Method and System of Identifying Environment Features for Use in Analyzing Asset Operation

Patent metadata

Field	Value
Publication number	US-2018211204-A1
Application number	US-201715416996-A
Country	US
Kind code	A1
Filing date	Jan 26, 2017
Priority date	Jan 26, 2017
Publication date	Jul 26, 2018
Grant date	—

Abstract

Based on an analysis of asset attribute data associated with a plurality of assets, a platform may detect a locality that is a possible instance of a given type of environment, such as a mine or construction site. In response, the platform may obtain image data associated with the detected locality and input that image data into a model that outputs likelihood data indicating a likelihood that any portion of the detected locality comprises a given feature of the given type of environment (e.g., a boundary, navigation route, hazard, etc.), where this model is defined based on training data. Based on the likelihood data, the platform may then generate output data indicating a location of any portion of the detected locality that is likely to comprise the given feature. In turn, the platform may use the output data to simulate asset operation in the detected locality.

First claim

What is claimed is:

1 . A method comprising:
receiving asset attribute data associated with a plurality of assets; based on an analysis of the received asset attribute data, detecting a locality that is a possible instance of a given type of environment; in response to the detecting, obtaining image data associated with the detected locality; inputting the obtained image data into a model that outputs likelihood data indicating a likelihood that any portion of the detected locality comprises a given feature of the given type of environment, wherein the model is defined based on training data that includes respective image data for each of a plurality of known instances of the given type of environment; based on the likelihood data, generating output data indicating a location of any portion of the detected locality that is likely to comprise the given feature; and using the output data to simulate asset operation in the detected locality.

2 . The method of claim 1 , wherein the given type of environment is one of a mining site, a construction site, a manufacturing plant, or a distribution center.

3 . The method of claim 1 , wherein the given feature is a one of a boundary, a navigation route, a hazard, an asset, or a structure.

4 . The method of claim 1 , wherein the training data further includes, for the respective image data for each of the plurality of known instances of the given type of environment, label data indicating which one or more portions of the image data comprise the given feature.

5 . The method of claim 4 , wherein the respective image data for each of the plurality of known instances of the given type of environment comprises an array of pixels, and wherein the label data comprises a plurality of labels that each correspond to a respective subset of the array of pixels.

6 . The method of claim 5 , wherein the model embodies a relationship between characteristics of pixels and a likelihood that pixels represent the given feature.

7 . The method of claim 1 , wherein the training data further includes asset attribute data associated with assets located in the known instances of the given type of environment.

8 . The method of claim 1 , wherein the received asset attribute data associated with the plurality of assets comprises location data for the plurality of assets.

9 . The method of claim 1 , wherein the obtained image data for the detected locality comprises an array of pixels, and wherein the likelihood data comprises a set of likelihood values that each correspond to a subset of the array of pixels.

10 . The method of claim 1 , further comprising:
transmitting the output data to a computing device to facilitate causing the computing device to display a visualization of the output data.

11 . The method of claim 1 , wherein the image data for the detected locality comprises first image data, the likelihood data for the detected locality comprises first likelihood data, and the output data for the detected locality comprises first output data, the method further comprising:
obtaining second image data associated with the detected locality, wherein the second image data is captured at a different time than first image data; inputting the obtained second image data into a model that outputs second likelihood data indicating a likelihood that any portion of the detected locality comprises the given feature of the given type of environment, wherein the model that outputs second likelihood data is defined based on respective training data that includes respective image data for each of the plurality of known instances of the given type of environment; based on the second likelihood data, generating the second output data indicating a location of any portion of the detected locality that is likely to comprise the given feature; comparing the second output data with the first output data; and based on the comparison, identifying a change in at least one instance of the given feature in the detected locality.

12 . A computing system comprising:
a network interface configured to facilitate communications over a communication network with one or more data sources; at least one processor; a non-transitory computer-readable medium; and program instructions stored on the non-transitory computer-readable medium that are executable by the at least one processor to cause the computing system to:
receive, via the network interface, asset attribute data associated with a plurality of assets;
based on an analysis of the received asset attribute data, detect a locality that is a possible instance of a given type of environment;
in response to the detecting, obtain, via the network interface, image data associated with the detected locality;
input the obtained image data into a model that outputs likelihood data indicating a likelihood that any portion of the detected locality comprises a given feature of the given type of environment, wherein the model is defined based on training data that includes respective image data for each of a plurality of known instances of the given type of environment;
based on the likelihood data, generate output data indicating a location of any portion of the detected locality that is likely to comprise the given feature; and
use the output data to simulate asset operation in the detected locality.

13 . The computing system claim 12 , wherein the given type of environment is one of a mining site, a construction site, a manufacturing plant, or a distribution center.

14 . The computing system of claim 12 , wherein the given feature is a one of a boundary, a navigation route, a hazard, an asset, or a structure.

15 . The computing system of claim 12 , wherein the training data further includes, for the respective image data for each of the plurality of known instances of the given type of environment, label data indicating which one or more portions of the image data comprise the given feature.

16 . The computing system of claim 12 , wherein the model embodies a relationship between characteristics of pixels and a likelihood that pixels represent the given feature.

17 . The computing system of claim 12 , wherein the training data further includes asset attribute data associated with assets located in the known instances of the given type of environment.

18 . The computing system of claim 12 , wherein the obtained image data for the detected locality comprises an array of pixels, and wherein the likelihood data comprises a set of likelihood values that each correspond to a subset of the array of pixels.

19 . The computing system of claim 12 , further comprising:
transmitting the output data to a computing device to facilitate causing the computing device to display a visualization of the output data.

20 . A non-transitory computer-readable medium having program instructions stored thereon that are executable to cause a computing device to:
receive asset attribute data associated with a plurality of assets; based on an analysis of the received asset attribute data, detect a locality that is a possible instance of a given type of environment; in response to the detecting, obtain image data associated with the detected locality; input the obtained image data into a model that outputs likelihood data indicating a likelihood that any portion of the detected locality comprises a given feature of the given type of environment, wherein the model is defined based on training data that includes respective image data for each of a pl

Assignees

• Uptake Tech Inc

Inventors

• Bruns Tyler Eilert
• Keating Brian Robert
• Mcelhinney Adam

Classifications

• G06Q10/087 Primary

  Inventory or stock management, e.g. order filling, procurement or balancing against orders · CPC title

• G06F18/28

  Determining representative reference patterns, e.g. by averaging or distorting; Generating dictionaries · CPC title

• G06K9/4604

  Physics · mapped topic

• G06K9/6256

  Physics · mapped topic

• G06K9/6202

  Physics · mapped topic