Jie Hua Chen

8423454, Apr 16, 2013

Jan 6, 2012

13/344709

Jie Chen - Chappaqua NY, US
Timothy John Breault - Huntersville NC, US
Fernando Cela Diaz - New York NY, US
William Anthony Nobili - Charlotte NC, US
Sandi Setiawan - Charlottle NC, US
Harsh Singhal - Charlotte NC, US
Agus Sudjianto - Charlotte NC, US
Andrea Renee Turner - Rock Hill SC, US
Bradford Timothy Winkelman - Wilmington DE, US

Bank of America Corporation - Charlotte NC

G06Q 40/00

705 38

Embodiments of the present invention relate to methods and apparatuses for determining leading indicators and/or for modeling one or more time series. For example, in some embodiments, a method is provided that includes: (a) receiving first data indicating the value of a total income amount for a plurality of consumers over a period of time; (b) receiving second data indicating the value of a total debt amount for a plurality of consumers over a period of time; (c) selecting a consumer leverage time series that compares the total income amount to the total debt amount over a period of time; (d) modeling the consumer leverage time series based at least partially on the first and second data; (e) determining, using a processor, the value of the cycle component for a particular time; and (f) outputting an indication of the value of the cycle component for the particular time.

20210081673, Mar 18, 2021

Sep 9, 2020

17/016260

- Princeton NJ, US
Asim KADAV - Jersey City NJ, US
Jie CHEN - Bellevue WA, US

NEC LABORATORIES AMERICA, INC - Princeton NJ

G06K 9/00

Aspects of the present disclosure describe systems, methods, and structures that provide action recognition with high-order interaction with spatio-temporal object tracking. Image and object features are organized into into tracks, which advantageously facilitates many possible learnable embeddings and intra/inter-track interaction(s). Operationally, our systems, method, and structures according to the present disclosure employ an efficient high-order interaction model to learn embeddings and intra/inter object track interaction across the space and time for AR. Each frame is detected by an object detector to locate visual objects. Those objects are linked through time to form object tracks. The object tracks are then organized and combined with the embeddings as the input to our model. The model is trained to generate representative embeddings and discriminative video features through high-order interaction which is formulated as an efficient matrix operation without iterative processing delay.