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 .
This office action is in response to communication filed on 05/01/2025.
Claims 1-19 present for examination.
Information Disclosure Statement
It is hereby acknowledged that the following papers have been received and placed of record in the file:
Information Disclosure Statement(s) as received on 05/23/2025 and 10/22/2025 is/are considered by the Examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-16, 19, and 25 of U.S. Patent No. 12,512,209 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims in the patent anticipate all the claims in the applications.
Instant Application
Patent (US 12,512,209 B2)
1. A gateway edge system comprising:
a data orchestration engine comprising a plurality of modules configured to receive and process data according to a workflow and dynamically route the processed data to one or more entities that are in communication with the gateway edge system, wherein the gateway edge system is configured to be deployed within a firewall of the healthcare system, and wherein the plurality of modules comprises an ingestion and normalization module configured to:
i) receive input data from a plurality of electronic data sources located within the healthcare system, wherein the plurality of electronic data sources comprises at least two different sources and wherein the at least two different sources comprise an electronic health record (EHR) system, a radiology electronic clinical data system, Picture Archiving and Communication System (PACS), radiation dose data, a billing system or a claim system; and
ii) normalize the input data by mapping to a standardized data model.3. The gateway edge system of claim 1, wherein the plurality of modules further comprises a translation engine configured to process the normalized input data to extract a plurality of elements and translate the plurality of elements into a plurality of intermediate variables.
1. A gateway edge system comprising:
a data orchestration engine comprising a plurality of modules configured to receive and process data according to a workflow and dynamically route the processed data to one or more entities that are in communication with the gateway edge system, wherein the gateway edge system is configured to be deployed within a firewall of a healthcare system, wherein the plurality of modules comprises an ingestion and normalization module configured to:
i) receive input data from a plurality of electronic data sources located within the healthcare system, wherein the plurality of electronic data sources comprises at least two different sources and wherein the at least two different sources comprise an electronic health record (EHR) system, a radiology electronic clinical data system, Picture Archiving and Communication System (PACS), radiation dose data, a billing system or a claim system; and
ii) normalize the input data by mapping to a standardized data model, and wherein the plurality of modules further comprises a translation engine configured to process the normalized input data to extract a plurality of elements and translate the plurality of elements into a plurality of variables.
2. The gateway edge system of claim 1, wherein the standardized data model comprises a Fast Healthcare Interoperability Resources (FHIR) standard.
2. The gateway edge system of claim 1, wherein the standardized data model comprises a Fast Healthcare Interoperability Resources (FHIR) standard.
4. The gateway edge system of claim 3, wherein the plurality of modules further comprises a clinical quality measure computation component configured to compute, based at least in part on the plurality of intermediate variables, a clinical quality measure indicative of whether a radiation dose is excessive, within a safe range, or inadequate.
3. The gateway edge system of claim 1, wherein the plurality of modules further comprises a clinical quality measure computation component configured to compute, based at least in part on the plurality of variables, a clinical quality measure indicative of whether a radiation dose is excessive, within a safe range, or inadequate.
5. The gateway edge system of claim 1, wherein the data received from the EHR system comprises at least one of a radiology report, a pathology report, a clinical report, a radiation dose data, diagnostic data, study or test order related data including International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) code and Current Procedural Terminology (CPT) codes associated with a test, a study request including Logical Observation Identifiers Names and Codes (LOINC), a clinical indication for the test, or data related to a reason for the test.
4. The gateway edge system of claim 1, wherein the data received from the EHR system comprises at least one of a radiology report, a pathology report, a clinical report, a radiation dose data, diagnostic data, study or test order related data including International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) code and Current Procedural Terminology (CPT) codes associated with a test, a study request including Logical Observation Identifiers Names and Codes (LOINC), a clinical indication for the test, or data related to a reason for the test.
6. The gateway edge system of claim 1, wherein the data comprises at least one of image data, video data, free text-based data, or structured data, and wherein the ingestion and normalization module is further configured to provide predefined and configurable clinical data endpoints for immediate connectivity.
16. A gateway edge system comprising:
a data orchestration engine comprising a plurality of modules configured to receive and process data according to a workflow and dynamically route the processed data to one or more entities that are in communication with the gateway edge system, wherein the gateway edge system is configured to be deployed within a firewall of a healthcare system, and wherein the plurality of modules comprises an ingestion and normalization module configured to:
i) receive input data from a plurality of electronic data sources located within the healthcare system, wherein the plurality of electronic data sources comprises at least two different sources and wherein the at least two different sources comprise an electronic health record (EHR) system, a radiology electronic clinical data system, Picture Archiving and Communication System (PACS), radiation dose data, a billing system or a claim system; and
ii) normalize the input data by mapping to a standardized data model;
wherein the ingestion and normalization module is further configured to provide predefined and configurable clinical data endpoints for immediate connectivity,
and wherein the data comprises at least one of image data, video data, free text-based data, or structured data.
7. The gateway edge system of claim 1, wherein the ingestion and normalization module is further configured to combine two or more data sets into a single cohort based at least in part on a patient name or key identifier identified from the two or more data sets.
19. A gateway edge system comprising:
a data orchestration engine comprising a plurality of modules configured to receive and process data according to a workflow and dynamically route the processed data to one or more entities that are in communication with the gateway edge system, wherein the gateway edge system is configured to be deployed within a firewall of a healthcare system, and wherein the plurality of modules comprises an ingestion and normalization module configured to:
i) receive input data from a plurality of electronic data sources located within the healthcare system, wherein the plurality of electronic data sources comprises at least two different sources and wherein the at least two different sources comprise an electronic health record (EHR) system, a radiology electronic clinical data system, Picture Archiving and Communication System (PACS), radiation dose data, a billing system or a claim system;
ii) normalize the input data by mapping to a standardized data model;
iii) combine two or more data sets into a single cohort based at least in part on a patient name or key identifier identified from the two or more data sets.
8. The gateway edge system of claim 1, wherein the plurality of modules further comprises a machine-learning based module configured to process the data locally within the firewall of the healthcare system, and wherein the machine-learning based module is further configured to perform multi-step data processing.
25. A gateway edge system comprising:
a data orchestration engine comprising a plurality of modules configured to receive and process data according to a workflow and dynamically route the processed data to one or more entities that are in communication with the gateway edge system, wherein the gateway edge system is configured to be deployed within a firewall of a healthcare system, and wherein the plurality of modules comprises an ingestion and normalization module configured to:
i) receive input data from a plurality of electronic data sources located within the healthcare system, wherein the plurality of electronic data sources comprises at least two different sources and wherein the at least two different sources comprise an electronic health record (EHR) system, a radiology electronic clinical data system, Picture Archiving and Communication System (PACS), radiation dose data, a billing system or a claim system;
ii) normalize the input data by mapping to a standardized data model;
iii) combine two or more data sets into a single cohort based at least in part on a patient name or key identifier identified from the two or more data sets,
wherein the plurality of modules further comprises a machine-learning based module configured to process the data locally within the firewall of the healthcare system, and wherein the machine-learning based module is further configured to perform multi-step data processing.
9. The gateway edge system of claim 1, wherein a selection and an order of modules in the plurality of modules are determined based at least in part on the workflow and a type of the data.
5. The gateway edge system of claim 1, wherein a selection and an order of modules in the plurality of modules are determined based at least in part on the workflow and a type of the data.
10. The gateway edge system of claim 9, wherein the selection and the order of modules in the plurality of modules are determined based at least in part by utilizing a trained machine learning model or a large language model (LLM).
6. The gateway edge system of claim 5, wherein the selection and the order of modules in the plurality of modules are determined based at least in part by utilizing a trained machine learning model or a large language model (LLM).
11. The gateway edge system of claim 1, wherein the gateway edge system is configured to be deployed in a virtual infrastructure of the healthcare system, and wherein the gateway edge system is further configured to interface with the one or more electronic data sources within the healthcare system to receive the data via a local network.
7. The gateway edge system of claim 1, wherein the gateway edge system is configured to be deployed in a virtual infrastructure of the healthcare system, and wherein the gateway edge system is further configured to interface with the one or more electronic data sources within the healthcare system to receive the data via a local network.
12. The gateway edge system of claim 1, wherein the gateway edge system is configured to provide a standardized connection between the healthcare system and the one or more entities.
8. The gateway edge system of claim 1, wherein the gateway edge system is configured to provide a standardized connection between the healthcare system and the one or more entities.
13. The gateway edge system of claim 1, wherein the gateway edge system is configured to be remotely managed by a cloud console.
9. The gateway edge system of claim 1, wherein the gateway edge system is configured to be remotely managed by a cloud console.
14. The gateway edge system of claim 1, wherein the gateway edge system is configured to calculate the clinical quality measure substantially in real-time upon receipt of the data, and wherein the data is received via a data push model of a data orchestration engine.
10. The gateway edge system of claim 1, wherein the gateway edge system is configured to calculate the clinical quality measure substantially in real-time upon receipt of the data, and wherein the data is received via a data push model of a data orchestration engine.
15. The gateway edge system of claim 1, wherein the data comprises medical image data, and wherein the gateway edge system is configured to execute a containerized code at runtime for processing the medical image data.
11. The gateway edge system of claim 1, wherein the data comprises medical image data, and wherein the gateway edge system is configured to execute a containerized code at runtime for processing the medical image data.
16. The gateway edge system of claim 15, wherein the gateway edge system is configured to be in remote communication with a cloud platform comprising a registry, and wherein the registry comprises at least one code module configured to be deployed to the gateway edge system for execution.
12. The gateway edge system of claim 11, wherein the gateway edge system is configured to be in remote communication with a cloud platform comprising a registry, and wherein the registry comprises at least one code module configured to be deployed to the gateway edge system for execution.
17. The gateway edge system of claim 16, wherein the at least one code module comprises a container-wrapped code module.
13. The gateway edge system of claim 12, wherein the at least one code module comprises a container-wrapped code module.
18. The gateway edge system of claim 16, wherein the at least one code module is configured to be tested for vulnerability and is in compliance with a wrapper application program interface (API) of the gateway edge system.
14. The gateway edge system of claim 12, wherein the at least one code module is configured to be tested for vulnerability and is in compliance with a wrapper application program interface (API) of the gateway edge system.
19. The gateway edge system of claim 1, wherein the data comprises patient names and/or key identifiers, and wherein the gateway edge system comprises a visualization module configured to allow external users or entities to access the data without leaving the firewall of the healthcare system.
15. The gateway edge system of claim 1, wherein the data comprises patient names and/or key identifiers, and wherein the gateway edge system comprises a visualization module configured to allow external users or entities to access the data without leaving the firewall of the healthcare system.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because these claims are directed to software per se. Referring to claims 1-19, claim 1 recites the limitation, “a gateway edge system comprising: a data orchestration engine”, which directs the claim to software per se.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "the healthcare system" in line 5. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 8, claim limitation recites “the data” in line 2, which renders the claim vague and indefinite. It is unclear whether “the data” is referring to “data” in claim 1, line 3, or “the processed data” in claim 1, line 3, or to “input data” in claim 1, line 8, or to different/distinct data.
Regarding claim 9, claim limitation recites “the data” in line 2, which renders the claim vague and indefinite. It is unclear whether “the data” is referring to “data” in claim 1, line 3, or “the processed data” in claim 1, line 3, or to “input data” in claim 1, line 8, or to different/distinct data.
Claim 11 recites the limitation "the one or more electronic data sources" in line 3. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 11, claim limitation recites “the data” in line 4, which renders the claim vague and indefinite. It is unclear whether “the data” is referring to “data” in claim 1, line 3, or “the processed data” in claim 1, line 3, or to “input data” in claim 1, line 8, or to different/distinct data.
Claim 14 recites the limitation "the clinical quality measure" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 14, claim limitation recites “the data” in line 2, which renders the claim vague and indefinite. It is unclear whether “the data” is referring to “data” in claim 1, line 3, or “the processed data” in claim 1, line 3, or to “input data” in claim 1, line 8, or to different/distinct data.
Regarding claim 14, claim limitation recites “the data” in line 3, which renders the claim vague and indefinite. It is unclear whether “the data” is referring to “data” in claim 1, line 3, or “the processed data” in claim 1, line 3, or to “input data” in claim 1, line 8, or to different/distinct data.
Regarding claim 15, claim limitation recites “the data” in line 1, which renders the claim vague and indefinite. It is unclear whether “the data” is referring to “data” in claim 1, line 3, or “the processed data” in claim 1, line 3, or to “input data” in claim 1, line 8, or to different/distinct data.
Regarding claim 19, claim limitation recites “the data” in line 1, which renders the claim vague and indefinite. It is unclear whether “the data” is referring to “data” in claim 1, line 3, or “the processed data” in claim 1, line 3, or to “input data” in claim 1, line 8, or to different/distinct data.
Regarding claim 19, claim limitation recites “the data” in line 3, which renders the claim vague and indefinite. It is unclear whether “the data” is referring to “data” in claim 1, line 3, or “the processed data” in claim 1, line 3, or to “input data” in claim 1, line 8, or to different/distinct data.
All dependent claims are rejected as having the same deficiencies as the claims they depend from.
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) 1-3, 5, 6, 11-13, and 19 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Olivares et al. (US 2023/0162837 A1), hereinafter Olivares.
Regarding claim 1, Olivares discloses
A gateway edge system comprising:
a data orchestration engine comprising a plurality of modules configured to receive and process data according to a workflow and dynamically route the processed data to one or more entities that are in communication with the gateway edge system, wherein the gateway edge system is configured to be deployed within a firewall of the healthcare system ([0128]: the medical information is imagery in this scenario that is formatted in DICOM and transmitted through the communications tool 1424 and the organization’s firewall 1503 to the medical information sharing platform 1403 using DICOMWeb; using the workflow engine disclosed above, the medical information is transmitted, again using DICOMWeb to a DICOMWeb image storage 1506, from whence it may be transferred to FHIR clinical storage 1509; & [0133]: platform security features with respect to isolation include limitations that (1) all clinical (e.g., FHIR) and imaging (e.g., DICOM) data is segmented and isolated in the cloud per organization, and (2) the platform gateway only passes data related to the logged organization), and wherein the plurality of modules comprises an ingestion and normalization module configured to:
receive input data from a plurality of electronic data sources located within the healthcare system, wherein the plurality of electronic data sources comprises at least two different sources and wherein the at least two different sources comprise an electronic health record (EHR) system, a radiology electronic clinical data system, Picture Archiving and Communication System (PACS), radiation dose data, a billing system or a claim system ([0093]: receiving HL7 clinical orders for imaging from an Electronic Medical Records (“EMR”) system 4 and feeding a radiology modality with the worklist of orders using DICOM protocol); and
ii) normalize the input data by mapping to a standardized data model ([0006]: clinical data integration systems deal with all the variety of standards and their sub-variants and perform translations (also called mappings) between them; & [0034]: mapping individual data elements into other values; transforming datasets into a different clinical format).
Regarding claim 2, Olivares discloses the gateway edge system as described in claim 1. Olivares further discloses
the standardized data model comprises a Fast Healthcare Interoperability Resources (FHIR) standard ([0032]: Fast Healthcare Interoperability Resources (“FHIR”) is a specification which is a standard for exchanging healthcare information electronically).
Regarding claim 3, Olivares discloses the gateway edge system as described in claim 1. Olivares further discloses
the plurality of modules further comprises a translation engine configured to process the normalized input data to extract a plurality of elements and translate the plurality of elements into a plurality of intermediate variables ([0006]: clinical data integration systems deal with all the variety of standards and their sub-variants and perform translations (also called mappings) between them; & [0034]: mapping individual data elements into other values; transforming datasets into a different clinical format).
Regarding claim 5, Olivares discloses the gateway edge system as described in claim 1. Olivares further discloses
the data received from the EHR system comprises at least one of a radiology report, a pathology report, a clinical report, a radiation dose data, diagnostic data, study or test order related data including International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) code and Current Procedural Terminology (CPT) codes associated with a test, a study request including Logical Observation Identifiers Names and Codes (LOINC), a clinical indication for the test, or data related to a reason for the test ([0093]: receiving HL7 clinical orders for imaging from an Electronic Medical Records (“EMR”) system 4).
Regarding claim 6, Olivares discloses the gateway edge system as described in claim 1. Olivares further discloses
the data comprises at least one of image data ([0039]: the data object 600 may be, for example and without limitation, a radiological image or a patient’s chart, in any medical data format), video data, free text-based data, or structured data, wherein the ingestion and normalization module is further configured to provide predefined and configurable clinical data endpoints for immediate connectivity ([0061]: implements an HTTP endpoint for receiving HL7 Clinical Document Architecture (“CDA”) documents and its derivatives, like Consolidated-Clinical Document Architecture (“C-CDA”) and Continuity of Care Document (“CCR”) documents).
Regarding claim 11, Olivares discloses the gateway edge system as described in claim 1. Olivares further discloses
the gateway edge system is configured to be deployed in a virtual infrastructure of the healthcare system, and wherein the gateway edge system is further configured to interface with the one or more electronic data sources within the healthcare system to receive the data via a local network ([0110]: network 1208 may include one or more computing networks available today, such as other LANs, wide area networks (“WAN”), the Internet, and/or other remote networks, in order to transfer data between client devices 1204A-D and cloud service provider network 1210 (e.g., a cloud service provider hosting the disclosed clinical data workflow engine application)).
Regarding claim 12, Olivares discloses the gateway edge system as described in claim 1. Olivares further discloses
the gateway edge system is configured to provide a standardized connection between the healthcare system and the one or more entities ([0110]: network 1208 may include one or more computing networks available today, such as other LANs, wide area networks (“WAN”), the Internet, and/or other remote networks, in order to transfer data between client devices 1204A-D and cloud service provider network 1210 (e.g., a cloud service provider hosting the disclosed clinical data workflow engine application)).
Regarding claim 13, Olivares discloses the gateway edge system as described in claim 1. Olivares further discloses
the gateway edge system is configured to be remotely managed by a cloud console ([0100]: perform one or more functions of the clinical data workflow engine cloud-based or distributed application).
Regarding claim 19, Olivares discloses the gateway edge system as described in claim 1. Olivares further discloses
the data comprises patient names and/or key identifiers, and wherein the gateway edge system comprises a visualization module configured to allow external users or entities to access the data without leaving the firewall of the healthcare system ([0128]: the medical information is imagery in this scenario that is formatted in DICOM and transmitted through the communications tool 1424 and the organization’s firewall 1503 to the medical information sharing platform 1403 using DICOMWeb).
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) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Olivares in view of Kawamura (US 2019/0090837 A1).
Regarding claim 4, Olivares discloses the gateway edge system as described in claim 3. Olivares does not explicitly disclose
the plurality of modules further comprises a clinical quality measure computation component configured to compute, based at least in part on the plurality of intermediate variables, a clinical quality measure indicative of whether a radiation dose is excessive, within a safe range, or inadequate.
However, Kawamura discloses
the plurality of modules further comprises a clinical quality measure computation component configured to compute, based at least in part on the plurality of intermediate variables, a clinical quality measure indicative of whether a radiation dose is excessive, within a safe range, or inadequate ([0068]: determine the quality of the radiographic image on the basis of the determination result of whether the amount of radiation R is excessive or insufficient).
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 feature of Kawamura in Olivares because Olivares discloses sending radiology images ([0088]) and Kawamura further suggests to determine whether the amount of radiation is excessive or insufficient ([0068]).
One of ordinary skill in the art would be motivated to utilize the teachings of Kawamura in the Olivares system in order to ensure to provide sufficient amount of radiation.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Olivares in view of Chen et al. (US 2022/0165394 A1), hereinafter Chen.
Regarding claim 7, Olivares discloses the gateway edge system as described in claim 1. Olivares does not explicitly disclose
the ingestion and normalization module is further configured to combine two or more data sets into a single cohort based at least in part on a patient name or key identifier identified from the two or more data sets.
However, Chen discloses
the ingestion and normalization module is further configured to combine two or more data sets into a single cohort based at least in part on a patient name or key identifier identified from the two or more data sets ([0020]: arrange identifiers to the medical images; the identifiers are configured to identify individual patients; & [0021]: group the medical images according to the identifiers, thus each group of medical images is corresponding to one individual patient).
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 feature of Chen in Olivares because Olivares discloses sending radiology images ([0088]) and Chen further suggests to group medical images according to patient identifier ([0020-0021]).
One of ordinary skill in the art would be motivated to utilize the teachings of Chen in the Olivares system in order to better manage medical images.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Olivares in view of Liang et al. (US 2022/0084173 A1), hereinafter Liang.
Regarding claim 8, Olivares discloses the gateway edge system as described in claim 1. Olivares further discloses
the plurality of modules further comprises a machine-learning based module configured to process data locally within the firewall of the healthcare system ([0092]: forward the image to a local DICOM destination through the LAN 2)
Olivares does not explicitly disclose
the machine-learning based module is further configured to perform multi-step data processing.
However, Liang discloses
the machine-learning based module is further configured to perform multi-step data processing ([0032]: and if so, could medical image processing applications be improved through a multi-step operation in which a trained GAN type machine learning model first “virtually heals” a patient by turning that patient’s medical image, with an unknown health status (e.g., either diseased or healthy), into a healthy medical image, such that diseased regions of that patient’s medical image (if present) may then be revealed through subsequent processing which subtracts the two images, thus revealing any diseased region as the difference between a machine generated “healthy” image and the actual and unmodified image with potentially diseased regions).
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 feature of Liang in Olivares because Olivares discloses sending radiology images ([0088]) and Liang further suggests to perform multi-step operation on patient’s medical image ([0032]).
One of ordinary skill in the art would be motivated to utilize the teachings of Liang in the Olivares system in order to enhance image-to-image translation.
Claim(s) 9 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Olivares in view of VanAntwerp et al. (US 11,550,565 B1), hereinafter VanAntwerp.
Regarding claim 9, Olivares discloses the gateway edge system as described in claim 1. Olivares does not explicitly disclose
a selection and an order of modules in the plurality of modules are determined based at least in part on the workflow and a type of the data.
However, VanAntwerp discloses
a selection and an order of modules in the plurality of modules are determined based at least in part on the workflow and a type of the data (Col. 17, lines 55-59: the workflow module 504 may identify, based upon the selected workflow, the collection of steps included therein, as well as current state information associated with the workflow, such as a step pointer to the currently active step in the workflow).
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 feature of VanAntwerp in Olivares because Olivares discloses execute workflow [0044]) and VanAntwerp further suggests identify collection of steps based upon workflow (Col. 17, lines 55-59).
One of ordinary skill in the art would be motivated to utilize the teachings of VanAntwerp in the Olivares system in order to provide an efficient system.
Regarding claim 10, Olivares and VanAntwerp disclose the gateway edge system as described in claim 9. Olivares further discloses
the selection and the order of modules in the plurality of modules are determined based at least in part by utilizing a trained machine learning model or a large language model (LLM) ([0134]: deployed with artificial intelligence (“AI”)).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Olivares in view of Chan et al. (US 10,733,566 B1), hereinafter Chan.
Regarding claim 14, Olivares discloses the gateway edge system as described in claim 1. Olivares does not explicitly disclose
the gateway edge system is configured to calculate the clinical quality measure substantially in real-time upon receipt of the data, and wherein the data is received via a data push model of a data orchestration engine.
However, Chan discloses
the gateway edge system is configured to calculate the clinical quality measure substantially in real-time upon receipt of the data (Col. 9, lines 26-29: parse newly received real-time medical data concerning a patient and identify a body mass index (BMI) of the patient as an entity of interest), and wherein the data is received via a data push model of a data orchestration engine (Col. 10, lines 35-40: an ensemble may contain a number of condition models, with each condition model comprising a set of factors or indicators that are predictive of whether a particular medical condition is reflected in the received real-time clinical data (for a diagnosis model) or in the documentation (for a documentation model)).
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 feature of Chan in Olivares because Olivares discloses sending radiology images ([0088]) and Chan further suggests parse real-time medical data to identify a body mass index of the patient (Col. 9, lines 26-29).
One of ordinary skill in the art would be motivated to utilize the teachings of Chan in the Olivares system in order to provide an efficient system.
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Olivares in view of Tran et al. (US 2023/0089026 A1), hereinafter Tran.
Regarding claim 15, Olivares discloses the gateway edge system as described in claim 1. Olivares further discloses
the data comprises medical image data ([0125]: image files and other medical information are saved in redundant repositories residing on the Microsoft Azure Cloud and are easily accessible).
Olivares does not explicitly disclose
the gateway edge system is configured to execute a containerized code at runtime for processing the medical image data.
However, Tran discloses
the gateway edge system is configured to execute a containerized code at runtime for processing the medical image data ([0434]: the MLPS 720 is a containerized service comprising code and dependencies packaged to execute quickly and reliably from one computing environment to another; & [0002]: automated analysis of medical image by employing machine learning techniques).
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 feature of Tran in Olivares because Olivares discloses sending radiology images ([0088]) and Tran further suggests automated analysis of medical image ([0434]).
One of ordinary skill in the art would be motivated to utilize the teachings of Tran in the Olivares system in order to provide reliable and efficient system as suggested by Tran ([0434]).
Claim(s) 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Olivares in view of Tran, and further in view of Hoffer (US 9,928,379 B1).
Regarding claim 16, Olivares and Tran disclose the gateway edge system as described in claim 15. Olivares and Tran do not explicitly disclose
the gateway edge system is configured to be in remote communication with a cloud platform comprising a registry, and wherein the registry comprises at least one code module configured to be deployed to the gateway edge system for execution.
However, Hoffer discloses
the gateway edge system is configured to be in remote communication with a cloud platform comprising a registry, and wherein the registry comprises at least one code module configured to be deployed to the gateway edge system for execution (Col. 36, lines 14-17: some configurations may deploy conventional databases, registries, or directories as components to manage storage, while others may use different information storage units 32 or customized data management systems).
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 feature of Hoffer in Olivares and Tran because Olivares and Tran disclose deploy artificial intelligence (Olivares: [0134]) and Hoffer further suggests deploy registries (Col. 36, lines 14-17).
One of ordinary skill in the art would be motivated to utilize the teachings of Hoffer in the Olivares and Tran system in order to reduce infrastructure latency.
Regarding claim 17, Olivares, Tran, and Hoffer disclose the gateway edge system as described in claim 16. Olivares further discloses
the at least one code module comprises a container-wrapped code module ([0037]: a workflow 370 is composed of a set of Transactions 380 and a set of Workflow Elements 390).
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Olivares in view of Tran, in view of Hoffer, and further in view of Cohen (US 2024/0291863 A1).
Regarding claim 18, Olivares, Tran, and Hoffer disclose the gateway edge system as described in claim 16. Olivares, Tran, and Hoffer do not explicitly disclose
the at least one code module is configured to be tested for vulnerability and is in compliance with a wrapper application program interface (API) of the gateway edge system.
However, Cohen discloses
the at least one code module is configured to be tested for vulnerability and is in compliance with a wrapper application program interface (API) of the gateway edge system ([0120]: influencing execution of the native API based on the invocation source identifier includes modifying the execution when the invocation source identifier is determined to violate a security rule; performing one or more checks (e.g., relating to security, validation, authentication, data integrity)).
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 feature of Cohen in Olivares, Tran, and Hoffer because Olivares, Tran, and Hoffer disclose control the test operation of the Workflow Engine (Olivares: [0046]) and Cohen further suggests perform checks relating to security ([0120]).
One of ordinary skill in the art would be motivated to utilize the teachings of Cohen in the Olivares, Tran, and Hoffer system in order to provide a secure system.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ayalon (US 11,574,710 B1). Receive, from the agent service and by the agent application and based upon the identifier for the patient, community data for the patient that is aggregated from a plurality of electronic sources.
Ahmed et al. (US 2021/0319866 A1). Combine health records of the patient received from the plurality of healthcare networks to generate a combined health record of the patient.
Bernstein et al. (US 9,760,679 B2). Aggregate data from multiple analyte measurement devices according to a stored patient ID.
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Kaylee Huang
06/23/2026
/KAYLEE J HUANG/Primary Examiner, Art Unit 2447
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