Incident category selection optimization

Patent metadata

Field	Value
Publication number	US-12238347-B2
Application number	US-202318240289-A
Country	US
Kind code	B2
Filing date	Aug 30, 2023
Priority date	Jan 7, 2022
Publication date	Feb 25, 2025
Grant date	Feb 25, 2025

Abstract

This disclosure describes techniques that enable a categorization controller to detect activation of a portable recording device that is configured to capture a real-time multimedia stream of the current event. The categorization controller may further identify a set of categories that are likely associated with the real-time multimedia stream, determine an ordered ranking of the set of categories, and generate a ranked category dataset for delivery to the portable recording device. In doing so, the portable recording device may present the ordered ranking of the set of categories at a user interface.

First claim

What is claimed:

1. A system, comprising:
one or more processors;
memory coupled to the one or more processors, the memory including one or more modules that are executable by the one or more processors to:
identify a set of categories that are likely associated with a real-time multimedia stream;
generate a data model to infer an ordered ranking of individual incident categories, based at least in part on historical event data;
determine, by the data model, the ordered ranking of the individual incident categories of the set of categories;
generate incident category data to present the ordered ranking of the individual incident categories via a portable recording device; and
send the incident category data to the portable recording device.

2. The system of claim 1 , wherein the one or more modules are further executable by the one or more processors to:
retrieve, from a network operations controller, a computer-aided dispatch (CAD) identifier associated with a current event,
wherein to identify the set of categories is based at least in part on the CAD identifier.

3. The system of claim 1 , wherein the one or more modules are further executable by the one or more processors to:
retrieve, from a third-party server, environmental data associated with a current event, and
wherein, to identify the set of categories is based at least in part on the environmental data.

4. The system of claim 1 , wherein the one or more modules are further executable by the one or more processors to:
determine a geolocation of the portable recording device; and
retrieve, from a third-party server, environmental data associated with the geolocation,
wherein to identify the set of categories is based at least in part on the environmental data.

5. The system of claim 1 , wherein the one or more modules are further executable by the one or more processors to:
receive, from the portable recording device, sensor data associated with a surrounding environment proximate to the portable recording device,
wherein to identify the set of categories is based at least in part on the sensor data.

6. The system of claim 1 , wherein the one or more modules are further executable by the one or more processors to:
receive, from the portable recording device, audio data that describes a current event,
wherein to identify the set of categories is based at least in part on analysis of the audio data.

7. The system of claim 1 , wherein the one or more modules are further executable by the one or more processors to:
receive input data that is associated with a current event, the input data including at least one of a CAD identifier, environmental data, or sensor data associated with the portable recording device,
wherein to determine, by the data model, the ordered ranking of the individual incident categories is based at least in part on an analysis of the input data using the data model.

8. The system of claim 7 , wherein the one or more modules are further executable by the one or more processors to:
generate an accuracy score for the individual incident categories of the set of categories, based at least in part on the analysis of the input data, the accuracy score to indicate a likelihood that the individual incident categories are associated with the current event,
wherein to determine, by the data model, the ordered ranking of the individual incident categories is further based at least in part on the accuracy score of the individual incident categories.

9. The system of claim 7 , wherein the incident category data further includes computer-executable instructions that cause the ordered ranking of the individual incident categories to be presented for selection via a category selector of the portable recording device, wherein a first selection of the category selector corresponds to a superior ranked individual incident category of the ordered ranking of the individual incident categories.

10. One or more non-transitory computer-readable media collectively storing computer-executable instructions that, when executed with one or more processors, collectively cause computers to perform acts comprising:
retrieving, from a network operations center, a computer-aided dispatch (CAD) identifier;
identifying a set of categories that are likely associated with a real-time multimedia stream, based at least in part on the CAD identifier;
generating a data model to determine an ordered ranking of individual incident categories, based at least in part on historical event data;
determining, by the data model, the ordered ranking of the individual incident categories of the set of categories;
generating incident category data to present the ordered ranking of the individual incident categories via a portable recording device; and
send the incident category data to the portable recording device.

11. The one or more non-transitory computer-readable media of claim 10 , wherein the acts further comprise:
retrieving, from the portable recording device, sensor data associated with a surrounding environment proximate to the portable recording device,
wherein determining, by the data model, the ordered ranking of the individual incident categories is based at least in part on the sensor data.

12. The one or more non-transitory computer-readable media of claim 11 , wherein the sensor data comprises at least one of a geolocation or the real-time multimedia stream.

13. The one or more non-transitory computer-readable media of claim 10 , wherein the acts further comprise:
retrieving from a third-party server, environmental data associated with a geolocation of the portable recording device,
wherein determining, by the data model, the ordered ranking of the individual incident categories is based at least in part on the environmental data.

14. The one or more non-transitory computer-readable media of claim 10 , wherein the acts further comprise:
receiving input data associated with a current event, the input data corresponding to one of the CAD identifier, environmental data, or sensor data associated with the portable recording device,
wherein determining, by the data model, the ordered ranking of the individual incident categories is based at least in part on an analysis of the input data using the data model.

15. The one or more non-transitory computer-readable media of claim 14 , wherein the acts further comprise:
generating an accuracy score for the individual incident categories of the set of categories, based at least in part on the analysis of the input data using the data model,
wherein determining, by the data model, the ordered ranking of the individual incident categories is further based at least in part on the accuracy score of the individual incident categories.

16. A portable recording device, comprising:
a user interface;
one or more sensors;
one or more processors; and
memory coupled to the one or more processors, the memory including one or more modules that are executable by the one or more processors to:
capture, via the one or more sensors, sensor data associated with a surrounding environment proximate to the portable recording device;
transmit the sensor data to a categorization controller;
generate a data model to infer an ordered ranking of individual incident categories, based at least in part on historical event data;
determine, by the data model, the ordered ranking of the individual incident categories;
receive, from the categorization controller, incident category data, based at least in part on the sensor data, the incident category data including computer-execut

Assignees

• Getac Technology Corp
• Whp Workflow Solutions Inc

Inventors

• Adeel Muhammad
• Guzik Thomas

Classifications

• G06F16/45

  Clustering; Classification · CPC title

• G06F16/38

  Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually · CPC title

• H04N21/251

  Learning process for intelligent management, e.g. learning user preferences for recommending movies (details of learning user preferences for the retrieval of video data in a video database G06F16/739; computer systems using learning methods G06N3/08) · CPC title

• H04N21/44

  Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs · CPC title

• H04N21/422

  Input-only peripherals {, i.e. input devices connected to specially adapted client devices}, e.g. global positioning system [GPS] {(input devices also receiving signals from specially adapted client devices H04N21/4104)} · CPC title