Fmr Corp.

Pu Li

Yu Zhang

Hua Hao

Dong Han

Liang Chen

Yucai Yang

Maohua Sun

XiaoJia Yang

Zanyu Xie

Junmei Liu

Yulong Zhang

Chuanlu Yu

Yuanjun Liu

Jianhua Sun

Methods and apparatuses are described for unstructured computer text is analyzed for masking of confidential information using artificial intelligence. A client device generates a message comprising unstructured computer text including confidential information. A server trains a word embedding model using the unstructured text. The server generates a multidimensional vector for each word in the unstructured text, generates a mapping table comprising a predetermined set of words corresponding to confidential information from the unstructured text, and determines one or more neighboring words in the trained word embedding model using the predetermined set of words. The server updates the mapping table to incorporate the one or more neighboring words and executes rules on the unstructured text that filter out one or more words, and applies the updated mapping table to match words in the updated mapping table with words in the filtered text and mask the matching words in the unstructured text.

Automated masking of confidential information in unstructured computer text using artificial intelligence

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 computer text classification and routing using artificial intelligence transfer learning

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.

Automated log analysis and problem solving using intelligent operation and deep learning

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.

Story cycle time anomaly prediction and root cause identification in an agile development environment

Methods and apparatuses are described for automated execution of computer software using intelligent speech recognition techniques. A server captures a digitized voice segment from a remote device, the first digitized voice segment corresponding to speech submitted by a user of the remote device during a voice call. The server analyzes the digitized voice segment to convert the digitized voice segment into text and extract a set of keywords from the converted text. A client computing device displays the extracted keywords in a user interface of a display device. The server determines one or more computer software applications accessible to the client computing device and selects least one of the computer software applications that include functionality responsive to the keywords. The client computing device executes the functionality of the selected computer software applications that is responsive to the keywords.

Automated software execution using intelligent speech recognition

Growing community of inventors

Dalian, China

Pu Li