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 .
Comments
The Amendment – After Non-Final Rejection filed on September 9, 2026 has been entered and made of record.
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.
Claims 1, 3, 4, 6-11, 13, 14, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Evron (WO 2021/044431) in view of Bundsgaard et al. (U.S. Pub. No. 2022/0400940) and Dacosta (U.S. Pub. No. 2017/0236281).
As to claims 1, 11 and 20, Evron teaches a computer-implemented method (i.e., “system for diagnosing or predicting a medical condition 100”, page 18 lines 16-18)/a computer-implemented system comprising: a processor (i.e., “processor”, page 49 lines 10-16); and a non-transitory computer-readable storage medium (i.e., “machine-readable medium”, page 50 lines 4-22) storing instructions that, when executed by one or more processors, cause an application server to perform operations comprising/a non-transitory computer-readable storage medium (i.e., “machine-readable medium”, page 50 lines 4-22) storing instructions that, when executed by one or more processors, cause an application server to perform operations comprising:
receiving, via a network connection and by an application server from a mobile application executing on a mobile device of a subject (i.e., “The photos can be sent from the mobile phone or other device via the internet cloud 122 and stored on the cloud and/or at an application server 132 and/or database server”, page 18 lines 23-26), a set of one or more throat images capturing an inside of the subject’s throat (i.e., “a new user/patient may have a sore throat. He/she activates a cellphone App and takes one or more photos of his/her throat using mobile 110”, page 23 lines 2-6);
receiving, by the application server from the mobile application via the network connection, a set of clinical factors associated with the subject (i.e., “metadata”, page 21 line 15 through page 23 line 1) that were input by the subject in one or more user interfaces that provide step-by-step instructions for entering the set of clinical factors, the step-by-step instructions including a set of questions (See for example, page 21 line 15 through page 23 line 1) asking the subject to input information including identifying information for the subject and at least one symptom (See for example, “The user uploads the photo/images to the cellphone App, as well as the answers to the questions in a capturing metadata”, page 23 lines 2-6);
inputting, at the application server, the set of throat images into a machine-learned model (i.e., “It takes an image and metadata () and outputs a prediction whether the patient suffers from bacterial or viral pharyngitis”, page 23 lines 23-27; and “analyzing said image and/or said metadata of said patient using a machine learning system previously trained with said training samples”, claim 1 at page 55) trained on a plurality of training throat images labeled with respective training labels indicating presence or absence of one or more bacterial or viral pathogens (i.e., “obtaining training images and/or metadata step 202, images and/or metadata associated with a specific disease, disorder or condition are obtained. For example, in diagnosing a strep throat, images of throats, previously determined by laboratory tests to be with a streptococcal throat infection (e.g. Fig. 3B) and images without a streptococcus- infected throat, but with a viral pharyngitis (e.g. Fig. 3A) are obtained”, page 21 lines 15-23);
determining, at the application server (i.e., “Servers 130, 132 are configured to perform the method(s) of the present invention”, page 19 lines 15-16), a disease state prediction for the subject based on at least an output of the machine-learned model, wherein the disease state prediction indicates whether a bacterial or viral pathogen is present in the subject (i.e., “distinguish between viral and bacterial”, page 27 lines 26-29);
transmitting, from the application server to the mobile device via the network connection, the disease state prediction for the subject (See for example, “The input for the application is obtained from the mobile phone or computer camera in addition to the metadata that is also fed by the user. The diagnostic algorithm is a software code that sits in the cloud to which the input is transferred, consisting of the image and the metadata, and where the input processing takes place. The output of the processing software is a medical diagnosis of the disease”, page 34 lines 4-9); and
presenting, via the mobile application executing on the mobile device, a user interface including the disease state prediction for the subject (i.e., “The outputs of the software may be displayed, such as on the App on cellphone/mobile 110, or on smart device 112”, page 19 lines 17-18).
However, Evron does not explicitly disclose the set of throat images captured using one or more user interfaces that provide step-by-step instructions with guided assistance for positioning an integrated camera of the mobile device to capture the set of throat images, and wherein the disease state prediction comprises one or more feature metrics determined from the set of throat images, each of the one or more feature metrics indicating a presence or a property of a respective feature of the subject's throat identified in the set of throat images.
Bundsgaard et al. teaches a set of throat images that are captured using one or more user interfaces that provide step-by-step instructions with guided assistance for positioning an integrated camera of the mobile device to capture the set of throat images (See for example, “the user 101 is instructed by the app to position him-/herself in front of a mirror 102, as shown in FIGS. 1 and 3. FIG. 3 is a diagrammatic representation of the user 101, mirror 102, and mobile device 103 of FIG. 1, the reflections of the user 101’ and the mobile device 103’ in the mirror, and the obtaining of an image of an area of the user’s oral cavity. In an embodiment, the mirror 102 is at a given distance from the user’s face, preferably at a given distance from the user’s oral cavity, and the app instructs the user to position him/herself accordingly”, Paragraph [0063]; and Paragraph [0064]).
The combination of Evron and Bundsgaard et al. do not explicitly disclose wherein the disease state prediction comprises one or more feature metrics determined from the set of throat images, each of the one or more feature metrics indicating a presence or a property of a respective feature of the subject's throat identified in the set of throat images.
Dacosta teaches a disease state prediction (i.e., “Tissue autofluorescence imaging provides a unique means of obtaining biologically relevant information of normal and diseased tissues in real-time, thus allowing differentiation between normal and diseased tissue states”, Paragraph [0048]) that comprises one or more feature metrics determined from the set of throat images (i.e., “the device may be used to image and/or monitor targets such as a skin target, an oral target, an ear-nose-throat target”, Paragraph [0147]), each of the one or more feature metrics indicating a presence or a property of a respective feature of the subject’s throat identified in the set of throat images (i.e., “biological components of the wound and surrounding normal tissues including connective tissues, blood, microvascularity, bacteria, microorganisms, etc. as well as fluorescently labeled drugs/pharmacological agents”, Paragraph [0161]).
Evron, Bundsgaard et al. and Dacosta are analogous art because they are from the field of digital image processing for medical imaging.
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 Evron by incorporating the set of throat images captured using one or more user interfaces that provide step-by-step instructions with guided assistance for positioning an integrated camera of the mobile device to capture the set of throat images, as taught by Bundsgaard et al., and the disease state prediction comprises one or more feature metrics determined from the set of throat images, each of the one or more feature metrics indicating a presence or a property of a respective feature of the subject’s throat identified in the set of throat images, as taught by Dacosta.
The suggestion/motivation for doing so would have been to obtain one or more images of an area of his/her oral cavity without the need of dedicated equipment or any other tools that are not normally present in an average household, and to provide early identification of a disease to guide therapeutic intervention.
Therefore, it would have been obvious to combine Bundsgaard et al. and Dacosta with Evron to obtain the invention as specified in claims 1, 11 and 20.
As to claims 3 and 13, Evron teaches wherein the one or more user interfaces used to capture the set of throat images instruct the subject on how to position or orient the integrated camera (See for example, “Camera guidance networks”, and “The second neural network is classification-based and is set up to classify each frame obtained from the camera with the at least one of the following outputs: move camera right, move camera left, move camera up, move camera down, open mouth, lower tongue, perfect placement”, page 25 lines 13-23).
As to claims 4 and 14, Evron does not explicitly disclose wherein the one or more user interfaces used to capture the set of throat images instruct the subject on how to use a mirror to capture the set of throat images with the integrated camera.
Bundsgaard et al. teaches one or more user interfaces that instruct the subject on how to use a mirror to capture the set of throat images with the integrated camera (See for example, “the user 101 is instructed by the app to position him-/herself in front of a mirror 102, as shown in FIGS. 1 and 3. FIG. 3 is a diagrammatic representation of the user 101, mirror 102, and mobile device 103 of FIG. 1, the reflections of the user 101’ and the mobile device 103’ in the mirror, and the obtaining of an image of an area of the user's oral cavity. In an embodiment, the mirror 102 is at a given distance from the user’s face, preferably at a given distance from the user’s oral cavity, and the app instructs the user to position him/herself accordingly. The mirror may be e.g. a wall-mounted mirror, a floor mirror, a tabletop mirror, or a handheld mirror”, Paragraph [0063]).
Therefore, in view of Bundsgaard et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Evron by incorporating the one or more user interfaces instruct the subject on how to use a mirror to capture the one or more throat images with the camera, as taught by Bundsgaard et al., in order to allow the user to be able to obtain one or more images of an area of his/her oral cavity without the need of dedicated equipment or any other tools that are not normally present in an average household
As to claim 6, Evron teaches wherein the mobile application automatically evaluates a quality of the set of throat images (i.e., “the methods and systems employ another algorithm that checks and ensures image quality”, page 24 lines 14-15; and “the camera placement and the image quality algorithms are performed on a local device, for example the same device that captured the video input (smartphone, tablet etc.)”, page 25 lines 8-11).
As to claim 7, Evron does not explicitly disclose wherein the mobile application automatically instructs the subject to capture a new image if the quality of the set of throat images is not sufficient.
Bundsgaard et al. teaches a mobile application (i.e., “the mobile device 103 is configured to execute an application software (“app”) and comprises a main camera 207 for capturing images”, Paragraph [0062]) that automatically instructs the subject to capture a new image if the quality of the set of throat images is not sufficient (i.e., “The user 101 is instructed by the app to capture another image of his/her oral cavity, if the image quality is below a threshold for image quality, i.e. if the images do not have clinical image quality”, Paragraph [0077]).
Therefore, in view of Bundsgaard et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further Evron by incorporating the mobile application automatically instructs the subject to capture a new image if the quality of the set of throat images is not sufficient, as taught by Bundsgaard et al., in order to allow the user to obtain images of high quality.
As to claims 8 and 17, Evron teaches wherein the disease state prediction comprises at least one of: a probability of a viral pathogen infection; a probability of a bacterial pathogen infection; and a probability of no pathogen infection (See for example, “In the inference stage: The input is the image and its metadata, and the output is probability of viral and bacterial pharyngitis”, page 10 lines 22-23).
As to claims 9 and 18, Evron teaches wherein the set of clinical factors comprises at least one from a group consisting of: age (i.e., “By “metadata” is meant all the non-visuals signs and symptoms of the disease or disorder, such as, but not limited to … patient’s age”, page 18 lines 13-15); a presence or absence of swollen lymph nodes (i.e., “enlarged lymph nodes”, page 18 lines 13-15); subject temperature (i.e., “3) How high is your temperature?”, page 22 line 14); a presence or absence of fever (i.e., “fever”, page 18 lines 13-15); a presence or absence of a cough (i.e., “coughing”, page 18 lines 13-15); a presence or absence of a runny nose (i.e., “runny nose”, page 18 lines 13-15); a presence or absence of a headache (i.e., “headache”, page 18 lines 13-15); a presence or absence of body aches; a presence or absence of vomiting; a presence or absence of diarrhea; a presence or absence of fatigue; a presence or absence of chills; and a duration of pharyngitis.
As to claims 10 and 19, Evron teaches wherein at least one of the set of throat images is pre-processed before being input into the machine-learned model, the pre-processing comprising at least one from a group consisting of: uniform aspect ratio correction; rescaling; normalization; object detection; segmentation; cropping; dimensionality reduction; dimensionality increment; brightness adjustment; image shifting; image flipping; zoom in or out; image rotation; image quality filtering; and image pixel correction (i.e., “Camera guidance networks: The camera placement algorithm is based on DNN (Deep Neural net algorithms) which is actually based on two neural networks that are combined together. The input to these two networks are the video frames. One of these networks performs semantic segmentation of the oro-pharyngeal organs and tissues, such as the uvula, tonsils, teeth, lips, tongue and gums”, page 25 lines 13-18).
As to claim 16, Evron teaches wherein the mobile application automatically evaluates a quality the set of throat images (i.e., “the methods and systems employ another algorithm that checks and ensures image quality”, page 24 lines 14-15; and “the camera placement and the image quality algorithms are performed on a local device, for example the same device that captured the video input (smartphone, tablet etc.)”, page 25 lines 8-11).
However, Evron does not explicitly disclose wherein the mobile application automatically instructs the subject to capture a new image if the quality of the set of throat images is not sufficient.
Bundsgaard et al. teaches a mobile application (i.e., “the mobile device 103 is configured to execute an application software (“app”) and comprises a main camera 207 for capturing images”, Paragraph [0062]) that automatically instructs the subject to capture a new image if the quality of the set of throat images is not sufficient (i.e., “The user 101 is instructed by the app to capture another image of his/her oral cavity, if the image quality is below a threshold for image quality, i.e. if the images do not have clinical image quality”, Paragraph [0077]).
Therefore, in view of Bundsgaard et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further Evron by incorporating the mobile application automatically instructs the subject to capture a new image if the quality of the set of throat images is not sufficient, as taught by Bundsgaard et al., in order to allow the user to obtain images of high quality.
Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Evron in view of Bundsgaard et al. and Dacosta as applied to claims 1 and 11 above, and further in view of Tran (U.S. Pub. No 2020/0405148). The teachings of Evron, Bundsgaard et al. and Dacosta have been discussed above.
As to claims 2 and 12, Evron, Bundsgaard et al. and Dacosta do not explicitly disclose receiving, by the application server from the mobile device via the network connection, profile information of the subject created using one or more user interfaces that instruct the subject to create a profile.
Tran teaches receiving, by the application server from the mobile device via the network connection, profile information of the subject created using one or more user interfaces that instruct the subject to create a profile (i.e., “a patient or healthcare provider may create a patient profile comprising, e.g., identifying, characterizing, and/or medical information, including information about a patient’s medical history, profession, and/or lifestyle”, Paragraph [0253]; and Paragraphs [0254]-[0255]).
Evron, Bundsgaard et al., Dacosta and Tran are analogous art because they are from the field of digital image processing for medical imaging.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to further modify Evron, Bundsgaard et al. and Dacosta by incorporating receiving, by the application server from the mobile device via the network connection, profile information of the subject created using one or more user interfaces that instruct the subject to create a profile, as taught by Tran.
The suggestion/motivation for doing so would have been to further store auxiliary data that can be analyzed for the disease state prediction.
Therefore, it would have been obvious to combine Tran with Evron, Bundsgaard et al. and Dacosta to obtain the invention as specified in claims 2 and 12.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Evron in view of Bundsgaard et al. and Dacosta as applied to claims 1 and 11 above, and further in view of Hefez et al. (U.S. Pub. No. 2018/0160887). The teachings of Evron, Bundsgaard et al. and Dacosta have been discussed above.
As to claims 5 and 15, Evron, Bundsgaard et al. and Dacosta do not explicitly disclose wherein the one or more user interfaces used to capture the set of throat images provide diagrams or tutorials illustrating specific features of the throat that should be within view to capture the set of throat images.
Hefez et al. teaches one or more user interfaces used to capture the set of throat images that provide diagrams or tutorials illustrating specific features of the throat that should be within view to capture the set of throat images (See for example, Paragraphs [0150] and [0154]; “examination logic module 408 can be configured to utilize guiding module 406 in order to provide various guidance data instructing user 102 how to maneuver system 200 to the desired system 200 spatial disposition with respect to patient’s 103 body”, Paragraph [0191]; and Paragraph [0192]).
Evron, Bundsgaard et al., Dacosta and Hefez et al. are analogous art because they are from the field of digital image processing for medical imaging.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to further modify Evron, Bundsgaard et al. and Dacosta by incorporating the one or more user interfaces used to capture the set of throat images provide diagrams or tutorials illustrating specific features of the throat that should be within view to capture the one or more throat images, as taught by Hefez et al.
The suggestion/motivation for doing so would have been to trigger the capturing of images when the throat features are present within the field of view of the camera.
Therefore, it would have been obvious to combine Hefez et al. with Evron, Bundsgaard et al. and Dacosta to obtain the invention as specified in claims 5 and 15.
Claims 21-24, 29 and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Evron (WO 2021/044431) in view of Dacosta (U.S. Pub. No. 2017/0236281).
As to claim 21, Evron teaches a computer-implemented method (i.e., “system for diagnosing or predicting a medical condition 100”, page 18 lines 16-18) comprising:
receiving, by an application server from a mobile application executing on a device of a subject (i.e., “The photos can be sent from the mobile phone or other device via the internet cloud 122 and stored on the cloud and/or at an application server 132 and/or database server”, page 18 lines 23-26), one or more throat images capturing an inside of the subject’s throat (i.e., “a new user/patient may have a sore throat. He/she activates a cellphone App and takes one or more photos of his/her throat using mobile 110”, page 23 lines 2-6), the throat images captured using one or more user interfaces that instruct the subject to use a camera of the device to capture the throat images (See for example, “Camera guidance networks”, and “The second neural network is classification-based and is set up to classify each frame obtained from the camera with the at least one of the following outputs: move camera right, move camera left, move camera up, move camera down, open mouth, lower tongue, perfect placement”, page 25 lines 13-23);
receiving, by the application server from the mobile application, a set of clinical factors associated with the subject (i.e., “metadata”, page 21 line 15 through page 23 line 1) that were input by the subject in one or more user interfaces that instructed subject to input the set of clinical factors (See for example, “The user uploads the photo/images to the cellphone App, as well as the answers to the questions in a capturing metadata”, page 23 lines 2-6);
inputting, at the application server, the throat images into a machine-learned model (i.e., “It takes an image and metadata () and outputs a prediction whether the patient suffers from bacterial or viral pharyngitis”, page 23 lines 23-27; and “analyzing said image and/or said metadata of said patient using a machine learning system previously trained with said training samples”, claim 1 at page 55) trained on a plurality of training throat images labeled with respective training labels indicating presence or absence of one or more bacterial or viral pathogens (i.e., “obtaining training images and/or metadata step 202, images and/or metadata associated with a specific disease, disorder or condition are obtained. For example, in diagnosing a strep throat, images of throats, previously determined by laboratory tests to be with a streptococcal throat infection (e.g. Fig. 3B) and images without a streptococcus- infected throat, but with a viral pharyngitis (e.g. Fig. 3A) are obtained”, page 21 lines 15-23);
determining, at the application server (i.e., “Servers 130, 132 are configured to perform the method(s) of the present invention”, page 19 lines 15-16), a disease state prediction for the subject based on at least an output of the machine-learned model, wherein the disease state prediction indicates whether a bacterial or viral pathogen is present in the subject (i.e., “distinguish between viral and bacterial”, page 27 lines 26-29);
transmitting, from the application server to the device via a network connection, the disease state prediction for the subject (See for example, “The input for the application is obtained from the mobile phone or computer camera in addition to the metadata that is also fed by the user. The diagnostic algorithm is a software code that sits in the cloud to which the input is transferred, consisting of the image and the metadata, and where the input processing takes place. The output of the processing software is a medical diagnosis of the disease”, page 34 lines 4-9); and
presenting, via the mobile application executing on the device, a user interface including the disease state prediction for the subject (i.e., “The outputs of the software may be displayed, such as on the App on cellphone/mobile 110, or on smart device 112”, page 19 lines 17-18).
However, Evron does not explicitly disclose wherein the disease state prediction comprises one or more feature metrics determined from the set of throat images, each of the one or more feature metrics indicating a presence or a property of a respective feature of the subject's throat identified in the set of throat images.
Dacosta teaches a disease state prediction (i.e., “Tissue autofluorescence imaging provides a unique means of obtaining biologically relevant information of normal and diseased tissues in real-time, thus allowing differentiation between normal and diseased tissue states”, Paragraph [0048]) that comprises one or more feature metrics determined from the set of throat images (i.e., “the device may be used to image and/or monitor targets such as a skin target, an oral target, an ear-nose-throat target”, Paragraph [0147]), each of the one or more feature metrics indicating a presence or a property of a respective feature of the subject’s throat identified in the set of throat images (i.e., “biological components of the wound and surrounding normal tissues including connective tissues, blood, microvascularity, bacteria, microorganisms, etc. as well as fluorescently labeled drugs/pharmacological agents”, Paragraph [0161]).
Evron and Dacosta are analogous art because they are from the field of digital image processing for medical imaging.
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 Evron by incorporating the disease state prediction comprises one or more feature metrics determined from the set of throat images, each of the one or more feature metrics indicating a presence or a property of a respective feature of the subject's throat identified in the set of throat images, as taught by Dacosta.
The suggestion/motivation for doing so would have been to provide early identification of a disease to guide therapeutic intervention.
Therefore, it would have been obvious to combine Dacosta with Evron to obtain the invention as specified in claim 21.
As to claim 22, Evron teaches wherein the one or more user interfaces used to capture the one or more throat images provide guided assistance for capturing the one or more throat images with the camera (See for example, “Camera guidance networks”, and “The second neural network is classification-based and is set up to classify each frame obtained from the camera with the at least one of the following outputs: move camera right, move camera left, move camera up, move camera down, open mouth, lower tongue, perfect placement”, page 25 lines 13-23).
As to claim 23, Evron teaches wherein the one or more user interfaces used to capture the one or more throat images instruct the subject on how to position or orient the camera (See for example, “Camera guidance networks”, and “The second neural network is classification-based and is set up to classify each frame obtained from the camera with the at least one of the following outputs: move camera right, move camera left, move camera up, move camera down, open mouth, lower tongue, perfect placement”, page 25 lines 13-23).
As to claim 24, Evron teaches wherein the one or more user interfaces used to capture the one or more throat images ask a set of questions, including having the subject input information about the subject (See for example, “metadata may be used, such as binary “yes-no” question responses, and/or qualitative and/or quantitative user/patient question responses”, page 21 line 15 through page 23 line 1).
As to claim 29, Evron teaches wherein the disease state prediction comprises at least one of: a probability of a viral pathogen infection; a probability of a bacterial pathogen infection; and a probability of no pathogen infection (See for example, “In the inference stage: The input is the image and its metadata, and the output is probability of viral and bacterial pharyngitis”, page 10 lines 22-23).
As to claim 30, Evron teaches wherein the set of clinical factors comprises at least one from a group consisting of: age (i.e., “By “metadata” is meant all the non-visuals signs and symptoms of the disease or disorder, such as, but not limited to … patient’s age”, page 18 lines 13-15); a presence or absence of swollen lymph nodes (i.e., “enlarged lymph nodes”, page 18 lines 13-15); subject temperature (i.e., “3) How high is your temperature?”, page 22 line 14); a presence or absence of fever (i.e., “fever”, page 18 lines 13-15); a presence or absence of a cough (i.e., “coughing”, page 18 lines 13-15); a presence or absence of a runny nose (i.e., “runny nose”, page 18 lines 13-15); a presence or absence of a headache (i.e., “headache”, page 18 lines 13-15); a presence or absence of body aches; a presence or absence of vomiting; a presence or absence of diarrhea; a presence or absence of fatigue; a presence or absence of chills; and a duration of pharyngitis.
Claim 25 is rejected under 35 U.S.C. 103 as being unpatentable over Evron in view of Dacosta as applied to claim 21 above, and further in view of Tran (U.S. Pub. No. 2020/0405148). The teachings of Evron and Dacosta have been discussed above.
As to claim 25, Evron and Dacosta do not explicitly disclose receiving, by the application server from the device via the network connection, profile information of the subject created using one or more user interfaces that instruct the subject to create a profile.
Tran teaches receiving, by the application server from the device via the network connection, profile information of the subject created using one or more user interfaces that instruct the subject to create a profile (i.e., “a patient or healthcare provider may create a patient profile comprising, e.g., identifying, characterizing, and/or medical information, including information about a patient’s medical history, profession, and/or lifestyle”, Paragraph [0253]; and Paragraphs [0254]-[0255]).
Evron, Dacosta and Tran are analogous art because they are from the field of digital image processing for medical imaging.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to further modify Evron and Dacosta by incorporating the receiving, by the application server from the device via the network connection, profile information of the subject created using one or more user interfaces that instruct the subject to create a profile, as taught by Tran.
The suggestion/motivation for doing so would have been to further store auxiliary data that can be analyzed for the disease state prediction.
Therefore, it would have been obvious to combine Tran with Evron and Dacosta to obtain the invention as specified in claim 25.
Claims 26 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Evron in view of Dacosta as applied to claim 21 above, and further in view of Bundsgaard et al. (U.S. Pub. No. 2022/0400940). The teachings of Evron and Dacosta have been discussed above.
As to claim 26, Evron and Dacosta do not explicitly disclose wherein the one or more user interfaces used to capture the one or more throat images instruct the subject on how to use a mirror to capture the one or more throat images with the camera.
Bundsgaard et al. teaches one or more user interfaces that instruct the subject on how to use a mirror to capture the one or more throat images with the camera (See for example, “the user 101 is instructed by the app to position him-/herself in front of a mirror 102, as shown in FIGS. 1 and 3. FIG. 3 is a diagrammatic representation of the user 101, mirror 102, and mobile device 103 of FIG. 1, the reflections of the user 101’ and the mobile device 103’ in the mirror, and the obtaining of an image of an area of the user's oral cavity. In an embodiment, the mirror 102 is at a given distance from the user’s face, preferably at a given distance from the user’s oral cavity, and the app instructs the user to position him/herself accordingly. The mirror may be e.g. a wall-mounted mirror, a floor mirror, a tabletop mirror, or a handheld mirror”, Paragraph [0063]).
Evron, Dacosta and Bundsgaard et al. are analogous art because they are from the field of digital image processing for medical imaging.
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 Evron and Dacosta by incorporating the one or more user interfaces instruct the subject on how to use a mirror to capture the one or more throat images with the camera, as taught by Bundsgaard et al.
The suggestion/motivation for doing so would have been to allow the user to be able to obtain one or more images of an area of his/her oral cavity without the need of dedicated equipment or any other tools that are not normally present in an average household.
Therefore, it would have been obvious to combine Bundsgaard et al. with Evron and Dacosta to obtain the invention as specified in claim 26.
As to claim 28, Evron teaches wherein the mobile application automatically evaluates a quality of the one or more throat images (i.e., “the methods and systems employ another algorithm that checks and ensures image quality”, page 24 lines 14-15; and “the camera placement and the image quality algorithms are performed on a local device, for example the same device that captured the video input (smartphone, tablet etc.)”, page 25 lines 8-11).
However, Evron and Dacosta do not explicitly disclose wherein the mobile application automatically instructs the subject to capture a new image if the quality of the one or more throat images is not sufficient.
Bundsgaard et al. teaches a mobile application (i.e., “the mobile device 103 is configured to execute an application software (“app”) and comprises a main camera 207 for capturing images”, Paragraph [0062]) that automatically instructs the subject to capture a new image if the quality of the one or more throat images is not sufficient (i.e., “The user 101 is instructed by the app to capture another image of his/her oral cavity, if the image quality is below a threshold for image quality, i.e. if the images do not have clinical image quality”, Paragraph [0077]).
Therefore, in view of Bundsgaard et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Evron and Dacosta by incorporating the mobile application automatically instructs the subject to capture a new image if the quality of the one or more throat images is not sufficient, as taught by Bundsgaard et al., in order to allow the user to obtain images of high quality.
Claim 27 is rejected under 35 U.S.C. 103 as being unpatentable over Evron in view of Dacosta as applied to claim 21 above, and further in view of Hefez et al. (U.S. Pub. No. 2018/0160887). The teachings of Evron and Dacosta have been discussed above.
As to claim 27, Evron and Dacosta do not explicitly disclose wherein the one or more user interfaces used to capture the one or more throat images provide diagrams or tutorials illustrating specific features of the throat that should be within view to capture the one or more throat images.
Hefez et al. teaches one or more user interfaces that provide diagrams or tutorials illustrating specific features of the throat that should be within view to capture the one or more throat images (See for example, Paragraphs [0150] and [0154]; “examination logic module 408 can be configured to utilize guiding module 406 in order to provide various guidance data instructing user 102 how to maneuver system 200 to the desired system 200 spatial disposition with respect to patient’s 103 body”, Paragraph [0191]; and Paragraph [0192]).
Evron, Dacosta and Hefez et al. are analogous art because they are from the field of digital image processing for medical imaging.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to further modify Evron and Dacosta by incorporating the one or more user interfaces provide diagrams or tutorials illustrating specific features of the throat that should be within view to capture the one or more throat images, as taught by Hefez et al.
The suggestion/motivation for doing so would have been to trigger the capturing of images when the throat features are present within the field of view of the camera.
Therefore, it would have been obvious to combine Hefez et al. with Evron and Dacosta to obtain the invention as specified in claim 27.
Response to Arguments
Claim Objections
With respect to claim 21, Applicant has amended the claim in order to correct for minor informalities. Therefore, the objection has been withdrawn.
Claim Rejections - 35 USC § 112
With respect to claims 3-5, 13-15, 22024, 26, and 27, Applicant has amended the claims in order to particularly point out and distinctly claim the subject matter which the inventor regards as the invention. Therefore, the rejections have been withdrawn.
Claim Rejections - 35 USC §§ 102 and 103
With respect to claims 1-30, Applicant’s arguments (Remarks dated September 9, 2026, pages 12-14) have been fully considered. However, they are moot in view of the new ground(s) of rejection (Refer to Claim Rejections - 35 USC § 103 Section above).
Conclusion
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSE M TORRES whose telephone number is (571)270-1356. The examiner can normally be reached Monday thru Friday; 10:00 AM to 6:00 PM EST.
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/JOSE M TORRES/Examiner, Art Unit 2664 09/25/2026
/PING Y HSIEH/Primary Examiner, Art Unit 2664
