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 .
Response to Arguments
Applicant’s arguments with respect to claims 1, 10, and 19 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. The claims are rejected over Long/Stojadinovic in view of newly cited reference Burnett as set forth below.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-5, 9-20 and 23-25 are rejected under 35 U.S.C. 103 as being unpatentable over Long (US 20210077670 A1) in view of Stojadinovic (US 20200335179 A1), further in view of Burnett (US 20190343445 A1).
Regarding Claim 1, Long discloses a method ([abstract][0002][0006] methods for providing negative pressure with instillation fluids to a tissue site are disclosed) comprising: receiving patient information (¶[0006-0011] apparatus may include a pH sensor, a humidity sensor, a temperature sensor and a pressure sensor embodied on a single pad proximate the tissue site to provide data indicative of acidity, humidity, temperature and pressure. Such apparatus may further comprise an algorithm for processing such data for detecting leakage and blockage as well as providing information relating to the progression of healing of wounds at the tissue site.); selecting, based at least in part on the patient information, at least one negative-pressure wound therapy (NPWT) parameter setting for controlling a fluid at a wound site via a NPWT dressing that determines current causal relationships between a set of NPWT parameter settings and a set of effects of controlling the fluid at the wound site (Fig 18-19A-B, ¶[0002][0123-0130] providing negative-pressure therapy with fluid instillation therapy and sensing properties of wound exudates extracted from a tissue site (properties of wound exudate are considered patient information). The fluid instillation control algorithm 706 may include the detection of dressing flow characteristics within the system and an assessment of sensor properties based on the property data stored on the controller of the therapy system. The fluid instillation therapy algorithm 706 may commence by initializing the therapy settings at 603 including logging the baseline readings of the sensors at 703 and setting the initial values of the property signals provided by the sensors. The fill assist algorithm 710 may also continue providing fill assist at 716 by refilling the therapy cavity until previous fill volumes are achieved for another cycle of instillation therapy. Whenever the desired fill volume or fill volumes are achieved, the fill assist algorithm 710 may proceed back to the fluid instillation control algorithm 706 so that the sensor readings may be logged and assessed at 720 along with a measurement of the amount of fluid dispensed to the tissue interface, i.e., the dispensed fill volume.); and controlling the fluid at the wound site via the NPWT dressing based on the selected at least one NPWT parameter setting (Fig 18-19A-B, ¶[0123-0130] the fluid instillation control algorithm 706 may be configured to assess the pH data, the humidity data, the temperature data, and the dispensed volume of fluids, and then use such assessment data to evaluate the status of wound health of the tissue site at 721. The fluid instillation control algorithm 706 may be configured further to return to the wound pressure control algorithm 605 after such assessments have been completed.).
Long is silent in regards to a NPWT dressing based on a causal model that determines, in real time, current causal relationships between a set of NPWT parameter settings and a set of effects of controlling the fluid at the wound site; and controlling the fluid at the wound site via the NPWT dressing based on the selected at least one NPWT parameter setting.
However, Stojadinovic teaches a clinical decision module for wound therapy, thus from the same field of endeavor, wherein a NPWT dressing is based on a causal model ([abstract][0002][0016][0034][ fully unsupervised, machine-learned, cross-validated, and dynamic Bayesian Belief Network (BBN) model that utilizes clinical parameters for determined a patient-specific probability of the healing rate of an acute traumatic wound. Negative-pressure and vacuum-assisted closure (VAC) have improved wound management. BBN is trained using a machine learning algorithm applied to the specific patient study population) because the BBN-ML provides the surgical team with an estimate of wound-healing rate and the likelihood of healing success if the wound were to be closed. Providing such a quantitative and objective measure of wound status greatly reduces intra-observer variability and improves personalized, and in some cases wound-specific, treatment of trauma patients. The BBN-ML has machine-learning capabilities in that the accuracy of the BBN-ML improves with each additional patient's biomarker information entered into the database. Thus, data from the clinical study is collected for model refinement (¶[0144])
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Long so that a NPWT dressing is based on a causal model, as taught by Stojadinovic because the causal model provides the surgical team with an estimate of wound-healing rate and the likelihood of healing success if the wound were to be closed. Providing such a quantitative and objective measure of wound status greatly reduces intra-observer variability and improves personalized, and in some cases wound-specific, treatment of trauma patients. The causal model has machine-learning capabilities in that the accuracy of the causal model improves with each additional patient's biomarker information entered into the database. Thus, data from the clinical study is collected for model refinement (as motivated by Stojadinovic ¶[0144]).
Long/Stojadinovic is silent whether a causal model determines, in real time, current causal relationships between a set of NPWT parameter settings and a set of effects of controlling the fluid at the wound site; and controlling the fluid at the wound site via the NPWT dressing based on the selected at least one NPWT parameter setting.
However, Burnett teaches a negative pressure system for bodily fluids, thus from a similar field of endeavor as the claimed invention wherein a causal model determines, in real time, current causal relationships between a set of NPWT parameter settings and a set of effects of controlling the fluid at the wound site(¶[0238-0241][0246] loop controller uses patient parameters and system parameters to monitor and control therapies in a manner that is informed by machine learning and algorithmic tuning); and controlling the fluid at the wound site via the NPWT dressing based on the selected at least one NPWT parameter setting (¶[0246-0247] loop controller receives parameter inputs and used the information provided by the parameters to control the pump) in order to alert the provider to a worsening of the patient’s condition and may be used to provide automated adjustment (¶[0241]).
Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to modify the method of Long/Stojadinovic so that a causal model determines, in real time, current causal relationships between a set of NPWT parameter settings and a set of effects of controlling the fluid at the treatment site; and controlling the fluid at the treatment site via the NPWT dressing based on the selected at least one NPWT parameter setting in order to alert the provider to a worsening of the patient’s condition and may be used to provide automated adjustment (as motivated by Burnett ¶[0241]).
Regarding Claim 2, Long/Stojadinovic/Burnett discloses receiving a measure of an effect of controlling the fluid at the wound site; and adjusting, based on the received measure of the effect of controlling the fluid at the wound site, the causal model (Fig 18-19A-B ¶[0002][006-0011][0123-0130] fluid instillation control algorithm 706 may include the detection of dressing flow characteristics within the system and an assessment of sensor properties based on the property data stored on the controller of the therapy system… configured to assess the pH data, humidity data, temperature data, and the dispensed volume of fluids, and the use such assessment data to evaluate the status of wound health of the tissue site at 721).
Regarding Claim 3, Long/Stojadinovic/Burnett discloses wherein the at least one NPWT parameter setting is an at least one first NPWT parameter setting, the method further comprising: selecting, based on the adjusted causal model, at least one second NPWT parameter setting for controlling the fluid at the wound site; and controlling the fluid at the wound site via the NPWT dressing based on the selected at least one second NPWT parameter setting (¶[0097-0106] controller 110 may also be programmed with a negative pressure control algorithm that assesses the logged property signals to assess the status of wound healing and with that assessment adjust the pump pressure and/or the pump duty cycle if necessary to maintain the wound pressure proximate the desired target pressure. Dressing interface 400 of Fig 4, for applying negative-pressure therapy with fluid instillation therapy and sensing properties of wound exudates extracted from a tissue site as shown at 600).
Regarding Claim 4, Long/Stojadinovic/Burnett discloses wherein patient information comprises at least one of user input patient information, patient biometric information, and wound measurement information, wherein wound measurement information comprises the measure of the effect of controlling the fluid at the wound site (¶[0006-0011][0097-0106][0123-0130] include a pH sensor, a humidity sensor, a temperature sensor and a pressure sensor embodied on a single pad proximate the tissue site to provide data indicative of acidity, humidity, temperature, and pressure. Comprises an algorithm for processing such data for detecting leakage and blockage as well as providing information relating to the progression of healing of wounds at the tissue site ).
Regarding Claim 5, Long is silent wherein patient information comprises an aggregate of patient information of a plurality of patients.
However, Stojadinovic teaches a clinical decision module for wound therapy, thus from the same field of endeavor, wherein patient information comprises an aggregate of patient information of a plurality of patients (¶[0034][0091-0097][0141-0145]) BBN model is trained using a machine learning algorithm applied to the specific patient study population. BBN-ML has machine-learning capabilities in that the accuracy of the BBN-ML improves with each additional patient’s biomarker information entered into the database) to reduce intra-observer variability and improve personalized and wound specific treatment (¶[0144]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Long so that patient information comprises an aggregate of patient information of a plurality of patients, as taught by Stojadinovic to reduce intra-observer variability and improve personalized and wound specific treatment (as motivated by Stojadinovic ¶[0144]).
Regarding Claim 9, Long/Stojadinovic/Burnett discloses wherein controlling the fluid at the wound site comprises providing the fluid to the wound site via the NPWT therapy dressing (¶[0007] a dressing interface adapted to couple a source of negative-pressure to the tissue interface, wherein the dressing interface comprises a housing having a therapy cavity including an opening configured to be disposed in fluid communication with the tissue interface, and a negative-pressure port adapted to fluidly couple the therapy cavity to the source of negative-pressure.).
Regarding Claim 10, Long discloses a system ([abstract][0002][0006] methods for providing negative pressure with instillation fluids to a tissue site are disclosed) comprising: a memory; and one or more processors in communication with the memory (Fig 1¶[0062] controller 110 may be a microcontroller, which generally comprises an integrated circuit containing a processor core and a memory programmed to directly or indirectly control one or more operating parameters of the therapy system 100) and configured to: receive patient information (¶[0006-0011] apparatus may include a pH sensor, a humidity sensor, a temperature sensor and a pressure sensor embodied on a single pad proximate the tissue site to provide data indicative of acidity, humidity, temperature and pressure. Such apparatus may further comprise an algorithm for processing such data for detecting leakage and blockage as well as providing information relating to the progression of healing of wounds at the tissue site.); select, based at least in part on the patient information, at least one negative-pressure wound therapy (NPWT) parameter setting that determines current causal relationships between a set of NPWT parameter settings and a set of effects of controlling the fluid at the wound site (Fig 18-19A-B, ¶[0002][0123-0130] providing negative-pressure therapy with fluid instillation therapy and sensing properties of wound exudates extracted from a tissue site (properties of wound exudate are considered patient information). The fluid instillation control algorithm 706 may include the detection of dressing flow characteristics within the system and an assessment of sensor properties based on the property data stored on the controller of the therapy system. The fluid instillation therapy algorithm 706 may commence by initializing the therapy settings at 603 including logging the baseline readings of the sensors at 703 and setting the initial values of the property signals provided by the sensors. The fill assist algorithm 710 may also continue providing fill assist at 716 by refilling the therapy cavity until previous fill volumes are achieved for another cycle of instillation therapy. Whenever the desired fill volume or fill volumes are achieved, the fill assist algorithm 710 may proceed back to the fluid instillation control algorithm 706 so that the sensor readings may be logged and assessed at 720 along with a measurement of the amount of fluid dispensed to the tissue interface, i.e., the dispensed fill volume.); and control the fluid at the wound site via the NPWT dressing based on the selected at least one NPWT parameter setting (Fig 18-19A-B, ¶[0123-0130] the fluid instillation control algorithm 706 may be configured to assess the pH data, the humidity data, the temperature data, and the dispensed volume of fluids, and then use such assessment data to evaluate the status of wound health of the tissue site at 721. The fluid instillation control algorithm 706 may be configured further to return to the wound pressure control algorithm 605 after such assessments have been completed.).
Long is silent in regards to a NPWT dressing based on a causal model that determines, in real time, current causal relationships between a set of NPWT parameter settings and a set of effects of controlling the fluid at the wound site; and controlling the fluid at the wound site via the NPWT dressing based on the selected at least one NPWT parameter setting.
However, Stojadinovic teaches a clinical decision module for wound therapy, thus from the same field of endeavor, wherein a NPWT dressing based on a causal model ([abstract][0002][0016][0034][ fully unsupervised, machine-learned, cross-validated, and dynamic Bayesian Belief Network (BBN) model that utilizes clinical parameters for determined a patient-specific probability of the healing rate of an acute traumatic wound. Negative-pressure and vacuum-assisted closure (VAC) have improved wound management. BBN is trained using a machine learning algorithm applied to the specific patient study population) because the BBN-ML provides the surgical team with an estimate of wound-healing rate and the likelihood of healing success if the wound were to be closed. Providing such a quantitative and objective measure of wound status greatly reduces intra-observer variability and improves personalized, and in some cases wound-specific, treatment of trauma patients. The BBN-ML has machine-learning capabilities in that the accuracy of the BBN-ML improves with each additional patient's biomarker information entered into the database. Thus, data from the clinical study is collected for model refinement (¶[0144])
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Long so that a NPWT dressing is based on a causal model, as taught by Stojadinovic because the causal model provides the surgical team with an estimate of wound-healing rate and the likelihood of healing success if the wound were to be closed. Providing such a quantitative and objective measure of wound status greatly reduces intra-observer variability and improves personalized, and in some cases wound-specific, treatment of trauma patients. The causal model has machine-learning capabilities in that the accuracy of the causal model improves with each additional patient's biomarker information entered into the database. Thus, data from the clinical study is collected for model refinement (as motivated by Stojadinovic ¶[0144]).
Long/Stojadinovic is silent whether a causal model determines, in real time, current causal relationships between a set of NPWT parameter settings and a set of effects of controlling the fluid at the wound site; and controlling the fluid at the wound site via the NPWT dressing based on the selected at least one NPWT parameter setting.
However, Burnett teaches a negative pressure system for bodily fluids, thus from a similar field of endeavor as the claimed invention wherein a causal model determines, in real time, current causal relationships between a set of NPWT parameter settings and a set of effects of controlling the fluid at the wound site(¶[0238-0241][0246] loop controller uses patient parameters and system parameters to monitor and control therapies in a manner that is informed by machine learning and algorithmic tuning); and controlling the fluid at the wound site via the NPWT dressing based on the selected at least one NPWT parameter setting (¶[0246-0247] loop controller receives parameter inputs and used the information provided by the parameters to control the pump) in order to alert the provider to a worsening of the patient’s condition and may be used to provide automated adjustment (¶[0241]).
Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to modify the system of Long/Stojadinovic so that a causal model determines, in real time, current causal relationships between a set of NPWT parameter settings and a set of effects of controlling the fluid at the treatment site; and controlling the fluid at the treatment site via the NPWT dressing based on the selected at least one NPWT parameter setting in order to alert the provider to a worsening of the patient’s condition and may be used to provide automated adjustment (as motivated by Burnett ¶[0241]).
Regarding Claim 11, Long/Stojadinovic/Burnett discloses receiving a measure of an effect of controlling the fluid at the wound site; and adjusting, based on the received measure of the effect of controlling the fluid at the wound site, the causal model (Fig 18-19A-B ¶[0002][006-0011][0123-0130] fluid instillation control algorithm 706 may include the detection of dressing flow characteristics within the system and an assessment of sensor properties based on the property data stored on the controller of the therapy system… configured to assess the pH data, humidity data, temperature data, and the dispensed volume of fluids, and the use such assessment data to evaluate the status of wound health of the tissue site at 721).
Regarding Claim 12, Long/Stojadinovic/Burnett discloses wherein the at least one NPWT parameter setting is an at least one first NPWT parameter setting, wherein the one or more processors are further configured to: select based on the adjusted causal model, at least one second NPWT parameter setting for controlling the fluid at the wound site; and controlling the fluid at the wound site via the NPWT dressing based on the selected at least one second NPWT parameter setting (¶[0097-0106] controller 110 may also be programmed with a negative pressure control algorithm that assesses the logged property signals to assess the status of wound healing and with that assessment adjust the pump pressure and/or the pump duty cycle if necessary to maintain the wound pressure proximate the desired target pressure. Dressing interface 400 of Fig 4, for applying negative-pressure therapy with fluid instillation therapy and sensing properties of wound exudates extracted from a tissue site as shown at 600).
Regarding Claim 13, Long/Stojadinovic/Burnett discloses wherein patient information comprises at least one of user input patient information, patient biometric information, and wound measurement information, wherein wound measurement information comprises the measure of the effect of controlling the fluid at the wound site (¶[0006-0011][0097-0106][0123-0130] include a pH sensor, a humidity sensor, a temperature sensor and a pressure sensor embodied on a single pad proximate the tissue site to provide data indicative of acidity, humidity, temperature, and pressure. Comprises an algorithm for processing such data for detecting leakage and blockage as well as providing information relating to the progression of healing of wounds at the tissue site ).
Regarding Claim 14, Long is silent wherein patient information comprises an aggregate of patient information of a plurality of patients.
However, Stojadinovic teaches a clinical decision module for wound therapy, thus from the same field of endeavor, wherein patient information comprises an aggregate of patient information of a plurality of patients (¶[0034][0091-0097][0141-0145]) BBN model is trained using a machine learning algorithm applied to the specific patient study population. BBN-ML has machine-learning capabilities in that the accuracy of the BBN-ML improves with each additional patient’s biomarker information entered into the database) to reduce intra-observer variability and improve personalized and wound specific treatment (¶[0144]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Long so that patient information comprises an aggregate of patient information of a plurality of patients, as taught by Stojadinovic to reduce intra-observer variability and improve personalized and wound specific treatment (as motivated by Stojadinovic ¶[0144]).
Regarding Claim 15, Long/Stojadinovic/Burnett discloses wherein wound measurement information and the measure of the effect of controlling the fluid at the wound site comprises at least one of an impedance measurement of wounded tissue, an oxygen measurement of a wound bed, an oxygen measurement of the fluid, a carbon dioxide measurement of the wound bed, a temperature measurement, an analyte sensor measurement, and an optical measurement of the wound bed (¶[0006-0011][0063][0081] pressure sensor, humidity sensor, temperature sensor, oxygen sensor).
Regarding Claim 16, Long/Stojadinovic/Burnett discloses wherein the at least one first NPWT parameter setting and the at least one second NPWT parameter setting each comprise a setting of a NPWT parameter of a set of NPWT parameters, the set of NPWT parameters comprising at least one of a negative pressure level, a negative pressure cycling, a continuous negative pressure application, a fluid flow rate, a fluid volume, a fluid pressure, a fluid temperature, a fluid composition, a fluid dwell time, or a fluid purge time (¶[0006-0011][0097-0106][0111][0123-0130] therapy settings for the fluid instillation therapy phase may further include any initial values associated with the fill volume and the soak time, as well as an instillation pump pressure, an instillation duty cycle, and a desired fluid pressure of the instillation pump 116 for the fluid instillation therapy phase of the therapy treatment).
Regarding Claim 17, Long/Stojadinovic/Burnett discloses wherein the at least one NPWT parameter setting comprises a predetermined range of values (Fig 13A-C ¶[0006-0011][0097-0106][0111][0123-0130] blockage/leak detection algorithm 620 determines whether there is a dead space detection variance of less than a predetermined value, e.g. 200cc, at 633. If the variance is not less than the predetermined value, i.e., if the variance is greater than the predetermined value, the blockage/leak detection algorithm 620 generates a fluid leak alarm at 631. If the blockage/leak detection algorithm 620 determines that the variance is less than the predetermined value, the blockage/leak detection algorithm 620 generates a canister full alert 667 (not shown in FIG. 13A) and then progresses to log a new set of sensor readings at 650.).
Regarding Claim 18, Long/Stojadinovic/Burnett discloses wherein controlling the fluid at the wound site comprises providing the fluid to the wound site via the NPWT therapy dressing (¶[0007] a dressing interface adapted to couple a source of negative-pressure to the tissue interface, wherein the dressing interface comprises a housing having a therapy cavity including an opening configured to be disposed in fluid communication with the tissue interface, and a negative-pressure port adapted to fluidly couple the therapy cavity to the source of negative-pressure.).
Regarding Claim 19, Long discloses a computer readable medium comprising instructions that when executed cause one or more processors ([abstract][¶[0062] A controller, such as the controller 110, may be a microprocessor or computer programmed to operate one or more components of the therapy system 100, such as the negative-pressure source 104. In some embodiments, for example, the controller 110 may be a microcontroller, which generally comprises an integrated circuit containing a processor core and a memory programmed to directly or indirectly control one or more operating parameters of the therapy system 100.) to: receive patient information (¶[0006-0011] apparatus may include a pH sensor, a humidity sensor, a temperature sensor and a pressure sensor embodied on a single pad proximate the tissue site to provide data indicative of acidity, humidity, temperature and pressure. Such apparatus may further comprise an algorithm for processing such data for detecting leakage and blockage as well as providing information relating to the progression of healing of wounds at the tissue site.); select, based at least in part on the patient information, at least one negative-pressure wound therapy (NPWT) parameter setting that determines current causal relationships between a set of NPWT parameter settings and a set of effects of controlling the fluid at the wound site (Fig 18-19A-B, ¶[0002][0123-0130] providing negative-pressure therapy with fluid instillation therapy and sensing properties of wound exudates extracted from a tissue site (properties of wound exudate are considered patient information). The fluid instillation control algorithm 706 may include the detection of dressing flow characteristics within the system and an assessment of sensor properties based on the property data stored on the controller of the therapy system. The fluid instillation therapy algorithm 706 may commence by initializing the therapy settings at 603 including logging the baseline readings of the sensors at 703 and setting the initial values of the property signals provided by the sensors. The fill assist algorithm 710 may also continue providing fill assist at 716 by refilling the therapy cavity until previous fill volumes are achieved for another cycle of instillation therapy. Whenever the desired fill volume or fill volumes are achieved, the fill assist algorithm 710 may proceed back to the fluid instillation control algorithm 706 so that the sensor readings may be logged and assessed at 720 along with a measurement of the amount of fluid dispensed to the tissue interface, i.e., the dispensed fill volume.); and control the fluid at the wound site via the NPWT dressing based on the selected at least one first NPWT parameter setting (Fig 18-19A-B, ¶[0123-0130] the fluid instillation control algorithm 706 may be configured to assess the pH data, the humidity data, the temperature data, and the dispensed volume of fluids, and then use such assessment data to evaluate the status of wound health of the tissue site at 721. The fluid instillation control algorithm 706 may be configured further to return to the wound pressure control algorithm 605 after such assessments have been completed.).
Long is silent in regards to a NPWT dressing based on a causal model that determines, in real time, current causal relationships between a set of NPWT parameter settings and a set of effects of controlling the fluid at the wound site; and controlling the fluid at the wound site via the NPWT dressing based on the selected at least one NPWT parameter setting.
However, Stojadinovic teaches a clinical decision module for wound therapy, thus from the same field of endeavor, wherein a NPWT dressing based on a causal model ([abstract][0002][0016][0034][ fully unsupervised, machine-learned, cross-validated, and dynamic Bayesian Belief Network (BBN) model that utilizes clinical parameters for determined a patient-specific probability of the healing rate of an acute traumatic wound. Negative-pressure and vacuum-assisted closure (VAC) have improved wound management. BBN is trained using a machine learning algorithm applied to the specific patient study population) because the BBN-ML provides the surgical team with an estimate of wound-healing rate and the likelihood of healing success if the wound were to be closed. Providing such a quantitative and objective measure of wound status greatly reduces intra-observer variability and improves personalized, and in some cases wound-specific, treatment of trauma patients. The BBN-ML has machine-learning capabilities in that the accuracy of the BBN-ML improves with each additional patient's biomarker information entered into the database. Thus, data from the clinical study is collected for model refinement (¶[0144])
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Long so that a NPWT dressing is based on a causal model, as taught by Stojadinovic because the causal model provides the surgical team with an estimate of wound-healing rate and the likelihood of healing success if the wound were to be closed. Providing such a quantitative and objective measure of wound status greatly reduces intra-observer variability and improves personalized, and in some cases wound-specific, treatment of trauma patients. The causal model has machine-learning capabilities in that the accuracy of the causal model improves with each additional patient's biomarker information entered into the database. Thus, data from the clinical study is collected for model refinement (as motivated by Stojadinovic ¶[0144]).
Long/Stojadinovic is silent whether a causal model determines, in real time, current causal relationships between a set of NPWT parameter settings and a set of effects of controlling the fluid at the wound site; and controlling the fluid at the wound site via the NPWT dressing based on the selected at least one NPWT parameter setting.
However, Burnett teaches a negative pressure system for bodily fluids, thus from a similar field of endeavor as the claimed invention wherein a causal model determines, in real time, current causal relationships between a set of NPWT parameter settings and a set of effects of controlling the fluid at the wound site (¶[0238-0241][0246] loop controller uses patient parameters and system parameters to monitor and control therapies in a manner that is informed by machine learning and algorithmic tuning); and controlling the fluid at the wound site via the NPWT dressing based on the selected at least one NPWT parameter setting (¶[0246-0247] loop controller receives parameter inputs and used the information provided by the parameters to control the pump) in order to alert the provider to a worsening of the patient’s condition and may be used to provide automated adjustment (¶[0241]).
Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to modify the device of Long/Stojadinovic so that a causal model determines, in real time, current causal relationships between a set of NPWT parameter settings and a set of effects of controlling the fluid at the treatment site; and controlling the fluid at the treatment site via the NPWT dressing based on the selected at least one NPWT parameter setting in order to alert the provider to a worsening of the patient’s condition and may be used to provide automated adjustment (as motivated by Burnett ¶[0241]).
Regarding Claim 20, Long/Stojadinovic/Burnett discloses wherein the at least one NPWT parameter setting is an at least one first NPWT parameter setting, the computer readable medium further comprising instructions that when executed cause one or more processors to: select based on the adjusted causal model, at least one second NPWT parameter setting for controlling the fluid at the wound site; and controlling the fluid at the wound site via the NPWT dressing based on the selected at least one second NPWT parameter setting (¶[0097-0106] controller 110 may also be programmed with a negative pressure control algorithm that assesses the logged property signals to assess the status of wound healing and with that assessment adjust the pump pressure and/or the pump duty cycle if necessary to maintain the wound pressure proximate the desired target pressure. Dressing interface 400 of Fig 4, for applying negative-pressure therapy with fluid instillation therapy and sensing properties of wound exudates extracted from a tissue site as shown at 600).
Regarding Claim 23, Long/Stojadinovic is silent whether the causal model is implemented as a feedback control system that updates the selection of the at least one NPWT parameter setting based on measured effects of controlling the fluid at the wound site.
However, Burnett teaches a negative pressure system for bodily fluids, thus from a similar field of endeavor as the claimed invention wherein the causal model is implemented as a feedback control system that updates the selection of the at least one NPWT parameter setting based on measured effects of controlling the fluid at the wound site (¶[0238-0241][0246-0247] loop controller uses patient parameters and system parameters to monitor and control therapies in a manner that is informed by machine learning and algorithmic tuning. Loop controller receives parameter inputs and uses the information provided by the parameters to control the pump) in order to alert the provider to a worsening of the patient’s condition and may be used to provide automated adjustment (¶[0241]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Long/Stojadinovic so that the causal model is implemented as a feedback control system that updates the selection of the at least one NPWT parameter setting based on measured effects of controlling the fluid at the wound site, as taught by Burnett in order to alert the provider to a worsening of the patient’s condition and may be used to provide automated adjustment (as motivated by Burnett ¶[0241]).
Regarding Claim 24, Long/Stojadinovic is silent whether the feedback control system repeatedly modifies the determination of current causal relationships between NPWT parameter settings and effects at the wound site based on ongoing sensor data collected during therapy.
However, Burnett teaches a negative pressure system for bodily fluids, thus from a similar field of endeavor as the claimed invention, wherein the feedback control system repeatedly modifies the determination of current causal relationships between NPWT parameter settings and effects at the wound site based on ongoing sensor data collected during therapy (¶[0246-0248] loop controller continues to monitor patient parameters and adjust the treating medical devices accordingly) in order to alert the provider to a worsening of the patient’s condition and may be used to provide automated adjustment (¶[0241]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Long/Stojadinovic so that the feedback control system repeatedly modifies the determination of current causal relationships between NPWT parameter settings and effects at the wound site based on ongoing sensor data collected during therapy, as taught by Burnett in order to alert the provider to a worsening of the patient’s condition and may be used to provide automated adjustment (as motivated by Burnett ¶[0241]).
Regarding Claim 25, Long/Stojadinovic is silent whether the causal model determines and updates current causal relationships between a set of NPWT parameter settings and a set of effects of controlling the fluid at the wound site during therapy, based on ongoing sensor data.
However, Burnett teaches a negative pressure system for bodily fluids, thus from a similar field of endeavor as the claimed invention, wherein the causal model determines and updates current causal relationships between a set of NPWT parameter settings and a set of effects of controlling the fluid at the wound site during therapy, based on ongoing sensor data (¶[0246-0248] loop controller continues to monitor multiple patient parameters and adjust the multiple treating medical devices accordingly) in order to alert the provider to a worsening of the patient’s condition and may be used to provide automated adjustment (¶[0241]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device of Long/Stojadinovic so that the causal model determines and updates current causal relationships between a set of NPWT parameter settings and a set of effects of controlling the fluid at the wound site during therapy, based on ongoing sensor data, as taught by Burnett, in order to alert the provider to a worsening of the patient’s condition and may be used to provide automated adjustment (as motivated by Burnett ¶[0241]).
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.
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TIMOTHY LEE. FLYNN
Examiner
Art Unit 3781
/REBECCA E EISENBERG/Supervisory Patent Examiner, Art Unit 3781
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