Sana A Lee, 43Hayward, CA

2022023, Jul 28, 2022

Jan 28, 2021

17/161406

- San Jose CA, US
Eunyee Koh - San Jose CA, US
Fan Du - Milpitas CA, US
Tak Yeon Lee - San Jose CA, US
Sana Malik Lee - Brea CA, US
Ryan Rossi - Santa Clara CA, US

G06F 16/738
G06F 16/901
G06F 16/9032
G06F 16/34
G06F 16/783

This disclosure describes one or more embodiments of systems, non-transitory computer-readable media, and methods that intelligently and automatically analyze input data and generate visual data stories depicting graphical visualizations from data insights determined from the input data. For example, the disclosed systems automatically extract data insights utilizing an in-depth statistical analysis of dataset groups from data-attribute categories within the input data. Based on the data insights, the disclosed systems can automatically generate exportable visual data stories to visualize the data insights, provide textual or audio-based natural language summaries of the data insights, and animate such data insights in videos. In some embodiments, the disclosed systems generate a visual-data-story graph comprising nodes representing visual data stories and edges representing similarities between the visual data stories. Based on the visual-data-story graph, the disclosed systems can select a relevant visual data story to display on a graphical user interface.

2023013, Apr 27, 2023

Dec 21, 2022

18/069561

- San Jose CA, US
Eunyee Koh - San Jose CA, US
Fan Du - Milpitas CA, US
Tak Yeon Lee - San Jose CA, US
Sana Malik Lee - Brea CA, US
Ryan Rossi - Santa Clara CA, US

G06F 16/738
G06F 16/901
G06F 16/783
G06F 16/34
G06F 16/9032
G06F 16/44

This disclosure describes one or more embodiments of systems, non-transitory computer-readable media, and methods that intelligently and automatically analyze input data and generate visual data stories depicting graphical visualizations from data insights determined from the input data. For example, the disclosed systems automatically extract data insights utilizing an in-depth statistical analysis of dataset groups from data-attribute categories within the input data. Based on the data insights, the disclosed systems can automatically generate exportable visual data stories to visualize the data insights, provide textual or audio-based natural language summaries of the data insights, and animate such data insights in videos. In some embodiments, the disclosed systems generate a visual-data-story graph comprising nodes representing visual data stories and edges representing similarities between the visual data stories. Based on the visual-data-story graph, the disclosed systems can select a relevant visual data story to display on a graphical user interface.

2021035, Nov 18, 2021

May 5, 2020

16/867104

- SAN JOSE CA, US
Ryan A. Rossi - Santa Clara CA, US
Sana Malik Lee - Cupertino CA, US
Georgios Theocharous - San Jose CA, US
Handong Zhao - San Jose CA, US
Gang Wu - San Jose CA, US
Youngsuk Park - Stanford CA, US

G06F 9/50
G06F 11/34
G06F 11/30
G06F 17/10

A system and method for automatically adjusting computing resources provisioned for a computer service or application by applying historical resource usage data to a predictive model to generate predictive resource usage. The predictive resource usage is then simulated for various service configurations, determining scaling requirements and resource wastage for each configuration. A cost value is generated based on the scaling requirement and resource wastage, with the cost value for each service configuration used to automatically select a configuration to apply to the service. Alternatively, the method for automatically adjusting computer resources provisioned for a service may include receiving resource usage data of the service, applying it to a linear quadratic regulator (LQR) to find an optimal stationary policy (treating the resource usage data as states and resource-provisioning variables as actions), and providing instructions for configuring the service based on the optimal stationary policy.

2021032, Oct 21, 2021

Jun 30, 2021

17/364480

- San Jose CA, US
Sana Malik Lee - Cupertino CA, US
Georgios Theocharous - San Jose CA, US
Eunyee Koh - San Jose CA, US

G01C 21/34
H04W 4/024
G06Q 10/04
H04W 4/021

The present disclosure relates to generating and modifying recommended event sequences utilizing a dynamic user preference interface. For example, in one or more embodiments, the system generates a recommended event sequence using a recommendation model trained based on a plurality of historical event sequences. The system then provides, for display via a client device, the recommendation, a plurality of interactive elements for entry of user preferences, and a visual representation of historical event sequences. Upon detecting input of user preferences, the system can modify a reward function of the recommendation model and provide a modified recommended event sequence together with the plurality of interactive elements. In one or more embodiments, as a user enters user preferences, the system additionally modifies the visual representation to display subsets of the plurality of historical event sequences corresponding to the preferences.

2020034, Oct 29, 2020

Apr 25, 2019

16/394227

- San Jose CA, US
Eunyee Koh - San Jose CA, US
Sungchul Kim - San Jose CA, US
Shunan Guo - ShangHai, CN
Sana Malik Lee - Cupertino CA, US

G06N 3/08
G06N 5/02
G06N 20/10
G06N 7/00

Disclosed systems and methods predict and visualize outcomes based on past events. For example, an analysis application encodes a sequence of events into a feature vector that includes, for each event, a numerical representation of a respective category and a respective timestamp. The application applies a time-aware recurrent neural network to the feature vector, resulting in one or more of (i) a set of future events in which each event is associated with a probability and a predicted duration and (ii) a sequence embedding that contains information about predicted outcomes and temporal patterns observed in the sequence of events. The application applies a support vector model classifier to the sequence embedding. The support vector model classifier computes a likelihood of a categorical outcome for each of the events in the probability distribution. The application modifies interactive content according to the categorical outcomes and probability distribution.

2020003, Jan 30, 2020

Jul 27, 2018

16/047908

- San Jose CA, US
Sana Malik Lee - Cupertino CA, US
Georgios Theocharous - San Jose CA, US
Eunyee Koh - San Jose CA, US

G01C 21/34
G06Q 10/04
H04W 4/021
H04W 4/024

The present disclosure relates to generating and modifying recommended event sequences utilizing a dynamic user preference interface. For example, in one or more embodiments, the system generates a recommended event sequence using a recommendation model trained based on a plurality of historical event sequences. The system then provides, for display via a client device, the recommendation, a plurality of interactive elements for entry of user preferences, and a visual representation of historical event sequences. Upon detecting input of user preferences, the system can modify a reward function of the recommendation model and provide a modified recommended event sequence together with the plurality of interactive elements. In one or more embodiments, as a user enters user preferences, the system additionally modifies the visual representation to display subsets of the plurality of historical event sequences corresponding to the preferences.