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    Spatial and temporal sequence-to-sequence modeling for handwriting recognition
    12.
    发明授权
    Spatial and temporal sequence-to-sequence modeling for handwriting recognition 有权

    公开(公告)号：US11270104B2

    公开(公告)日：2022-03-08

    申请号：US16741384

    申请日：2020-01-13

    Applicant: Apple Inc.

    Inventor： Dominic L. Howell ,  Jerome R. Bellegarda

    IPC: G06K9/00 ,  G06F3/04883 ,  G06K9/62

    Abstract: An example process for recognizing handwritten input includes obtaining input data representing handwritten input, where the handwritten input is associated with a first dimension and a second dimension relative to the handwritten input; sampling the input data to obtain a plurality of coordinates representing the handwritten input; determining, based on the plurality of coordinates, a sequence of vectors representing a respective plurality of portions of the handwritten input, where: each portion of the respective plurality of portions is associated with a respective height and width corresponding respectively to the first and second dimensions, the respective height being greater than the respective width; and consecutive vectors of the sequence of vectors represent respective consecutive portions of the handwritten input; generating, using a handwriting recognition model, based on the sequence of vectors, one or more characters for the handwritten input; and causing the one or more characters to be displayed.

    Efficient word encoding for recurrent neural network language models
    16.
    发明授权
    Efficient word encoding for recurrent neural network language models 有权

    公开(公告)号：US10366158B2

    公开(公告)日：2019-07-30

    申请号：US15141660

    申请日：2016-04-28

    Applicant: Apple Inc.

    Inventor： Jerome R. Bellegarda ,  Jannes G. Dolfing

    IPC: G06F17/27 ,  G10L15/16 ,  G06F16/35 ,  G06N3/02 ,  G10L15/183

    Abstract: Systems and processes for efficient word encoding are provided. In accordance with one example, a method includes, at an electronic device with one or more processors and memory, receiving a user input including a word sequence, and providing a representation of a current word of the word sequence. The representation of the current word may be indicative of a class of a plurality of classes and a word associated with the class. The method further includes determining a current word context based on the representation of the current word and a previous word context, and providing a representation of a next word of the word sequence. The representation of the next word of the word sequence may be based on the current word context. The method further includes displaying, proximate to the user input, the next word of the word sequence.

    Language identification from short strings
    18.
    发明授权
    Language identification from short strings 有权

    公开(公告)号：US10127220B2

    公开(公告)日：2018-11-13

    申请号：US14845180

    申请日：2015-09-03

    Applicant: Apple Inc.

    Inventor： Jerome R. Bellegarda ,  Bishal Barman

    IPC: G06F17/20 ,  G06F17/27 ,  G06F17/28 ,  G10L15/26

    Abstract: Systems and processes for language identification from short strings are provided. In accordance with one example, a method includes, at a first electronic device with one or more processors and memory, receiving user input including an n-gram and determining a similarity between a representation of the n-gram and a representation of a first language. The representation of the first language is based on an occurrence of each of a plurality of n-grams in the first language and an occurrence of each of the plurality of n-grams in a second language. The method further includes determining whether the similarity between the representation of the n-gram and the representation of the first language satisfies a threshold.

    Parsimonious handling of word inflection via categorical stem + suffix N-gram language models
    19.
    发明授权
    Parsimonious handling of word inflection via categorical stem + suffix N-gram language models 有权

    公开(公告)号：US09886432B2

    公开(公告)日：2018-02-06

    申请号：US14839806

    申请日：2015-08-28

    Applicant: Apple Inc.

    Inventor： Jerome R. Bellegarda ,  Sibel Yaman

    IPC: G06F17/27 ,  G06F17/20 ,  G10L15/197

    CPC classification number: G06F17/276 ,  G10L15/197

    Abstract: Systems and processes are disclosed for predicting words using a categorical stem and suffix word n-gram language model. A word prediction includes determining a stem probability using a stem language model. The word prediction also includes determining a suffix probability using suffix language model decoupled from the stem model, in view of one or more stem categories. The word prediction also includes determine a probability of the stem belonging to the stem category. A joint probability is determined based on the foregoing, and one or more word predictions having sufficient likelihood. In this way, the categorical stem and suffix language model constraints predicted suffixes to those that would be grammatically valid with predicted stems, thereby producing word predictions with grammatically valid stem and suffix combinations.

    Sentiment prediction from textual data
    20.
    发明授权
    Sentiment prediction from textual data 有权

    公开(公告)号：US11010561B2

    公开(公告)日：2021-05-18

    申请号：US16234080

    申请日：2018-12-27

    Applicant: Apple Inc.

    Inventor： Jerome R. Bellegarda

    IPC: G06F40/30 ,  G06F40/10 ,  G06N3/02

    Abstract: Techniques for predicting sentiment from textual data are described herein. In some examples, the described techniques utilize a sentiment prediction model having bidirectional long short-term memory (LSTM) networks with one or more convolution-and-pooling stages. The bidirectional LSTM networks process vector representations of words in a textual word sequence to determine forward and backward word-level context feature vectors. Forward and backward phrase-level feature vectors are determined based on the forward and backward word-level context feature vectors. The one or more convolution-and-pooling stages pool the forward and backward phrase-level feature vectors to determine pooled phrase-level feature vectors. A sentiment representing the textual word sequence is determined based on the pooled phrase-level feature vectors.

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