International Business Machines Corporation

Robert J Paquin

Philip Siconolfi

Jenny S Li

Conor D Proce

Matthew J Webber

Martin G Keen

Rajaram B Krishnamurthy

Vijai Kalathur

Moses J Vaughan

Jeffrey E Bisti

Tyler Nicole King

Rita Beisel

Cristina Olivia McComic

An embodiment for a method of using machine learning algorithms to proactively identify potential errors in code instance data for creating technical documentation. The embodiment may receive code instance data. The embodiment may generate error classifications for the received code instance data using a convolutional neural network and natural language processing techniques. The embodiment may perform a correlation analysis to derive correlations between the generated error classifications for the received code instance data and similarly occurring classifications in one or more historical code instances. The embodiment may calculate a score for each of the derived correlations, the calculated score corresponding to a likelihood that the similarly occurring classifications in the one or more other code instances represents a potential error. The embodiment may output notifications to a user for each of the derived correlations for which the calculated score exceeds a threshold value.

Proactively identifying errors in technical documentation code

Aspects include receiving, at a portable storage device, a file from a digital device. The file is stored into an unprotected storage area on the portable storage device and a storage policy associated with the digital device is accessed by the portable storage device. It is determined, by the portable storage device, based at least in part on the portable storage policy, whether to store a copy of the file in a protected storage area on the storage device. Access to the protected storage area is restricted to authenticated users. A copy of the file is stored in the protected storage area in response to determining that a copy of the file should be stored in the protected storage area.

Access verification on portable mass storage devices

Embodiments include methods, systems and computer program products method for cognitive storage management for a mobile computing device. The computer-implemented method includes monitoring, using a processor, content generation and usage on a mobile computing device by a user associated with the mobile computing device to determine usage habits of the user. The processor determines a storage capacity for the mobile computing device. The processor predicts future content generation and usage by the user based on the determined usage habits. The processor determines whether future storage capacity issues could exist based on the predicted future content usage and the determined storage capacity. The processor, in response to a determination that future storage capacity issues could exist based on the predicted future content usage and the determined storage capacity, causes at least a portion of content stored on the mobile computing device for storage in a remote storage.

Cognitive storage management for a mobile computing device

Aspects of the invention include intercepting a rendering of a picture at a website, the picture uploaded to a server of the website by a content provider. Access criteria associated with a user at a user device is determined. The access criteria includes a relationship between the user and one or both of the content provider and content of the picture. A resolution of a modified rendering of the picture is determined based at least in part on the access criteria. The modified rendering of the picture is generated based at least in part on the resolution. The modified rendering of the picture is displayed at the user device.

Dynamic picture sizing based on user access criteria

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Wappingers Falls, NY, United States of America

Robert J Paquin