IP Library › Granted Patent US 12695704

Granted Patent B2

US 12695704 · App. 18/581,926 · Granted Jul 28, 2026

Selecting periods of time for payload transmission based on detected attributes

Inventors: Deepak Cherian (Newark, CA); Vijayaraghavan Doraiswami (Santa Clara, CA); Senthil Arunachalam (Fremont, CA)

Assignee: Ordr Inc.

H04L47/24 H04L41/5022

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Quick Facts

Patent No.: US 12695704
App. No.: 18/581,926
Granted: Jul 28, 2026
Kind: B2

External Links

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Abstract

Techniques for determining whether a device communicating in a network should transmit a payload, e.g., priority dataset, to a server based on the device type, analysis of the current attributes of the device, and the location of the device are disclosed. When the current attributes of the device, in combination with the device location, are determined to meet criticality criteria, instructions are provided to the device to refrain from transmitting the payload. When the current attributes of the device, in combination with the device location, are determined to not meet criticality criteria, instructions are provided to the device to permit transmission of the payload by the device.

Claims (76)

1 . One or more non-transitory computer readable media comprising instructions which, when executed by one or more hardware processors, causes performance of operations comprising:

obtaining, at a first time, a first set of current attributes corresponding to a device;

training a machine learning model using training datasets to predict criticality levels for operations being executed by the device, each training dataset comprising a particular set of current attributes and a corresponding criticality level;

based on the first set of current attributes, determining that the device is currently executing an operation that meets one or more criticality criteria, wherein determining that the device is currently executing the operation that meets one or more criticality criteria comprises:

applying the machine learning model to first set of current attributes to determine a criticality level of the operation being executed by the device; and

comparing the criticality level to the one or more criticality criteria; and

responsive to determining that the device is currently executing the operation that meets the one or more criticality criteria, causing the device to refrain from transmitting a particular dataset generated by the device.

2 . The one or more non-transitory computer readable media of claim 1 , wherein determining that the device is currently executing the operation that meets one or more criticality criteria comprises:

detecting data being transmitted by or to the device on a device port; and

determining that the device port is mapped to a criticality category that meets one or more criticality criteria.

3 . The one or more non-transitory computer readable media of claim 1 , wherein determining that the device is currently executing the operation that meets one or more criticality criteria comprises:

detecting a current location of the device; and

determining that the current location is mapped to a criticality category that meets one or more criticality criteria.

4 . The one or more non-transitory computer readable media of claim 1 , wherein determining that the device is currently executing the operation that meets one or more criticality criteria comprises:

detecting data transmitted to or from the device; and

analyzing the data to determine that the data is mapped to a criticality category that meets one or more criticality criteria.

5 . The one or more non-transitory computer readable media of claim 1 , wherein determining that the device is currently executing the operation that meets one or more criticality criteria comprises:

detecting a transmission protocol corresponding to data transmitted to or from the device; and

analyzing the transmission protocol to determine that the transmission protocol is mapped to a criticality category that meets one or more criticality criteria.

6 . The one or more non-transitory computer readable media of claim 1 , wherein the operations further comprise:

determining the one or more criticality criteria based on a device type corresponding to the device.

7 . The one or more non-transitory computer readable media of claim 6 , wherein the operations further comprise:

determining the device type, wherein determining the device type comprises:

training a second machine learning model using training datasets to predict device types, each training dataset comprising a particular set of device attributes and a corresponding particular device type;

applying the second machine learning model to one or more attributes of the device to determine the device type;

receiving feedback on the device type determined for the device; and

retraining the second machine learning model based on the feedback.

8 . The one or more non-transitory computer readable media of claim 1 , wherein the operations further comprise:

obtaining, at a second time, a second set of current attributes corresponding to the device;

based on the second set of current attributes, determining that the device is not currently executing any operation that meets the one or more criticality criteria; and

responsive to determining that the device is not currently executing any operation that meets the one or more criticality criteria, causing the device to transmit the particular dataset generated by the device.

9 . The one or more non-transitory computer readable media of claim 1 , wherein the operations further comprise:

prior to obtaining the first set of current attributes, obtaining, from the device, a request for instructions regarding whether the device is permitted to transmit the particular dataset.

10 . A method comprising:

obtaining, at a first time, a first set of current attributes corresponding to a device;

applying the machine learning model to first set of current attributes to determine a criticality level of the operation being executed by the device; and

comparing the criticality level to the one or more criticality criteria; and

wherein the method is performed by at least one device including a hardware processor.

11 . A system comprising:

at least one device including a hardware processor; and

the system being configured to perform operations comprising:

obtaining, at a first time, a first set of current attributes corresponding to a device;

based on the first set of current attributes, determining that the device is currently executing an operation for a patient that meets one or more criticality criteria, wherein determining that the device is currently executing the operation that meets one or more criticality criteria comprises:

applying the machine learning model to first set of current attributes to determine a criticality level of the operation being executed by the device, and

comparing the criticality level to the one or more criticality criteria; and

12 . The method of claim 10 , wherein determining that the device is currently executing the operation that meets one or more criticality criteria comprises:

detecting data being transmitted by or to the device on a device port; and

determining that the device port is mapped to a criticality category that meets one or more criticality criteria.

13 . The method of claim 10 , wherein determining that the device is currently executing the operation that meets one or more criticality criteria comprises:

detecting a current location of the device; and

determining that the current location is mapped to a criticality category that meets one or more criticality criteria.

14 . The method of claim 10 , wherein determining that the device is currently executing the operation that meets one or more criticality criteria comprises:

detecting data transmitted to or from the device; and

analyzing the data to determine that the data is mapped to a criticality category that meets one or more criticality criteria.

15 . The method of claim 10 , wherein determining that the device is currently executing the operation that meets one or more criticality criteria comprises:

detecting a transmission protocol corresponding to data transmitted to or from the device; and

analyzing the transmission protocol to determine that the transmission protocol is mapped to a criticality category that meets one or more criticality criteria.

16 . The method of claim 10 , further comprising:

determining the one or more criticality criteria based on a device type corresponding to the device.

17 . The method of claim 16 , further comprising:

determining the device type, wherein determining the device type comprises:

applying the second machine learning model to one or more attributes of the device to determine the device type;

receiving feedback on the device type determined for the device; and

retraining the second machine learning model based on the feedback.

18 . The method of claim 10 , further comprising:

obtaining, at a second time, a second set of current attributes corresponding to the device;

based on the second set of current attributes, determining that the device is not currently executing any operation that meets the one or more criticality criteria; and

19 . The method of claim 10 , further comprising:

prior to obtaining the first set of current attributes, obtaining, from the device, a request for instructions regarding whether the device is permitted to transmit the particular dataset.