Preferred Networks, Inc.

Nippon Telegraph and Telephone Corporation

Shohei Hido

Daisuke Okanohara

Justin B Clayton

Nobuyuki Kubota

Seiya Tokui

Toru Nishikawa

Nobuyuki Ota

Daisuke Tanaka

Takahiro Hamada

Keigo Kawaai

Yuminobu Igarashi

A server device configured to communicate, via a communication network, with at least one device including a learner configured to perform processing by using a learned model, includes processor, a transmitter, and a storage configured to store a plurality of shared models pre-learned in accordance with environments and conditions of various devices. The processor is configured to acquire device data including information on an environment and conditions from the at least one device, and select an optimum shared model for the at least one device based on the acquired device data. The transmitter is configured to transmit a selected shared model to the at least one device.

Server device, learned model providing program, learned model providing method, and learned model providing system

Systems, methods, and apparatus for time series data adaptation, including sensor fusion, are disclosed. For example, a system includes a variational inference machine, a sequential data forecast machine including a hidden state, and a machine learning model. The sequential data forecast machine exports a version of the hidden state. The variational inference machine receives as inputs time series data and the version of the hidden state, and outputs a time dependency infused latent distribution. The sequential data forecast machine obtains the version of the hidden state, receives as inputs the time series data and the time dependency infused latent distribution, and updates the hidden state based on the time series data, the time dependency infused latent distribution, and the version of the hidden state to generate a second version of the hidden state. The time dependency infused latent distribution is input into the machine learning model, which outputs a result.

Time series data adaptation and sensor fusion systems, methods, and apparatus

A control apparatus performs analysis by using partial information and determines whether or not communication is abnormal. If the communication is determined to be abnormal, the control apparatus controls a communication route for a communication control device such that the communication is transmitted from a communication apparatus to the control apparatus. Further, the control apparatus determines whether or not the communication transmitted by the control of the communication route is malicious communication. As a result, if the communication is determined to be malicious communication, the control apparatus controls the communication control device to restrict the malicious communication.

Network system

Machine learning with model filtering and model mixing for edge devices in a heterogeneous environment is disclosed. In an example embodiment, an edge device includes a communication module, a data collection device, a memory, a machine learning module, and a model mixing module. The edge device analyzes collected data with a model for a first task, outputs a result, and updates the model to create a local model. The edge device communicates with other edge devices in a heterogeneous group, transmits a request for local models to the heterogeneous group, and receives local models from the heterogeneous group. The edge device filters the local models by structure metadata, including second local models, which relate to a second task. The edge device performs a mix operation of the second local models to generate a mixed model which relates to the second task, and transmits the mixed model to the heterogeneous group.

Machine learning with model filtering and model mixing for edge devices in a heterogeneous environment

A machine learning heterogeneous edge device, method, and system are disclosed. In an example embodiment, an edge device includes a communication module, a data collection device, a memory, a machine learning module, a group determination module, and a leader election module. The edge device analyzes collected data with a model, outputs a result, and updates the model to create a local model. The edge device communicates with other edge devices in a heterogeneous group. The edge device determines group membership and determines a leader edge device. The edge device receives a request for the local model, transmits the local model to the leader edge device, receives a mixed model created by the leader edge device performing a mix operation of the local model and a different local model, and replaces the local model with the mixed model.

Growing community of inventors

Burlingame, CA, United States of America

Shohei Hido