International Business Machines Corporation

Philip Siconolfi

Jeffrey E Bisti

Marna Walle

Danielle Judka

Molly Stevens

Charlie Bryant

Ryan Bertsche

Kirsten Brunner-McDonald

Robert J Paquin

Rajaram B Krishnamurthy

Vijai Kalathur

Moses J Vaughan

Jenny S Li

Jacky M Doll

Robert J C Paquin

Conor D Proce

Matthew J Webber

Emily M Metruck

Kirsten Brunner McDonald

Frank J De Gilio

Tyler Nicole King

Weon Weian Yuan

Mary T Fetterman

Kathleen M Pfeiffer

Robert F Pfeiffer

William J Bartolomeo

Anne M Romanowski

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

Methods, systems and computer program products for dynamic routing are provided. Aspects include receiving a plurality of stops from a user, wherein the plurality of stops includes at least one fixed location and at least one variable location and obtaining an address for at least one of the plurality of stops. Aspects also include obtaining a routing goal associated with the user and generating a route, including each of the plurality of stops, based on the routing goal. Aspects further include monitoring a movement of the user and updating the route based at least in part on the movement of the user.

Dynamic routing system

A computer-implemented method for recording file dependencies. A non-limiting example of the computer-implemented method includes creating, by the processor, a new inter-dependencies object in an inter-dependencies registry and writing, by the processor, an inter-dependency relationship between a first file and a second file from which the first file depends to the inter-dependency object in the inter-dependencies registry. The computer-implemented method further provides, by the processor, the inter-dependencies registry to a system upon receiving a call to backup the system.

File entity relationship management and source product backup

An example operation may include one or more of initiating a session between one or more users, identifying an active game status associated with the one or more users during the session, creating a link to the active games status, storing the link in a blockchain, creating a message comprising the link to the active game status, and broadcasting the message to a plurality of potential users.

Game data offloading to a blockchain

Technical solutions are described for execution of a machine-assisted workflow, the workflow including a sequence of workflow steps. An example method includes pulling a preliminary update for the workflow from a data source and modifying the workflow dynamically. The method further includes selecting a workflow step, pulling an updated information for the workflow step from the data source and modifying the workflow step dynamically using the updated information. The method further includes retrieving, from the data source, first preview information, the first preview information indicative of previews of one or more operations from the updated workflow step. The method further includes to the first preview information, executing the updated workflow step. The method further includes accumulating execution information including second preview information based on the execution and pushing the execution information to the data source upon completion of the workflow step.

Cognitive learning workflow execution

Embodiments include method, systems and computer program products for enhancing a customer service process. Aspects include receiving customer data associated with a customer and creating a customer profile based on the customer data. Aspects also include obtaining customer service representative data comprising a customer service profile for each of a plurality of customer service representatives and determining a matching subset of the plurality of customer service representatives for the customer based upon the customer profile and the customer service representative data. Next, aspects include displaying, to the customer, the matching subset of the plurality of customer services representatives for the customer. Aspects also include selecting a first customer service representative from the matching subset of the plurality of customer service representatives for the customer based on a customer input.

Enhancing customer service processing using data analytics and cognitive computing

Technical solutions are described for a machine-assisted workflow execution. An example method includes selecting a workflow for interactive execution, the workflow includes a sequence of workflow steps, and responsively, identifying a preliminary update for the workflow from a data source. The method further includes authenticating the data source, and if the data source is a trusted data source, pulling the preliminary update from the data source and modifying the workflow dynamically. The method further includes selecting a workflow step for execution, and responsively, pulling an updated information for the workflow step from the data source. The method further includes executing the workflow step using the pulled updated information, the execution including changing a target system. The method further includes accumulating execution information and pushing the execution information to the data source upon completion of the workflow step.

Technical solutions are described for machine-assisted workflow execution, the workflow includes a sequence of workflow steps. An example method includes, pulling a preliminary update for the workflow from a data source and modifying the workflow dynamically. The method further includes selecting a workflow step for execution, and responsively, pulling updated information for the workflow step from the data source. The method further includes retrieving, from the data source, first reasoning information being indicative of whether to update the workflow step. If the first reasoning information is indicative of performing an update, updating the workflow step dynamically using the updated information, and executing the updated workflow step. The method further includes accumulating execution information including attributes of the execution of the workflow step, and second reasoning information for the updating of the workflow step, and pushing the execution information to the data source upon completion of the workflow step.

Technical solutions are described for machine-assisted workflow execution. An example method includes selecting a workflow for interactive execution, the workflow including a sequence of workflow steps. The method further includes pulling a preliminary update for the workflow from a data source and modifying the workflow dynamically. The method further includes selecting a workflow step from the workflow for execution, and pulling updated information for the workflow step from the data source. The method further includes executing the workflow step using the pulled updated information, the execution includes changing a target system. The method further includes accumulating execution information including attributes associated with execution of the workflow step, and pushing the execution information to the data source upon completion of the workflow step.

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

A method, a computer program product, and a computer system for providing a cognitive solution for maintaining a microenvironment. A cognitive service in an infrastructural computing system receives from a user computing device data of one or more sensors in the microenvironment. The cognitive service receives from the user computing device a profile of a user of the microenvironment. The cognitive service determines one or more current conditions of the microenvironment, based on the data of the one or more sensors. The cognitive service determines a cognitive solution for maintaining the microenvironment, based on the profile of the user and the one or more current conditions of the microenvironment. The cognitive service sends to the user computing device the cognitive solution. The user computing device adjusts one or more devices based on the cognitive solution.

Cognitive solution for microenvironment

An example operation may include one or more of initiating a session between one or more users, identifying a current status of user activities associated with the one or more users during the session, determining whether the current status of the user activities requires updates to a local blockchain or a session blockchain, and storing the current status of the user activities in one or more of a local blockchain and a session blockchain.

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Wappingers Falls, NY, United States of America

Philip Siconolfi