公开(公告)号：US20240280526A1

公开(公告)日：2024-08-22

申请号：US18440741

申请日：2024-02-13

Applicant: Lyten, Inc.

Inventor： Daniel Cook ,  Michael Stowell ,  Karel Vanheusden ,  George Clayton Gibbs ,  Jacques Nicole ,  Carlos Montalvo ,  Kyle Matthys ,  Bruce Lanning ,  Sung Lim ,  John Chmiola

IPC: G01N27/22 ,  G01N27/414 ,  G01N27/447

CPC classification number: G01N27/221 ,  G01N27/4145 ,  G01N27/447 ,  G01N2027/222

Abstract: Methods and system to learn precise sensing fingerprints based on machine learning integration are disclosed herein. In use, the system receives at least one first parameter associated with at least one sensor and associates the first parameter with a pre-identified first digital signature in a signature database. A machine learning system is trained based on the first parameter and the pre-identified digital signature. The system then receives at least one second parameter from the at least one sensor and determines that the second parameter is independent of a digital signature in the signature database. Using the machine learning system, a second digital signature for the second parameter is identified and saved in the signature database.

公开(公告)号：US20250076233A1

公开(公告)日：2025-03-06

申请号：US18952878

申请日：2024-11-19

Applicant: Lyten, Inc.

Inventor： Daniel Cook ,  Michael Stowell ,  Karel Vanheusden ,  George Clayton Gibbs ,  Jacques Nicole ,  Carlos Montalvo ,  Kyle Matthys ,  Bruce Lanning ,  Sung Lim ,  John Chmiola

IPC: G01N27/02 ,  G01N27/22 ,  G01N27/414 ,  G01N27/447 ,  G01N27/72 ,  G02F1/167 ,  G06Q30/018 ,  G06Q50/06 ,  H04L9/32 ,  H04L9/40

Abstract: Disclosed herein is a sensors-as-a-service ecosystem. In use, the system includes functions for receiving first sensor data at a sensors as a service platform, where the first sensor data corresponds to a first level of capabilities for a first sensor. The system also receives a selection of a sensor upgrade for the first sensor and provisions enhanced sensor capabilities for the sensor upgrade based on the selection. Furthermore, the system sends a sensor update with the enhanced sensor capabilities from the sensors as a service platform to the first sensor. Finally, the system receives second sensor data from the first sensor at the sensors as a service platform, where the second sensor data corresponds to a second level of capabilities for the first sensor.

公开(公告)号：US20240417669A1

公开(公告)日：2024-12-19

申请号：US18814320

申请日：2024-08-23

Applicant: Lyten, Inc.

Inventor： Sung Lim ,  Daniel Cook ,  Jacques Nicole ,  Michael Stowell ,  Ashley Lim

IPC: C12M1/34 ,  C12Q1/04

Abstract: A resonant sensor is embedded within or applied to a component of a medical diagnostic apparatus. The resonant sensor is formed from a composite material. The resonant sensor undergoes a change of permittivity and/or change in permeability due to metabolic activity of a microorganism that is involved in the medical diagnostic and proximal to the resonant sensor. The medical diagnostic apparatus may be a blood culture bottle that is configured to contain a blood culture medium. The resonant sensor may be embedded in or applied to the exterior or interior wall of the blood culture bottle. The resonant sensor may undergo a change in permittivity and/or a change in permeability due to production of carbon dioxide by the microorganism. The composite material may comprise a carbonaceous material such as graphene.

公开(公告)号：US20240288381A1

公开(公告)日：2024-08-29

申请号：US18440753

申请日：2024-02-13

Applicant: Lyten, Inc.

Inventor： Michael Stowell ,  Daniel Cook ,  Carlos Montalvo ,  George Clayton Gibbs ,  Jacques Nicole ,  Karel Vanheusden ,  Kyle Matthys ,  Bruce Lanning ,  Sung Lim ,  John Chmiola

IPC: G01N22/00 ,  H01Q13/20

CPC classification number: G01N22/00 ,  H01Q13/20

Abstract: Methods and system to learn precise sensing fingerprints based on machine learning integration are disclosed herein. In use, the system receives at least one first parameter associated with at least one sensor and associates the first parameter with a pre-identified first digital signature in a signature database. A machine learning system is trained based on the first parameter and the pre-identified digital signature. The system then receives at least one second parameter from the at least one sensor and determines that the second parameter is independent of a digital signature in the signature database. Using the machine learning system, a second digital signature for the second parameter is identified and saved in the signature database.

公开(公告)号：US20240272103A1

公开(公告)日：2024-08-15

申请号：US18440806

申请日：2024-02-13

Applicant: Lyten, Inc.

Inventor： Daniel Cook ,  Michael Stowell ,  Karel Vanheusden ,  George Clayton Gibbs ,  Jacques Nicole ,  Carlos Montalvo ,  Kyle Matthys ,  Bruce Lanning ,  Sung Lim ,  John Chmiola

IPC: G01N27/02 ,  G01N27/72

CPC classification number: G01N27/02 ,  G01N27/72

Abstract: Methods and system to learn precise sensing fingerprints based on machine learning integration are disclosed herein. In use, the system receives at least one first parameter associated with at least one sensor and associates the first parameter with a pre-identified first digital signature in a signature database. A machine learning system is trained based on the first parameter and the pre-identified digital signature. The system then receives at least one second parameter from the at least one sensor and determines that the second parameter is independent of a digital signature in the signature database. Using the machine learning system, a second digital signature for the second parameter is identified and saved in the signature database.

公开(公告)号：US20240275608A1

公开(公告)日：2024-08-15

申请号：US18440719

申请日：2024-02-13

Applicant: Lyten, Inc.

Inventor： Daniel Cook ,  Michael Stowell ,  Karel Vanheusden ,  George Clayton Gibbs ,  Jacques Nicole ,  Carlos Montalvo ,  Kyle Matthys ,  Bruce Lanning ,  Sung Lim ,  John Chmiola

IPC: H04L9/32 ,  G02F1/167 ,  H04L9/40

CPC classification number: H04L9/3247 ,  G02F1/167 ,  H04L63/1416

Abstract: Methods and system to learn precise sensing fingerprints based on machine learning integration are disclosed herein. In use, the system receives at least one first parameter associated with at least one sensor and associates the first parameter with a pre-identified first digital signature in a signature database. A machine learning system is trained based on the first parameter and the pre-identified digital signature. The system then receives at least one second parameter from the at least one sensor and determines that the second parameter is independent of a digital signature in the signature database. Using the machine learning system, a second digital signature for the second parameter is identified and saved in the signature database.

公开(公告)号：US20240273648A1

公开(公告)日：2024-08-15

申请号：US18440769

申请日：2024-02-13

Applicant: Lyten, Inc.

Inventor： Daniel Cook ,  Keith Norman ,  Kyle Matthys ,  Michael Stowell ,  Karel Vanheusden ,  George Clayton Gibbs ,  Jacques Nicole ,  Carlos Montalvo ,  Bruce Lanning ,  Sung Lim ,  John Chmiola

IPC: G06Q50/06 ,  G06Q30/018

CPC classification number: G06Q50/06 ,  G06Q30/018

Abstract: Methods and system to learn precise sensing fingerprints based on machine learning integration are disclosed herein. In use, the system receives at least one first parameter associated with at least one sensor and associates the first parameter with a pre-identified first digital signature in a signature database. A machine learning system is trained based on the first parameter and the pre-identified digital signature. The system then receives at least one second parameter from the at least one sensor and determines that the second parameter is independent of a digital signature in the signature database. Using the machine learning system, a second digital signature for the second parameter is identified and saved in the signature database.