-
公开(公告)号：US20220148146A1
公开(公告)日：2022-05-12
申请号：US17095678
申请日：2020-11-11
Applicant: DISH Network L.L.C.
Inventor： Adam Morzos
IPC: G06T7/00
Abstract: Examples of the present disclosure describe systems and methods for detecting and remediating compression artifacts in multimedia items. In example aspects, a machine learning model is trained on a dataset related to compression artifacts and non-compression artifacts. Input data may then be collected by a data collection module and provided to a pattern recognition module. The pattern recognition module may extract visual and audio features of the multimedia item and provide the extracted features to the trained machine learning model. The trained machine learning model may compare the extracted features to the model, and a confidence value may be generated. The confidence value may be compared to a confidence value threshold. If the confidence value is equal to or exceeds the confidence threshold, then the input data may be classified as containing at least one compression artifact. Remedial action may subsequently be applied (e.g., boosting the system with technical resources).
-
公开(公告)号：US20220030045A1
公开(公告)日：2022-01-27
申请号：US17473593
申请日：2021-09-13
Applicant: DISH NETWORK L.L.C.
Inventor： Christopher Bruhn , Adam Morzos
Abstract: The present disclosure is generally related to wireless data transmission. An example method for transmitting a data file from a base station to multiple Internet of Things (IoT) devices includes obtaining information on connections between the base station and IoT devices as well as device-to-device connections. The method also includes generating schemes for splitting the data file and transmitting each data portion accordingly. The method optionally includes obtaining updates on connection information and adjusting the data splitting schemes based thereon.
-
公开(公告)号：US20230076776A1
公开(公告)日：2023-03-09
申请号：US18055786
申请日：2022-11-15
Applicant: DISH Network L.L.C.
Inventor： Adam Morzos
IPC: G06T7/00
Abstract: Examples of the present disclosure describe systems and methods for detecting and remediating compression artifacts in multimedia items. In example aspects, a machine learning model is trained on a dataset related to compression artifacts and non-compression artifacts. Input data may then be collected by a data collection module and provided to a pattern recognition module. The pattern recognition module may extract visual and audio features of the multimedia item and provide the extracted features to the trained machine learning model. The trained machine learning model may compare the extracted features to the model, and a confidence value may be generated. The confidence value may be compared to a confidence value threshold. If the confidence value is equal to or exceeds the confidence threshold, then the input data may be classified as containing at least one compression artifact. Remedial action may subsequently be applied (e.g., boosting the system with technical resources).
-
公开(公告)号：US20210227015A1
公开(公告)日：2021-07-22
申请号：US16748531
申请日：2020-01-21
Applicant: DISH Network L.L.C.
Inventor： Christopher Bruhn , Adam Morzos
Abstract: The present disclosure is generally related to wireless data transmission. An example method for transmitting a data file from a base station to multiple Internet of Things (IoT) devices includes obtaining information on connections between the base station and IoT devices as well as device-to-device connections. The method also includes generating schemes for splitting the data file and transmitting each data portion accordingly. The method optionally includes obtaining updates on connection information and adjusting the data splitting schemes based thereon.
-
公开(公告)号：US11659016B2
公开(公告)日：2023-05-23
申请号：US17473593
申请日：2021-09-13
Applicant: DISH NETWORK L.L.C.
Inventor： Christopher Bruhn , Adam Morzos
IPC: H04L65/752 , H04W4/70 , G06F21/64 , H04L67/04 , H04L67/06 , H04L67/12 , H04W28/06 , H04W40/12 , H04W40/24 , H04L65/613 , H04L47/34 , A63F13/34
CPC classification number: H04L65/752 , A63F13/34 , G06F21/64 , H04L47/34 , H04L65/613 , H04L67/04 , H04L67/06 , H04L67/12 , H04W4/70 , H04W28/06 , H04W40/12 , H04W40/246
Abstract: The present disclosure is generally related to wireless data transmission. An example method for transmitting a data file from a base station to multiple Internet of Things (IoT) devices includes obtaining information on connections between the base station and IoT devices as well as device-to-device connections. The method also includes generating schemes for splitting the data file and transmitting each data portion accordingly. The method optionally includes obtaining updates on connection information and adjusting the data splitting schemes based thereon.
-
Patent Agency Ranking
公开(公告)号：US11508053B2
公开(公告)日：2022-11-22
申请号：US17095678
申请日：2020-11-11
Applicant: DISH Network L.L.C.
Inventor： Adam Morzos
Abstract: Examples of the present disclosure describe systems and methods for detecting and remediating compression artifacts in multimedia items. In example aspects, a machine learning model is trained on a dataset related to compression artifacts and non-compression artifacts. Input data may then be collected by a data collection module and provided to a pattern recognition module. The pattern recognition module may extract visual and audio features of the multimedia item and provide the extracted features to the trained machine learning model. The trained machine learning model may compare the extracted features to the model, and a confidence value may be generated. The confidence value may be compared to a confidence value threshold. If the confidence value is equal to or exceeds the confidence threshold, then the input data may be classified as containing at least one compression artifact. Remedial action may subsequently be applied (e.g., boosting the system with technical resources).
-
公开(公告)号：US11146605B2
公开(公告)日：2021-10-12
申请号：US16748531
申请日：2020-01-21
Applicant: DISH Network L.L.C.
Inventor： Christopher Bruhn , Adam Morzos
Abstract: The present disclosure is generally related to wireless data transmission. An example method for transmitting a data file from a base station to multiple Internet of Things (IoT) devices includes obtaining information on connections between the base station and IoT devices as well as device-to-device connections. The method also includes generating schemes for splitting the data file and transmitting each data portion accordingly. The method optionally includes obtaining updates on connection information and adjusting the data splitting schemes based thereon.
-
公开(公告)号：US12229931B2
公开(公告)日：2025-02-18
申请号：US18497853
申请日：2023-10-30
Applicant: DISH Network L.L.C.
Inventor： Adam Morzos
Abstract: Examples of the present disclosure describe systems and methods for detecting and remediating compression artifacts in multimedia items. In example aspects, a machine learning model is trained on a dataset related to compression artifacts and non-compression artifacts. Input data may then be collected by a data collection module and provided to a pattern recognition module. The pattern recognition module may extract visual and audio features of the multimedia item and provide the extracted features to the trained machine learning model. The trained machine learning model may compare the extracted features to the model, and a confidence value may be generated. The confidence value may be compared to a confidence value threshold. If the confidence value is equal to or exceeds the confidence threshold, then the input data may be classified as containing at least one compression artifact. Remedial action may subsequently be applied (e.g., boosting the system with technical resources).
-
公开(公告)号：US20240112317A1
公开(公告)日：2024-04-04
申请号：US18497853
申请日：2023-10-30
Applicant: DISH Network L.L.C.
Inventor： Adam Morzos
IPC: G06T7/00
CPC classification number: G06T7/0002 , G06T2207/20081 , G06T2207/30168
Abstract: Examples of the present disclosure describe systems and methods for detecting and remediating compression artifacts in multimedia items. In example aspects, a machine learning model is trained on a dataset related to compression artifacts and non-compression artifacts. Input data may then be collected by a data collection module and provided to a pattern recognition module. The pattern recognition module may extract visual and audio features of the multimedia item and provide the extracted features to the trained machine learning model. The trained machine learning model may compare the extracted features to the model, and a confidence value may be generated. The confidence value may be compared to a confidence value threshold. If the confidence value is equal to or exceeds the confidence threshold, then the input data may be classified as containing at least one compression artifact. Remedial action may subsequently be applied (e.g., boosting the system with technical resources).
-
公开(公告)号：US11810283B2
公开(公告)日：2023-11-07
申请号：US18055786
申请日：2022-11-15
Applicant: DISH Network L.L.C.
Inventor： Adam Morzos
CPC classification number: G06T7/0002 , G06T2207/20081 , G06T2207/30168
Abstract: Examples of the present disclosure describe systems and methods for detecting and remediating compression artifacts in multimedia items. In example aspects, a machine learning model is trained on a dataset related to compression artifacts and non-compression artifacts. Input data may then be collected by a data collection module and provided to a pattern recognition module. The pattern recognition module may extract visual and audio features of the multimedia item and provide the extracted features to the trained machine learning model. The trained machine learning model may compare the extracted features to the model, and a confidence value may be generated. The confidence value may be compared to a confidence value threshold. If the confidence value is equal to or exceeds the confidence threshold, then the input data may be classified as containing at least one compression artifact. Remedial action may subsequently be applied (e.g., boosting the system with technical resources).
-
-
-
-
-
-
-
-
-
Search Results
10
records
(took 0.014 seconds)
