Processing method and device and electronic equipment

Abstract

The invention provides a processing method. The processing method is applied to first electronic equipment and comprises the following steps: responding to received communication information sent by second electronic equipment, and if the first electronic equipment is in a first state, acquiring sensor data of the first electronic equipment; based on the sensor data, first state information representing the current behavior state of the user is determined. And generating target reply information containing the first state information. And sending the target reply information to the second electronic equipment. Wherein the first state represents that the first electronic equipment does not reply the communication information.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, and in particular to a processing method, device and electronic device. Background Art

[0002] In instant calls, emails, social media messages, chat apps, or customer service systems, you can set up an automatic reply with a preset message when the user is unable to respond immediately. This automatic reply feature can help manage user expectations, notifying the other party that the message has been received and that a specific response may be expected at a later time.

[0003] However, current automatic reply methods generally use fixed template messages and are unable to provide personalized replies based on different scenarios. Summary of the invention

[0004] One aspect of the present disclosure provides a processing method, which is applied to a first electronic device, comprising: in response to receiving a communication message sent by a second electronic device, if the first electronic device is in a first state, obtaining sensor data of the first electronic device. Based on the sensor data, determining first state information representing the current behavior state of the user. Generating target reply information including the first state information. Sending the target reply information to the second electronic device. Wherein, the first state represents that the first electronic device has not replied to the communication message.

[0005] Optionally, generating the target reply information including the first status information includes: if the user using the first electronic device is the first user, generating the target reply information including the first status information as the first reply information; if the user using the first electronic device is the second user, generating the target reply information including the first status information as the second reply information.

[0006] Optionally, based on the sensor data, determining the first state information characterizing the current behavior state of the user includes: inputting the sensor data into the target intelligent engine to obtain the first state information characterizing the current behavior state of the user.

[0007] Optionally, the target intelligent engine is used to determine the state information corresponding to the sensor data in a target state information set, the target state information set includes at least two state information, and the first state information belongs to the target state information set.

[0008] Optionally, the sensor data includes first sensor data and second sensor data. Inputting the sensor data into the target intelligent engine includes: processing the first sensor data with a first processing strategy to obtain a first sensor feature. Processing the second sensor data with a second processing strategy to obtain a second sensor feature. Inputting the first sensor feature and the second sensor feature into the target intelligent engine.

[0009] Optionally, generating a target reply message including the first status information includes: if the communication message comes from a third user, generating the target reply message including the first status information in a first generating method. If the communication message comes from a fourth user, generating the target reply message including the first status information in a second generating method. The language style of the target reply message generated in the first generating method is different from the language style of the target reply message generated in the second generating method.

[0010] Optionally, responding to receiving the communication information sent by the second electronic device includes: responding to receiving an instant communication request initiated by the second electronic device, or responding to receiving an instant communication message initiated by the second electronic device.

[0011] Optionally, before generating the target reply information, if the user's current behavior state is a non-detectable behavior state when determining the target state information based on the sensor data, the processing method further includes: obtaining associated device information of the first electronic device. Based on the associated device information, determining the target state information representing the user's current behavior state.

[0012] Another aspect of the present disclosure provides a processing device, which is applied to a first electronic device, including: an acquisition module, which is used to obtain sensor data of the first electronic device in response to receiving communication information sent by a second electronic device, if the first electronic device is in a first state. A determination module, which is used to determine first state information representing the current behavior state of the user based on the sensor data. A generation module, which is used to generate target reply information including the first state information. A sending module, which is used to send the target reply information to the second electronic device. Wherein, the first state represents that the first electronic device has not replied to the communication information.

[0013] Another aspect of the present disclosure provides a first electronic device, including: one or more sensors, used to obtain sensor data based on the state of the first electronic device. One or more processors, used to determine first state information representing the current behavior state of the user based on the sensor data in response to receiving communication information sent by the second electronic device, and generate target reply information including the first state information, and send the target reply information to the second electronic device. The first state represents that the first electronic device does not reply to the communication information.

[0014] Another aspect of the present disclosure provides a non-volatile storage medium storing computer executable instructions, wherein the instructions are used to implement any of the above methods when executed.

[0015] Another aspect of the present disclosure provides a computer program, which includes computer executable instructions, and the instructions are used to implement any of the above methods when executed. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0017] Figure 1 The application scenario diagram of the processing method according to the embodiment of the present disclosure is schematically shown;

[0018] Figure 2 A flowchart schematically shows a processing method according to an embodiment of the present disclosure;

[0019] Figure 3 A flowchart of a method for generating target reply information according to an embodiment of the present disclosure is schematically shown;

[0020] Figure 4 A flowchart of a method for determining first state information according to an embodiment of the present disclosure is schematically shown;

[0021] Figure 5 A flowchart of a method for determining first state information according to another embodiment of the present disclosure is schematically shown;

[0022] Fig. 6A A flowchart of a method for generating target reply information according to another embodiment of the present disclosure is schematically shown; Figure 6B The following schematically shows a logic flow chart of a processing method according to an embodiment of the present disclosure;

[0023] Figure 7 Schematically shows a flow chart of a method for responding to receiving communication information according to an embodiment of the present disclosure;

[0024] Figure 8 A flowchart schematically shows a processing method according to another embodiment of the present disclosure;

[0025] Fig. 9 The structure block diagram of the processing device according to the embodiment of the present disclosure is schematically shown;

[0026] Fig.10 A block diagram of a first electronic device suitable for implementing a processing method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0027] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0028] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0029] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0030] Some block diagrams and / or flow charts are shown in the accompanying drawings. It should be understood that some blocks or combinations thereof in the block diagrams and / or flow charts may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that these instructions, when executed by the processor, may create a device for implementing the functions / operations described in these block diagrams and / or flow charts.

[0031] Therefore, the technology of the present disclosure can be implemented in the form of hardware and / or software (including firmware, microcode, etc.). In addition, the technology of the present disclosure can take the form of a computer program product on a computer-readable medium storing instructions, which can be used by an instruction execution system or in combination with an instruction execution system. In the context of the present disclosure, a computer-readable medium can be any medium that can contain, store, transmit, propagate, or transmit instructions. For example, a computer-readable medium can include, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, device, or propagation medium. Specific examples of computer-readable media include: magnetic storage devices, such as magnetic tape or hard disk (HDD); optical storage devices, such as compact disk (CD-ROM); memory, such as random access memory (RAM) or flash memory; and / or wired / wireless communication links.

[0032] An embodiment of the present disclosure provides a processing method for improving the diversity of automatic replies. The processing method is applied to a first electronic device, and includes: in response to receiving a communication message sent by a second electronic device, if the first electronic device is in a first state, obtaining sensor data of the first electronic device. Based on the sensor data, first state information characterizing the current behavior state of the user is determined. Target reply information containing the first state information is generated. The target reply information is sent to the second electronic device. Among them, the first state characterizes that the first electronic device has not replied to the communication message. By generating automatic reply information based on sensor data characterizing the current state of the user, different automatic reply contents can be obtained according to different user states, thereby improving the diversity of the user's automatic reply content to missed messages.

[0033] Figure 1 The application scenario diagram of the processing method according to the embodiment of the present disclosure is schematically shown.

[0034] like Figure 1 As shown, the application scenario 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for a communication link between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0035] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc., and realize communication between terminal devices 101, 102, 103. Various communication client applications can be installed on terminal devices 101, 102, 103, such as security applications, shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only for example).

[0036] The terminal devices 101 , 102 , and 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0037] The server 105 may be a server that provides various services, such as a background management server (only an example) that provides support for websites browsed or applications logged in by users using terminal devices 101, 102, 103. The background management server may analyze and process received data such as user requests, and feed back processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device to provide personalized automatic reply services for communications between terminal devices of multiple users.

[0038] It should be noted that the processing method provided in the embodiment of the present disclosure can generally be executed by the server 105 or by the terminal device. Accordingly, the processing device provided in the embodiment of the present disclosure can generally be set in the server 105. The processing method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105. Accordingly, the processing device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105.

[0039] It should be understood that Figure 1The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0040] The following will be based on Figure 1 The scene described by Figure 2~Figure 8 The processing method of the disclosed embodiment is described in detail.

[0041] Figure 2 The flowchart of the processing method according to the embodiment of the present disclosure is schematically shown.

[0042] According to the embodiments of the present disclosure, Figure 2 As shown, the processing method of this embodiment is applied to a first electronic device, for example, including operations S210 to S240.

[0043] In operation S210, in response to receiving the communication information sent by the second electronic device, if the first electronic device is in a first state, the sensor data of the first electronic device is acquired, wherein the first state indicates that the first electronic device has not responded to the communication information.

[0044] The first electronic device will respond to receiving the communication information from the second electronic device. This is the trigger point of the entire processing flow, which means that when the first electronic device receives the communication information, the subsequent processing steps will be activated.

[0045] After receiving the communication information, the method checks whether the first electronic device is currently in a first state. The "first state" here specifically refers to a state in which the first electronic device has not responded to the communication information.

[0046] For example, the communication information sent by the second electronic device may be an incoming call, a text message or a message from another instant messaging tool, and no reply is received from the first electronic device within a preset time for the communication information.

[0047] If the first electronic device is indeed in the first state (i.e., the unreply state), the method further acquires sensor data of the first electronic device. Such sensor data may include, but is not limited to, location information, motion state, ambient noise, etc., depending on the sensor configuration of the device and the actual requirements of the method.

[0048] In operation S220, first state information representing a current behavior state of a user is determined based on the sensor data.

[0049] Based on the acquired sensor data, the method performs analysis and processing to determine first state information representing a current behavior state of the user.

[0050] For example, by analyzing location and motion status data, we can infer whether the user is moving or in a specific location, etc.; by analyzing environmental noise data, we can determine whether the user is in a noisy environment and may not be able to respond in time, etc.

[0051] In operation S230, target reply information including the first state information is generated.

[0052] After determining the current behavior state of the user, the method generates a target reply message including the first state information, which is intended to convey the current state of the user to the second electronic device so that the second electronic device can understand why the user did not reply to the communication message in time.

[0053] For example, if you are currently in a noisy environment and there is no reply within the preset time, the message "The owner is surrounded by noisy environment and may not have noticed the relevant notification" can be automatically generated.

[0054] For example, if the user is exercising and does not reply within the preset time, the message "Hello, the owner is exercising and cannot reply to the message in time" can be automatically generated.

[0055] In operation S240, the target reply information is transmitted to the second electronic device.

[0056] Finally, the first electronic device sends the generated target reply information including the information representing the current behavior status of the user to the second electronic device, so that the second electronic device can understand the user's status according to the received information.

[0057] This embodiment uses the sensor data of the first electronic device to infer the user's current behavior state, and accordingly generates and sends target reply information containing the state information to the second electronic device, thereby achieving a personalized reply to the user's current state.

[0058] Figure 3 The flowchart of the method for generating target reply information according to an embodiment of the present disclosure is schematically shown.

[0059] According to the embodiments of the present disclosure, Figure 3 In addition to the above references Figure 2 In addition to the operations S210 to S240 described above, the processing method of this embodiment further generates target reply information including the first state information through operations S331 to S332. For the sake of brevity, the description of operations S210 to S240 is omitted here, and the subsequent related method embodiments are similar and will not be repeated.

[0060] In operation S331, if the user using the first electronic device is the first user, target reply information including the first state information is generated as the first reply information.

[0061] In operation S332, if the user using the first electronic device is the second user, target reply information including the first state information is generated as second reply information.

[0062] Specifically, different users may respond differently to the same thing. For example, when they are driving, some users are accustomed to replying with text messages, while some users are accustomed to replying with messages containing emoticons. When they are exercising, some users are accustomed to replying with a specific exercise name, such as lifting weights, while some users are accustomed to giving general replies, such as exercising.

[0063] Therefore, in one or more embodiments of the present application, based on the historical reply data of the user, the model is trained to generate the automatic reply content. For the same sensor data, the automatic reply content of the devices used by different users may be the same or different.

[0064] Specifically, if the model is used to classify sensor data and a reply message is generated based on the classification result, different replies will be generated even if the classification result of the sensor data is the same. For example, based on different exercise methods, although the results of sensor data classification are all that the user is moving or exercising, the automatic reply content of the device used by different users can be different, such as "I am exercising", "I am practicing yoga", "I am practicing Tai Chi", etc.

[0065] For devices shared by multiple people, the content of the device's automatic reply can also be determined based on the judgment of the current user.

[0066] For example, on smart devices, personalized automatic replies can be provided based on the user's different identities (such as family members, work partners) and current status to enhance the user experience.

[0067] Specifically, different users are distinguished based on the account logged into the smart device, and personalized automatic replies are provided in combination with the account logged into the smart device and the sensor data collected by the smart device.

[0068] In addition to considering the device status and sensor data, the automatic reply content generated in this embodiment may also be combined with the important factor of user identity to achieve a more personalized and flexible communication experience.

[0069] Figure 4 A flow chart of a method for determining first state information according to an embodiment of the present disclosure is schematically shown.

[0070] According to the embodiments of the present disclosure, Figure 4 As shown, for example, first state information representing the current behavior state of the user is determined based on the sensor data through operation S421.

[0071] In operation S421, the sensor data is input into the target intelligent engine to obtain first state information representing the current behavior state of the user.

[0072] In some embodiments, if the device is in an unresponsive state, the method acquires sensor data of the first electronic device, which may include location information, motion status, environmental noise, biometric information, etc.

[0073] The acquired sensor data is input into a pre-trained target intelligent engine (such as a machine learning model, deep learning network or artificial intelligence algorithm). The intelligent engine can analyze and infer the user's current behavior state or context based on the input sensor data.

[0074] The intelligent engine outputs first state information representing the current behavior state of the user, which may be one or more labels, categories, or probability distributions, used to describe the user's current behavior, emotions, environment, etc.

[0075] According to the obtained first status information, the method generates a target reply message including the status information. The content of the reply message can be customized based on the specific content of the status information and a preset reply template.

[0076] For example, in a smart home system, the intelligent engine analyzes sensor data (such as human activity monitoring, ambient temperature and humidity, etc.) and automatically generates a reply message that matches the user's current status, such as "I'm cooking in the kitchen and will reply to your message later."

[0077] It should be noted that the target intelligent engine may need to be trained and optimized through a large amount of historical sensor data and user behavior data to improve its accuracy and generalization ability. During the training process, supervised learning, unsupervised learning or semi-supervised learning methods can be used to select appropriate algorithms and models based on specific business needs and data characteristics.

[0078] This embodiment uses an intelligent engine to process and analyze sensor data, so as to more accurately determine the user's current behavior state and generate more intelligent and personalized automatic reply information accordingly.

[0079] According to an embodiment of the present disclosure, the target intelligent engine is used to determine the state information corresponding to the sensor data in the target state information set, the target state information set includes at least two state information, and the first state information belongs to the target state information set.

[0080] In some embodiments, the target intelligent engine may be a classification model for classifying reasons for non-reply. Taking missed calls as an example, the classification model for classifying reasons for non-reply may classify missed calls into a variety of reasons for missed calls.

[0081] If the device triggers a missed call event, the method obtains sensor data of the first electronic device, which may include location information (GPS), acceleration data (used to detect motion status), ambient noise level, microphone activity status (to detect whether there are other calls or voice activities), screen status (whether it is lit), etc.

[0082] The acquired sensor data is fed into a pre-trained classification model for classifying reasons for missed calls. This model is an instance of the Targeted Intelligence Engine that is specifically designed to analyze and classify possible reasons for missed calls.

[0083] The classification model for classifying the reasons for non-reply outputs one or more possible reasons for missed calls based on the input sensor data as the first state information representing the current behavior state of the user. These reasons may include but are not limited to: the user is driving, the user is in a meeting, the user's mobile phone is muted / vibrated and is not noticed, the user is in a noisy environment and cannot answer the call, etc.

[0084] According to the determined missed call reasons (i.e., first status information), the method generates a target reply message including the reason information. The content of the reply message can be customized based on a preset reply template to meet different communication scenarios and user needs.

[0085] The generated target reply information (including the reason for the missed call) is sent to the second electronic device (such as the caller's mobile phone) to realize the automatic reply function, and at the same time provide an explanation for the reason for the missed call.

[0086] It should be noted that the missed connection reason generation model needs to be trained with a large amount of historical sensor data and corresponding missed connection events to learn the association between different sensor data and missed connection reasons. During the training process, supervised learning methods can be used to annotate the true missed connection reason for each training sample to guide the model training process.

[0087] In order to improve the accuracy and generalization ability of the model, technical means such as data enhancement, regularization, and ensemble learning can also be used for optimization.

[0088] This embodiment utilizes a specific target intelligent engine, the missed call reason generation model, to process and analyze sensor data, thereby more accurately determining the reasons why the user missed a call, and generating more intelligent and personalized automatic reply information accordingly.

[0089] Figure 5 A flow chart of a method for determining first state information according to another embodiment of the present disclosure is schematically shown.

[0090] According to an embodiment of the present disclosure, the sensor data includes, for example, first sensor data and second sensor data. Figure 5 As shown, for example, by operating S5211~S5213, the sensor data is input into the target intelligent engine to determine the first state information.

[0091] In operation S5211, first sensor data is processed using a first processing strategy to obtain a first sensor feature.

[0092] In operation S5212, the second sensor data is processed using a second processing strategy to obtain a second sensor feature.

[0093] In operation S5213, the first sensor feature and the second sensor feature are input into the target intelligent engine.

[0094] In some embodiments, the first electronic device includes a plurality of sensors, such as a gyroscope, an infrared sensor, an acceleration and gravity sensor (A+G sensor), etc. These sensors will provide different types of sensor data.

[0095] When a first electronic device (such as a smart phone) receives a communication message (such as an incoming call) from a second electronic device and does not answer the call, the system checks whether the device is in an active state and is ready to receive sensor data.

[0096] The system simultaneously collects data from multiple sensor sources including gyroscopes, infrared sensors, and A+G sensors.

[0097] Gyroscope data may reflect the rotation state of the device; infrared sensor data may reflect the objects or temperature of the device's surrounding environment; A+G sensor data provides the device's acceleration and gravity direction information.

[0098] For the gyroscope data, the system applies specific signal processing algorithms (such as filtering and denoising) to extract the rotation characteristics of the device, such as angular velocity and rotation direction, as the first sensor characteristics.

[0099] For the infrared sensor data, the system applies image processing algorithms (such as edge detection and object recognition) to extract environmental features, such as the number of surrounding objects and temperature distribution, as the second sensor features.

[0100] For the A+G sensor data, the system applies motion analysis algorithms (such as gait recognition and activity classification) to extract motion features, such as walking, running, and stillness, as the third sensor features.

[0101] Since different sensor data have different dimensions and feature representations, the system needs to map these features into the same dimensional space so that they can be input into the target intelligent engine for subsequent processing.

[0102] The system applies one or more feature mapping technologies (such as principal component analysis PCA, linear discriminant analysis LDA, neural network, etc.) to map the first sensor features, the second sensor features, the third sensor features, etc. to the same feature space to form a unified feature vector. This unified feature vector contains information from different sensors and can more comprehensively reflect the user's current behavior and situation.

[0103] The mapped feature vector is input into the target intelligent engine (such as the missed call cause generation model), and the model makes predictions and classifications based on the input feature vector.

[0104] Then, the model outputs the most likely cause of missed connection as the first state information, which is based on the comprehensive analysis of multi-source sensor data.

[0105] Finally, based on the first status information and the preference information in the user's historical behavior, the system generates personalized reply information and sends it to the second electronic device to implement an intelligent automatic reply function.

[0106] For example, the system can also collect user feedback on automatic replies, including satisfaction, accuracy of reply content, etc. Based on user feedback, the system continuously optimizes the algorithm of the target intelligent engine and multi-sensor data processing strategy to improve the accuracy of automatic replies and user satisfaction.

[0107] By introducing a variety of sensor data and advanced feature mapping technology, this embodiment enables the system to more comprehensively capture the user's current behavior and context, thereby providing the user with a more intelligent and personalized service experience.

[0108] Fig. 6A A flowchart of a method for generating target reply information according to another embodiment of the present disclosure is schematically shown. Figure 6B The following schematically shows a logic flow chart of a processing method according to an embodiment of the present disclosure.

[0109] According to the embodiments of the present disclosure, Fig. 6A As shown, for example, target reply information including the first state information is generated through operations S631-S632.

[0110] In operation S631, if the communication information comes from a third user, a target reply message including first status information is generated in a first generating manner.

[0111] In operation S632, if the communication information comes from the fourth user, a target reply message including the first status information is generated in a second generation mode, wherein the language style of the target reply message generated in the first generation mode is different from the language style of the target reply message generated in the second generation mode.

[0112] In some embodiments, Figure 6B As shown, the target intelligent engine, for example, includes a missed call reason generation model, a content style generation model, and a reply behavior classification model.

[0113] The system collects and stores users’ personal information, historical behavior data, and interaction records with other users for subsequent user-specific analysis.

[0114] When the first electronic device (such as a smartphone) receives communication information (such as an incoming call) from the second electronic device and does not answer the call, the system triggers the acquisition of sensor data and caller information in the notification phase and starts timing. Through timing, detection of missed notifications, mobile phone unlocking status or sensor strategy timeout, the user is notified of a missed call event.

[0115] The system identifies the source of the communication information, that is, determines whether the communication information comes from the third user or the fourth user. Then, based on the user's historical behavior data and interaction records, the system analyzes the relationship between the user and the third user and the fourth user, such as the degree of intimacy and business relationship.

[0116] The system collects and analyzes sensor data (such as location, acceleration, microphone activity, etc.), combines it with user historical behavior data, and applies the missed call cause generation model in the target intelligent engine to determine the possible reasons why the user did not answer the call, that is, the first status information.

[0117] If the communication information comes from a third user (assuming that the user's friend or close contact), the system selects the first generation method, which emphasizes the intimacy, informality and personalization of the language.

[0118] If the communication information comes from a fourth user (assuming that the user's colleague, client or business partner), the system selects the second generation method, which emphasizes the formality, professionalism and politeness of the language.

[0119] The system inputs the notification duration, first status information and user-specific information (such as the relationship between the user and the source of the communication information) into the target intelligent engine's trained missed call reason generation model, content style generation model, reply behavior classification model, and infers the automatic reply strategy, including whether to automatically execute the reply, whether to give reply suggestions, whether to explain the reason for the missed call, reply tone and content generation, etc., and applies the strategy to complete the automatic reply processing flow.

[0120] Among them, the content style generation model generates target reply information that meets user specificity and content style requirements based on the input information and the preset style template.

[0121] For example, the first generation method may generate a reply similar to “Hi, [third user name], I was driving and missed the call, I will get back to you later~”. The second generation method may generate a reply similar to “Dear [fourth user name], I am very sorry for not answering your call in time. I am currently dealing with urgent matters and expect to reply to your message after [specific time].”

[0122] The system sends the generated target reply information to the second electronic device (i.e. the source device of the communication information) to realize the intelligent automatic reply function.

[0123] In addition, regarding the reply behavior classification model, the system can also determine whether to automatically execute a reply based on the user's historical reply actions, such as immediate reply, delayed reply, and no reply. When the reply is not automatically executed, the system can also generate a reply suggestion for the user based on the processing results of the target intelligent engine, and display the reply suggestion to the user when the user opens the unprocessed message.

[0124] It is understandable that the system can also use the sensor data of this round and the content of the user's reply to incrementally train the reasons for missed calls that may be provided in the automatic reply. Use the reply content of the user in this round to incrementally train the tone and main content of the automatic reply. And use the caller information, timing information and the user's method of handling missed call notifications in this round to incrementally train the strategy for replying to the notification.

[0125] By introducing user-specific analysis and advanced content style generation technology, this embodiment enables the system to automatically adjust the reply style according to different contacts, thereby providing users with a more intelligent and personalized service experience.

[0126] Figure 7 The flowchart of the method for responding to receiving communication information according to an embodiment of the present disclosure is schematically shown.

[0127] According to the embodiments of the present disclosure, Figure 7 As shown, for example, operations S711 to S712 are performed to respond to and receive communication information sent by the second electronic device.

[0128] In operation S711, in response to receiving an instant messaging request initiated by a second electronic device.

[0129] In operation S712, in response to receiving an instant messaging message initiated by the second electronic device.

[0130] In some embodiments, for example, when the first electronic device is in an inactive state (such as locked, muted, in a call, etc.), the system can receive an instant messaging request from the second electronic device, including an audio or video call request, a video chat request, etc.

[0131] The system identifies the type of instant messaging request and checks the current state of the first electronic device, such as whether it is in a state where it can be answered, whether the do not disturb mode has been set, etc.

[0132] If the first electronic device is in an unavailable state, the system applies the missed call analysis model in the target intelligent engine, combined with sensor data (such as location, acceleration, microphone activity, etc.) and user historical behavior data, to analyze the possible reasons why the user did not answer the instant messaging request.

[0133] Based on the analysis results, the system generates a target reply message containing the first status information. This can be a simple text message to inform the other user of the current status (such as "I am driving, I will reply later" or "I am in a meeting and cannot answer").

[0134] The system sends the target reply information to the second electronic device, and records the unanswered instant messaging request and the reason for it for subsequent analysis.

[0135] For another example, when the first electronic device is in any state, the system can receive instant messaging messages from the second electronic device, including text messages, picture messages, voice messages, etc.

[0136] The system identifies the type and content of instant messaging messages, and determines whether an automatic reply needs to be generated based on the user's historical behavior data and preference information.

[0137] If the system determines that an automatic reply needs to be generated, it will apply the content generation model in the target intelligent engine, combine user-specific information (such as the relationship between the user and the user of the second electronic device, the user's historical reply style, etc.) and the content of the instant messaging message, to generate personalized target reply information.

[0138] The system sends the target reply information to the second electronic device and records the instant messaging message and its reply for subsequent analysis.

[0139] For example, the system can also record all received communication information (including instant messaging requests and instant messaging messages), generated reply information, and user feedback, and regularly analyze this data to identify user behavior patterns, changes in preferences, and possible system improvement points.

[0140] By introducing the processing logic of instant messaging requests and the automatic reply function of instant messaging messages, the system in this embodiment can provide users with more comprehensive and intelligent communication information processing services.

[0141] Figure 8 The flowchart schematically shows a processing method according to another embodiment of the present disclosure.

[0142] According to an embodiment of the present disclosure, before generating the target reply information, if the target state information is determined based on the sensor data, the user's current behavior state is a non-detectable behavior state, such as Figure 8 As shown, the processing method, for example, also includes operations S810 to S820.

[0143] In operation S810, associated device information of a first electronic device is acquired.

[0144] In operation S820, target state information representing a current behavior state of the user is determined based on the associated device information.

[0145] In some embodiments, the first electronic device is configured with access permissions for other associated devices (such as smart home devices, smart wearable devices, etc.).

[0146] When the first electronic device receives communication information (such as an incoming call, an instant messaging message, etc.) from the second electronic device and does not answer the call, the system prepares to generate a reply message.

[0147] The system first attempts to infer the user's current behavior state through sensor data (such as position, acceleration, microphone activity, etc.) of the first electronic device.

[0148] If the sensor data is not sufficient to determine the specific behavior that the user is performing, that is, the user's current behavior state is a non-detectable behavior state, the system enters the next processing flow.

[0149] The system accesses information of other devices associated with the first electronic device, which may include smart home devices (such as smart door locks, smart lighting, smart appliances, etc.) and smart wearable devices (such as smart watches, smart bracelets, etc.).

[0150] The system collects status information of these devices, such as the switch status of smart door locks, the brightness of smart lighting, heart rate monitoring of smart bracelets, etc., to assist in inferring the user's current behavior status.

[0151] The system applies the algorithm model in the target intelligent engine and combines the information of related devices to conduct a comprehensive analysis of the user's current behavior status.

[0152] For example, if a smart door lock is on and smart lighting is off, and a smart bracelet shows that the user is moving, the system may infer that the user is going out or doing activities outdoors.

[0153] Based on the inference results, the system generates target state information that represents the user's current behavior state.

[0154] The system inputs the target state information and user-specific information (such as the relationship between the user and the user of the second electronic device, the user's historical reply style, etc.) into the content generation model in the target intelligent engine.

[0155] The content generation model generates target reply information that meets user specificity and content style requirements based on the input information and the preset reply template.

[0156] The system sends the generated target reply information to the second electronic device to realize the intelligent automatic reply function.

[0157] By introducing the concept of device interconnection, this embodiment enables the system to provide users with more accurate and personalized missed call processing services.

[0158] Based on the above method, the present disclosure also provides a processing device. Fig. 9 The processing device is described in detail.

[0159] Fig. 9 The structural block diagram of a processing device according to an embodiment of the present disclosure is schematically shown.

[0160] like Fig. 9 As shown, the processing device 900 of this embodiment is applied to a first electronic device, and includes, for example: an acquisition module 910, a determination module 920, a generation module 930, and a sending module 940. The processing device 900 can execute the above reference Figure 2~Figure 8 The processing method described is used to realize personalized automatic reply to the second electronic device.

[0161] Specifically, the acquisition module 910 is used to, in response to receiving the communication information sent by the second electronic device, acquire the sensor data of the first electronic device if the first electronic device is in the first state. In one embodiment, the acquisition module 910 can be used to perform the operation S210 described above, which will not be repeated here.

[0162] The determination module 920 is used to determine the first state information representing the current behavior state of the user based on the sensor data. In one embodiment, the determination module 920 can be used to perform the operation S220 described above, which will not be described in detail here.

[0163] The generating module 930 is used to generate target reply information including the first state information. In one embodiment, the generating module 930 can be used to perform the operation S230 described above, which will not be described in detail here.

[0164] The sending module 940 is used to send the target reply information to the second electronic device. The first state represents that the first electronic device does not reply to the communication information. In one embodiment, the sending module 940 can be used to perform the operation S240 described above, which will not be repeated here.

[0165] It is understandable that the acquisition module 910, the determination module 920, the generation module 930 and the sending module 940 can be combined in one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the acquisition module 910, the determination module 920, the generation module 930 and the sending module 940 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented in hardware or firmware in any other reasonable way of integrating or packaging the circuit, or in a proper combination of software, hardware and firmware. Alternatively, at least one of the acquisition module 910, the determination module 920, the generation module 930 and the sending module 940 can be at least partially implemented as a computer program module, and when the program is run by a computer, the function of the corresponding module can be executed.

[0166] Fig.10 A block diagram of a first electronic device suitable for implementing a processing method according to an embodiment of the present disclosure is schematically shown.

[0167] like Fig.10 As shown, the first electronic device 1000 according to an embodiment of the present disclosure includes a processor 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage part 1008 to a random access memory (RAM) 1003. The processor 1001 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 1001 may also include an onboard memory for caching purposes. The processor 1001 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0168] For example, processor 1001 is one or more, and is used to determine first state information representing the current behavior state of the user based on sensor data in response to receiving communication information sent by the second electronic device, and generate target reply information including the first state information, and send the target reply information to the second electronic device.

[0169] In RAM 1003, various programs and data required for the operation of the first electronic device 1000 are stored. The processor 1001, ROM 1002 and RAM 1003 are connected to each other via a bus 1004. The processor 1001 performs various operations of the method flow according to the embodiment of the present disclosure by executing the programs in ROM 1002 and / or RAM 1003. It should be noted that the program can also be stored in one or more memories other than ROM 1002 and RAM 1003. The processor 1001 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the programs stored in the one or more memories.

[0170] According to an embodiment of the present disclosure, the first electronic device 1000 may further include an input / output (I / O) interface 1005, which is also connected to the bus 1004. The first electronic device 1000 may further include one or more of the following components connected to the I / O interface 1005: an input part 1006 including a keyboard, a mouse, etc.; an output part 1007 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage part 1008 including a hard disk, etc.; and a communication part 1009 including a network interface card such as a LAN card, a modem, etc. The communication part 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the I / O interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1010 as needed, so that a computer program read therefrom is installed into the storage part 1008 as needed.

[0171] For example, the input portion 1006 may also include one or more sensors for obtaining sensor data based on the state of the first electronic device. The first state indicates that the first electronic device has not responded to the communication information.

[0172] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the processing method according to the embodiment of the present disclosure is implemented.

[0173] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 1002 and / or RAM 1003 described above and / or one or more memories other than ROM 1002 and RAM 1003.

[0174] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the processing method provided by the embodiment of the present disclosure.

[0175] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 1001. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0176] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 1009, and / or installed from the removable medium 1011. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0177] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 1009, and / or installed from the removable medium 1011. When the computer program is executed by the processor 1001, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0178] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0179] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0180] It will be appreciated by those skilled in the art that the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations and / or combinations are not explicitly described in the present disclosure. In particular, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways without departing from the spirit and teachings of the present disclosure. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0181] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the attached claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A processing method, applied to a first electronic device, comprising: In response to receiving the communication information sent by the second electronic device, if the first electronic device is in a first state, acquiring sensor data of the first electronic device; Based on the sensor data, determining first state information representing a current behavior state of the user; generating target reply information including the first state information; sending the target reply information to the second electronic device; The first state represents that the first electronic device has not responded to the communication information.

2. The method according to claim 1, wherein: The generating target reply information including the first state information comprises: If the user using the first electronic device is the first user, generating the target reply information including the first state information as the first reply information; If the user using the first electronic device is a second user, the target reply information including the first state information is generated as the second reply information.

3. The method according to claim 1, wherein: The determining, based on the sensor data, first state information representing the current behavior state of the user includes: The sensor data is input into a target intelligent engine to obtain first state information representing a current behavior state of the user.

4. The method according to claim 3, wherein: The target intelligent engine is used to determine the state information corresponding to the sensor data in a target state information set, the target state information set includes at least two state information, and the first state information belongs to the target state information set.

5. The method according to claim 3, wherein: The sensor data includes first sensor data and second sensor data; The step of inputting the sensor data into a target intelligent engine comprises: Processing the first sensor data with a first processing strategy to obtain a first sensor feature; Processing the second sensor data with a second processing strategy to obtain a second sensor feature; The first sensor feature and the second sensor feature are input into the target intelligence engine.

6. The method according to claim 1, wherein: Generating target reply information including the first state information, including: If the communication information comes from a third user, generating a target reply message including the first state information in a first generating manner; If the communication information comes from a fourth user, generating a target reply message including the first status information in a second generating manner; The language style of the target reply information generated by the first generation method is different from the language style of the target reply information generated by the second generation method.

7. The method according to claim 1, wherein: The step of responding to receiving the communication information sent by the second electronic device includes: In response to receiving an instant messaging request initiated by a second electronic device; or In response to receiving an instant messaging message initiated by a second electronic device.

8. The method according to claim 1, wherein: Before generating the target reply information, if the current behavior state of the user is a non-detectable behavior state when determining the target state information based on the sensor data, the method further includes: Acquire associated device information of the first electronic device; Based on the associated device information, target state information representing the current behavior state of the user is determined.

9. A processing device, applied to a first electronic device, comprising: an acquisition module, configured to, in response to receiving communication information sent by a second electronic device, acquire sensor data of the first electronic device if the first electronic device is in a first state; A determination module, configured to determine first state information representing a current behavior state of a user based on the sensor data; A generating module, used for generating target reply information including the first state information; A sending module, used for sending the target reply information to the second electronic device; The first state represents that the first electronic device has not responded to the communication information.

10. A first electronic device, comprising: One or more sensors, configured to obtain sensor data based on a state of the first electronic device; One or more processors, configured to, in response to receiving a communication message sent by a second electronic device, determine first state information representing a current behavior state of a user based on the sensor data, generate target reply information including the first state information, and send the target reply information to the second electronic device; The first state represents that the first electronic device has not responded to the communication information.

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