Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for machine learning for home understanding and notification. In one aspect, a method includes obtaining reference videos from a camera within a premises of a home, determining, from the reference videos, timing of actions in a routine that a particular person performs before leaving the home, determining from a sample video from the camera within the home that the particular person appears to be out of sync in performing a particular action based on the timing of actions in the routine determined from the reference videos, and in response, providing a notification to the particular person.

An automatic generation system of a training image and a method thereof are provided. The disclosure generates a training image and records the target category and the target position. The disclosure adds the target image to the container image as a candidate image, calculates a reliability of the candidate image, and repeatedly executes the process until the reliability of the candidate image meets a threshold condition for generating the training image. The disclosure is able to generate the training images automatically, and the recognition difficulty of the training image is adjustable by the user, so as to be suitable for customized recognition training.

Techniques for a perception system of a vehicle that can detect and track objects in an environment are described herein. The perception system may include a machine-learned model that includes one or more different portions, such as different components, subprocesses, or the like. In some instances, the techniques may include training the machine-learned model end-to-end such that outputs of a first portion of the machine-learned model are tailored for use as inputs to another portion of the machine-learned model. Additionally, or alternatively, the perception system described herein may utilize temporal data to track objects in the environment of the vehicle and associate tracking data with specific objects in the environment detected by the machine-learned model. That is, the architecture of the machine-learned model may include both a detection portion and a tracking portion in the same loop.

Techniques for a perception system of a vehicle that can detect and track objects in an environment are described herein. The perception system may include a machine-learned model that includes one or more different portions, such as different components, subprocesses, or the like. In some instances, the techniques may include training the machine-learned model end-to-end such that outputs of a first portion of the machine-learned model are tailored for use as inputs to another portion of the machine-learned model. Additionally, or alternatively, the perception system described herein may utilize temporal data to track objects in the environment of the vehicle and associate tracking data with specific objects in the environment detected by the machine-learned model. That is, the architecture of the machine-learned model may include both a detection portion and a tracking portion in the same loop.

A system and method for analyzing risk and providing risk mitigation instructions. The system receives analyzes sensor data and other data corresponding to a user to determine a test group. The system uses the test group to determine a risk score, and, subsequently, a risk mitigation strategy. Machine learning techniques are implemented to refine how the test group, risk score, and mitigation are each selected.

Systems and methods electronically provide information regarding digital rules related to a potential relationship instance. Users often wish to know which digital rules apply to a specified item before engaging in a relationship instance with a host entity regarding the item. The system and methods described herein allow a computing facility to identify an item and receive resource information related to the item and the digital rules applicable to the item.

A wine label recognition method, a wine information management method and apparatus, a computer device, and a computer-readable storage medium are provided. The method includes: obtaining a wine image, and performing optical character recognition (OCR) on the wine image in a preset OCR manner, to obtain text included in the wine image (S21); performing deep learning recognition on the wine image in a preset deep learning recognition manner, to obtain an image feature included in the wine image (S22); and sifting out a target wine label matching the text and the image feature from a preset wine label database according to the text and the image feature, and using the target wine label as a wine label corresponding to the wine image (S33). Advantages of deep learning and OCR are fully utilized thereby improving accuracy and efficiency of wine label recognition and improving automation efficiency of wine information management.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for contextually disambiguating queries are disclosed. In an aspect, a method includes receiving an image being presented on a display of a computing device and a transcription of an utterance spoken by a user of the computing device, identifying a particular sub-image that is included in the image, and based on performing image recognition on the particular sub-image, determining one or more first labels that indicate a context of the particular sub-image. The method also includes, based on performing text recognition on a portion of the image other than the particular sub-image, determining one or more second labels that indicate the context of the particular sub-image, based on the transcription, the first labels, and the second labels, generating a search query, and providing, for output, the search query.

The disclosed systems and methods provide hands-free medication tracking. A method includes providing an augmented reality device attachable to a face of a user. The method also includes determining, using one or more sensors of the augmented reality device, a user action to be carried out with respect to a medication. The method also includes presenting, via a display interface of the augmented reality device, a visual indicator to assist with the user action. The method also includes confirming, via the one or more sensors of the augmented reality device, a completion of the user action. The method also includes sending, via a communication interface of the augmented reality device, an update message to a server indicating the completion of the user action, wherein the update message causes the server to update a medication inventory in a database.

The disclosed systems and methods provide hands-free medication tracking. A method includes providing an augmented reality device attachable to a face of a user. The method also includes determining, using one or more sensors of the augmented reality device, a user action to be carried out with respect to a medication. The method also includes presenting, via a display interface of the augmented reality device, a visual indicator to assist with the user action. The method also includes confirming, via the one or more sensors of the augmented reality device, a completion of the user action. The method also includes sending, via a communication interface of the augmented reality device, an update message to a server indicating the completion of the user action, wherein the update message causes the server to update a medication inventory in a database.