IP Library › Granted Patent US 12,222,689
Granted Patent B2
US 12,222,689 · App. 18/204,631 · Granted Feb 11, 2025

Building system with string mapping based on a sequence to sequence neural network

Inventors: Surajit Borah (Bangalore, IN); Santle Camilus (Sunnyvale, CA); ZhongYi Jin (Santa Clara, CA); Vish Ramamurti (San Leandro, CA); Young M. Lee (Old Westbury, NY)
Assignee: Tyco Fire & Security GmbH
G05B13/027G05B13/04G06F40/30G06N3/049
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Quick Facts
Patent No.
US 12,222,689
App. No.
18/204,631
Granted
Feb 11, 2025
Kind
B2
Abstract

A building system including one or more memory devices configured to store instructions that, when executed by one or more processors, cause the one or more processors to receive training data including acronym strings and tag strings, train a sequence to sequence neural network based on the training data, receive an acronym string for labeling, the acronym string comprising a particular plurality of acronyms, and generate a tag string for the acronym string with the sequence to sequence neural network, wherein the sequence to sequence neural network outputs a tag of the tag string for one acronym of the particular plurality of acronyms based on the one acronym and contextual information of the acronym string, wherein the contextual information includes other acronyms of the particular plurality of acronyms.

Claims (68)

1. A building system comprising one or more memory devices configured to store instructions thereon that, when executed by one or more processors, cause the one or more processors to:

receive training data comprising a plurality of tag strings, wherein a tag string of the plurality of tag strings is a translation of an acronym string of a plurality of acronym strings, and wherein the plurality of acronym strings include a plurality of acronyms that represent a plurality of entities of a building;

train a sequence to sequence neural network based on the training data;

receive an acronym string comprising a particular plurality of acronyms; and

output, using the sequence to sequence neural network, a tag of a tag string for one acronym of the particular plurality of acronyms based on a placement of the one acronym within the acronym string.

2. The building system of claim 1 , wherein the instructions cause the one or more processors to:

receive, from a user via a user device, a selection of one or more first acronym strings of the plurality of acronym strings;

train the sequence to sequence neural network based on the one or more first acronym strings of the plurality of acronym strings; and

determine, responsive to training the sequence to sequence neural network, labels for one or more second acronym strings of the plurality of acronym strings.

3. The building system of claim 1 , wherein the instructions cause the one or more processors to train the sequence to sequence neural network by:

identifying one or more first acronym strings of the plurality of acronym strings that are similar to one or more second acronym strings of the plurality of acronym strings;

selecting, from at least one of the one or more first acronym strings or the one or more second acronym strings, a given acronym string to represent the one or more first acronym strings and the one or more second acronym strings; and

training the sequence to sequence neural network based on the given acronym string.

4. The building system of claim 1 , wherein the instructions cause the one or more processors to:

identify, based on characteristics of the plurality of acronym strings, a given acronym string to represent one or more acronym strings of the plurality of acronym strings;

display, via a display of a user device, the given acronym string; and

receive, from a user via the user device, a translation of the given acronym string.

5. The building system of claim 4 , wherein the sequence to sequence neural network is trained based on the translation of the given acronym string.

6. The building system of claim 1 , wherein the sequence to sequence neural network is a long-short term memory (LSTM) sequence to sequence neural network.

7. The building system of claim 1 , wherein the instructions cause the one or more processors to:

determine a number of strings of the particular plurality of acronym strings;

compare the number of strings to a threshold level; and

select the sequence to sequence neural network to translate the acronym string from a group of available models in response to a determination that the number of strings is greater than the threshold level.

8. The building system of claim 1 , wherein the instructions cause the one or more processors to receive the training data from a database, and wherein the training data is based on data of one or more buildings and the acronym string is associated with the building.

9. The building system of claim 1 , wherein the instructions cause the one or more processors to:

remove at least one of spaces or special characters from the plurality of acronym strings;

apply a segmentation model to the plurality of acronym strings to identify the plurality of acronyms;

generate an acronym vocabulary by removing redundant acronyms from the plurality of acronyms; and

train the sequence to sequence neural network based on the training data and the acronym vocabulary.

10. The building system of claim 1 , wherein the instructions cause the one or more processors to:

receive a set of acronym strings for the building for translation;

select the plurality of acronym strings from the set of acronym strings;

receive the plurality of tag strings from a user device, each tag string of the plurality of tag strings being a translation of respective acronym strings of the plurality of acronym strings;

train the sequence to sequence neural network based on the training data; and

translate remaining acronym strings of the set of acronym strings with the sequence to sequence neural network.

11. The building system of claim 1 , wherein the instructions cause the one or more processors to receive a manual selection of the plurality of acronym strings from a user device.

12. The building system of claim 1 , wherein the instructions cause the one or more processors to receive the plurality of tag strings from a user device via user input provided by a user via the user device, the user input indicating tag translations of particular acronyms of the plurality of acronym strings.

13. The building system of claim 1 , wherein the instructions cause the one or more processors to cause a user device to display the plurality of acronym strings to a user for manual translation.

14. A method comprising:

receiving, by one or more processing circuits, training data comprising a plurality of tag strings, wherein a tag string of the plurality of tag strings is a translation of an acronym string of a plurality of acronym strings, and wherein the plurality of acronym strings include a plurality of acronyms that represent a plurality of entities of a building;

training, by the one or more processing circuits, a sequence to sequence neural network based on the training data;

receiving, by the one or more processing circuits, an acronym string comprising a particular plurality of acronyms; and

outputting, by the one or more processing circuits using the sequence to sequence neural network, a tag of a tag string for one acronym of the particular plurality of acronyms based on a placement of the one acronym within the acronym string.

15. The method of claim 14 , further comprising:

receiving, by the one or more processing circuits, from a user via a user device, a selection of one or more first acronym strings of the plurality of acronym strings;

training, by the one or more processing circuits, the sequence to sequence neural network based on the one or more first acronym strings of the plurality of acronym strings; and

determining, by the one or more processing circuits, responsive to training the sequence to sequence neural network, labels for one or more second acronym strings of the plurality of acronym strings.

16. The method of claim 14 , wherein training the sequence to sequence neural network includes:

identifying, by the one or more processing circuits, one or more first acronym strings of the plurality of acronym strings that are similar to one or more second acronym strings of the plurality of acronym strings;

selecting, by the one or more processing circuits, from at least one of the one or more first acronym strings or the one or more second acronym strings, a given acronym string to represent the one or more first acronym strings and the one or more second acronym strings; and

training, by the one or more processing circuits, the sequence to sequence neural network based on the given acronym string.

17. The method of claim 14 , further comprising:

identifying, by the one or more processing circuits, based on characteristics of the plurality of acronym strings, a given acronym string to represent one or more acronym strings of the plurality of acronym strings;

displaying, by the one or more processing circuits via a display of a user device, the given acronym string; and

receiving, by the one or more processing circuits, from a user via the user device, a translation of the given acronym string.

18. One or more non-transitory storage medium configured to store instructions thereon that, when executed by one or more processors, cause the one or more processors to:

receive training data comprising a plurality of tag strings, wherein a tag string of the plurality of tag strings is a translation of an acronym string of a plurality of acronym strings, and wherein the plurality of acronym strings include a plurality of acronyms that represent a plurality of entities of a building;

train a sequence to sequence neural network based on the training data;

receive an acronym string comprising a particular plurality of acronyms; and

output, using the sequence to sequence neural network, a tag of a tag string for one acronym of the particular plurality of acronyms based on a placement of the one acronym within the acronym string.

19. The one or more non-transitory storage medium of claim 18 , wherein the instructions cause the one or more processors to:

receive, from a user via a user device, a selection of one or more first acronym strings of the plurality of acronym strings;

train the sequence to sequence neural network based on the one or more first acronym strings of the plurality of acronym strings; and

determine, responsive to training the sequence to sequence neural network, labels for one or more second acronym strings of the plurality of acronym strings.

20. The one or more non-transitory storage medium of claim 18 , wherein the instructions cause the one or more processors to train the sequence to sequence neural network by:

identifying one or more first acronym strings of the plurality of acronym strings that are similar to one or more second acronym strings of the plurality of acronym strings;

selecting, from at least one of the one or more first acronym strings or the one or more second acronym strings, a given acronym string to represent the one or more first acronym strings and the one or more second acronym strings; and

training the sequence to sequence neural network based on the given acronym string.

Assignments (3)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 9, 2024
From: JOHNSON CONTROLS TYCO IP HOLDINGS LLP
To: TYCO FIRE & SECURITY GMBH
Reel/Frame 067056/0552 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 22, 2023
From: BORAH, SURAJIT; CAMILUS, SANTLE; JIN, ZHONGYI; RAMAMURTI, VISH; LEE, YOUNG M.
To: JOHNSON CONTROLS TECHNOLOGY COMPANY
Reel/Frame 064033/0339 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 22, 2023
From: JOHNSON CONTROLS TECHNOLOGY COMPANY
To: JOHNSON CONTROLS TYCO IP HOLDINGS LLP
Reel/Frame 064033/0488 →
Continuity (2)
Continuation 16885968 · May 28, 2020
Related Publication 20230393539A1 · Dec 7, 2023
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