Abstract: A question-answering system that receive a natural-language question includes a database to provide a basis for that answer and a structured-query generator that constructs a structured query from the question and uses it to obtain an answer to the question from the database.

Abstract: Disclosed are methods, systems, devices, apparatus, media, design structures, and other implementations, including a method that includes receiving, at a local device from a remote device, query data representative of a question relating to source content of a source document of a repository of a plurality of source documents, with the source content being associated with transformed content accessible from the local device. The method further includes generating, in response to determination, at the local device, of a match between the query data and at least one portion of the transformed content, output data comprising one or more of a pointer to access, in the source document accessible from the remote device, at least one portion of the source document corresponding to the at least one portion of the transformed content, or a copy of the at least one portion of the source document.

Abstract: A question-answering system that receive a natural-language question includes a database to provide a basis for that answer and a structured-query generator that constructs a structured query from the question and uses it to obtain an answer to the question from the database.

Abstract: A drug candidate discovery system may include: a project management module configured to create a project for adding a task to perform drug candidate discovery; a simulation management module configured to create a simulation on the created project; a simulation setting module configured to set a simulation workflow for the simulation based on input from a user, using a canvas area and a simulation setting area containing a protein structure data input area and a task module selection area, each containing one or more objects that can be dragged and dropped onto the canvas area and converted into a node; a simulation workflow management module configured to manage information on nodes that can precede or follow in the simulation workflow; and a simulation execution module configured to manage and execute a task for running the simulation workflow by dividing and allocating GPU resource to the respective nodes.

Abstract: Disclosed are methods, systems, devices, apparatus, media, and other implementations for improved search-time content retrieval performed by an information retrieval platform. The implementations include a method including determining by a searching system, at a first time instance, one or more first search results for a first query comprising one or more query terms associated with a first concept, modifying the query, based on the determined one or more first search results, to include one or more modified query terms associated with one or more concepts hierarchically related to the first concept, and determining at a subsequent time instance one or more subsequent search results for the modified query comprising the one or more modified query terms.

Abstract: A question-answering system that receive a natural-language question includes a database to provide a basis for that answer and a structured-query generator that constructs a structured query from the question and uses it to obtain an answer to the question from the database. The method further comprises generating a first answer to said first question, wherein generating said first answer comprises causing said question to traverse a first path through said question-answering system, wherein the first path comprises a knowledge base constructed from content of the document collection using a machine learning BERT model; and causing said question to traverse a second path through said question-answering system to generate a second answer to said question, said second answer differing from said first answer, wherein the second path comprises a structured query generator and the database, the database being distinct from the knowledge base.

Abstract: Disclosed are methods, systems, devices, apparatus, media, and other implementations that include a method for document processing (particularly for training of a machine learning question answering platform, and for ingestion of documents). The method includes obtaining a question dataset (e.g., either from public or private repositories of questions) comprising one or more source questions for document processing by a machine learning question-and-answer system that provides answer data in response to question data submitted by a user, modifying a source question from the question dataset to generate one or more augmented questions with equivalent semantic meanings as that of the source question, and processing a document with the one or more augmented questions.

Abstract: A drug candidate discovery system may include: a project management module configured to create a project for adding a task to perform drug candidate discovery; a simulation management module configured to create a simulation on the created project; a simulation setting module configured to set a simulation workflow for the simulation based on input from a user, using a canvas area and a simulation setting area containing a protein structure data input area and a task module selection area, each containing one or more objects that can be dragged and dropped onto the canvas area and converted into a node; a simulation workflow management module configured to manage information on nodes that can precede or follow in the simulation workflow; and a simulation execution module configured to manage and execute a task for running the simulation workflow by dividing and allocating GPU resource to the respective nodes.

Abstract: A method for operating a protein structure-based candidate discovery system may include: displaying a first screen for receiving input of a plurality of ligands to be docked to a target protein; calculating predicted binding energy by sequentially performing molecular docking on the plurality of ligands based on settings input by a user via the first screen; displaying a first list on a second screen, the first list containing rows including a user interface control for user selection, names of the plurality of ligands, and the calculated binding energy values; predicting binding affinity for the rows selected by the user via the user interface control from the first list displayed on the second screen; and displaying a second list on a third screen, the second list containing rows including the names of the plurality of ligands, values of the binding energy, and values of the predicted binding affinity.

Abstract: Provided are a new drug candidate discovery system and a computer program implementing a new drug candidate discovery platform. A new drug candidate discovery system may include an automatic data preprocessing module configured to receive a target protein information from a user through a web interface, and perform preprocessing on a protein structure file obtained based on the target protein information; a simulation setting module configured to predict an Enzymatically Active Pocket for Docking Calculation (EAPDC) from the protein structure file using an artificial intelligence language model, and determine a docking calculation site; and a docking simulation module configured to perform a docking simulation for the docking calculation site.

Abstract: A question-answering system that receive a natural-language question includes a database to provide a basis for that answer and a structured-query generator that constructs a structured query from the question and uses it to obtain an answer to the question from the database.

Abstract: Disclosed are methods, systems, devices, apparatus, media, and other implementations that include a method for document processing (particularly for training of a machine learning question answering platform, and for ingestion of documents). The method includes obtaining a question dataset (e.g., either from public or private repositories of questions) comprising one or more source questions for document processing by a machine learning question-and-answer system that provides answer data in response to question data submitted by a user, modifying a source question from the question dataset to generate one or more augmented questions with equivalent semantic meanings as that of the source question, and processing a document with the one or more augmented questions.

Abstract: A question-answering system that receive a natural-language question includes a database to provide a basis for that answer and a structured-query generator that constructs a structured query from the question and uses it to obtain an answer to the question from the database.

Abstract: Disclosed are methods, systems, devices, apparatus, media, design structures, and other implementations, including a method that includes receiving, at a local device from a remote device, query data representative of a question relating to source content of a source document of a repository of a plurality of source documents, with the source content being associated with transformed content accessible from the local device. The method further includes generating, in response to determination, at the local device, of a match between the query data and at least one portion of the transformed content, output data comprising one or more of a pointer to access, in the source document accessible from the remote device, at least one portion of the source document corresponding to the at least one portion of the transformed content, or a copy of the at least one portion of the source document.

Abstract: A question-answering system that receive a natural-language question includes a database to provide a basis for that answer and a structured-query generator that constructs a structured query from the question and uses it to obtain an answer to the question from the database.

Abstract: Disclosed are methods, systems, devices, apparatus, media, design structures, and other implementations, including a method that includes receiving a source document, applying one or more pre-processes to the source document to produce contextual information representative of the structure and content of the source document, and transforming the source document, based on the contextual information, to generate a question-and-answer searchable document.

Abstract: Disclosed are methods, systems, devices, apparatus, media, design structures, and other implementations, including a method that includes receiving, at a local device from a remote device, query data representative of a question relating to source content of a source document of a repository of a plurality of source documents, with the source content being associated with transformed content accessible from the local device. The method further includes generating, in response to determination, at the local device, of a match between the query data and at least one portion of the transformed content, output data comprising one or more of a pointer to access, in the source document accessible from the remote device, at least one portion of the source document corresponding to the at least one portion of the transformed content, or a copy of the at least one portion of the source document.

Abstract: Disclosed are methods, systems, devices, apparatus, media, design structures, and other implementations, including a method that includes receiving a source document, applying one or more pre-processes to the source document to produce contextual information representative of the structure and content of the source document, and transforming the source document, based on the contextual information, to generate a question-and-answer searchable document.

Abstract: An artificial intelligence system monitors and records usage of a set of mobile devices that have been “paired” or grouped together. The system, over time, accumulates a history of times and locations associated with events that occur with respect to the devices. From this history, the system develops a usage model for the devices. The system, if it detects current usage diverges significantly from an expected usage, performs responsive actions which may include: notifying a user, via a paired device, that the mobile device may be lost or misplaced; command the “lost” mobile device to enter a reduced power state, to prolong battery life; and/or command the “lost” device to emit audible and/or visible signals intended to facilitate locating the device. In some embodiments, the system predicts an imminent loss of the device before it becomes lost, based on current usage vs. expected usage embodied in the model.

Abstract: An approach is provided for determining a product value score. Features of a product are identified. Ranking data for each identified feature is collected. The ranking data includes popularity, stability, availability, and memory usage of each feature. Additional data for each feature are determined. The additional data includes an importance, a conversion rate, and a marketing impact of each feature. Based on the ranking data and the additional data, normalized data is generated according to characteristics of the features. Based on a data science prediction analysis which uses the identified features, weights of the features are determined. Based on initial results of the data science prediction analysis, a key product is selected from a set of products. Based on the key product and the weights, product value scores of products in the set of products are determined.