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    Auto generating reasoning query on a knowledge graph
    1.
    发明授权
    Auto generating reasoning query on a knowledge graph 有权

    公开(公告)号：US11227018B2

    公开(公告)日：2022-01-18

    申请号：US16454774

    申请日：2019-06-27

    Applicant: International Business Machines Corporation

    Inventor： Geetha Adinarayan ,  Hari Hara Prasad Viswanathan ,  Sathiskumar Palaniappan ,  Amit Mohan Mangalvedkar

    IPC: G06F16/901 ,  G06F16/9032 ,  G06F16/904

    Abstract: Aspects of the present invention disclose a method for generating a reasoning query based on a user selection of a generated data visualization of a knowledge graph. The method includes one or more processors generating a knowledge graph of a domain. The method further includes constructing a hierarchy of the knowledge graph. The method further includes generating a data visualization of the domain based at least in part on the hierarchy of the knowledge graph. The method further includes identifying a user selection of one or more nodes of the data visualization. The method further includes generating a reasoning query corresponding to the domain based on the data visualization of the domain and the user selection. The method further includes determining whether the knowledge graph includes a collection of nodes that are on a level of the constructed hierarchy that corresponds to a level of the one or more nodes.

    Identifying anomalies in data during data outage
    2.
    发明授权
    Identifying anomalies in data during data outage 有权

    公开(公告)号：US11221934B2

    公开(公告)日：2022-01-11

    申请号：US16739496

    申请日：2020-01-10

    Applicant: International Business Machines Corporation

    Inventor： Mansoor Ahmed ,  Sattwati Kundu ,  Nair Raghunath Eledath ,  Geetha Adinarayan

    IPC: G06F11/30 ,  G06N20/00 ,  G06F11/32 ,  G06F11/07

    Abstract: A computer-implemented method, system and computer program product for identifying anomalies in data during a data outage. An anomaly detection model is built using data received from a sensor at a characterized granularity. Once a period of service occurs following a data outage, a quantum of missing data during the data outage is identified using predictive modeling if the data during the data outage is not available at the granularity in which the anomaly detection model is built. The identified quantum of missing data is retrofitted into a predicted pattern during the data outage and the analytics are then re-run on the retrofitted quantum of missing data in the predicted pattern to identify anomalies during the data outage. In this manner, anomalies in data, such as data from sensor readings, can be identified during the data outage thereby enabling the model to provide more accurate predictions of anomalies occurring during the data outage.

    Identifying and controlling unwanted calls
    4.
    发明授权
    Identifying and controlling unwanted calls 有权

    公开(公告)号：US10212279B1

    公开(公告)日：2019-02-19

    申请号：US15837074

    申请日：2017-12-11

    Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

    Inventor： Geetha Adinarayan ,  Dinesh Radhakrishnan ,  Akshat Dixit ,  Gandhi Sivakumar

    IPC: H04M3/436 ,  G10L25/63 ,  H04M1/2745 ,  H04N1/32

    Abstract: Systems, methods and tools for leveraging computer networks, data sharing, data analytics and sentiment analysis to control the receipt of unwanted telephone calls and preventing unwanted telephone calls from disturbing a user through the implementation of contacts list and/or a shared table of unique calls. Contact lists and shared tables of unique calls may identify incoming calls that may be directed to the user or filtered out based on whether the incoming call's number is in the user's contact list and/or based on the past experiences of the individual users engaging with the incoming caller. Embodiments may track the past call durations for unique incoming number and track the sentiment of users during these calls and use the collected call data and sentiment determine whether subsequent incoming calls from a phone number should be forwarded to the user's telephone enabled device or automatically responded to with further instructions.

    ANALYTICALLY SELECTING WHICH TESTS ARE TO BE EXECUTED IN A CONTINUOUS DELIVERY PROCESS
    5.
    发明申请
    ANALYTICALLY SELECTING WHICH TESTS ARE TO BE EXECUTED IN A CONTINUOUS DELIVERY PROCESS 审中-公开

    公开(公告)号：US20170364435A1

    公开(公告)日：2017-12-21

    申请号：US15689468

    申请日：2017-08-29

    Applicant: International Business Machines Corporation

    Inventor： Geetha Adinarayan ,  Shaw-Ben S. Shi ,  Gandhi Sivakumar ,  Meng H. Tsai

    IPC: G06F11/36

    CPC classification number: G06F11/3672 ,  G06F11/368 ,  G06F11/3688

    Abstract: A method, system and computer program product for analytically selecting which tests are to be executed in a continuous delivery process. An analytics processing system constructs a code dependency tree to analyze the tests that are affected by changes in code after a new build is generated. After analyzing the code dependency tree, the system eliminates those tests in the code dependency tree that do not depend on the changed code. The system then analyzes the historical execution records for those tests that have not been eliminated for execution to obtain an execution rate and a failure rate for those tests. A score is generated for each of the tests from the code dependency tree that were not eliminated for execution based on the historical execution rate and failure rate of the test. Tests that have a score that exceeds a threshold are included for execution.

    PERSONALIZED MARKETING INCENTIVES BASED ON HISTORICAL INFORMATION AND MOBILITY MONITORING
    6.
    发明申请
    PERSONALIZED MARKETING INCENTIVES BASED ON HISTORICAL INFORMATION AND MOBILITY MONITORING 审中-公开

    公开(公告)号：US20170091823A1

    公开(公告)日：2017-03-30

    申请号：US14870479

    申请日：2015-09-30

    Applicant: International Business Machines Corporation

    Inventor： Geetha Adinarayan ,  Shaw-Ben S. Shi ,  Gandhi Sivakumar ,  Meng Hong Tsai

    IPC: G06Q30/02 ,  H04W4/04

    CPC classification number: G06Q30/0269 ,  G06Q30/0267 ,  H04W4/026 ,  H04W4/027 ,  H04W4/029 ,  H04W4/33

    Abstract: One embodiment for determining a marketing incentive for a user of an electronic device. In one embodiment, a computer processor detects a first electronic device within a retail environment utilizing a second electronic device that also identifies information associated with the first electronic device. In one embodiment, a computer processor determines a behavior associated with the first electronic device based, at least in part, on movement of the first electronic device within the retail environment. In one embodiment, a computer processor identifies data associated with the retail environment that includes information associated with a retailer associated with the retail environment and information associated with the first electronic device. In one embodiment, a computer processor determines a first marketing incentive based, at least in part, on the determined behavior associated with the first electronic device and the identified data associated with the retail environment.

    Data quality-based confidence computations for KPIs derived from time-series data
    8.
    发明授权
    Data quality-based confidence computations for KPIs derived from time-series data 有权

    公开(公告)号：US11314584B1

    公开(公告)日：2022-04-26

    申请号：US17105036

    申请日：2020-11-25

    Applicant: International Business Machines Corporation

    Inventor： Vitobha Munigala ,  Diptikalyan Saha ,  Sattwati Kundu ,  Geetha Adinarayan

    IPC: G06F11/00 ,  G06F11/07 ,  G06N5/04 ,  G06F16/23

    Abstract: A system, computer program product, and method are presented for providing confidence values for replacement data for data that has issues indicative of errors, where the data issues, the replacement data, and confidence values are related to one or more KPIs. The method includes identifying one or more potentially erroneous data instances and determining one or more predicted replacement values for the potentially erroneous data instances. The method further includes determining a confidence value for each predicted replacement value and resolving the one or more potentially erroneous data instances with one predicted replacement value of the one or more predicted replacement values. The method also includes generating an explanatory basis for the resolution of the one or more potentially erroneous data instances.

    Identifying anomalies in data during data outage
    9.
    发明授权
    Identifying anomalies in data during data outage 有权

    公开(公告)号：US11288155B2

    公开(公告)日：2022-03-29

    申请号：US17128016

    申请日：2020-12-19

    Applicant: International Business Machines Corporation

    Inventor： Mansoor Ahmed ,  Sattwati Kundu ,  Nair Raghunath Eledath ,  Geetha Adinarayan

    IPC: G06F11/30 ,  G06N20/00 ,  G06F11/32 ,  G06F11/07

    Abstract: A computer-implemented method, system and computer program product for identifying anomalies in data during a data outage. An anomaly detection model is built using data received from a sensor at a characterized granularity. Once a period of service occurs following a data outage, a quantum of missing data during the data outage is identified using predictive modeling if the data during the data outage is not available at the granularity in which the anomaly detection model is built. The identified quantum of missing data is retrofitted into a predicted pattern during the data outage and the analytics are then re-run on the retrofitted quantum of missing data in the predicted pattern to identify anomalies during the data outage. In this manner, anomalies in data, such as data from sensor readings, can be identified during the data outage thereby enabling the model to provide more accurate predictions of anomalies occurring during the data outage.

    IDENTIFYING ANOMALIES IN DATA DURING DATA OUTAGE
    10.
    发明申请
    IDENTIFYING ANOMALIES IN DATA DURING DATA OUTAGE 有权

    公开(公告)号：US20210216422A1

    公开(公告)日：2021-07-15

    申请号：US16739496

    申请日：2020-01-10

    Applicant: International Business Machines Corporation

    Inventor： Mansoor Ahmed ,  Sattwati Kundu ,  Nair Raghunath Eledath ,  Geetha Adinarayan

    IPC: G06F11/30 ,  G06F11/32 ,  G06F11/07 ,  G06N20/00

    Abstract: A computer-implemented method, system and computer program product for identifying anomalies in data during a data outage. An anomaly detection model is built using data received from a sensor at a characterized granularity. Once a period of service occurs following a data outage, a quantum of missing data during the data outage is identified using predictive modeling if the data during the data outage is not available at the granularity in which the anomaly detection model is built. The identified quantum of missing data is retrofitted into a predicted pattern during the data outage and the analytics are then re-run on the retrofitted quantum of missing data in the predicted pattern to identify anomalies during the data outage. In this manner, anomalies in data, such as data from sensor readings, can be identified during the data outage thereby enabling the model to provide more accurate predictions of anomalies occurring during the data outage.

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