公开(公告)号：US20180137037A1

公开(公告)日：2018-05-17

申请号：US15885879

申请日：2018-02-01

Applicant: International Business Machines Corporation

Inventor： Da L. Huang ,  Zhang Wu ,  Lu Yu ,  Xin Zhang ,  Yun Jie Zhou

IPC: G06F11/36

CPC classification number: G06F11/3684 ,  G06F11/3676 ,  G06F11/3688

Abstract: The source code of a software artifact may be scanned, and a call tree model with leaf nodes may be generated based on the scan. A set of test cases can be executed against the software artifact and log data from the execution can be collected. A set of untested leaf nodes can be detected and a new set of test cases can be generated to test the untested nodes. The new set of test cases are executed and a subset of the test cases which cover the previously untested nodes are added to the existing set of test cases.

公开(公告)号：US09916230B1

公开(公告)日：2018-03-13

申请号：US15677189

申请日：2017-08-15

Applicant: International Business Machines Corporation

Inventor： Da L. Huang ,  Zhang Wu ,  Lu Yu ,  Xin Zhang ,  Yun Jie Zhou

IPC: G06F9/45 ,  G06F11/36

CPC classification number: G06F11/3684 ,  G06F11/3676 ,  G06F11/3688

Abstract: The source code of a software artifact may be scanned, and a call tree model with leaf nodes may be generated based on the scan. A set of test cases can be executed against the software artifact and log data from the execution can be collected. A set of untested leaf nodes can be detected and a new set of test cases can be generated to test the untested nodes. The new set of test cases are executed and a subset of the test cases which cover the previously untested nodes are added to the existing set of test cases.

公开(公告)号：US11972525B2

公开(公告)日：2024-04-30

申请号：US17676444

申请日：2022-02-21

Applicant: International Business Machines Corporation

Inventor： Kun Yan Yin ,  Zhong Fang Yuan ,  Yi Chen Zhong ,  Lu Yu ,  Tong Liu

IPC: G06T17/10

CPC classification number: G06T17/10

Abstract: An example operation may include one or more of generating a three-dimensional (3D) model of an object via execution of a machine learning model on one or more images of the object, capturing a plurality of snapshots of the 3D model of the object at different angles to generate a plurality of snapshot images of the object, fusing a feature into each of the plurality of snapshots to generate a plurality of fused snapshots of the 3D model of the object, and storing the plurality of fused snapshots of the 3D model of the object in memory.

公开(公告)号：US11928117B2

公开(公告)日：2024-03-12

申请号：US17355312

申请日：2021-06-23

Applicant: International Business Machines Corporation

Inventor： Wen Wang ,  Yi Chen Zhong ,  Kun Yan Yin ,  De Shuo Kong ,  Lu Yu ,  Yi Ming Wang

IPC: G06F16/24 ,  G06F16/2457 ,  G06N5/02 ,  H04N21/4788 ,  G06F40/205 ,  H04L67/10

CPC classification number: G06F16/24578 ,  G06N5/02 ,  H04N21/4788 ,  G06F40/205 ,  H04L67/10

Abstract: Embodiments of the present invention relate to methods, systems, and computer program products for managing a plurality of live comments. A plurality of live comments is obtained for a video, the plurality of live comments being associated with a plurality of fragments in the video, respectively. A plurality of features are extracted from the plurality of live comments, respectively. A knowledge base is generated for the plurality of live comments based on the plurality of features. With these embodiments, the live comments may be managed in an effective way. Further, the knowledge base may provide answers to a user query.

公开(公告)号：US20230121812A1

公开(公告)日：2023-04-20

申请号：US17502791

申请日：2021-10-15

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Kun Yan Yin ,  Xue Ping Liu ,  Yun Jing Zhao ,  Fei Wang ,  Yu Tao Wu ,  Lu Yu

IPC: G06N3/08 ,  G06K9/62

Abstract: Data augmentation is described to train an artificial intelligence model that includes analyzing a first data set to measure an amount of data in the data set and the variation in the amount of data in the first data set to determine deficiencies for training an artificial intelligence model. Augmenting data is added for the first data set having an amount of data measured that fails to meet a threshold value. Deficiencies in the variation in the amount of data in the first data set are augmented using augmentation methods outside the variation scope of the first data set to provide a second data set of augmented data. An artificial intelligence model is trained with a combined data set of the first data set, and the second data set of augmented data when the first and second data set have an amount of data meeting the threshold value.

公开(公告)号：US20180089070A1

公开(公告)日：2018-03-29

申请号：US15677189

申请日：2017-08-15

Applicant: International Business Machines Corporation

Inventor： Da L. Huang ,  Zhang Wu ,  Lu Yu ,  Xin Zhang ,  Yun Jie Zhou

IPC: G06F11/36

CPC classification number: G06F11/3684 ,  G06F11/3676 ,  G06F11/3688

Abstract: The source code of a software artifact may be scanned, and a call tree model with leaf nodes may be generated based on the scan. A set of test cases can be executed against the software artifact and log data from the execution can be collected. A set of untested leaf nodes can be detected and a new set of test cases can be generated to test the untested nodes. The new set of test cases are executed and a subset of the test cases which cover the previously untested nodes are added to the existing set of test cases.

公开(公告)号：US20230041181A1

公开(公告)日：2023-02-09

申请号：US17444382

申请日：2021-08-03

Applicant: International Business Machines Corporation

Inventor： Wen Wang ,  Yi Chen Zhong ,  Yi Ming Wang ,  Lu Yu ,  Liu Yao He

IPC: G06F16/242 ,  G06F16/2455 ,  G06N20/00

Abstract: A computer retrieves data from a database. The computer retrieves a Machine Learning (ML) model trained to generate database queries. The computer applies the ML model to generate a primary database query based, at least in part, on a user inquiry available to the computer. The computer retrieves the primary database query, an initial set of data from a database available to the computer. The computer, in response to retrieving the initial set of data, receives feedback assessing the initial set of data. The computer, in response to receiving the feedback, applies a Natural Language Processing (NLP) model to identify query adjustment content within the feedback. The computer revises the primary database query based, at least in part, on the model adjustment content, to generate a secondary database query. The computer retrieves using the secondary database query, a secondary set of data from the database.