David Fai Yan

20210349929, Nov 11, 2021

Jul 26, 2021

17/384972

- Menlo Park CA, US
David Yan - Portola Valley CA, US
Marina Chilingaryan - Menlo Park CA, US

G06F 16/35
G06F 16/93
G06F 16/33

An example method of method of document cluster labeling comprises: selecting a current document cluster of a plurality of document clusters; initializing a label associated with the current document cluster; selecting a term from a list of terms comprised by the document cluster; appending the term to the label associated with the current document cluster; responsive to determining that the label is found in a label dictionary, iteratively selecting a next term from the list of terms comprised by the document cluster and appending the next term to the label associated with the current document cluster; responsive to failing to locate the label in the label dictionary, inserting the label into the label dictionary; and associating the label with the current document cluster.

20190012629, Jan 10, 2019

Jul 9, 2018

16/030598

- Menlo Park CA, US
David YAN - Portola Valley CA, US

G06Q 10/06
G06Q 10/10
G06N 3/08

An example method of employee performance evaluation comprises: processing a plurality of documents which record communications of a person to identify a task assigned to the person; identifying a subset of the plurality of documents, wherein the subset of documents is associated with the task; analyzing the subset of documents to identify a completion status of the task; and determining a value of a performance metric associated with the person, wherein the value of the performance metric reflects the completion status of the task.

20190014070, Jan 10, 2019

Jul 9, 2018

16/030558

- Menlo Park CA, US
Alexander MAKUSHEV - Balashikha, RU
Victor KUZNETSOV - Sunnyvale CA, US
Sergei RYKOV - Moscow, RU
Victor BOCHAROV - Saint Petersburg, RU
David YAN - Portola Valley CA, US
Marina CHILINGARYAN - Berkeley CA, US

H04L 12/58
G06F 17/27

A task assistant executed by a processing device receives a copy of a first correspondence sent by a first user in a first communication thread, wherein the first correspondence is addressed to a second user and the copy is sent to an automated task assistant. The task assistant determines that the first correspondence includes a request and a due date and monitors additional correspondence in the first communication thread to determine whether the request has been satisfied by the due date. Responsive to determining that the request has not been satisfied, the task assistant provides a notification with a reminder of the request to a client device associated with the second user in advance of the due date.

20180329989, Nov 15, 2018

May 7, 2018

15/972952

- Menlo Parks CA, US
David Yan - Portola Valley CA, US
Marina Chilingaryan - Menlo Park CA, US

G06F 17/30

An example method of document clustering comprises: representing each document of a plurality of documents by a vector comprising a first plurality of real values, wherein each real value of the first plurality of real values reflects a first frequency-based metric of a term comprised by the document; partitioning the plurality of documents into a first set of document clusters based on distances between vectors representing the documents; representing each document cluster of the first set of document clusters by a vector comprising a second plurality of real values, wherein each real value of the second plurality of real values reflects a second frequency-based metric of a term comprised by the document cluster; and partitioning the first set of document clusters into a second set of document clusters based on distances between vectors representing the document clusters of the first set of document clusters.

20170257425, Sep 7, 2017

May 16, 2017

15/597147

- San Jose CA, US
Chandi Singh - Foster City CA, US
Thomas Weise - Mountain View CA, US
Pramod Immaneni - San Jose CA, US
David Yan - Campbell CA, US
Gaurav Gupta - San Jose CA, US
Amol Kekre - Fremont CA, US

H04L 29/08
H04L 29/06

Software for a distributed streaming platform receives an application. The application is structured as a directed acyclic graph (DAG) with instances of operators as nodes and streams as edges between nodes. Multiple instances of an operator access a shared database. The software receives a pre-defined hint associated with the application. The pre-defined hint sets a maximum period of time for local caching of a result from a query of the database by each of the multiple instances. The software launches the application by assigning the instances of operators to one or more containers provided by the streaming platform and initiating the streams. Each container is associated with a local cache. The software then receives a request from the application to make a dynamic adjustment that increases the maximum period of time for local caching of a result from a query of the database by each of the multiple instances.

20150334065, Nov 19, 2015

May 15, 2015

14/713531

David Yan - Portola Valley CA, US
Alena Yan - Portola Valley CA, US
Donald Wisniewski - Bartlett IL, US

H04L 12/58
G06F 15/02
H04W 4/20
G10L 15/08
H04M 1/725
H04L 29/08
H04L 29/06

A method according to a set of instructions stored on a memory of a computing device includes receiving, by a processor of the computing device, first and second user-profile information associated with a first and second user, respectively. The method further includes determining a match between the first and second users based on the first and second user-profile information. The method further includes establishing, in response to determining the match, a real time voice communication over a network between a first user device and a second user device. The method further includes sending or receiving information to the first user device and the second user device during the real time voice communication. First contact information associated with the first user device is not sent to the second user device. Second contact information associated with the second user device is not sent to the first user device.

20150186420, Jul 2, 2015

Jun 23, 2014

14/311952

David Yan - Portola Valley CA, US
Konstantin Anisimovich - Moscow, RU

ABBYY InfoPoisk LLC - Moscow

G06F 17/30

Various embodiments are provided for enabling tagging of image files based on tagging or commenting on images on a social networking site. The tagging or commenting on an image on the social networking site is detected by the system. The social network tag or comment is analyzed to determine a textual tag to be assigned to image files corresponding to the social network images that have been tagged or commented on. In some implementations semantic analysis of text component of the social network tags or comments is performed. In some implementations the textual tags are then propagated to other image files associated with the user.

20150187353, Jul 2, 2015

Jun 23, 2014

14/311851

David Yan - Portola Valley CA, US
Konstantin Anisimovich - Moscow, RU

ABBYY InfoPoisk LLC - Moscow

G10L 15/18

Various embodiments are provided for enabling audio tagging of image files. The audio messages are obtained by the system, usually by recording an audio message from a user, and then converted into a textual tag, using speech recognition technology. In some implementations semantic analysis of text component of these massages is performed. In some implementations the textual tags are then propagated to other image files associated with the user.