Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/22/2026 has been entered.
Response to Arguments
Applicant’s arguments, see “Remarks” filed on 06/22/2026 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Stephany (US 20020171746 A1).
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 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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-6, and 8-20 are rejected under 35 U.S.C. 103 as being unpatentable over Pedersen et al. (US 20150119721 A1) in view of Stephany (US 20020171746 A1), and further in view of DiMaio et al. (US 20220142484 A1) herein after Di.
Regarding claim 1, Pedersen et al. teaches the system comprising: a mobile device comprising at least one processor and a display (see para [0128]; “view his/her wound on the LCD display of the smartphone camera”, see also para [0132]; “a mobile communication system 280 includes a processor 250 and one or more memories 260. In the embodiment shown in FIG. 14, a camera 265, where the camera as an objective lens 267, can also supply the physiological indicators signal to the mobile communication device 280”); at least one camera communicatively coupled to the mobile device (see para [0132]; “a camera 265, where the camera as an objective lens 267, can also supply the physiological indicators signal to the mobile communication device 280”); and one or more non-transitory computer-readable media storing computer-executable instructions that, when executed by the at least one processor (see para [0147]; “A tangible machine readable medium can be used to store software and data that, when executed by a computing device, causes the computing device to perform a method(s) as may be recited”), receiving inputs by a user to obtain the plurality of images (see para [0136]; “one or more processors 155 are operatively connected to an input component 160, which could receive the images transmitted by the handheld portable electronic/communication device”), recognizing burns and burn locations on the patient in the plurality of images (see para [0137]; “the one or more processors 155 to receive the image from the handheld portable electronic device, extract a boundary of the wound area, perform color segmentation within the boundary of the wound area, wherein the wound area is divided into a plurality of segments, each segment being associated with a color indicating a healing condition of the segment and evaluate the wound area”). However, Pedersen et al. does not teach a system for creating a burn chart charting burns of a patient, perform a method of creating the burn chart, the method comprising: displaying one or more pose templates on the display of the mobile device, wherein the one or more pose templates comprise a body outline illustrating one or more body positions to assist in obtaining one or more patient poses for capturing a plurality of images, obtaining the plurality of images by the at least one camera when each pose template is displayed on the display.
In the same field of endeavor Stepheny et al. teaches the method comprising: displaying one or more pose templates on the display of the mobile device (see para [0034]; “a template 24 (shown in dashed lines) is displayed in image display 20”), wherein the one or more pose templates comprise a body outline illustrating one or more body positions (see para [0034]; “template 24 is configured as an outline or silhouette of a frontal view of a head and upper body of a person”, para [0214]; “Illustrated in FIGS. 4(a) through 4(d) are four templates 29(a)-29(d) each showing a predetermined pose for the subject For example, template 29(a) shows a pose at 0 degrees for a frontal view, template 29(b) shows a pose at 90 degrees for a left side view, template 29(c) shows a pose at 180 degrees for a back side view, and 29(d) shows a pose at 270 degrees for a right side view”) to assist in obtaining one or more patient poses for capturing a plurality of images (see para [0034]; “The photographer locates the subject in viewfinder 18 and aligns template 24 with the subject so as to frame the subject within template 24 of image display 20”, see also para [0037]-[0039] further teaches using different front, back, side and opposite-side templates to obtain images of subject at the corresponding predetermined poses); obtaining the plurality of images by the at least one camera when each pose template is displayed on the display (see para [0214]; “template 29(a) is displayed in image display 20 (step 102). The photographer aligns template 29(a) with the subject (step 104) and activates activation member 16 to capture a first image (step 106)….displays template 29(b) in image display 20 (step 108). The photographer aligns template 29(b) with the subject (step 110) and activates activation member 16 to capture a second image (step 112). These steps are repeated to capture a third image using template 29(c) (steps 114-118) and a fourth image using template 29(d) (steps 120-124))”, see also claim 7; “providing an image capture device, the image capture device having an image display and first, second, third, and fourth templates …(b) displaying the first template in the image display; (c) aligning the first template with the subject; (d) capturing a first image of the subject; (e) repeating steps (b) through (d) using the second, third, and fourth templates to capture a second, third, and fourth image”). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify a wound assessing method to provide a convenient, quantitative mechanism for diabetic foot ulcer assessment of Pedersen et al. in view of the use of method of generating an animation model of Stephany et al. in order to generate sufficient information for lifelike animations (see para [0034]). However, the combination of Pedersen et al. and Stephany et al. does not teach a system for creating a burn chart charting burns of a patient and combining the plurality of images to create a burn chart.
In the same field of endeavor, Di teaches a system for creating a burn chart charting burns of a patient (see para [0152]; “may reconstruct a three-dimensional model from multiple two-dimensional images (e.g., images 212, 214, 216, and 218)….. Once the three-dimensional model is created using the two-dimensional images, the % TBSA burned can be estimated”, see also para [0151]; “Once the three-dimensional body model is created, the classified tissue regions can be projected onto areas of the three-dimensional body model”), perform a method of creating the burn chart, and combining the plurality of images to create a burn chart (see para [0157]; “the entire surface area of a tissue classification may be pieced together from various images. For example, mosaic portions 211 and 212 may be some of the images used to estimate the surface area afflicted with tissue condition such as a burn. The process of piecing the images together takes the plurality of images of the tissue classified as the tissue condition and combines them to estimate the surface area of the classified region” see para [154]; “This figure shows a mosaic technique, wherein several pictures are added together to calculate a % TBSA burned”). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify a wound assessing method to provide a convenient, quantitative mechanism for diabetic foot ulcer assessment of Pedersen et al. in view of the use of method of generating an animation model of Stephany et al. and techniques for non-invasive optical image assessing the presence and severity of tissue conditions of burns and other wounds of Di in order to allow the un-imaged leg surface to be estimated (see para [0154]).
Regarding claim 2, the rejection of claim 1 is incorporated herein.
Di in the combination further teach wherein the burn chart is an enhanced Lund and Browder chart, and wherein the method further comprises determining a percentage of a total body of the patient that is burned (see para [0159]; “There are other formulas that can be used to estimate the surface area of the various parts of a subject. For example FIG. 5 shows the Rule of Nines and Lund-Browder Charts. For example, illustration 500 shows the Rule of Nines, wherein the head and neck, and arm are each estimated to be 9% of total body surface area. For example, the total surface area of arm 501 can be estimated to be 9% of the total body surface area of the illustrated person under the Rule of Nines”).
Regarding claim 3, the rejection of claim 2 is incorporated herein.
Di in the combination further teach wherein the method further comprises: determining a burn score for the patient based on the percentage (see para [0163]; “For example, in burns, fatality rates increase with increasing % TBSA burned”), and labeling the enhanced Lund and Browder chart with the burn score, patient information, and a treatment regimen see para [0210]; “the devices described herein can physically locate and identify burns, including their burn severity (e.g., the degree of the burn and whether it is superficial, shallow partial thickness burns, deep partial, or full thickness), and also find % TBSA of burns in general or for each burn severity”, see also para [0220]; “For example, output 220 shows an example where the % TBSA of burns is calculated … may also display other information such as a mortality estimate or other pertinent information to the treatment of the subject In the example of a mortality estimate, data such as the data in the chart of FIG. 6…. the mortality rate based on % TBSA and/or the age of the subject”, see also para [0162]; “the age of the subject may be effectively used in an estimation of relative percentage of body surface area using a Lund-Browder Chart. In alternative examples, other data, including gender, weight, height, body type, body shape, skin tone, race, orientation of an imaged body, and/or any relevant data mentioned in this disclosure may also be inputted or acquired for calculating % TBSA”).
Regarding claim 4, the rejection of claim 1 is incorporated herein.
Di in the combination further teach wherein the method further comprises: generating a multispectral image; and determining burn severity based on the multispectral image (see para [0011]; “Multispectral Imaging (MSI), measures the reflectance of select wavelengths of visible and near-infrared light from the surface of a burn… These light-tissue interactions produce unique reflectance signatures captured by MSI that can be used to classify burn severity”).
Regarding claim 5, the rejection of claim 1 is incorporated herein.
Pedersen in the combination further teach wherein automatically recognizing the burn locations on the patient comprises utilizing one or more machine learning algorithms to: recognizing skin of the patient; and recognize the burn locations on the skin of the patient (see para [0035]; “A more accurate method may be used for wound boundary detection based on skills and insight by experienced wound clinicians. For this purpose, machine learning methods, such as the Support Vector Machine, may be used to train the wound analysis system to learn about the essential features about the wound. [0036] (iii) Component configured for color image segmentation. The color segmentation method is instrumental in determining the healing state of the wound where red indicates healing, yellow indicates inflamed, and black indicates necrotic”).
Regarding claim 6, the rejection of claim 5 is incorporated herein.
Pedersen in the combination further teach wherein the one or more machine learning algorithms are further configured to perform: recognizing a background in the plurality of images, recognize distractors in the plurality of images; and classifying the distractors in the plurality of images (see para [0078]; “In object recognition field, three major tasks needed to be solved to achieve the best recognition performance: 1) find the best representation to distinguish the object and background, 2) find the most efficient object search method and 3) design the most effective machine learning based classifier to determine whether a representation belongs to the object category or not”).
Regarding claim 8, the scope of claim 8 is fully incorporated in claim 1, and the
rejection of claim 1 is equally applicable here.
Regarding claim 9, the rejection of claim 8 is incorporated herein.
Pedersen et al. in the combination further teach comprises recognizing the burn locations (see para [0075]; “The above disclosed method mainly classifies the wound locations into three categories: 1) wound in the middle of the foot, 2) wound at the edge of the foot without toe-amputation and 3) wound at the edge of the foot with toe-amputation”), and classifying the burn locations and the severity of the burns by one or more machine learning algorithms trained on images of burns (see para [0076]; “analyzing the image includes using a trained classifier and, in the system of these teachings, the image analysis component is configured to use a trained classifier. [0077] A machine learning based solutions has been developed in which the wound boundary determination is an object recognition task since it is claimed that the machine learning (ML) is currently the only known way to develop computer vision systems that are robust and easily reusable in different environments. Herein below, the term "wound recognition" is used as the equivalent expression of "wound boundary determination", since both have the same goal” Note: shows 3D charting framework).
Di in the combination further teach wherein the method further, recognizing severity of the burns, (see Abstract; “Additionally, alternatives described herein are used with a variety of tissue classification applications, including assessing the presence and severity of tissue conditions, such as burns and other wounds” Note: converting 2D images into 3D model of the patient body surface).
Regarding claim 10, the rejection of claim 8 is incorporated herein.
Pedersen et al. in the combination further teach and labeling the burn chart with the burn score, patient information, and a treatment regimen (see para [0220]; “The system is able to display Physical Exam (PEx) Findings (i.e., descriptive elaboration of the patient skin lesions), Problem, and Biopsy and Treatment sites on the Body mapping component 152”).
Di in the combination further teach wherein the method further comprises: determining a percentage of area of the skin that is burned; determining a burn score for the patient based on the percentage (see para [0149]; “the % TBSA burned may be estimated by generating a first count that is the sum of all the pixels classified as burned in all the images, generating a second count that is the sum of all the pixels of the subject in all the images, and dividing the first count by the second count. For example, to calculate the % TBSA that is third degree burned, the system may count the pixels of regions 222, 230, and 236, and divide that total by the total number of pixels of all surfaces of the subject 250 by counting and adding the total pixels of the subject 250 found in each of images 212, 214, 216, and 218”);
Regarding claim 11, the rejection of claim 8 is incorporated herein.
Pedersen et al. in the combination further teach wherein the display and the one or more cameras are integrated into the mobile device (see para [0130]; [0132]; disclose smartphone with display and integrated camera).
Regarding claim 12, the rejection of claim 8 is incorporated herein.
Di in the combination further teach wherein the one or more cameras comprises a red-green-blue camera (see para [0547]; “FIG. 67B illustrates an RGB real image”), a multispectral camera, and a light detection and ranging camera (see para [0562]; “the multispectral images described herein can be captured, in some embodiments, by a fiber optic cable having both light emitters and a light detector at the same end of a probe. The light emitters can be capable of emitting around 1000 different wavelengths of light between 400 nm and 1100 nm to provide for a smooth range of illumination of the subject at different wavelengths”).
Regarding claim 13, the rejection of claim 8 is incorporated herein.
Di in the combination further teach wherein the method further comprises displaying the burn chart as an anterior pose and a posterior pose (see Fig. 3, and Fig. 5 para [0159]; “Under the Rule of Nines, each leg and each of the anterior and posterior surfaces of the trunk are estimated to be 18% of the total body surface area”).
Regarding claim 14, the rejection of claim 8 is incorporated herein.
Di in the combination further teach wherein the anterior pose and the posterior pose are displayed as two-dimension poses (see Fig. 3, and Fig. 5 disclose two-dimension poses).
Regarding claim 15, the scope of claim 15 is fully incorporated in claim 1, and the
rejection of claim 1 is equally applicable here. Additionally,
Pedersen in the combination further teach classifying the burn locations and the burn severity (see para [0076]; “analyzing the image includes using a trained classifier and, in the system of these teachings, the image analysis component is configured to use a trained classifier, see also para [0075] “method mainly classifies the wound locations into three categories: 1) wound in the middle of the foot, 2) wound at the edge of the foot without toe-amputation and 3) wound at the edge of the foot with toe-amputation” Note: the wound analysis method disclosed includes categorizing wound positions relative to anatomical regions of the foot).
Di in the combination further teach recognizing burn severity of the burns on the patient in the plurality of images (see Abstract; “with a variety of tissue classification applications, including assessing the presence and severity of tissue conditions, such as burns and other wounds”), labeling the burn chart with the burn locations and the burn severity (see para [0210]; “the devices described herein can physically locate and identify burns, including their burn severity (e.g., the degree of the burn and whether it is superficial, shallow partial thickness burns, deep partial, or full thickness), and also find % TBSA of burns in general or for each burn severity”).
Regarding claim 16, the rejection of claim 15 is incorporated herein.
Di in the combination further teach further comprising labeling the burn chart with patient information and a treatment regimen (see para [0210]; “the devices described herein can physically locate and identify burns, including their burn severity (e.g., the degree of the burn and whether it is superficial, shallow partial thickness burns, deep partial, or full thickness), and also find % TBSA of burns in general or for each burn severity”, see also para [0220]; “For example, output 220 shows an example where the % TBSA of burns is calculated … may also display other information such as a mortality estimate or other pertinent information to the treatment of the subject In the example of a mortality estimate, data such as the data in the chart of FIG. 6…. the mortality rate based on % TBSA and/or the age of the subject”, see also para [0162]; “the age of the subject may be effectively used in an estimation of relative percentage of body surface area using a Lund-Browder Chart. In alternative examples, other data, including gender, weight, height, body type, body shape, skin tone, race, orientation of an imaged body, and/or any relevant data mentioned in this disclosure may also be inputted or acquired for calculating % TBSA”).
Regarding claim 17, the rejection of claim 15 is incorporated herein.
Di in the combination further teach wherein the burn chart is an enhanced Lund and Browder chart (see para [0162]; “the age of the subject may be effectively used in an estimation of relative percentage of body surface area using a Lund-Browder Chart”), and the enhanced Lund and Browder chart is displayed as a posterior pose and an anterior pose (see Fig. 3, and Fig. 5 para [0159]; “Under the Rule of Nines, each leg and each of the anterior and posterior surfaces of the trunk are estimated to be 18% of the total body surface area”).
Regarding claim 18, the rejection of claim 15 is incorporated herein.
Pedersen et al. in the combination further teach comprises recognizing the burn locations (see para [0075]; “The above disclosed method mainly classifies the wound locations into three categories: 1) wound in the middle of the foot, 2) wound at the edge of the foot without toe-amputation and 3) wound at the edge of the foot with toe-amputation”), and classifying the burn locations and the severity of the burns by one or more machine learning algorithms trained on images of burns (see para [0076]; “analyzing the image includes using a trained classifier and, in the system of these teachings, the image analysis component is configured to use a trained classifier. [0077] A machine learning based solutions has been developed in which the wound boundary determination is an object recognition task since it is claimed that the machine learning (ML) is currently the only known way to develop computer vision systems that are robust and easily reusable in different environments. Herein below, the term "wound recognition" is used as the equivalent expression of "wound boundary determination", since both have the same goal”).
Di in the combination further teach wherein the method further, recognizing severity of the burns, (see Abstract; “Additionally, alternatives described herein are used with a variety of tissue classification applications, including assessing the presence and severity of tissue conditions, such as burns and other wounds”).
Regarding claim 19, the rejection of claim 15 is incorporated herein.
Di in the combination further teach further comprising: recognizing skin of the patient (see para [0086]; “FIG. 44 illustrates example burn injured skin”); recognize the burn locations on the skin of the patient (see para [0296]; “FIG. 24 illustrates the location of burn injuries on dorsum of the pig”); and determining a percentage of the skin that is burned (see para [0162]; “Accordingly, the imaging techniques described herein can provide a more accurate % TBSA burned calculation than relying only on these charts as is conventionally done”).
Regarding claim 20, the rejection of claim 15 is incorporated herein.
Pedersen et al. in the combination further teach further comprising: recognizing distractors in the plurality of images; and classifying the distractors in the plurality of images (see para [0078]; “In object recognition field, three major tasks needed to be solved to achieve the best recognition performance: 1) find the best representation to distinguish the object and background, 2) find the most efficient object search method and 3) design the most effective machine learning based classifier to determine whether a representation belongs to the object category or not”).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Pedersen et al. and Stephany in view of Di. As applied in claim 1 above, and further in view of Ma et al. (US 20150213646 A1).
Regarding claim 7, the rejection of claim 1 is incorporated herein. The combination of Pedersen et al., Stephany et al. and Di as a whole does not teach wherein the method further comprises: detecting depth by the at least one camera; generating a point cloud model of the patient, and generating a three-dimensional model of the patient by fitting a mesh to the point cloud model of the patient.
In the same field of endeavor, Ma et al. teach wherein the method further comprises: detecting depth by the at least one camera; generating a point cloud model of the patient (see Abstract; “apparatus for generating a 3D personalized mesh of a person from a depth camera image for medical imaging scan planning is disclosed. A depth camera image of a subject is converted to a 3D point cloud”), and generating a three-dimensional model of the patient by fitting a mesh to the point cloud model of the patient (see para [0151]; “the template mesh is detected in the 3D point cloud”). Accordingly, it would have been obvious to one of ordinary skill in the art at the time of the invention to modify a wound assessing method to provide a convenient, quantitative mechanism for diabetic foot ulcer assessment of Pedersen et al. in view of the use of method of generating an animation model of Stephany et al. and further in view of techniques for non-invasive optical image assessing the presence and severity of tissue conditions of burns and other wounds of Di and apparatus for generating a 3D personalized mesh of a person from a depth camera image for medical imaging scan apparatus for generating a 3D personalized mesh of a person from a depth camera image for medical imaging scan of Ma et al. in order to constrain the search range for the PBT classifiers used to detect the joint landmarks (see Abstract).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WINTA GEBRESLASSIE whose telephone number is (571)272-3475. The examiner can normally be reached Monday-Friday9:00-5:00.
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, Andrew Bee can be reached at 571-270-5180. 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.
/WINTA GEBRESLASSIE/Examiner, Art Unit 2677
Prosecution Insights