Starkey Laboratories, Inc.

David Alan Fabry

Achintya K Bhowmik

Justin R Burwinkel

Christopher Larry Howes

Amit Shahar

Jeffrey Paul Solum

Sidney A Higgins

Thomas Howard Burns

Thomas Scheller

Jumana Harianawala

Kyle Walsh

Jaymin Baroda

Ke Zhou

Clifford Anthony Tallman

Martin McKinney

Brantly Sturgeon

Kenneth Kragh Jensen

Jeffery Lee Crukley

Olabanji Yussuf Shonibare

Mark Mercury

Fue Yang

William F Austin

Chris Howes

Manfred Stoifl

Martin Mckinney

Achintya Kumar Bhowmik

An ear-worn electronic device comprises a microphone configured to sense sound in an acoustic environment, an acoustic transducer, and a non-volatile memory configured to store a plurality of parameter value sets, each of the parameter value sets associated with a different acoustic environment. A control input is configured to receive a control input signal produced by at least one of a user-actuatable control of the ear-worn electronic device and an external electronic device communicatively coupled to the ear-worn electronic device in response to a user action. A processor is configured to classify the acoustic environment using the sensed sound and determine a listening intent preference of the user. The processor is configured to apply, in response to the control input signal, one of the parameter value sets appropriate for the classification and the listening intent preference of the user.

Ear-worn electronic device employing user-initiated acoustic environment adaptation

Embodiments herein relate to a local assistant system responding to voice input using an ear-wearable device. The system detects a wake-up signal and receives a first voice input communicating a first query content. The system includes speech recognition circuitry to determine the first query content, speech generation circuitry, and an input database of locally-handled user inputs. If the first audio input matches one of the locally-handled user inputs, then the system takes a local responsive action. If the first audio input does not match one of the locally-handled user inputs, then the system transmits at least a portion of the first query content over a wireless network to a network resource.

Local artificial intelligence assistant system with ear-wearable device

Embodiments herein relate to devices and related systems and methods for motion sickness prevention and mitigation. In an embodiment, a method of preventing or mitigating motion sickness in a subject is included, the method tracking motion of the subject using a first motion sensor; estimating a vestibular system input based on tracked motion of the subject; tracking head position of the subject using the first motion sensor; estimating a visual system input based on tracked head position of the subject; estimating consistency between the vestibular system input and the visual system input; and initiating a responsive measure if the estimated consistency crosses a threshold value. Other embodiments are also included herein.

Hearing assistance devices with motion sickness prevention and mitigation features

A system and method of automatic transcription using a visual display device and an ear-wearable device. The system is configured to process an input audio signal at the display device to identify a first voice signal and a second voice signal from the input audio signal. A representation of the first voice signal and the second voice signal can be displayed on the display device and input can be received comprising the user selecting one of the first voice signal and the second voice signal as a selected voice signal. The system is configured to convert the selected voice signal to text data and display a transcript on the display device. The system can further generate an output signal sound at the first transducer of the ear-wearable device based on the input audio signal.

Audio signal processing for automatic transcription using ear-wearable device

Embodiments herein relate to systems, devices and methods for fitting hearing assistance devices. In a first aspect, a method of fitting a hearing assistance device is included, the method including providing an audio sample to a hearing assistance device wearer, receiving input from the hearing assistance device wearer regarding a preferred sound volume or perceived loudness, receiving input from the hearing assistance device wearer with the external device regarding a bass/treble balance, and determining a maximum power output of the hearing assistance device that does not exceed a loudness discomfort level (LDL). Other embodiments are also included herein.

Systems, devices and methods for fitting hearing assistance devices

An ear-worn electronic device comprises a microphone arrangement configured to sense sound in an acoustic environment, an acoustic transducer, and a non-volatile memory configured to store parameter value sets each associated with a different acoustic environment, at least one of which is associated with an acoustic environment with muffled speech. A control input of the device is configured to receive a control input signal produced by a user-actuatable control, a sensor or an external electronic device. A processor is configured to classify the acoustic environment as one with muffled speech using the sensed sound and, in response to a signal received from the control input, apply one or more of the parameter value sets appropriate for the classification to enhance intelligibility of muffled speech.

Ear-worn electronic device employing acoustic environment adaptation for muffled speech

An ear-worn electronic device comprises a housing, electronic circuitry, a power source, a microphone, and a receiver or speaker. The housing comprises a nozzle dimensioned for deployment within at least a portion of an ear canal. The nozzle comprises a distal portion, a proximal portion, a cavity within the nozzle, and a terminus surface at the distal portion comprising a sound port through which sound can pass out of the nozzle. The nozzle comprises an acoustical vent arrangement comprising a proximal port provided at the proximal portion, a plurality of distal ports provided at the terminus surface, and at least a portion of the nozzle cavity in fluid communication with the proximal port and the distal ports. Each of the distal ports has a volume smaller than that of the proximal port, and a volume of the proximal port is substantially equal to that of the distal ports cumulatively.

Ear-wearable hearing device

Systems and methods may be used to determine a fit for a hearing assistance device shell model. For example, a method may include receiving an image of anatomy of a patient including at least a portion of a canal aperture of an ear of the patient, generating a patient model of a portion of the anatomy of the patient, the patient model indicating at least one of a height or width of the canal aperture, and determining, using the patient model, a best fit model from a set of hearing assistance device shell models generated using a machine learning technique. The method may include outputting an identification of the best fit model.

Generating a hearing assistance device shell

A hearing assistance system and method are described herein. The system includes a first ear-worn device, a second ear-worn device, and a case. The system is configured to detect a vibration sequence at an IMU and decide whether to pair wireless communication devices of the first and second ear-worn devices based on the vibration sequence detected at the IMU.

Pairing system and method for ear-worn devices

A computing system comprising one or more electronic computing devices receives data from a hearing-assistance device. The computing system determines, based on the data received from the hearing-assistance device, a cognitive benefit measure for a wearer of the hearing-assistance device. The cognitive benefit measure being an indication of a change of a cognitive benefit of the wearer of the hearing-assistance device attributable to use of the hearing-assistance device by the wearer of the hearing-assistance device. The computing device outputs an indication of the cognitive benefit measure.

Growing community of inventors

Eden Prairie, MN, United States of America

David Alan Fabry