Apple Inc.

Baidu USA LLC

Lin Li

Xiao Charles Yang

Ashish Garg

Mubarak Ali Seyed Ibrahim

Deepak Muralidharan

Justine Kao

Saurabh Adya

Rushin N Shah

Myra C Lukens

Alkeshkumar M Patel

Gennaro Frazzingaro

Matthew S Seigel

Blaise Thomson

Yu Zhu

Yingjie Zheng

Hong Yu

John G Keesling

Jianpeng Cheng

Shruti Bhargava

Stephen G Pulman

Anuj Kumar

AtishKumar Kothari

Dhivya Piraviperumal

Lavanya Colinjivadi Viswanathan

Systems and processes for operating a digital assistant are provided. An example process for performing a task includes, at an electronic device having one or more processors and memory, receiving a spoken input including a request, receiving an image input including a plurality of objects, selecting a reference resolution module of a plurality of reference resolution modules based on the request and the image input, determining, with the selected reference resolution module, whether the request references a first object of the plurality of objects based on at least the spoken input, and in accordance with a determination that the request references the first object of the plurality of objects, determining a response to the request including information about the first object.

Digital assistant reference resolution

Systems and processes for operating an intelligent automated assistant are provided. In accordance with one example, a method includes, at an electronic device with one or more processors and memory, obtaining a first set of data corresponding to one or more interactions between a user and the digital assistant on the electronic device; obtaining a second set of data corresponding to one or more interactions between the user and an application on the electronic device; and storing the first set of data and the second set of data. The method further includes receiving a set of executable instructions; processing, based on the received set of executable instructions, the first set of data and the second set of data to obtain a third set of data; and transmitting the third set of data to a remote electronic device.

Feedback analysis of a digital assistant

Systems and processes for processing natural language input are described. An example process for processing natural language input includes receiving a natural language input and determining a domain corresponding to the natural language input. The example process further includes, in accordance with determining the domain corresponding to the natural language input, determining, based on the natural language input, a first value for a first property of the domain and determining, based on a named entity model and the natural language input, a second value for the first property of the domain, where the second value defines a parameter for a task corresponding to the natural language input. The example process further includes performing the task based on the parameter and providing a result based on the performed task.

Named entity normalization in a spoken dialog system

Systems and processes for operating an intelligent automated assistant are provided. An example process receives natural language input and determines a first and a second parsing result for the natural language input. The first and the second parsing results include respective mappings of one or more properties of a domain corresponding to the natural language input to one or more words of the natural language input. The process determines whether the second parsing result corresponds to a data item in a knowledge base, and in accordance with determining that the second parsing result corresponds to the data item in the knowledge base, the process ranks the second parsing result higher than the first parsing result. Based on the ranking, the process generates a task flow using the second parsing result and executes the task flow to provide an output based on the data item.

Knowledge-based framework for improving natural language understanding

Described is a technique for associating words used in a search query with categories. This technique aims to produce potentially more relevant search results by improving the associations with words used for a search. A machine learning technique is implemented to train a classification model, which may include a word embedding model. The classification model is trained to receive words as input and to create vectors of the words as output. These word vectors may then be mapped to a vector space and the technique may then perform a cluster analysis of the vectors. Based on the cluster analysis, clusters may be identified and each cluster may be associated with a corresponding category.

Method and system of determining categories associated with keywords using a trained model

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Lin Li