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    Automated computer text classification and routing using artificial intelligence transfer learning
    1.
    发明授权
    Automated computer text classification and routing using artificial intelligence transfer learning 有权

    公开(公告)号：US10678830B2

    公开(公告)日：2020-06-09

    申请号：US15994278

    申请日：2018-05-31

    申请人： FMR LLC

    发明人： Pu Li ,  Chuanlu Yu ,  Hua Hao ,  Yu Zhang ,  Dong Han

    IPC分类号： G06F16/35 ,  G06N3/04 ,  G06F17/16 ,  G06N3/08 ,  G06F16/31 ,  G06F16/335 ,  G06F16/33

    摘要： Methods and apparatuses are described for automated computer text classification and routing using artificial intelligence transfer learning. A server trains a word embedding model using one-hot vectors of word pairs from a filtered first corpus of unstructured computer text and a filtered second corpus of unstructured computer text, using an artificial intelligence neural network. The server trains a long short-term memory model using vector matrices that correspond to sentences in the filtered second corpus of unstructured computer text, and labels. The server receives a message, generates a matrix for each sentence in the message by applying the trained word embedding model, generates one or more labels, and a probability for each label, for each sentence in the message by applying the trained long short-term memory model, and routes the message to a second client computing device based upon an assigned label.

    Automated log analysis and problem solving using intelligent operation and deep learning
    2.
    发明授权
    Automated log analysis and problem solving using intelligent operation and deep learning 有权

    公开(公告)号：US10649882B2

    公开(公告)日：2020-05-12

    申请号：US15689559

    申请日：2017-08-29

    申请人： FMR LLC

    发明人： Pu Li ,  Maohua Sun ,  Hua Hao ,  Junmei Liu ,  Yuanjun Liu ,  Yucai Yang

    IPC分类号： G06F11/36 ,  G06N3/04 ,  G06N3/08 ,  G06F11/00

    摘要： A computer-implemented method of training, using a computer log file, an application error prediction engine to identify one or more application errors includes parsing the computer log file into a plurality of data sets. Each data set is associated with a unique computing session having a session identifier and ending in an application or. The method also includes extracting, from each data set, values for a specified set of parameters in each data set. The method also includes encoding the extracted values for each data set into a corresponding data structure. The method also includes generating, for each data structure, a corresponding vector, the corresponding vectors collectively forming a matrix. The method also includes calculating, based on the matrix, a set of clusters, each cluster corresponding to a known error type, the set of clusters used to create a model used to identify new error types.

    Intelligent generation of code for imputation of missing data in a machine learning dataset
    4.
    发明授权
    Intelligent generation of code for imputation of missing data in a machine learning dataset 有权

    公开(公告)号：US12014157B2

    公开(公告)日：2024-06-18

    申请号：US17898815

    申请日：2022-08-30

    申请人： FMR LLC

    发明人： Pu Li ,  Pranay Tiwari ,  Manish Worlikar ,  Alain Wilkinson ,  Ray Zhang

    IPC分类号： G06F9/44 ,  G06F8/35 ,  G06N5/01 ,  G06N5/022

    CPC分类号： G06F8/35 ,  G06N5/01 ,  G06N5/022

    摘要： Methods and apparatuses are described for intelligent imputation of missing data in a machine learning (ML) dataset comprised of a plurality of features. Each feature includes a plurality of values, where at least a portion of the values for one or more features are missing. A server analyzes the ML dataset to generate characteristics of the missing values in the ML dataset. The server selects an imputation algorithm for filling in the missing values based upon the identified characteristics. The server determines a computing environment in which the imputation algorithm is executed based upon one or more of a size of the ML dataset or the selected algorithm. The server generates code that comprises instructions for executing the imputation algorithm on the ML dataset in the computing environment. The server integrates the code into an ML platform that executes the code to assign replacement values to the missing values.

    AUTOMATED COMPUTER TEXT CLASSIFICATION AND ROUTING USING ARTIFICIAL INTELLIGENCE TRANSFER LEARNING
    6.
    发明申请
    AUTOMATED COMPUTER TEXT CLASSIFICATION AND ROUTING USING ARTIFICIAL INTELLIGENCE TRANSFER LEARNING 审中-公开

    公开(公告)号：US20190370394A1

    公开(公告)日：2019-12-05

    申请号：US15994278

    申请日：2018-05-31

    申请人： FMR LLC

    发明人： Pu Li ,  Chuanlu Yu ,  Hua Hao ,  Yu Zhang ,  Dong Han

    IPC分类号： G06F17/30 ,  G06F17/16 ,  G06N3/08 ,  G06N3/04

    摘要： Methods and apparatuses are described for automated computer text classification and routing using artificial intelligence transfer learning. A server trains a word embedding model using one-hot vectors of word pairs from a filtered first corpus of unstructured computer text and a filtered second corpus of unstructured computer text, using an artificial intelligence neural network. The server trains a long short-term memory model using vector matrices that correspond to sentences in the filtered second corpus of unstructured computer text, and labels. The server receives a message, generates a matrix for each sentence in the message by applying the trained word embedding model, generates one or more labels, and a probability for each label, for each sentence in the message by applying the trained long short-term memory model, and routes the message to a second client computing device based upon an assigned label.

    Automated Log Analysis and Problem Solving Using Intelligent Operation and Deep Learning
    7.
    发明申请
    Automated Log Analysis and Problem Solving Using Intelligent Operation and Deep Learning 审中-公开

    公开(公告)号：US20190065343A1

    公开(公告)日：2019-02-28

    申请号：US15689559

    申请日：2017-08-29

    申请人： FMR LLC

    发明人： Pu Li ,  Maohua Sun ,  Hua Hao ,  Junmei Liu ,  Yuanjun Liu ,  Yucai Yang

    IPC分类号： G06F11/36 ,  G06N3/08 ,  G06N3/04

    摘要： A computer-implemented method of training, using a computer log file, an application error prediction engine to identify one or more application errors includes parsing the computer log file into a plurality of data sets. Each data set is associated with a unique computing session having a session identifier and ending in an application or. The method also includes extracting, from each data set, values for a specified set of parameters in each data set. The method also includes encoding the extracted values for each data set into a corresponding data structure. The method also includes generating, for each data structure, a corresponding vector, the corresponding vectors collectively forming a matrix. The method also includes calculating, based on the matrix, a set of clusters, each cluster corresponding to a known error type, the set of clusters used to create a model used to identify new error types.

    INTELLIGENT GENERATION OF CODE FOR IMPUTATION OF MISSING DATA IN A MACHINE LEARNING DATASET
    8.
    发明公开
    INTELLIGENT GENERATION OF CODE FOR IMPUTATION OF MISSING DATA IN A MACHINE LEARNING DATASET 审中-公开

    公开(公告)号：US20240069874A1

    公开(公告)日：2024-02-29

    申请号：US17898815

    申请日：2022-08-30

    申请人： FMR LLC

    发明人： Pu Li ,  Pranay Tiwari ,  Manish Worlikar ,  Alain Wilkinson ,  Ray Zhang

    IPC分类号： G06F8/35 ,  G06N5/00 ,  G06N5/02

    CPC分类号： G06F8/35 ,  G06N5/003 ,  G06N5/022

    摘要： Methods and apparatuses are described for intelligent imputation of missing data in a machine learning (ML) dataset comprised of a plurality of features. Each feature includes a plurality of values, where at least a portion of the values for one or more features are missing. A server analyzes the ML dataset to generate characteristics of the missing values in the ML dataset. The server selects an imputation algorithm for filling in the missing values based upon the identified characteristics. The server determines a computing environment in which the imputation algorithm is executed based upon one or more of a size of the ML dataset or the selected algorithm. The server generates code that comprises instructions for executing the imputation algorithm on the ML dataset in the computing environment. The server integrates the code into an ML platform that executes the code to assign replacement values to the missing values.

    Story cycle time anomaly prediction and root cause identification in an agile development environment
    9.
    发明授权
    Story cycle time anomaly prediction and root cause identification in an agile development environment 有权

    公开(公告)号：US10540573B1

    公开(公告)日：2020-01-21

    申请号：US16211968

    申请日：2018-12-06

    申请人： FMR LLC

    发明人： Pu Li ,  Liang Chen ,  XiaoJia Yang ,  Zanyu Xie ,  Yulong Zhang

    IPC分类号： G06F9/44 ,  G06K9/62 ,  G06N3/08 ,  G06F11/36 ,  G06N5/00 ,  G06N20/00

    摘要： Methods and apparatuses are described for automated computer text classification and routing using artificial intelligence transfer learning. A server captures historical story data from an Agile development tracking system. For each completed story, the server generates a vector based upon story-specific features and assigns a label to the vector based upon a cycle time associated with the story. The server trains a classification model using a neural network on the vectors and labels. The server captures new story data from the Agile development tracking system. For each new story, the server generates a vector based upon story-specific features and executes the trained model on the vector to generate a cycle time prediction for the new story. Based upon the cycle time prediction, the server identifies deficiencies in the new story and generates an alert message.