Abstract: Techniques for improving a natural language processing (NLP) system by ingesting information from various sources and generating personalized results in response to a user query. With user permission, the system may ingest personal data from a variety of data sources, allowing the system to access existing personal data in order to identify correlated information, make inferences and predictions, prompt the user when appropriate, and generate improved results. The ingestion step may process both textual and non-textual data (e.g., images, audio, video). Using this consolidated data storage, the system may search amongst multiple different sources of data and identify correlations to improve processing. In addition, the system may proactively provide suggestions to a customer (e.g., detect upcoming event and suggest an action corresponding to the event) or may provide personalized results (e.g., filter results based on user preferences determined by the inferences and predictions).

Abstract: Techniques for improving a natural language processing (NLP) system by ingesting information from various sources and generating personalized results in response to a user query. With user permission, the system may ingest personal data from a variety of data sources, allowing the system to access existing personal data in order to identify correlated information, make inferences and predictions, prompt the user when appropriate, and generate improved results. The ingestion step may process both textual and non-textual data (e.g., images, audio, video). Using this consolidated data storage, the system may search amongst multiple different sources of data and identify correlations to improve processing. In addition, the system may proactively provide suggestions to a customer (e.g., detect upcoming event and suggest an action corresponding to the event) or may provide personalized results (e.g., filter results based on user preferences determined by the inferences and predictions).