International Business Machines Corporation

Anand Pikle

Amol Ashok Dhondse

Gandhi Sivakumar

Faried Abrahams

Harish Bharti

Abhay Kumar Patra

Rajesh Kumar Saxena

Kerrie Lamont Holley

Lennox Epstein Thomas

Laura Irina Rusu

Maharaj Mukherjee

Raman Harishankar

Krishnan K Ramachandran

Dale K Davis Jones

Chitra Dorai

Michael Edward Alexander

Rashida A Hodge

Ronald Ambrosio

Susan L Sylvia

Rakesh Shinde

Deepak Sekar

Bruce A Jones

Ana P H Veerkamp

Selene M O Nunez

A system and method for intelligent classification for product pedigree identification are presented. A transactional block may be dynamically created for recording both pedigree information for one or more supply ingredients at a time of origin and one or more parameters relating to the one or more supply ingredients at each temporal event. One or more transactional blocks may be linked via a shared ledger according to the one or more supply ingredients and the one or more parameters.

Intelligent classification for product pedigree identification

A computer system message generated by an application programming interface (API) or addressed to the API can be received. A first data sensitivity score for at least a first of a plurality of data elements in a payload of the computer system message and at least second data sensitivity score for at least a second of the plurality of data elements in the payload of the computer system message can be determined. Based on the first data sensitivity score and at least the second data sensitivity score, a differential security can be applied to the computer system message. Applying the differential security can include masking the first of the plurality of data elements and not masking the second of the plurality of data elements. The computer system message can be electronically communicated the to a destination to which the message is addressed.

Applying differential security to API message payload data elements

An approach includes a system implemented in a computer infrastructure including a processor. The approach further includes the processor configured to receive dimensionally aware linkages. The approach further includes the processor configured to retrieve aggregated data from a data set. The approach further includes the processor configured to identify at least one affinity cluster related to the dimensionally aware linkages in at least two dimensions and the retrieved aggregated data by performing at least one lookup linkage which matches at least one dimension of the at least two dimensions of the dimensionally aware linkages with the retrieved aggregated data. The approach further includes the processor configured to provide personalized content to a user having same or similar user preferences as the received dimensionally aware linkages based on the identified at least one affinity cluster.

Personalized content and services based on profile information

An approach for securing sensitive information in communication data is provided. In an embodiment, communication data is received from an ongoing communication (e.g., phone conversation, exchange of text messages, etc.) between a user and at least one other person. Contextual input is derived based on the content of the communication data and historical data from one or more external sources. A sensitivity index score for each element (e.g., portion) of the communication data is derived based on an assessment of the contextual input. The elements of the communication data having a sensitivity index score greater than a predefined threshold value are deemed to be security sensitive and, therefore, are encoded prior to transmission to its intended recipient.

Selective cognitive security for communication data

A computer-implemented method includes: receiving, by a computing device, information regarding an application programming interface (API); classifying, by the computing device, the API using natural language classification on to a baseline taxonomy; extracting, by the computing device, information regarding features of the API based on the classifying to the baseline taxonomy; performing, by the computing device, an assessment on the API relative to other APIs within a same class as the API based on the extracting the information regarding the API features and the classifying the API; and updating, by the computing device, the API based on the performing the assessment.

API evolution and adaptation based on cognitive selection and unsupervised feature learning

A mapping is constructed between a node of a cultural hierarchy and a node of an organizational hierarchy. The cultural hierarchy is a data structure representing an arrangement of characteristics of different social levels relative to one another in a culture. The organizational hierarchy is a data structure representing an arrangement of characteristics of different roles of people relative to one another in a group. A set of cultural attributes is constructed to represent a persona of a human user according to mapping. Using a cultural attribute, an adjustment rule is selected from a library. A machine output is modified according to the adjustment rule. The machine output is in response to a dialog between a data processing system and human user in a machine-human interaction. The adjusted output is transformed and presented as a response to the human user in the machine-human interaction.

Culturally-aware cognitive system for human interactions

Approaches presented herein enable automatically fulfilling an obligation under a smart contract. A block is added to a blockchain ledger in response to an event that triggers the obligation. The block includes data related to the event. Inferences related to the event are derived based on an analysis of event data and historical data incorporated in prior blocks in the blockchain ledger. Based on the inferences, a potential cause of the event is derived. Based on the potential cause, an interested stakeholder to the fulfilling of the obligation is identified. The interested stakeholder is added to a group that is allowed access to the blockchain ledger corresponding to the smart contract.

Granting access to a blockchain ledger

A method, system and computer program product for determining API pricing. Consumption parameters are identified using a supervised learning model. The API consumption parameters refer to any parameters that can be used to describe an API (functionality or otherwise) and can be used to compare other comparable APIs in similar domains provided by other providers. Furthermore, reference pricing is determined using machine learning using the identified API consumption parameters. Additionally, the API price is determined dynamically using the identified API consumption parameters and the determined reference pricing. An API pricing score is then derived for the API price using the supervised learning model. The API price is selected as the suggested price for the API in response to the API pricing score exceeding a threshold value. In this manner, an API price is established that reflects the true value of the API assessed by the API consumer.

API pricing based on relative value of API for its consumers

A method for providing bidding support to a user during an auction using cognitive modeling comprises obtaining data associated with the auction, with the obtained data comprising data associated with at least one bidder. The obtained data is analyzed, and at least one output is generated based on the analysis. The at least one output is sent to at least one device associated with the user. The at least one device is configured to generate at least one alert based on the at least one output, with the at least one alert providing decision support to the user with respect to bidding strategy.

Implementing cognitive modeling techniques to provide bidding support

Approaches presented herein enable optimizing resource allocation to a bid request response based on a cognitive analysis of natural language artifacts. More specifically, a bid request and a plurality of supporting artifacts in a natural language are obtained. A cognitive analysis of the request and supporting artifacts is performed to extract a set of information entities. The extracted information entities are normalized using a lexical-relations based graph database to classify the set of extracted information entities as standardized concepts. A subset of the set of the standardized concepts is identified as a set of parameters corresponding to a set of predetermined variables. Each variable of the set of predetermined variables is weighted according to a likelihood that the variable indicates a relevance of a resource. A probability that a particular resource is relevant is determined based on the weighting and that resource is assigned to the bid request response.

Optimizing resource allocation to a bid request response based on cognitive analysis of natural language documentation

Attribute-based application programming interface (API) comparative benchmarking is provided. In response to determining that a target API maps to an existing API classification based on attributes of the target API, a weighted average of benchmark confidence scores of other APIs in a same class as the target API is determined. A benchmark confidence score is determined for the target API based on feedback, reviews, and ratings. The benchmark confidence score of the target API is compared with the weighted average of benchmark scores. An attribute-based API classification mapping is updated based on the comparison. Pricing for the target API is determined based on a weighted average of API pricing across the other APIs in the same class as the target API.

Feature-based application programming interface cognitive comparative benchmarking

A security control system for a kinematic robot uses a cognitive assessment agent to map proposed instructions to either legitimate or illegitimate actions based on contextual variables. The agent computes a security anomaly index score representing a variance of a likely kinematic action of the robot compared to acceptable actions. If the score exceeds a predetermined threshold, a security alert is generated for the robot's administrator. The contextual variables include a user profile, a user location, and subject matter of the kinematic actions. The analysis compares input text to predefined classification metadata, and can also compare verbal phrases or body gestures to corresponding baselines. Different numeric weights can be applied to the contextual variables. The computing begins with a default value for the score and thereafter increments or decrements the score based on the weights. The weights can be adjusted based on a supervisory appraisal of the computed score.

Cognitive learning to counter security threats for kinematic actions in robots

Embodiments for utilizing a human compound eye using internet of things ('HCEI') for obstacle protection of a user by a processor. One or more objects may be determined within an obstacle threshold distance in relation to a user according to data captured from one or more internet of things (IoT) devices associated with a wound dressing, a mobility assistance device, or a combination thereof. The user may be alerted of the one or more objects within the obstacle threshold distance.

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Pune, India

Anand Pikle