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 .
DETAILED ACTION
This action is responsive to amendment filed 2/20/2026
This action is made Final.
Claims 1-6 and 8-15 are pending in the case. Claims 1 and 10 are independent claims. Claims 1 and 10 are currently amended. Claim 7 has been canceled.
Response to Arguments
Applicant's arguments filed 2/20/2026 have been fully considered but they are not persuasive. Applicant remarks that Comaniciu in view of Hatamizadeh does not teach every feature of amended claims 1 and 10. The Examiner disagrees. More specifically, Applicant remarks that the cited references do not teach the selecting of the medical imaging data for input into a machine learning model based on an image type (page 6-7). The Examiner disagrees. Hatamizadeh discusses “for each type of imaging device (e.g., CT, MM, X-Ray, ultrasound, sonography, echocardiography, etc.), sequencing device, radiology device, genomics device, etc., there may be any number of containers that may perform a data processing task with respect to imaging data 3808 (or other data types, such as those described herein) generated by a device. In at least one embodiment, an advanced processing and inferencing pipeline may be defined based on selections of different containers that are desired or required for processing imaging data 3808, in addition to containers that receive and configure imaging data for use by each container and/or for use by facility 3802 after processing through a pipeline (emphasis added). The Examiner maintains that Hatamizadeh’s performing particular data processing steps depending on the output data type of the imaging data is equivalent to the claimed selecting of the medical imaging data for input into a machine learning model based on an image type. Therefore the claims remain rejected over Comaniciu in view of Hatamizadeh.
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.
Claim(s) 1-6 and 8-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Comaniciu et al (EP 4134977 A1) in view of Hatamizadeh et al (USPUB 20230145535 A1).
Claim 1:
Comaniciu teaches A method for distributing data, comprising: receiving, by a computing service and from a medical data server, medical image data; inputting the medical image data into a machine learning model hosted by the computing service; generating, by the machine learning model, additional biometric data corresponding to the medical image data; and sending the additional biometric data to the medical data server, wherein the additional biometric data and the medical image data is incorporated into an electronic health record (EHR) (0012, 0015 and 0026: features from the EMR and/or radiology text can be used to train a machine learning model, e.g., a deep learning model, which is configured to process the findings and/or abnormalities stated by a radiologist when the radiologist interprets and diagnoses medical images. The method can then alert and/or recommend that certain additional abnormalities should be considered based on common radiologic presentations of specific diseases, as learned from large volumes of EMR and radiology reports...the text data representing the at least one radiologic finding relating to the patient which serves as input into the machine-learning model may be automatically generated by an automated analysis of one or more medical images of the patient. For example, an imaging Al model may be used to generate the text data automatically. Accordingly, this aspect of the invention supports an automated reading of medical images by Al models)... if the user input indicates that the at least one predicted additional radiologic finding is accepted, adding the at least one predicted additional radiologic finding to a radiology report of the patient. This way, it is ensured that only findings validated by a human are finally added to the radiology report).
Comaniciu, by itself, does not seem to completely teach a picture archiving and communication system (PACS); selecting the medical image data for inputting into a machine learning model hosted by the computing service based on an image type of the medical image data, wherein the computing service allows users to upload machine learning models and configure corresponding machine learning models to interface with the medical data server.
The Examiner maintains that these features were previously well-known as taught by Hatamizadeh.
Hatamizadeh teaches a picture archiving and communication system (PACS) (0523); selecting the medical image data for inputting into a machine learning model hosted by the computing service based on an image type of the medical image data, wherein the computing service allows users to upload machine learning models and configure corresponding machine learning models to interface with the medical data server (0529, 0589-590: for each type of imaging device (e.g., CT, MM, X-Ray, ultrasound, sonography, echocardiography, etc.), sequencing device, radiology device, genomics device, etc., there may be any number of containers that may perform a data processing task with respect to imaging data 3808 (or other data types, such as those described herein) generated by a device. In at least one embodiment, an advanced processing and inferencing pipeline may be defined based on selections of different containers that are desired or required for processing imaging data 3808, in addition to containers that receive and configure imaging data for use by each container and/or for use by facility 3802 after processing through a pipeline...once customer dataset 4206 has associated ground truth data, ground truth data (e.g., from AI-assisted annotation, manual labeling, etc.) may be used by during model training 3814 to generate refined model... customer dataset 4206 may be applied to initial model 4204 any number of times, and ground truth data may be used to update parameters of initial model 4204 until an acceptable level of accuracy is attained for refined model... refined model 4212 may be uploaded to pre-trained models 3906 in model registry).
Comaniciu and Hatamizadeh are analogous art because they are from the same problem-solving area, using machine learning to analyze medical images and generate data.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Comaniciu and Hatamizadeh before him or her, to combine the teachings of Comaniciu and Hatamizadeh. The rationale for doing so would have been to obtain the benefit of allowing for refined models to be utilized to provide the desired data.
Therefore, it would have been obvious to combine Comaniciu and Hatamizadeh to obtain the invention as specified in the instant claim(s).
Claim 2:
Comaniciu teaches the medical image data comprises a radiological image (0003).
Claim 3:
Comaniciu, by itself, does not seem to completely teach the medical image data is stored in a Digital Imaging and Communications in Medicine (DICOM) format.
The Examiner maintains that these features were previously well-known as taught by Hatamizadeh.
Hatamizadeh teaches the medical image data is stored in a Digital Imaging and Communications in Medicine (DICOM) format (0529).
Comaniciu and Hatamizadeh are analogous art because they are from the same problem-solving area, using machine learning to analyze medical images and generate data.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Comaniciu and Hatamizadeh before him or her, to combine the teachings of Comaniciu and Hatamizadeh. The rationale for doing so would have been to obtain the benefit of allowing various types of image data to be processed.
Therefore, it would have been obvious to combine Comaniciu and Hatamizadeh to obtain the invention as specified in the instant claim(s).
Claim 4:
Comaniciu teaches receiving population data associated with the medical image data; generating normative medical data from the population data and the additional biometric data; and sending the normative medical data to the medical data server, wherein the normative medical data is incorporated into the EHR (0007 and 0010: training a model on images and biopsy data to guide the radiologist's interpretation using correlations with biopsy findings... The machine-learning model may be based on training data, the training data comprising text data from radiology reports and text data from electronic medical records of a plurality of patients).
Claim 5:
Comaniciu teaches the medical image data comprises anonymized data, wherein the method further comprises: receiving a token from the medical data server associated with the medical image data; and sending, to the medical data server, the token with the additional biometric data (0039: the FHIR data is then tokenized and fed to an information extraction model to detect and extract relevant (e.g. pre-defined) medical concepts. In one embodiment, the extracted medical concepts are selected from the group comprising demographics, radiologic findings, past medical history, lab results and/or medications).
Claim 6:
Comaniciu by itself, does not seem to completely teach the machine learning model comprises a convolutional neural network (CNN).
The Examiner maintains that these features were previously well-known as taught by Hatamizadeh
Hatamizadeh teaches the machine learning model comprises a convolutional neural network (CNN) (0174).
Comaniciu and Hatamizadeh are analogous art because they are from the same problem-solving area, using machine learning to analyze medical images and generate data.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Comaniciu and Hatamizadeh before him or her, to combine the teachings of Comaniciu and Hatamizadeh. The rationale for doing so would have been to obtain the benefit of utilizing various neural networks to generate the best data regarding the image.
Therefore, it would have been obvious to combine Comaniciu and Hatamizadeh to obtain the invention as specified in the instant claim(s).
Claim 8:
Comaniciu teaches receiving, by the computing service and from the medical data server, a plurality of medical image datasets; inputting the plurality of medical image datasets into the machine learning model; and training the machine learning model with the plurality of medical image datasets (0010: The machine-learning model may be based on training data, the training data comprising text data from radiology reports and text data from electronic medical records of a plurality of patients. In particular, the machine learning model may be based on training date by the machine-learning model having been trained using the training data using a training algorithm).
Claim 9:
Comaniciu teaches An apparatus comprising a processor, memory, and computer-executable instructions stored in the memory that, when executed by the processor, cause the apparatus to perform the method of claim 1 (0028).
Claim 10:
Comaniciu teaches A system for data distribution, comprising: a medical data server configured to: receive medical image data; store the medical image data as an electronic health record; and send the medical data; a computing service configured to: receive the medical image data from the medical data server; input the medical image data into an machine learning model hosted by the computing service; generate, by the machine learning model, additional biometric data corresponding to the medical image data; and send the additional biometric data to the medical data server (0011-12, 0015 and 0026: combining information that typically forms electronic medical records (EMR), such as the reason for exam (RFS), e.g., including factors such as demographics, past medical history, medications, laboratory results and the like, as part of the medical imaging order/request, with findings and/or impressions from (retrospective) radiology reports of a large population of patients... features from the EMR and/or radiology text can be used to train a machine learning model, e.g., a deep learning model, which is configured to process the findings and/or abnormalities stated by a radiologist when the radiologist interprets and diagnoses medical images. The method can then alert and/or recommend that certain additional abnormalities should be considered based on common radiologic presentations of specific diseases, as learned from large volumes of EMR and radiology reports...the text data representing the at least one radiologic finding relating to the patient which serves as input into the machine-learning model may be automatically generated by an automated analysis of one or more medical images of the patient. For example, an imaging Al model may be used to generate the text data automatically. Accordingly, this aspect of the invention supports an automated reading of medical images by Al models)... if the user input indicates that the at least one predicted additional radiologic finding is accepted, adding the at least one predicted additional radiologic finding to a radiology report of the patient. This way, it is ensured that only findings validated by a human are finally added to the radiology report).
Comaniciu, by itself, does not seem to completely teach select the medical image data for inputting into a machine learning model based on an image type of the medical image data, wherein the computing service allows users to upload machine learning models and configure corresponding machine learning models to interface with the medical data server.
The Examiner maintains that these features were previously well-known as taught by Hatamizadeh.
Hatamizadeh teaches select the medical image data for inputting into a machine learning model based on an image type of the medical image data, wherein the computing service allows users to upload machine learning models and configure corresponding machine learning models to interface with the medical data server (0529, 589-590: for each type of imaging device (e.g., CT, MM, X-Ray, ultrasound, sonography, echocardiography, etc.), sequencing device, radiology device, genomics device, etc., there may be any number of containers that may perform a data processing task with respect to imaging data 3808 (or other data types, such as those described herein) generated by a device. In at least one embodiment, an advanced processing and inferencing pipeline may be defined based on selections of different containers that are desired or required for processing imaging data 3808, in addition to containers that receive and configure imaging data for use by each container and/or for use by facility 3802 after processing through a pipeline...once customer dataset 4206 has associated ground truth data, ground truth data (e.g., from AI-assisted annotation, manual labeling, etc.) may be used by during model training 3814 to generate refined model... customer dataset 4206 may be applied to initial model 4204 any number of times, and ground truth data may be used to update parameters of initial model 4204 until an acceptable level of accuracy is attained for refined model... refined model 4212 may be uploaded to pre-trained models 3906 in model registry).
Comaniciu and Hatamizadeh are analogous art because they are from the same problem-solving area, using machine learning to analyze medical images and generate data.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Comaniciu and Hatamizadeh before him or her, to combine the teachings of Comaniciu and Hatamizadeh. The rationale for doing so would have been to obtain the benefit of allowing for refined models to be utilized to provide the desired data.
Therefore, it would have been obvious to combine Comaniciu and Hatamizadeh to obtain the invention as specified in the instant claim(s).
Claim 11:
Comaniciu teaches the medical data server is further configured to store the additional biometric data in an electronic Health Record (EHR) (0026).
Claim 12:
Comaniciu, by itself, does not seem to completely teach the medical image data comprises a radiological image, and wherein the system further comprises a camera configured to capture the radiological image.
The Examiner maintains that these features were previously well-known as taught by Hatamizadeh.
Hatamizadeh teaches the medical image data comprises a radiological image, and wherein the system further comprises a camera configured to capture the radiological image (0522).
Comaniciu and Hatamizadeh are analogous art because they are from the same problem-solving area, using machine learning to analyze medical images and generate data.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Comaniciu and Hatamizadeh before him or her, to combine the teachings of Comaniciu and Hatamizadeh. The rationale for doing so would have been to obtain the benefit of allowing various types of image data to be processed.
Therefore, it would have been obvious to combine Comaniciu and Hatamizadeh to obtain the invention as specified in the instant claim(s).
Claim 13:
Comaniciu, by itself, does not seem to completely teach the camera comprises a positron emission tomography (PET) camera, an ultrasound camera, a magnetic resonance imaging (MRI) camera, an X-ray camera, or a computerized tomography (CT) camera.
The Examiner maintains that these features were previously well-known as taught by Hatamizadeh.
Hatamizadeh teaches the camera comprises a positron emission tomography (PET) camera, an ultrasound camera, a magnetic resonance imaging (MRI) camera, an X-ray camera, or a computerized tomography (CT) camera (0522).
Comaniciu and Hatamizadeh are analogous art because they are from the same problem-solving area, using machine learning to analyze medical images and generate data.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Comaniciu and Hatamizadeh before him or her, to combine the teachings of Comaniciu and Hatamizadeh. The rationale for doing so would have been to obtain the benefit of allowing various types of image data to be processed.
Therefore, it would have been obvious to combine Comaniciu and Hatamizadeh to obtain the invention as specified in the instant claim(s).
Claim 14:
Comaniciu teaches receive population data associated with the medical image data; generate normative medical data from the population data and the additional biometric data; and send the normative medical data to the medical data server (0007 and 0010: training a model on images and biopsy data to guide the radiologist's interpretation using correlations with biopsy findings... The machine-learning model may be based on training data, the training data comprising text data from radiology reports and text data from electronic medical records of a plurality of patients).
Claim 15:
Comaniciu teaches the medical data server is further configured to store the normative medical data in an electronic health record (EHR) (0007 and 0010: training a model on images and biopsy data to guide the radiologist's interpretation using correlations with biopsy findings... The machine-learning model may be based on training data, the training data comprising text data from radiology reports and text data from electronic medical records of a plurality of patients).
Note
The Examiner cites particular columns, line numbers and/or paragraph numbers in the references as applied to the claims below for the convenience of the Applicant(s). Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. See MPEP 2123.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED H ZUBERI whose telephone number is (571)270-7761. The examiner can normally be reached Mon – Th 10AM-8PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen Hong can be reached on (571) 272-4124. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMMED H ZUBERI/Primary Examiner, Art Unit 2178
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