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Небесная энциклопедия

Космические корабли и станции, автоматические КА и методы их проектирования, бортовые комплексы управления, системы и средства жизнеобеспечения, особенности технологии производства ракетно-космических систем

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Мониторинг СМИ и социальных сетей. Сканирование интернета, новостных сайтов, специализированных контентных площадок на базе мессенджеров. Гибкие настройки фильтров и первоначальных источников.

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Применить Всего найдено 4. Отображено 4.
03-12-2015 дата публикации

Predicting the Severity of an Active Support Ticket

Номер: US20150347906A1
Автор: Gabriele Bodda, Ryan Currier, Venkitesh Subramanian, Prerna Makanawala, Rei Kasai, Devasena Rajamohan, Amith Manoharan Chithambaram, Terence Chesire, Kiran Karadi
Принадлежит:

An issue tracking system capable of predicting the likelihood that an outcome of interest will occur during the lifecycle of an active support ticket. The likelihood can be represented as a severity index score. The issue tracking system can apply a predictive algorithm on attributes of the active support ticket to generate the severity index score. The predictive algorithm to use can depend on the outcome of interest while the correlation factors used to configure the predictive algorithm can depend on support tickets that have already been completed. 1. A computer-implemented method , comprising:identifying, by a processor, an outcome of interest related to support tickets created to report issues with a product or service;retrieving, by the processor and from a predictive analysis engine, a plurality of correlation factors that are correlated with the outcome of interest;retrieving, by the processor, an active support ticket; andapplying, by the processor, a predictive algorithm to the active support ticket to generate a severity index score configured to measure the likelihood that the outcome of interest will result during the lifecycle of the active support ticket, wherein the predictive algorithm is configured using the plurality of correlation factors.2. The computer-implemented method of claim 1 , wherein the plurality of correlation factors are ranked.3. The computer-implemented method of claim 1 , wherein each of the correlation factors is an attribute configurable on the support tickets.4. The computer-implemented method of claim 1 , further comprising:selecting, by the processor, the predictive algorithm according to the outcome of interest.5. The computer-implemented method of claim 1 , further comprising:detecting, by the processor, a change to the active support ticket; andreapplying, by the processor, the predictive algorithm to the active support ticket to regenerate the severity index score.6. The computer-implemented method of claim 1 , wherein ...

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Номер записи: 1
10-10-2017 дата публикации

Predicting the severity of an active support ticket

Номер: US0009785918B2
Автор: Gabriele Bodda, Ryan Currier, Venkitesh Subramanian, Prerna Makanawala, Rei Kasai, Devasena Rajamohan, Amith Manoharan Chithambaram, Terence Chesire, Kiran Karadi, BODDA GABRIELE, CURRIER RYAN, SUBRAMANIAN VENKITESH, MAKANAWALA PRERNA, KASAI REI, RAJAMOHAN DEVASENA, CHITHAMBARAM AMITH MANOHARAN, CHESIRE TERENCE, KARADI KIRAN, Bodda Gabriele, Currier Ryan, Subramanian Venkitesh, Makanawala Prerna, Kasai Rei, Rajamohan Devasena, Chithambaram Amith Manoharan, Chesire Terence, Karadi Kiran
Принадлежит: SAP SE, BODDA GABRIELE, CURRIER RYAN, SUBRAMANIAN VENKITESH, MAKANAWALA PRERNA, KASAI REI, RAJAMOHAN DEVASENA, CHITHAMBARAM AMITH MANOHARAN, CHESIRE TERENCE, KARADI KIRAN, Bodda Gabriele, Currier Ryan, Subramanian Venkitesh, Makanawala Prerna, Kasai Rei, Rajamohan Devasena, Chithambaram Amith Manoharan, Chesire Terence, Karadi Kiran

An issue tracking system capable of predicting the likelihood that an outcome of interest will occur during the lifecycle of an active support ticket. The likelihood can be represented as a severity index score. The issue tracking system can apply a predictive algorithm on attributes of the active support ticket to generate the severity index score. The predictive algorithm to use can depend on the outcome of interest while the correlation factors used to configure the predictive algorithm can depend on support tickets that have already been completed.

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Номер записи: 2
03-12-2015 дата публикации

Providing Recommendations Through Predictive Analytics

Номер: US20150348051A1
Автор: Gabriele Bodda, Ryan Currier, Venkitesh Subramanian, Prerna Makanawala, Rei Kasai, Devasena Rajamohan, Amith Manoharan Chithambaram, Terence Chesire, Kiran Karadi
Принадлежит:

A recommendation engine analyzes metrics on an active support ticket to provide recommended solutions, technicians and offers. The recommendation can communicate with a predictive analysis engine to identify solutions, technicians, or offers that are highly correlated with the input parameters. In some embodiments, instructions can be provided to a client device for measuring a metric that is used as an input parameter of the predictive analysis engine. The customer or the technician can follow the instructions to measure the metric. 1. A computer-implemented method , comprising:receiving, from a client device, an issue report configured to report a problem experienced with a sales item;identifying, by a processor, a metric associated with the sales item that is missing in the issue report;transmitting, by the processor, a measurement request to the client device to retrieve the metric;receiving, by the processor, the metric from the client device;performing, by the processor, a query on a predictive analysis engine to generate a ranked list of solutions that are applicable to the issue, the query including the metric;selecting, by the processor, a recommended solution from the ranked list; andtransmitting, by the processor, the recommended solution to the client device.2. The computer-implemented method of claim 1 , wherein identifying the metric comprises:identifying, by the processor, a plurality of metrics utilized by the predictive algorithm to generate the ranked list; anddetermining, by the processor, that the metric is missing in the issue report.3. The computer-implemented method of claim 1 , wherein the measurement request includes at least one user instruction to retrieve the metric using the client device.4. The computer-implemented method of claim 3 , wherein the metric is measured using a sensor on the client device.5. The computer-implemented method of claim 1 , wherein the recommended solution is an on-site visit from a technician.6. The computer- ...

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Номер записи: 3
03-12-2015 дата публикации

Predicting the Severity of an Active Support Ticket

Номер: US20150346918A1
Автор: Gabriele Bodda, Ryan Currier, Venkitesh Subramanian, Prerna Mankanawala, Rei Kasai, Devasena Rajamohan, Amith Manoharan Chithambaram, Terence Chesire, Kiran Karadi
Принадлежит:

An issue tracking system capable of predicting the likelihood that an outcome of interest will occur during the lifecycle of an active support ticket. The likelihood can be represented as a severity index score. The issue tracking system can apply a predictive algorithm on attributes of the active support ticket to generate the severity index score. A graphics engine can present each active support ticket as a line positioned along the perimeter of an inner circle where the length of the line represents the severity index score of the corresponding active support ticket. In some examples, the lines can extend towards an outer circle that represents a predefined threshold value for the severity index score. 1. A computer-implemented method , comprising:providing, by a processor, a graphical user interface configured to represent a plurality of members belonging to a dataset, the graphical user interface including a first figure having a plurality of lines positioned along the perimeter of the first figure and extending perpendicularly away from the first figure, wherein a first line from the plurality of lines that represents a first member of the dataset is positioned at a first position on the perimeter of the first figure according to a first attribute of the first member, the first line having a length that is proportional to a second attribute of the first member;detecting, by the processor, that a new member has been added to the dataset; andrepositioning, by the processor, the first line to a second position on the perimeter of the first figure based on the first attribute of the first member and the first attribute of the new member.2. The computer-implemented method of claim 1 , wherein the graphical user interface further includes a second figure that is substantially the same shape as the first figure claim 1 , wherein the size of the second figure is proportional to a predefined threshold value for the second attribute.3. The computer-implemented method ...

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Номер записи: 4