CN111782061B - Method and device for recommending input mode of smart watch - Google Patents
Method and device for recommending input mode of smart watch Download PDFInfo
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Abstract
The application discloses a method, a device, electronic equipment and a storage medium for recommending an input mode of a smart watch, and relates to the technical field of input methods. The specific implementation scheme is as follows: acquiring watch state information; determining a scene of a user according to watch state information; in response to receiving the message, determining a message type of the message; acquiring a pre-trained historical preference model; and inputting the message type and the scene into a historical preference model, and determining the recommended input mode and/or the content of the quick reply. The embodiment intelligently judges the most suitable input mode or reply content at the time, thereby avoiding the user from manually switching different input modes.
Description
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to the technical field of input methods.
Background
The intelligent watch is used as auxiliary wearable equipment of the intelligent mobile phone, provides functions of message receiving, reminding and replying, and meets the instant messaging requirement when a user is inconvenient to use the mobile phone. However, due to the screen size limitation of the smart watch, the conventional keyboard/handwriting input has low input efficiency on the smart watch, and the voice input or quick reply with higher input efficiency cannot meet the requirements of users in many input scenes due to the situation limitation. Therefore, users often need to switch between different input modes to match the input modes that conform to the current use situation and personal habits.
The input modes supported by the current intelligent watch mainly comprise: 1) The voice-based input mode comprises voice message or voice-to-text, wherein the user inputs the message content in a voice mode, and the intelligent watch converts the voice into text and then sends the text; 2) Quick reply, the watch provides a plurality of reply contents defined in advance for the user to select; 3) Keyboard input, adapting a full keyboard or a nine-grid keyboard of mobile phone pinyin input to a watch interface; 4) Handwriting input is performed on the watch dial.
When a user needs to input, the input mode of the intelligent watch presents strategies mainly based on two types of fixation: 1) Providing all input modes, and enabling a user to manually select before inputting is needed each time; 2) A default input mode is provided, and when input is needed, the default input mode interface is entered, but an entry for switching to other input modes is reserved.
Disclosure of Invention
The disclosure provides a method, a device, equipment and a storage medium for recommending an input mode of a smart watch.
According to a first aspect of the present disclosure, there is provided a method for recommending an input mode of a smart watch, comprising: acquiring watch state information; determining a scene of a user according to watch state information; in response to receiving the message, determining a message type of the message; acquiring a pre-trained historical preference model; and inputting the message type and the scene into a historical preference model, and determining the recommended input mode and/or the content of the quick reply.
According to a second aspect of the present disclosure, there is provided an apparatus for recommending an input mode of a smart watch, including: a state acquisition unit configured to acquire watch state information; a scene determination unit configured to determine a scene in which the user is located according to the watch status information; a type determining unit configured to determine a message type of the message in response to receiving the message; a model acquisition unit configured to acquire a history preference model trained in advance; and the recommending unit is configured to input the message type and the scene into the historical preference model and determine the recommended input mode and/or the content of the quick reply.
According to a third aspect of the present disclosure, there is provided an electronic apparatus, characterized by comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of the first aspects.
According to a fourth aspect of the present disclosure there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of the first aspects.
According to a fifth aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the method according to any of the first aspects.
Aiming at the text input problem of the intelligent watch, the application provides an intelligent watch input personalized presentation scheme of scene intelligence, the user can judge the scene of the user at the moment by the watch sensor and the schedule set by the user independently, and judge the proper input mode or reply content at the moment by combining the message type and the historical preference of the user to the input mode, thereby being convenient for the user to reply the message on the watch.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are included to provide a better understanding of the present application and are not to be construed as limiting the application. Wherein:
FIG. 1 is an exemplary system architecture diagram in which an embodiment of the present application may be applied;
FIG. 2 is a flow chart of one embodiment of a method for recommending an input mode for a smart watch in accordance with the present application;
FIG. 3 is a schematic diagram of one application scenario of a method for recommending an input mode for a smart watch according to the present application;
FIG. 4 is a flow chart of yet another embodiment of a method for recommending an input mode for a smart watch in accordance with the present application;
FIG. 5 is a schematic diagram of one embodiment of an apparatus for recommending an input mode for a smart watch in accordance with the present application;
Fig. 6 is a block diagram of an electronic device for implementing a method for recommending an input mode for a smart watch according to an embodiment of the present application.
Detailed Description
Exemplary embodiments of the present application will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present application are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
FIG. 1 illustrates an exemplary system architecture 100 for a method and apparatus for recommending an input mode for a smart watch to which embodiments of the application may be applied.
As shown in fig. 1, system architecture 100 may include smartwatches 101, 102, network 103, database server 104, and server 105. Network 103 is used to provide a medium for communication links between smart watches 101, 102, database server 104, and server 105. The network 103 may include various connection types, such as wired, wireless communication links, and the like.
Users 110, 120 may interact with server 105 via network 103 using smartwatches 101, 102, respectively, to receive or send messages, etc. Various client applications may be installed on smartwatches 101, 102, such as model training class applications, input methods, shopping class applications, payment class applications, web browsers, instant messaging tools, and the like. Instant messages, such as WeChat, spike, QQ, etc., may be communicated between smartwatches 101, 102 through server 105 or other servers.
The smart watches 101, 102 herein may include a variety of sensors, e.g., acceleration sensors, heart rate sensors, GPS, sound sensors (e.g., microphones).
Database server 104 may be a database server that provides various services. For example, a database server may have stored therein a sample set. The sample set contains a large number of samples. The sample may include a scene, a message type, and an input manner or content of a shortcut reply corresponding to the scene, the message type. Thus, after receiving a message from the smart watch 101 or 102, the user 110 or 120 selects the content of the input mode or quick reply, and stores the current scene, the message type, and the selection result as a sample in the database server 104.
Server 105 may also be a server that provides various services, such as a background server that provides support for a historical preference model used on smart watches 101, 102. The background server may train the initial model using samples in the sample set sent by smart watches 101, 102 and may send training results (e.g., generated historical preference models) to smart watches 101, 102. In this way, smart watches 101, 102 may apply the generated historical preference model to make automatic recommendations for input means or quick replies.
The database server 104 and the server 105 may be hardware or software. When they are hardware, they may be implemented as a distributed server cluster composed of a plurality of servers, or as a single server. When they are software, they may be implemented as a plurality of software or software modules (e.g., to provide distributed services), or as a single software or software module. The present invention is not particularly limited herein.
It should be noted that, the method for recommending the input mode of the smart watch provided by the embodiment of the application is generally executed by the smart watch. Accordingly, a device for recommending an input mode of the smart watch is also generally provided in the smart watch.
It should be noted that the database server 104 may not be provided in the system architecture 100 in cases where the server 105 may implement the relevant functions of the database server 104.
It should be understood that the numbers of smartwatches, networks, database servers, and servers in fig. 1 are merely illustrative. There may be any number of smartwatches, networks, database servers, and servers, as desired for implementation.
With continued reference to FIG. 2, a flow 200 of one embodiment of a method for generating a face detection model in accordance with the present application is shown. The method for generating a face detection model may include the steps of:
step 201, obtaining watch status information.
In this embodiment, an execution subject of a method for recommending an input mode of a smart watch (e.g., the smart watch shown in fig. 1) may obtain watch status information, where the watch status information includes at least one of the following: sensor information, schedule information and running APP information.
1) Acquiring sensor information by a watch sensor: acceleration sensors may be used to detect movement of the user, heart rate sensors may be used to detect heart rate of the user, and GPS may locate the user and his trajectory. In addition, the microphone of the watch may be used to measure the noise level of the environment in which it is located to determine whether it is suitable for voice input.
2) Acquiring schedule information through a schedule set by a user: at present, the intelligent watch supports schedule connection with a mobile phone, and reminds before the schedule starts.
3) Cell phone operation APP: the APP running on the handset may also be used to assist in determining the current user status.
Step 202, determining the scene of the user according to the watch state information.
In this embodiment, the scenario where the user is located is mainly determined based on several input sources:
1) From the sensor information, it can be determined that the user is: ① A relatively stationary state, where the heart rate is within a quiet heart rate, the GPS and acceleration sensor display that the user is relatively stationary, where the user may be in relatively stationary activities such as office, meeting, class, watching television, etc.; ② The movement state, when the GPS displays that the user is moving, the heart rate and acceleration sensor can judge that the user is walking, riding or other moving modes according to the movement speed. ③ The exercise state can judge whether the user is in the exercise state according to the heart rate, and the outdoor exercise or the indoor exercise can be further judged by combining the heart rate change rule and the GPS.
2) The schedule set by the user can be used for judging whether the user is likely to be in a busy state such as meeting, sports and the like, and then the information acquired by the watch sensor is verified. When the sensor information is insufficient to judge the specific state of the user, the judgment is assisted by the schedule information. For example, if the user is judged to be in a relatively stationary state by the sensor, and the user may be in a relatively stationary activity such as office, meeting, lesson, watching television, etc., if the schedule information shows that the user is in a meeting, the user may be confirmed to be in the meeting.
3) Judging whether a user is likely to be in a motion, no disturbance or navigation state or not through the running APP information, and verifying through information acquired by a watch sensor, wherein a mobile phone can possibly start a corresponding motion APP to provide motion guidance or record when the user is in the motion state; during a meeting, the mobile phone may be in a do-not-disturb mode; while on the road, it may be in a map navigation mode. For example, if the user is determined to be in a moving state by the sensor, the user may be walking, riding or in other moving modes. If the running APP is self-driving navigation, then it can be confirmed that the user is in the car.
In response to receiving the message, a message type of the message is determined 203.
In this embodiment, the user may customize the granularity of the message types, and according to the defined types, a personalized input scheme may be provided. If the message source is divided into working communication and private communication, or a preferential or non-preferential input mode is set for a specific object; the reply content may be classified as specific or non-specific, single or multiple times according to the message. Different message types, the user may have different input mode preferences. For example, messages from staples are classified as work class communications and messages from QQ are classified as private communications. The working class communication and the private communication can also be determined according to the grouping of the same APP, for example, the messages from the colleagues in the WeChat can be divided into the working class communication, and the messages from the family group can be divided into the private communication. Non-priority voice inputs may be set for the leader and priority voice inputs may be set for the family.
Alternatively, the intent of the received message may be identified by semantic recognition techniques, determining whether the message reply content is specific or non-specific, single or multiple times. For example, if the received message is "meeting at 4 pm, and a request acknowledgement is received," it may be identified that the message only needs to be replied to "received" once. And if the received message is "what is you going to play in tomorrow? We sell a bar "then it can be identified that this message requires multiple replies. The type of the reply message can also be determined by identifying the affinity of the received message, for example, when the received message is "old man, the old man goes home in the morning, the relationship between the received message and the old man is identified, the content of the quick reply can be automatically added with" wife "above the original" good ", and then the reply content becomes" good, wife ". Affinity may also be determined based on the contact groupings to prevent misleading of received messages. For example, although the received message is "wife", the person sending the message is not wife, and the reply cannot be automatically added with "wife". Thus, a better user experience is obtained, and the user receiving the reply does not feel the opposite party to apply the diffraction.
Step 204, a pre-trained historical preference model is obtained.
In this embodiment, a cold start mode is adopted, and the input mode of the user for the current state preference is gradually learned. The method comprises the steps of presenting a conventional input scheme to a user for selection, and then learning the selection of the user to form judgment on the preference of the user to the input mode in a specific state, and gradually replacing the presented scheme with a personalized input scheme. The historical preference model can be a simple frequency statistical table or a trained neural network. The input method supports content that is quickly replied to from editing. The context and the content of the selected shortcut reply each time the user receives the message may be recorded.
Step 205, inputting the message type and the scene into a historical preference model to determine the recommended input mode and/or the content of the shortcut reply.
In this embodiment, in combination with the judgment of the scenario in which the user is located and learning of the user history input preference for different message types and scenarios, a specific input mode and/or a shortcut reply option of personalized content may be presented. If the message is not suitable for the quick reply, the content of the quick reply is not provided, if the message is suitable for the quick reply, the content of the quick reply can be preferentially presented for the user to select, and if the user does not select the quick reply, the method is switched to a recommended input mode. If the user selects the shortcut reply, the input mode does not need to be switched.
1) The input mode presents: currently, the autonomous input modes of users mainly include keyboard input (full keyboard/nine-square), handwriting input and voice input. They can be broadly divided into two categories: silence input (keyboard input and handwriting input) and non-silence input (voice input), generally, the silence input and the non-silence input are complementary relationship for a certain user, and have different adaptability in different situations, while different input modes in the silence input are mutually alternative relationship, and the user often has a single preferred input mode in the silence input. Thus, the voice input or the silence input mode preferred by the user can be presented according to whether the scene of the user is suitable for voice input and the input tendency of the user in the scene. In addition, the shortcut reply provides a message reply mode which is not independently input.
2) The quick reply content: the quick reply mainly plays two roles: for a message informing of a class, representing an acknowledgement; for messages requiring a specific reply, the specific reply time is deferred while indicating receipt of the message. According to the current situation of the user, the quick reply can show the specific reason of deferring the reply, is more genuine, and has different affinities according to personal habits when facing different message types. The priority is also different according to user history preference compared to the shortcut reply and the autonomous input mode.
With continued reference to fig. 3, fig. 3 is a schematic diagram of an application scenario of a method for recommending an input mode of a smart watch according to the present embodiment. In the application scenario of fig. 3, the user's smart watch acquires watch status information in real time. Detecting the heart rate 110 of the user through a heart rate sensor, detecting that the user is in a motion state based on the heart rate, and displaying that the user is in regular heart rate acceleration and recovery according to the law of heart rate variation; detecting that the user is in the building through the GPS; the noise detection display of the microphone can be used for voice input; the schedule set by the user shows that the user is in sports, and the mobile phone runs a sports APP course, and the user is inferred to be likely to perform the training of the apparatus in the gym.
According to the input mode selection display of the user history, when the user performs the training of the apparatus in the gym, the user tends to use the shortcut input to represent confirmation or delay the reply for the working message, when the shortcut reply is not applicable, the user tends to reply in a nine-grid input mode, and the user never adopts a voice input mode for the working message; and in the case of private messages, the user always tends to reply with voice input.
When the user receives the message from the nail, the message is judged to be of a work type, the quick reply content is presented preferentially according to the preference, and the user is confirmed to be ' good ' or ' I are moving, and you reply after about half an hour. When the shortcut reply cannot meet the user requirement, the input mode of the nine-grid can be quickly switched. When the user receives the private message, the user is input by presenting the voice input mode according to the preference.
The method provided by the embodiment of the application comprehensively utilizes a plurality of information sources which can be acquired by the existing intelligent watch, including watch sensor, user set schedule, connected mobile phone state and other information, provides a method for judging the situation of the user based on the acquired information, combines the information type and the user history input mode preference, intelligently provides the input mode of the user tendency and the content of quick reply, and enables the input information to be more intelligent, personalized and efficient. The application optimizes the input information distribution experience of the intelligent watch to a certain extent and widens the use scene of the intelligent watch.
With further reference to fig. 4, a flow 400 of yet another embodiment of a method for recommending an input modality for a smart watch is shown. The process 400 of the method for recommending an input mode of a smart watch includes the following steps:
step 401, determining a network structure of an initial neural network and initializing network parameters of the initial neural network.
In this embodiment, the execution subject of steps 401-404 may be the same as or different from the execution subject of steps 201-205. If so, the execution subject of steps 401-404 may store the trained neural network structure information and parameter values of the network parameters locally after training the neural network. If not, the execution body of steps 401-404 may send the trained network structure information of the neural network and the parameter values of the network parameters to the execution body of the method of steps 201-205 after training the neural network.
In this embodiment, the execution body of steps 401-404 may first determine the network structure of the initial neural network. For example, it is necessary to determine which layers the initial neural network includes, the connection order relationship between layers, and which neurons each layer includes, the weight (weight) and bias term (bias) corresponding to each neuron, the activation function of each layer, and so on.
It will be appreciated that since the neural network may comprise various types of neural networks, the network structure that needs to be determined is also different for different types of neural networks.
Step 402, a training sample set is obtained.
In this embodiment, the training sample set may be obtained locally or remotely from other electronic devices connected to the execution subject network. The training sample comprises a scene, a message type and contents of an input mode or a shortcut reply selected by a user. For example, indoor sports scenes, working messages, and user selection shortcut replies "good, receives" or selects 9 palace lattice input modes. The training sample set includes user selections of various message types in various scenarios. The sample may come from the user himself, if the sample size is insufficient, reference may also be made to the choices of other users.
Step 403, taking the scene and the message type in the training samples in the training sample set as the input of the initial neural network, taking the input mode selected by the user or the content of the quick reply as the expected output of the initial neural network, and training the initial neural network by using a machine learning method.
In this embodiment, the difference between the obtained input mode or quick reply content and the expected output in the training sample is calculated by using a preset loss function first, for example, the difference between the obtained input mode or quick reply content and the input mode or quick reply content in the training sample may be calculated by using an L2 norm as the loss function. Then, based on the calculated difference, the network parameters of the initial neural network may be adjusted, and the training may be ended if a preset training end condition is satisfied. For example, the training end conditions preset herein may include, but are not limited to, at least one of: the training time exceeds the preset duration; the training times exceed the preset times; the calculated variance is less than a preset variance threshold.
Here, various implementations may be employed to adjust network parameters of the initial neural network based on the calculated input pattern or the difference between the content of the quick reply and the desired output. For example, a BP (Back Propagation) algorithm or an SGD (Stochastic GRADIENT DESCENT, random gradient descent) algorithm may be employed to adjust network parameters of the initial neural network.
Step 404, determining the initial neural network obtained by training as a pre-trained historical preference model.
In this embodiment, the initial neural network trained in step 403 may be determined as a pre-trained historical preference model.
As can be seen from fig. 4, compared with the corresponding embodiment of fig. 2, the flow 400 of the method for recommending the input mode of the smart watch in this embodiment embodies the step of generating the history preference model. Therefore, the scheme described in the embodiment can generate a history preference model of each user, and improve the hit rate of the input mode or the quick reply content recommendation.
With further reference to fig. 5, as an implementation of the method shown in the foregoing figures, the present application provides an embodiment of an apparatus for recommending an input mode of a smart watch, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied to various electronic devices.
As shown in fig. 5, the apparatus 500 for recommending an input mode of a smart watch according to the present embodiment includes: a state acquisition unit 501, a scene determination unit 502, a type determination unit 503, a model acquisition unit 504, and a recommendation unit 505. Wherein the state acquisition unit 501 is configured to acquire watch state information; a scene determination unit 502 configured to determine a scene in which the user is located according to the watch status information; a type determining unit 503 configured to determine a message type of the message in response to receiving the message; a model acquisition unit 504 configured to acquire a history preference model trained in advance; a recommending unit 505 configured to input the message type and the scene into the history preference model, determine the recommended input mode and/or the content of the shortcut reply.
In this embodiment, specific processes of the state acquisition unit 501, the scene determination unit 502, the type determination unit 503, the model acquisition unit 504, and the recommendation unit 505 of the apparatus 500 for recommending a smart watch may refer to steps 201, 202, 203, 204, 205 in the corresponding embodiment of fig. 2.
In some alternative implementations of the present embodiment, the watch status information includes at least one of: sensor information, schedule information and running APP information.
In some optional implementations of the present embodiment, the state acquisition unit 501 is further configured to acquire the sensor information by at least one of: detecting movement information of a user through an acceleration sensor; detecting a heart rate of the user by a heart rate sensor; positioning the user and the moving track through a GPS; the ambient noise level is measured by a microphone.
In some optional implementations of the present embodiment, the scene determination unit 502 is further configured to: determining a scene where a user is located according to the sensor information; and verifying the scene where the user is located through the schedule information.
In some optional implementations of the present embodiment, the scene determination unit 502 is further configured to: determining a scene where a user is located according to the sensor information; and verifying the scene where the user is located through the running APP information.
In some optional implementations of the present embodiment, the type determining unit 503 is further configured to: dividing into working classes or private classes according to the source of the message; or setting a preferential or non-preferential input mode for a specific object; or the message is subjected to semantic understanding, the type of the reply content of the message is determined, and the reply content is divided into specific or non-specific single or multiple times.
In some optional implementations of the present embodiment, the apparatus 500 further includes a model training unit (not shown in the drawings) configured to: determining a network structure of an initial neural network and initializing network parameters of the initial neural network; acquiring a training sample set, wherein the training sample comprises a scene, a message type and the content of an input mode or quick reply selected by a user; taking the scene and the message type in the training samples in the training sample set as the input of the initial neural network, taking the input mode selected by the user or the content of the quick reply as the expected output of the initial neural network, and training the initial neural network by using a machine learning device; and determining the initial neural network obtained through training as a pre-trained historical preference model.
According to embodiments of the present application, the present application also provides an electronic device, a readable storage medium and a computer program product.
Fig. 6 is a block diagram of an electronic device according to an embodiment of the present application, which is a method for recommending an input mode of a smart watch. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the applications described and/or claimed herein.
As shown in fig. 6, the electronic device includes: one or more processors 601, memory 602, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the electronic device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In other embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple electronic devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 601 is illustrated in fig. 6.
The memory 602 is a non-transitory computer readable storage medium provided by the present application. The memory stores instructions executable by the at least one processor to cause the at least one processor to execute the method for recommending the input mode of the smart watch provided by the application. The non-transitory computer readable storage medium of the present application stores computer instructions for causing a computer to execute the method for recommending an input mode of a smart watch provided by the present application.
The memory 602 is used as a non-transitory computer readable storage medium, and may be used to store a non-transitory software program, a non-transitory computer executable program, and modules, such as program instructions/modules corresponding to a method for recommending an input manner of a smart watch in an embodiment of the present application (for example, the state acquisition unit 501, the scene determination unit 502, the type determination unit 503, the model acquisition unit 504, and the recommendation unit 505 shown in fig. 5). The processor 601 executes various functional applications of the server and data processing by running non-transitory software programs, instructions and modules stored in the memory 602, that is, implements the method for recommending an input method of the smart watch in the above-described method embodiment.
The memory 602 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for a function; the storage data area may store data created according to the use of the electronic device for recommending an input manner of the smart watch, etc. In addition, the memory 602 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory 602 may optionally include memory remotely located with respect to processor 601, which may be connected via a network to an electronic device for recommending an input modality for the smartwatch. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device for recommending the input mode of the smart watch may further include: an input device 603 and an output device 604. The processor 601, memory 602, input device 603 and output device 604 may be connected by a bus or otherwise, for example in fig. 6.
The input device 603 may receive input numeric or character information and generate key signal inputs related to user settings and function controls of the electronic device for recommending an input mode for the smart watch, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointer stick, one or more mouse buttons, a track ball, a joystick, etc. input devices. The output means 604 may include a display device, auxiliary lighting means (e.g., LEDs), tactile feedback means (e.g., vibration motors), and the like. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device may be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASIC (application specific integrated circuit), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
These computing programs (also referred to as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
According to the technical scheme provided by the embodiment of the application, an intelligent watch input personalized presentation scheme of scene intelligence is provided, the scene of the user at the moment is judged by the APP running by the mobile phone through the watch sensor and the schedule which is set by the user independently, and the proper input mode or reply content at the moment is judged by combining the message type and the historical preference of the user to the input mode.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present application may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed embodiments are achieved, and are not limited herein.
The above embodiments do not limit the scope of the present application. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of the present application.
Claims (13)
1. A method for recommending an input mode for a smart watch, comprising:
obtaining watch status information, wherein the watch status information comprises at least one of the following: sensor information, schedule information and running APP information;
Determining the scene of the user according to the watch state information, including: determining a scene where a user is located according to the sensor information; verifying the scene where the user is located through the schedule information;
in response to receiving a message, determining a message type of the message;
Acquiring a pre-trained historical preference model;
and inputting the message type and the scene into the historical preference model, and determining a recommended input mode and/or contents of quick replies.
2. The method of claim 1, wherein the sensor information is obtained by at least one of:
Detecting movement information of a user through an acceleration sensor;
detecting a heart rate of the user by a heart rate sensor;
positioning the user and the moving track through a GPS;
the ambient noise level is measured by a microphone.
3. The method of claim 1, wherein the determining, from the watch status information, a scene in which the user is located, comprises:
determining a scene where a user is located according to the sensor information;
and verifying the scene where the user is located through the running APP information.
4. The method of claim 1, wherein the determining the message type of the message comprises:
Dividing into working classes or private classes according to the source of the message; or alternatively
Setting a preferential or non-preferential input mode for a specific object; or alternatively
And carrying out semantic understanding on the message, determining the type of the reply content of the message, and dividing the message into specific and non-specific types, and single or multiple times.
5. The method of any of claims 1-4, wherein the historical preference model is trained by:
determining a network structure of an initial neural network and initializing network parameters of the initial neural network;
acquiring a training sample set, wherein the training sample comprises a scene, a message type and the content of an input mode or quick reply selected by a user;
taking a scene and a message type in a training sample in the training sample set as input of the initial neural network, taking an input mode selected by a user or content of quick reply as expected output of the initial neural network, and training the initial neural network by using a machine learning method;
And determining the initial neural network obtained through training as the pre-trained historical preference model.
6. An apparatus for recommending an input mode for a smart watch, comprising:
A state acquisition unit configured to acquire watch state information, wherein the watch state information includes at least one of: sensor information, schedule information and running APP information;
A scene determining unit configured to determine a scene in which a user is located according to the watch status information, including: determining a scene where a user is located according to the sensor information; verifying the scene where the user is located through the schedule information;
a type determining unit configured to determine a message type of a message in response to receiving the message;
A model acquisition unit configured to acquire a history preference model trained in advance;
and the recommending unit is configured to input the message type and the scene into the historical preference model and determine a recommended input mode and/or contents of quick replies.
7. The apparatus of claim 6, wherein the status acquisition unit is further configured to acquire sensor information by at least one of:
Detecting movement information of a user through an acceleration sensor;
detecting a heart rate of the user by a heart rate sensor;
positioning the user and the moving track through a GPS;
the ambient noise level is measured by a microphone.
8. The apparatus of claim 6, wherein the scene determination unit is further configured to:
determining a scene where a user is located according to the sensor information;
and verifying the scene where the user is located through the running APP information.
9. The apparatus of claim 6, wherein the type determination unit is further configured to:
Dividing into working classes or private classes according to the source of the message; or alternatively
Setting a preferential or non-preferential input mode for a specific object; or alternatively
And carrying out semantic understanding on the message, determining the type of the reply content of the message, and dividing the message into specific and non-specific types, and single or multiple times.
10. The apparatus according to any of claims 6-9, wherein the apparatus further comprises a model training unit configured to:
determining a network structure of an initial neural network and initializing network parameters of the initial neural network;
acquiring a training sample set, wherein the training sample comprises a scene, a message type and the content of an input mode or quick reply selected by a user;
Taking a scene and a message type in a training sample in the training sample set as input of the initial neural network, taking an input mode selected by a user or content of quick reply as expected output of the initial neural network, and training the initial neural network by using a machine learning device;
And determining the initial neural network obtained through training as the pre-trained historical preference model.
11. An electronic device, comprising:
at least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.
12. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-5.
13. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any of claims 1-5.
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