Ricoh Company, Ltd.

Ricoh Corporation

Clearcare, Inc.

Duke University

Other

The State University of New York

Ricoh Silicon Valley Corporation

Jonathan J Hull

Jamey Graham

Berna Erol

Peter E Hart

Dar-Shyang Lee

Kurt W Piersol

John F Cullen

Jorge Moraleda

Mark Peairs

Geoffrey Nudd

Michael Alan Baxter

Gregory J Wolff

Ken Gudan

Sergey Chemishkian

Qifa Ke

Matthew Stephen Reynolds

David Cristman

Kiyoshi Suzuki

Daniel G Van Olst

Hidenobu Kishi

Stephen Weyl

Kathrin Berkner

John Taylor

Derek E Poppink

John W Barrus

Pamela Gage

Xu Liu

Marko Balabanovic

Kenneth F Gudan

Emilio R Antunez

Shuai Shao

Hideki Segawa

Michael J Gormish

Max E McFarland

Daja Phillips

Michael G E Griffin

Sargur N Srihari

Ichiro Sakikawa

Kevin Ridout

Greg Wolff

Andrew Lookingbill

Sarah Cook

Bala Krishna Nakshatrala

Tao Hong

Sadao Takahashi

Sapna A Shroff

Vikas Krishna

Bradley J Rhodes

Sri Rama Prasanna Pavani

Takahiro Furukawa

Joshua F Ensworth

Toshio Kanoh

Sergey Chemishkain

Mike Griffin

Landry Huet

Robert Alexander

Harsha Binnamangalam

Daja Strassberg-Phillips

Arne Elliott

Masayuki Nishimoto

Qifa Le

Chih-Chau Kuan

Shigeharu Uda

Ke Qifa

Lee Dar-Shyang

Hidenobu c/o Ricoh Innovations Inc Kishi

Xiaoye Lu

A system collects information associated with the work performance and work satisfaction of caregiver's who provide in-home care services to seniors. A machine learning system is trained to predict caregiver attrition and generate a user interface display indicative of a risk an individual caregiver will attrit. The system may also be used to determine action steps to reduce the risk of attrition of a caregiver.

Machine learning system and method for predicting caregiver attrition

The present disclosure describes a conversation facilitation system for facilitating conversation-based social interactions to improve senior health, one or more operations and functions being efficiently achieved via this system comprise: receiving a dialog act of a conversation, applying natural language understanding (NLU) processing on the dialog act, computing a conversation metric, and generating a result of the conversation to conclude the conversation based on the conversation metric.

Conversation facilitation system for mitigating loneliness

The in-home care of seniors is augmented using Internet of Things (IOT) technologies. In-home sensors monitor a senior and their caregiver. Physical conditions and psychological conditions may be monitored. In some implementations, a machine learning system has a classifier trained to detect a specified condition, such as depression. The system may perform various transformations of raw sensor data into a format indicative of a particular condition. In one implementation, a psychological or medical condition has symptoms in which each symptom has one or more measurable events. Mappings between symptoms, events, sensor data, and sensor transformation functions may be supported.

Automatic in-home senior care system augmented with internet of things technologies

The present disclosure describes a system to use conversation data of patients to detect dangerous mental or physical conditions, such as suicidal thoughts, physical abuse, recent falls, and viral infection. A machine learning system may be trained to identify a dangerous mental or physical condition from conversations based on examples of patients evaluated to have a specific mental or physical condition. Conversations of patients may be monitored, natural language understanding (NLU) processing performed, and a machine learning system used to detect dangerous mental or physical conditions.

Conversation system for detecting a dangerous mental or physical condition

A machine learning system passively monitors sensor data from the living space of a patient. The sensor data may include audio data. Audio features are generated. A trained machine learning model is used to detect a change in condition. In some implementations, the machine learning model is trained in a learning phase based on training data that includes questionnaires completed by caregivers and identified audio features.

Automatic change in condition monitoring by passive sensor monitoring and machine learning

An individual antenna element is described. In one embodiment, the individual antenna element is a patch antenna which has an electrically small radiating element with a U-shaped slot on an antenna substrate, with a gain greater than 2 dBi with +/&#x2212;45 degree coverage.

Individual antenna element

A battery charger for charging a battery is disclosed. In one embodiment, the battery charger comprises an input for receiving a first voltage and for coupling to a positive terminal of the battery; a first transistor having a first gate, a first drain and a first source, wherein the first gate is for coupling to a charging signal for the battery and the first drain is coupled to the battery negative terminal and, the first transistor to cause energy to be transferred from the input into the battery when turned on; and a group of transistors coupled to the first transistor and the input to control when the first transistor is turned on, wherein the group of transistors comprises a second transistor having a second gate coupled to the input and coupled to a third transistor to turn off the third transistor after a delay occurs after the voltage on the input reaches a predetermined level, and further herein the third transistor causes the first transistor to turn on when the third transistor is turned off.

Battery charger

A reconfigurable array antenna and system for including the same are described. In one embodiment, the antenna apparatus comprises a feeding network and an array coupled to the feeding network, wherein the array has a plurality of locations, with each location capable of having an antenna element attached thereto, and wherein the feeding network is operable to combine energy received by antenna elements in the array, and the energy is combined by the feeding network in RF.

Reconfigurable reconstructive antenna array

A DC-DC boost converter and apparatus including the same are disclosed. In one embodiment, the DC-DC converter comprises an input for receiving a first voltage at a first level, where the first voltage is negative; a transformer coupled to the input to convert the first voltage to a second voltage at a second level, where the second level is higher than the first level and the second voltage is positive; and an output connected to the transformer to output the second voltage.

DC-DC boost converter

Growing community of inventors

San Carlos, CA, United States of America

Jonathan J Hull