DETAILED ACTION
This office action is based on the claim set filed on 03/14/2025.
Claims 21 have been amended.
Claims 6-8, 10-12, 15-20, 22-25, 29-54, 57, 59-61, 63-65, 68-75, 77-100, and 102 have been canceled.
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 .
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on 03/14/2025 and 05/12/2025 are in accordance with the provisions of 37 CFR 1.97 and are considered by the Examiner.
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;
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 (Original), 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]);
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]).
Prior Art Cited but not Applied
The following document(s) were found relevant to the disclosure but not applied:
US 2025/0118444 “Ianni” discloses an electronic neural network that has been trained on a set of training data that comprises a plurality of reference subject medical data sets that are each labeled with a medical determination and are each assigned a ground truth concordance score generated by a plurality of experts in which a value of a given ground truth concordance score comprises a fraction of the plurality of experts.
US 2021/0374953 “Asiedu” discloses detection of cervical pre-cancer extracting features from the at least one pre-processed cervigram and classifying the cervigram as negative or positive for cervical pre-cancer based on the extracted features.
US20230012989 “Dalvin” discloses pre-trained weights for a neural network or other date to recognize common EEG artifacts.
The references are relevant since it discloses analyzing a subject data to determine disease state based on a score.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAAELDIN ELSHAER whose telephone number is (571)272-8284. The examiner can normally be reached M-Th 8:30-5:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MAMON OBEID can be reached at Mamon.Obeid@USPTO.GOV. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ALAAELDIN M. ELSHAER/Primary Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687
Prosecution Insights