Oizea Type

Oizea Type, which means Input All Easier, is a patent pending predictive text technology for mobile phones, developed by Oizea.com .
Design
The objective is to make it easier to type text messages by eliminating the delay introduced by hunt and peck and hence can be adapted by muscle memory. Another objective is reducing word candidates provided to user by applying temporal ambiguity.
Shape-Based
Finding letters on telephone keypad can be slower (~0.6 sec) than finding numerals.
Oizea Type introduces intuitive shape-based associations between letters and numerals. The traditional letter hunt and peck is replaced by numeral touch typing. This mapping has KSPC ~1.35 compared with 2.2 of Multi-tap.
It can be viewed as a numeral dialect of Leet that makes Leet more useful than just a slang.
Spatial Ambiguity
Mobile device text entry encounters a typical problem of assigning letters to a much limited numbers of keys (12 keys for telephone keypad).
Traditional predictive text tries to solve this problem based on spatial ambiguity, i.e. several letters are associated with each key, such that they cannot be distinguished in a specific region of space (here, a key press).
The ambiguity is exponential in proportion to the input length hence 3 variations for "HOME" as illustrated.
The following is the feasible combinations of the input sequence "4663" disambiguated by dictionary-based mechanism:
* HOME, GOOD, GONE, HOOD, HOOF, HONE, GOOF, IMME, INNE, HOND, INOF and GOOE (12).
Temporal Ambiguity
Oizea Type instead utilizes temporal ambiguity to tackle this problem.
Letters can then be assigned to new states introduced by temporal relationship of input sequence.
For example, letter 'i' is assigned to numeral sequence '1' and letter 'H' is assigned to numeral sequence '11', respectively. An input sequence "11" can be treat as "ii" as well as "H".
The letters may not be distinguished in a specific period of time (here, an input sequence).
The advantage of this scheme is that the ambiguity is not exponential in proportion to input length as in traditional spatial ambiguity. There are only 3x2x1=6 variations for "HOME" as illustrated in second figure.
The collisions in the coding space are much lesser if temporal ambiguity is applied for dictionary-based mechanism. For example, there are less than 50 pairs of word collision against a 39,000-word dictionary and just a handful of three-word collision. The word candidates provided by predictive text software can be greatly reduced and prevent user from distraction of looking up various possibilities.

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