Khoa Dang Le

20210149933, May 20, 2021

Apr 28, 2020

15/929364

- San Francisco CA, US
Weiping Peng - San Francisco CA, US
Na Cheng - Dublin CA, US
Feifei Jiang - San Mateo CA, US
Jacob Nathaniel Huffman - Oakland CA, US
Nandini Suresh Kumar - San Francisco CA, US
Khoa Le - San Francisco CA, US
Christopher Larry - San Francisco CA, US

salesforce.com, Inc. - San Francisco CA

G06F 16/31
H04L 12/58
G06F 16/34
G06F 16/35

Computing systems, database systems, and related methods are provided for recommending values for fields of database objects and dynamically updating a recommended value for a field of a database record in response to updated auxiliary data associated with the database record. One method involves obtaining associated conversational data, segmenting the conversational data, converting each respective segment of conversational data into a numerical representation, generating a combined numerical representation of the conversational data based on the sequence of numerical representations using an aggregation model, generating the recommended value based on the combined numerical representation of the conversational data using a prediction model associated with the field, and autopopulating the field of the case database object with the recommended value.

20210150146, May 20, 2021

Nov 18, 2019

16/687626

- San Francisco CA, US
Edgar Gerardo Velasco - San Francisco CA, US
Victor Winslow Yee - Berkeley CA, US
Na Cheng - Dublin CA, US
Khoa Le - San Francisco CA, US

G06F 40/30
G06F 16/33
G06F 16/332
G06N 20/00

A system is configured to analyze a corpus of historical chat data to identify the list of “best” responses. As such, the user is not required to identify a list of canned responses for input into the system. The described system uses a context word embedding function and response word embedding function to generate context vectors and response vectors corresponding to the corpus of conversation data, and the vectors are represented by a respective context matrix and a response matrix. The system processes these matrices to generate scores for responses, clusters the responses, and identifies the responses corresponding to the best scores for each cluster.

20200097616, Mar 26, 2020

Sep 24, 2018

16/140418

- San Francisco CA, US
Aditya SAKHUJA - San Francisco CA, US
Hui S. FISHER - Bellevue WA, US
Anjan GOSWAMI - San Francisco CA, US
Khoa LE - San Francisco CA, US

G06F 17/30
G06F 17/27
G06F 9/451

A method is provided for generating a ranked list of candidate responders. In some embodiments, the method includes receiving a question from a user and generating a question feature vector representing an intent of the question and a first skill set inferred from the question. The method also includes for one or more candidate responders, generating a candidate feature vector representing a skill set and questions associated with the respective candidate responder; computing a reputation score based on questions and user feedback associated with the respective candidate responder; and computing, based on the question feature vector, candidate feature vector, and reputation score, a probability score representing a prediction of the quality of an answer that would be provided by the respective candidate responder if the input question were routed to the respective candidate responder. The method further includes generating a ranked list of candidate responders using the computed probability scores.