Predictive conversion of language input

Patent metadata

Field	Value
Publication number	US-9842101-B2
Application number	US-201514839830-A
Country	US
Kind code	B2
Filing date	Aug 28, 2015
Priority date	May 30, 2014
Publication date	Dec 12, 2017
Grant date	Dec 12, 2017

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.

First claim

What is claimed is:

1. A method for converting language input, the method comprising:
at an electronic device having one or more processors and memory:
obtaining a corpus of text composed by a user;
after obtaining the corpus of text:
receiving, from the user, input comprising a sequence of symbols of a first symbolic system;
determining a plurality of candidate word strings corresponding to the sequence of symbols, each candidate word string of the plurality of candidate word strings comprising two or more words of a second symbolic system, wherein the obtained corpus of text comprises words of the second symbolic system;
ranking the plurality of candidate word strings based on a probability of occurrence of each candidate word string of the plurality of candidate word strings in the obtained corpus of text; and
displaying, based on the ranking, a portion of the plurality of candidate word strings for selection by the user.

2. The method of claim 1 , wherein the first symbolic system is different from the second symbolic system.

3. The method of claim 1 , further comprising:
generating a first language model using the obtained corpus of text; and
determining, using the first language model, the probability of occurrence of each candidate word string of the plurality of candidate word strings in the obtained corpus of text.

4. The method of claim 3 , wherein the first language model is an n-gram language model.

5. The method of claim 3 , further comprising:
receiving, from the user, a selection of a candidate word string from the displayed portion of the plurality of candidate word strings; and
displaying the selected candidate word string in a text field of the electronic device.

6. The method of claim 5 , further comprising:
receiving an indication that the user has committed to the selected candidate word string; and
in response to receiving the indication, updating the first language model using the selected candidate word string.

7. The method of claim 6 , wherein receiving the indication further comprises receiving a full stop input for a sentence containing the selected candidate word string.

8. The method of claim 6 , wherein receiving the indication further comprises receiving a command to send a message containing the selected candidate word string.

9. The method of claim 5 , further comprising:
determining a predicted word of the second symbolic system based on a probability of occurrence of a sequence of words in the obtained corpus of text, the sequence of words comprising the selected candidate word string and the predicted text; and
displaying the predicted word adjacent to the selected candidate word string in the text field.

10. The method of claim 1 , wherein:
the obtained corpus of text is associated with a first context;
the input is associated with an input context; and
ranking the plurality of candidate word strings is based on a degree of similarity between the input context and the first context.

11. The method of claim 10 , wherein the first context includes a first recipient and a first application of the electronic device, and wherein the input context includes a second recipient and a second application of the electronic device.

12. The method of claim 10 , wherein the first context and input context are determined using a sensor of the electronic device.

13. The method of claim 1 , further comprising:
obtaining a second corpus of text composed by the user, wherein:
the obtained second corpus of text is associated with a second context;
ranking the plurality of candidate word strings is based on a probability of occurrence of each candidate word string of the plurality of candidate word strings in the second obtained corpus of text; and
the ranking the plurality of candidate word strings is based on a degree of similarity between the input context and the second context.

14. The method of claim 13 , wherein the obtained second corpus of text comprises words of the second symbolic system.

15. The method of claim 1 , further comprising:
determining, using a second language model, a probability of occurrence of each candidate word string of the plurality of candidate word strings in a general corpus of text, wherein ranking the plurality of candidate word strings is based on the probability of occurrence of each candidate word string of the plurality of candidate word strings in the general corpus of text, and wherein the general corpus of text is not composed by the user.

16. The method of claim 1 , wherein the first symbolic system comprises a phonetic system for transcribing a language.

17. The method of claim 1 , wherein the first symbolic system comprises Chinese Pinyin.

18. The method of claim 1 , wherein the first symbolic system comprises Chinese Zhuyin.

19. The method of claim 1 , wherein the second symbolic system comprises Chinese characters.

20. The method of claim 1 , wherein each word of the two or more words is a monosyllabic Chinese character.

21. The method of claim 1 , wherein the probability of occurrence of each candidate word string of the plurality of candidate word strings in the obtained corpus of text is the probability that each candidate word string of the plurality of candidate word strings occurs within the obtained corpus of text composed by the user.

22. A non-transitory computer-readable storage medium comprising computer-executable instructions, which when executed by one or more processors, cause the one or more processors to:
obtain a corpus of text composed by a user;
after obtaining the corpus of text:
receive, from the user, input comprising a sequence of symbols of a first symbolic system;
determine a plurality of candidate word strings corresponding to the sequence of symbols, each candidate word string of the plurality of candidate word strings comprising two or more words of a second symbolic system, wherein the obtained corpus of text comprises words of the second symbolic system;
rank the plurality of candidate word strings based on a probability of occurrence of each candidate word string of the plurality of candidate word strings in the obtained corpus of text; and
display, based on the ranking, a portion of the plurality of candidate word strings for selection by the user.

23. A system comprising:
one or more processors;
memory storing computer-readable instructions, which when executed by the one or more processors, cause the one or more processors to:
obtain a corpus of text composed by a user;
after obtaining the corpus of text:
receive, from the user, input comprising a sequence of symbols of a first symbolic system;
determine a plurality of candidate word strings corresponding to the sequence of symbols, each candidate word string of the plurality of candidate word strings comprising two or more words of a second symbolic system, wherein the obtained corpus of text comprises words of the second symbolic system;
rank the plurality of candidate word strings based on a probability of occurrence of each candidate word string of the plurality of candidate word strings in the obtained corpus of text; and
display, based on the ranking, a portion of the plurality of candidate word strings for selection by the user.

24. The system of claim 23 , wherein the computer-readable instructions further cause the one or more processors to:
generate a first language model using the obtained corpus of text; and
determi

Assignees

• Apple Inc

Inventors

• Wang Xin
• Ramerth Brent D

Classifications

• G06F16/3329

  Natural language query formulation · CPC title

• G06F40/274 Primary

  Converting codes to words; Guess-ahead of partial word inputs · CPC title

• G06F16/3322

  using system suggestions (G06F16/3325 takes precedence) · CPC title

• G06F3/018 Primary

  Input/output arrangements for oriental characters · CPC title

• G06F40/129

  Handling non-Latin characters, e.g. kana-to-kanji conversion · CPC title