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 .
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on January 20th, 2025 has not been considered, because the document does not include the required IDS Size Fee Assertion.
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because it includes legal phraseology (i.e., “said human body”). A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
The disclosure is objected to because of the following informalities:
In Paragraph [03], "…thermography is however the…" should read "…thermography is, however, the…"
In Paragraph [25], "focussing" should read "focusing"
In Paragraph [30], "…CD or DVD, -ROM disk…" should read "…CD or DVD-ROM disk…" or "…CD-ROM or DVD-ROM disk…"
Appropriate correction is required.
Claim Objections
Claim 9 objected to under 37 CFR 1.75(c) as being in improper form because a multiple dependent claim should refer to. See MPEP § 608.01(n).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 14 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter as follows.
Claim 14 recites "A computer program product comprising…". Computer programs, per se, are not in one of the statutory categories of invention because a computer program is merely a set of instructions capable of being executed by a computer - the computer program itself is not a process. MPEP § 2106.
A computer program, at best, is a functional descriptive material per se. Descriptive material can be characterized as either "functional descriptive material" or "nonfunctional descriptive material." Both types of "descriptive material" are nonstatutory when claimed as descriptive material per se, 33 F.3d at 1360, 31 USPQ2d at 1759. When functional descriptive material is recorded on some computer-readable medium, it becomes structurally and functionally interrelated to the medium and will be statutory in most cases since use of technology permits the function of the descriptive material to be realized. Compare In re Lowry, 32 F.3d 1579, 1583-84, 32 USPQ2d 1031, 1035 (Fed. Cir. 1994) )(discussing patentable weight of data structure limitations in the context of a statutory claim to a data structure stored on a computer readable medium that increases computer efficiency) and > >In re Warmerdam, 33 F.3d 1354, 1360- 61,31 USPQ2d *>1754, 1759 (claim to computer having a specific data structure stored in memory held statutory product-by-process claim) with Warmerdam, 33 F.3d at 1361,31 USPQ2d at 1760 (claim to a data structure per se held nonstatutory). See MPEP 2106.01.
Claim 15 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter as follows.
Claim 15 recites “A computer readable storage medium…”. The specification issilent with respect to the definition of a "computer readable storage medium" and only provides examples of the storage medium. The broadest reasonable interpretation of a claim drawn to a computer readable storage medium typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent. See Subject Matter Eligibility of Computer Readable Media, 1351 OG 212 (26 Jan 2010). See MPEP 2111.01. Signals are nothing but the physical characteristics of a form of energy, and as such is nonstatutory natural phenomena. See, e.g., In re Nuitjen, 500 F. 3d 1346, 1357 (Fed. Cir. 2007)(slip. op. at 18)("A transitory, propagating signal like Nuitjen's is not a process, machine, manufacture, or composition of matter.' ... Thus, such a signal cannot be patentable subject matter."). Thus, Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Examiner recommends the claim to be amended to recite “A non-transitory computer readable storage medium…” in order to overcome the 101 rejection.
The rejection of claim 15 above may be overcome by amending the claim to recite, for example, “A non-transitory computer-readable medium storing a computer program, when is executed by a computer, causing the computer to …”.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.\
Claims 1-4, 6, 8, 11-15 are rejected under 35 U.S.C. 103 as being unpatentable over Sa et al. (US 20200271507).
Regarding Claim 1, Sa discloses “A computer-implemented method for processing a plurality of camera images of a human body surface target area over time, the method comprising the steps of:” (Sa, Paragraph [0036], discloses: “In one embodiment, the surface data may include different representations of the patient. Two or more channels are created. For example, two images have pixel intensity modulated by the amplitude of the information for the channel (e.g., one by depth and the other by color). In one embodiment, given a 3D surface of the patient's body (skin surface), 2D projections of this data—skin surface image (e.g., height of the surface from the scanner table at each location in the image) and depth image (e.g., measure the thickness of the person at each location in the image)—are formed by image processing from the output of the sensor.”); “obtaining a plurality of images of a human body surface target area of a human body captured by a first camera over time” (Sa, Paragraph [0032], discloses “In act 10, a sensor captures an outer surface of a patient. The sensor is a depth sensor, such as a 2.5D or RGBD sensor (e.g., Microsoft Kinect 2 or ASUS Xtion Pro). The depth sensor may directly measure depths, such as using time-of-flight, interferometry, or coded aperture. The depth sensor may be a camera or cameras capturing a grid projected onto the patient. The sensor may be multiple cameras capturing 2D images from different directions, allowing reconstruction of the outer surface from multiple images without transmission of structured light. Other optical or non-ionizing sensors may be used.”); “obtaining physical data of said human body, wherein said physical data include one or more of a length and/or a weight of said human body, a circumference of a hip, waist and/or a chest of said human body, an age and/or a gender of said human body” (Sa, Paragraph [0003], discloses “Anthropometric measurements may be used to estimate human body weight. In one approach, anthropometric features are manually measured from an image to learn a correlation of features and body weight through regression. In a more automated approach, features are extracted directly from a depth image. Directly extracting features from the depth image may be problematic when the scene is cluttered and noisy.”); “generating a three-dimensional (3D) model of at least said human body surface target area based on a statistical shape model (SSM) of a human body and on said obtained physical data of said human body” (Sa, Paragraph [0042], discloses “In one embodiment, the depth camera image 20 of a subject is converted to a 3D point cloud. A plurality of anatomical landmarks is detected in the 3D point cloud. A 3D avatar mesh is initialized by aligning a template mesh to the 3D point cloud based on the detected anatomical landmarks. A personalized 3D avatar mesh of the subject is generated by optimizing the 3D avatar mesh using a trained parametric deformable model (PDM). The optimization is subject to constraints that take into account clothing worn by the subject and the presence of a table on which the subject in lying.”); and “mapping said plurality of images on the generated SSM-based 3D model of said human body surface target area, thereby obtaining an anatomical alignment of said plurality of images of said human body surface target area” (Sa, Paragraph [0005], discloses “In a first aspect, a method is provided for patient weight estimation from surface data in a medical imaging system. The surface data is data representing a surface or outside of the patient, such as by capturing an outer surface of a patient with a sensor. A patient model is fit to the surface data. The patient model is a mesh, statistical shape model, or other generic parameterization of an outer surface. The fit deforms the patient model to fit to the surface data of the particular patient. A value for each of one or more features are extracted from the patient model as fit. The features are shape features or other characterization of the patient model. A weight of the patient is estimated by input of the value or values for the one or more features to a machine-learned regressor. The patient is dosed based on the weight.”; Paragraph [0042] discloses “Any now known or later developed fit of a body surface model to captured surface data for a patient may be used. For example, a SCAPE model is fit to the surface data based on minimization of differences. In one embodiment, the depth camera image 20 of a subject is converted to a 3D point cloud. A plurality of anatomical landmarks is detected in the 3D point cloud. A 3D avatar mesh is initialized by aligning a template mesh to the 3D point cloud based on the detected anatomical landmarks.”; Paragraph [0035] and Figure 3 discloses “The outer surface is captured as depths from the sensor to different locations on the patient, an image or photograph of the outside of the patient, or both. The sensor outputs the sensed image and/or depths. The measurements of the outer surface from the sensor are surface data for the patient. FIG. 3 shows an example image 20 from surface data where the intensity in grayscale is mapped to the sensed depth. Alternatively, the sensor measurements are processed to determine the outer surface information, such as stereoscopically determining the outer surface from camera images from different angles with image processing.”
PNG
media_image1.png
232
133
media_image1.png
Greyscale
From the aforementioned paragraphs, it is important to note that surface data is acquired from a plurality of images (as seen in Figure [0036]), whereas the surface data is being mapped directly to the model which as shown in Paragraph [0005] is a statistical shape model. As a result, we see in paragraph [0042] how the anatomical landmarks between the surface data fit to a body surface model and the 3D model (the 3D avatar mesh) are aligned to match with one another.). Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to use the Sa techniques of processing a plurality of camera images of a human body target surface area over time to achieve a more complete computer-implemented method. By using the Sa techniques of acquiring the plurality of images of the human body surface target area, obtaining physical data of the human body target surface area, generating a 3D model of the human body target surface area, and mapping the images on the 3D model, one of ordinary skill in the art allows the user to carefully evaluate the motion, progression, or movement of a person having a specific medical condition very accurately. Therefore, it would have been obvious for one of ordinary skill in the art to use the Sa reference to achieve the same method described in Claim 1.
Regarding Claim 2, Sa discloses “The method according to claim 1, wherein the first camera is one of an infrared camera, a UV camera or a visual light camera.” (Sa, Paragraph [0068], discloses “The sensor 77 is a depth sensor or camera. LIDAR, 2.5D, RGBD, stereoscopic optical sensor, or other depth sensor may be used.”)
Regarding Claim 3, Sa discloses “The method according to claim 1, wherein said first camera is configured to capture 2D images of said human body surface target area” (Sa, Paragraph [0036], discloses “In one embodiment, given a 3D surface of the patient's body (skin surface), 2D projections of this data—skin surface image (e.g., height of the surface from the scanner table at each location in the image) and depth image (e.g., measure the thickness of the person at each location in the image)—are formed by image processing from the output of the sensor.”; Paragraph [0032], discloses “The sensor may be multiple cameras capturing 2D images from different directions, allowing reconstruction of the outer surface from multiple images without transmission of structured light.”).
Regarding Claim 4, Sa discloses “The method according to claim 1, further comprising the step of obtaining a plurality of visual images of at least said human body surface target area of said human body captured by a second camera over time, wherein the capturing of said plurality of images by said second camera has been done substantially simultaneously with the capturing of said plurality of images by said first camera” (Sa, Paragraph [0036], discloses “In one embodiment, the surface data may include different representations of the patient. Two or more channels are created. For example, two images have pixel intensity modulated by the amplitude of the information for the channel (e.g., one by depth and the other by color). In one embodiment, given a 3D surface of the patient's body (skin surface), 2D projections of this data—skin surface image (e.g., height of the surface from the scanner table at each location in the image) and depth image (e.g., measure the thickness of the person at each location in the image)—are formed by image processing from the output of the sensor.”; Paragraph [0068] discloses “The sensor 77 is a depth sensor or camera. LIDAR, 2.5D, RGBD, stereoscopic optical sensor, or other depth sensor may be used. One sensor 77 is shown, but multiple sensors may be used. A light projector may be provided. The sensor 77 may directly measure depth from the sensor 77 to the patient. The sensor 77 may include a separate processor for determining depth measurements from images, or the image processor 72 determines the depth measurements from images captured by the sensor 77. The depth may be relative to the sensor 77 and/or a bed or table 79.”; It is important to note that the sensor can be defined as two cameras taking an image of the surface of a patient’s human body, this making a second camera exist in this invention with the second camera substantially simultaneously taking images of the surface of a patient’s body alongside the first camera).
Regarding Claim 6, Sa discloses “The method according to claim 4, further comprising the step of skeletonizing the obtained visual images of at least said human body surface target area.” (Sa, Paragraph [0044], discloses “In yet another embodiment, a personalized 3D mesh of a person is generated by a model-based approach to fit a human skeleton model to depth image data of the person. The estimated pose skeleton is then used to initialize a detailed parametrized deformable mesh (PDM) that was learned in an offline training phase.”).
Regarding Claim 11, Sa discloses “A system for processing a plurality of camera images of a human body surface target area over time comprising:” (Sa, Paragraph [0036], discloses “In one embodiment, the surface data may include different representations of the patient. Two or more channels are created. For example, two images have pixel intensity modulated by the amplitude of the information for the channel (e.g., one by depth and the other by color). In one embodiment, given a 3D surface of the patient's body (skin surface), 2D projections of this data—skin surface image (e.g., height of the surface from the scanner table at each location in the image) and depth image (e.g., measure the thickness of the person at each location in the image)—are formed by image processing from the output of the sensor); “a first camera configured to capture a plurality of images of at least said human body surface target area of a human body over time” (Sa, Paragraph [0032], discloses “In act 10, a sensor captures an outer surface of a patient. The sensor is a depth sensor, such as a 2.5D or RGBD sensor (e.g., Microsoft Kinect 2 or ASUS Xtion Pro). The depth sensor may directly measure depths, such as using time-of-flight, interferometry, or coded aperture. The depth sensor may be a camera or cameras capturing a grid projected onto the patient. The sensor may be multiple cameras capturing 2D images from different directions, allowing reconstruction of the outer surface from multiple images without transmission of structured light. Other optical or non-ionizing sensors may be used.”); and “a controller and a memory with computer program code configured to perform” (Sa, Paragraph [0063], discloses “In another example, the image processor configures a diagnostic medical scanner (e.g., computed tomography, fluoroscopy, or x-ray) based on the weight. The medical scanner may configure itself. The image processor may provide information to a controller of the medical scanner to configure. The image processor may configure by direct control the medical scanner. Alternatively, the user manually configures the medical scanner based on the weight by entry with one or more controls.”; Paragraph [0066] discloses “The medical imaging system includes the display 70, memory 74, and image processor 72. The display 70, image processor 72, and memory 74 may be part of the medical therapy system 76, a computer, server, workstation, or other system for image processing medical images from a scan of a patient.”; Paragraph [0079] discloses “The functions, acts or tasks are independent of the particular type of instructions set, storage media, processor or processing strategy and may be performed by software, hardware, integrated circuits, firmware, micro code and the like, operating alone or in combination.”; It is important to note that based on BRI, the image processor is defined as a controller in this reference); “the method according to Claim 1” (Please refer to the above-described analysis for Claim 1). Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to use the Sa technique for processing a plurality of camera images of a human body surface target area over time to have a more complete system. By using the techniques for image acquisition and processing of the human body surface target area using the method described in Claim 1 seen in Sa, one of ordinary skill in the art allows for a user (such as a researcher or medical professional) to have an implemented system that can help track the movement or progression of a specific ailment of the human body seen in a specific patient. Therefore, it would have been obvious for one of ordinary skill in the art to use the Sa reference in order to achieve the same method described in Claim 11.
Regarding Claim 12, Sa discloses “A system according to claim 11, further comprising a second camera configured to capture a plurality of visual images of at least said human body surface target area over time substantially simultaneously with the capturing of images by the first camera” (Sa, Paragraph [0036], discloses “In one embodiment, the surface data may include different representations of the patient. Two or more channels are created. For example, two images have pixel intensity modulated by the amplitude of the information for the channel (e.g., one by depth and the other by color). In one embodiment, given a 3D surface of the patient's body (skin surface), 2D projections of this data—skin surface image (e.g., height of the surface from the scanner table at each location in the image) and depth image (e.g., measure the thickness of the person at each location in the image)—are formed by image processing from the output of the sensor.”; Paragraph [0068] discloses “The sensor 77 is a depth sensor or camera. LIDAR, 2.5D, RGBD, stereoscopic optical sensor, or other depth sensor may be used. One sensor 77 is shown, but multiple sensors may be used. A light projector may be provided. The sensor 77 may directly measure depth from the sensor 77 to the patient. The sensor 77 may include a separate processor for determining depth measurements from images, or the image processor 72 determines the depth measurements from images captured by the sensor 77. The depth may be relative to the sensor 77 and/or a bed or table 79.”; As mentioned in Claim 4, the sensor can be defined as two cameras taking an image of the surface of a patient’s body, thus making a second camera exist in this invention with the second camera substantially simultaneously taking images of the surface of a patient’s body alongside the first camera).
Regarding Claim 13, Sa discloses “A controller comprising at least one processor and at least one memory including computer program code, the at least one memory and computer program code configured to, with the at least one processor, cause the controller to perform” (Sa, Paragraph [0063], discloses “In another example, the image processor configures a diagnostic medical scanner (e.g., computed tomography, fluoroscopy, or x-ray) based on the weight. The medical scanner may configure itself. The image processor may provide information to a controller of the medical scanner to configure. The image processor may configure by direct control the medical scanner. Alternatively, the user manually configures the medical scanner based on the weight by entry with one or more controls.”; Paragraph [0066] discloses “The medical imaging system includes the display 70, memory 74, and image processor 72. The display 70, image processor 72, and memory 74 may be part of the medical therapy system 76, a computer, server, workstation, or other system for image processing medical images from a scan of a patient.”; Paragraph [0079] discloses “The functions, acts or tasks are independent of the particular type of instructions set, storage media, processor or processing strategy and may be performed by software, hardware, integrated circuits, firmware, micro code and the like, operating alone or in combination.”; As mentioned in the analysis of Claim 11, the image processor is defined as a controller in this reference); “the methods according to claim 1” (Please refer to the above-described analysis regarding Claim 1). Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to use the techniques for using the method of Claim 1 of processing images of a human body surface target area over time to improve the controller of Claim 15 in the same way.
Regarding Claim 14, Sa discloses “A computer program product comprising computer-executable instructions for performing the methods according to claim 1 when the program is run on a computer” (Sa, Paragraph [0079], discloses “The instructions for implementing the training or application processes, the methods, and/or the techniques discussed herein are provided on non-transitory computer-readable storage media or memories, such as a cache, buffer, RAM, removable media, hard drive or other computer readable storage media (e.g., the memory 74).”, where computer-readable instructions for a computer-readable storage media are clearly stated, which can be interpreted as a computer program product; Please refer to the above-described analysis for Claim 1).
Regarding Claim 15, Sa discloses “A computer readable storage medium comprising computer-executable instructions for performing the methods according to claim 1 when the program is run on a computer.” (Sa, Paragraph [0079], discloses “The instructions for implementing the training or application processes, the methods, and/or the techniques discussed herein are provided on non-transitory computer-readable storage media or memories, such as a cache, buffer, RAM, removable media, hard drive or other computer readable storage media (e.g., the memory 74).”; Please refer to the above-described analysis regarding Claim 1). Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to use the techniques for using the method of Claim 1 of processing images of a human body surface target area over time to improve the controller of Claim 15 in the same way.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Sa in view of Yoo et al. (KR 20140103788 A).
Regarding Claim 5, Sa discloses “The method according to claim 4 wherein the second camera is” (Please refer to the above-described analysis for Claim 4) the vascular imaging apparatus acquires RGB images of the subject organism using RGB cameras.” (Yoo, Paragraph [0049]). Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method seen in the combination of Sa and Berend with the RGB camera seen in Yoo to result in an improved method for processing human body surface target area images over time.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Sa in view of Berend et al. (US 20160278868).
Regarding Claim 7, the Sa discloses “The method according to claim 4” (Please refer to the above-described analysis for Claim 4) issues in the anatomic joint based on the range of motion video; templating a prosthetic implant on two-dimensional images or a three-dimensional model of the anatomic joint using the identified potential impingement and stability issues of the anatomic joint to obtain position and orientation data; and electronically saving the position and orientation data of the prosthetic implant for use with a surgical navigation system” (Berend, Paragraph [0081]). Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method seen in Sa with the technique of mapping the skeletonized images with the 3D model to result in an improved method for processing images of a human body surface target area over time. By combining the Berend technique of mapping the skeletonized images with the 3D model with the method seen in Sa, one of ordinary skill in the art allows not only for the surface to be observed over time, but also the physical movement of the human body to be observed closely as various activities are being performed as the limbs and joints of the skeleton move in different directions. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Sa and Berend references to achieve the same method seen in Claim 7.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Sa in view of Ostrovsky-Berman (US 20100128953 A1).
Regarding Claim 9, Sa discloses “The method according to claims 4 and claim 8” (Please refer to the above-described analysis for Claims 4 and 8), (Sa, Paragraph [0042], discloses “In one embodiment, the depth camera image 20 of a subject is converted to a 3D point cloud. A plurality of anatomical landmarks is detected in the 3D point cloud. A 3D avatar mesh is initialized by aligning a template mesh to the 3D point cloud based on the detected anatomical landmarks. A personalized 3D avatar mesh of the subject is generated by optimizing the 3D avatar mesh using a trained parametric deformable model (PDM). The optimization is subject to constraints that take into account clothing worn by the subject and the presence of a table on which the subject in lying.”; Sa, Paragraph [0005], discloses “The patient model is a mesh, statistical shape model, or other generic parameterization of an outer surface.”); (Otrovsky-Berman, Paragraph [0021]). Otrovsky-Berman further discloses that “More optionally, the statistical model mapping a distance between two bones of the skeletal structure, at least one of the plurality of warping transformations being directed according to a level of compliance of a current registration of two respective the bones with the distance” (Otrovsky-Berman, Paragraph [0022]). Otrovsky finally discloses that “Optionally, the medical image is registered by identifying one or more lungs related anatomical characteristics therein and registering them with one or more respective anatomical characteristics in the skeletal atlas. Optionally, the registering is based on a plurality of warping transformations. Additionally or alternatively, the registration is confined and/or directed according to structural constraints, for example according to distances between the reference anatomical elements, such as reference bones. In such an embodiment, the distances constrain and/or direct the registration by scoring or sub-scoring the warping transformations” (Otrovsky-Berman, Paragraph [0045]). Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention combine the method seen in Sa with the technique of warping the model based on the skeletal visual images to result in an improved method for processing human body surface target area images over time.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Sa in view of Chiou et al. (US 12,211,151 w/ an EFD of April 8th, 2022) and Zeien (US 2020/0187764)
Regarding Claim 10, Sa discloses “The method according to any of the preceding claims including at least claim 4, further comprising the step of obtaining a plurality of visual images of at least said human body surface target area of said human body captured by a second camera over time, wherein the capturing of said plurality of images by said second camera has been done substantially simultaneously with the capturing of said plurality of images by said first camera” (Sa, Paragraph [0036], discloses “In one embodiment, the surface data may include different representations of the patient. Two or more channels are created. For example, two images have pixel intensity modulated by the amplitude of the information for the channel (e.g., one by depth and the other by color). In one embodiment, given a 3D surface of the patient's body (skin surface), 2D projections of this data—skin surface image (e.g., height of the surface from the scanner table at each location in the image) and depth image (e.g., measure the thickness of the person at each location in the image)—are formed by image processing from the output of the sensor.”; Paragraph [0068] discloses “The sensor 77 is a depth sensor or camera. LIDAR, 2.5D, RGBD, stereoscopic optical sensor, or other depth sensor may be used. One sensor 77 is shown, but multiple sensors may be used. A light projector may be provided. The sensor 77 may directly measure depth from the sensor 77 to the patient. The sensor 77 may include a separate processor for determining depth measurements from images, or the image processor 72 determines the depth measurements from images captured by the sensor 77. The depth may be relative to the sensor 77 and/or a bed or table 79.”; As mentioned in Claim 4 and 12, the sensor can be defined as two cameras taking an image of the surface of a patient’s body, this making a second camera exist in this invention with the second camera substantially simultaneously taking images of the surface of a patient’s body alongside the first camera); (Sa, Paragraph [0042], discloses “In one embodiment, the depth camera image 20 of a subject is converted to a 3D point cloud. A plurality of anatomical landmarks is detected in the 3D point cloud. A 3D avatar mesh is initialized by aligning a template mesh to the 3D point cloud based on the detected anatomical landmarks. A personalized 3D avatar mesh of the subject is generated by optimizing the 3D avatar mesh using a trained parametric deformable model (PDM). The optimization is subject to constraints that take into account clothing worn by the subject and the presence of a table on which the subject in lying.”; Sa, Paragraph [0005], discloses “The patient model is a mesh, statistical shape model, or other generic parameterization of an outer surface.”)
Sa does not explicitly disclose “and further comprising the step of estimating a relative position of said first camera with respect to said second camera based on a virtual image of the (Chiou, Col 144, Lines 41-57). Here, we can clearly see how the first and second cameras have their positions estimated by calculating the difference in projection in order to determine the user’s head position throughout the course of imaging the person. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method of using a second camera substantially simultaneously with the first camera seen in Sa with the Chiou technique of estimating the relative position of the first camera with respect to the second camera to result in an improved method for processing images of the human body surface target area over time. By combining the method seen in Sa with the Chiou technique of estimating the first and second camera positions, one of ordinary skill in the art enables a user of this method to accurately assess where the cameras are oriented in terms of the body to calibrate the method if necessary. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the Sa and Chiou references to achieve the above-described limitations seen in Claim 10.
The combination of Sa and Chiou does not explicitly disclose “a virtual image of the (Zeien, Paragraph [0111]). Zeien further discloses that “As persons having ordinary skill in the art would understand from the above descriptions and accompanying drawings, various systems and methods are disclosed herein for providing three-dimensional representations of a target surface of a human body.” (Zeien, Paragraph [0118]). Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method seen in the combination of Sa and Chiou with the Zeien technique of obtaining a virtual image of the 3D model of a human body surface target area to have an improved method for processing images of a human body surface target area over time. By combining the method seen in the combination of Sa and Chiou with the Zeien technique of obtaining a virtual image of the 3D model of a human target surface area, one of ordinary skill in the art allows the virtual image to be saved and modified based on the changes that are present in between different shots of images that are acquired to monitor a condition. Therefore, it would have been obvious for one of ordinary skill in the art to combine the Sa, Chiou and Zeien references to achieve the same method described in Claim 10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Smith et al. (US 11,854,146 w/ EFD of June 25th, 2021) teaches systems and methods directed to generation of a dimensionally accurate three-dimensional (“3D”) model of a body, such as a human body, based on two-dimensional (“2D”) images of at least a portion of that body.
Li et al. (US 2021/0012558) teaches a method and an apparatus for reconstructing a three-dimensional model of a human body, and a storage medium.
Penney et al. (US 2009/0089034) teaches a method, apparatus and computer program code for automatically planning at least a part of a surgical procedure to be carried out on a body part of a patient
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SORIE I KOROMA JR whose telephone number is (571)272-9259. The examiner can normally be reached Monday - Friday 8AM-6:00PM; Alternate Fridays Off.
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, Amandeep Saini can be reached at 571-272-3382. 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.
/SORIE I KOROMA JR/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
Prosecution Insights