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 .
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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, 5-10, and 16-18 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Gardella et al., US 2024/0281971.
Regarding claim 1, Gardella discloses a method (Abstract; para 0008; methods for analyzing medical imaging using spatiotemporal neural networks for detecting cardiovascular anomalies and/or conditions such as CHD), comprising:
operating a first spatiotemporal model (figs. 3A-3C; para 0058, 0065, and 0101; one or more spatiotemporal CNNs including one or more spatial CNN and one or more temporal CNN; the spatiotemporal CNN system may be implemented using independent CNNs, including a spatial CNN and a temporal CNN; Thus, a first spatiotemporal model corresponds to the spatial model/CNN forming the spatial portion of the spatiotemporal CNN system) upon a plurality of frames of an anatomic video of a patient to determine a first prediction (figs. 2A, element 226 and 4, element 412; para 0039, 0045, 0069, and 0072; image data 206 may include still frames and/or video clips and may include RGB and/or grey scale pixel information; Spatial output 226 may include a vector or matrix including a score or value for one or more frames corresponding to the likelihood of CHD and/or other cardiovascular anomaly; generate a spatial output using the image data and the trained spatial model), wherein the first spatiotemporal model is configured to determine the first prediction using a plurality of hand-crafted features extracted from the plurality of frames (figs. 2A, elements 216 and 220 and 4, elements 406 and 414; para 0041-0046 and 0070-0074; Sampled image data 216, pre-processed image data 212, and/or image data 206 may then be applied to spatial model 222 to generate spatial output 226 which may be a spatial CNN such as an spatial CNN trained for image processing; optical flow data 220 may be applied to temporal model 224, which may be a temporal CNN such as an temporal CNN trained for image processing and/or trained for processing optical flow data to generate temporal output 228);
operating a second spatiotemporal model (figs. 3A-3C; para 0058, 0065, and 0101; one or more spatiotemporal CNNs including one or more spatial CNN and one or more temporal CNN; the spatiotemporal CNN system may be implemented using independent CNNs, including a spatial CNN and a temporal CNN; Thus, a second spatiotemporal model corresponds to the temporal model/CNN forming the temporal portion of the spatiotemporal CNN system), comprising one or more deep learning models (para 0025; one or more neural network may be a deep neural network (DNN)), upon the plurality of frames of the anatomic video to determine a second prediction (figs. 2A, element 228 and 4, element 420; para 0039, 0046, 0069, and 0074; image data 206 may include still frames and/or video clips and may include RGB and/or grey scale pixel information; Spatial output 226 may include a vector or matrix including a score or value for one or more frames corresponding to the likelihood of CHD and/or other cardiovascular anomaly; temporal model 224 may generate temporal output 228 which may indicate for each optical flow data set a score or value indicative of a likelihood of a presence of one or more CHD and/or other cardiovascular anomaly. Temporal output 228 may optionally further include a score or value indicative of a likelihood of one or more views or orientations of the sensor device for which the image data corresponds to; generate a temporal output using the optical flow data and the trained temporal model); and
generating a medical prediction based upon a combination of the first prediction and the second prediction (figs. 2A, element 232 and 4, element 422; para 0047 and 0074; Spatial output 226 and temporal output 228 may both be input into fuser 230 to fuse spatial model 222 and temporal model 224 to generate spatiotemporal output 232; fusion may be performed on the temporal output and spatial output to determine a spatiotemporal output).
Regarding claim 5, the method of claim 1, Gardella discloses further comprising: adding or removing one or more frames from the anatomic video prior to operating upon the plurality of frames with the first spatiotemporal model or the second spatiotemporal model (para 0010).
Regarding claim 6, the method of claim 1, Gardella discloses further comprising:
extracting a plurality of radiomic features for each of the plurality of frames of the anatomic video (figs. 2A, elements 216 and 220 and 4, elements 406 and 414; para 0041-0046 and 0070-0074);
generating a time-series feature from the plurality of radiomic features (figs. 2A, element 216 and 220 and 4, element 406 and 414; para 0041-0046 and 0070-0074); and
operating upon the time-series feature with a machine learning model to determine the first prediction (figs. 2A, element 226 and 4, element 412; para 0039, 0045, 0069, and 0072).
Regarding claim 7, the method of claim 6, Gardella discloses wherein the plurality of radiomic features include one or more of a shape feature, a texture feature, and a first order statistic (figs. 2A, element 216 and 4, element 406; para 0039, 0044,and 0070).
Regarding claim 8, the method of claim 1, Gardella discloses wherein the second spatiotemporal model comprises a first transformer encoder and a second transformer encoder arranged in series (para 0025).
Regarding claim 9, the method of claim 8, Gardella discloses
wherein the first transformer encoder is configured to determine a spatial relationship between tokens from a same frame of the plurality of frames (figs. 2A, element 216 and 4, element 406; para 0039, 0044-0045, and 0070-0072); and
wherein the second transformer encoder is configured to determine one or more temporal relationships between tokens from different ones of the plurality of frames (figs. 2A, element 220 and 4, element 414; para 0039, 0046, and 0073-0074).
Regarding claim 10, the method of claim 1, Gardella discloses further comprising: generating a linear combination of the first prediction and the second prediction to determine the medical prediction (figs. 2A, element 232 and 4, element 422; para 0047 and 0074).
Regarding claim 16, Gardella discloses an apparatus (Abstract; para 0008; systems for analyzing medical imaging using spatiotemporal neural networks for detecting cardiovascular anomalies and/or conditions such as CHD), comprising:
electronic memory configured to store an imaging data set (fig. 1, element 112; para 0032; Datastore 112 may receive and store image data (e.g., image data 118)) comprising an anatomic video from a patient, wherein the anatomic video has a plurality of frames (figs. 2A, element 206 and 4, element 402; para 0039 and 0069; image data 206 may include still frames and/or video clips and may include RGB and/or grey scale pixel information);
a first spatiotemporal model (figs. 3A-3C; para 0058, 0065, and 0101; one or more spatiotemporal CNNs including one or more spatial CNN and one or more temporal CNN; the spatiotemporal CNN system may be implemented using independent CNNs, including a spatial CNN and a temporal CNN; Thus, a first spatiotemporal model corresponds to the spatial model/CNN forming the spatial portion of the spatiotemporal CNN system) configured to operate on the anatomic video to extract a plurality of radiomic features and a time-series feature from the plurality of frames (figs. 2A, elements 216 and 220 and 4, elements 406 and 414; para 0041-0046 and 0070-0074; Sampled image data 216, pre-processed image data 212, and/or image data 206 may then be applied to spatial model 222 to generate spatial output 226 which may be a spatial CNN such as an spatial CNN trained for image processing; optical flow data 220 may be applied to temporal model 224, which may be a temporal CNN such as an temporal CNN trained for image processing and/or trained for processing optical flow data to generate temporal output 228) and to determine a first prediction from the time-series feature (figs. 2A, element 226 and 4, element 412; para 0039, 0045, 0069, and 0072; image data 206 may include still frames and/or video clips and may include RGB and/or grey scale pixel information; Spatial output 226 may include a vector or matrix including a score or value for one or more frames corresponding to the likelihood of CHD and/or other cardiovascular anomaly; generate a spatial output using the image data and the trained spatial model);
a second spatiotemporal model (figs. 3A-3C; para 0058, 0065, and 0101; one or more spatiotemporal CNNs including one or more spatial CNN and one or more temporal CNN; the spatiotemporal CNN system may be implemented using independent CNNs, including a spatial CNN and a temporal CNN; Thus, a second spatiotemporal model corresponds to the temporal model/CNN forming the temporal portion of the spatiotemporal CNN system) configured to operate on the anatomic video to determine a second prediction (figs. 2A, element 228 and 4, element 420; para 0039, 0046, 0069, and 0074; image data 206 may include still frames and/or video clips and may include RGB and/or grey scale pixel information; Spatial output 226 may include a vector or matrix including a score or value for one or more frames corresponding to the likelihood of CHD and/or other cardiovascular anomaly; temporal model 224 may generate temporal output 228 which may indicate for each optical flow data set a score or value indicative of a likelihood of a presence of one or more CHD and/or other cardiovascular anomaly. Temporal output 228 may optionally further include a score or value indicative of a likelihood of one or more views or orientations of the sensor device for which the image data corresponds to; generate a temporal output using the optical flow data and the trained temporal model); and
an evaluation tool configured to generate a medical prediction using both the first prediction and the second prediction (figs. 2A, element 232 and 4, element 422; para 0047 and 0074; Spatial output 226 and temporal output 228 may both be input into fuser 230 to fuse spatial model 222 and temporal model 224 to generate spatiotemporal output 232; fusion may be performed on the temporal output and spatial output to determine a spatiotemporal output).
Regarding claim 17, the apparatus of claim 16, Gardella discloses wherein the time-series feature is determined from radiomic features extracted from multiple ones of the plurality of frames (figs. 2A, elements 216 and 220 and 4, elements 406 and 414; para 0041-0046 and 0070-0074).
Regarding claim 18, the apparatus of claim 16, Gardella discloses
wherein the series of transformer encoders comprise a first transformer encoder configured to determine a spatial relationship between tokens from a same frame of the plurality of frames (figs. 2A, element 216 and 4, element 406; para 0039, 0044-0045, and 0070-0072); and
wherein the series of transformer encoders comprise a second transformer encoder configured to determine one or more temporal relationships between tokens from different ones of the plurality of frames (figs. 2A, element 220 and 4, element 414; para 0039, 0046, and 0073-0074).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 2-3 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gardella et al., US 2024/0281971 in view of Appadurai et al., US 2024/0428414.
Regarding claim 2, the method of claim 1, Gardella discloses wherein the plurality of frames comprise a plurality of segmented frames (para 0024 and 0041).
Gardella discloses claim 2 as enumerated above, but Gardella does not explicitly disclose masked to identify a left ventricle wall of a heart of the patient as claimed.
However, Appadurai discloses the diagnosis of hypertrophic cardiomyopathy was defined as left ventricular hypertrophy (LVH) ≥15 millimeters (mm) anywhere in the left ventricle (LV) wall in the absence of any other identifiable cause such as hypertension or valve disease, and was confirmed by a cardiologist in the heart failure with preserved ejection fraction clinic (para 0031).
Therefore, taking the combined disclosures of Gardella and Appadurai as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the diagnosis of hypertrophic cardiomyopathy was defined as left ventricular hypertrophy (LVH) ≥15 millimeters (mm) anywhere in the left ventricle (LV) wall in the absence of any other identifiable cause such as hypertension or valve disease, and was confirmed by a cardiologist in the heart failure with preserved ejection fraction clinic as taught by Appadurai into the invention of Gardella for the benefit of imaging modalities that can differentiate myocardial alterations between CA subtypes are important for accurate diagnosis and management (Appadurai: para 0027).
Regarding claim 3, the method of claim 2, Appadurai in the combination disclose wherein the plurality of hand-crafted features are associated with spatiotemporal changes in the left ventricle wall of the heart (para 0031).
Regarding claim 19, the apparatus of claim 16, Gardella discloses wherein the anatomic video is an echocardiography video of a heart of the patient (para 0023, 0039 and 0069).
Gardella discloses claim 19 as enumerated above, but Gardella does not explicitly disclose the patient has chronic kidney disease as claimed.
However, Appadurai discloses established echocardiographic phenocopies of CA, such as hypertrophic cardiomyopathy (HCM) and advanced chronic kidney disease (CKD) patients were included as comparator groups (para 0031).
Therefore, taking the combined disclosures of Gardella and Appadurai as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate established echocardiographic phenocopies of CA, such as hypertrophic cardiomyopathy (HCM) and advanced chronic kidney disease (CKD) patients were included as comparator groups as taught by Appadurai into the invention of Gardella for the benefit of imaging modalities that can differentiate myocardial alterations between CA subtypes are important for accurate diagnosis and management (Appadurai: para 0027).
Claim(s) 4 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gardella et al., US 2024/0281971 in view of Sengupta et al., US 2024/0312636.
Regarding claim 4, the method of claim 1, Gardella discloses wherein the plurality of frames span a time period (para 0039-0041).
Gardella discloses claim 4 as enumerated above, but Gardella does not explicitly disclose equal to a heartbeat cycle of the patient as claimed.
However, Sengupta discloses speckle tracking echocardiography (STE) can use a tracking system based on grayscale B-mode images and can be obtained by automatic measurement of the distance between 2 pixels of an LV segment during the cardiac cycle. Each video can be composed of two-beat regular rhythm cine-loop including complete diastolic and systolic cycles to evaluate the end-diastolic phase and end-systolic phase, respectively (para 0042-0043).
Therefore, taking the combined disclosures of Gardella and Sengupta as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate speckle tracking echocardiography (STE) can use a tracking system based on grayscale B-mode images and can be obtained by automatic measurement of the distance between 2 pixels of an LV segment during the cardiac cycle. Each video can be composed of two-beat regular rhythm cine-loop including complete diastolic and systolic cycles to evaluate the end-diastolic phase and end-systolic phase, respectively as taught by Sengupta into the invention of Gardella for the benefit of accurately risk stratify the AMI patients. (Sengupta: para 0020).
Regarding claim 20, this claim recites substantially the same limitations that are performed by claim 4 above, and it is rejected for the same reasons.
Claim(s) 11-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gardella et al., US 2024/0281971 in view of Appadurai et al., US 2024/0428414 and further in view of Sengupta et al., US 2024/0312636.
Regarding claim 11, Gardella disclose a non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations (Abstract; para 0008; methods for analyzing medical imaging using spatiotemporal neural networks for detecting cardiovascular anomalies and/or conditions such as CHD), comprising:
accessing an echocardiography video of a heart of a patient (figs. 2A, element 206 and 4, element 402; para 0023, 0039 and 0069; echocardiogram system generates image data 206 may include still frames and/or video clips and may include RGB and/or grey scale pixel information) (para 0039; image data 206 may include two-dimensional representations)
operating upon the echocardiography video with a first spatiotemporal model (figs. 3A-3C; para 0058, 0065, and 0101; one or more spatiotemporal CNNs including one or more spatial CNN and one or more temporal CNN; the spatiotemporal CNN system may be implemented using independent CNNs, including a spatial CNN and a temporal CNN; Thus, a first spatiotemporal model corresponds to the spatial model/CNN forming the spatial portion of the spatiotemporal CNN system) that is configured to determine a first prediction (figs. 2A, element 226 and 4, element 412; para 0039, 0045, 0069, and 0072; image data 206 may include still frames and/or video clips and may include RGB and/or grey scale pixel information; Spatial output 226 may include a vector or matrix including a score or value for one or more frames corresponding to the likelihood of CHD and/or other cardiovascular anomaly; generate a spatial output using the image data and the trained spatial model) from a first plurality of hand-crafted features extracted from the plurality of 2D frames (figs. 2A, elements 216 and 220 and 4, elements 406 and 414; para 0041-0046 and 0070-0074; Sampled image data 216, pre-processed image data 212, and/or image data 206 may then be applied to spatial model 222 to generate spatial output 226 which may be a spatial CNN such as an spatial CNN trained for image processing; optical flow data 220 may be applied to temporal model 224, which may be a temporal CNN such as an temporal CNN trained for image processing and/or trained for processing optical flow data to generate temporal output 228);
operating upon the echocardiography video with a second spatiotemporal model (figs. 3A-3C; para 0058, 0065, and 0101; one or more spatiotemporal CNNs including one or more spatial CNN and one or more temporal CNN; the spatiotemporal CNN system may be implemented using independent CNNs, including a spatial CNN and a temporal CNN; Thus, a second spatiotemporal model corresponds to the temporal model/CNN forming the temporal portion of the spatiotemporal CNN system) that is configured to determine a second prediction (figs. 2A, element 228 and 4, element 420; para 0039, 0046, 0069, and 0074; image data 206 may include still frames and/or video clips and may include RGB and/or grey scale pixel information; Spatial output 226 may include a vector or matrix including a score or value for one or more frames corresponding to the likelihood of CHD and/or other cardiovascular anomaly; temporal model 224 may generate temporal output 228 which may indicate for each optical flow data set a score or value indicative of a likelihood of a presence of one or more CHD and/or other cardiovascular anomaly. Temporal output 228 may optionally further include a score or value indicative of a likelihood of one or more views or orientations of the sensor device for which the image data corresponds to; generate a temporal output using the optical flow data and the trained temporal model) using deep learning (para 0025; one or more neural network may be a deep neural network (DNN)), wherein the second spatiotemporal model comprises a transformer model (para 0025; one or more neural network may be a deep neural network (DNN));
combining the first prediction and the second prediction (figs. 2A, element 232 and 4, element 422; para 0047 and 0074; Spatial output 226 and temporal output 228 may both be input into fuser 230 to fuse spatial model 222 and temporal model 224 to generate spatiotemporal output 232; fusion may be performed on the temporal output and spatial output to determine a spatiotemporal output) to generate a biomarker (para 0023 and 0049; The images may also be processed by the spatiotemporal neural network to determine detection of key-points corresponding to cardiovascular anatomy (e.g., the apex of the heart, etc.), such data referred to as key-point data, and/or contours and/or segmentation of elements and/or features of the cardiovascular anatomy (e.g., the contours of one or more ventricles, one or more atria, etc.), such data referred to as contour data, and this information may be used to compute measurements (e.g., length, area, ratios) and/or be used for the detection of features and/or anatomy of the fetus (e.g., detection of the heart, the lung, parts of the heart such as the atria, the septum, the ventricles, and the like); and
utilizing the biomarker to generate a medical prediction (figs. 2A, element 232 and 4, element 422; para 0047 and 0074; Spatial output 226 and temporal output 228 may both be input into fuser 230 to fuse spatial model 222 and temporal model 224 to generate spatiotemporal output 232; fusion may be performed on the temporal output and spatial output to determine a spatiotemporal output).
Gardella discloses claim 11 as enumerated above, but Gardella does not explicitly disclose chronic kidney disease
However, Appadurai discloses established echocardiographic phenocopies of CA, such as hypertrophic cardiomyopathy (HCM) and advanced chronic kidney disease (CKD) patients were included as comparator groups. Furthermore, the diagnosis of hypertrophic cardiomyopathy was defined as left ventricular hypertrophy (LVH) ≥15 millimeters (mm) anywhere in the left ventricle (LV) wall in the absence of any other identifiable cause such as hypertension or valve disease, and was confirmed by a cardiologist in the heart failure with preserved ejection fraction clinic (para 0031).
Therefore, taking the combined disclosures of Gardella and Appadurai as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate established echocardiographic phenocopies of CA, such as hypertrophic cardiomyopathy (HCM) and advanced chronic kidney disease (CKD) patients were included as comparator groups. Furthermore, the diagnosis of hypertrophic cardiomyopathy was defined as left ventricular hypertrophy (LVH) ≥15 millimeters (mm) anywhere in the left ventricle (LV) wall in the absence of any other identifiable cause such as hypertension or valve disease, and was confirmed by a cardiologist in the heart failure with preserved ejection fraction clinic as taught by Appadurai into the invention of Gardella for the benefit of imaging modalities that can differentiate myocardial alterations between CA subtypes are important for accurate diagnosis and management (Appadurai: para 0027).
Gardella and Appadurai in the combination disclose claim 11 as enumerated above, but Gardella and Appadurai in the combination do not explicitly disclose a heartbeat cycle as claimed.
However, Sengupta discloses speckle tracking echocardiography (STE) can use a tracking system based on grayscale B-mode images and can be obtained by automatic measurement of the distance between 2 pixels of an LV segment during the cardiac cycle. Each video can be composed of two-beat regular rhythm cine-loop including complete diastolic and systolic cycles to evaluate the end-diastolic phase and end-systolic phase, respectively (para 0042-0043).
Therefore, taking the combined disclosures of Gardella, Appadurai, and Sengupta as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate speckle tracking echocardiography (STE) can use a tracking system based on grayscale B-mode images and can be obtained by automatic measurement of the distance between 2 pixels of an LV segment during the cardiac cycle. Each video can be composed of two-beat regular rhythm cine-loop including complete diastolic and systolic cycles to evaluate the end-diastolic phase and end-systolic phase, respectively as taught by Sengupta into the inventions of Gardella and Appadurai for the benefit of accurately risk stratify the AMI patients. (Sengupta: para 0020).
Regarding claim 12, the non-transitory computer-readable medium of claim 11, Gardella in the combination disclose wherein the operations further comprise:
receiving the echocardiography video, wherein the echocardiography video has a first number of frames (figs. 2A, element 206 and 4, element 402; para 0023, 0039 and 0069); and
adjusting a number of frames within the echocardiography video (para 0040-0041 and 0069).
Regarding claim 13, the non-transitory computer-readable medium of claim 11, Gardella in the combination disclose wherein the operations further comprise:
extracting a plurality of radiomic features from respective ones of the plurality of 2D frames (figs. 2A, elements 216 and 220 and 4, elements 406 and 414; para 0041-0046 and 0070-0074);
generating a time-series feature from the plurality of radiomic features extracted from respective ones of the plurality of 2D frames (figs. 2A, elements 216 and 220 and 4, elements 406 and 414; para 0041-0046 and 0070-0074); and
determining the first prediction from the time-series feature (figs. 2A, element 226 and 4, element 412; para 0039, 0045, 0069, and 0072).
Regarding claim 14, the non-transitory computer-readable medium of claim 13, Gardella in the combination disclose wherein the plurality of radiomic features include one or more of shape features, texture features, and first order statistics (figs. 2A, element 216 and 4, element 406; para 0039, 0044,and 0070).
Regarding claim 15, the non-transitory computer-readable medium of claim 11, Gardella in the combination disclose wherein the second spatiotemporal model comprises:
a first transformer encoder (para 0025); and
a second transformer encoder arranged downstream of the first transformer encoder (para 0025).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Madabhushi et al., US 2021/0097682 discloses training and/or employing a combined model employing machine learning and deep learning outputs to generate prognoses for treatment of tumors.
Arnab et al., US 2023/0017072 discloses a computer-implemented method for classifying video data with improved accuracy.
Ouyang et al., US 2024/0296952 discloses a method for analyzing kidney health in an individual comprises receiving data associated with one or more cardiac characteristics of the individual.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VAN D HUYNH whose telephone number is (571)270-1937. The examiner can normally be reached 8AM-6PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen R Koziol can be reached at (408) 918-7630. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/VAN D HUYNH/Primary Examiner, Art Unit 2665
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