IP Library Granted Patent US 12,499,319
Granted Patent B2
US 12,499,319 · App. 18/064,724 · Granted Dec 16, 2025

Database systems with user-configurable automated metadata assignment

Inventors: Zachary Alexander (Berkeley, CA); Yixin Mao (San Francisco, CA)
G06F40/35G06F16/164
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Quick Facts
Patent No.
US 12,499,319
App. No.
18/064,724
Granted
Dec 16, 2025
Kind
B2
Abstract

Database systems and methods are provided for assigning structural metadata to records and creating automations using the structural metadata. One method of assigning structural metadata to a group of conversation records involves receiving a user input modification pertaining to a group of semantically similar conversations, automatically reassigning a conversation to a different group of semantically similar conversations based on its representative utterance in a manner that is influenced by the user input modification, and automatically updating, at a database system, a record associated with the conversation to include metadata identifying the different group of semantically similar conversations.

Claims (52)

1 . A method comprising:

receiving user input including a modification pertaining to at least one of a plurality of preexisting groups of semantically similar conversations, a respective group of the plurality of preexisting groups comprising a subset of conversations previously assigned to the respective group based on a respective representative utterance associated with the respective conversation of the subset of conversations;

automatically reassigning a first conversation of the subset of conversations from the respective group to a different group of semantically similar conversations based on the representative utterance associated with the first conversation in a manner that is influenced by the modification using a user-constrained assignment algorithm comprising one or more constraints corresponding to the modification;

automatically updating, at a database system, a record associated with the first conversation to include metadata identifying the different group of semantically similar conversations in lieu of the respective group previously assigned to the first conversation;

determining a value for a performance metric associated with the different group of the plurality of preexisting groups after automatically reassigning the first conversation, wherein the value for the performance metric is influenced by one or more performance metrics associated with the first conversation and an updated value for the performance metric associated with the respective group is influenced by reassignment of the first conversation; and

providing one or more graphical indicia influenced by the value of the performance metric associated with the different group and the updated value for the performance metric associated with the respective group.

2 . The method of claim 1 , wherein:

the modification comprises a new group to be added to the plurality of preexisting groups of semantically similar conversations;

automatically reassigning the first conversation comprises automatically reassigning the first conversation from a preexisting group of the plurality of preexisting groups of semantically similar conversations to the new group based on the representative utterance associated with the first conversation; and

automatically updating the record comprises automatically updating the record to include an updated group value for a first field of the metadata identifying the new group in lieu of a previous group value for the first field of the metadata identifying the preexisting group.

3 . The method of claim 2 , wherein:

the user input includes a semantic representation assigned to the new group; and

automatically reassigning the first conversation comprises automatically reassigning the first conversation based on a relationship between the semantic representation assigned to the new group and the representative utterance associated with the first conversation.

4 . The method of claim 3 , further comprising:

generating a first numerical representation of the representative utterance associated with the first conversation; and

generating a second numerical representation of the semantic representation assigned to the new group, wherein automatically reassigning the first conversation comprises automatically reassigning the conversation to the new group when a difference between the first numerical representation and the second numerical representation is less than a second difference between the first numerical representation and a third numerical representation of a second semantic representation assigned to the preexisting group.

5 . The method of claim 1 , wherein:

the modification comprises a second conversation to be assigned to the different group of the plurality of preexisting groups; and

automatically reassigning the first conversation comprises automatically reassigning the first conversation from a preexisting group of the plurality of preexisting groups of semantically similar conversations to the different group based on a relationship between the representative utterance associated with the first conversation and the respective representative utterance associated with the second conversation.

6 . The method of claim 1 , wherein:

the modification comprises removal of a first group of the plurality of preexisting groups of semantically similar conversations; and

automatically reassigning the first conversation comprises automatically reassigning the first conversation from the first group of the plurality of preexisting groups of semantically similar conversations to the different group based on the representative utterance associated with the first conversation.

7 . The method of claim 1 , wherein:

the modification comprises merging a first group of the plurality of preexisting groups of semantically similar conversations with a second group of the plurality of preexisting groups of semantically similar conversations; and

automatically reassigning the first conversation comprises automatically reassigning the first conversation from the first group of the plurality of preexisting groups of semantically similar conversations to the second group based on the representative utterance associated with the first conversation.

8 . The method of claim 1 , further comprising configuring one or more parameters of a Gaussian mixture model (GMM) using the modification to obtain a semi-supervised GMM constrained by the user input, wherein automatically reassigning the first conversation comprises:

inputting a numerical representation of the representative utterance associated with the first conversation to the semi-supervised GMM to obtain an output from the semi-supervised GMM influenced by the numerical representation of the representative utterance associated with the first conversation; and

reassigning the first conversation from a first group of the plurality of preexisting groups of semantically similar conversations to a second group of the plurality of preexisting groups of semantically similar conversations when the output from the semi-supervised GMM is indicative of the second group.

9 . The method of claim 8 , wherein each group of the plurality of preexisting groups is distinct relative to other groups of the plurality of preexisting groups.

10 . At least one non-transitory machine-readable storage medium that provides instructions that, when executed by at least one processor, are configurable to cause the at least one processor to perform operations comprising:

receiving user input including a modification pertaining to at least one of a plurality of preexisting groups of semantically similar conversations, a respective group of the plurality of preexisting groups comprising a subset of conversations previously assigned to the respective group based on a respective representative utterance associated with the respective conversation of the subset of conversations;

automatically reassigning a first conversation of the subset of conversations from the respective group to a different group of semantically similar conversations based on the representative utterance associated with the first conversation in a manner that is influenced by the modification using a user-constrained assignment algorithm comprising one or more constraints corresponding to the modification;

automatically updating a record associated with the first conversation to include metadata identifying the different group of semantically similar conversations in lieu of the respective group previously assigned to the first conversation;

determining a value for a performance metric associated with the different group of the plurality of preexisting groups after automatically reassigning the first conversation, wherein the value for the performance metric is influenced by one or more performance metrics associated with the first conversation and an updated value for the performance metric associated with the respective group is influenced by reassignment of the first conversation; and

providing one or more graphical indicia influenced by the value of the performance metric associated with the different group and the updated value for the performance metric associated with the respective group.

11 . The at least one non-transitory machine-readable storage medium of claim 10 , wherein the modification comprises a new group to be added to the plurality of preexisting groups of semantically similar conversations and the instructions are configurable to cause the at least one processor to automatically reassign the first conversation from a preexisting group of the plurality of preexisting groups of semantically similar conversations to the new group based on the representative utterance associated with the first conversation.

12 . The at least one non-transitory machine-readable storage medium of claim 11 , wherein the user input includes a semantic representation assigned to the new group and the instructions are configurable to cause the at least one processor to automatically reassign the first conversation based on a relationship between the semantic representation assigned to the new group and the representative utterance associated with the first conversation.

13 . The at least one non-transitory machine-readable storage medium of claim 12 , wherein the instructions are configurable to cause the at least one processor to:

generate a first numerical representation of the representative utterance associated with the first conversation; and

generate a second numerical representation of the semantic representation assigned to the new group, wherein automatically reassigning the first conversation comprises automatically reassigning the conversation to the new group when a difference between the first numerical representation and the second numerical representation is less than a second difference between the first numerical representation and a third numerical representation of a second semantic representation assigned to the preexisting group.

14 . The at least one non-transitory machine-readable storage medium of claim 10 , wherein the modification comprises a second conversation to be assigned to the different group of the plurality of preexisting groups and the instructions are configurable to cause the at least one processor to automatically reassign the first conversation from a preexisting group of the plurality of preexisting groups of semantically similar conversations to the different group based on a relationship between the representative utterance associated with the first conversation and the respective representative utterance associated with the second conversation.

15 . The at least one non-transitory machine-readable storage medium of claim 10 , wherein the modification comprises removal of a first group of the plurality of preexisting groups of semantically similar conversations and the instructions are configurable to cause the at least one processor to automatically reassign the first conversation from the first group of the plurality of preexisting groups of semantically similar conversations to the different group based on the representative utterance associated with the first conversation.

16 . The at least one non-transitory machine-readable storage medium of claim 10 , wherein the modification comprises merging a first group of the plurality of preexisting groups of semantically similar conversations with a second group of the plurality of preexisting groups of semantically similar conversations and the instructions are configurable to cause the at least one processor to automatically reassign the first conversation from the first group of the plurality of preexisting groups of semantically similar conversations to the second group based on the representative utterance associated with the first conversation.

17 . The at least one non-transitory machine-readable storage medium of claim 10 , wherein the instructions are configurable to cause the at least one processor to configure one or more parameters of a Gaussian mixture model (GMM) using the modification to obtain a semi-supervised GMM constrained by the user input, wherein automatically reassigning the first conversation comprises:

inputting a numerical representation of the representative utterance associated with the first conversation to the semi-supervised GMM to obtain an output from the semi-supervised GMM influenced by the numerical representation of the representative utterance associated with the first conversation; and

reassigning the first conversation from a first group of the plurality of preexisting groups of semantically similar conversations to a second group of the plurality of preexisting groups of semantically similar conversations when the output from the semi-supervised GMM is indicative of the second group.

18 . A method comprising:

receiving user input including a modification to at least one of a plurality of groups of semantically similar conversations, a respective group of the plurality of groups comprising a subset of conversations assigned to the respective group based on a respective representative utterance associated with the respective conversation of the subset of conversations;

configuring one or more parameters of a Gaussian mixture model (GMM) using the modification to obtain a semi-supervised GMM constrained by the user input;

inputting a numerical representation of the representative utterance associated with a first conversation of the subset of conversations to the semi-supervised GMM to obtain an output from the semi-supervised GMM influenced by the numerical representation of the representative utterance associated with the first conversation;

automatically reassigning the first conversation from a first group of the plurality of groups of semantically similar conversations to a second group of the plurality of groups of semantically similar conversations different from the first group when the output from the semi-supervised GMM is indicative of the second group; and

automatically updating, at a database system, a record associated with the first conversation to include metadata identifying the second group of semantically similar conversations.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 12, 2022
From: ALEXANDER, ZACHARY; MAO, YIXIN
To: SALESFORCE, INC.
Reel/Frame 062059/0809 →
Continuity (1)
Related Publication 20240193373A1 · Jun 13, 2024
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