International Business Machines Corporation

Yi-Min Chee

Krishna Chaitanya Ratakonda

Ashish Jagmohan

Jarir Kamel Chaar

Daniel V Oppenheim

Fausto Bernardini

Saeed Reza Bagheri

Thomas A Jobson, Jr

Joseph P Huchel

Nianjun Zhou

Rong Liu

Liang-Jie Zhang

Hao Chen

Satyanarayana Venkata Nitta

Shao Chun Li

Ravindranath Kokku

Ya Bin Dang

Feng Liu

Li Jun Mei

Ahamed Jalaldeen

Justin Gregory Manweiler

Lav Raj Varshney

Richard B Segal

Ali P Arsanjani

Sara Rosenthal

Guang Tai Liang

Abhilash Narendra

Julie MacNaught

Nathan R Barry

Ryan Enderby

Sheela Shetty

Angela Schoergendorfer

Pamela N Luna

Piyawadee Sukaviriya

Feng Li

Xin Zhou

Fan Jing Meng

Qian Ma

Rakesh Mohan

Bengt-Olaf Schneider

Danny Soroker

Ramesh Ambat Gopinath

Anshul Sheopuri

Shivali Agarwal

Michael S Karasick

Wesley M Gifford

Ru Fang

Renuka Sindhgatta

Zhi Le Zou

Shota Aki

Jose Gomes

Samuel Monk Weber

Paul Robert Carini

John J Ambrose

Suzanne M Rodeman

Teresa Abdel-Hamid

Peide Zhong

DingDing Lu

Dingding Lu

An example operation may include one or more of generating, by a blockchain node, an asset containment world-state by execution of a linear-time ingestion algorithm, detecting, by the blockchain node, an asset aggregation or disaggregation event, updating, by the blockchain node, the world-state based on the asset aggregation or disaggregation event, and computing supply-chain metrics based on the updated world-state.

Computation of containment relationships

An example operation may include one or more of acquiring, by a processor node, identification data from an asset producer node over a supply blockchain, generating, by the processor node, a unique identifier (ID) for the asset producer node, and executing a smart contract to: map the ID to the identification data and provide an entitlement to the asset producer node to a plurality of supplier nodes based on the mapped ID.

Anonymization of partners

An example operation may include one or more of generating, by a processor node, an identifier (ID) for an asset producer node, mapping, by the processor node, the ID to an identity of an asset producer node, receiving, by the processor node, a request from a supplier node from a plurality of supplier nodes for the asset producer node to be added to a supply blockchain, and providing entitlements to the plurality of the supplier nodes based on the mapped ID.

An example operation may include one or more of receiving a data file submitted by a node, the data file comprising information about an event-driven process for a chain of nodes, retrieving an entitlement mode of the data file from a data block that is stored among a hash-linked chain of data blocks on a distributed ledger, determining access rights of the data file with respect to another node in the chain of nodes based on the retrieved entitlement mode of the data file, and transmitting information about the event-driven process to the other node based on the determined access rights of the other node.

Dynamic entitlement for blockchain data

An example operation may include one or more of releasing, by a subscription service node, at least one blockchain transaction to a plurality of subscribing blockchain nodes requiring direct entitlement access, executing, by the subscription service node, a smart contract to calculate secondary entitlements triggered by the at least one blockchain transaction, determining, by the subscription service node, portions of second entitlement data sets allowed to be accessed by a subset of the plurality of the subscribing blockchain nodes, and sending verification data to the plurality of the subscribing blockchain nodes for verification of the second entitlement data sets.

Subscription service for networks

An example operation may include one or more of detecting, by a blockchain node, an asset aggregation or disaggregation event, performing, by the blockchain node, a single-shot update of an asset containment world-state at an ingestion of the asset aggregation or disaggregation event, determining, by the blockchain node, parent-child duration parameters for each instance of an asset parent-child association defined by the asset containment world-state, and executing a linear-time algorithm to calculate supply-chain metrics based on all combinations of the asset aggregation or disaggregation orderings based on the parent-child duration parameters.

According to embodiments, methods, systems, and computer program products are provided for receiving one or more input compositions comprising one or more materials, assigning a material vector to each material, learning, for each of the input compositions, a composition vector based on the material vectors of the materials that form each composition, assigning predicted rating values having a confidence level to each of the composition vectors, selecting a composition to be rated based on the confidence levels, presenting the selected composition to be rated to a user, receiving a user rating for the composition to be rated; adjusting the predicted rating values and confidence levels of the composition vectors that have not been rated by the user, and generating a predictive model to predict a user's ratings for compositions when confidence levels of each composition vector is above a predetermined threshold value.

System and method for identifying composition preferences

A computer system, method, and computer-readable product for providing data for career advice, such as job or education recommendations, from automated review and analysis of career-related data for an individual, which is at least initially obtained from documents, such as resumes and writing samples. For a designated individual, career-related data is obtained from the documents and an initial personality estimate is created for the designated individual based upon, at least, the obtained career-related data. One or more latent factor models for known career-personality matches are then gathered from a database and a questionnaire is provided to the individual to gather further information and augment the personality estimate. The created personality estimate is integrated with the latent factor model(s) to create career advice data.

Recommending personalized job recommendations from automated review of writing samples and resumes

A computer system, method, and computer-readable product for automatically generating a hierarchy of concepts described in a video using semantic concept matching that assists persons in searching for specific types of content within videos. A video segment is obtained, with the segment having a distinct running time with discrete information related to one or more topics within its content. The discrete information of the video segment is then parsed out and related to a predetermined relevant topic, and then one or more attributes for weighting each discrete information relevant topic within the video segment are determined. A hierarchy of concepts of the video segments is then created, based upon, at least, the weighting of the information topic within the running time of the video segment based upon the presence of the one or more attributes. Other information related to the video segment can also be used to modify the hierarchy.

Hierarchical video concept tagging and indexing system for learning content orchestration

An example operation may include one or more of storing data via a blockchain ledger based on a consensus of a set of anchor blockchain nodes which are subset of nodes included in larger set of nodes in a blockchain network, receiving an access request from a blockchain member not included in the set of anchor blockchain nodes and which does not maintain a copy of the blockchain ledger, verifying a previously stored portion of the blockchain ledger which is accessible to the blockchain member based on the received access request, and sharing the previously stored portion of the blockchain ledger with the a computing system of the blockchain member based on the verification.

Blockchain trust anchor

Method and systems for tagging code include organizing code units in a hierarchical structure that associates the code units with respective description units. The hierarchical structure is filtered to remove un-meaningful description units from the hierarchical structure. Feature tags are generated based on a weighted sum of term frequencies in the description units of the hierarchical structure to associate features with respective code units.

Cross-validation based code feature tagging

Identifying one or more patterns of content consumption across multiple entities and determining an engagement action for a user of an entity based on the patterns may include receiving information associated with content, cross-industry user data associated with consumption of the content, and a given organization user data associated with consumption of the content. A first set of consumption profile vectors associated with the given organization and one or more second set of consumption profile vectors associated respectively with one or more cross-industry organizations may be generated. Information associated with a target user in the given organization may be received. A customized learning plan for the target user in the given organization may be generated based on the first set of consumption profile vectors, the one or more second set of consumption profile vectors, and the information associated with the target user.

Identifying patterns of learning content consumption across multiple entities and automatically determining a customized learning plan based on the patterns

A method for analyzing a virtual discussion. The method may include identifying, with a processing device, a first concept relevant to a first subdiscussion associated with an online discussion. The method may also include identifying a second concept relevant to the first subdiscussion, and determining a relation between the first concept and the second concept.

Computer-based analysis of virtual discussions for products and services

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Yorktown Heights, NY, United States of America

Yi-Min Chee