Abstract: Methods and apparatus for generating medical records from a doctor-patient dialogue are provided. A main content portion of a written doctor-patient conversation is identified. The main content portion of the conversation is extracted from the conversation. The main content of the conversation is divided into sections according to a pre-defined set of sections, and, based on the sections and their respective content, a medical record is generated according to a pre-defined template. The pre-defined template is one of a hard medical record format or a soft medical record format.

Abstract: Traffic jam patterns can be identified and, based on historical traffic data, pre-traffic jam patterns that are likely to result in the traffic jam patterns can be identified as well. Real-time traffic data regarding a driving road of a community can be received and analyzed to determine whether the real-time traffic data match with a pre-traffic jam pattern. If the data matches a pre-traffic jam pattern, an alerting signal for predicting a traffic jam can be transmitted.

Abstract: Methods and systems for processing records include extracting feature vectors from words in an unstructured portion of a record. The feature vectors are weighted based similarity to a topic vector from a structured portion of the record associated with the unstructured portion. The weighted feature vectors are classified using a machine learning model to determine respective probability vectors that assign a probability to each of a set of possible relations for each feature vector. Relations between entities are determined within the record based on the probability vectors. An action is performed responsive to the determined relations.

Abstract: Method and apparatus are presented for receiving a medical or medical condition related input term or phrase in a source language, and translating the term or phrase from the source language into at least one target language to obtain a set of translated terms of the input term. For each translated term in the set of translations, the method and apparatus further translate the set of translations back into the source language to obtain an output list of standard versions of the input term, scoring each entry of the output list as to probability of being the most standard version of the input term, and providing the entry of the output list that has the highest score to a user.

Abstract: Embodiments of the present disclosure relate to object detection in an image. In an embodiment, a computer-implemented method is disclosed. According to the method, image data representing a scene is obtained and sound distribution information related to the scene is obtained. A detection strategy to be applied in object detection is determined based on the sound distribution information. The object detection is performed on the image data by applying the detection strategy. In other embodiments, a system and a computer program product are disclosed.

Abstract: Traffic jam patterns can be identified and, based on historical traffic data, pre-traffic jam patterns that are likely to result in the traffic jam patterns can be identified as well. Real-time traffic data regarding a driving road of a community can be received and analyzed to determine whether the real-time traffic data match with a pre-traffic jam pattern. If the data matches a pre-traffic jam pattern, an alerting signal for predicting a traffic jam can be transmitted.

Abstract: Embodiments of the present disclosure relate to object detection in an image. In an embodiment, a computer-implemented method is disclosed. According to the method, image data representing a scene is obtained and sound distribution information related to the scene is obtained. A detection strategy to be applied in object detection is determined based on the sound distribution information. The object detection is performed on the image data by applying the detection strategy. In other embodiments, a system and a computer program product are disclosed.

Abstract: Methods and apparatus for generating medical records from a doctor-patient dialogue are provided. A main content portion of a written doctor-patient conversation is identified. The main content portion of the conversation is extracted from the conversation. The main content of the conversation is divided into sections according to a pre-defined set of sections, and, based on the sections and their respective content, a medical record is generated according to a pre-defined template. The pre-defined template is one of a hard medical record format or a soft medical record format.

Abstract: Method and apparatus are presented for receiving a medical or medical condition related input term or phrase in a source language, and translating the term or phrase from the source language into at least one target language to obtain a set of translated terms of the input term. For each translated term in the set of translations, the method and apparatus further translate the set of translations back into the source language to obtain an output list of standard versions of the input term, scoring each entry of the output list as to probability of being the most standard version of the input term, and providing the entry of the output list that has the highest score to a user.

Abstract: Methods and systems for processing records include extracting feature vectors from words in an unstructured portion of a record. The feature vectors are weighted based similarity to a topic vector from a structured portion of the record associated with the unstructured portion. The weighted feature vectors are classified using a machine learning model to determine respective probability vectors that assign a probability to each of a set of possible relations for each feature vector. Relations between entities are determined within the record based on the probability vectors. An action is performed responsive to the determined relations.