CN115373523A

CN115373523A - Input method, input device and input device

Info

Publication number: CN115373523A
Application number: CN202110542553.XA
Authority: CN
Inventors: 张文略; 卓兴中; 韩秦
Original assignee: Beijing Sogou Technology Development Co Ltd
Current assignee: Beijing Sogou Technology Development Co Ltd
Priority date: 2021-05-18
Filing date: 2021-05-18
Publication date: 2022-11-22

Abstract

The embodiment of the application discloses an input method, an input device and a device for inputting. An embodiment of the method comprises: acquiring a candidate word sequence corresponding to a current input string; acquiring the above information of the input string, and adjusting the sequence of candidate words in the candidate word sequence based on the above information; and selecting at least one candidate word from the candidate word sequence for display based on the adjusted sequence. The implementation method improves the matching degree of the display sequence of the candidate words and the user intention.

Description

Input method, input device and input device

Technical Field

The embodiment of the application relates to the technical field of computers, in particular to an input method, an input device and an input device.

Background

With the development of computer technology, word libraries applied by input methods are more and more abundant. The candidate bar of the input method interface may present multiple candidate words for selection by the user.

In the prior art, after a user inputs a certain input string, the arrangement sequence of candidate words is usually decided according to the word frequency or historical screen frequency of the candidate words. The sorting mode considers one-sidedly, so that the display sequence of the candidate words cannot be matched with the intention of the user.

Disclosure of Invention

The embodiment of the application provides an input method, an input device and an input device, and aims to solve the technical problem that the matching degree of the display sequence of candidate words and the user intention is low in the prior art.

In a first aspect, an embodiment of the present application provides an input method, where the method includes: acquiring a candidate word sequence corresponding to a current input string; acquiring the above information of the input string, and adjusting the sequence of candidate words in the candidate word sequence based on the above information; and selecting at least one candidate word from the candidate word sequence for display based on the adjusted sequence.

In a second aspect, an embodiment of the present application provides an input device, including: the acquisition unit is configured to acquire a candidate word sequence corresponding to a current input string; the sequence adjusting unit is configured to acquire the above information of the input string and adjust the sequence of the candidate words in the candidate word sequence based on the above information; and the display unit is configured to select at least one candidate word from the candidate word sequence to display based on the adjusted sequence.

In a third aspect, an embodiment of the present application provides an apparatus for input, comprising a memory, and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs are configured to be executed by the one or more processors and include instructions for: acquiring a candidate word sequence corresponding to a current input string; acquiring the above information of the input string, and adjusting the sequence of candidate words in the candidate word sequence based on the above information; and selecting at least one candidate word from the candidate word sequence for display based on the adjusted sequence.

In a fourth aspect, embodiments of the present application provide a computer-readable medium on which a computer program is stored, which when executed by a processor, implements the method as described in the first aspect above.

According to the input method, the input device and the input device provided by the embodiment of the application, the candidate word sequence corresponding to the current input string and the above information of the input string are obtained, and the sequence of the candidate words in the candidate word sequence is adjusted based on the above information, so that at least one candidate word is selected from the candidate word sequence for display based on the adjusted sequence. Therefore, the order of the candidate words in the candidate word sequence can be adjusted according to the above information of the input string, the candidate words are considered, the more accurate arrangement order of the candidate words can be obtained in a context scene by combining the above information, and the matching degree of the display order of the candidate words and the user intention is improved.

Drawings

Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:

FIG. 1 is a flow diagram of one embodiment of an input method according to the present application;

FIG. 2 is a flow diagram of yet another embodiment of an input method according to the present application;

FIG. 3 is a schematic diagram of an embodiment of an input device according to the present application;

FIG. 4 is a schematic diagram of a structure of an apparatus for input according to the present application;

FIG. 5 is a schematic diagram of a server in accordance with some embodiments of the present application.

Detailed Description

The present application will be described in further detail with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.

It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

Referring to FIG. 1, a flow 100 of one embodiment of an input method according to the present application is shown. The input method can be operated in various electronic devices including but not limited to: a server, a smart phone, a tablet computer, an e-book reader, an MP3 (moving Picture Experts Group Audio Layer III) player, an MP4 (moving Picture Experts Group Audio Layer IV) player, a laptop, a vehicle-mounted computer, a desktop computer, a set-top box, a smart tv, a wearable device, and so on.

The input method application mentioned in the embodiment of the application can support various input methods. The input method may be an encoding method used for inputting various symbols to electronic devices such as computers and mobile phones, and a user may conveniently input a desired character or character string to the electronic devices using the input method application. It should be noted that, in the embodiment of the present application, in addition to the common chinese input method (such as pinyin input method, wubi input method, zhuyin input method, phonetic input method, handwriting input method, etc.), the input method may also support other languages (such as english input method, japanese hiragana input method, korean input method, etc.), and the input method and the language category of the input method are not limited at all.

The input method in this embodiment may include the following steps:

step 101, acquiring a candidate word sequence corresponding to a current input string.

In this embodiment, an execution main body of the input method (e.g., the electronic device) may receive an input string currently input by a user in real time, and retrieve candidate words of the input string from a lexicon (e.g., one or more lexicons such as a system lexicon, a user lexicon, a cloud lexicon, and the like). Then, the retrieved candidate words can be ranked in a conventional manner to obtain a candidate word sequence. As an example, if the user inputs the pinyin string "zuowei", the candidate words in the candidate word sequence may be "as", "seat", "sitting", "Zuo Wei", "sitting", and the like in order.

Here, the ordering manner adopted by the candidate word sequence may include, but is not limited to, at least one of the following: sorting according to the word frequency of the candidate words, sorting according to the historical screen-up times of the user on the candidate words, sorting by combining the word frequency of the candidate words and the historical screen-up times of the user on the candidate words, and the like.

Here, the user may use any input method, such as encoding, voice, handwriting, etc., to input in the input method interface, which is not limited herein. When the coding mode is used for inputting, the coding mode can include but is not limited to pinyin, wubi and the like. Taking pinyin input as an example, the user can input in any one of various ways such as full pinyin, simple pinyin and final character simple pinyin.

Step 102, obtaining the above information of the input string, and adjusting the order of the candidate words in the candidate word sequence based on the above information.

In this embodiment, the execution body may first acquire the above information of the input string. The above information may be the content that was last on screen. For example, after the user screens "you," the pinyin string "hao" is entered. The current input string is "hao" and the above information of the current input string is "you".

After the above information is obtained, the execution subject may adjust an order of the candidate words in the candidate word sequence based on the above information. Here, whether each candidate word currently in the same history input scenario (i.e., the same above information and the same input string) is frequently displayed may be determined by counting the history input data of the full number of users or users with similar characteristics. For a candidate word, if the word is frequently displayed, the order of the candidate word may be moved forward to update the order of the candidate words in the sequence of candidate words.

It is understood that the original sequence of the candidate word sequence only considers the information of the candidate words themselves, such as sorting according to the word frequency of the candidate words only, sorting according to the historical screen times of the candidate words of the user only, or sorting according to the two factors. But this sort result does not take into account the above information. For example, the user has a large number of times of historically displaying the character "hao", so that the head of the candidate word is "hao" whenever the input string "hao" is input, both in the case of an empty text (i.e., there is no text information) and in the case of text information. However, if the user intends to input "hello", the first position is the "hao" character after "you" is input on the screen, which not only violates the user intention, but also interferes with the input efficiency of the user. Therefore, the order of the candidate words in the candidate word sequence is adjusted by combining the above information, so that the situation can be avoided, and the display order of the candidate words is more matched with the input intention of the user.

In some alternative implementations, the common word lexicon can be formulated in advance based on historical input data. Wherein, the common words can be determined in advance in a statistical manner. For example, the determination may be based on one or more of word frequency, user demand probability, word length. In the above example, the word "hello" is frequently used in a plurality of input scenarios, and the word length satisfies the preset condition, and the user demand probability is greater than a certain threshold, then the word can be used as a common word. "Helao" is not frequently used in various input scenarios, and therefore is not a common word.

In general, the screen-up probability of the user for the frequently-used word is greater than that of the frequently-used word, so that the order of the "good" word in the candidate word sequence in the previous example should be earlier than the order of the "heucha" word. Therefore, based on a pre-established common word library, when the current candidate word and the above information are determined to form the common word, the sequence of the candidate word can be rearranged based on the common word. The operation of performing candidate word reordering based on common words may be performed according to the following substeps S11 to S13:

and a substep S11, determining a target candidate word which forms the common word with the information in the candidate word sequence based on the pre-generated common word library.

Here, the common word bank may be obtained by counting historical input data of a full number of users or a specific set of users. For example, the common words may be determined in advance based on statistical information of the words, thereby generating a common word bank. The statistical information may include, but is not limited to, at least one of: word frequency, user demand probability, word length. The execution main body can splice the above information with the candidate words, and perform character string matching on the spliced words and the words in the common word bank to determine the target candidate words in the candidate word sequence, which form the common words with the above information.

Optionally, the common word bank may be generated by the following steps:

firstly, determining words with word frequency and word length meeting preset conditions as candidate common words. The preset condition may be set to a word frequency greater than a certain preset value and a word length of 2 or 3, and the like, which is not limited herein.

And secondly, counting the user demand probability of each candidate common word under different input scenes based on the historical input string corresponding to each candidate common word and the historical information.

The input scene may be divided based on at least one of: word segmentation mode, keyboard type, input mode. For example, each combination of word segmentation mode, input mode, and keyboard type may correspond to an input scenario. The word segmentation mode can include but is not limited to single word segmentation, multi-word segmentation and the like. The input modes can include but are not limited to full spellings, simple spellings, final spellings, and the like. The types of keyboards described above may include, but are not limited to, 9-key keyboards, 26-key keyboards, and the like.

In some examples, the input scenario may be determined based on both the word segmentation approach (e.g., whether the word segmentation input is) and the input approach (e.g., a full pinyin, a short pinyin, an end short pinyin, etc.). Based on the historical input string and the historical information, the word segmentation mode and the input mode can be known, and therefore the input scene can be known.

In other examples, the input scenario may be further subdivided based on historical input strings and historical above information, further in conjunction with keyboard types. For example, for the candidate common word "hello", a user enters the spell "nihao" with a 26-key keyboard as an input scenario. A user inputs abbreviated spelling 'NH' by using a 9-key keyboard, and the input scene can be another input scene. A user first screens "you" with a 26-key keyboard and then enters the spell "hao" as yet another input scenario.

Here, for each candidate common word, the user requirement probability of the candidate common word in a certain input scene may refer to a probability that the user screens the candidate common word in the input scene, and specifically may be a ratio of the number of times that the candidate common word is screened in the scene to the total number of times that all candidate words are screened in the scene.

In some examples, when determining the user requirement probability of each candidate common word in different input scenes, each candidate common word may be sequentially used as a target word, and the following steps are performed: first, at least one input scene of the target word is determined based on the historical input string and the historical previous information of the target word. Then, for each input scene in the at least one input scene, determining the user requirement probability of the target word in the input scene based on the screen-on times of the target word in the input scene and the total screen-on times corresponding to the input scene. Specifically, the ratio of the screen-on frequency of the target word in the input scene to the total screen-on frequency corresponding to the input scene (the total screen-on frequency of all candidate words in the scene) may be used as the user requirement probability of the target word in the input scene.

And thirdly, screening the candidate common words based on the user demand probability to obtain a common word library. Here, the candidate common words may be screened according to a predetermined rule.

As an example, the candidate common words with the user demand probability greater than a certain preset value may be classified into a common word bank.

As yet another example, a historical input string corresponding to each candidate common word may be obtained first. Then, for each acquired historical input string, determining a historical input scene corresponding to the historical input string, and making a decision in various situations according to the number of the candidate common words (which may be called hit words) hit by the historical input string and the historical input scene. The method comprises the following specific steps:

for each acquired historical input string, if the historical input string only hits one candidate common word (i.e. only one hit word), and the user demand probability of the hit word is greater than a first preset value (e.g. 99%) in the historical input scene corresponding to the historical input string, the hit word may be classified into the common word bank.

For each acquired historical input string, if the historical input string hits only one hit word and the user demand probability of the hit word is greater than a second preset value (for example, 50%) in the historical input scene corresponding to the historical input string, and meanwhile the user demand probability of the hit word in the historical input scene is greater than the input scene probability, the hit word can be classified into a common word bank. The input scene probability of the hit word in a certain historical input scene may be a ratio of the screen-up times of the hit word in the historical input scene to the screen-up times of the hit word in all corresponding input scenes.

And for each acquired historical input string, if the historical input string has at least two hit words, selecting the first two hit words according to the sequence of the probability of user demand from large to small. If the following three conditions are met at the same time, the hit word corresponding to the history input string is deleted (namely, the hit word is not taken as the final common word and is not classified into the common word bank). If the following three conditions are not met simultaneously, the two selected hit words can be classified into a common word bank. The first condition is as follows: the user demand probability of the preferred hit word under the history input scene corresponding to the history input string is smaller than a third preset value (such as 50%); and a second condition: the user demand probability of the preferred hit word under the historical input scene corresponding to the historical input string is smaller than the input scene probability; and (3) carrying out a third condition: and the sum of the user demand probabilities of the two selected hit words under the historical input scene corresponding to the historical input string is smaller than the sum of the input scene probabilities.

It should be noted that, based on the user demand probability, the candidate common words may also be screened in other manners, which is not described in detail herein.

And a substep S12 of determining a current input scene based on the input string and the above information. The method for determining the current input scene may refer to the description in the step of generating the common word bank, and is not described herein again.

And a substep S13 of adjusting the sequence of the target candidate words in the candidate word sequence based on the user demand probability of the common words corresponding to the target candidate words in the current input scene. The user requirement probability may be obtained in advance, and the calculation manner may refer to the description in the step of generating the common word bank, which is not described herein again.

Here, the common word corresponding to the target candidate word is the common word formed by the target candidate word and the above information. Based on the user demand probability of the common word corresponding to the target candidate word in the current input scene, the sequence of the target candidate word in the candidate word sequence can be adjusted in various ways, so that the adjustment of the sequence of the candidate word in the candidate word sequence is realized.

As an example, the execution subject may determine the magnitude of the sequence difference of the forward movement of the target candidate word based on the magnitude of the user requirement probability of the common word corresponding to the target candidate word in the current input scene. The larger the user demand probability, the larger the difference in the order in which the target candidate words move forward. The relation between the user demand probability and the forward movement order difference can be preset, so that the order of the common words can be adjusted.

As yet another example, each candidate word in the original sequence of candidate words may have a score. The executing body may first obtain a score of each candidate word in the candidate word sequence. And then, determining the priority of the target candidate words based on the user demand probability of the common words corresponding to the target candidate words in the current input scene. The priority of the target candidate word may be a priority of a corresponding common word (i.e., a common word composed of the target candidate word and the above information) in the current input scene, and the priority of the common word in each input scene may be preset according to a user requirement probability of the common word in each input scene, or may be set by combining other factors, which is not limited herein. After obtaining the priority of the target candidate word, the execution subject may update the score of the target candidate word based on the priority. In practice, if the score is smaller and the rank is higher, the score of the target candidate word can be reduced, and the rank of the target candidate word is advanced. Specifically, the higher the priority, the more its score is reduced, so that the lower the score, the higher the ranking. Therefore, the sequence of the target candidate words in the candidate word sequence is more advanced relative to the original sequence, so that the candidate words in the candidate word sequence after the sequence is adjusted are more matched with the input intention of the user.

And 103, selecting at least one candidate word from the candidate word sequence for display based on the adjusted sequence.

In this embodiment, the execution main body selects at least one candidate word from the candidate word sequence to display based on the adjusted sequence. The number of the candidate words and the display style may be set according to the requirement, and are not limited specifically here.

According to the method provided by the embodiment of the application, the candidate word sequence corresponding to the current input string is obtained, then the above information of the input string is obtained, and the sequence of the candidate words in the candidate word sequence is adjusted based on the above information, so that at least one candidate word is selected from the candidate word sequence for display based on the adjusted sequence. Therefore, the order of the candidate words in the candidate word sequence can be adjusted according to the above information of the input string, the candidate words are considered, the more accurate arrangement order of the candidate words can be obtained in a context scene by combining the above information, and the matching degree of the display order of the candidate words and the intention of the user is improved.

With further reference to FIG. 2, a flow 200 of yet another embodiment of an input method is illustrated. The process 200 of the input method comprises the following steps:

step 201, acquiring a candidate word sequence corresponding to a current input string.

Step 201 in this embodiment can refer to step 101 in the corresponding embodiment of fig. 1, and is not described herein again.

In step 202, the above information of the input string is obtained.

Step 202 in this embodiment can refer to step 102 in the corresponding embodiment of fig. 1, and is not described herein again.

Step 203, based on the input string and the above information, determining the user behavior characteristics.

In this embodiment, the execution subject of the input method may determine the user behavior feature based on the input string and the above information. Here, the user behavior characteristics may include a plurality of items of information, such as may include, but not be limited to, input strings, above information, input times, and the like. The user behavior features may characterize the current input behavior of the user.

And 204, when the user behavior characteristics meet preset order adjusting conditions, adjusting the order of the candidate words in the candidate word sequence based on the information.

In this embodiment, the execution body may store therein a specific behavior feature. When the user behavior characteristics are matched with the specific behavior characteristics, the user can be considered to be extremely better than a certain word, at this time, the preset order adjusting condition can be considered not to be met, and the step of subsequently adjusting the order of the candidate words in the candidate word sequence is not executed. Otherwise, when the user behavior characteristics meet the preset order adjusting conditions, the order of the candidate words in the candidate word sequence can be adjusted based on the above information. The step of adjusting the order of the candidate words in the candidate word sequence based on the above information may refer to step 102 in fig. 1, and is not described herein again.

As an example, the user frequently inputs a certain fixed word during the history input process, such as "you" is first displayed, then "hao" is displayed by inputting the pinyin string, "hao", then the binary relationship between "you" and "hao" can be recorded, and the behavior characteristic is recorded as a specific behavior characteristic. If the user screens 'you' in the input process and continues to input the pinyin string 'hao', the behavior characteristics of the user are considered to be in record matching with the recorded characteristics, the user can be considered to be extremely better to input the word 'you Hao', and therefore the step of subsequently adjusting the sequence of the candidate words in the candidate word sequence is not executed. Otherwise, if the user behavior characteristics are not matched with the recorded characteristics, the order of the candidate words in the candidate word sequence can be adjusted based on the above information.

Step 205, based on the adjusted sequence, selecting at least one candidate word from the candidate word sequence for display.

Step 205 in this embodiment can refer to step 103 in the corresponding embodiment of fig. 1, and is not described herein again.

As can be seen from fig. 2, compared with the embodiment corresponding to fig. 1, the process 200 of the input method in this embodiment involves a step of determining whether to adjust candidate words in the candidate word sequence based on the user behavior characteristics, so that the order adjustment may not be performed when the order adjustment condition is not satisfied, and the personalized input behavior of the user may not be affected by the order adjustment.

With further reference to fig. 3, as an implementation of the methods shown in the above figures, the present application provides an embodiment of an input device, which corresponds to the embodiment of the method shown in fig. 1, and which is particularly applicable to various electronic devices.

As shown in fig. 3, the input device 300 of the present embodiment includes: an obtaining unit 301 configured to obtain a candidate word sequence corresponding to a current input string; an order adjusting unit 302 configured to obtain the above information of the input string, and adjust the order of the candidate words in the candidate word sequence based on the above information; the presentation unit 303 is configured to select at least one candidate word from the candidate word sequence for presentation based on the adjusted order.

In some optional implementations of this embodiment, the foregoing order adjusting unit is further configured to: determining user behavior characteristics based on the input string and the above information; and when the user behavior characteristics meet the preset order-adjusting condition, adjusting the order of the common words in the candidate word sequence based on the above information of the input string.

In some optional implementations of this embodiment, the foregoing order adjusting unit is further configured to: determining a target candidate word which forms a common word with the above information in the candidate word sequence based on a pre-generated common word library; determining a current input scene based on the input string and the above information; and adjusting the sequence of the target candidate words in the candidate word sequence based on the user demand probability of the common words corresponding to the target candidate words in the current input scene.

In some optional implementations of this embodiment, the foregoing order adjusting unit is further configured to: obtaining scores of all candidate words in the candidate word sequence; determining the priority of the target candidate word based on the user demand probability of the common word corresponding to the target candidate word in the current input scene; updating the score of the target candidate word based on the priority; and adjusting the sequence of the target candidate words in the candidate word sequence based on the updated scores.

In some optional implementations of this embodiment, the common words in the common word bank are determined based on statistical information of words, where the statistical information includes at least one of: word frequency, user demand probability, word length.

In some optional implementations of this embodiment, the common word bank is generated by: taking words with word frequency and word length meeting preset conditions as candidate common words, and counting user demand probabilities of the candidate common words under different input scenes on the basis of historical input strings and historical previous information corresponding to the candidate common words; and screening the candidate common words based on the counted user demand probability to obtain a common word library.

In some optional implementations of the embodiment, the input scenario is divided based on at least one of: word segmentation mode, keyboard type and input mode; the input mode comprises at least one of the following modes: full spelling, simple spelling, and final word simple spelling.

In some optional implementation manners of this embodiment, the counting, based on the historical input string and the historical previous information corresponding to each candidate common word, the user requirement probability of each candidate common word in different input scenes includes: taking each candidate common word as a target word, and executing the following steps: determining at least one input scene of the target word based on the historical input string and the historical previous information of the target word; and for each input scene in the at least one input scene, determining the user demand probability of the target word in the input scene based on the screen-on times of the target word in the input scene and the total screen-on times corresponding to the input scene.

The device provided by the above embodiment of the present application selects at least one target candidate word from the candidate word sequence for display based on the adjusted order by obtaining the candidate word sequence corresponding to the current input string, then obtaining the above information of the input string, and adjusting the order of the candidate words in the candidate word sequence based on the above information. Therefore, the order of the candidate words in the candidate word sequence can be adjusted according to the above information of the input string, the candidate words are considered, the more accurate arrangement order of the candidate words can be obtained in a context scene by combining the above information, and the matching degree of the display order of the candidate words and the intention of the user is improved.

Fig. 4 is a block diagram illustrating an apparatus 400 for input according to an example embodiment, where the apparatus 400 may be an intelligent terminal or a server. For example, the apparatus 400 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, and the like.

Referring to fig. 4, the apparatus 400 may include one or more of the following components: processing components 402, memory 404, power components 406, multimedia components 408, audio components 410, input/output (I/O) interfaces 412, sensor components 414, and communication components 416.

The processing component 402 generally controls overall operation of the apparatus 400, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing element 402 may include one or more processors 420 to execute instructions to perform all or part of the steps of the methods described above. Further, the processing component 402 can include one or more modules that facilitate interaction between the processing component 402 and other components. For example, the processing component 402 can include a multimedia module to facilitate interaction between the multimedia component 408 and the processing component 402.

The memory 404 is configured to store various types of data to support operations at the apparatus 400. Examples of such data include instructions for any application or method operating on the device 400, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 404 may be implemented by any type or combination of volatile or non-volatile 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 disks.

Power supply components 406 provide power to the various components of device 400. The power components 406 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the apparatus 400.

The multimedia component 408 includes a screen providing an output interface between the device 400 and a user. 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 an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of the touch or slide action but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 408 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the device 400 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 a focal length and optical zoom capability.

The audio component 410 is configured to output and/or input audio signals. For example, audio component 410 includes a Microphone (MIC) configured to receive external audio signals when apparatus 400 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 404 or transmitted via the communication component 416. In some embodiments, audio component 410 also includes a speaker for outputting audio signals.

The I/O interface 412 provides an interface between the processing component 402 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.

The sensor component 414 includes one or more sensors for providing various aspects of status assessment for the apparatus 400. For example, the sensor assembly 414 may detect an open/closed state of the device 400, the relative positioning of the components, such as a display and keypad of the apparatus 400, the sensor assembly 414 may also detect a change in the position of the apparatus 400 or a component of the apparatus 400, the presence or absence of user contact with the apparatus 400, orientation or acceleration/deceleration of the apparatus 400, and a change in the temperature of the apparatus 400. The sensor assembly 414 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 414 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 414 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 416 is configured to facilitate wired or wireless communication between the apparatus 400 and other devices. The apparatus 400 may access a wireless network based on a communication standard, such as WiFi,2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 416 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 416 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 apparatus 400 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, micro-controllers, microprocessors or other electronic components for performing the above-described methods.

In an exemplary embodiment, a non-transitory computer-readable storage medium comprising instructions, such as the memory 404 comprising instructions, executable by the processor 420 of the apparatus 400 to perform the above-described method is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

Fig. 5 is a schematic diagram of a server in some embodiments of the present application. The server 500 may vary widely in configuration or performance and may include one or more Central Processing Units (CPUs) 522 (e.g., one or more processors) and memory 532, one or more storage media 530 (e.g., one or more mass storage devices) storing applications 542 or data 544. Memory 532 and storage media 530 may be, among other things, transient storage or persistent storage. The program stored on the storage medium 530 may include one or more modules (not shown), each of which may include a series of instruction operations for the server. Still further, the central processor 522 may be configured to communicate with the storage medium 530, and execute a series of instruction operations in the storage medium 530 on the server 500.

The server 500 may also include one or more power supplies 526, one or more wired or wireless network interfaces 550, one or more input-output interfaces 558, one or more keyboards 556, and/or one or more operating systems 541, such as Windows Server, mac OS XTM, unixTM, linuxTM, freeBSDTM, etc.

A non-transitory computer readable storage medium having instructions therein which, when executed by a processor of an apparatus (smart terminal or server), enable the apparatus to perform an input method, the method comprising: acquiring a candidate word sequence corresponding to a current input string; acquiring the above information of the input string, and adjusting the sequence of candidate words in the candidate word sequence based on the above information; and selecting at least one candidate word from the candidate word sequence for display based on the adjusted sequence.

Optionally, the adjusting the order of the candidate words in the candidate word sequence based on the above information includes: determining user behavior characteristics based on the input string and the above information; and when the user behavior characteristics meet the preset order adjusting conditions, adjusting the order of the candidate words in the candidate word sequence based on the above information.

Optionally, the adjusting, based on the above information, an order of the candidate words in the candidate word sequence includes: determining a target candidate word of the common word which is formed by the candidate word sequence and the above information based on a pre-generated common word library; determining a current input scene based on the input string and the above information; and adjusting the sequence of the target candidate words in the candidate word sequence based on the user demand probability of the common words corresponding to the target candidate words in the current input scene.

Optionally, the adjusting, based on the user demand probability, an order of the common word in the candidate word sequence includes: obtaining scores of all candidate words in the candidate word sequence; determining the priority of the target candidate word based on the user demand probability of the common word corresponding to the target candidate word in the current input scene; updating the score of the target candidate word based on the priority; and adjusting the sequence of the target candidate words in the candidate word sequence based on the updated scores.

Optionally, the common words in the common word bank are determined based on statistical information of words, where the statistical information includes at least one of the following: word frequency, user demand probability, word length.

Optionally, the common word bank is generated by the following steps: taking words with word frequency and word length meeting preset conditions as candidate common words, and counting user demand probabilities of the candidate common words under different input scenes on the basis of historical input strings and historical previous information corresponding to the candidate common words; and screening the candidate common words based on the counted user demand probability to obtain a common word library.

Optionally, the input scenario is divided based on at least one of: word segmentation mode, keyboard type and input mode; the input means includes at least one of: full spelling, simple spelling, and final word simple spelling.

Optionally, the calculating, based on the historical input string and the historical previous information corresponding to each candidate common word, the user demand probability of each candidate common word in different input scenes includes: taking each candidate common word as a target word, and executing the following steps: determining at least one input scene of the target word based on the historical input string and the historical previous information of the target word; for each input scene in the at least one input scene, determining the user demand probability of the target word in the input scene based on the screen-on times of the target word in the input scene and the total screen-on times corresponding to the input scene.

Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice in 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 application being indicated by the following claims.

It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

The above description is only a preferred embodiment of the present application and should not be taken as limiting the present application, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present application should be included in the protection scope of the present application.

The present application provides an input method, an input device and an input device, and the principles and embodiments of the present application are described herein using specific examples, and the descriptions of the above examples are only used to help understand the method and the core ideas of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, the specific implementation manner and the application scope may be changed, and in summary, the content of the present specification should not be construed as a limitation to the present application.

Claims

1. An input method, characterized in that the method comprises:

acquiring a candidate word sequence corresponding to a current input string;

acquiring the above information of the input string, and adjusting the sequence of candidate words in the candidate word sequence based on the above information;

and selecting at least one candidate word from the candidate word sequence for display based on the adjusted sequence.

2. The input method according to claim 1, wherein the adjusting the order of the candidate words in the candidate word sequence based on the above information comprises:

determining user behavior characteristics based on the input string and the above information;

and when the user behavior characteristics meet a preset order adjusting condition, adjusting the order of the candidate words in the candidate word sequence based on the above information.

3. The input method according to claim 1, wherein the adjusting the order of the candidate words in the sequence of candidate words based on the above information comprises:

determining a target candidate word of the common word which is formed by the candidate word sequence and the above information based on a pre-generated common word library;

determining a current input scene based on the input string and the above information;

and adjusting the sequence of the target candidate words in the candidate word sequence based on the user demand probability of the common words corresponding to the target candidate words in the current input scene.

4. The input method according to claim 3, wherein the adjusting the order of the target candidate word in the candidate word sequence based on the user demand probability of the common word corresponding to the target candidate word in the current input scene comprises:

obtaining scores of all candidate words in the candidate word sequence;

determining the priority of the target candidate word based on the user demand probability of the common word corresponding to the target candidate word in the current input scene;

updating the score of the target candidate word based on the priority;

and adjusting the sequence of the target candidate words in the candidate word sequence based on the updated scores.

5. The input method according to claim 3, wherein the common words in the common word lexicon are determined based on statistical information of words, the statistical information comprising at least one of: word frequency, user demand probability, word length.

6. The input method according to claim 5, wherein the common word lexicon is generated by:

taking words with word frequency and word length meeting preset conditions as candidate common words, and counting user demand probabilities of the candidate common words under different input scenes on the basis of historical input strings and historical previous information corresponding to the candidate common words;

and screening the candidate common words based on the counted user demand probability to obtain a common word library.

7. The input method according to claim 6, wherein the input scene is divided based on at least one of: word segmentation mode, keyboard type and input mode; the input means includes at least one of: full spelling, simple spelling, and final word simple spelling.

8. The input method according to claim 6, wherein the counting the user demand probability of each candidate common word in different input scenes based on the historical input string and the historical previous information corresponding to each candidate common word comprises:

taking each candidate common word as a target word, and executing the following steps:

determining at least one input scene of the target word based on the historical input string and the historical previous information of the target word;

for each input scene in the at least one input scene, determining the user requirement probability of the target word in the input scene based on the screen-on times of the target word in the input scene and the total screen-on times corresponding to the input scene.

9. An input device, the device comprising:

the acquisition unit is configured to acquire a candidate word sequence corresponding to a current input string;

the sequence adjusting unit is configured to acquire the above information of the input string and adjust the sequence of the candidate words in the candidate word sequence based on the above information;

and the display unit is configured to select at least one candidate word from the candidate word sequence to display based on the adjusted sequence.

10. The input device of claim 9, wherein the ordering unit is further configured to:

and when the user behavior characteristics meet a preset order adjusting condition, adjusting the order of the common words in the candidate word sequence based on the above information of the input string.

11. The input device of claim 9, wherein the ordering unit is further configured to:

12. The input device of claim 11, wherein the ordering unit is further configured to:

obtaining scores of all candidate words in the candidate word sequence;

updating the score of the target candidate word based on the priority;

13. The input device of claim 11, wherein common words in the common word corpus are determined based on statistical information of words, the statistical information comprising at least one of: word frequency, user demand probability, word length.

14. An apparatus for input, 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 a candidate word sequence corresponding to a current input string;

determining common words in the candidate word sequence, and adjusting the sequence of the common words in the candidate word sequence based on the above information of the input string;

15. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-8.