Abstract:
Systems and processes for predictive conversion of language input are provided. In one example process, text composed by a user can be obtained. Input comprising a sequence of symbols of a first symbolic system can be received from the user. Candidate word strings corresponding to the sequence of symbols can be determined. Each candidate word string can comprise two or more words of a second symbolic system. The candidate word strings can be ranked based on a probability of occurrence of each candidate word string in the obtained text. Based on the ranking, a portion of the candidate word strings can be displayed for selection by the user.
Abstract:
Systems and methods for proactively assisting users with accurately locating a parked vehicle are disclosed herein. An example method includes: automatically, and without instructions from a user: determining that a user of the electronic device is in a vehicle that has come to rest at a geographic location. Upon determining that the user has left the vehicle at the geographic location, the method includes automatically, and without instructions from a user: determining whether positioning information, retrieved from the location sensor to identify the geographic location, satisfies accuracy criteria. Upon determining that the positioning information does not satisfy the accuracy criteria, the method includes: providing a prompt to the user to input information about the geographic location. In response to providing the prompt, the method includes receiving information from the user about the geographic location and storing the information as vehicle location information.
Abstract:
The present disclosure generally relates to methods and user interfaces for managing visual content at a computer system. In some embodiments, methods and user interfaces for managing visual content in media are described. In some embodiments, methods and user interfaces for managing visual indicators for visual content in media are described. In some embodiments, methods and user interfaces for inserting visual content in media are described. In some embodiments, methods and user interfaces for identifying visual content in media are described. In some embodiments, methods and user interfaces for translating visual content in media are described.
Abstract:
In one implementation, a method is performed for generating metadata estimations based on metadata subdivisions. The method includes: obtaining an input image; obtaining metadata associated with the input image; subdividing the metadata into a plurality of metadata subdivisions; determining a viewport relative to the input image based on at least one of head pose information and eye tracking information; generating one or more metadata estimations by performing an estimation algorithm on at least a portion of the plurality of metadata subdivisions based on the viewport; and generating an output image by performing an image processing algorithm on the input image based on the one or more metadata estimations.
Abstract:
A device comprises memory, a display characterized by a display characteristic, and processors coupled to the memory. The processors execute instructions causing the processors to receive data indicative of the display characteristic, data indicative of ambient lighting, and data indicative of content characteristics for a content item; determine a tone mapping curve for the content item based on the data indicative of content characteristics; determine a first, so-called “anchor” point along the tone mapping curve; modify a first portion of the tone mapping curve below the anchor point based on the data indicative of ambient lighting; modify a second portion of the tone mapping curve above the anchor point based on the data indicative of the display characteristic; perform tone mapping for the content item based on the modified toned mapping curve to obtain a tone mapped content item; and cause the display to display the tone mapped content item.
Abstract:
The present disclosure generally relates to integrated text conversion and prediction. In an example process, a current character input of a first writing system is received. A first current character context in the first writing system is determined based on the current character input and a first previous character context in the first writing system. A second current character context in a second writing system is determined based on the first current character context, a second previous character context in the second writing system, and a character representation in the second writing system. A current word context in the second writing system is determined based on the second current character context, a previous word context in the second writing system, and a word representation in the second writing system. Based on the current word context, a probability distribution over a word inventory in the second writing system is determined.