Patent Document ID: 9176941
Application ID: 14232737

Base Claim:
1. A text inputting method, comprising: obtaining a user input; generating a candidate sentence list according to the user input; for each candidate sentence in the candidate sentence list, respectively calculating a standard conditional probability of each word in the candidate sentence according to a universal language model; respectively calculating a cache conditional probability of each word in the candidate sentence according to a preconfigured modeling policy, the user input and a precached user input; calculating a mixed conditional probability of each word according to the standard conditional probability and the cache conditional probability; obtaining an on-screen probability of the candidate sentence according to the mixed conditional probability; sorting candidate sentences in the candidate sentence list according to their on-screen probabilities; and outputting the sorted candidate sentence list; wherein: the cache conditional probability of each word in the candidate sentence is positively related to a time function value of the word, and the time function value is related to a time when the word enters a cache area used for the pre-cached user input; wherein the time function value is a result obtained by dividing a preconfigured constant by a time interval between an ith word enters the cache area and a word currently inputted by the user.

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Claim 5:
5. The text inputting method of claim 1 , wherein the process of respectively calculating the standard conditional probability of each word in the candidate sentence according to the universal language model comprises: respectively calculating the standard conditional probability of each the candidate sentence according to a pre-created standard Ngram language model, including: obtaining a number of times k′i that a word sequence which includes the i th word and a preconfigured constant number of words before the i th word emerges in training material of the standard N gram language model; obtaining a number of times k′i−1 that a word sequence which includes the preconfigured constant number of words before the ith word emerges in the training material of the standard Ngram language model; calculating a ratio of k′i to k′i−1, and taking the ratio as the standard conditional probability of the ith word of the candidate sentence.