公开(公告)号：US20240420850A1

公开(公告)日：2024-12-19

申请号：US18640453

申请日：2024-04-19

Applicant: Quantum-Si Incorporated

Inventor： Marylens Hernandez ,  Umut Eser ,  Michael Meyer ,  Henri Lichenstein ,  Tian Xu ,  Jonathan M. Rothberg

IPC: G16H70/40 ,  G16H50/20 ,  G16H50/50

Abstract: Methods and apparatus for predicting an association between input data in a first modality and data in a second modality using a statistical model trained to represent interactions between data having a plurality of modalities including the first modality and the second modality, the statistical model comprising a plurality of encoders and decoders, each of which is trained to process data for one of the plurality of modalities, and a joint-modality representation coupling the plurality of encoders and decoders. The method comprises selecting, based on the first modality and the second modality, an encoder/decoder pair or a pair of encoders, from among the plurality of encoders and decoders, and processing the input data with the joint-modality representation and the selected encoder/decoder pair or pair of encoders to predict the association between the input data and the data in the second modality.

公开(公告)号：US20190371476A1

公开(公告)日：2019-12-05

申请号：US16406993

申请日：2019-05-08

Applicant: Quantum-Si Incorporated

Inventor： Marylens Hernandez ,  Umut Eser ,  Michael Meyer ,  Henri Lichenstein ,  Tian Xu ,  Jonathan M. Rothberg

IPC: G16H70/40 ,  G16H50/20 ,  G16H50/50

Abstract: Methods and apparatus for predicting an association between input data in a first modality and data in a second modality using a statistical model trained to represent interactions between data having a plurality of modalities including the first modality and the second modality, the statistical model comprising a plurality of encoders and decoders, each of which is trained to process data for one of the plurality of modalities, and a joint-modality representation coupling the plurality of encoders and decoders. The method comprises selecting, based on the first modality and the second modality, an encoder/decoder pair or a pair of encoders, from among the plurality of encoders and decoders, and processing the input data with the joint-modality representation and the selected encoder/decoder pair or pair of encoders to predict the association between the input data and the data in the second modality.

公开(公告)号：US11967436B2

公开(公告)日：2024-04-23

申请号：US16406993

申请日：2019-05-08

Applicant: Quantum-Si Incorporated

Inventor： Marylens Hernandez ,  Umut Eser ,  Michael Meyer ,  Henri Lichenstein ,  Tian Xu ,  Jonathan M. Rothberg

IPC: G16H70/40 ,  G16H50/20 ,  G16H50/50

CPC classification number: G16H70/40 ,  G16H50/20 ,  G16H50/50

Abstract: Methods and apparatus for predicting an association between input data in a first modality and data in a second modality using a statistical model trained to represent interactions between data having a plurality of modalities including the first modality and the second modality, the statistical model comprising a plurality of encoders and decoders, each of which is trained to process data for one of the plurality of modalities, and a joint-modality representation coupling the plurality of encoders and decoders. The method comprises selecting, based on the first modality and the second modality, an encoder/decoder pair or a pair of encoders, from among the plurality of encoders and decoders, and processing the input data with the joint-modality representation and the selected encoder/decoder pair or pair of encoders to predict the association between the input data and the data in the second modality.

公开(公告)号：US20200350081A9

公开(公告)日：2020-11-05

申请号：US16406993

申请日：2019-05-08

Applicant: Quantum-Si Incorporated

Inventor： Marylens Hernandez ,  Umut Eser ,  Michael Meyer ,  Henri Lichenstein ,  Tian Xu ,  Jonathan M. Rothberg

IPC: G16H70/40 ,  G16H50/20 ,  G16H50/50

Abstract: Methods and apparatus for predicting an association between input data in a first modality and data in a second modality using a statistical model trained to represent interactions between data having a plurality of modalities including the first modality and the second modality, the statistical model comprising a plurality of encoders and decoders, each of which is trained to process data for one of the plurality of modalities, and a joint-modality representation coupling the plurality of encoders and decoders. The method comprises selecting, based on the first modality and the second modality, an encoder/decoder pair or a pair of encoders, from among the plurality of encoders and decoders, and processing the input data with the joint-modality representation and the selected encoder/decoder pair or pair of encoders to predict the association between the input data and the data in the second modality.