International Business Machines Corporation

Benjamin L Johnson

Donna Karen Byron

Florian Pinel

Carmine M DiMascio

Carmine Dimascio

Charles Evan Beller

Dan G Tecuci

Lakshminarayanan Krishnamurthy

Timothy P Winkler

Jacquelyn Annette Martino

Krishna Kummamuru

Mary Swift

Ladislav Kunc

Priscilla S Moraes

Christian Ewen

Joanne M Santiago

Christie Ewen

Mechanisms for automatically modifying a set of instructions based on an expanded domain specific knowledge base is provided. The mechanisms generate a domain specific knowledge base comprising a set of entities and corresponding domain specific attributes and expand the domain specific knowledge base to include values for the domain specific attributes through an automated bootstrap learning process that performs natural language processing and analysis of natural language content using a set of pre-condition annotated action terms. The mechanisms receive electronic content comprising an initial set of instructions to perform an operation and evaluate the initial set of instructions based on the expanded domain specific knowledge base to identify a missing instruction. The mechanisms modify the initial set of instructions to include an additional instruction based on the missing instruction and thereby generate a modified set of instructions.

Modifying a set of instructions based on bootstrapped knowledge acquisition from a limited knowledge domain

A method includes receiving, by a first device of a transportation provider, a device ID of a particular device of a user and purchase information regarding a purchase, by the user, of one or more goods or services. The method includes sending, by the first device, the device ID of the particular user device to a central server. The method includes receiving, by the first device from the central server, information regarding the particular user device. The method includes determining, by the first device, a selected transportation vehicle based on the purchase information and the information regarding the particular user device. The method includes sending, by the first device, a vehicle ID of the selected transportation vehicle to the central server. The method includes providing a transportation service to the user using the particular user device and the selected transportation vehicle.

Devices, systems, and methods for secure and adaptable transportation of goods and/or persons

An automated leveling system for maintaining a level of a surface, comprising a surface traveling device and a leveling module attached to the surface traveling device. The leveling module comprises a depression detector arm assembly, at least one computing device, and a level-sensing detector. The level-sensing detector senses a level of a surface as the surface traveling device operates across the surface. The level-sensing detector moves across the surface according to the operation of the surface traveling device, and transmits a depression reporting signal to the computing device. The depression reporting signal indicates the presence of a depression in the surface if the level-sensing detector contacts a depression in the surface over a depression threshold.

Automated leveling system for maintaining level of a surface

A chatbot answer database can be generated from a question and answer document. A question/answer processor can receive a document that includes questions and answers. A set of entities can be determined from the answers in the document. A set of triplets can be constructed from the answers, and stored in a chatbot database. For each answer, the question/answer processor can determine text for slots of the triplet based on the entities extracted from the answer. The triplet can be stored as a tree structure in a database of tree structures associated with the document. A chatbot can receive questions, and provide answers based on the database of tree structures associated with the document.

Generating a chatbot from an FAQ

A website navigation system has an analysis system which receives a request for an Internet web page from a client device. The analysis system receives web page data associated with the web page from the Internet and performs a data analysis process to organize the web page data for use in a virtual conversation with the user in order to present the web page in an audible format. The analysis system identifies separate elements of the web page from the web page data and extracts information from the separate elements based on the web page data. The analysis system groups the separate elements into categories based on the extracted information and sorts the groups of separate elements based on usage statistics. The analysis system then generates a prompt for being output to the user by the client device as audible output based on the sorted groups of separate elements.

System enabling audio-based navigation and presentation of a website

A system for entity-level clarification in conversation services includes a memory having instructions therein. The system also includes at least one processor in communication with the memory. The at least one processor is configured to execute the instructions to receive a conversation services training example set, build an entity usage map using the conversation services training example set, receive a user utterance, and, responsive to a reception of the user utterance, generate a clarification response using the entity usage map. The at least one processor is also configured to execute the instructions to provide the clarification response to a user.

Entity-level clarification in conversation services

A method, apparatus and program product utilize an automated educational tool to generate or modify one or more multiple choice questions in a question set in connection with tracking a user's attention state to dynamically vary the difficulty of a user assessment performed by the automated educational tool and thereby encourage the user to pay greater attention to the material being presented to the user.

Generating probing questions to test attention to automated educational materials

A computer-implemented method for building a semantic analysis model. In one embodiment, the computer-implemented method includes creating proxy tags comprising a set of surface form variants. The computer-implemented method creates training examples comprising a combination of terminal tokens and at least one of the proxy tags. The computer-implemented method builds the semantic analysis model using the training examples.

Abstraction and portability to intent recognition

A life-logging system is configured to register a plurality of cameras and a plurality of users to a life-logging network. The life-logging system receives a plurality of camera profiles associated with the plurality of cameras and a user profile associated with a user. The life-logging system determines location information associated with the user and identifies a camera of the plurality of cameras for capturing an image of the user based on the plurality of camera profiles, the user preferences, and the location information. The life-logging system arranges for the image of the user captured by the camera to be delivered to a client device.

Life-logging system with third-person perspective

A method, system and computer-usable medium for augmenting the training of a conversational system. In certain embodiments, the method comprises: ingesting a training set to be used in training of the conversational system, the training set including objects for use in the training, wherein the objects include one or more object types, wherein the object types include one or more of an entity or intent; generating proposed reductions for inclusion in an augmented training set, wherein the proposed reductions include one or more of: reduction candidates generated using properties of objects included in the training set; reduction candidates obtained from search queries of one or more external resources, wherein the search queries relate to one or more objects included in the training set; filtering the proposed reductions to generate a reduced set of proposed reductions; and augmenting the training set with the reduced set of proposed reductions.

System for augmenting conversational system training with reductions

Embodiments provide dynamic consumer incentive generation by generating a combination discount offer that provides a total discount value to a customer in response to verifying that the customer executes a commercial activity at each of different (first and second) ones of a discount grouping plurality of businesses, in response to determining that it is probable as a function of historic purchasing data that the customer will purchase an item from the first business when they make a purchase from the second business; and allocating different portions of the total discount value as costs to the first and second businesses that have different values determined as a function of a difference between a first probability that the customer will purchase an item from the first business and a second probability that the customer will purchase an item from the second business.

Dynamic incentive generator

A computer program product for computer generation of digital content, said computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processing resource to cause the processing resource to determine demographic designators associated with users of user devices that render interactive digital contents; group together similar input questions input into the interactive digital contents of the user devices; for each group of similar questions, create an answer to each group of similar questions; create a script from each answer; record each script and the corresponding group of similar input questions in a data repository; and insert a script from the data repository into a digital content template.

Digital content generation based on user feedback

Systems, methods, and computer-readable media for constructing a conversation model using documentation of an application programming interface (API) are disclosed. The conversation model can be used to train a natural language classifier. API endpoints may be represented in the API documentation as (verb, resource, element) tuples. These tuples can be converted into intent and parameters of the API endpoints can be converted into entities. In addition, example utterances may be created for each intent. The conversation model can be generated using the intents, example utterances, and/or entities.

Bootstrapping a conversation service using documentation of a rest API

Mechanisms for training a human user to perform an operation and provided. The mechanisms generate a domain specific knowledge base comprising a set of entities and corresponding domain specific attributes and expand the domain specific knowledge base to include values for the domain specific attributes through an automated bootstrap learning process that performs natural language processing and analysis of natural language content using a set of pre-condition annotated action terms, thereby generating an expanded domain specific knowledge base. The mechanisms evaluate an input from another device identifying an action associated with an entity in the set of entities, based on a retrieved domain specific attribute value and the retrieved pre-condition annotation from the expanded domain specific knowledge base. The mechanisms output a notification to a user computing device indicating whether the input is correct or incorrect to thereby train a user associated with the user computing device.

Growing community of inventors

Baltimore, MD, United States of America

Benjamin L Johnson