IDENTIFYING MULTIMEDIA ASSET SIMILARITY USING BLENDED SEMANTIC AND LATENT FEATURE ANALYSIS

    公开(公告)号:US20240202455A1

    公开(公告)日:2024-06-20

    申请号:US18592140

    申请日:2024-02-29

    CPC classification number: G06F40/30 G06F16/41 G06F16/43

    Abstract: Methods and system for determining a similarity relationship between a plurality of digital assets and a target digital asset comprises creating a normalized semantic feature vector associated with a search query, discovering the target asset based on the normalized semantic feature vector, generating a normalized latent feature vector associated with the target asset, comparing the normalized semantic feature vector with semantic feature vectors for each of the digital assets to generate a semantic comparison value, comparing the normalized target latent feature vector with latent feature vectors for each of the digital assets to generate a latent comparison value, blending the semantic comparison vector value with the latent feature comparison vector value to create a target comparison value for each of the digital assets, and reporting the digital assets having the highest target comparison values to the user or group of users.

    Identifying multimedia asset similarity using blended semantic and latent feature analysis

    公开(公告)号:US11580306B2

    公开(公告)日:2023-02-14

    申请号:US16419547

    申请日:2019-05-22

    Abstract: Methods and system for determining a similarity relationship between a plurality of digital assets and a target digital asset comprises creating a normalized semantic feature vector associated with a search query, discovering the target asset based on the normalized semantic feature vector, generating a normalized latent feature vector associated with the target asset, comparing the normalized semantic feature vector with semantic feature vectors for each of the digital assets to generate a semantic comparison value, comparing the normalized target latent feature vector with latent feature vectors for each of the digital assets to generate a latent comparison value, blending the semantic comparison vector value with the latent feature comparison vector value to create a target comparison value for each of the digital assets, and reporting the digital assets having the highest target comparison values to the user or group of users.

    IDENTIFYING MULTIMEDIA ASSET SIMILARITY USING BLENDED SEMANTIC AND LATENT FEATURE ANALYSIS

    公开(公告)号:US20230161967A1

    公开(公告)日:2023-05-25

    申请号:US18093924

    申请日:2023-01-06

    CPC classification number: G06F40/30 G06F16/43 G06F16/41

    Abstract: Methods and system for determining a similarity relationship between a plurality of digital assets and a target digital asset comprises creating a normalized semantic feature vector associated with a search query, discovering the target asset based on the normalized semantic feature vector, generating a normalized latent feature vector associated with the target asset, comparing the normalized semantic feature vector with semantic feature vectors for each of the digital assets to generate a semantic comparison value, comparing the normalized target latent feature vector with latent feature vectors for each of the digital assets to generate a latent comparison value, blending the semantic comparison vector value with the latent feature comparison vector value to create a target comparison value for each of the digital assets, and reporting the digital assets having the highest target comparison values to the user or group of users.

    IDENTIFYING MULTIMEDIA ASSET SIMILARITY USING BLENDED SEMANTIC AND LATENT FEATURE ANALYSIS

    公开(公告)号:US20190272326A1

    公开(公告)日:2019-09-05

    申请号:US16419547

    申请日:2019-05-22

    Abstract: Methods and system for determining a similarity relationship between a plurality of digital assets and a target digital asset comprises creating a normalized semantic feature vector associated with a search query, discovering the target asset based on the normalized semantic feature vector, generating a normalized latent feature vector associated with the target asset, comparing the normalized semantic feature vector with semantic feature vectors for each of the digital assets to generate a semantic comparison value, comparing the normalized target latent feature vector with latent feature vectors for each of the digital assets to generate a latent comparison value, blending the semantic comparison vector value with the latent feature comparison vector value to create a target comparison value for each of the digital assets, and reporting the digital assets having the highest target comparison values to the user or group of users.

    Identifying multimedia asset similarity using blended semantic and latent feature analysis

    公开(公告)号:US10331785B2

    公开(公告)日:2019-06-25

    申请号:US13778771

    申请日:2013-02-27

    Abstract: Methods and system for determining a similarity relationship between a plurality of digital assets and a target digital asset comprises creating a normalized semantic feature vector associated with a search query, discovering the target asset based on the normalized semantic feature vector, generating a normalized latent feature vector associated with the target asset, comparing the normalized semantic feature vector with semantic feature vectors for each of the digital assets to generate a semantic comparison value, comparing the normalized target latent feature vector with latent feature vectors for each of the digital assets to generate a latent comparison value, blending the semantic comparison vector value with the latent feature comparison vector value to create a target comparison value for each of the digital assets, and reporting the digital assets having the highest target comparison values to the user or group of users.

    IDENTIFYING MULTIMEDIA ASSET SIMILARITY USING BLENDED SEMANTIC AND LATENT FEATURE ANALYSIS

    公开(公告)号:US20240202456A1

    公开(公告)日:2024-06-20

    申请号:US18592150

    申请日:2024-02-29

    CPC classification number: G06F40/30 G06F16/41 G06F16/43

    Abstract: Methods and system for determining a similarity relationship between a plurality of digital assets and a target digital asset comprises creating a normalized semantic feature vector associated with a search query, discovering the target asset based on the normalized semantic feature vector, generating a normalized latent feature vector associated with the target asset, comparing the normalized semantic feature vector with semantic feature vectors for each of the digital assets to generate a semantic comparison value, comparing the normalized target latent feature vector with latent feature vectors for each of the digital assets to generate a latent comparison value, blending the semantic comparison vector value with the latent feature comparison vector value to create a target comparison value for each of the digital assets, and reporting the digital assets having the highest target comparison values to the user or group of users.

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