea events, the quantity of hypopnea events, or both in association with the temporal duration (¶[0204], where “The user may directly set at least one of … a threshold value for a duration of an apnea state, and a threshold value for a number of apnea states according to a degree of the apnea state of the electronic device.” Examiner takes the position that since a user can select at least one of these thresholds that both can be selected in order to set a threshold condition.).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Lee, which teaches identifying a quantity of apnea events, a quantity of hypopnea events, or both in association with a temporal duration, based at least in part on the waveform signal; and setting a threshold condition based at least in part on the quantity of apnea events, the quantity of hypopnea events, or both in association with the temporal duration, with the modified invention of Addison since, if a duration of the apnea state is greater than a certain time, the apnea state may detrimentally influence the health of the user (Lee ¶[0194]).
Regarding claim 4, Addison in combination with Wu and Lee teaches all limitations of claim 1 as described in the rejection above.
Addison teaches that the instructions are further executable by the processor to: compare one or more characteristics of the waveform signal to a target set of criteria (¶[0100], where “The V(t) signal (also referred to herein as the volume signal, the tidal volume, and/or the tidal volume signal) over the same time frame is compared to the reference tidal volume, and a calibration factor is determined so that the range of V(t) matches the reference tidal volume measured by the flow measurement device,” ¶[0105], where “Another type of smart ROI determination may use respiration rate (RR) modulations power analysis. This compares a power while breathing to a power while not breathing to filter noise and determine more accurate ROIs and tidal volumes”),
wherein setting the threshold condition comprises maintaining or modifying the threshold condition based at least in part on a result of the comparison (¶[0105], where “movement of various points on the chest may be compared with a known or expected respiration rate to ensure that a good point is selected ... Points that modulate at the respiration rate and above a threshold amplitude are added to the ROI, and points that do not modulate at that rate or at that amplitude are discarded. This ROI can be updated dynamically, so that the ROI is continually refreshing to capture the portions of the chest that are moving with breaths, or to track the chest as the patient moves across the field of view”).
Regarding claim 5, Addison in combination with Wu and Lee teaches all limitations of claim 4 as described in the rejection above.
Addison teaches that the one or more characteristics of the waveform signal comprise a pattern of the waveform signal (¶[0087], where “the ROI or portions of the ROI may be determined to move in accordance with respiratory patterns, to determine a tidal volume of the patient”).
Regarding claim 8, Addison in combination with Wu and Lee teaches all limitations of claim 1 as described in the rejection above.
Wu teaches that the instructions are further executable by the processor to: deliver, via the stimulation device, the therapy treatment to the patient based at least in part on the control signal (¶[0148], where “IMD 324 includes a memory (shown in FIG. 21) to store a plurality of therapy programs that each define a set of therapy parameter values. In some examples, IMD 324 may select a therapy program from the memory based on various parameters, such as sensed patient parameters and the identified patient behaviors. IMD 324 may generate electrical stimulation based on the selected therapy program to manage the patient symptoms,” ¶[0175], where “Memory 360 may store computer-readable instructions that, when executed by processor 350, cause IMD 324 to perform various functions.” Examiner takes the position that there is an inherent control signal from the processor to initiate stimulation since the processor executes the instructions to cause the IMD to generate electrical stimulation.).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Wu, which teaches that the instructions are further executable by the processor to: deliver, via the stimulation device, a therapy treatment to the patient based at least in part on the control signal, with the modified invention of Addison in order to generate stimulation based on a selected therapy program to manage patient symptoms (Wu ¶[0148]).
Regarding claim 9, Addison in combination with Wu and Lee teaches all limitations of claim 1 as described in the rejection above.
Addison teaches that the clinical data comprises a data record associated with the biometric parameter (¶[0121], where “The tidal volume measurement (TVm) may also be used to determine whether a patient is exhibiting hypoventilation. FIG. 23 is a graph showing tidal volume measurements and a respiratory compromise threshold … A distinct kink in the data at the respiratory compromise threshold indicates a lower threshold of normoventilation, below which hypoventilation may be taking place … Such a plot may indicate to a clinician that the patient is exhibiting hypoventilation and that an intervention is necessary.” Examiner takes the position that the tidal volume is a biometric parameter and that the plot that may be presented to a clinician is equivalent to clinical data.).
Furthermore, Wu teaches that the instructions are further executable by the processor to: provide clinical data to a medical provider (¶[0136], where “Processors 80 may output the one or more candidate frames for presentation to a user (e.g., clinician 22).” Examiner takes the position that the candidate frames are equivalent to clinical data.).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Wu, which teaches that the instructions are further executable by the processor to: provide clinical data to a medical provider, with the modified invention of Addison so that the clinician may define one or more sample areas so that analysis of the information may be performed (Wu ¶[0136]).
Regarding claim 10, Addison in combination with Wu and Lee teaches all limitations of claim 1 as described in the rejection above.
Addison teaches that the instructions are further executable by the processor to: output an alert based at least in part on the value of the biometric parameter, one or more characteristics of the waveform signal, or both (¶[0155], where “various types of alerts that may be used in accordance with tidal volume monitoring systems, methods, and computer readable media. For example, an alert may be triggered when a hypoventilation as described herein is detected. An alert may also be triggered if a tidal volume falls below a predetermined threshold. An alert may be triggered if a minute volume falls below a predetermined threshold. An alert may be triggered if no breathing activity is detected, or if no breathing activity is detected for at least a certain duration of time”).
Regarding claim 11, Addison in combination with Wu and Lee teaches all limitations of claim 1 as described in the rejection above.
Addison teaches that the instructions are further executable by the processor to: calculate one or more values of the biometric parameter based at least in part on the depth data (¶[0095], where “The image includes a patient 390 and a region of interest (ROI) 395. The ROI 395 can be used to determine a volume measurement from the chest of the patient 390. The ROI 395 is located on the patient's chest … Because the image includes depth data, such as from a depth sensing camera, information on the spatial location of the patient 390, and therefore the patient's chest and the ROI 395, can also be determined … As the patient 390 breathes, the patient's chest moves toward and away from the camera, changing the depth information associated with the images over time,” ¶[0096], where “tidal volume associated with a region of interest (ROI) may be calculated according to various embodiments described herein. Vectors associated with points within the ROI 395 are depicted in FIG. 5, where a schematic of the box values are shown to change over time. For example, these vectors represent movement of a patient's chest toward a camera as the patient's chest expands forward with inhalation. Similarly, the vectors will then move backward, away from the camera, when the patient's chest contrasts with exhalation. This movement forward and backward can be tracked to determine a respiration rate. Furthermore, this movement forward and backward can be integrated to determine a tidal volume”).
Regarding claim 13, see the rejection of claim 1 above, where the claim is directed to a method comprising substantially the same subject matter of claim 1, and is rejected under substantially the same sections of Addison in combination with Wu and Lee. However, claim 13 adds “a method comprising: … identifying a quantity of apnea events, a quantity of hypopnea events, or both from one or more characteristics of the waveform signal; setting a threshold condition based at least in part on the identified quantity or quantities; … an implantable neurostimulator … ; and monitoring a second set of depth data of the target subject to determine whether the control signal to the stimulation device requires modification”.
Addison teaches a method (Abstract, where “The present invention relates to the field of medical monitoring, and in particular non-contact video monitoring to measure tidal volume of a patient. Systems, methods, and computer readable media are described for determining a region of interest of a patient and monitoring that region of interest to determine tidal volume of the patient”) comprising:
setting a threshold condition (¶[0122], where “A threshold minute volume may also be determined as shown in FIG. 24. … a threshold minute volume may be determined that indicates a patient may be in the hypoventilation region. In some embodiments, a moving average may be used since some of the data points in the normoventilation region fall below the threshold minute volume”).
Although Addison teaches the collection of depth data from a depth sensing camera, Addison does not teach identifying a quantity of apnea events, a quantity of hypopnea events, or both from one or more characteristics of the waveform signal, setting a threshold condition based at least in part on the identified quantity or quantities; an implantable neurostimulator; nor monitoring a second set of depth data of the target subject to determine whether the control signal to the stimulation device requires modification.
Wu teaches outputting a control signal to an implantable neurostimulator (¶[0156], where “IMD 324 may determine the therapy by selecting one or more therapy parameter values … The therapy may include one or more of electrical stimulation therapy,” ¶[0163], where “IMD 324 is described as delivering electrical stimulation therapy to brain 322,” ¶[0175], where “IMD 324 includes processor 350, memory 360, stimulation generator 354, sensing module 356, switch module 352, telemetry module 358, sensor 359, and power source 370 … Memory 360 may store computer-readable instructions that, when executed by processor 350, cause IMD 324 to perform various functions,” ¶[0179], where “Stimulation generator 354, under the control of processor 350, generates stimulation signals for delivery to patient 12A”); and
monitoring a second set of depth data of the target subject to determine whether the control signal to the stimulation device requires modification (¶[0162], where “networked server 44 may be configured to request capture of supplemental video information of patient motion during a second period of time different than the first period of the previous video information 50. Based on the supplemental video information, networked server 44 may identify any patient behavior within the supplemental video information. Networked server 44 may receive an indication of the patient behavior during the second period and use the patient behavior to determine a different therapy to be delivered to patient 12A based on the indication of the patient behavior during the second period of time … networked server 44 may update the correlations or calibrations of the patient parameter values obtained during the second period of time to the newly identified patient behaviors”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Wu, which teaches outputting a control signal to an implantable neurostimulator; and monitoring a second set of depth data of the target subject to determine whether the control signal to the stimulation device requires modification, with the modified invention of Addison since identifying patient behaviors may be used to control or improve the delivery of therapy to patient, where identified patient behavior may be used as direct feedback used to control therapy delivery or to calibrate other sensors that provide sensed patient parameter values used as feedback in controlling therapy (Wu ¶[0137]), and in order to improve the precision with which therapy is directed to patient behavior (Wu ¶[0162]).
Although Addison teaches a waveform based on tidal volume of an individual, neither Addison nor Wu teach the processor identifying a quantity of apnea events, a quantity of hypopnea events, or both from one or more characteristics of the waveform signal, nor setting a threshold condition based at least in part on the identified quantity or quantities.
Lee teaches identifying a quantity of apnea events, a quantity of hypopnea events, or both from one or more characteristics of the waveform signal (¶[0037], where “an electronic device providing information on an apnea state according to distortion of a PPG signal,” and where a PPG signal is a type of waveform signal, ¶[0190], where “the electronic device 301 may provide information on an apnea state during sleeping. For example, the electronic device 301 may provide and store information on a number and/or a duration of apnea states during sleeping”); and
setting a threshold condition based at least in part on the identified quantity or quantities (¶[0204], where “The user may directly set at least one of … a threshold value for a duration of an apnea state, and a threshold value for a number of apnea states according to a degree of the apnea state of the electronic device.” Examiner takes the position that since a user can select at least one of these thresholds that both can be selected in order to set a threshold condition.).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Lee, which teaches the processor identifying a quantity of apnea events, a quantity of hypopnea events, or both from one or more characteristics of the waveform signal, and setting a threshold condition based at least in part on the identified quantity or quantities, with the modified invention of Addison since, if a duration of the apnea state is greater than a certain time, the apnea state may detrimentally influence the health of the user (Lee ¶[0194]).
Regarding claim 15, Addison in combination with Wu and Lee teaches all limitations of claim 13 as described in the rejection above. Furthermore, regarding claim 15, see the rejection of claim 4 above.
Regarding claim 16, Addison in combination with Wu and Lee teaches all limitations of claim 13 as described in the rejection above.
Wu teaches modifying the control signal based on the second set of depth data (¶[0162], where “networked server 44 may be configured to request capture of supplemental video information of patient motion during a second period of time different than the first period of the previous video information 50. Based on the supplemental video information, networked server 44 may identify any patient behavior within the supplemental video information. Networked server 44 may receive an indication of the patient behavior during the second period and use the patient behavior to determine a different therapy to be delivered to patient 12A based on the indication of the patient behavior during the second period of time … networked server 44 may update the correlations or calibrations of the patient parameter values obtained during the second period of time to the newly identified patient behaviors … programmer 24 or IMD 324 may perform the updates to the calibrations and/or associations of patient parameter values to therapy parameters”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Wu, which teaches modifying the control signal based on the second set of depth data, with the modified invention of Addison since identifying patient behaviors may be used to control or improve the delivery of therapy to patient, where identified patient behavior may be used as direct feedback used to control therapy delivery or to calibrate other sensors that provide sensed patient parameter values used as feedback in controlling therapy (Wu ¶[0137]), and in order to improve the precision with which therapy is directed to patient behavior (Wu ¶[0162]).
Regarding claim 17, Addison in combination with Wu and Lee teaches all limitations of claim 16 as described in the rejection above.
Wu teaches that modifying the control signal comprises modifying the threshold condition (¶[0161], where “A variety of different patient parameters may be monitored and used to provide feedback to control stimulation therapy. For example, a patient parameter may be a … relative motion between two locations of the patient, blood pressure, heart rate, patient speech pattern, patient breathing pattern, sleep indication, or a chemical indication,” ¶[0178], where “Feedback control 364 may include instructions that determine what feedback to use when controlling therapy delivery such as which therapy programs, therapy parameter sets, or individual therapy parameter values to select ... feedback control 364 may include associations of values for one or more sensed patient parameters (e.g., LFP signals or patient accelerations) to respective therapy parameter sets. The values of the sensed patient parameters may be calibrated or correlated with identified patient behaviors from captured video information. In any case, IMD 324 may use the instructions within feedback control 364 to adjust the therapy delivered to patient 12A.” Examiner takes the position that, since the patient parameters are being monitored and utilized to provide feedback which controls the stimulation therapy and selects specific therapy, there is an inherent threshold being met in order to apply stimulation to a patient. Furthermore, the threshold condition is modified since the patient parameters are continuously monitored in order to provide therapy feedback and adjust the delivered therapy. As the feedback is gathered and stimulation adjusted, the parameters will change in response to the stimulation, which consequently modifies the threshold during therapy.).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Wu, which teaches that modifying the control signal comprises modifying the threshold condition, with the modified invention of Addison since identifying patient behaviors may be used to control or improve the delivery of therapy to patient, where identified patient behavior may be used as direct feedback used to control therapy delivery or to calibrate other sensors that provide sensed patient parameter values used as feedback in controlling therapy (Wu ¶[0137]), and in order to improve the precision with which therapy is directed to patient behavior (Wu ¶[0162]).
Regarding claim 18, Addison in combination with Wu and Lee teaches all limitations of claim 17 as described in the rejection above.
Wu teaches that modifying the threshold condition comprises iteratively modifying the threshold condition (¶[0161], where “A variety of different patient parameters may be monitored and used to provide feedback to control stimulation therapy. For example, a patient parameter may be a … relative motion between two locations of the patient, blood pressure, heart rate, patient speech pattern, patient breathing pattern, sleep indication, or a chemical indication,” ¶[0178], where “Feedback control 364 may include instructions that determine what feedback to use when controlling therapy delivery such as which therapy programs, therapy parameter sets, or individual therapy parameter values to select ... feedback control 364 may include associations of values for one or more sensed patient parameters (e.g., LFP signals or patient accelerations) to respective therapy parameter sets. The values of the sensed patient parameters may be calibrated or correlated with identified patient behaviors from captured video information. In any case, IMD 324 may use the instructions within feedback control 364 to adjust the therapy delivered to patient 12A.” Examiner takes the position that since the patient parameters are being monitored to control stimulation therapy that there is an inherent threshold that is met in order to apply stimulation to a patient. Furthermore, the threshold condition is iteratively modified since the patient parameters are continuously monitored in order to provide therapy feedback. As the feedback is gathered and stimulation adjusted, the parameters will change in response to the stimulation, which consequently modifies the threshold during therapy.), and outputting the control signal until the waveform signal satisfies a set of criteria (¶[0178], where “Feedback control 364 may include instructions that determine what feedback to use when controlling therapy delivery such as which therapy programs, therapy parameter sets, or individual therapy parameter values to select ... feedback control 364 may include associations of values for one or more sensed patient parameters (e.g., LFP signals or patient accelerations) to respective therapy parameter sets. The values of the sensed patient parameters may be calibrated or correlated with identified patient behaviors from captured video information. In any case, IMD 324 may use the instructions within feedback control 364 to adjust the therapy delivered to patient 12A.” Examiner takes the position that, since the feedback control is continuous, that the control signal is output until a set of criteria is satisfied since that is when treatment completes.).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Wu, which teaches that that modifying the threshold condition comprises iteratively modifying the threshold condition and outputting the control signal until the waveform signal satisfies a set of criteria, with the modified invention of Addison since identifying patient behaviors may be used to control or improve the delivery of therapy to patient, where identified patient behavior may be used as direct feedback used to control therapy delivery or to calibrate other sensors that provide sensed patient parameter values used as feedback in controlling therapy (Wu ¶[0137]), and in order to improve the precision with which therapy is directed to patient behavior (Wu ¶[0162]).
Regarding claim 19, Addison in combination with Wu and Lee teaches all limitations of claim 18 as described in the rejection above.
Wu teaches that the set of criteria comprises a target waveform (¶[0142], where “the stimulation generator of IMD 324 may be configured to generate and deliver a continuous wave signal, e.g., a sine wave or triangle wave,” ¶[0178], where “Feedback control 364 may include instructions that determine what feedback to use when controlling therapy delivery such as which therapy programs, therapy parameter sets, or individual therapy parameter values to select ... feedback control 364 may include associations of values for one or more sensed patient parameters (e.g., LFP signals or patient accelerations) to respective therapy parameter sets. The values of the sensed patient parameters may be calibrated or correlated with identified patient behaviors from captured video information. In any case, IMD 324 may use the instructions within feedback control 364 to adjust the therapy delivered to patient 12A.” Examiner takes the position that, since the feedback control is continuous, that the control signal is output until a set of criteria is satisfied since that is when treatment completes and that there is a target waveform since a specific wave signal is utilized for effective treatment.).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Wu, which teaches that the set of criteria comprises a target waveform, with the modified invention of Addison in order to improve the precision with which therapy is directed to patient behavior (Wu ¶[0162]).
Regarding claim 20, Addison in combination with Wu and Lee teaches all limitations of claim 1 as described in the rejection above.
Wu teaches that the stimulation device comprises an implantable neurostimulator, a cardiac pacemaker, a cardioverter-defibrillator, or a drug delivery device (¶[0156], where “IMD 324 may determine the therapy by selecting one or more therapy parameter values … The therapy may include one or more of electrical stimulation therapy, drug delivery therapy (e.g., drug delivered from an implantable or external drug pump), or oral medication therapy,” ¶[0163], where “IMD 324 is described as delivering electrical stimulation therapy to brain 322 … system 320 may include an implantable drug pump in addition to, or in place of, electrical stimulator 324”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Wu, which teaches that the stimulation device comprises an implantable neurostimulator, a cardiac pacemaker, a cardioverter-defibrillator, or a drug delivery device, with the modified invention of Addison in order to treat a patient with a movement disorder using one or more therapies (Wu ¶[0004]).
Regarding claim 21, Addison in combination with Wu and Lee teaches all limitations of claim 1 as described in the rejection above.
Wu teaches that the instructions further comprise iteratively modifying the threshold condition (¶[0161], where “A variety of different patient parameters may be monitored and used to provide feedback to control stimulation therapy. For example, a patient parameter may be a … relative motion between two locations of the patient, blood pressure, heart rate, patient speech pattern, patient breathing pattern, sleep indication, or a chemical indication,” ¶[0178], where “Feedback control 364 may include instructions that determine what feedback to use when controlling therapy delivery such as which therapy programs, therapy parameter sets, or individual therapy parameter values to select ... feedback control 364 may include associations of values for one or more sensed patient parameters (e.g., LFP signals or patient accelerations) to respective therapy parameter sets. The values of the sensed patient parameters may be calibrated or correlated with identified patient behaviors from captured video information. In any case, IMD 324 may use the instructions within feedback control 364 to adjust the therapy delivered to patient 12A.” Examiner takes the position that since the patient parameters are being monitored to control stimulation therapy that there is an inherent threshold that is met in order to apply stimulation to a patient. Furthermore, the threshold condition is iteratively modified since the patient parameters are continuously monitored in order to provide therapy feedback. As the feedback is gathered and stimulation adjusted, the parameters will change in response to the stimulation, which consequently modifies the threshold during therapy.), and outputting the control signal based on the waveform signal satisfying the modified threshold condition until the waveform signal satisfies a set of criteria (¶[0178], where “Feedback control 364 may include instructions that determine what feedback to use when controlling therapy delivery such as which therapy programs, therapy parameter sets, or individual therapy parameter values to select ... feedback control 364 may include associations of values for one or more sensed patient parameters (e.g., LFP signals or patient accelerations) to respective therapy parameter sets. The values of the sensed patient parameters may be calibrated or correlated with identified patient behaviors from captured video information. In any case, IMD 324 may use the instructions within feedback control 364 to adjust the therapy delivered to patient 12A.” Examiner takes the position that, since the feedback control is continuous, that the control signal is output until a set of criteria is satisfied since that is when treatment completes.).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Wu, which teaches that the instructions further comprise iteratively modifying the threshold condition and outputting the control signal based on the waveform signal satisfying the modified threshold condition until the waveform signal satisfies a set of criteria, with the modified invention of Addison since identifying patient behaviors may be used to control or improve the delivery of therapy to patient, where identified patient behavior may be used as direct feedback used to control therapy delivery or to calibrate other sensors that provide sensed patient parameter values used as feedback in controlling therapy (Wu ¶[0137]), and in order to improve the precision with which therapy is directed to patient behavior (Wu ¶[0162]).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Addison in view of Wu and Lee as applied to the rejection of claim 1 above, and further in view of Shouldice et al. (hereinafter “Shouldice”) (U.S. Pub. No. 2018/0106897 A1).
Regarding claim 6, Addison in combination with Wu and Lee teaches all limitations of claim 4 as described in the rejection above.
None of Addison, Wu, nor Lee teach that the one or more characteristics of the waveform signal comprise a ratio associated with a first amplitude of the waveform signal and a second amplitude of the waveform signal.
Shouldice teaches sensors configured to detect characteristics of moving objects (Abstract, where “One or more processors, such as in a system of sensors or that control a sensor, may be configured to process signals from the one or more sensors to identify a person. The processing may include evaluating features from the signals such as breathing rate, respiration depth, degree of movement and heart rate”), where the one or more characteristics of the waveform signal (Figure 2A, which shows a raw motion signal equivalent to a waveform) comprise a ratio associated with a first amplitude of the waveform signal and a second amplitude of the waveform signal (¶[0030], where “evaluation may include classification of features determined from the signals. The features may include one or more of: a spectral peak ratio … a peak trough ratio …a ratio of maximum to minimum amplitude of a breathing cycle”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Shouldice, which teaches that the one or more characteristics of the waveform signal comprise a ratio associated with a first amplitude of the waveform signal and a second amplitude of the waveform signal, with the modified invention of Addison in order to identify or recognize a particular person (Shouldice ¶[0287]) and set operation of a respiratory treatment apparatus based on the person identified (Shouldice ¶[0036]).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Addison in view of Wu as applied to the rejection of claim 1 above, and further in view of Hartley et al. (hereinafter “Hartley”) (U.S. Pub. No. 2022/0133156 A1).
Regarding claim 12, Addison in combination with Wu and Lee teaches all limitations of claim 1 as described in the rejection above.
Addison teaches that the depth data comprises motion data (¶[0095], where “The image includes a patient 390 and a region of interest (ROI) 395. The ROI 395 can be used to determine a volume measurement from the chest of the patient 390. The ROI 395 is located on the patient's chest … Because the image includes depth data, such as from a depth sensing camera, information on the spatial location of the patient 390, and therefore the patient's chest and the ROI 395, can also be determined … As the patient 390 breathes, the patient's chest moves toward and away from the camera, changing the depth information associated with the images over time.” Examiner interprets that since the change in depth information is the movement of the chest as the chest moves towards and away from the camera that the depth data comprises motion data.).
None of Addison, Wu, nor Lee teaches that the instructions are further executable by the processor to: provide at least a portion of the motion data to a machine learning model; and receive an output from the machine learning model in response to the machine learning model processing at least the portion of the motion data, the output comprising one or more values of the biometric parameter.
Hartley teaches a system and method for monitoring vital signs of a subject, such as a sleeping patient (Abstract), and further teaches that the instructions are further executable by the processor to: provide at least a portion of the motion data to a machine learning model; and receive an output from the machine learning model in response to the machine learning model processing at least the portion of the motion data, the output comprising one or more values of the biometric parameter (¶[0046], where “The magnitude and profile of chest motion can be used in training a machine learning model 160, provided with actual measured breath volume, to predict tidal volume. Thus chest motion can be predictive of tidal volume, while the frequency is used to determine breathing rate. This motion can be detected from a variety of angles and under covers”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine the above-described teachings of Hartley, which teaches that the instructions are further executable by the processor to: provide at least a portion of the motion data to a machine learning model; and receive an output from the machine learning model in response to the machine learning model processing at least the portion of the motion data, the output comprising one or more values of the biometric parameter, with the modified invention of Addison since knowledge of the tidal volume is an important indication of the subject's health and since although, under ideal conditions, a sensor may detect a mass of hot, moist air being exhaled, this technique is sensitive to humidity, ambient temperature, and viewing angle. Utilizing the machine learning model may overcome these factors to predict tidal volume by incorporating contributions of these factors to the overall factorization machine (Hartley ¶[0045]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Yoon et al. (U.S. Pub. No. 2018/0206783 A1)
Kayyali et al. (U.S. Pat. No. 9,533,114 B1).
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/SEFRA D. MANOS/Examiner, Art Unit 3792
/UNSU JUNG/Supervisory Patent Examiner, Art Unit 3792