IP Library Granted Patent US 11,599,343
Granted Patent B2
US 11,599,343 · App. 16/403,809 · Granted Mar 7, 2023

Methods and devices for modifying a runtime environment of imaging applications on a medical device

Inventors: Lutz Dominick (Eggolsheim, DE); Vladyslav Ukis (Nuremberg, DE)
Assignee: SIEMENS HEALTHCARE GMBH
G06F8/443G06F8/64G06F8/71G06F11/302G06F11/3013G06F11/3495G06F11/3664G06F11/3672G06F11/3688G06F11/3692G06F16/2455G16H40/40
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Quick Facts
Patent No.
US 11,599,343
App. No.
16/403,809
Granted
Mar 7, 2023
Kind
B2
Abstract

A method, an improvement node, a system and a computer program for computing an improvement result for a runtime environment of at least one application, on a device in a medical context. An embodiment of the method includes detecting a state of the runtime environment on the device; accessing a database with the state detected, to retrieve a corresponding at least one candidate improvement result; using the at least one candidate improvement result retrieved, for test-wise execution on a test infrastructure in which the state of the runtime environment detected is provided identically; measuring improvement parameters of the test-wise execution; and adding, upon the improvement parameters measured meeting defined requirements, candidate improvement results, of the at least one corresponding candidate improvement result retrieved, for which the improvement parameters measured meet defined requirements.

Claims (73)

1. A method for computing an improvement result for a runtime environment of at least one application, the at least one application being installed on a medical device, and the method comprising:

detecting a first state of the runtime environment on the medical device;

accessing a first database to retrieve at least one candidate improvement result, the at least one candidate improvement result corresponding to the first state;

performing test-wise execution on a test infrastructure based on the at least one candidate improvement result, the test infrastructure having the first state;

measuring improvement parameters of the test-wise execution; and

obtaining the improvement result by adding a set of candidate improvement results among the at least one candidate improvement result to the improvement result, the set of candidate improvement results including one or more among the at least one candidate improvement results for which the improvement parameters meet a criterion.

2. The method of claim 1 , further comprising:

generating a set of computer instructions to be executed in the runtime environment based on the improvement result.

3. The method of claim 2 , wherein generating a set of computer instructions generates the set of computer instructions in response to a confirmation signal issued by at least one of the medical device or a central improvement node.

4. The method of claim 2 , wherein

the at least one candidate improvement result includes a plurality of candidates improvement results;

the performing performs the test-wise execution on a respective candidate improvement result among the plurality of candidate improvement results;

the measuring measures corresponding improvement parameters of the test-wise execution on the respective candidate improvement result;

the obtaining includes adding the respective candidate improvement result to the improvement result in response to determining the corresponding improvement parameters meet the criterion; and

the method further comprises iteratively executing the performing, the measuring and the obtaining for each of the plurality of candidate improvement results to obtain the improvement result.

5. The method of claim 2 , wherein

the at least one candidate improvement result includes a plurality of candidates improvement results;

the set of candidate improvement results includes two or more candidate improvement results among the plurality of candidate improvement results; and

the method further comprises prioritizing the two or more candidate improvement results based on the improvement parameters to obtain a prioritized improvement result.

6. The method of claim 2 , wherein the accessing a first database comprises executing a correlation algorithm for calculating correlations of usage patterns on at least one of other devices, other runtime environments or other user contexts.

7. The method of claim 6 , wherein the correlation algorithm is based on at least one of a similarity concept or a recommendation concept.

8. The method of claim 1 , further comprising:

determining at least one target object based on the first state, the at least one target object including at least one improvement target for the medical device, and the at least one improvement target corresponding to the first state,

wherein the at least one candidate improvement result retrieved from the first database corresponds to the at least one improvement target.

9. The method of claim 8 , wherein

the at least one candidate improvement result includes a plurality of candidates improvement results;

the performing performs the test-wise execution on a respective candidate improvement result among the plurality of candidate improvement results;

the measuring measures corresponding improvement parameters of the test-wise execution on the respective candidate improvement result;

the obtaining includes adding the respective candidate improvement result to the improvement result in response to determining the corresponding improvement parameters meet the criterion; and

the method further comprises iteratively executing the performing, the measuring and the obtaining for each of the plurality of candidates improvement results to obtain the improvement result.

10. The method of claim 8 , wherein

the at least one candidate improvement result includes a plurality of candidates improvement results;

the set of candidate improvement results includes two or more candidate improvement results among the plurality of candidate improvement results; and

the method further comprises prioritizing the two or more candidate improvement results based on the improvement parameters to obtain a prioritized improvement result.

11. The method of claim 8 , wherein the accessing a first database comprises executing a correlation algorithm for calculating correlations of usage patterns on at least one of other devices, other runtime environments or other user contexts.

12. The method of claim 11 , wherein the correlation algorithm is based on at least one of a similarity concept or a recommendation concept.

13. The method of claim 1 , wherein

the at least one candidate improvement result includes a plurality of candidates improvement results;

the performing performs the test-wise execution on a respective candidate improvement result among the plurality of candidate improvement results;

the measuring measures corresponding improvement parameters of the test-wise execution on the respective candidate improvement result;

the obtaining includes adding the respective candidate improvement result to the improvement result in response to determining the corresponding improvement parameters meet the criterion; and

the method further comprises iteratively executing the performing, the measuring and the obtaining for each of the plurality of candidate improvement results to obtain the improvement result.

14. The method of claim 1 , wherein

the at least one candidate improvement result includes a plurality of candidates improvement results;

the set of candidate improvement results includes two or more candidate improvement results among the plurality of candidate improvement results; and

the method further comprises prioritizing the two or more candidate improvement results based on the improvement parameters to obtain a prioritized improvement result.

15. The method of claim 1 , wherein the improvement result comprises at least one of:

a new-install instruction to newly install new application on the medical device,

a configuration instruction to apply a new configuration of a first installed application,

a replacement instruction for replacing a second installed application with a replacement application, or

an add-instruction for provisioning of additional data sources, the provisioning of the additional data sources including activation of corresponding interfaces and new functions in a third installed application.

16. The method of claim 1 , wherein the measuring dynamically measures the improvement parameters based on data of the medical device.

17. The method of claim 1 , wherein the first state includes parameters indicating: installed applications, a number of logged-on users, used formats, access rights, roles, user context data, physical parameters of the medical device and logical parameters of the medical device.

18. The method of claim 1 , further comprising:

providing an installation package based on the improvement result, the installation package being configured to be installed on the medical device with the runtime environment.

19. The method of claim 1 , further comprising:

storing the improvement result a second database.

20. The method of claim 1 , wherein the accessing a first database comprises executing a correlation algorithm for calculating correlations of usage patterns on at least one of other devices, other runtime environments or other user contexts.

21. The method of claim 20 , wherein the correlation algorithm is based on at least one of a similarity concept or a recommendation concept.

22. An improvement node for computing an improvement result for a runtime environment of at least one application, the at least one application being installed on a medical device, and the improvement node comprising:

a state interface configured to detect a first state of the runtime environment on the medical device;

a database interface configured to access at least one candidate improvement result from a database, the database storing a correlations between states, improvement targets and candidate improvement results, and the at least one candidate improvement result corresponding to the first state;

processing circuitry configured to

implement a respective test infrastructure for each of a plurality of different devices, the respective test infrastructure representing a detected state of a runtime environment of a corresponding device among the plurality of different devices, and the respective test infrastructure being configured to perform test-wise execution of the at least one candidate improvement result, and

measure improvement parameters during the test-wise execution on the respective test infrastructure to obtain an improvement result, the improvement result including one or more candidate improvement results among the at least one candidate improvement result for which the improvement parameters meet a criterion; and

an output interface configured to provide the improvement result.

23. A system for computing an improvement result for a medical device within a network of medical devices, the system comprising:

the improvement node of claim 22 ;

a plurality of devices, each of the plurality of devices including a respective runtime environment for execution of a plurality of applications; and

a database.

24. A non-transitory computer readable medium storing a computer program that, when executed on an electronic device, causes the electronic device to execute the method of claim 1 .

25. A non-transitory computer readable medium storing a computer program that, when executed on an electronic device, causes the electronic device to execute the method of claim 2 .

26. A non-transitory computer readable medium storing a computer program that, when executed on an electronic device, causes the electronic device to execute the method of claim 8 .

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 20, 2023
From: SIEMENS HEALTHCARE GMBH
To: SIEMENS HEALTHINEERS AG
Reel/Frame 066267/0346 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 20, 2019
From: DOMINICK, LUTZ; UKIS, VLADYSLAV
To: SIEMENS HEALTHCARE GMBH
Reel/Frame 050102/0086 →
Priority Claims (1)
EP 18171239 · May 8, 2018 · regional
Continuity (1)
Related Publication 20190347186A1 · Nov 14, 2019
Cited By (1)
US 12,423,166