International Business Machines Corporation

Nuance Communications, Inc.

Michael C Hollinger

Andrew J Geissler

Christina Karen Lauridsen

Jennifer Elizabeth King

Nicholas E Bofferding

Fabian F Morgan

Jacob Daniel Eisinger

Igor Roditis Jablokov

Hilary A Pike

Timothy R Chavez

Daniel M Smith

David W Wintermute

Michael R Reddock

Michael A Zaitzeff

Charles Willard Cross, Jr

Jason Szecheong Ma

Mitchell P Harding

Thomas W Aarts

Jose Luis Ortiz

Gundam Raghuswamyreddy

Kevin J Cawlfield

Chang W Chen

Ched D Hays

Benjamin D Lewis

Adriana Kobylak

Kulvir Singh Bhogal

Robert Ross Peterson

Balaji Venkata Atyam

Jeremy R Fox

Kelley Anders

Grant Douglas Miller

Perry L Jones

Jason B Akers

Ross B Clay

Ryan A Holt

Ted M Pacyga

Rodolfo Lopez

Charles W Cross

Chang Chen

Mal Pattiarachi

Juan G Rivera

David B Lewis

Abhinav Pratap Singh

Jennifer E Oliver

Sivas Srr

A method can include determining metadata for a file responsive to initiating an operation on the file stored within a client system and determining a storage location within a file repository of a master version of the file from the metadata. The method also can include determining, from the storage location, whether the master version of the file is more recent than the file stored within the client system using a processor of the client system.

Automatic file version verification within electronic mail

In an approach for creating synthetic visual inspection data sets for training an artificial intelligence computer vision deep learning model utilizing augmented reality, a processor enables a user to capture a plurality of images of an anchor object using a camera on a user computing device. A processor receives the plurality of images of the anchor object from the user. A processor generates a baseline model of an anchor object. A processor generates a training data set. A processor trains the baseline model of the anchor object. A processor creates a trained Artificial Intelligence (AI) computer vision deep learning model. A processor enables the user to interact with the trained AI computer vision deep learning model in an access mode.

Creating synthetic visual inspection data sets using augmented reality

Embodiments for providing demographic reach with anonymity by a processor. User data access may be managed via a data access agent by generating a unique user privacy profile having a selected level of anonymity for each relationship between a user and an application service.

Managing access to data for demographic reach with anonymity

A sourcing interface enables a particular device communicatively connected on a network to share one or more resources of the particular device with other devices communicatively connected to the network and coordinate task bidding with the other through broadcasting requests on the network. The sourcing interface manages dispatch of a particular task to one or more selected devices, the one or more selected devices selected based on one or more bids received by the particular device that best match a service level of particular request by the particular device, the dispatched task migrated to the one or more selected devices instead of handled by a web service provider. The sourcing interface monitors, at the particular device, for a result of execution of the particular task from the one or more selected devices, the one or more selected devices collecting telemetry data for usage reporting to the web service provider.

Managing resource sharing and task bidding on the internet of things (IoT)

Method and apparatus for incentivizing ride share users, where offers are presented to the users and based on selected offers, corresponding fares may be reduced. A ride share incentivization may include user profile database, a business database, a route generator, and an advertisement generator. The route generator is configured to generate a first route and a first fare based on a first ride share request, and a second route and a second fare based on a second ride share request. The advertisement generator may generate offers for the rider share users and adjust the fares of the ride share users based on the selected offers. The advertisement generator may further adjust one or more offers when no offers are selected.

Adjusting route parameters using a centralized server

Embodiments for managing temporal access to data by a processor. Permission for communicating with a user for providing temporal consent data during a selected time window based on a plurality of factors may be established. The temporal consent data may be delivered during the selected time window.

Reverse-tiered model to manage temporal access to data

An apparatus, method, and system for optimal device selection for streaming content are disclosed. One embodiment of an apparatus includes a stream query module that determines playback parameters of available streaming options for playing user-selected content. A playback environment module determines playback capabilities of connected playback devices and a playback environment of a user of the user-selected content. An analysis module performs a comparison of the available streaming options with the connected playback devices in the playback environment. A device optimization module determines an optimal playback device for playing the user-selected content in the playback environment using the comparison and the playback environment. A computer-implemented method and computer program product also perform the functions of the apparatus.

Optimal device selection for streaming content

A method for correcting improper repair actions in a computer system includes assigning a first algorithm identifier to a first algorithm and storing the first algorithm identifier and a first input to the first algorithm. The method includes executing the first algorithm with the first input and storing one or more results of the first algorithm, where the one or more results includes a repair action. The method includes determining that the repair action is faulty and storing the algorithm identifier for the first algorithm in a blacklist. The method also includes assigning a second algorithm identifier to a second algorithm and storing the second algorithm identifier and a second input to the second algorithm. The method includes executing the second algorithm with the second input, where the second algorithm corrects the faulty repair action caused by the first algorithm, and storing a result of the second algorithm.

Method and apparatus to rollback memory DIMM lane sparing

Systems, methods, and computer program products to perform an operation comprising identifying a first commit of a plurality for a software project, a source code of the first commit executable in a first system architecture, determining, based on a set of tests run against a built source code of the first commit for the first and second system architectures, that the first commit is a latest working build for the architectures, computing a score for each commit in a first set of the plurality, identifying a second commit of the first set of commits based on the scores for each commit, building the source code of the second commit for execution on the second system architecture, and determining that the source code of the second commit did not build successfully for the second system architecture based on an error at a first line of the source code.

Efficient detection of architecture related bugs during the porting process

Dynamic message selection and presentation based on integration of data from two or more applications is provided. Data from two or more applications on a first device is integrated utilizing an authentication token associated with the first device. Responsive to a real-time message request associated with an application, a data storage having one or more messages is interrogated. A first message from the interrogated data storage is selected and presented in real-time based on the message request, route traversal information from a first application, and profile data associated with a second application.

Local advertisement generation and display based on applications

A computer system may identify a source code for a program. The source code may include one or more instructions. The computer system may then receive a selection of two or more assessment metrics for evaluating the source code. The computer system may then generate an assessment score for each instruction in the source code based on the two or more assessment metrics. The computer system may then provide an assessment score indicator to for each instruction in the source code.

Analytic and layer-based augmentation of code reviews

Techniques are disclosed for identifying an optimal set of test cases of a larger test case bucket to execute on a new software release. The release includes one or more patches modifying development code of a previous release of the software, and each test case includes metadata specifying a history of the test case relative to an execution on the previous release. In each patch, it is determined, based on the metadata, whether at a test case targets the development code modified by the patch. Upon determining that the test case targets the development code, a score associated with the test case is incremented. The test cases are prioritized for execution based on score.

Focus area integration test heuristics

A computer-implemented method and system for assessing risk of a software program and software updates to a program to prioritize verification resources, which includes receiving code for a software product for a testing assessment. The code is analyzed according to a risk assessment criteria, and the risk assessment criteria includes risk assessment factors. The risk assessment factors for the code are weighted as part of the criteria. A risk assessment score of the code is determined based on the criteria. Testing resources are allocated in response to the risk assessment score.

Assessing risk of software commits to prioritize verification resources

Growing community of inventors

Round Rock, TX, United States of America

Michael C Hollinger