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 .
Priority
This application discloses and claims only subject matter disclosed in prior application number 18/749,457, filed 06/20/2024 and names the inventor or at least one joint inventor named in the prior application. Accordingly, this application has been examined as a continuation.
Response to Preliminary Amendment
This Office Action is responsive to communications filed on 10/14/2024. Claims 1-12, 14-16, 18-22 are pending in the instant application. Claim 1 is the sole independent claim. In response to the Notice to File Missing Parts of Nonprovisional Application dated October 23, 2024. Claims 13 and 17 are canceled. Claim 1 has been amended to remove the recitation of "using the equation: V/P= -D(c)*t-+ q, where Vis a volume of wound, P isa perimeter of wound, Dc is a continuous linear healing rate, t is time in between evaluation, and q is time of closure." Claim 14 has been amended to recite "comprise a classifier." Applicant submits that support for this amendment is found at para [0016]. Claim 22 is new. Applicant submits that support for claim 22 is found in original claim 1. Claims 1-22 are pending. Applicant submits no new matter has been added. An Office Action on the merits follows here below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/27/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 8-12, 14-16, 18-22 are rejected under 35 U.S.C. 103 as being unpatentable over Fan (US 20200193597 A1) in combination with Xu, Y. “Personalized prediction of chronic wound healing: An exponential mixed effects model using stereophotogrammetric measurement” Journal of Tissue Viability (2014) 23, pages 48-59.
Regarding Claim 1 (Currently Amended): Fan discloses a method of determining a wound healing rate of a wound or a portion thereof on a subject (Refer to para [003]; “The systems and methods disclosed herein are directed to medical imaging, and, more particularly, to wound assessment, healing prediction, and treatment using machine learning techniques.”) comprising: obtaining image data comprising an image of a wound or a portion thereof of a subject (Refer to para [059]; “In some embodiments, images for wound assessment may be captured with spectral imaging systems configured to image light within a single wavelength band.”) automatically segmenting the image into a plurality of regions by a first trained model (Refer to para [144]; “Some implementations may also incorporate patient metrics into the input data to further increase classification accuracy, or may segment training data based on patient metrics to train different instances of the machine learning model 1532 for use with other patients having those same patient metrics.”) automatically determining a boundary of a wound area of the wound or portion thereof by a second trained model based on the plurality of regions from segmentation (Also at para [144]; “Patient metrics can include textual information or medical history or aspects thereof describing characteristics of the patient or the patient's health status, for example the area of a wound, lesion, or ulcer, the BMI of the patient, the diabetic status of the patient, the existence of peripheral vascular disease or chronic inflammation in the patient, the number of other wounds the patient has or has had, whether the patient is or has recently taken immunosuppressant drugs (e.g., chemotherapy) or other drugs that positively or adversely affect wound healing rate, HbA1c, chronic kidney failure stage IV, type II vs type I diabetes, chronic anemia, asthma, drug use, smoking status, diabetic neuropathy, deep vein thrombosis, previous myocardial infarction, transient ischemic attacks, or sleep apnea or any combination thereof.”) determining three-dimensional characteristics of the wound area comprising a length, a width, and a depth of the wound or portion thereof (Refer to para [170]; “the layers of a CNN can have nodes arranged in three dimensions: width, height, and depth, corresponding to the 2?2 array of pixel values in each image frame (e.g., the width and height) and to the number of image frames in a sequence of images (e.g., the depth). In some embodiments, the nodes of a layer may only be locally connected to a small region of the width and height of the preceding layer, called a receptive field.”).
Fan does not expressly disclose the equation to determine the wound healing rate.
Xu expressly discloses determining a wound healing rate of the wound based on the three-dimensional characteristics of the wound area (Refer to page 55; para [004] Results, Section 3.1; “The best fit for the clinical study data was determined to be a personalized mixed effect exponential model(pMEE), with the initial wound size wi and time tij as predictors and observed wound size yij as the response variable. The healing rates and shapes of the healing curves differed from patient to patient. These personalized differences among patients were captured in the random effects.”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Fan by expressly describing the wound healing equation as rejected above by Xu. One of ordinary skill in the art could have easily added these variables to expressly detail the wound healing rate equation.
The suggestion/motivation for combining the teachings of Fan and Xu would have been to enhance the system for determining wound rates with greater accuracy.
Xu discloses “…The current model development indicates that we can utilize accurate monitoring of wound geometry to adaptively predict healing progression during the second phase of the healing process. Accuracy of the prediction curve in the current model will improve with each additional evaluation. When no wound geometry information for a patient other than initial wound size is known, b0 and b1, which are fixed numbers estimated from the current study, are used to predict the healing curve, with a0 and a1 set to zero. As measurements of the same patient become available through time, corresponding expectations of a0 and a1 are updated adaptively and included in the modeling equation, resulting in a more accurate prediction curve tailored to the patient. Given the initial wound geometry, e.g., initial wound perimeter, our model gives a predictive trajectory curve of future wound healing, from which the time until a certain percentage of wound closure is achieved can be estimated.” (Section 4, Discussion, page 58, para [003, left}, Xu).
Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Fan and Xu in order to obtain the specified claimed elements of Claim 1. It is for at least the aforementioned reasons that the Examiner has reached a conclusion of obviousness with respect to the claim in question.
Regarding Claim 2 (Original): Fan discloses determining the wound healing rate over a predetermined time period (Refer to para [011]; “a method of predicting wound healing using a system for assessing or predicting wound healing comprises illuminating the tissue region with light of at least the first wavelength such that the tissue region reflects at least a portion of the light to the at least one light detection element, using the system to generate the at least one scalar value, and determining the predicted healing parameter over the predetermined time interval.”).
Regarding Claim 3 (Original): Fan discloses determining the wound healing rate after the predetermined time period has lapsed (Refer to para [012]; “the method further comprises measuring one or more dimensions of the wound after the predetermined time interval has elapsed following the determination of the predicted amount of healing of the wound, determining an actual amount of healing of the wound over the predetermined time interval, and updating at least one machine learning algorithm of the one or more machine learning algorithms by providing at least the image and the actual amount of healing of the wound as training data.”).
Regarding Claim 4 (Original): Fan discloses predicting an area reduction of the wound or a portion thereof over the predetermined time period (Refer to para [156 and 157]; “The following implementations may include the acquisition of one or more images of a wound in one or more known wavelength bands, and may include, based on the one or more images, any one or more the following: segmentation of the image into a wound portion and a non-wound portion of the image, prediction of percent area reduction of the wound after a predetermined time period, prediction of healing potentional of individual sections of the wound after a predetermined time period, display of a visual representation associated with any such segmentation or prediction, indication of a selection between a standard wound care therapy and an advanced wound care therapy, and the like. In various embodiments, a wound assessment system or a clinician can determine an appropriate level of wound care therapy based on the results of the machine learning algorithms disclosed herein. For example, if an output of a wound healing prediction system indicates that an imaged wound will close by more than 50% within 30 days, the system can apply or inform a health care practitioner or patient to apply a standard of care therapy; if the output indicates that the wound will not close by more than 50% in 30 days, the system can apply or inform the health care practitioner or patient to use one or more advanced wound care therapies.”).
Regarding Claim 5 (Original): Fan discloses predicting an area reduction of the wound or a portion thereof over the predetermined time period (Refer to para [144]; “For example, the training data can include multispectral data cubes (the input) and classified mappings (the expected output) that have been labeled, for example by a clinician who has designated areas of the wound that correspond to certain clinical states, and/or with healing (1) or non-healing (0) labels sometime after initial imaging of the wound when actual healing is known. Other implementations of the machine learning model 1532 can be trained to make other types of predictions, for example the likelihood of a wound healing to a particular percentage area reduction over a specified time period (e.g., at least 50% area reduction within 30 days) or wound states such as, hemostasis, inflammation, pathogen colonization, proliferation, remodeling or healthy skin categories.”) is performed by a third trained model (Refer to para [166]; “The system shown in FIG. 25 can be considered as a single machine learning system having multiple machine learning models as well as the patient metric vector generator. In some embodiments, this entire system can be trained in an end-to-end fashion such that the CNN and fully connected network tune their parameters through backpropagation in order to be able to generate predicted healing parameters from input images, with the patient metric vector added to the values passed between the CNN and the fully connected network.”).
Regarding Claim 8 (Original): Fan discloses determining an expected time period needed for the wound or portion thereof to heal (Refer to para [141]; “The nodes in each layer connect to some or all nodes in the subsequent layer and the weights of these connections are typically learnt from data during the training process, for example through backpropagation in which the network parameters are tuned to produce expected outputs given corresponding inputs in labeled training data. Thus, an artificial neural network is an adaptive system that is configured to change its structure (e.g., the connection configuration and/or weights) based on information that flows through the network during training, and the weights of the hidden layers can be considered as an encoding of meaningful patterns in the data.”).
Regarding Claim 9 (Original): Fan discloses determining the wound healing rate after the expected time period needed for the wound or portion thereof to heal (Refer to para [162 and 162]; “FIG. 25 presents another approach to providing such healing predictions. As illustrated, an image (or set of multispectral images captured at different wavelengths, either at different times or simultaneously using a multispectral image sensor) is provided as input into a neural network such as a convolutional neural network (“CNN”). The CNN takes this two-dimensional (“2D”) array of pixel values (e.g., values along both the height and width of the image sensor used to capture the image data) and outputs a one-dimensional (“1D) representation of the image. These values can represent classifications of each pixel in the input image, for example according to one or more physiological states pertaining to ulcers or other wounds. As shown in FIG. 25, a patient metric data repository can store other types of information about the patient, referred to herein as patient metrics, clinical variables, or health metric values. Patient metrics can include textual information describing characteristics of the patient, for example, the area of the ulcer, the body mass index (BMI) of the patient, the number of other wounds the patient has or has had, diabetic status, whether the patient is or has recently taken immunosuppressant drugs (e.g., chemotherapy) or other drugs that positively or adversely affect wound healing rate, HbA1c, chronic kidney failure stage IV, type II vs. type I diabetes, chronic anemia, asthma, drug use, smoking status, diabetic neuropathy, deep vein thrombosis, previous myocardial infarction, transient ischemic attacks, or sleep apnea or any combination thereof. However, a variety of other metrics may be used. A number of example metrics are provided in Table 1 below.”).
Regarding Claim 10 (Original): Fan discloses selecting, prior to an end of the predetermined time period, between a standard wound care therapy and an advanced wound care therapy based at least in part on the wound healing rate (Refer to para [056]; “It is generally accepted that DFUs with greater than 50% area reduction (PAR) after 30 days will heal by 12 weeks with standard of care therapy. However, using this metric requires four weeks of wound care before one can determine if a more effective therapy (e.g., an advanced care therapy) should be used. In a typical clinical approach to wound care for non-urgent initial presentation, such as for a DFU, a patient receives standard wound care therapy (e.g., correction of vascular problems, optimization of nutrition, glucose control, debridement, dressings, and/or off-loading) for approximately 30 days following the presentation and initial assessment of the wound.”).
Regarding Claim 11 (Original): Fan discloses comparing a historical wound healing rate and the wound healing rate (Refer to para [133]; “The outputs of both non-linear mapping modules 1310A, 1310B are then provided to the depth calculation module 1335, which can compute a depth of a particular region of interest in the image data. For example, the depth may represent the distance between the object and the image sensor. In some implementations, multiple depth values can be computed and compared to determine the depth of the object relative to something other than the image sensor. For example, a greatest depth of a wound bed can be determined, as well as a depth (greatest, lowest, or average) of healthy tissue surrounding the wound bed. By subtracting the depth of the healthy tissue from the depth of the wound bed, the deepest depth of the wound can be determined. This depth comparison can additionally be performed at other points in the wound bed (e.g., all or some predetermined sampling) in order to build a 3D map of the depth of the wound at various points (shown in FIG. 14 as z(x,y) where z would be a depth value). In some embodiments, greater disparity may improve the depth calculation, although greater disparity may also result in more computationally intensive algorithms for such depth calculations.”).
Regarding Claim 12 (Original): Fan discloses the first trained model, the second trained model or the third trained model comprises one or more machine learning models (Refer to para [166]; “The system shown in FIG. 25 can be considered as a single machine learning system having multiple machine learning models as well as the patient metric vector generator. In some embodiments, this entire system can be trained in an end-to-end fashion such that the CNN and fully connected network tune their parameters through backpropagation in order to be able to generate predicted healing parameters from input images, with the patient metric vector added to the values passed between the CNN and the fully connected network.”).
Regarding Claim 14 (Currently Amended): Fan discloses the one or more machine learning models comprise a classier or (Refer to para [169]; “This reduced-dimensionality representation of the images can be used by another machine learning model, for example, the classifier of FIG. 25 or a suitable CNN or other neural network, in order to output a predicted healing parameter.”) comprise a neural network, a convolutional neural network (CNN), a deep convolutional neural network (DCNN), a cascaded deep convolutional neural network, a simplified CNN, a shallow CNN, or a combination thereof (Refer to para [162]; “FIG. 25 presents another approach to providing such healing predictions. As illustrated, an image (or set of multispectral images captured at different wavelengths, either at different times or simultaneously using a multispectral image sensor) is provided as input into a neural network such as a convolutional neural network (“CNN”). The CNN takes this two-dimensional (“2D”) array of pixel values (e.g., values along both the height and width of the image sensor used to capture the image data) and outputs a one-dimensional (“1D) representation of the image. These values can represent classifications of each pixel in the input image, for example according to one or more physiological states pertaining to ulcers or other wounds.”).
Regarding Claim 15 (Original): Fan discloses the one or more machine learning models are trained using a wound, burn, or ulcer image set. (Refer to para [160]; “In further examples, systems of the present technology may provide a binary yes/no or a percentage likelihood of healing with regard to smaller portions of a wound, such as for individual pixels or subsets of pixels of a wound image, with the yes/no or percentage likelihood indicating whether each individual portion of the wound is likely to be healing tissue or non-healing tissue following the predetermined time period.”).
Regarding Claim 16 (Original): Fan discloses the three-dimensional characteristics of the wound area comprise topology information of the wound area (Refer to para [163, 182 and 194]; “As shown in FIG. 25, a patient metric data repository can store other types of information about the patient, referred to herein as patient metrics, clinical variables, or health metric values. Patient metrics can include textual information describing characteristics of the patient, for example, the area of the ulcer, the body mass index (BMI) of the patient, the number of other wounds the patient has or has had, diabetic status, whether the patient is or has recently taken immunosuppressant drugs (e.g., chemotherapy) or other drugs that positively or adversely affect wound healing rate, HbA1c, chronic kidney failure stage IV, type II vs. type I diabetes, chronic anemia, asthma, drug use, smoking status, diabetic neuropathy, deep vein thrombosis, previous myocardial infarction, transient ischemic attacks, or sleep apnea or any combination thereof. However, a variety of other metrics may be used.”).
Regarding Claim 18 (Original): Fan discloses identifying the wound or portion thereof as granulation, slough, or eschar tissue (Refer to para [157-160]; “In various embodiments, a wound assessment system or a clinician can determine an appropriate level of wound care therapy based on the results of the machine learning algorithms disclosed herein. For example, if an output of a wound healing prediction system indicates that an imaged wound will close by more than 50% within 30 days, the system can apply or inform a health care practitioner or patient to apply a standard of care therapy; if the output indicates that the wound will not close by more than 50% in 30 days, the system can apply or inform the health care practitioner or patient to use one or more advanced wound care therapies. Under existing wound treatment, a wound such as a diabetic foot ulcer (DFU) may initially receive one or more standard wound care therapies for the initial 30 days of treatment, such as Standard of Care (SOC) therapy as defined by the Centers for Medicare and Medicaid. As one example of a standard wound care regimen, SOC therapy can include one or more of: optimization of nutritional status; debridement by any means to remove devitalized tissue; maintenance of a clean, moist bed of granulation tissue with appropriate moist dressings; necessary treatment to resolve any infection that may be present; addressing any deficiencies in vascular perfusion to the extremity with the DFU; offloading of pressure from the DFU; and appropriate glucose control. During this initial period of 30 days of SOC therapy, measurable signs of DFU healing are defined as: decrease in DFU size (either wound surface area or wound volume), decrease in amount of DFU exudate, and decrease in amount of necrotic tissue within the DFU. An example progression of a healing DFU is illustrated in FIG. 22. If healing is not observed during this initial period of 30 days of SOC therapy, Advanced Wound Care (AWC) therapies are generally indicated. The Centers for Medicare and Medicaid have no summary or definition of AWC therapies but are considered to be any therapy outside of SOC therapy as defined above. AWC therapies are an area of intense research and innovation with near-constant introduction of new options to be used in clinical practice. Therefore, coverage of AWC therapies are determined on an individual basis and a treatment considered AWC may not be covered for reimbursement for some patients. Based on this understanding, AWC therapies include, but are not limited to, any one or more of: hyperbaric oxygen therapy; negative-pressure wound therapy; bioengineered skin substitutes; synthetic growth factors; extracellular matrix proteins; matrix metalloproteinase modulators; and electrical stimulation therapy. An example progression of a non-healing DFU is illustrated in FIG. 23. In various embodiments, wound assessment and/or healing predictions described herein may be accomplished based on one or more images of the wound, either alone or based on a combination of both patient health data (e.g., one or more health metric values, clinical features, etc.) and images of the wound.”).
Regarding Claim 19 (Original): Fan discloses the image data is acquired from an imaging device comprising one or more imaging sensors (Refer to para [062]; “It will be understood that changes to lenses, image sensors, aperture sizes, or other components of the presently disclosed imaging systems may involve other adjustments to the imaging system as would be known to those of ordinary skill in the art. The technology of the present disclosure also provides improvements over other multi-spectral imaging systems in that the components that perform the function of resolving wavelengths or causing the system as a whole to be able to resolve wavelengths (e.g., optical filters or the like) can be separable from the components that transduce light energy into digital outputs (e.g., image sensors or the like).”).
Regarding Claim 20 (Original): Fan discloses the imaging device is contained in a mobile device (Refer to para [059]; “In other embodiments, images may be captured with spectral imaging systems configured to capture two or more wavelength bands. In one particular example, images may be captured with a monochrome, RGB, and/or infrared imaging device such as those included in commercially available mobile devices.”).
Regarding Claim 21 (Original): Fan discloses a system for determining a wound healing rate of a wound or a portion thereof on a subject (Refer to para [007]; “…a system for assessing or predicting wound healing comprises at least one light detection element configured to collect light of at least a first wavelength after being reflected from a tissue region comprising a wound, and one or more processors in communication with the at least one light detection element.”) comprising one or more processors configured to implement the method of claim 1 (Refer to para [147]; “As depicted the datacube analysis servers 1615 may include one or more computers, perhaps arranged in a cluster of servers or as a server farm. The memory and processors that make up these computers may be located within one computer or distributed throughout many computers (including computers that are remote from one another).”).
Regarding Claim 22 (New): Xu teaches determining the wound healing rate comprises using the equation: V/P= -D(c)*t + q, where V is a volume of wound, P is a perimeter of wound, Dc is a continuous linear healing rate, t is time in between evaluation, and q is time of closure (Refer to page 55; para [004] Results, Section 3.1; "The best fit for the clinical study data was determined to be a personalized mixed effect exponential model(pMEE), with the initial wound size Wi and time tij as predictors and observed wound size Yij as the response variable. The healing rates and shapes of the healing curves differed from patient to patient. These personalized differences among patients were captured in the random effects.").
Allowable Subject Matter
Claims 6 and 7 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The prior art either singly or in combination does not teach, disclose or suggest at least the following claim limitation(s): “… determining an actual amount of area reduction of the wound or portion thereof over the predetermined time period and updating the third trained model with new training data comprising at least the image of the wound or portion thereof and the actual amount of area reduction of the wound or portion thereof.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MIA M THOMAS whose telephone number is (571)270-1583. The examiner can normally be reached M-Th 8:30am-4:30pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen (Steve) Koziol can be reached at (408) 918-7630. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
MIA M. THOMAS
Primary Examiner
Art Unit 2665
/MIA M THOMAS/Primary Examiner
Art Unit 2665
Prosecution Insights