DETAILED ACTION
Notice to Applicant
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This communication is in response to the amendment filed 11/18/25. Claims 1-9, 17, 25, and 29-31 are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/18/25 has been entered.
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 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.
Claims 1, 4-9,17, 21-25, 27-29, and 31 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dala et al (US 2013/0024382 A1 -hereinafter Dala) in view of Bernard et al (US 20190057769 A1), and in further view of Chilamkurhy et al (US 10504227 B1- hereinafter Chila)
Regarding claim 1, Dala discloses a method (methods; Abstract):
receiving, at a health data analysis system in a first region from a health data input-output system in a second region (fig. 1, par. 61; par. 63-64; par. 93 receiving imaging and DICOM files from data servers (110-113) to physician system devices and user console for analysis), a unique case file containing one or more anonymous Digital Imaging and Communications in Medicine (DICOM) object(s) for analysis, wherein the one or more anonymous DICOM objects include one or more images files devoid of patient privacy information (receiving transmissions on mobile computing devices, physicians may review a patient's medical data; encrypted medical data; patients' medical records and may include information such as patients' imaging data; enable communicating medical images to a physician's handheld device and providing tools to enable the physician to remotely review such images; medical images may be collected by an image server with known format 112, such as DICOM; para [0012], [0047], [0080], [0113]; )
uncompressing and validating at least one of the one or more anonymous DICOM object (s) (the handheld device may send a download request back to the server which then transmits the data; medical records may be downloaded (uncompressed) from an EMR database server; encrypted medical data; patients' medical records and may include information such as patients' imaging data; medical images may be collected by an image server with known format 112, such as DICOM; validate transmissions from the computing device; para [0047], [0064], [0080], [0090], [0113], [0333]); and
transmitting an analysis of at least one of the one or more anonymous DICOM object(s) (medical records may be downloaded (uncompressed) from an EMR database server; the system may distinguish (analysis) between conditions based on determined priorities and may deliver (transmit) medical data to the physician in order of highest priority first; validate transmissions from the computing device; para [0090], [0272], [0333]),
the analysis having been completed according to the priority level of the unique case file (the system may distinguish (analysis) between conditions based on determined priorities and may deliver medical data to the physician in order of highest priority first; for example, the console may order a heart attack condition as a higher priority than a high temperature condition; para [0272]),
wherein the analysis includes a diagnosis or treatment analysis (par. 275-the auto-analysis routine may interpret testing and patient medical data in view of recorded symptoms to determine the presence of a STEMI condition; par. 281-If the auto-analysis routine determines the presence of a STEMI event based on the patient medical data (i.e., decision block 2050="YES"), the console may transmit data for furthering treatment of the patient)
Dala fails to disclose a computer-implemented method of analyzing health data over a decentralized cloud-computing platform. Bernard teaches a system and method for distributing and analyzing health data over a decentralized cloud-computing platform (Fig. 1-3; par. 41-43; par. 355-: the processing devices may be distributedly located (e.g., cloud computing via indirect coupling via a local area network and/or a wide area network). At the time of filing, it would have been obvious to one of ordinary skill in the art to modify the system/method of Dala with the teaching of Bernard to include analyzing health data over a decentralized cloud-computing platform. One would have been motivated to include this feature to facilitate greater accessibility to healthcare resources and to support medical decision making, thereby improving the quality and cost effectiveness of healthcare.
Claim 1 further recites:
determining a presence, at the health data analysis system, of stored health data associated with the one or more anonymous DICOM object(s),the stored health data being present at the health data analysis system prior to receiving the one or more anonymous DICOM object(s);
upon determining the presence, at the health data analysis system, of stored health data associated with the one or more anonymous DICOM object(s), the stored health data being present prior to receiving the one or more anonymous DICOM object(s), setting a priority level for the unique case file to a high priority
As cited above, Dala teaches setting a priority level for the unique case file based on priorities associated with the one or more anonymous DICOM object(s) (par. 122- The image processing software may be configured to assign priority to certain optimization steps-e.g. priority setting which prioritizes resolution; or prioritizing aspect ratio of an image; see also par. 271-275-priority based upon STEMI events) Dala further discloses setting or assessing priority levels of a case file based on associated medical condition (par. 272- the auto-analysis may determine the priority regarding the patient's current condition. For example, if the auto-analysis routine detects strong STEMI characteristics based on the medical data and testing information, the console may qualify the condition as a high urgency concern. Priority or urgency assessments are described in detail below with reference to FIG. 21. In an embodiment, the system may distinguish between conditions based on determined priorities and may deliver medical data to the physician in order of highest priority first)
Dala does not expressly disclose, but Bernard teaches:
determining a presence, at the health data analysis system, of stored health data associated with the one or more anonymous DICOM object(s),the stored health data being present at the health data analysis system prior to receiving the one or more anonymous DICOM object(s); (inclusion of image attributes/ determining the presence of stored information: par. 52-56: inclusion of image attributes (e.g. DICOM objects) Each medical scan entry 352 can be identified by its own medical scan identifier 353, and can include or otherwise map to scan image data 410, and metadata such as scan classifier data 420, patient history data 430, diagnosis data 440, annotation author data 450, confidence score data 460, display parameter data 470, similar scan data 480, training set data 490, and/or other data relating to the medical scan… The medical scan entries 352 and the associated data as described herein can also refer to data associated with a medical scan that is not stored by the medical scan database, for example, that is uploaded by a client device for direct transmission to a subsystem, data generated by a subsystem and used as input to another subsystem or transmitted directly to a client device, or other data associated with a medical scan that is received and or generated without being stored in the medical scan database 342…some or all of the structure and data attributes described with respect to a medical scan entry 352 can also correspond to structure and/or data attribute of data objects or other data generated by and/or transmitted between subsystems and/or client devices that correspond to a medical scan; par. 67-68-comparison of image scans (i.e. DICOM attributes/ objects) to determine using scan similarity; par. 60-system use of DICOM standard/format including annotation data )
upon determining the presence, at the health data analysis system, of stored health data associated with the one or more anonymous DICOM object(s), the stored health data being present prior to receiving the one or more anonymous DICOM object(s), setting a priority level for the unique case file to a high priority (par. 55-classification and prioritizing based on image attribute data: The scan classifier data 420 can include scan date data 426 indicating when the scan was taken. The scan classifier data 420 can include scan priority data 427, which can indicate a priority score, ranking, number in a queue, or other priority data with regard to triaging and/or review. A priority score, ranking, or queue number of the scan priority data 427 can be generated by automatically by a subsystem based on the scan priority data 427, based on a severity of patient symptoms or other indicators in the risk factor data 432, based on a priority corresponding to the originating entity, based on previously generated diagnosis data 440 for the scan, and/or can be assigned by the originating entity and/or a user of the system.)
At the time of filing, it would have been obvious to one of ordinary skill in the art to further modify the system and method of Dala with the teaching of Bernard. One would have been motivated to include this feature to facilitate prompt review/analysis of scans regarding medical conditions that are particularly severe, rare, and/or time-sensitive. (par. 504)
Claim 1 has been further amended to recite: determining an analysis of at least one of the one or more anonymous DICOM objects in an order-according to the set priority level of the unique case file among a plurality of case files wherein the analysis includes a diagnosis or treatment analysis.
Dala and Bernard do not disclose, but Chila teaches determining an analysis of at least one of the one or more anonymous DICOM objects in an order-according to the set priority level of the unique case file among a plurality of case files, (col. 5, lines 35-45: Based on the result of the machine learning analysis, the medical evaluation for the images and the associated imaging procedure may be prioritized, or otherwise changed or modified. Further, the detection of the medical conditions may be used to assist the assignment of the medical imaging data to particular evaluators, the evaluation process for the medical imaging data, or implement other actions prior to, or concurrent with, the medical imaging evaluation (or the generation of a data item such as a report from such medical imaging evaluation); see also claim 1: prioritizing an assignment of a medical evaluation to an evaluator based on the score generated for the images; claims 5-6: detecting and localizing medical abnormalities of the images and prioritizing the assignment of a medical evaluation to an evaluator are carried out in the cloud server to obtain a prioritization status for DICOM images… wherein the prioritization status for DICOM images is transferred to the local server and a Health Level Seven (HL7) message is constructed in response to the prioritization status for DICOM images)
At the time of effective filing, it would have been obvious to one of ordinary skill in the art to modify the system and method of Dala and Bernard with the teaching of Chila with the motivation of facilitating automated detection, indication, or confirmation of certain medical conditions within the images, such as the detection of urgent or life-critical medical conditions, clinically serious abnormalities, and other key findings, and expediting critical treatments for patients.
Regarding claim 4, Dala in view of Bernard discloses the computer-implemented method of claim 1. In addition, Dala discloses further comprising:
monitoring a status of the unique case file (data may also be collected by a patient's home monitoring systems. which may report physical, chemical, electrical or other patient's medical parameters; the system may distinguish between conditions based on determined priorities and may deliver medical data to the physician in order of highest priority; para (0062), (0272]); and
advancing analysis of the unique case file based on the status and the priority level of the unique case file (the auto-analysis may determine the priority; priority or urgency assessments; the system may distinguish between conditions based on determined priorities and may deliver medical data to the physician in order of highest priority first; For example, the console may order a heart attack condition as a higher priority than a high temperature condition; (para [0272]).
Regarding claim 5, Dala in view of Bernard discloses the computer-implemented method of claim 1. In addition, Dala discloses further
comprising:
setting the priority level for the unique case file is further based an entity analyzing the one or more anonymous DICOM objects (par. 122-123-priority regarding resolution and transmission based upon the recipient/ recipient device reviewing imaging data, par. 272-priority of patient’s medical condition; See also para [0080], [0083], [0113]: encrypted medical data; medical data stored in electronic format may be analyzed by the console or server; medical images may be collected by an image server with known format, such as DICOM);
Regarding claim 6, Dala in view of Bernard discloses the computer-implemented method of claim 1. In addition, Dala discloses further comprising: generating a draft result report based on the received analysis (receives and displays the transmitted data, such as multiple pages of summarized data; the data may be displayed in a keyword format so that the physician may quickly grasp the data of most
interest, or click on specific key words to view a detailed listing of the related underlying patient-physician interaction record; the physician may then update the current data record; para (01311); and generating an interactive user interface of the draft result report (the user uses tools (e.g., a graphical user interface) on a user console 120 to select medical data to transmit to the physician; para [00641).
Regarding claim 7, Dala in view of Bernard discloses the computer-implemented method of claim 6. In addition, Dala discloses further comprising:
receiving a modification to the draft result report (underlying patient-physician interaction record; the physician may then update (modify) the current data record or create a new one, perhaps with new observations or a new treatment plan; para [0131];
generating a final result report based on the modification (underlying patient-physician interaction record; the physician may then update the current data record or create a new one; para [0131]); and storing the final result report (the patient's health records (store online); para [0273], a pre-defined identified network address, or a requesting web interface (a hospital may post patient medical records on a website
portal; data interactions, such as on-demand data transfers between electronic medical record databases and the physician's mobile device; para (00501).
Regarding claim 8, Dala in view of Bernard discloses the computer implemented method of claim 7. In addition, Dala discloses wherein the final result report is pushed to a database, a pre-defined identified network address, or a requesting web interface using secure Hypertext Transfer Protocol (HTTPS) (a hospital may post patient medical records on a website portal; data interactions, such as on-demand data transfers between electronic medical record databases and the physician's mobile device; para [0050].
Regarding claim 9, Dala discloses a system (systems; Abstract) comprising:
one or more processors configured to execute the instructions to perform a method (processor is configured with processor-executable instructions to perform operations; claim 22) including:
receiving, at a health data analysis system in a first region from a health data input-output system in a second region (fig. 1, par. 61; par. 63-64; par. 93 receiving imaging and DICOM files from data servers (110-113) to physician system devices and user console for analysis), a unique case file containing one or more anonymous Digital Imaging and Communications in Medicine (DICOM) object(s) for analysis, wherein the one or more anonymous DICOM objects include one or more imaqe files devoid of patient privacy information; (receiving transmissions on mobile computing devices, physicians may review a patient's medical data; encrypted medical data; patients' medical records and may include information such as patients' imaging data; enable communicating medical images to a physician's handheld device and providing tools to enable the physician to remotely review such images; medical images may be collected by an image server with known format 112, such as DICOM; para [0012], [0047], [0080], [0113]; )
uncompressing and validating at least one of the one or more anonymous DICOM object (s) (the handheld device may send a download request back to the server which then transmits the data; medical records may be downloaded (uncompressed) from an EMR database server; encrypted medical data; patients' medical records and may include information such as patients' imaging data; medical images may be collected by an image server with known format 112, such as DICOM; validate transmissions from the computing device; para [0047], [0064], [0080], [0090], [0113], [0333]); and
transmitting an analysis of at least one of the one or more uncompressed and validated anonymous DICOM object(s) (medical records may be downloaded (uncompressed) from an EMR database server; the system may distinguish (analysis) between conditions based on determined priorities and may deliver (transmit) medical data to the physician in order of highest priority first; validate transmissions from the computing device; para [0090], [0272], [0333]),
the analysis having been completed according to the priority level of the unique case file (the system may distinguish (analysis) between conditions based on determined priorities and may deliver medical data to the physician in order of highest priority first; for example, the console may order a heart attack condition as a higher priority than a high temperature condition; para [0272]),
wherein the analysis includes a diagnosis or treatment analysis (par. 275-the auto-analysis routine may interpret testing and patient medical data in view of recorded symptoms to determine the presence of a STEMI condition; par. 281-If the auto-analysis routine determines the presence of a STEMI event based on the patient medical data (i.e., decision block 2050="YES"), the console may transmit data for furthering treatment of the patient)
Dala fails to disclose a computer-implemented method of analyzing health data over a decentralized cloud-computing platform. Bernard teaches a system and method for distributing and analyzing health data over a decentralized cloud-computing platform (Fig. 1-3; par. 41-43; par. 355-: the processing devices may be distributedly located (e.g., cloud computing via indirect coupling via a local area network and/or a wide area network). At the time of filing, it would have been obvious to one of ordinary skill in the art to modify the system/method of Dala with the teaching of Bernard to include analyzing health data over a decentralized cloud-computing platform. One would have been motivated to include this feature to facilitate greater accessibility to healthcare resources and to support medical decision making, thereby improving the quality and cost effectiveness of healthcare.
Claim 9 also recites:
determining a presence, at the health data analysis system, of stored health data associated with the one or more anonymous DICOM object(s),the stored health data being present at the health data analysis system prior to receiving the one or more anonymous DICOM object(s);
upon determining the presence, at the health data analysis system, of stored health data associated with the one or more anonymous DICOM object(s), the stored health data being present prior to receiving the one or more anonymous DICOM object(s), setting a priority level for the unique case file to a high priority
As cited above, Dala teaches setting a priority level for the unique case file based on priorities associated with the one or more anonymous DICOM object(s) (par. 122- The image processing software may be configured to assign priority to certain optimization steps-e.g. priority setting which prioritizes resolution; or prioritizing aspect ratio of an image; see also par. 271-275-priority based upon STEMI events) Dala further discloses setting or assessing priority levels of a case file based on associated medical condition (par. 272- the auto-analysis may determine the priority regarding the patient's current condition. For example, if the auto-analysis routine detects strong STEMI characteristics based on the medical data and testing information, the console may qualify the condition as a high urgency concern. Priority or urgency assessments are described in detail below with reference to FIG. 21. In an embodiment, the system may distinguish between conditions based on determined priorities and may deliver medical data to the physician in order of highest priority first)
Dala does not expressly disclose, but Bernard teaches:
determining a presence, at the health data analysis system, of stored health data associated with the one or more anonymous DICOM object(s),the stored health data being present at the health data analysis system prior to receiving the one or more anonymous DICOM object(s); (inclusion of image attributes/ determining the presence of stored information: par. 52-56: inclusion of image attributes (e.g. DICOM objects) Each medical scan entry 352 can be identified by its own medical scan identifier 353, and can include or otherwise map to scan image data 410, and metadata such as scan classifier data 420, patient history data 430, diagnosis data 440, annotation author data 450, confidence score data 460, display parameter data 470, similar scan data 480, training set data 490, and/or other data relating to the medical scan… The medical scan entries 352 and the associated data as described herein can also refer to data associated with a medical scan that is not stored by the medical scan database, for example, that is uploaded by a client device for direct transmission to a subsystem, data generated by a subsystem and used as input to another subsystem or transmitted directly to a client device, or other data associated with a medical scan that is received and or generated without being stored in the medical scan database 342…some or all of the structure and data attributes described with respect to a medical scan entry 352 can also correspond to structure and/or data attribute of data objects or other data generated by and/or transmitted between subsystems and/or client devices that correspond to a medical scan; par. 67-68-comparison of image scans (i.e. DICOM attributes/ objects) to determine using scan similarity; par. 60-system use of DICOM standard/format including annotation data )
upon determining the presence, at the health data analysis system, of stored health data associated with the one or more anonymous DICOM object(s), the stored health data being present prior to receiving the one or more anonymous DICOM object(s), setting a priority level for the unique case file to a high priority (par. 55-classification and prioritizing based on image attribute data: The scan classifier data 420 can include scan date data 426 indicating when the scan was taken. The scan classifier data 420 can include scan priority data 427, which can indicate a priority score, ranking, number in a queue, or other priority data with regard to triaging and/or review. A priority score, ranking, or queue number of the scan priority data 427 can be generated by automatically by a subsystem based on the scan priority data 427, based on a severity of patient symptoms or other indicators in the risk factor data 432, based on a priority corresponding to the originating entity, based on previously generated diagnosis data 440 for the scan, and/or can be assigned by the originating entity and/or a user of the system.)
At the time of filing, it would have been obvious to one of ordinary skill in the art to further modify the system and method of Dala with the teaching of Bernard. One would have been motivated to include this feature to facilitate prompt review/analysis of scans regarding medical conditions that are particularly severe, rare, and/or time-sensitive. (par. 504)
Claim 9 has been further amended to recite: determining an analysis of at least one of the one or more anonymous DICOM objects in an order-according to the set priority level of the unique case file among a plurality of case files, wherein the analysis includes a diagnosis or treatment analysis.
Dala and Bernard do not disclose, but Chila teaches determining an analysis of at least one of the one or more anonymous DICOM objects in an order-according to the set priority level of the unique case file among a plurality of case files. (col. 5, lines 35-45: Based on the result of the machine learning analysis, the medical evaluation for the images and the associated imaging procedure may be prioritized, or otherwise changed or modified. Further, the detection of the medical conditions may be used to assist the assignment of the medical imaging data to particular evaluators, the evaluation process for the medical imaging data, or implement other actions prior to, or concurrent with, the medical imaging evaluation (or the generation of a data item such as a report from such medical imaging evaluation); see also claim 1: prioritizing an assignment of a medical evaluation to an evaluator based on the score generated for the images; claims 5-6: detecting and localizing medical abnormalities of the images and prioritizing the assignment of a medical evaluation to an evaluator are carried out in the cloud server to obtain a prioritization status for DICOM images… wherein the prioritization status for DICOM images is transferred to the local server and a Health Level Seven (HL7) message is constructed in response to the prioritization status for DICOM images)
At the time of effective filing, it would have been obvious to one of ordinary skill in the art to modify the system and method of Dala and Bernard with the teaching of Chila with the motivation of facilitating automated detection, indication, or confirmation of certain medical conditions within the images, such as the detection of urgent or life-critical medical conditions, clinically serious abnormalities, and other key findings, and expediting critical treatments for patients.
Regarding claim 17, Dala in view of Bernard discloses a method (methods; Abstract) comprising:
receiving, at a health data analysis system in a first region from a health data input-output system in a second region (fig. 1, par. 61; par. 63-64; par. 93 receiving imaging and DICOM files from data servers (110-113) to physician system devices and user console for analysis), a unique case file containing one or more anonymous Digital Imaging and Communications in Medicine (DICOM) object(s) for analysis, wherein the one or more anonymous DICOM objects include one or more imaqe files devoid of patient privacy information; (receiving transmissions on mobile computing devices, physicians may review a patient's medical data; encrypted medical data; patients' medical records and may include information such as patients' imaging data; enable communicating medical images to a physician's handheld device and providing tools to enable the physician to remotely review such images; medical images may be collected by an image server with known format 112, such as DICOM; para [0012], [0047], [0080], [0113]; )
uncompressing and validating at least one of the one or more anonymous DICOM object (s) (the handheld device may send a download request back to the server which then transmits the data; medical records may be downloaded (uncompressed) from an EMR database server; encrypted medical data; patients' medical records and may include information such as patients' imaging data; medical images may be collected by an image server with known format 112, such as DICOM; validate transmissions from the computing device; para [0047], [0064], [0080], [0090], [0113], [0333]); and
transmitting an analysis of at least one of the one or more anonymous DICOM object(s) (medical records may be downloaded (uncompressed) from an EMR database server; the system may distinguish (analysis) between conditions based on determined priorities and may deliver (transmit) medical data to the physician in order of highest priority first; validate transmissions from the computing device; para [0090], [0272], [0333]),
the analysis having been completed according to the priority level of the unique case file (the system may distinguish (analysis) between conditions based on determined priorities and may deliver medical data to the physician in order of highest priority first; for example, the console may order a heart attack condition as a higher priority than a high temperature condition; para [0272]),
wherein the analysis includes a diagnosis or treatment analysis (par. 275-the auto-analysis routine may interpret testing and patient medical data in view of recorded symptoms to determine the presence of a STEMI condition; par. 281-If the auto-analysis routine determines the presence of a STEMI event based on the patient medical data (i.e., decision block 2050="YES"), the console may transmit data for furthering treatment of the patient)
Dala fails to disclose a computer-implemented method of analyzing health data over a decentralized cloud-computing platform. Bernard teaches a system and method for distributing and analyzing health data over a decentralized cloud-computing platform (Fig. 1-3; par. 41-43; par. 355-: the processing devices may be distributedly located (e.g., cloud computing via indirect coupling via a local area network and/or a wide area network). At the time of filing, it would have been obvious to one of ordinary skill in the art to modify the system/method of Dala with the teaching of Bernard to include analyzing health data over a decentralized cloud-computing platform. One would have been motivated to include this feature to facilitate greater accessibility to healthcare resources and to support medical decision making, thereby improving the quality and cost effectiveness of healthcare.
Claim 17 has been amended to recite:
determining a presence, at the health data analysis system, of stored health data associated with the one or more anonymous DICOM object(s),the stored health data being present at the health data analysis system prior to receiving the one or more anonymous DICOM object(s);
upon determining the presence, at the health data analysis system, of stored health data associated with the one or more anonymous DICOM object(s), the stored health data being present prior to receiving the one or more anonymous DICOM object(s), setting a priority level for the unique case file to a high priority
As cited above, Dala teaches setting a priority level for the unique case file based on priorities associated with the one or more anonymous DICOM object(s) (par. 122- The image processing software may be configured to assign priority to certain optimization steps-e.g. priority setting which prioritizes resolution; or prioritizing aspect ratio of an image; see also par. 271-275-priority based upon STEMI events) Dala further discloses setting or assessing priority levels of a case file based on associated medical condition (par. 272- the auto-analysis may determine the priority regarding the patient's current condition. For example, if the auto-analysis routine detects strong STEMI characteristics based on the medical data and testing information, the console may qualify the condition as a high urgency concern. Priority or urgency assessments are described in detail below with reference to FIG. 21. In an embodiment, the system may distinguish between conditions based on determined priorities and may deliver medical data to the physician in order of highest priority first)
Dala does not expressly disclose, but Bernard teaches:
determining a presence, at the health data analysis system, of stored health data associated with the one or more anonymous DICOM object(s),the stored health data being present at the health data analysis system prior to receiving the one or more anonymous DICOM object(s); (inclusion of image attributes/ determining the presence of stored information: par. 52-56: inclusion of image attributes (e.g. DICOM objects) Each medical scan entry 352 can be identified by its own medical scan identifier 353, and can include or otherwise map to scan image data 410, and metadata such as scan classifier data 420, patient history data 430, diagnosis data 440, annotation author data 450, confidence score data 460, display parameter data 470, similar scan data 480, training set data 490, and/or other data relating to the medical scan… The medical scan entries 352 and the associated data as described herein can also refer to data associated with a medical scan that is not stored by the medical scan database, for example, that is uploaded by a client device for direct transmission to a subsystem, data generated by a subsystem and used as input to another subsystem or transmitted directly to a client device, or other data associated with a medical scan that is received and or generated without being stored in the medical scan database 342…some or all of the structure and data attributes described with respect to a medical scan entry 352 can also correspond to structure and/or data attribute of data objects or other data generated by and/or transmitted between subsystems and/or client devices that correspond to a medical scan; par. 67-68-comparison of image scans (i.e. DICOM attributes/ objects) to determine using scan similarity; par. 60-system use of DICOM standard/format including annotation data )
upon determining the presence, at the health data analysis system, of stored health data associated with the one or more anonymous DICOM object(s), the stored health data being present prior to receiving the one or more anonymous DICOM object(s), setting a priority level for the unique case file to a high priority (par. 55-classification and prioritizing based on image attribute data: The scan classifier data 420 can include scan date data 426 indicating when the scan was taken. The scan classifier data 420 can include scan priority data 427, which can indicate a priority score, ranking, number in a queue, or other priority data with regard to triaging and/or review. A priority score, ranking, or queue number of the scan priority data 427 can be generated by automatically by a subsystem based on the scan priority data 427, based on a severity of patient symptoms or other indicators in the risk factor data 432, based on a priority corresponding to the originating entity, based on previously generated diagnosis data 440 for the scan, and/or can be assigned by the originating entity and/or a user of the system.)
At the time of filing, it would have been obvious to one of ordinary skill in the art to further modify the system and method of Dala with the teaching of Bernard. One would have been motivated to include this feature to facilitate prompt review/analysis of scans regarding medical conditions that are particularly severe, rare, and/or time-sensitive. (par. 504)
Claim 17 has been further amended to recite: determining an analysis of at least one of the one or more anonymous DICOM objects in an order-according to the set priority level of the unique case file among a plurality of case files, wherein the analysis includes a diagnosis or treatment analysis.
Dala and Bernard do not disclose, but Chila teaches determining an analysis of at least one of the one or more anonymous DICOM objects in an order-according to the set priority level of the unique case file among a plurality of case files. (col. 5, lines 35-45: Based on the result of the machine learning analysis, the medical evaluation for the images and the associated imaging procedure may be prioritized, or otherwise changed or modified. Further, the detection of the medical conditions may be used to assist the assignment of the medical imaging data to particular evaluators, the evaluation process for the medical imaging data, or implement other actions prior to, or concurrent with, the medical imaging evaluation (or the generation of a data item such as a report from such medical imaging evaluation); see also claim 1: prioritizing an assignment of a medical evaluation to an evaluator based on the score generated for the images; claims 5-6: detecting and localizing medical abnormalities of the images and prioritizing the assignment of a medical evaluation to an evaluator are carried out in the cloud server to obtain a prioritization status for DICOM images… wherein the prioritization status for DICOM images is transferred to the local server and a Health Level Seven (HL7) message is constructed in response to the prioritization status for DICOM images)
At the time of effective filing, it would have been obvious to one of ordinary skill in the art to modify the system and method of Dala and Bernard with the teaching of Chila with the motivation of facilitating automated detection, indication, or confirmation of certain medical conditions within the images, such as the detection of urgent or life-critical medical conditions, clinically serious abnormalities, and other key findings, and expediting critical treatments for patients.
claim 25. Dala teaches the method of claim 1, wherein transmitting the analysis of the one or more uncompressed and validated anonymous DICOM objects further occurs based on whether a pull mechanism is initiated. (par. 80-data (including DICOM data) transmitted upon validation of data requestor’s credentials- The transmitted data may be encrypted or scrambled, and various user access validation steps may be incorporated to protect the integrity of the data and the privacy of the patient. For example, encrypted medical data may be transmitted from the server 130 to a physician's mobile device 150 only when the identities of the physician and the physician's mobile device are authenticated. The identity of a physician may be authenticated by requiring a time-sensitive log-in process with a strong password known only to the physician.
claim 29. Dala teaches the method of claim 1, wherein validating at least one of the one or more anonymous DICOM object(s) includes determining whether a DICOM is missing a DICOM image, whether a DICOM image size of the DICOM objects is outside of a defined range, or whether the DICOM image does not meet defined quality criteria. (par. 80-validation steps; par. 146- validation and successful transmission are based upon size constraints of the image file for resizing ability, image clarity and speed of transmission; See also par. 121-122 for checking of resolution and display parameters)
claim 31. Dala teaches the method of claim 1, wherein the analysis occurs in two or more stages, the priority level is editable at each stage, and the analysis at each stage occurs according to the priority level. (par. 98-message is classified as urgent/ non-urgent; par. 121-122-second prioritization of imaging data for optimization of resolution, transmission time, or aspect ratio.)
Claims 2 and 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dala et al. (US 2013/0024382 A1 -hereinafter Dala), Bernard et al (US 20190057769 A1), and Chilamkurhy et al (US 10504227 B1- hereinafter Chila) as applied to claim 1, and in further view of Lyman et al (US 2020/0161005 A1)
Regarding claim 2, Dala ,Bernard, and Chila in combination disclose the computer-implemented method of claim 1 as explained in the rejection of claim 1.
Dala ,Bernard, and Chila in combination fails to disclose, but Lyman teaches a method wherein the analysis comprises:
generating a volume image file based on at least one of the one or more anonymous DICOM objects of the unique case file, the volume image file representing a three- dimensional (3D) volume; (par. 59-image analysis scans include 3D regions; par. 142-feature vectors include pixel data from 3D images; par. 152- abnormality size and volume determined based on a number of pixels determined to be part of the detected abnormality);
generating an archive file including analysis data associated with the volume image file; and (par. 158-160- medical picture archive integration system 2600 includes a de-identification system that includes a first memory designated for protected health information (PHI), operable to perform a de-identification function on a DICOM image, received from a medical picture archive system, to identify at least one patient identifier and generate a de-identified medical scan that does not include the at least one patient identifier)
generating a patient-specific model based on the volume image file and the archive file. (par. 139-141: generating a neural network model using patient 3D scan data in the training set; par. 142-feature vectors include pixel data from 3D images; par. 397- the plurality of sub-models can be configured generate the same or different types of output and/or can include the and/or different number of output nodes. For example, output to some or all inference functions that utilize of the plurality of sub-models can be configured to include probabilities indicating whether one or more types of abnormalities are present, can indicate region of interest data localizing one or more detected abnormalities, and/or can include characterization data describing size, volume, or other characterization data describing one or more detected abnormalities).
At the time of filing, it would have been obvious to one of ordinary skill in the art or modify the system/method of Dala ,Bernard, and Chila in combination in combination with the teaching of Lyman to include the recited features, with the motivation of improving the quality and cost effectiveness of healthcare by facilitating greater accessibility to healthcare resources and providing greater support for medical decision making.
claim 30. Dala teaches a method of claim 2, wherein the patient-specific model is a model of the patient's vasculature (par. 179-2D and 3D renderings of patient anatomical structures; server may also perform three-dimensional renderings of three-dimensional data sets…Such renderings of three-dimensional image data may be in the form of rotatable two-dimensional slice images, or isometric renderings, or movies where the isometric rendering is rotated by a given angle frame to frame…par. 180: server can stream real-time cardiac tests and images- a real-time stream of clinical data, such as an ECG trace, or image data, such as an ultrasound image of a heart);
validating the at least one of the one or more anonymous DICOM object(s) includes determining that an image quality is greater than or equal to a predetermined threshold, (par. 146- validation and successful transmission are based upon size constraints of the image file for resizing ability, image clarity and speed of transmission. See also 120-121) and
the analysis includes a blood flow simulation (par. 179, par.180-server can stream real-time cardiac tests and images- a real-time stream of clinical data, such as an ECG trace, or image data, such as an ultrasound image of a heart)
Claims 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dala et al. (US 2013/0024382 A1 -hereinafter Dala), Bernard et al (US 20190057769 A1), Chilamkurhy et al (US 10504227 B1- hereinafter Chila) and Lyman et al (US 2020/0161005 A1) as applied to claim 2, and in further view of Zavislan (US 2002/0010394 A1).
Regarding claim 3, Dala, Bernard, Chila and Lyman in combination discloses the computer-implemented method of claim 2. , Dala, Bernard, Chila and Lyman fail to disclose wherein the volume image file includes a file checksum that correlates the volume image file to the archive file.
Zavislan discloses wherein the volume image file includes a file checksum that correlates the volume image file to the archive file (stores into file structure integrity check data for the confocal images stored in the file structure; this integrity check data may be a CHECKSUM value representing the total number of bits of the stored confocal images in file structure; para [0038]. [0040], [0042]). It would have been obvious to one of ordinary skill in the art at the time the invention was made to include wherein the volume image file includes a file checksum that correlates the volume image file to the archive file as taught by Zavislan into the system of , Dala, Bernard, Chila and Lyman in combination for the purpose of providing a file integrity check using the integrity check data of the stored file structure in order to assure that after transmission of file structure all the bits of the confocal images are property received.
Response to Arguments
Applicant's arguments filed 11/18/25 have been fully considered but they are not persuasive.
(A) Applicant argues that the claims as amended are not taught by the prior art of record.
In response, the prior art rejections have been updated to address the claim as amended, and new grounds of rejection have been added for applicant’s consideration.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bar-Aviv (US 20090028403 A1) teaches a system and method including setting priorities associated with one or more anonymous DICOM objects, being based on a presence of previously-created health data associated with the one or more anonymous DICOM object(s), the previously-created health data being created prior to receiving the unique case file
Kopylov (US 20180239868 A1) discloses priority values assigned to imaging files including DICOM files. (par. 324-325)
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/Rachel L. Porter/ Primary Examiner, Art Unit 3626