A machine-learning ecosystem includes a correlation module for building at least one prediction model based on at least one data input including at least one input parameter and at least one output parameter, the prediction model relating the output parameter to the input parameter. The correlation module performs at least one threshold check on the prediction model to assess the robustness of the prediction model. The ecosystem further includes a decision module communicatively coupled to the correlation module and receiving the prediction model from the correlation module. Based on a verification check at the decision module, a confirmation, a deferral, or a rejection of the prediction model is sent from the decision module to the correlation module.

A translation apparatus includes a translation unit which translates content of a document into a different language, a history creating unit which, in translation of the content from a first language into a second language, creates history information including a correspondence between original text in the first language and translated text in the second language, an extraction unit which, in translation of the content from the second language into another language, if content (present content) of the document in the second language is present in the history information, extracts content (absent content) that is not present in the history information, and a combining unit which combines a translation result obtained by translating the present content from the second language into the other language, with a replacement result obtained by replacing the absent content from the second language to the other language based on the history information.

Systems, methods, and devices of a voice-directed inspection system that supports multiple inspectors in the inspection of business assets are described. Inspection plans for large and complex business assets can involve several steps. It is advantageous to split large inspection plans into non-overlapping sections to allow multiple inspectors to perform concurrent inspections. Such sectionalizing is also useful in training new inspectors.

Techniques are disclosed that enable automating user interface input by generating a sequence of actions to perform a task utilizing a multi-agent reinforcement learning framework. Various implementations process an intent associated with received user interface input using a holistic reinforcement policy network to select a software reinforcement learning policy network. The sequence of actions can be generated by processing the intent, as well as a sequence of software client state data, using the selected software reinforcement learning policy network. The sequence of actions are utilized to control the software client corresponding to the selected software reinforcement learning policy network.

Techniques are disclosed that enable automating user interface input by generating a sequence of actions to perform a task utilizing a multi-agent reinforcement learning framework. Various implementations process an intent associated with received user interface input using a holistic reinforcement policy network to select a software reinforcement learning policy network. The sequence of actions can be generated by processing the intent, as well as a sequence of software client state data, using the selected software reinforcement learning policy network. The sequence of actions are utilized to control the software client corresponding to the selected software reinforcement learning policy network.

Systems and methods for a media guidance application that generates results in multiple languages for search queries. In particular, the media guidance application resolves multiple language barriers by taking automatic and manual user language settings and applying those settings to a variety of potential search results.

Remote automated assistant component(s) generate client device notification(s) based on a received IoT state change notification that indicates a change in at least one state associated with at least one IoT device. The generated client device notification(s) can each indicate the change in state associated with the at least one IoT device, and can optionally indicate the at least one IoT device. Further, the remote automated assistant component(s) can identify candidate assistant client devices that are associated with the at least one IoT device, and determine whether each of the one or more of the candidate assistant client device(s) should render a corresponding client device notification. The remote automated assistant component(s) can then transmit a corresponding command to each of the assistant client device(s) it determines should render a corresponding client device notification, where each transmitted command causes the corresponding assistant client device to render the corresponding client device notification.

A communication system includes a group calling controller configured to perform first processing of broadcasting utterance voice data received from one of mobile communication terminals to other mobile communication terminals and second processing of chronologically accumulating the result of utterance voice recognition from voice recognition processing on the received utterance voice data as a communication history and allowing users to see the communication history in synchronization, and an individual calling controller configured to transmit utterance voice data only to a specified user during group calling. A communication controller including the controllers is configured to identify a user participating in an individual calling mode that utterance voice data being transmitted only to the specified user during the broadcast of the first processing and to perform, after end of the individual calling mode, processing for notifying the identified user that the broadcast was performed during the individual calling mode.

A communication system is configured to broadcast utterance voice data received from one of mobile communication terminals to other mobile communication terminals, to control text delivery such that a result of utterance voice recognition from voice recognition processing on the received utterance voice data is displayed on the mobile communication terminals in synchronization, and to use the result of utterance voice recognition to perform communication evaluation. The communication evaluation includes a first evaluation including evaluating a dialogue between users based on a group dialogue index to produce group communication evaluation information, a second evaluation including evaluating utterances constituting the dialogue between the users based on a personal utterance index to produce personal utterance evaluation information, and a third evaluation including using the group communication evaluation information and the personal utterance evaluation information to produce entire communication group evaluation information.

Provided are a wearable device for providing a multi-modality, and an operation method of the wearable device. The operation method of the wearable device including obtaining source data including at least one of image data, text data, or sound data, determining whether the image data, the text data, and the sound data are included in the source data, based on determining that at least one of the image data, the text data, or the sound data is not included in the source data, generating the image data, the text data, and the sound data, which are not included in the source data, by using a generator of an generative adversarial network (GAN), which receives the source data as an input, generating a pulse-width modulation (PWM) signal based on the sound data, and outputting the multi-modality based on the image data, the text data, the sound data, and the PWM signal.