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    Key-value memory network for predicting time-series metrics of target entities
    2.
    发明授权
    Key-value memory network for predicting time-series metrics of target entities 有权

    公开(公告)号：US11501107B2

    公开(公告)日：2022-11-15

    申请号：US16868942

    申请日：2020-05-07

    Applicant: Adobe Inc.

    Inventor： Ayush Chauhan ,  Shiv Kumar Saini ,  Parth Gupta ,  Archiki Prasad ,  Amireddy Prashanth Reddy ,  Ritwick Chaudhry

    IPC: G06K9/62 ,  G06N3/063 ,  G11C16/14 ,  G06Q10/10 ,  G06F7/544

    Abstract: This disclosure involves using key-value memory networks to predict time-series data. For instance, a computing system retrieves, for a target entity, static feature data and target time-series feature data. The computing system can normalize the target time-series feature data based on a normalization scale. The computing system also generates input data by, for example, concatenating the static feature data, the normalized time-series feature data, and time-specific feature data. The computing system generates predicted time-series data for the target metric of the target entity by applying a key-value memory network to the input data. The key-value memory network can include a key matrix learned from training static feature data and training time-series feature data, a value matrix representing time-series trends, and an output layer with a continuous activation function for generating predicted time-series data.

    KEY-VALUE MEMORY NETWORK FOR PREDICTING TIME-SERIES METRICS OF TARGET ENTITIES
    3.
    发明申请
    KEY-VALUE MEMORY NETWORK FOR PREDICTING TIME-SERIES METRICS OF TARGET ENTITIES 有权

    公开(公告)号：US20230031050A1

    公开(公告)日：2023-02-02

    申请号：US17960585

    申请日：2022-10-05

    Applicant: Adobe Inc.

    Inventor： Ayush Chauhan ,  Shiv Kumar Saini ,  Parth Gupta ,  Archiki Prasad ,  Amireddy Prashanth Reddy ,  Ritwick Chaudhry

    IPC: G06K9/62 ,  G06N3/063

    Abstract: A system implements a key value memory network including a key matrix with key vectors learned from training static feature data and time-series feature data, a value matrix with value vectors representing time-series trends, and an input layer to receive, for a target entity, input data comprising a concatenation of static feature data of the target entity, time-specific feature data, and time-series feature data for the target entity. The key value memory network also includes an entity-embedding layer to generate an input vector from the input data, a key-addressing layer to generate a weight vector indicating similarities between the key vectors and the input vector, a value-reading layer to compute a context vector from the weight and value vectors, and an output layer to generate predicted time-series data for a target metric of the target entity by applying a continuous activation function to the context vector and the input vector.

    Chart question answering
    4.
    发明授权
    Chart question answering 有权

    公开(公告)号：US11288324B2

    公开(公告)日：2022-03-29

    申请号：US16749044

    申请日：2020-01-22

    Applicant: ADOBE INC.

    Inventor： Sumit Shekhar ,  Ritwick Chaudhry ,  Utkarsh Gupta ,  Prann Bansal ,  Ajay Shridhar Joshi

    IPC: G06F17/00 ,  G06F16/903 ,  G06F16/9032 ,  G06N3/08

    Abstract: A method, apparatus, and non-transitory computer readable medium for chart question answering are described. The method, apparatus, and non-transitory computer readable medium may receive a text query about a chart, identify a plurality of chart elements in the chart, associate a text string from the text query with corresponding chart elements from the plurality of chart elements, replace the text string in the text query with arbitrary rare words based on the association to produce an encoded query, generate an embedded query based on the encoded query, generate an image feature vector based on the chart, combine the embedded query and the image feature vector to produce a combined feature vector, compute an answer probability vector based on the combined feature vector, and provide an answer to the text query based on the answer probability vector.

    Parsing and reflowing infographics using structured lists and groups
    7.
    发明授权
    Parsing and reflowing infographics using structured lists and groups 有权

    公开(公告)号：US11769006B2

    公开(公告)日：2023-09-26

    申请号：US16929903

    申请日：2020-07-15

    Applicant: Adobe Inc.

    Inventor： Sumit Shekhar ,  Zoya Bylinskii ,  Tushar Gurjar ,  Ritwick Chaudhry ,  Ayush Goyal

    IPC: G06F40/205 ,  G06F16/9538 ,  G06F16/9032 ,  G06N20/00

    CPC classification number: G06F40/205 ,  G06F16/90332 ,  G06F16/9538 ,  G06N20/00

    Abstract: This disclosure describes methods, systems, and non-transitory computer readable media for automatically parsing infographics into segments corresponding to structured groups or lists and displaying the identified segments or reflowing the segments into various computing tasks. For example, the disclosed systems may utilize a novel infographic grouping taxonomy and annotation system to group elements within infographics. The disclosed systems can train and apply a machine-learning-detection model to generate infographic segments according to the infographic grouping taxonomy. By generating infographic segments, the disclosed systems can facilitate computing tasks, such as converting infographics into digital presentation graphics (e.g., slide carousels), reflow the infographic into query-and-response models, perform search functions, or other computational tasks.

    KEY-VALUE MEMORY NETWORK FOR PREDICTING TIME-SERIES METRICS OF TARGET ENTITIES
    9.
    发明申请
    KEY-VALUE MEMORY NETWORK FOR PREDICTING TIME-SERIES METRICS OF TARGET ENTITIES 有权

    公开(公告)号：US20210350175A1

    公开(公告)日：2021-11-11

    申请号：US16868942

    申请日：2020-05-07

    Applicant: Adobe Inc.

    Inventor： Ayush Chauhan ,  Shiv Kumar Saini ,  Parth Gupta ,  Archiki Prasad ,  Amireddy Prashanth Reddy ,  Ritwick Chaudhry

    IPC: G06K9/62 ,  G06F7/544 ,  G11C16/14 ,  G06Q10/10 ,  G06N3/063

    Abstract: This disclosure involves using key-value memory networks to predict time-series data. For instance, a computing system retrieves, for a target entity, static feature data and target time-series feature data. The computing system can normalize the target time-series feature data based on a normalization scale. The computing system also generates input data by, for example, concatenating the static feature data, the normalized time-series feature data, and time-specific feature data. The computing system generates predicted time-series data for the target metric of the target entity by applying a key-value memory network to the input data. The key-value memory network can include a key matrix learned from training static feature data and training time-series feature data, a value matrix representing time-series trends, and an output layer with a continuous activation function for generating predicted time-series data.

    Personalized e-learning using a deep-learning-based knowledge tracing and hint-taking propensity model
    10.
    发明授权
    Personalized e-learning using a deep-learning-based knowledge tracing and hint-taking propensity model 有权

    公开(公告)号：US10943497B2

    公开(公告)日：2021-03-09

    申请号：US15964869

    申请日：2018-04-27

    Applicant: ADOBE INC.

    Inventor： Shiv Kumar Saini ,  Ritwick Chaudhry ,  Pradeep Dogga ,  Harvineet Singh

    IPC: G09B7/00 ,  G06N7/00 ,  G09B5/06 ,  G06N20/00

    Abstract: Techniques are described for jointly modeling knowledge tracing and hint-taking propensity. During a read phase, a co-learning model accepts as inputs an identification of a question and the current knowledge state for a learner, and the model predicts probabilities that the learner will answer the question correctly and that the learner will use a learning aid (e.g., accept a hint). The predictions are used to personalize an e-learning plan, for example, to provide a personalized assessment. By using these predictions to personalize a learner's experience, for example, by offering hints at optimal times, the co-learning system increases efficiencies in learning and improves learning outcomes. Once a learner has interacted with a question, the interaction is encoded and provided to the co-learning model to update the learner's knowledge state during an update phase.

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