DETAILED ACTION
This office action is based on the claim(s) filed on 08/05/2026.
Claims 1, 55, and 102 have been amended.
Claims 1-5, 9, 13-14, 21, 26-28, 55-56, 58, 62, 66-67, 76, and 101 are currently pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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 1-5, 9, 13-14, 21, 26-28, 55-56, 58, 62, 66-67, 76, and 101 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-5, 9, 13-14, 21, and 26-28, are drawn to a method and Claim 55-56, 58, 62, 66-67, and 76, are drawn to a system/device, and Claim 101 is directed to an art of manufacturer, and each of which is within the four statutory categories (i.e., a machine and a process). Claims 1-5, 9, 13-14, 21, 26-28, 55-56, 58, 62, 66-67, 76, and 101 are further directed to an abstract idea on the grounds set out in detail below.
Under Step 2A, Prong 1, the steps of the claim for the invention represents an abstract idea of a series of steps that recite a process for predicting and determine a disease state. Collecting a user data/images to estimate behavior relationship with characteristics and predict the user future behavior are steps that could have been performed by a human mind but for the fact that the claims recite a general-purpose computer processor to implement the abstract idea for which both the instant claims and the abstract idea are defined as Metal Process that can be performed using human mind with the aid of pencil and paper.
Independent Claim 1 recites the steps of:
“A computer-implemented method of generating a prediction score for a disease state in a test subject, the method comprising:
passing a first set of features extracted from oral cavity-related data obtained from a test subject through an electronic neural network, wherein the electronic neural network has been trained on a first set of training data that comprises a plurality of sets of features extracted from oral cavity-related data obtained from reference subjects, wherein the oral cavity-related data obtained from the reference subjects are each labeled with a positive or negative disease state ground truth classification for a given reference subject, and wherein one or more predictions for a positive or negative disease state classification for the given reference subject are made based on the oral cavity-related data obtained from the given reference subject, which predictions are compared to the ground truth classification for the given reference subject when the electronic neural network is trained;
extracting the first set of features from a series of oral cavity images obtained from the oral cavity-related data of the test subject, wherein the series of oral cavity images comprises multiple frames from a video of the test subject;
aggregating the extracted features across the multiple frames to generate an estimated size of a region of interest;
outputting from the electronic neural network the prediction score for the disease state in the test subject indicated by the first set of features extracted from the oral cavity-related data from the test subject”.
Independent Claim 55 and 101 recite similar steps as in Claim 1.
These limitations, as drafted, given the broadest reasonable interpretation cover performance of the limitations by a human mind with aid of pen and paper reciting an abstract idea for Mental Process but for the recitation of generic computer components. For example, the limitations encompass a user the ability to collect a test subject data, process the data against a data of a reference subject data labeled based on a ground truth classification, to calculate/predict a score for the test subject disease state, which are steps that that could have been performed by a human to implement the abstract idea and are steps reciting mental process that could have been performed using a human mind with aid of pen and paper, but other than the mere nominal recitation of "processor, memory, neural network", to implement the abstract idea for performing the steps of observing, evaluating, judgment and opinion which can be performed using a human mind with the aid of pencil and paper, see MPEP § 2106.04(a)(2)(III). Accordingly, the claim limitations (in BOLD) recite an abstract idea. Any limitations not identified above as part of the Mental Process are deemed "additional elements," and will be discussed in further detail below.
Under Step 2A, Prong 2, this judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas, linking the abstract idea to a particular technological environment. In particular, the claims recite the additional elements such as “processor, memory, non-transitory computer readable media, neural network” that iteratively takes input data and analyzes said data to determine an output to performing generic computer functions, e.g., passing set of features extracted and obtained from a test subject through an electronic neural network for predicting a disease classification, such that it amounts no more than adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f), generally linking the use of the judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h), and a mere data gathering process that does not add a meaningful limitation to the above abstract idea, see MPEP 2106.04(d). For example, the claim recite a neural network that is trained using a set of training data which is recited at a high level of generality and is described in the specification in an arbitrary form without disclosing how a specific algorithm using available data for allowing the model to learn patterns and relationships within the data and implement it to perform the claimed function. As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 "merely include[ing] instructions to implement an abstract idea on a computer" is an example of when an abstract idea has not been integrated into a practical application. Accordingly, looking at the claim as a whole, individually and in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Under step 2B, the claims do not include additional elements that are sufficient to amount to "significantly more" than the judicial exception because as mentioned above, the additional elements amount to no more than generic computing components, recited at a high level of generality, do not present improvements to another technology or technical field, nor do they affect an improvement to the functioning of the computer itself, that amount to no more than mere instruction to perform the abstract idea such that it amounts no more than adding the words "apply it" (or an equivalent) to apply the exception using generic computer component, see MPEP 2106.05(f). There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and mere instructions to apply an exception using a generic computer component cannot provide an inventive concept, See Alice, 573 U.S. at 223 ("mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention."). The claims are not patent eligible.
Dependent Claims 2-5, 9, 13-14, 21, 26-28, 56, 58, 62, 66-67, 76, include all of the limitations of claim(s) 1 and 55, and therefore likewise incorporate the above-described abstract idea. While the depending claims add additional limitations, such as
As for claims 2 and 56, the claim(s) recite limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human mind with the aid of pen and paper, reciting an abstract idea for Mental Process. The claims recite additional elements “neural network” that implement the identified abstract idea. These hardware components are recited in the claim(s) at a high level that it amounts to no more than mere instructions to perform the steps of the abstract idea such that it amounts no more than adding the words "apply it" (or an equivalent) to apply the exception using generic computer component, see MPEP 2106.05(f), merely uses the computer as a tool to perform the abstract idea, see MPEP 2106.05(h), and a mere data gathering process that does not add a meaningful limitation to the above abstract idea, see MPEP 2106.05(d). Thus, the judicial exceptions recited in claims is/are not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more").
As for claim 3, the claim(s) recite limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human mind with the aid of pen and paper, reciting an abstract idea for Mental Process. The claims recite additional elements “neural network” that implement the identified abstract idea. These hardware components are recited in the claim(s) at a high level that it amounts to no more than mere instructions to perform the steps of the abstract idea such that it amounts no more than adding the words "apply it" (or an equivalent) to apply the exception using generic computer component, see MPEP 2106.05(f), merely uses the computer as a tool to perform the abstract idea, see MPEP 2106.05(h), adding insignificant extra/post extra-solution activity to the judicial exception, (i.e. “administrating a therapy…”), see MPEP 2106.05(g), and a mere data gathering process that does not add a meaningful limitation to the above abstract idea, see MPEP 2106.05(d). Thus, the judicial exceptions recited in claims is/are not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more").
As for claims 4-5, 9, 14, 21, 58, 62, and 67, the claim(s) recites limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human mind with the aid of pen and paper which are similarly rejected because, neither of the claims, further, defined the abstract idea and do not further limit the claim to a practical application or provide an inventive concept.
As for claims 13 and 66, the claim(s) recites limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human mind with the aid of pen and paper reciting an abstract idea for Mental Process along with mathematical calculations and relationships that constitute Mathematical Concepts. For example, probability of a positive or negative classification, is/are Mathematical Concepts which are similarly rejected because, neither of the claims, further, defined the abstract idea and do not further limit the claim to a practical application or provide an inventive concept.
As for claims 26-28, and 76, the claim(s) recite limitations that are under the broadest reasonable interpretation, further define the abstract idea noted in the independent claim(s) that covers performance by a human mind with the aid of pen and paper, reciting an abstract idea for Mental Process along with mathematical calculations and relationships that constitute Mathematical Concepts, but for the recitation of generic computer components. The claims recite additional elements “neural network” that implement the identified abstract idea. These hardware components are recited in the claim(s) at a high level that it amounts to no more than mere instructions to perform the steps of “training” to perform the abstract idea such that it amounts no more than adding the words "apply it" (or an equivalent) to apply the exception using generic computer component, see MPEP 2106.05(f), for example, passing data through trained neural network without reciting steps of training process, that is merely uses the computer as a tool to perform the abstract idea, see MPEP 2106.05(h), adding insignificant extra/post extra-solution activity to the judicial exception, (i.e. “administrating a therapy…”), see MPEP 2106.05(g), and a mere data gathering process that does not add a meaningful limitation to the above abstract idea, see MPEP 2106.05(d).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 4-5, 9, 13-14, 26, 55, 58, 62, 66-67, 76, and 101 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Sarkaria et al. (US 2022/0273245 A1- “Sarkaria”)
Regarding Claim 1 (Currently Amended), Sarkaria teaches a computer-implemented method of generating a prediction score for a disease state in a test subject Sarkaria discloses a method for determining a predicated disease state score of a test subject (Sarkaria: [Fig. 1-3, 4-8], [Abs], [0089]), the method comprising:
passing a first set of features extracted from oral cavity-related data obtained from a test subject through an electronic neural network Sarkaria discloses the image parameter coefficients are determined so as to best represent the relationship between the first set of training subject throat images (oral cavity-related data) input into a function of the image model and their associated labels; generally, the image model 400 is a supervised machine learning technique; the image model is a convolutional neural network model (Sarkaria: [0079]),
wherein the electronic neural network has been trained on a first set of training data that comprises a plurality of sets of features extracted from oral cavity-related data obtained from reference subjects Sarkaria discloses the image parameter coefficients are determined so as to best represent the relationship between the first set of training subject throat images (oral cavity-related data) input into a function of the image model and their associated labels; generally, the image model is a supervised machine learning technique; the image model is a convolutional neural network model; the input and output vectors (feature) relevant to the chained model; the input vectors include the set of subject throat images (oral cavity-related data) and the clinical factors; the resulting output vector of the chained model is a disease state prediction, which includes probabilities for various types of infections in the subject; (Sarkaria: [0079], [0087])
wherein the oral cavity-related data obtained from the reference subjects are each labeled with a positive or negative disease state ground truth classification for a given reference subject Sarkaria discloses training labels may include a label indicating a presence of a pathogen in the subject associated with the respective training image; the label may be a categorical label (e.g., A, B, C); the first set of training throat images and the associated labels are provided by a training database 415; the first set of training throat images may be captured by the image capture device; the labels for the first set of training subjects is provided on the basis that disease states of the first set of training subjects are previously known (ground truth classification), for example as determined by traditional cell culturing and evaluation by one or more medical professionals evaluating the training set of subjects; the image model 400 is training with training images and corresponding training labels indicating the presence or absence of these conditions; (Sarkaria: [0004], [0069], [0072])
and wherein one or more predictions for a positive or negative disease state classification for the given reference subject are made based on the oral cavity-related data obtained from the given reference subject Sarkaria discloses the image model 400 is training with training images and corresponding training labels indicating the presence or absence of these conditions; the first set of training throat images may be captured by the image capture device; the labels for the first set of training subjects is provided on the basis that disease states of the first set of training subjects are previously known; (Sarkaria: [0004], [0069]),
which predictions are compared to the ground truth classification for the given reference subject when the electronic neural network is trained Sarkaria discloses the disease metrics generated by the image model and the clinical factors are inputted into a classifier to determine the disease state prediction, and the disease state prediction is returned; the first set of training throat images and the associated labels are provided by a training database 415; the first set of training throat images may be captured by the image capture device; the labels for the first set of training subjects is provided on the basis that disease states of the first set of training subjects are previously known (ground truth classification), for example as determined by traditional cell culturing and evaluation by one or more medical professionals evaluating the training set of subjects; generally, the image model is a supervised machine learning technique; the image model is a convolutional neural network model; (Sarkaria: [0069], [0079]);
extracting the first set of features from a series of oral cavity images obtained from the oral cavity-related data of the test subject, wherein the series of oral cavity images comprises multiple frames from a video of the test subject Sarkaria discloses collecting by an image capture device a set of subject throat images of the subject's throat (oral cavity-related data) and the clinical factors where the set of images is curated, centered and cropped [extracting] [Note: A set of video images is conceptually the same as video frames, where a single video frame is a single still image and a video is fundamentally a sequence of individual still pictures played in rapid succession] (Sarkaria: [Fig. 7], [0019], [0064-0065], [0079], [0087])
aggregating the extracted features across the multiple frames to generate an estimated size of a region of interest Sarkaria discloses the image model perform the feature detection on the images and color classification collected may be used [aggregating] to determine targeted feature metrics including, but not limited to: presence/size/shape/location of the oral cavity, etc. (Sarkaria: [0062], [0073])
outputting from the electronic neural network the prediction score for the disease state in the test subject indicated by the first set of features extracted from the oral cavity-related data from the test subject Sarkaria discloses the disease metrics (scores) generated by the image model and the clinical factors are inputted into a classifier to determine the disease state prediction, and the disease state prediction is returned; the image parameter coefficients are determined so as to best represent the relationship between the first set of training subject throat images (oral cavity-related data) input into a function of the image model and their associated labels; generally, the image model 400 is a supervised machine learning technique; the input and output vectors (feature) relevant to the chained model; the input vectors include the set of subject throat images (oral cavity-related data) and the clinical factors; the resulting output vector of the chained model is a disease state prediction, which includes probabilities (scores) for various types of infections in the subject (Sarkaria: [0079], [0087]).
Regarding Claim 4 (Original), Sarkaria teaches the computer-implemented method of claim 1, wherein the oral cavity-related data comprises oral cavity images Sarkaria discloses subject's throat collected by an image capture device; capture of the set of subject throat images; scan at least a rear surface of such oral cavity and/or a throat behind such oral cavity; (Sarkaria: [0019], [0020], [0054]).
Regarding Claim 5 (Original), Sarkaria teaches the computer-implemented method of claim 1, wherein the oral cavity-related data comprises image data, demographic data, symptom data, physical examination data, or a combination thereof Sarkaria discloses capture of the set of subject throat images; the subject profile may include identify information about the subject such as age, gender; derived data from those profiles such as aggregate demographic information; (Sarkaria: [0020], [0032], [0033]).
Regarding Claim 9 (Original), Sarkaria teaches the computer-implemented method of claim 1, wherein the disease state comprises a bacterial infection, a viral infection, or a peritonsillar abscess Sarkaria discloses disease state prediction may indicate a presence or probability of the subject having a streptococcal infection (Sarkaria: [0016]).
Regarding Claim 13 (Original), Sarkaria teaches the computer-implemented method of claim 1, wherein the prediction score comprises a probability of a positive or negative streptococcus pharyngitis classification for the test subject Sarkaria discloses detecting streptococcal infections in subjects experiencing pharyngitis; the disease state prediction may indicate a presence or probability of the subject having a streptococcal infection; the determined feature metrics and infection metrics may be provided without the prediction of a presence of a pathogen to the classifier as inputs for generating a patient's disease state; (Sarkaria [0016], [0074]).
Regarding Claim 14 (Original), Sarkaria teaches the computer-implemented method of claim 1, wherein the oral cavity-related data comprises oral cavity images from the test and reference subjects, which oral cavity images comprise a region of interest selected from the group consisting of: a throat area, a tonsil area, a tongue area, a palate area, uvula area, posterior oropharynx area, lips area, cheek area, and neck area Sarkaria discloses a set of subject throat images (oral cavity related data) of the subject's throat collected by an image capture device (Sarkaria: [0019]).
Regarding Claim 26 (Original), Sarkaria teaches the computer-implemented method of claim 1, wherein the first set of training data comprises oral cavity images and wherein the electronic neural network has been further trained on a second set of training data that comprises a plurality of sets of features extracted from numerical vectors representing sets of parameterized demographic data, symptom data, and/or physical examination data from the reference subjects Sarkaria discloses the first set of training throat (oral cavity) images and the associated labels are provided by a training database and the classifier is trained using a set of training predictions of pathogen presence generated by the pre-trained image model based on a second set of training throat images, training clinical factors associated with a second set of training subjects; the classifier is trained using feature metrics and infection metrics generated by the pre-trained image model based on the second set of training throat images; the classifier is a neural network mode the classifier is trained using feature metrics and infection metrics generated by the pre-trained image model based on the second set of training throat images; the classifier is a neural network model (Sarkaria: [0069], [0076], [0079]), and wherein the computer- implemented method further comprises passing a second set of features extracted from a numerical vector representing a set of parameterized demographic data, symptom data, and/or physical examination data from the test subject through the electronic neural network Sarkaria discloses a vector including one or more of the above numerical values; the classifier is a neural network model; the input vectors include the set of subject throat images and the clinical factors (physical examination data); (Sarkaria: [0072], [0079], [0087]).
Regarding Claim 55 (Original), Sarkaria teaches a system for generating a prediction score for a disease state in a test subject using an electronic neural network, the system comprising: a processor; and a memory communicatively coupled to the processor, the memory storing instructions which, when executed on the processor, Sarkaria: [Figs. 1, 2, 3A-3J, 4-8], [Abs], [0006], [0043], [0089]) perform operations comprising:
the claim recites substantially similar limitations to claim 1, as such, are rejected for similar reasons as given above.
Regarding Claims 58 (Original), the claim recites substantially similar limitations to claim 5, as such, are rejected for similar reasons as given above.
Regarding Claims 62 (Original), the claim recites substantially similar limitations to claim 9, as such, are rejected for similar reasons as given above.
Regarding Claims 66-67 (Original), the claims recite substantially similar limitations to claim 13-14, as such, are rejected for similar reasons as given above.
Regarding Claims 76 (Original), the claim recites substantially similar limitations to claim 26, as such, are rejected for similar reasons as given above.
Regarding Claim 101 (Original), Sarkaria teaches a computer readable media comprising non-transitory computer executable instructions which, when executed by at least one electronic processor (Sarkaria: [0043], [0046], [claim 23]), perform at least:
the claim recites substantially similar limitations to claim 1, as such, are rejected for similar reasons as given above.
Claim Rejections - 35 USC § 103
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.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2-3 and 56 are rejected under 35 U.S.C. 103 as being unpatentable over Sarkaria et al. (US 2022/0273245 A1- “Sarkaria”) in view of Maher et al. (US 2020/0185059 A1”- “Maher”)
Regarding Claim 2 (Original), Sarkaria teaches the computer-implemented method of claim 1, comprising generating a therapy recommendation for the test subject based upon the prediction score output from the electronic neural network
However, Sarkaria does not expressly disclose generating therapy recommendation based upon the prediction score output from the electronic neural network (as underlined).
Grail discloses classifier is based on a neural network algorithm; administering a treatment to a test subject based upon the cancer class of the test subject determined by the first trained classifier; in other words, the treatment is a treatment that is a known treatment for the cancer class the first trained classifier determines the test subject has; for instance, knowing the cancer class of the test subject provides a basis for determining which treatment regimen to provide the test subject using resources such as those provided by the American Society of Clinical Oncology; normalized scores were generated using abnormally methylated fragments (Grail: [0184], [0211], [0239]).
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have Sarkaria incorporate generating a therapy recommendation for the test subject based upon the prediction score output from the electronic neural network, as taught by Grail, improving screening and detection of cancer class from among several cancer classes, which serves to facilitate early and appropriate treatment
for subjects afflicted with cancer (Grail: [Abs]. [0045]).
Regarding Claim 3 (Original), Sarkaria teaches the computer-implemented method of claim 1, comprising administering a therapy to the test subject based upon the prediction score output from the electronic neural network
However, Sarkaria does not expressly disclose administrating a therapy based upon the prediction score output from the electronic neural network (as underlined).
Grail discloses classifier is based on a neural network algorithm; administering a treatment to a test subject based upon the cancer class of the test subject determined by the first trained classifier; in other words, the treatment is a treatment that is a known treatment for the cancer class the first trained classifier determines the test subject has; for instance, knowing the cancer class of the test subject provides a basis for determining which treatment regimen to provide the test subject using resources such as those provided by the American Society of Clinical Oncology; normalized scores were generated using abnormally methylated fragments; para [0184], [0211], [0239] (Grail: [0184], [0211], [0239]).
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have Sarkaria incorporate administrating a therapy for the test subject based upon the prediction score output from the electronic neural network, as taught by Grail, improving screening and detection of cancer class from among several cancer classes, which serves to facilitate early and appropriate treatment for subjects afflicted with cancer (Grail: [Abs]. [0045]).
Regarding Claims 56 (Original), the claim recites substantially similar limitations to claim 2, as such, are rejected for similar reasons as given above.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Sarkaria et al. (US 2022/0273245 A1- “Sarkaria”) in view of Elbaz et al. (US 2021/0128282 A1”- “Elbaz”)
Regarding Claim 21 (Currently Amended), Sarkaria teaches the computer-implemented method of claim 1, wherein the oral cavity data from the test and reference subjects are obtained from videos of the test and reference subjects
However, Sarkaria does not expressly disclose the oral cavity data from the test and reference subjects are obtained from videos of the test and reference subjects (as underlined)
Elbaz discloses displaying images from a three-dimensional (3D) volumetric model of a patient's dental arch, the method comprising: collecting the 3D volumetric model of the patient's dental arch; regions may be displayed to the user along with descriptive, analytic information about the scanned region; the images may be displayed as a time-lapse image (video, loop, etc.) (Elbaz: [0107], [0383]).
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have Sarkaria incorporate oral cavity data from videos of the test and reference subjects, as taught by Elbaz, improving screening and detection of cancer class from among several cancer classes, which tracking and showing changes over time (Elbaz: [0105]. [0427], [0428]).
Claim 27 is rejected under 35 U.S.C. 103 as being unpatentable over Sarkaria et al. (US 2022/0273245 A1- “Sarkaria”) in view of Brunk et al. (US 2016/0187199 A1”- “Brunk”)
Regarding Claim 27 (Original), Sarkaria teaches the computer-implemented method of claim 26, wherein the numerical vectors representing the set of parameterized demographic data, symptom data, and/or physical examination data from the reference subjects and from the test subject each comprise at least a 15-dimensional vector
However, Sarkaria does not expressly disclose the numerical vectors each comprise at least 15-dimenstional vector (as underlined)
Brunk discloses N dimensional vector of spectral ratios obtained by the above methods are mapped into 2 dimensional chromaticity space; this mapping can be adapted to map spectral vectors into a variety of color space standards, such as CJE and others; whilst a 15 dimensional vector value of a random point in the center of the scene is used as a 'reference value' and all the other measured spectricity vectors in the scene are compared to it, with simple Euclidean distance used as a modulator on the 'red' that gets overlaid into the image; regarding medical imaging applications (examination data), our techniques of using spectral information (Brunk: [0217], [00238], [0362]).
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have Sarkaria incorporate numerical vectors each comprise at least 15-dimenstional vector, as taught by Brunk, for providing spectral images processed for use in object identification, classification, and a variety of other applications, such as medical imaging (Brunk: [0352], [0463]).
Claim 28 is rejected under 35 U.S.C. 103 as being unpatentable over Sarkaria et al. (US 2022/0273245 A1- “Sarkaria”) in view of de Jonge et al. (US 2022/0167945 A1”- “Jonge”)
Regarding Claim 28 (Original), Sarkaria teaches the computer-implemented method of claim 26, further comprising mapping the first and second sets of features to a bidimensional vector that corresponds to the prediction score for the disease state in the test subject
However, Sarkaria does not expressly disclose mapping of features to bidimensional vector corresponding to predication score (as underlined).
Jonge discloses (real-time measurement prediction of ultrasound imaging is provided; the computing device may change a color of the medical parameters shown in the display 406 based on the value of the medical parameters comparing the received ultrasound image data with trained model data to predict, in real time, a landmark of the received ultrasound image data; through the bi-dimensional vectors, translations of the anatomy of interest are able to be handled (Jonge: [0103], [0202], [0286]).
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have Sarkaria incorporate mapping of features to bidimensional vector corresponding to predication score, as taught by Jonge, providing medical parameters displayed indicating that values are within a normal range, borderline abnormal range, and abnormal range (Jonge: [0202]).
Response to Amendment
Applicant's arguments filed 08/05/2026 have been fully considered by the Examiner and addressed as the following:
In the remarks, Applicant argues in substance that:
Applicant's arguments with respect to the 35 U.S.C. § 101 rejection on page 7-8.
On page 9 of the remarks, the Applicant argues “The claims are therefore not directed to a "Mental Process" abstract idea ... is no longer accurate in view of the amendments, which add concrete technical processing steps that improve how the data is prepared and featurized for the neural network”, Examiner respectfully disagree. Examiner asserts that the claims are given their broadest reasonable interpretation for the purpose of determining whether they encompass a judicial exception. The claim(s) limitations are directed towards generating a medical imaging protocol recite[ing], under BRI, a process that can be performed by a human receiving and analyzing oral cavity image information and to compare to a reference while extracting information from an image and aggregating the features and parameters which are generally classified as a form of data collection, manipulation, or processing, as such obtaining, capturing, extracting, or analyzing data from an image without a specific technological improvement, it is considered the judicial exception defining the abstract concept to determine a disease state which are steps reciting analyzing and predicting disease state, but for the fact that the claims recite a general-purpose computer processor and neural network to perform the steps, which recite a mental process that when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. “The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." see MPEP § 2106.04(a)(2)(III)(C).
Further, on page 9 of the remarks, the Applicant argues “Even assuming arguendo that an abstract idea is recited, the amended claims integrate that idea into a practical application. The claims as a whole are directed to a specific technological improvement in the field of telehealth and remote medical diagnostics specifically, an improved computer vision and machine learning pipeline that enables accurate, objective prediction of disease states....”, Examiner respectfully disagree. As described in the rejection above, the claim(s) does/do not describe a particular improvement of computer’s functionality or a technical field, rather using additional elements, “e.g., processor, neural network”, recited at a high level of generality without explaining how it solves or improves the alleged systems or solve a technical problem as such recited as tool(s) to perform the steps abstract idea such as obtaining, analyzing, determining, and extracting, aggregating information through leveraging computing technology (neural network) in a well understood manner as such merely reciting a generic neural network in a patent claim does not automatically integrate an abstract idea into a practical application in addition while improving upon an abstract idea does not make the abstract idea any less abstract.
In light of the Alice decision and the guidance provided in the 2019 PEG, the features listed in the claims, are not considered an improvement to another technology or technical field, or an improvement to the functioning of the computer itself rather describes an improvement to diagnosing protocol, which is solving a health facility and patient administrative problem, using computers. In addition, by relying on computing devices to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible (See Alice, 134 S. Ct. at 2359 "use of a computer to create electronic records, track multiple transactions, and issue simultaneous instructions" is not an inventive concept). Therefore, even when considering the claims additional elements, the claims as a whole, individually and in combination, provide no integration of the abstract ideas into a practical application that no meaningful limits on practicing the abstract idea are introduced, see MPEP 2106. The claims as a whole are therefore directed to an abstract idea.
Applicant further argues on page 10 of the remarks that “This constitutes a practical application in the medical diagnostics and telehealth technology fields. See MPEP § 2106.04(d); MPEP § 2106.0S(a) (improvements to another technology or technical field); Example 37 (medical diagnostics with specific technical steps)”, Examiner respectfully disagree. Example 37 describes a process of rearranging icons on a graphical user interface (GUI) based on tracking the usage of each icon over a determined period of time by allocating a memory to track each icon where the icons automatically rearranged based on usage ranking that provides an improvement to a computer system providing a concept that cannot be performed by a human mind. In contrast the claimed invention is reciting obtaining oral images and extracting information from the image data and present output for predicting a disease. The claimed concept of the current invention is still performed by a generic computer or using generic computing to perform the concept and the courts have repeatedly found the merely adding general purpose computing devices as tools to carry out an abstract idea is insufficient to transform an abstract idea into patent-eligible subject matter. See MPEP 2106.04(a)(2)(lll)(A), MPEP 2106.04(a)(3)(III)(A)(B). As explained in the 2019 PEG, the evaluation of Prong Two requires the use of the considerations (e.g. improving technology, effecting a particular treatment or prophylaxis, implementing with a particular machine, etc.) as such the claim does not affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition but a data collected using generic devices used to diagnose a person and predict a condition status and does not provide a treatment or treat a patient, see MPEP 2106.04(d)(2). Therefore, the invention as a whole is directed to an abstract idea “mental process” and is not integrated into a practical application.
Applicant further on page 10 of the remarks argues that “The claims also recite a particular machine-the electronic neural network specifically trained and configured to operate...”, Examiner disagrees. Examiner asserts the claims describes a trained neural network without describing any specific training techniques to perform the claimed steps as such it is viewed as simply instructing a generic computer to "apply" an abstract idea to perform the claimed steps and as mentioned above that merely reciting a generic neural network in a patent claim does not automatically integrate an abstract idea into a practical application.
On page 10 of the remarks argues that “The amended claims recite additional elements that, individually and in combination, amount to significantly more than any alleged abstract idea. The combination ... is not well-understood, routine, or conventional in the field of oral cavity disease state prediction... Unlike generic neural network applications, the claimed invention improves the functioning of the computer system itself in the domain of variable-quality medical image”, Examiner disagrees. The claims at issue do not require any nonconventional computer, network, or other components, or even a non-conventional and non-generic arrangement of known, conventional pieces but merely call for performance of the claimed functions on a set of generic computer components. The elements of the instant process, when taken alone, each execute in a manner conventionally expected of these elements. The elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone. Limitations that the courts have found to qualify as "significantly more" when recited in a claim with a judicial exception include adding a specific limitation other than what is well-understood, routine, conventional activity in the field, or adding unconventional steps that confine the claim to a particular useful application, e.g., a non-conventional and non-generic arrangement of various computer components.
Applicant further argues that “This is analogous to the patent-eligible improvements in computer functionality recognized in cases such as Enfish and McRO...”, Examiner disagress to the Applicant argument. In Enfish, the claim(s) provided an improvement to a computer function and/or technical field (self-pointing database) reciting a self-referential table for a computer database providing a particular improvement in the computer's functionality that improves the way a computer stores and retrieves data in memory whereas the instant claim(s) and specifications do not recite an improvement to technology, as in Enfish, but to performance of an abstract idea such as collecting and analyzing oral cavity image data data while using well-known computer system and components. Moreover, the Examiner notes that in McRO the as-filed disclosure explicitly described that computers could not previously be programmed to perform the particular type of animation described and that only human animators were previously capable of such animation. Because the claimed invention solved this particular problem, the court found that the claimed invention was an improvement to computer technology. There is no such problem described in Applicant's disclosure.
Therefore, the Examiner has addressed the Applicant argument(s) and found this argument is not found to be persuasive. Hence, Examiner remains the 101 rejections of claims which have been updated to address Applicant's amendments.
Applicant's arguments with respect to the 35 U.S.C. § 102 rejection on page 11-12.
On page 11 of the remarks, the Applicant argues “Sarkaria does not teach or suggest any multi-frame feature extraction from a series of video frames, any aggregation of features across multiple”, Examiner respectful disagree. Although the Applicant argument(s) is/are directed to a newly added feature that was/were not considered in the prior OA search and examination, Examiner assert that Sarkaria [0062-0065] discloses a set of subject throat images of the subject's throat collected by an image capture device and perform pre-processing techniques on the image features where the images are curated, centered, cropped, etc. which are extraction tools for image data. Moreover, Sarkaria discloses a set of video images which is conceptually the same as video frames, where a single video frame is a single still image and a video is fundamentally a sequence of individual still pictures played in rapid succession.
Therefore, Examiner find the Applicant argument is/are unpersuasive. Therefore, Examiner finds the Applicant argument is/are unpersuasive. Examiner remains the 102 rejection.
Applicant's arguments with respect to the 35 U.S.C. § 103 rejection on page 12-16.
On page 13-16 of the remarks, the Applicant argues that:
claims 2, 3, 56 being unpatentable over Sarkaria in view of Maher, argues “This rejection is overcome by the amendments to claim 1... Maher is directed to early cancer detection and classification (likely via blood-based cfDNA methylation or similar normalized scores), not oral cavity imaging, strep pharyngitis, or telehealth throat video analysis ... Moreover, even if combined, Maher's "administering a treatment... that is a known treatment for the cancer class" does not teach or suggest the specific, actionable therapy”, Examiner respectfully disagree. As described in the above rejection, Sarkaria discloses the argued amended feature(s). Moreover, the instant claims of the current application are directed to generating a predication score for a disease state and similarly Maher invention is directed to predict the probability of cancer tissue type which predication for disease state. While Maher discloses the generation of recommended therapy for predicted disease, It would be obvious to one of ordinary skill in the art to incorporate the teaching of Maher with Sarkaria to teach the argued feature.
claim 21 being unpatentable over Sarkaria in view of Elbaz, arguing the amended feature in claim 1 and arguing “The examiner's motivation statement again appears copied from a mismatched cancer context and does not fit the claimed invention's purpose (objective strep diagnosis to reduce unnecessary antibiotics/in-person visits). A POSITA in oral cavity ML diagnostics would not combine Elbaz's dental visualization tool with Sarkaria's strep image classifier in the manner claimed”, Examiner disagrees to the Applicant argument. As described in the above rejection, Sarkaria discloses the argued amended feature(s). Moreover, Elbaz in the same field of endeavor discloses 3d volumetric model for collecting patient dental arch in the oral cavity. It would be obvious to one of ordinary skill in the art to incorporate the teaching of Elbaz with Sarkaria to teach the argued feature.
claim 27 being unpatentable over Sarkaria in view of Brunk, arguing that “The 15-dimensional reference vector in Brunk is an example in a spectral scene analysis context, not a teaching of minimum dimensionality for clinical feature vectors in a disease prediction NN”, however, the argued feature “not a teaching of minimum dimensionality for clinical feature vectors” is not described in the claim nor the specification.
claim 28 being unpatentable over Sarkaria in view of de Jonge, arguing “It does not disclose mapping image-derived features+ clinical parameter vectors to a bi dimensional space for a disease state prediction score in an oral cavity/strep pharyngitis NN context... A POSITA in the relevant art would not look to ultrasound landmark detection for the specific bidimensional mapping feature in this oral ML diagnostic system. The combination, especially with the processing amendments, is non-obvious”, Examiner disagrees. As described in the above rejection, Sarkaria discloses the argued amended feature(s). Moreover, while the claim is mapping features only, Jonge in the same field of endeavor discloses comparing (mapping) through the bi-dimensional vectors the received image data with trained model data. It would be obvious to one of ordinary skill in the art to incorporate the teaching of Jonge with Sarkaria to teach the argued feature.
Therefore, Examiner finds the Applicant argument is/are unpersuasive. Examiner remains the 103 rejection.
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 extension fee 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 date of this final action.
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/ALAAELDIN M. ELSHAER/Primary Examiner, Art Unit 3687
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