CN108304078B - Input method and device and electronic equipment - Google Patents
Input method and device and electronic equipment Download PDFInfo
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- CN108304078B CN108304078B CN201710018203.7A CN201710018203A CN108304078B CN 108304078 B CN108304078 B CN 108304078B CN 201710018203 A CN201710018203 A CN 201710018203A CN 108304078 B CN108304078 B CN 108304078B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/02—Input arrangements using manually operated switches, e.g. using keyboards or dials
- G06F3/023—Arrangements for converting discrete items of information into a coded form, e.g. arrangements for interpreting keyboard generated codes as alphanumeric codes, operand codes or instruction codes
- G06F3/0233—Character input methods
- G06F3/0237—Character input methods using prediction or retrieval techniques
Abstract
The invention discloses an input method, an input device and electronic equipment, wherein the input method comprises the following steps: acquiring input behavior data of a user aiming at candidate items in an input history record of the user; based on the input behavior data, adjusting personalized candidate policies for the user; and providing target candidates for the target character string based on the adjusted personalized candidate strategy. According to the technical scheme, the personalized candidate strategies of the user are adjusted according to the input behavior data of the user, instead of adopting a constant candidate strategy, the continuous fitting of the input candidate strategies and the use habits of the user is realized, more accurate target candidate options are provided for the user, and therefore the input efficiency of the user is improved.
Description
Technical Field
The present invention relates to the field of software technologies, and in particular, to an input method, an input device, and an electronic device.
Background
With the continuous development of science and technology, portable electronic devices are rapidly developed and popularized, and man-machine interaction is becoming more frequent, and efficiency of man-machine interaction is becoming more important. The intelligent and individuation of the input method greatly improves the efficiency of man-machine interaction.
In the existing common input method, the personalization of the client is often realized in the form of a word stock, and the most common personalization means is a user word stock, namely, the vocabulary entries input by the user occupy the front positions of candidate areas of different users according to different people, and the candidate arrangement of each user is different according to the different user word stocks.
However, the candidate policies provided correspondingly are the same for different users, and are determined in advance by big data statistics, for example: when the number of candidates provided by the user word stock is less than 5, cloud candidates (candidates obtained by cloud service calculation) obtained by the cloud service are provided, so that each user starting the cloud calculation candidate function can execute the method, namely, many candidate strategies in the input method are written in codes, no updating space is provided at the user end, the fitting degree with personalized use habits of the user is not high, and the input efficiency of the user is not improved.
Disclosure of Invention
The embodiment of the invention provides an input method, an input device and electronic equipment, which are used for realizing continuous fitting of an input candidate strategy and a user using habit and improving the input efficiency of a user.
The embodiment of the application provides an input method, which comprises the following steps:
acquiring input behavior data of a user aiming at candidate items in an input history record of the user;
based on the input behavior data, adjusting personalized candidate policies for the user;
obtaining a target character string currently input by the user;
and providing target candidates for the target character string based on the adjusted personalized candidate strategy.
Optionally, the adjusting, based on the input behavior data, a personalized candidate policy for the user includes:
based on the input behavior data, the opening and closing of the personalized candidate strategies are adjusted, and/or the triggering conditions of the preset type candidates corresponding to the personalized candidate strategies are adjusted, and/or the number of the preset type candidates is adjusted.
Optionally, the adjusting the opening and closing of the personalized candidate policy based on the input behavior data includes:
obtaining a first ratio of the number of screen shots of preset type candidates in the input behavior data to the total number of screen shots in a first time period, wherein the first time period is adjacent to the current time period;
judging whether the first ratio is smaller than a first set threshold value or not;
if yes, closing the operation of the personalized candidate strategy corresponding to the preset type candidate item; and if not, starting the operation of the personalized candidate strategy corresponding to the preset type candidate item.
Optionally, the adjusting, based on the input behavior data, a trigger condition for displaying a preset type of candidate item corresponding to the personalized candidate policy includes:
acquiring the screen probability of preset type candidates in the input behavior data in each time period;
if the screen probability of the preset type candidate items is larger than a second set threshold value in the continuous N time periods, reducing the difficulty of showing the triggering conditions of the preset type candidate items, wherein N is an integer larger than 1; or alternatively
If the screen probability of the preset type candidate items in the continuous N time periods is smaller than a third set threshold value, the difficulty of showing the triggering conditions of the preset type candidate items is increased, and the third set threshold value is smaller than the second set threshold value.
Optionally, the triggering condition includes: a limit term and/or a limit threshold; the difficulty of the trigger condition is related to the number of limit terms and the size of the limit threshold.
Optionally, the adjusting the number of the preset types of candidate items to be displayed based on the input behavior data includes:
obtaining a first ratio of the number of screen shots of preset type candidates in the input behavior data to the total number of screen shots in a first time period, wherein the first time period is adjacent to the current time period;
obtaining a second ratio of the number of screen shots of preset type candidates in the input behavior data to the total number of screen shots in a second time period before the first time period;
comparing the magnitude of the first ratio to the second ratio;
if the first ratio is larger than the second ratio, increasing the number of the preset type candidates; and if the first ratio is smaller than the second ratio, reducing the display quantity of the preset type candidates.
Optionally, the personalized candidate policy includes:
candidate policies for providing cloud candidates, candidate policies for providing error correction candidates, and candidate policies for providing fuzzy voice candidates.
Optionally, each personalized candidate policy corresponds to a preset type of candidate item.
The embodiment of the application also provides an input device, which comprises:
the first acquisition unit is used for acquiring input behavior data of a user aiming at candidate items in an input history record of the user;
an adjustment unit for adjusting personalized candidate policies for the user based on the input behavior data;
the second acquisition unit is used for acquiring a target character string currently input by the user;
and the output unit is used for providing target candidate items for the target character string based on the personalized candidate strategy after adjustment.
Optionally, the adjusting unit includes:
the switch subunit is used for adjusting the opening and closing of the personalized candidate strategy based on the input behavior data; and/or the number of the groups of groups,
the condition adjustment subunit is used for adjusting the triggering condition of the preset type candidate items corresponding to the personalized candidate strategies; and/or the number of the groups of groups,
and the quantity adjusting subunit is used for adjusting the display quantity of the preset type candidates.
Optionally, the switch subunit is configured to:
obtaining a first ratio of the number of screen shots of preset type candidates in the input behavior data to the total number of screen shots in a first time period, wherein the first time period is adjacent to the current time period;
judging whether the first ratio is smaller than a first set threshold value or not;
if yes, closing the operation of the personalized candidate strategy corresponding to the preset type candidate item; and if not, starting the operation of the personalized candidate strategy corresponding to the preset type candidate item.
Optionally, the condition adjustment subunit is configured to:
acquiring the screen probability of preset type candidates in the input behavior data in each time period;
if the screen probability of the preset type candidate items is larger than a second set threshold value in the continuous N time periods, reducing the difficulty of showing the triggering conditions of the preset type candidate items, wherein N is an integer larger than 1; or alternatively
If the screen probability of the preset type candidate items in the continuous N time periods is smaller than a third set threshold value, the difficulty of showing the triggering conditions of the preset type candidate items is increased, and the third set threshold value is smaller than the second set threshold value.
Optionally, the triggering condition includes: a limit term and/or a limit threshold; the difficulty of the trigger condition is related to the number of limit terms and the size of the limit threshold.
Optionally, the number adjustment subunit is configured to:
obtaining a first ratio of the number of screen shots of preset type candidates in the input behavior data to the total number of screen shots in a first time period, wherein the first time period is adjacent to the current time period;
obtaining a second ratio of the number of screen shots of preset type candidates in the input behavior data to the total number of screen shots in a second time period before the first time period;
comparing the magnitude of the first ratio to the second ratio;
if the first ratio is larger than the second ratio, increasing the number of the preset type candidates; and if the first ratio is smaller than the second ratio, reducing the display quantity of the preset type candidates.
Optionally, the personalized candidate policy includes: candidate policies for providing cloud candidates, candidate policies for providing error correction candidates, and candidate policies for providing fuzzy voice candidates.
Optionally, each personalized candidate policy corresponds to a preset type of candidate item.
Embodiments of the present application also provide an electronic device comprising a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by one or more processors, the one or more programs comprising instructions for:
acquiring input behavior data of a user aiming at candidate items in an input history record of the user;
based on the input behavior data, adjusting personalized candidate policies for the user;
obtaining a target character string currently input by the user;
and providing target candidates for the target character string based on the adjusted personalized candidate strategy.
The above technical solutions in the embodiments of the present application at least have the following technical effects:
the method comprises the steps of obtaining input behavior data of candidate items from a historical input record of a user, adjusting personalized candidate strategies corresponding to various preset types of candidate items of the user based on the input behavior data, so as to provide target candidate items for a target character string currently input by the user based on the adjusted personalized candidate strategies, namely adjusting the personalized candidate strategies of the user according to the user input behavior data instead of adopting a constant candidate strategy, realizing continuous fitting of the input candidate strategies and the user using habits, and providing more accurate target candidate items for the user, thereby improving the input efficiency of the user.
Drawings
Fig. 1 is a flowchart of an input method provided in an embodiment of the present application;
FIG. 2 is a schematic diagram of an input device according to an embodiment of the present disclosure;
fig. 3 is a schematic diagram of an electronic device for implementing an input method according to an embodiment of the present application.
Detailed Description
According to the technical scheme provided by the embodiment of the application, the personalized candidate strategies of the user are adaptively adjusted according to the historical input behavior data of the user, so that the continuous fitting of the input candidate strategies and the use habits of the user is realized, more accurate candidate options are provided for the user, and the input efficiency of the user is improved.
The following describes in detail the main implementation principles of the technical solution of the embodiments of the present application, the specific implementation manner and the corresponding beneficial effects.
Example 1
Referring to fig. 1, an embodiment of the present application provides an input method, which includes:
s101: acquiring input behavior data of a user aiming at candidate items in an input history record of the user;
s102: based on the input behavior data, adjusting personalized candidate policies for the user;
s103: obtaining a target character string currently input by the user;
s104: and providing target candidates for the target character string based on the adjusted personalized candidate strategy.
In a specific implementation process, in a process of providing an input function for a user, an input method stores an input history record of the user, including: recording of user input character strings, recording of input behavior data for candidates provided by an input method, recording of screen character strings that have been input by user pruning, and the like. Wherein the input behavior data for the candidate item includes: candidates of the upper screen, types of the candidates, positions of the candidates, and the like.
S101, when input behavior data of a user aiming at candidate items is obtained in an input history record of the user, the input behavior data can be classified according to time and the type of the candidate item on the screen, so that S102 is conveniently executed, and personalized candidate strategies aiming at the user are adjusted based on the input behavior data. Wherein each personalized candidate policy corresponds to a preset type of candidate, for example: the candidate item type corresponding to the cloud candidate strategy is a cloud candidate item, the candidate item type corresponding to the error correction candidate strategy is an error correction candidate item, the candidate item type corresponding to the fuzzy sound candidate strategy is a fuzzy sound candidate item, the candidate item type corresponding to the simple spelling candidate strategy is a simple spelling candidate item, and the like. When the personalized candidate strategies are adjusted, the candidate strategies for providing cloud candidates, the candidate strategies for providing error correction candidates or the candidate strategies for providing fuzzy sound candidates can be independently carried out, and two or more candidate strategies can also be simultaneously carried out.
Each personalized candidate strategy has parameters such as display conditions, display positions, display quantity and the like of the respective preset type of candidate items. In order to make each personalized candidate strategy more personalized and more fit the use habit of the user, the embodiment of the application adjusts the opening and closing of each personalized candidate strategy based on the input behavior data of the user, and/or adjusts the triggering condition of displaying the preset type candidate item corresponding to each personalized candidate strategy, and/or adjusts the displaying quantity of the preset type candidate item.
1. Opening and closing of personalized candidate policies
In the actual use process, not all users need the candidate function of each personalized candidate strategy, and the same user may need different personalized candidate strategies in different time periods, so that a first ratio of the number of screen-on times to the total number of screen-on times of preset types of candidate items in the user input behavior data in a first time period is obtained based on the user input behavior data, the first time period is adjacent to the current time period, the duration of the first time period can be one week, 15 days, one month and the like, and the embodiment of the application does not limit the specific value of the first time period; and judging whether the first ratio is smaller than a first set threshold, if so, closing the personalized candidate strategy corresponding to the preset type candidate item, and if not, opening the personalized candidate strategy corresponding to the preset type candidate item. The first set threshold may be 10%, 15%, 25%, etc., and the embodiment of the present application does not limit the specific value of the first set threshold.
For example: the user A does not distinguish between "in" and "ing", the number of times that the word on the screen is the word of "in the first time period, such as one month, is obtained in the input behavior data of the user, the first ratio of the number of times to the total number of times is 12%, and is greater than the first set threshold value by 10%, so that the user needs a fuzzy sound candidate strategy, and the fuzzy sound candidate strategy is started, thereby being beneficial to providing accurate candidates for the user.
In contrast, if the user B opens the fuzzy sound candidate strategy, but in the first time period, the first ratio of the number of times of the fuzzy sound candidate item on the screen to the total number of times of the screen is only 1%, which indicates that the user does not actually need the fuzzy sound candidate strategy, the fuzzy sound candidate strategy is closed, and fuzzy sound candidate items which are not provided are avoided from occupying the candidate item positions and interfering with the screen operation of the user.
2. Trigger condition adjustment for personalized candidate policies
When the triggering condition is adjusted, the embodiment of the application obtains the screen probability of the preset type candidate item in the behavior data input by the user in each time period; judging the variation trend of the screen probability of the preset type candidate item; if the screen probability of the preset type candidate item is larger than a second set threshold value in the continuous N time periods, reducing the difficulty of showing the triggering condition of the preset type candidate item, wherein N is an integer larger than 1; or if the screen probability of the preset type candidate item is smaller than a third set threshold value in the continuous N time periods, increasing the probability of showing the triggering condition of the preset type candidate item, wherein the third set threshold value is smaller than the second set threshold value. Of course, the adjustment of the triggering condition can also be performed by adopting a method of opening and closing the candidate strategy, and when the first ratio is greater than the fourth set threshold, the difficulty of the triggering condition is reduced.
The triggering condition for limiting the display of the preset type candidate items in each personalized candidate strategy comprises the following steps: the difficulty of the trigger condition is related to the number of limit items and the size of the limit threshold. The more the limit items, the harder the limit threshold is reached, the greater the difficulty of the trigger condition, and conversely the lesser the difficulty. The adjustment of the triggering condition can be realized by closing or opening the limiting item, and can also be realized by adjusting the size of the limiting threshold value.
For example: in the cloud candidate strategy, the cloud candidate is generally displayed under the condition that the normal candidates are less than 5 and the length of the target character string input by the user is greater than 4. After a period of time, the screen probability of obtaining cloud candidates from the input behavior data of the user is obtained, and the screen probability is larger than a second set threshold value by 10% in 3 continuous time periods, which indicates that the user likes cloud candidates recommended by cloud computing, and then the difficulty of showing the triggering conditions of the cloud candidates is reduced: (1) closing the limit item: canceling the limitation that the number of normal candidates is less than 5; (2) the method comprises the following steps Adjusting the size of the limit threshold: and when the length of the target character string input by the user is greater than 3, providing cloud candidates for the user and displaying.
3. Display quantity adjustment of preset type candidates
The number of the preset type candidates provided by each personalized candidate strategy in a corresponding way is limited, and the number of the preset type candidates can be adjusted according to the use condition of each candidate strategy by a user. Specifically, a first ratio of the number of screen shots of preset type candidates in input behavior data to the total number of screen shots in a first time period is obtained, wherein the first time period is adjacent to the current time period; obtaining a second ratio of the number of screen shots of preset type candidates in the input behavior data to the total number of screen shots in a second time period before the first time period; comparing the magnitude of the first ratio to the second ratio; if the first ratio is larger than the second ratio, increasing the number of the preset type candidates; and if the first ratio is smaller than the second ratio, reducing the display quantity of the preset type candidates. Of course, the number of the preset types of candidates can also be adjusted by adopting the same adjustment method as the trigger condition, when the probability of displaying the preset candidates is larger and larger, the number of the preset types of candidates is increased, and when the probability of displaying the preset types of candidates is smaller and larger, the number of the preset types of candidates is reduced.
For example: for user B, the original error correction candidate strategy is: when the error correction candidates are presented, 2 error correction candidates are presented each time (error correction is performed on the target character string input by the user, such as "kaif" corrected to "kaif", and then "development" of the error correction candidates is obtained based on "kaif" conversion). After the input behavior data of the user is analyzed, the ratio of the number of the screen-on times of the error correction candidates to the total number of the screen-on times is increased in two time periods, namely the latest time period, which indicates that the preference degree and the dependence degree of the user on the error correction candidates are increased, so that the number of the error correction candidates provided for the target character string can be increased to 3, the screen-on of the user selection word is facilitated, otherwise, the number of the error correction candidates provided for the target character string is reduced to 1, and the interference of excessive error correction candidates on the user is avoided.
It should be noted that the above adjustment of the opening and closing of the candidate policies, the triggering condition, the number of candidates, etc. may be applied to each candidate policy. Through statistical analysis of the screen occupation ratios of different candidate items in the user input behavior data, specific use habits of the user are found, namely whether the user is a severe cloud user, a severe misinput user, a mild fuzzy voice user or a preference simple spelling input user, and the display and trigger conditions of relevant functions corresponding to personalized candidate strategies are automatically adjusted according to the statistical values, so that the input method can also automatically adapt to the use habits of the user in the strategy level, the input efficiency is further improved, and the user input experience is improved.
S103 and S104 are performed after S102: obtaining a target character string currently input by a user; and providing target candidate items for the target character string based on the adjusted personalized candidate strategy so as to enable the user to select to screen.
For example: a user frequently screens cloud candidates, hardly screens fuzzy voice candidates, frequently screens error correction candidates, and adjusts personalized candidate strategies of the user according to input behavior data of the user as follows: the triggering condition of the cloud candidate strategy is adjusted to provide cloud candidates each time of input, and the number of the cloud candidates is increased to 2; closing the function of the fuzzy sound candidate strategy; and the triggering condition of the error correction candidate strategy is adjusted to trigger when grammar errors occur in the target character string, and the number of the error correction candidate options is reduced to 1. After the personalized candidate strategy is adjusted, the user inputs a target character string 'lanshoua', the input method provides and displays cloud candidates 'blue-thin' and 'red-brush' according to the current personalized strategy, and simultaneously provides and displays error correction candidates 'awkward', and of course, conventional candidates are also provided and displayed, such as 'rotten hands', 'blue-longevity' and the like, so that the user can select to put on the screen.
In the above embodiment, no matter the cloud candidate strategy, the error correction candidate strategy, the fuzzy sound candidate strategy or other candidate strategies in the input method are subjected to self-adaptive adjustment based on the input behavior data of the user, the opening and closing of each strategy are adjusted, the triggering condition of the display candidate items is/are adjusted, and/or the display quantity of the candidate items is/are adjusted, so that the input method can be more fit with the use habit of the user, and the user experience is improved.
Referring to fig. 2, based on the input method provided in the foregoing embodiment, an embodiment of the present application further correspondingly provides an input device, where the input device includes:
a first obtaining unit 21, configured to obtain input behavior data of a user for candidate items in an input history of the user;
an adjustment unit 22 for adjusting personalized candidate policies for the user based on the input behavior data;
a second obtaining unit 23, configured to obtain a target character string currently input by the user;
an output unit 24, configured to provide target candidates for the target character string based on the personalized candidate policy after adjustment.
As an alternative embodiment, the adjusting unit 22 includes at least one of the following subunits: a switch subunit, a condition adjustment subunit and a quantity adjustment subunit. The switch subunit is used for adjusting the opening and closing of the personalized candidate strategy based on the input behavior data; the condition adjustment subunit is used for adjusting the triggering condition of the preset type candidate item corresponding to the personalized candidate strategy; the quantity adjusting subunit is used for adjusting the display quantity of the preset type candidates.
In a specific implementation process, the switch subunit is configured to: obtaining a first ratio of the number of screen shots of preset type candidates in the input behavior data to the total number of screen shots in a first time period, wherein the first time period is adjacent to the current time period; judging whether the first ratio is smaller than a first set threshold value or not; if yes, closing the operation of the personalized candidate strategy corresponding to the preset type candidate item; and if not, starting the operation of the personalized candidate strategy corresponding to the preset type candidate item.
The condition adjustment subunit is configured to: acquiring the screen probability of preset type candidates in the input behavior data in each time period; if the screen probability of the preset type candidate items is larger than a second set threshold value in the continuous N time periods, reducing the difficulty of showing the triggering conditions of the preset type candidate items, wherein N is an integer larger than 1; or if the screen probability of the preset type candidate items in the continuous N time periods is smaller than a third set threshold value, increasing the difficulty of showing the triggering condition of the preset type candidate items, wherein the third set threshold value is smaller than the second set threshold value. Optionally, the triggering condition may include: a limit term and/or a limit threshold; the difficulty of the trigger condition is related to the number of limit terms and the size of the limit threshold.
The number adjustment subunit is configured to: obtaining a first ratio of the number of screen shots of preset type candidates in the input behavior data to the total number of screen shots in a first time period, wherein the first time period is adjacent to the current time period; obtaining a second ratio of the number of screen shots of preset type candidates in the input behavior data to the total number of screen shots in a second time period before the first time period; comparing the magnitude of the first ratio to the second ratio; if the first ratio is larger than the second ratio, increasing the number of the preset type candidates; and if the first ratio is smaller than the second ratio, reducing the display quantity of the preset type candidates.
As an alternative embodiment, the personalized candidate policy may include: candidate policies for providing cloud candidates, candidate policies for providing error correction candidates, and candidate policies for providing fuzzy voice candidates. Each personalized candidate strategy corresponds to a preset type of candidate item.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Fig. 3 is a block diagram illustrating an electronic device 800 for implementing an input method, according to an example embodiment. For example, electronic device 800 may be a mobile phone, computer, digital broadcast terminal, messaging device, game console, tablet device, medical device, exercise device, personal digital assistant, or the like.
Referring to fig. 3, the electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, a sensor component 814, and a communication component 816.
The processing component 802 generally controls overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. Processing element 802 may include one or more processors 820 to execute instructions to perform all or part of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interactions between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.
The power supply component 806 provides power to the various components of the electronic device 800. The power components 806 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the electronic device 800.
The multimedia component 808 includes a screen between the electronic device 800 and the user that provides an output interface. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and/or a rear camera. The front camera and/or the rear camera may receive external multimedia data when the device 800 is in an operational mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.
The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 further includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be a keyboard, click wheel, buttons, etc. These buttons may include, but are not limited to: homepage button, volume button, start button, and lock button.
The sensor assembly 814 includes one or more sensors for providing status assessment of various aspects of the electronic device 800. For example, the sensor assembly 814 may detect an on/off state of the device 800, a relative positioning of the components, such as a display and keypad of the electronic device 800, the sensor assembly 814 may also detect a change in position of the electronic device 800 or a component of the electronic device 800, the presence or absence of a user's contact with the electronic device 800, an orientation or acceleration/deceleration of the electronic device 800, and a change in temperature of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscopic sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate communication between the electronic device 800 and other devices, either wired or wireless. The electronic device 800 may access a wireless network based on a communication standard, such as WiFi,2G, or 3G, or a combination thereof. In one exemplary embodiment, the communication part 816 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the electronic device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic elements for executing the methods described above.
In an exemplary embodiment, a non-transitory computer readable storage medium is also provided, such as memory 804 including instructions executable by processor 820 of electronic device 800 to perform the above-described method. For example, the non-transitory computer readable storage medium may be ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.
A non-transitory computer readable storage medium, which when executed by a processor of a mobile terminal, causes the mobile terminal to perform an input method, the method comprising: acquiring input behavior data of a user aiming at candidate items in an input history record of the user; based on the input behavior data, adjusting personalized candidate policies for the user; obtaining a target character string currently input by the user; and providing target candidates for the target character string based on the adjusted personalized candidate strategy.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It is to be understood that the invention is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the invention is limited only by the appended claims
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (12)
1. An input method, the method comprising:
acquiring input behavior data of a user aiming at candidate items in an input history record of the user; the input behavior data for the candidate item includes: candidates of the upper screen, the type of the candidates and the position of the candidates;
automatically adjusting personalized candidate policies for the user based on the input behavior data, comprising: based on the input behavior data, adjusting the opening and closing of the personalized candidate strategy, and/or adjusting the triggering condition of displaying the preset type candidate item corresponding to the personalized candidate strategy, and/or adjusting the displaying quantity of the preset type candidate item;
obtaining a target character string currently input by the user;
providing target candidate items for the target character string based on the adjusted personalized candidate strategy;
the personalized candidate policy includes:
providing candidate strategies of cloud candidates, providing candidate strategies of error correction candidates and providing candidate strategies of fuzzy sound candidates;
based on the input behavior data, adjusting a trigger condition for displaying a preset type of candidate item corresponding to the personalized candidate policy, including:
acquiring the screen probability of preset type candidates in the input behavior data in each time period;
if the screen probability of the preset type candidate items is larger than a second set threshold value in the continuous N time periods, reducing the difficulty of showing the triggering conditions of the preset type candidate items, wherein N is an integer larger than 1; or alternatively
If the screen probability of the preset type candidate items in the continuous N time periods is smaller than a third set threshold value, the difficulty of showing the triggering conditions of the preset type candidate items is increased, and the third set threshold value is smaller than the second set threshold value.
2. The method of claim 1, wherein the adjusting the opening and closing of the personalized candidate policy based on the input behavior data comprises:
obtaining a first ratio of the number of screen shots of preset type candidates in the input behavior data to the total number of screen shots in a first time period, wherein the first time period is adjacent to the current time period;
judging whether the first ratio is smaller than a first set threshold value or not;
if yes, closing the operation of the personalized candidate strategy corresponding to the preset type candidate item; and if not, starting the operation of the personalized candidate strategy corresponding to the preset type candidate item.
3. The method of claim 1, wherein the trigger condition comprises: a limit term and/or a limit threshold; the difficulty of the trigger condition is related to the number of limit terms and the size of the limit threshold.
4. The method of claim 1, wherein adjusting the number of presentations of the preset type of candidate items based on the input behavior data comprises:
obtaining a first ratio of the number of screen shots of preset type candidates in the input behavior data to the total number of screen shots in a first time period, wherein the first time period is adjacent to the current time period;
obtaining a second ratio of the number of screen shots of preset type candidates in the input behavior data to the total number of screen shots in a second time period before the first time period;
comparing the magnitude of the first ratio to the second ratio;
if the first ratio is larger than the second ratio, increasing the number of the preset type candidates; and if the first ratio is smaller than the second ratio, reducing the display quantity of the preset type candidates.
5. The method of any of claims 1-4, wherein each of the personalized candidate policies corresponds to a pre-set type of candidate.
6. An input device, the device comprising:
the first acquisition unit is used for acquiring input behavior data of a user aiming at candidate items in an input history record of the user; the input behavior data for the candidate item includes: candidates of the upper screen, the type of the candidates and the position of the candidates;
an adjustment unit, configured to automatically adjust a personalized candidate policy for the user based on the input behavior data; the adjusting unit includes: the switch subunit is used for adjusting the opening and closing of the personalized candidate strategy based on the input behavior data; and/or a condition adjustment subunit, configured to adjust a trigger condition for displaying a preset type of candidate item corresponding to the personalized candidate policy; and/or a quantity adjusting subunit, configured to adjust the number of presentations of the preset type of candidates;
the second acquisition unit is used for acquiring a target character string currently input by the user;
an output unit, configured to provide target candidates for the target character string based on the adjusted personalized candidate policy;
the personalized candidate policy includes:
providing candidate strategies of cloud candidates, providing candidate strategies of error correction candidates and providing candidate strategies of fuzzy sound candidates;
the condition adjustment subunit is configured to:
acquiring the screen probability of preset type candidates in the input behavior data in each time period;
if the screen probability of the preset type candidate items is larger than a second set threshold value in the continuous N time periods, reducing the difficulty of showing the triggering conditions of the preset type candidate items, wherein N is an integer larger than 1; or alternatively
If the screen probability of the preset type candidate items in the continuous N time periods is smaller than a third set threshold value, the difficulty of showing the triggering conditions of the preset type candidate items is increased, and the third set threshold value is smaller than the second set threshold value.
7. The apparatus of claim 6, wherein the switch subunit is to:
obtaining a first ratio of the number of screen shots of preset type candidates in the input behavior data to the total number of screen shots in a first time period, wherein the first time period is adjacent to the current time period;
judging whether the first ratio is smaller than a first set threshold value or not;
if yes, closing the operation of the personalized candidate strategy corresponding to the preset type candidate item; and if not, starting the operation of the personalized candidate strategy corresponding to the preset type candidate item.
8. The apparatus of claim 6, wherein the trigger condition comprises: a limit term and/or a limit threshold; the difficulty of the trigger condition is related to the number of limit terms and the size of the limit threshold.
9. The apparatus of claim 6, wherein the number adjustment subunit is to:
obtaining a first ratio of the number of screen shots of preset type candidates in the input behavior data to the total number of screen shots in a first time period, wherein the first time period is adjacent to the current time period;
obtaining a second ratio of the number of screen shots of preset type candidates in the input behavior data to the total number of screen shots in a second time period before the first time period;
comparing the magnitude of the first ratio to the second ratio;
if the first ratio is larger than the second ratio, increasing the number of the preset type candidates; and if the first ratio is smaller than the second ratio, reducing the display quantity of the preset type candidates.
10. The apparatus according to any of claims 6-9, wherein each of said personalized candidate policies corresponds to a pre-set type of candidate.
11. An electronic device comprising a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by one or more processors, the one or more programs comprising instructions for performing the method of any of claims 1-5.
12. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-5.
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| CN110874146A (en) * | 2018-08-30 | 2020-03-10 | 北京搜狗科技发展有限公司 | Input method and device and electronic equipment |
| CN109460158A (en) * | 2018-10-29 | 2019-03-12 | 维沃移动通信有限公司 | Characters input method, character correction model training method and mobile terminal |
| CN112083811B (en) * | 2019-06-14 | 2024-01-30 | 北京搜狗科技发展有限公司 | Candidate item display method and device |
| CN115079836A (en) * | 2021-03-10 | 2022-09-20 | 北京搜狗科技发展有限公司 | Request processing method, device and medium |
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