IP Library Granted Patent US 12,580,082
Granted Patent B2
US 12,580,082 · App. 18/017,539 · Granted Mar 17, 2026

Pain management system

Inventors: Mats Göran Barkfors (Lund, SE); Maria Linnea Elisabeth Rosén Klement (Lund, SE); Carl Arne Krister Borrebaeck (Lund, SE)
Assignee: PAINDRAINER AB
G16H50/30
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Quick Facts
Patent No.
US 12,580,082
App. No.
18/017,539
Granted
Mar 17, 2026
Kind
B2
Abstract

A pain-management system ( 300 ) configured to: receive a target-pain-score ( 314 ) that represents a level of pain that the user considers acceptable during a defined period of time: receive one or more target-input-parameters ( 304 ) which represent properties and/or activities of a user during the same defined period of time, wherein the one or more target-input-parameters ( 314 ) are a subset of a full list of input-parameters that are available: receive one or more settings ( 315 ) that represent one or more input-parameters that are to be increased or decreased: use a neural network that has been trained for the individual user to determine one or more calculated-user-parameters ( 316 ) based on the target-pain-score ( 314 ) and the target-input-parameters ( 304 ), wherein at least one of the calculated-user-parameters ( 316 ) is set based on the settings ( 315 ); and present the one or more calculated-user-parameters ( 316 ) using a user interface.

Claims (83)

1 . A pain-management system configured to:

receive a target-pain-score that represents a level of pain that the user considers acceptable during a defined period of time;

receive one or more target-input-parameters which represent properties and/or activities of a user during the same defined period of time, wherein the one or more target-input-parameters are a subset of a full list of input-parameters that are available;

receive one or more settings that represent one or more input-parameters that are to be increased or decreased, and wherein the one or more input-parameters represent one or more activity performed by the user;

compare the received target-pain-score and/or one or more of the target-input-parameters to historical pain-parameter-log-entries, each pain-parameter-log-entry representing user-input-parameters and corresponding user-input-pain-score, to locate a matched-pain-parameter-log-entry;

modify the matched-pain-parameter-log-entry based on the one or more received settings in order to generate a modified-pain-parameters-log-entry;

use a neural network that has been trained for the individual user to determine one or more calculated-user-parameters based on the target-pain-score, the target-input-parameters, and/or the modified-pain-parameters-log-entry, wherein at least one of the calculated-user-parameters is set based on the settings, and wherein the calculated-user-parameters include a calculated-pain-score and/or suggestions for values for one or more of the settings that are expected to achieve the target-pain-score; and

present, using a user interface: (i) one or more user-parameters of the modified-pain-parameters-log-entry as the calculated-user-parameters, and (ii) the calculated-pain-score.

2 . The system of claim 1 , wherein the system is configured to modify the matched-pain-parameters-log-entry to generate the modified-pain-parameters-log-entry by increasing or decreasing one or more user-input-parameters of the matched-pain-parameters-log-entry.

3 . The system of claim 1 , wherein the system is configured to:

inspect a set of historic pain-parameter-log-entries associated with the user to identify, as a matched-pain-parameters-log-entry, a pain-parameters-log-entry that is a match with the received target-pain-score and the target-input-parameters, wherein each pain-parameter-log-entry represents a plurality of user-input-parameters and a user-input pain-score;

modify the matched-pain-parameters-log-entry based on the settings in order to generate a modified-pain-parameters-log-entry;

apply the neural network to the modified-pain-parameters-log-entry to determine a calculated-pain-score; and

compare the calculated-pain-score to the target-pain-score, and:

if the calculated-pain-score is greater than the target-pain-score, then:

adjust one or more of the input-parameters of the modified-pain-parameters-log-entry;

apply the neural network to the adjusted modified-pain-parameters-log-entry to determine an adjusted calculated-pain-score; and

repeat the compare step one or more times for the adjusted calculated-pain-score; and

if the calculated-pain-score is less than or equal to the target-pain-score, then present using the user interface: (i) one or more user-parameters of the modified-pain-parameters-log-entry as the calculated-user-parameters, and (ii) the calculated-pain-score.

4 . The system of claim 3 , wherein the system is configured to repeat the compare step a plurality of times up to a predetermined maximum number.

5 . The system of claim 1 , wherein the system is further configured to:

present to the user via the user interface an option for modifying one or more of the calculated-user-parameters;

receive one or more modified calculated-user-parameters from the user interface representative of user input;

apply the neural network to the modified calculated-user-parameters to determine a modified calculated-pain-score; and

present the modified calculated-pain-score using the user interface.

6 . The system of claim 1 , further comprising:

a user interface configured to receive:

a plurality of user-input-parameters, which represent properties and/or activities of a user during a defined period of time; and

a user-input pain-score, which represents a degree of pain experienced by the user during the same defined period of time; and

an AI processor configured to:

set a plurality of weighting-values of the neural network based on the plurality of user-input-parameters and the user-input pain-score for the user.

7 . The system of claim 6 , wherein the system is configured to store the plurality of weighting-values associated with a user-identifier, wherein the user-identifier is uniquely associated with an individual user's profile.

8 . The system of claim 6 , wherein the system is further configured to:

modify the functionality of the user interface over time such that the user is presented with additional mechanisms for providing the user-input-parameters.

9 . The system of claim 6 , wherein the user-input-parameters and the target-input-parameters include one or more of the following characteristics:

(i) a duration-characteristic, which represents the duration that the user performed the activity/or exhibited the property;

(ii) an intensity-characteristic, which represents the intensity with which the user performed the activity;

(iii) a satisfaction-characteristic, which represents the degree of satisfaction that the user experienced when performing the activity; and

(iv) a type-characteristic.

10 . The system of claim 6 , wherein the AI processor is configured to store pain-parameters-log-entries in memory, wherein each pain-parameters-log-entry comprises:

the plurality of user-input-parameters;

the user-input-pain-score; and

a date-identifier associated with the corresponding user-input-parameters and user-input pain-score.

11 . The system of claim 10 , wherein each pain-parameters-log-entry further comprises:

a user-identifier that is uniquely associated with an individual user for which the plurality of user-input-parameters and the user-input-pain-score relates.

12 . The system of claim 10 , wherein the system is configured to determine a pain trigger by:

processing a plurality of pain-parameter-log-entries to identify pain-parameter log-entries that have a user-input pain-score that is increasing as high-pain-parameter-log-entries, wherein each pain-parameter-log-entry comprises one or more user-input-parameters and a user-input pain-score;

perform an analysis of the user-input-parameters of the high-pain parameter-log-entries to determine a correlation-score that represents the degree of correlation between values of corresponding user-input-parameters in the high-pain-parameter-log-entries; and

identify one or more user-parameters as a pain trigger if they have a correlation-score that satisfies a correlation-criterion.

13 . The system of claim 10 , wherein the system is configured to determine a pain protector by:

processing a plurality of pain-parameter-log-entries to identify pain-parameter log-entries that have a user-input pain-score that is decreasing as low-pain-parameter-log-entries, wherein each pain-parameter-log-entry comprises one or more user-input-parameters and a user-input pain-score;

perform an analysis of the user-input-parameters of the low-pain-parameter-log-entries to determine a correlation-score that represents the degree of correlation between values of corresponding user-input-parameters in the low-pain parameter-log-entries; and

identify one or more user-parameters as a pain protector if they have a correlation-score that satisfies a correlation-criterion.

14 . The system of claim 6 , wherein:

the plurality of user-input-parameters comprises one or more sensed-input parameters, wherein the sensed-input-parameters are provided directly or indirectly from a sensor.

15 . The system of claim 6 , wherein the user-input-parameters comprise one or more of:

a sleep-parameter;

a work-parameter;

a physical-activity-parameter; a housework-parameter;

a leisure-parameter;

a rest-parameter; and

a pain-range-parameter;

a heart-rate-parameter;

a blood-pressure-parameter;

a temperature-parameter

an energy level/tiredness fatigue parameter; and/or

a stress level parameter.

16 . A computer-implemented method comprising:

receiving a target-pain-score that represents a level of pain that the user considers acceptable during a defined period of time;

receiving one or more target-input-parameters which represent properties and/or activities of a user during the same defined period of time, wherein the one or more target-input-parameters are a subset of a full list of input-parameters that are available;

receiving one or more settings that represent one or more input-parameters that the user is looking to increase or decrease, and wherein the one or more input-parameters represent one or more activity performed by the user;

comparing the received target-pain-score and/or one or more of the target-input-parameters to historical pain-parameter-log-entries, each pain-parameter-log-entry representing user-input-parameters and corresponding user-input-pain-score, to locate a matched-pain-parameter-log-entry;

modifying the matched-pain-parameter-log-entry based on the one or more received settings in order to generate a modified-pain-parameters-log-entry;

using a neural network that has been trained for the individual user to determine one or more calculated-user-parameters based on the target-pain-score, and the target-input-parameters, and/or the modified-pain-parameters-log-entry, wherein at least one of the calculated-user-parameters is set based on the settings, and wherein the calculated-user-parameters include a calculated-pain-score and/or suggestions for values for one or more of the settings that are expected to achieve the target-pain-score; and

presenting (i) one or more user-parameters of the modified-pain-parameters-log-entry as the calculated-user-parameters, and (ii) the calculated-pain-score.

17 . A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to:

receive a target-pain-score that represents a level of pain that the user considers acceptable during a defined period of time;

receive one or more target-input-parameters which represent properties and/or activities of a user during the same defined period of time, wherein the one or more target-input-parameters are a subset of a full list of input-parameters that are available;

receive one or more settings that represent one or more input-parameters that the user is looking to increase or decrease, and wherein the one or more input-parameters represent one or more activity performed by the user;

comparing the received target-pain-score and/or one or more of the target-input-parameters to historical pain-parameter-log-entries, each pain-parameter-log-entry representing user-input-parameters and corresponding user-input-pain-score, to locate a matched-pain-parameter-log-entry;

modifying the matched-pain-parameter-log-entry based on the one or more received settings in order to generate a modified-pain-parameters-log-entry;

use a neural network that has been trained for the individual user to determine one or more calculated-user-parameters based on the target-pain-score, the target-input-parameters, and/or the modified-pain-parameters-log-entry, wherein at least one of the calculated-user-parameters is set based on the settings, and wherein the calculated-user-parameters include a calculated-pain-score and/or suggestions for values for one or more of the settings that are expected to achieve the target-pain-score; and

present (i) one or more user-parameters of the modified-pain-parameters-log-entry as the calculated-user-parameters, and (ii) the calculated-pain-score.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 17, 2023
From: BARKFORS, MATS GÖRAN; ROSÉN LINNEA KLEMENT, MARIA ELISABETH; BORREBAECK, CARLE ARNE KRISTER
To: PAINDRAINER AB
Reel/Frame 063344/0225 →
Priority Claims (1)
GB 2011500 · Jul 24, 2020 · national
Continuity (1)
Related Publication 20230268075A1 · Aug 24, 2023
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