A multicore apparatus determines fitness of a candidate for a role. The apparatus includes a multicore system processing device, a plurality of parallel multicore graphics processing devices, a network interface device, a storage device, and a system interface bus. The network interface device provides remote connection to the multicore system processing device. The storage device stores training data including positive and negative examples. The positive examples represent candidates who would be invited to an interview, and the negative examples represent candidates who would not be invited to an interview. The positive and negative examples are used by the plurality of parallel multicore graphics processing devices to train a deep learning model, which is used by the multicore system processing device to determine fitness of the candidate for the role while eliminating unwanted bias.

The present invention is intended to realize character string input using two or more devices by an efficient technique. A character string input system includes a first information processing apparatus and a second information processing apparatus. The first information processing apparatus acquires an operation done on a first input device and acquires a first character string on the basis of the acquired operation. The second information processing apparatus acquires an operation done on a second input device and acquires a second character string on the basis of the acquired operation. The first information processing apparatus outputs an input character string on the basis of the first character string acquired by the time input of the second character string is started by use of the second input device and the second character string.

The present invention is intended to realize character string input using two or more devices by an efficient technique. A character string input system includes a first information processing apparatus and a second information processing apparatus. The first information processing apparatus acquires an operation done on a first input device and acquires a first character string on the basis of the acquired operation. The second information processing apparatus acquires an operation done on a second input device and acquires a second character string on the basis of the acquired operation. The first information processing apparatus outputs an input character string on the basis of the first character string acquired by the time input of the second character string is started by use of the second input device and the second character string.

A virtual file organization system, method and program product are disclosed. Included is a system that assigns classification tags to files stored within a storage system based on a natural language processing (NLP) context analysis of each file; and a virtual smart folder that is viewable within a user interface, wherein: opening the virtual smart folder causes a set of virtual subfolders to be displayed in which each virtual subfolder includes a category title; opening of a virtual subfolder causes a set of files residing at disparate locations in the storage system to be displayed; and the files displayed by opening the virtual subfolder each include an assigned classification tag that is associated with the category title of the virtual subfolder.

A conference support apparatus includes an acquirer, an acceptor, a determiner, and an outputter. The acquirer acquires, for each predetermined member, related information including an evaluation value representing the association between a predefined word representing the classification of a conference and the member. The acceptor accepts input of information regarding a conference to be held and a member selected from the predetermined members as a potential participant of the conference to be held. The determiner determines whether or not a member who is a potential participant is suitable as a conference participant of the conference to be held, on the basis of the related information corresponding to the potential participant and the information regarding the conference to be held. The outputter outputs the determination result of the determiner.

A conference support apparatus includes an acquirer, an acceptor, a determiner, and an outputter. The acquirer acquires, for each predetermined member, related information including an evaluation value representing the association between a predefined word representing the classification of a conference and the member. The acceptor accepts input of information regarding a conference to be held and a member selected from the predetermined members as a potential participant of the conference to be held. The determiner determines whether or not a member who is a potential participant is suitable as a conference participant of the conference to be held, on the basis of the related information corresponding to the potential participant and the information regarding the conference to be held. The outputter outputs the determination result of the determiner.

A method for identifying documents that are similar in content to an input document includes receiving a request for identifying similar documents from among a plurality of candidate documents, retrieving document classification attributes for the input document and the candidate documents, where the document classification attributes are document level attributes. The method also includes comparing the document classification attributes of the input document with classification attributes of the candidate documents to identify a subset of the candidate documents having matching document classification attributes, retrieving semantic entities from the input document and from candidate documents in the subset, pairwise comparing the semantic entity attribute of the input document with the semantic entity attribute of the candidate documents in the subset to identify semantic entities having matching semantic attributes, calculating a content similarity score between the semantic entity of the input document and the semantic entity of the candidate document in the subset, calculating a total similarity score for the candidate documents in the subset based on the content similarity score, a number of matching document classification attributes, and weight factors, and selecting similar documents from the subset based on the total similarity score.

A method for providing a user interface (UI) for generating training data for an artificial intelligence (AI) model may include providing, for display via the UI, image information that depicts an object, a set of operations of the object, and a process associated with the set of operations. The method may include providing, for display via the UI, text information that describes the object, the set of operations of the object, and the process associated with the set of operations. The method may include receiving, via the UI, a user input that associates respective image information of the image information with corresponding text information of the text information. The method may include generating association information that associates the respective image information with the corresponding text information, based on the user input. The method may include generating discourse and semantic information from the text information associated to the image information.

The present disclosure relates to a vehicle quality problem management system and a data processing method thereof. The system includes a management server including a server communication device that performs wireless communication with a mobile device, and a server processing device connected to the server communication device, and the server processing device receives a voice signal containing a current quality problem from the mobile device, converts the current quality problem in the voice signal to text using speech to text (STT), and registers the current quality problem converted into the text in a database (DB).

Systems and methods for classifying images (e.g., ads) are described. An image is accessed. Optical character recognition is performed on at least a first portion of the image. Image recognition is performed via a convolutional neural network on at least a second portion of the image. At least one class for the image is automatically identified, via a fully connected neural network, based on one or more predictions, each of the one or more predictions being based on both the optical character recognition and the image recognition. Finally, the at least one class identified for the image is output.