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 .
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.
Claim(s) 1-2, 4-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kayser (US 20220108447 A1) in view of Shelton (US 20220240869 A1).
Regarding claim 1, Kayser teaches a method for tracking and assessing a wound ([abstract] This disclosure is directed towards a patient management system for analyzing images of wounds and tracking the progression of wounds over time), the method comprising: identifying a wound from an image output by a camera ([0031] To determine a stage of the wound 104 for the classification, the machine-learned model 122 may determine various characteristics of the wound from the image data 118); identifying a set of wound features based on the image ([0031] the image data 118 may comprise images and/or video, where the machine-learned model 122 may determine characteristics of the wound based on frames of the video input into the machine-learned model 122); detecting a temperature of the wound from at least one heat sensor ([0059] In some examples, the information 508 may include characteristics of the wound 506, such as temperature of the wound 506, blood flow to the wound 506, and so forth. The patient management system 112 may determine a temperature of the wound 506 from digital and/or thermal imaging of the wound 506 (e.g., via a thermographic camera)) ([0081] the patient management system 112 may use data collected using other sensor modalities to determine the progression of the wound over time, such as temperature measurements, mass measurements, volume measurements, and so forth); processing the set of wound features and wound temperature using machine learning ([0031] the image data 118 may comprise images and/or video, where the machine-learned model 122 may determine characteristics of the wound based on frames of the video input into the machine-learned model 122) ([0059] In some examples, the information 508 may include characteristics of the wound 506, such as temperature of the wound 506, blood flow to the wound 506, and so forth. The patient management system 112 may determine a temperature of the wound 506 from digital and/or thermal imaging of the wound 506 (e.g., via a thermographic camera)); determining an optimal wound healing trajectory based on the set of wound features and the wound temperature using machine learning ([0026] For instance, the machine-learned model 122 may represent future states of an entity (e.g., a wound), such as: 1) a probability distribution over the entity state space at each timestep; 2) multimodal (e.g., representing a plurality of possible progressions) to cover a diversity of possible implicit progressions an entity might follow (e.g., a rate of healing of the wound based on different levels of adherence to a treatment protocol); and 3) one-shot, meaning the ability to predict full progressions (and/or time sequences of state distributions) without iteratively applying a recurrence step) ([0058] The information 508 may also indicate that a wound specialist review has been ordered. For instance, the patient management system 112 may determine that the wound 506 is not progressing towards healing at a desired rate, and based on this determination, may automatically request a WOC to analyze the wound); predicting at least one future wound condition based on the optimal wound healing trajectory using machine learning (([0026] For instance, the machine-learned model 122 may represent future states of an entity (e.g., a wound), such as: 1) a probability distribution over the entity state space at each timestep; 2) multimodal (e.g., representing a plurality of possible progressions) to cover a diversity of possible implicit progressions an entity might follow (e.g., a rate of healing of the wound based on different levels of adherence to a treatment protocol)); and displaying the optimal wound healing trajectory and the at least one future wound condition on a display ([0044] The user interface 204 may be displayed in a touch interface of the device 202) ([0031] In some cases, the machine-learned model 122 may receive multiple instances of image data 118 that were captured at different times, and output the classification (e.g., the stage) based at least in part on differences in characteristics of the wound between the multiple instances of image data 118 and an amount of time between the instances of image data 118 being captured. In some instances, the machine-learned model 122 may use the stage information over time to predict progression of the wound in the future, similar to the description above)) ([0055] FIG. 5 is an example environment 500 including a device 502 displaying an image 504 depicting a wound 506 and information 508 related to the wound 506).
Kayser fails to fully teach triggering at least one response if the wound temperature is outside of a therapeutic temperature zone.
However, Shelton teaches triggering at least one response if the wound temperature is outside of a therapeutic temperature zone ([0055] For example, the core body temperature sensing system may detect abnormal temperature based on temperature being outside the range of 36.5° C. and 37.5° C) ([0056] For example, the body temperature sensing system may detect core body temperature data and trigger the sensing system to emit a cooling or heating element to raise or lower the body temperature in line with the measured ambient temperature). It would have been obvious to one of ordinary skill in the art before the effective filling date to have modified the invention of Kayser to include triggering at least one response if the wound temperature is outside of a therapeutic temperature zone. Doing so allows for alerting the user or correcting the temperature if the wound is out of a range for optimal healing.
Regarding claim 2, Kayser teaches the method according to claim 1, wherein at least one of the at least one heat sensor is a thermal camera ([0059] In some examples, the information 508 may include characteristics of the wound 506, such as temperature of the wound 506, blood flow to the wound 506, and so forth. The patient management system 112 may determine a temperature of the wound 506 from digital and/or thermal imaging of the wound 506 (e.g., via a thermographic camera)).
Regarding claim 4, Kayser teaches the method according to claim 1, wherein triggering the at least one response comprises sending an alert ([0034] the patient management system 112 may use machine learning and/or rules-based algorithms to determine whether a physician or WOC is needed to evaluate the wound 104, and if so, may send an alert 120 that an in-person evaluation is needed to a different one of the clinician devices 110 associated with the appropriate healthcare provider), but fails to teach if the wound temperature is outside of the therapeutic temperature zone.
However, Shelton teaches if the wound temperature is outside of the therapeutic temperature zone ([0055] For example, the core body temperature sensing system may detect abnormal temperature based on temperature being outside the range of 36.5° C. and 37.5° C. For example, the core body temperature sensing system may detect post-operation infection or sepsis based on certain temperature fluctuations and/or when core body temperature reaches abnormal levels). It would have been obvious to one of ordinary skill in the art before the effective filling date to have modified the invention of Kayser to include if the wound temperature is outside of the therapeutic temperature zone. Doing so allows for alerting the user if the wound is out of a range for optimal healing.
Regarding claim 5, Kayser teaches the method according to claim 1, but fails to teach wherein triggering the at least one response comprises warming the wound to the therapeutic temperature zone if the wound temperature is outside of the therapeutic temperature zone.
However, Shelton teaches wherein triggering the at least one response comprises warming the wound to the therapeutic temperature zone if the wound temperature is outside of the therapeutic temperature zone ([0056] For example, the body temperature sensing system may detect core body temperature data and trigger the sensing system to emit a cooling or heating element to raise or lower the body temperature in line with the measured ambient temperature). It would have been obvious to one of ordinary skill in the art before the effective filling date to have modified the invention of Kayser to include wherein triggering the at least one response comprises warming the wound to the therapeutic temperature zone if the wound temperature is outside of the therapeutic temperature zone. Doing so allows for maintaining the wound within a range for optimal healing.
Regarding claim 6, Kayser teaches the method according to claim 1, wherein triggering the at least one response comprises documenting the set of wound features and the wound temperature in an electronic medical record (0033] In some examples, the patient management system 112, patient devices 106, the healthcare establishment devices 108, and/or the clinician devices 110 may generate, store, and/or selectively share the patient data 116 and/or the image data 118 between one another to provide the patient 102 and/or persons assisting with treatment of the patient 102 with improved outcomes by effectively communicating information about the wound 104) ([0034] For example, the bedside nurse may use a camera of the clinician device 110 to capture an image and/or video of the wound 104 as described herein, and use the healthcare application to share the image and/or video (e.g., image data 118) with other healthcare providers who may have more knowledge about wounds than the bedside nurse (e.g., a physician or WOC), to store the image data 118 in an EMR associated with the patient 102).
Regarding claim 7, Kayser teaches the method according to claim 1, further comprising training a machine learning algorithm with a library of wound statistics ([0080] FIG. 9 is an example process 900 for training a machine-learned model to predict a progression of a wound, according to the techniques described herein) ([0084] the machine-learned model 122 is a supervised model, in which the model is trained using labeled training examples to generate an inferred function to map new, unlabeled examples. Alternatively or additionally, the machine-learned model 122 trained to determine a characteristic of a wound or predicted progression of wounds may be an unsupervised model, which may identify commonalities in an input data set and may react based on the presence or absence of such commonalities in each new piece of data) ([0086] According to some examples, the machine-learned model 122 may be trained using training data generated based on historical images (and/or previously generated outputs based on such historical data) from one or more perception logs or other sources of historical images. The training data may be generated by associating log data such as historical image data indicating the actual measured progression of the wound depicted in the image over time).
Regarding claim 8, Kayser teaches the method according to claim 7, wherein the library of wound statistics includes at least one of: similar wound features, similar wound temperatures, stages of wound healing, subject demographics, subject comorbidities, or therapies and procedures that impact wound healing ([0084] the machine-learned model 122 is a supervised model, in which the model is trained using labeled training examples to generate an inferred function to map new, unlabeled examples. Alternatively or additionally, the machine-learned model 122 trained to determine a characteristic of a wound or predicted progression of wounds may be an unsupervised model, which may identify commonalities in an input data set and may react based on the presence or absence of such commonalities in each new piece of data) ([0086] According to some examples, the machine-learned model 122 may be trained using training data generated based on historical images (and/or previously generated outputs based on such historical data) from one or more perception logs or other sources of historical images. The training data may be generated by associating log data such as historical image data indicating the actual measured progression of the wound depicted in the image over time. The log data may indicate a size, color, or the like of various features of the wound, which may be used to determine a progression of the wound over time. For instance, an image depicting a wound of Wound Classification Stage III can be labeled with an actual measured length, width, height, depth, and/or subcutaneous fat that is visible at the site of the wound at the time the image was captured (e.g., as may be provided by the user inputs from manual measurements of the wound depicted in the image) and/or at a time following the time at which the image was captured).
Regarding claim 9, Kayser teaches the method according to claim 1, further comprising outputting a wound healing recommendation as a result of an analysis of the set of wound features and wound temperature with the machine learning ([0039] Further, in some examples, the patient management system 112 may generate a treatment recommendation based on the determined efficacy of the treatment. The treatment recommendation may include one or more of increasing or decreasing a frequency of a treatment, adding an additional treatment to a current treatment, adding a different treatment while ceasing a current treatment, and so on).
Regarding claim 10, Kayser teaches the method according to claim 9, wherein the wound healing recommendation includes at least one of: best practices for care of the wound or risks associated with healing of the wound ([0039] Further, in some examples, the patient management system 112 may generate a treatment recommendation based on the determined efficacy of the treatment. The treatment recommendation may include one or more of increasing or decreasing a frequency of a treatment, adding an additional treatment to a current treatment, adding a different treatment while ceasing a current treatment, and so on) ([0039] the patient management system 112 may generate a treatment recommendation that turning the patient 102 in the hospital bed according to the set schedule is insufficient to improve the condition of the wound 104, and an oral medication is recommended to assist with healing the wound 104).
Regarding claim 11, Kayser teaches the method according to claim 1, wherein detecting the temperature of the wound from at least one heat sensor is performed in real time ([0059] In some examples, the information 508 may include characteristics of the wound 506, such as temperature of the wound 506, blood flow to the wound 506, and so forth. The patient management system 112 may determine a temperature of the wound 506 from digital and/or thermal imaging of the wound 506 (e.g., via a thermographic camera)) ([0081] the patient management system 112 may use data collected using other sensor modalities to determine the progression of the wound over time, such as temperature measurements, mass measurements, volume measurements, and so forth).
Regarding claim 12, Kayser teaches the method according to claim 1, but fails to teach further comprising detecting a humidity of the wound based on one or more signals obtained from the at least one heat sensor.
However, Shelton teaches further comprising detecting a humidity of the wound based on one or more signals obtained from the at least one heat sensor ([0122] In an example, the detection, prediction, and/or determination described herein may be performed by a computing system based on measured data and/or related biomarkers generated by the sweat sensing system. The sweat sensing system may locally process sweat data or transmit the sweat data to a processing unit). It would have been obvious to one of ordinary skill in the art before the effective filling date to have modified the invention of Kayser to include further comprising detecting a humidity of the wound based on one or more signals obtained from the at least one heat sensor. Doing so allows for sensing if the wound is within a range for optimal healing.
Regarding claim 13, Kayser teaches a system for tracking and assessing a wound ([abstract] This disclosure is directed towards a patient management system for analyzing images of wounds and tracking the progression of wounds over time), the system comprising: a display ([0044] The user interface 204 may be displayed in a touch interface of the device 202); a camera configured to output image data ([0031] the image data 118 may comprise images and/or video, where the machine-learned model 122 may determine characteristics of the wound based on frames of the video input into the machine-learned model 122); at least one heat sensor configured to output temperature data ([0059] In some examples, the information 508 may include characteristics of the wound 506, such as temperature of the wound 506, blood flow to the wound 506, and so forth. The patient management system 112 may determine a temperature of the wound 506 from digital and/or thermal imaging of the wound 506 (e.g., via a thermographic camera)) ([0081] the patient management system 112 may use data collected using other sensor modalities to determine the progression of the wound over time, such as temperature measurements, mass measurements, volume measurements, and so forth); a memory ([0103] The computer-readable media 1106 is illustrated as including a memory/storage component 1112. The memory/storage component 1112 represents memory/storage capacity associated with one or more computer-readable media) containing a machine readable medium including machine executable code having instructions stored thereon ([0107] The computer-readable storage media includes hardware such as volatile and non-volatile, removable and non-removable media and/or storage devices implemented in a method or technology suitable for storage of information such as computer-readable instructions, data structures, program modules, logic elements/circuits, or other data); a controller communicatively coupled to the display ([0104] Input/output interface(s) 1108 are representative of functionality to allow a user to enter commands and information to computing device 1102, and also allow information to be presented to the user and/or other components or devices using various input/output devices. Examples of input devices include a keyboard, a cursor control device (e.g., a mouse), a microphone, a scanner, touch functionality), camera ([0024] the image data 118 collected by a camera associated with the patient devices 106 and/or the clinician devices 110), at least one heat sensor ([0059] In some examples, the information 508 may include characteristics of the wound 506, such as temperature of the wound 506, blood flow to the wound 506, and so forth. The patient management system 112 may determine a temperature of the wound 506 from digital and/or thermal imaging of the wound 506 (e.g., via a thermographic camera)) ([0081] the patient management system 112 may use data collected using other sensor modalities to determine the progression of the wound over time, such as temperature measurements, mass measurements, volume measurements, and so forth), and memory ([0103] The computer-readable media 1106 is illustrated as including a memory/storage component 1112. The memory/storage component 1112 represents memory/storage capacity associated with one or more computer-readable media), the controller including one or more processors and configured to execute the machine executable code ([0007] In some examples, a system includes a camera, a display, one or more processors, and one or more computer-readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations) to cause the controller to: identify a wound and a set of wound features from the image data ([0007] the one or more processors may receive a selection of a body part type in a user interface, and receive a feed from the camera. In examples, the one or more processors cause an outline of the body part type to be displayed over the feed on the display, then capture an image of the feed. The one or more processors may determine that the image depicts a body part of the body part type associated with the outline and determine a size of the body part from the image and associated with the outline. In some examples, the one or more processors determine that the image depicts a wound, a classification of the wound, and determines a characteristic of the wound depicted in the image based at least in part on the size of the body part as depicted in the image); detect a temperature of the wound from the at least one heat sensor ([0059] In some examples, the information 508 may include characteristics of the wound 506, such as temperature of the wound 506, blood flow to the wound 506, and so forth. The patient management system 112 may determine a temperature of the wound 506 from digital and/or thermal imaging of the wound 506 (e.g., via a thermographic camera)) ([0081] the patient management system 112 may use data collected using other sensor modalities to determine the progression of the wound over time, such as temperature measurements, mass measurements, volume measurements, and so forth); process the set of wound features and wound temperature using machine learning ([0031] the image data 118 may comprise images and/or video, where the machine-learned model 122 may determine characteristics of the wound based on frames of the video input into the machine-learned model 122) ([0059] In some examples, the information 508 may include characteristics of the wound 506, such as temperature of the wound 506, blood flow to the wound 506, and so forth. The patient management system 112 may determine a temperature of the wound 506 from digital and/or thermal imaging of the wound 506 (e.g., via a thermographic camera)); determine an optimal wound healing trajectory based on the set of wound features and the wound temperature using machine learning ([0026] For instance, the machine-learned model 122 may represent future states of an entity (e.g., a wound), such as: 1) a probability distribution over the entity state space at each timestep; 2) multimodal (e.g., representing a plurality of possible progressions) to cover a diversity of possible implicit progressions an entity might follow (e.g., a rate of healing of the wound based on different levels of adherence to a treatment protocol); and 3) one-shot, meaning the ability to predict full progressions (and/or time sequences of state distributions) without iteratively applying a recurrence step) ([0058] The information 508 may also indicate that a wound specialist review has been ordered. For instance, the patient management system 112 may determine that the wound 506 is not progressing towards healing at a desired rate, and based on this determination, may automatically request a WOC to analyze the wound); predict at least one future wound condition based on the optimal wound healing trajectory using machine learning (([0026] For instance, the machine-learned model 122 may represent future states of an entity (e.g., a wound), such as: 1) a probability distribution over the entity state space at each timestep; 2) multimodal (e.g., representing a plurality of possible progressions) to cover a diversity of possible implicit progressions an entity might follow (e.g., a rate of healing of the wound based on different levels of adherence to a treatment protocol)); and display the optimal wound healing trajectory and the at least one future wound condition on the display ([0044] The user interface 204 may be displayed in a touch interface of the device 202) ([0031] In some cases, the machine-learned model 122 may receive multiple instances of image data 118 that were captured at different times, and output the classification (e.g., the stage) based at least in part on differences in characteristics of the wound between the multiple instances of image data 118 and an amount of time between the instances of image data 118 being captured. In some instances, the machine-learned model 122 may use the stage information over time to predict progression of the wound in the future, similar to the description above)) ([0055] FIG. 5 is an example environment 500 including a device 502 displaying an image 504 depicting a wound 506 and information 508 related to the wound 506).
Kayser fails to teach trigger at least one response if the wound temperature is outside of a therapeutic temperature zone.
However, Shelton teaches trigger at least one response if the wound temperature is outside of a therapeutic temperature zone ([0055] For example, the core body temperature sensing system may detect abnormal temperature based on temperature being outside the range of 36.5° C. and 37.5° C) ([0056] For example, the body temperature sensing system may detect core body temperature data and trigger the sensing system to emit a cooling or heating element to raise or lower the body temperature in line with the measured ambient temperature). It would have been obvious to one of ordinary skill in the art before the effective filling date to have modified the invention of Kayser to include trigger at least one response if the wound temperature is outside of a therapeutic temperature zone. Doing so allows for monitoring the temperature around the wound to ensure it is within a range for optimal healing.
Regarding claim 14, Kayser teaches the system according to claim 13, wherein the at least one heat sensor is a thermal camera ([0059] In some examples, the information 508 may include characteristics of the wound 506, such as temperature of the wound 506, blood flow to the wound 506, and so forth. The patient management system 112 may determine a temperature of the wound 506 from digital and/or thermal imaging of the wound 506 (e.g., via a thermographic camera)).
Claim(s) 3 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kayser (US 20220108447 A1) in view of Shelton (US 20220240869 A1), further in view of Fraden (US 20050043631 A1).
Regarding claim 3, Kayser teaches the method according to claim 1, butfails to teach wherein at least one of the at least one heat sensor is a temperature probe placed around a perimeter of the wound.
However, Fraden teaches wherein at least one of the at least one heat sensor is a temperature probe placed around a perimeter of the wound ([0021] Probe 3 touches skin (for example, forehead 23) of patient 22). It would have been obvious to one of ordinary skill in the art before the effective filling date to have modified the invention of Kayser to include wherein at least one of the at least one heat sensor is a temperature probe placed around a perimeter of the wound. Doing so allows for sensing the temperature around the wound to ensure it is within a range for optimal healing.
Regarding claim 15, Kayser teaches the method according to claim 13, butfails to teach wherein at least one of the at least one heat sensor is a temperature probe placed around a perimeter of the wound.
However, Fraden teaches wherein at least one of the at least one heat sensor is a temperature probe placed around a perimeter of the wound ([0021] Probe 3 touches skin (for example, forehead 23) of patient 22). It would have been obvious to one of ordinary skill in the art before the effective filling date to have modified the invention of Kayser to include wherein at least one of the at least one heat sensor is a temperature probe placed around a perimeter of the wound. Doing so allows for sensing the temperature around the wound to ensure it is within a range for optimal healing.
Claim(s) 16, 17, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kayser (US 20220108447 A1) in view of Shelton (US 20220240869 A1), further in view of Fraden (US 20050043631 A1) and Woloschek (US 20210393435 A1).
Regarding claim 16, Kayser teaches the system according to claim 13, but fails to teach further comprising a heater configured to supply at least one type of heat to the wound, wherein the controller is communicatively coupled to the heater.
However, Woloschek teaches further comprising a heater configured to supply at least one type of heat to the wound, wherein the controller is communicatively coupled to the heater ([0018] The patient monitoring system 50 includes a wireless physiological sensor 2 configured to communicate with a controller 24, which is a controller configure to facilitate physiological monitoring of the infant. The wireless physiological sensor 2 has a sensing element 4 arranged on a substrate 14. The sensor controller 10 receives physiological information detected by the sensing element 4. The sensing element 4 may be any type of device for sensing or detecting physiological information from the patient, which may include but is not limited to a skin electrode, temperature sensor). It would have been obvious to one of ordinary skill in the art before the effective filling date to have modified the invention of Kayser to include further comprising a heater configured to supply at least one type of heat to the wound, wherein the controller is communicatively coupled to the heater. Doing so allows for the heater to be controlled to keep the wound area within a predetermined range for optimal healing.
Regarding claim 17, Kayser teaches the system according to claim 16, but fails to teach wherein the at least one type of heat is radiant heat or convection heat.
However, Woloschek teaches wherein the at least one type of heat is radiant heat or convection heat ([0024] The incubator 20′, which in other embodiments could be another type of infant warming device 20 such as a radiant warmer, has a heater system providing a heated environment for the infant 1). It would have been obvious to one of ordinary skill in the art before the effective filling date to have modified the invention of Kayser to include wherein the at least one type of heat is radiant heat or convection heat. Doing so allows for localized heating of the wound area to keep within a predetermined range for optimal healing.
Regarding claim 19, Kayser teaches the system according to claim 16, but fails to teach wherein the controller is further configured to activate the heater until the wound temperature reaches the therapeutic temperature zone.
However, Shelton teaches wherein the controller is further configured to activate the heater until the wound temperature reaches the therapeutic temperature zone ([0056] For example, the body temperature sensing system may detect core body temperature data and trigger the sensing system to emit a cooling or heating element to raise or lower the body temperature in line with the measured ambient temperature). It would have been obvious to one of ordinary skill in the art before the effective filling date to have modified the invention of Kayser to include wherein the controller is further configured to activate the heater until the wound temperature reaches the therapeutic temperature zone. Doing so allows for the wound temperature to be held within a predetermined range for optimal healing.
Regarding claim 20, Kayser teaches the system according to claim 16, wherein detecting the temperature of the wound from the at least one heat sensor is performed in real time ([0059] In some examples, the information 508 may include characteristics of the wound 506, such as temperature of the wound 506, blood flow to the wound 506, and so forth. The patient management system 112 may determine a temperature of the wound 506 from digital and/or thermal imaging of the wound 506 (e.g., via a thermographic camera)) ([0081] the patient management system 112 may use data collected using other sensor modalities to determine the progression of the wound over time, such as temperature measurements, mass measurements, volume measurements, and so forth).
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kayser (US 20220108447 A1) in view of Shelton (US 20220240869 A1), further in view of Fraden (US 20050043631 A1) and Woloschek (US 20210393435 A1) and McPherson (US 20140243938 A1).
Regarding claim 18, Kayser teaches the system according to claim 16, but fails to teach wherein the heater is an electrically powered heating sleeve placed over the wound.
However, McPherson teaches wherein the heater is an electrically powered heating sleeve placed over the wound ([0020] FIG. 5 is a schematic diagram of a system including the body-enclosing garment of FIG. 4, shown with hand-enclosure portions and foot-enclosure portions being adapted to radiate electromagnetic energy to provide warmth to the hands and feet of the wearer). It would have been obvious to one of ordinary skill in the art before the effective filling date to have modified the invention of Kayser to include wherein the heater is an electrically powered heating sleeve placed over the wound. Doing so allows for the heating to enclose the wound for uniform heating.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASHLEIGH LAUREN KERN whose telephone number is (703)756-4577. The examiner can normally be reached 7:30 am - 4:30 pm.
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/ASHLEIGH LAUREN KERN/Examiner, Art Unit 3794
/ADAM Z MINCHELLA/Primary Examiner, Art Unit 3794
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