Task processing method and device, equipment, storage medium and program product

Abstract

The embodiment of the invention provides a task processing method and device, equipment, a storage medium and a program product. At a client, the method comprises the following steps: in response to a task request sent to server-side equipment, receiving a task execution instruction corresponding to the task request from the server-side equipment for execution; receiving prediction information for the task request from the server-side equipment, wherein the prediction information indicates at least one prediction execution result of the task execution instruction and a prediction response corresponding to the at least one prediction execution result; and after execution of the task execution instruction is completed, providing a response to the task request based on matching between a target execution result of the task execution instruction and the at least one predicted execution result. This helps to improve the stability and efficiency of the client device providing the response.

Description

Technical Field

[0001] The exemplary embodiments disclosed herein relate generally to the field of computers, and more particularly to methods, apparatuses, electronic devices, computer-readable storage media, and computer program products for task processing. Background Technology

[0002] With the development of information technology, various terminal devices can provide people with a variety of services in work and life. For example, terminal devices can be equipped with applications that provide services. Terminal devices or applications can provide users with task processing functions to assist users in using the terminal devices or applications. Terminal devices can receive task requests from users, execute the tasks corresponding to the task requests, and provide corresponding responses to users based on the task execution results. Summary of the Invention

[0003] In a first aspect of this disclosure, a method for task processing is provided. The method is implemented at a client device and includes: in response to sending a task request to a server device, receiving from the server device a task execution instruction corresponding to the task request for execution; receiving from the server device prediction information for the task request, the prediction information indicating at least one predicted execution result of the task execution instruction and a prediction response corresponding to each of the at least one predicted execution result; and after the execution of the task execution instruction is completed, providing a response to the task request based on a match between the target execution result of the task execution instruction and the at least one predicted execution result.

[0004] In a second aspect of this disclosure, a method for task processing is provided. The method is implemented at a server device and includes: in response to receiving a task request sent by a client device, determining a task execution instruction corresponding to the task request based on the task request; determining prediction information for the task request based on the task execution instruction; and sending the prediction information to the client device.

[0005] In a third aspect of this disclosure, an apparatus for task processing is provided. The apparatus is implemented at a client device and includes: a task request sending module configured to receive, in response to sending a task request to a server device, a task execution instruction corresponding to the task request from the server device for execution; a prediction information receiving module configured to receive prediction information for the task request from the server device, the prediction information indicating at least one predicted execution result of the task execution instruction and a prediction response corresponding to each of the at least one predicted execution result; and a response providing module configured to, after the execution of the task execution instruction is completed, provide a response to the task request based on the matching between the target execution result of the task execution instruction and at least one predicted execution result.

[0006] In a fourth aspect of this disclosure, an apparatus for task processing is provided. The apparatus is implemented at a server device and includes: a task request receiving module configured to, in response to receiving a task request sent by a client device, determine a task execution instruction corresponding to the task request based on the task request; a prediction information determining module configured to determine prediction information for the task request based on the task execution instruction; and a prediction information sending module configured to send the prediction information to the client device.

[0007] In a fifth aspect of this disclosure, an electronic device is provided. The device includes at least one processing unit; and at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit. When executed by the at least one processing unit, the instructions cause the electronic device to perform the methods of the first aspect and / or the second aspect.

[0008] In a sixth aspect of this disclosure, a computer-readable storage medium is provided. The medium stores a computer program that, when executed by a processor, implements the methods of the first and / or second aspects.

[0009] In a seventh aspect of this disclosure, a computer program product is provided. The product includes a computer program, which, when executed by a processor, implements the methods according to the first and / or second aspects of this disclosure.

[0010] It should be understood that the content described in this content section is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0011] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0012] Figure 1 A schematic diagram of an example environment in which embodiments of the present disclosure can be implemented is shown;

[0013] Figure 2 A flowchart of a signaling flow for task processing according to some embodiments of the present disclosure is shown;

[0014] Figure 3 A flowchart of a method for task processing according to some embodiments of the present disclosure is shown;

[0015] Figure 4 A flowchart of a method for task processing according to other embodiments of the present disclosure is shown;

[0016] Figure 5 An exemplary structural block diagram of an apparatus for task processing according to some embodiments of the present disclosure is shown;

[0017] Figure 6 Exemplary structural block diagrams of an apparatus for task processing according to other embodiments of the present disclosure are shown; and

[0018] Figure 7 A block diagram of an electronic device in which one or more embodiments of the present disclosure may be implemented is shown. Detailed Implementation

[0019] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0020] In the description of embodiments of this disclosure, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The term "some embodiments" should be understood as "at least some embodiments". Other explicit and implicit definitions may also be included below.

[0021] In this document, unless explicitly stated otherwise, performing a step in response to A does not mean that the step is performed immediately after A, but may include one or more intermediate steps.

[0022] It is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition, use, storage or deletion of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0023] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure through appropriate means in accordance with relevant laws and regulations, and user authorization should be obtained.

[0024] For example, in response to receiving a user's active request, a prompt message is sent to the user to clearly inform the user that the requested operation will require the acquisition and use of the user's personal information, thereby enabling the user to choose whether to provide personal information to the software or hardware such as electronic devices, applications, servers or storage media that perform the operation of the technical solution disclosed herein, based on the prompt message.

[0025] As an optional but non-restrictive implementation, in response to a user's active request, a prompt message can be sent to the user, such as a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0026] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0027] As used in this paper, the term "model" refers to a model that learns the relationship between inputs and outputs from training data, enabling it to generate corresponding outputs for a given input after training. Model generation can be based on machine learning techniques. Deep learning is a machine learning algorithm that processes inputs and provides corresponding outputs using multiple layers of processing units. A neural network model is an example of a deep learning-based model. In this paper, "model" may also be referred to as a "machine learning model," "learning model," "machine learning network," or "learning network," and these terms are used interchangeably.

[0028] A neural network is a machine learning network based on deep learning. A neural network processes input and provides a corresponding output, typically consisting of an input layer, an output layer, and one or more hidden layers between the input and output layers. Neural networks used in deep learning applications often include many hidden layers, thus increasing the network's depth. The layers of a neural network are connected sequentially, so that the output of the previous layer is provided as the input to the next layer. The input layer receives the input to the neural network, while the output layer's output serves as the final output. Each layer of a neural network includes one or more nodes (also called processing nodes or neurons), each node processing the input from the layer above.

[0029] Machine learning typically comprises three phases: training, testing, and application (also known as inference). In the training phase, a given model is trained using a large amount of training data, iteratively updating parameter values ​​until the model can consistently generate inferences that meet the expected goals from the training data. Through training, the model can be considered to have learned the relationship between inputs and outputs (also known as an input-output mapping) from the training data. The parameter values ​​of the trained model are determined. In the testing phase, test inputs are applied to the trained model to test whether it can provide the correct output, thus determining the model's performance. The testing phase can sometimes be integrated into the training phase. In the application or inference phase, the trained model can be used to process actual model inputs based on the trained parameter values ​​to determine the corresponding model output.

[0030] Figure 1 A schematic diagram of an example environment 100 in which embodiments of the present disclosure can be implemented is shown. In this example environment 100, an application 112 and a digital assistant 114 are installed on a client device 110. A user 140 can interact with the application 112 via the client device 110 and / or an attached device of the client device 110. In some implementations, the application 112 may be authorized to capture speech via an audio capture device (e.g., a microphone) of the client device 110, capture images via an image capture device (e.g., a camera) of the client device 110, and so on.

[0031] In some embodiments, application 112 and digital assistant 114 may be downloaded and installed on client device 110. In some embodiments, application 112 and digital assistant 114 may also be accessed in other ways, such as through a web page.

[0032] In embodiments of this disclosure, application 112 can be any suitable application with task processing capabilities, which may include, but is not limited to, one or more of the following: chat application components (also known as instant messaging application components), browser application components, planning application components, document application components, audio / video conferencing application components, email application components, task application components, calendar application components, goal and key results (OKR) application components, etc. It is understood that, although Figure 1 The image shows a single application component, but in reality, multiple application components can be installed on the client device 110. In some embodiments, application 112 may include a multi-functional collaboration platform, such as an office collaboration platform (also known as an office suite), which can provide integration of various types of business components to facilitate people's office work, communication, and other activities. In a multi-functional collaboration platform, people can launch different business components as needed to complete corresponding information processing, sharing, communication, etc.

[0033] In some embodiments, the digital assistant 114 may be provided by a separate application business component, or it may be integrated into an application 112 capable of providing content entities. The application business component providing the client interface for the digital assistant may correspond to a single-function application business component or a multi-functional collaboration platform, such as an office suite or other collaboration platform capable of integrating multiple components. It is understood that, similar to application business components, although... Figure 1 The image shows a single digital assistant, but there can actually be multiple digital assistants.

[0034] Digital assistant 114 is a user's intelligent assistant, possessing intelligent dialogue and information processing capabilities. In embodiments of this disclosure, digital assistant 114 is used to interact with user 140 to assist user 140 in using terminal devices or applications. In some embodiments, multiple interaction modes between user 140 and digital assistant 114 can be provided, and users can flexibly switch between these modes. When a certain interaction mode is triggered, a corresponding interaction area is presented to facilitate interaction between user 140 and digital assistant 114. The interaction methods between user 140 and digital assistant 114 differ under different interaction modes, thus flexibly adapting to the interaction needs of different application scenarios.

[0035] In environment 100, in response to the launch of application 112, client device 110 may present an interface 150 of application 112 and / or digital assistant 114. Interface 150 may, for example, include an interactive interface for application 112 and digital assistant 114. In some embodiments, interface 150 may present an interaction window between user 140 and digital assistant 114. In the interaction window, user 140 can converse with digital assistant 114 by inputting natural language, images, audio files, video files, web page files, etc., to instruct the digital assistant to assist in completing various tasks.

[0036] The interaction window between the digital assistant 114 and the user 140 may include a session window, such as a session window in an instant messaging application or an instant messaging module of a specific application. In the session window, the interaction between the digital assistant 114 and the user 140 may be presented in the form of session messages. Alternatively or additionally, the interaction window between the digital assistant 114 and the user 140 may also include other types of windows, such as a floating window, in which the user 140 can trigger the digital assistant 114 to perform corresponding operations by entering commands, selecting shortcuts, etc.

[0037] In some embodiments, the digital assistant 114 may support a conversation window interaction mode, also known as conversation mode. In this interaction mode, a conversation window is presented between the user 140 and the digital assistant 114, where the user 140 and the digital assistant 114 interact through conversation messages. In conversation mode, the digital assistant 114 can perform tasks based on the conversation messages in the conversation window. In the interaction window, the user 140 inputs interaction messages, and the digital assistant 114 responds to the user's input by providing a reply message. A conversation window with the digital assistant 114 can be opened by selecting the digital assistant 114. The conversation window may include interface elements for information interaction, such as input boxes, message lists, message bubbles, etc.

[0038] In some embodiments, a communication connection is established between the client device 110 and the server device 120. The communication connection can be established via wired or wireless means. The communication connection may include, but is not limited to, Bluetooth, mobile network, Universal Serial Bus (USB), and Wireless Fidelity (WiFi) connections, etc., and the embodiments of this disclosure are not limited in this respect. In the embodiments of this disclosure, the client device 110 and the server device 120 can perform signaling interaction through their communication connection to provide services to the application 112 and / or the digital assistant 114.

[0039] like Figure 1 As shown, server device 120 can invoke machine learning model 130 to support the functionality of application 112 and / or digital assistant 114 based on the output of machine learning model 130. Machine learning model 130 can be based on any suitable model architecture, including but not limited to Transformer models, convolutional neural networks (CNNs), recurrent neural networks (RNNs), deep neural networks (DNNs), etc. In some embodiments, machine learning model 130 can be based on a language model (LM). A language model, by learning from a large corpus, is capable of question-answering. Machine learning model 130 can also be based on other suitable models.

[0040] The machine learning model 130 can be deployed on the server device 120 or on other devices. The machine learning model 130 may include one or more machine learning models. It should be noted that if the machine learning model 130 includes multiple machine learning models, these multiple machine learning models may have different uses and functions, and this disclosure does not limit them.

[0041] Client device 110 can be any type of mobile terminal, fixed terminal, or portable terminal, including mobile phones, desktop computers, laptop computers, notebook computers, netbook computers, tablet computers, media computers, multimedia tablets, personal communication system (PCS) devices, personal navigation devices, personal digital assistants (PDAs), audio / video players, digital cameras / camcorders, positioning devices, television receivers, radio receivers, e-book devices, gaming devices, or any combination thereof, including accessories and peripherals of these devices or any combination thereof. In some embodiments, client device 110 may also support any type of user-facing interface (such as "wearable" circuitry).

[0042] The server-side device 120 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, and big data and artificial intelligence platforms. The server-side device 120 may include, for example, computing systems / servers such as mainframes, edge computing nodes, and computing devices in a cloud environment, etc.

[0043] It should be understood that the structure and function of the various elements in environment 100 are described for illustrative purposes only and do not imply any limitation on the scope of this disclosure.

[0044] During human-computer interaction, users can input task requests via voice or text on the client device. The server device and / or client device can analyze the task request to determine the task to be executed. Depending on the specific content of the task, it can be executed on the server device or the client device. If the task is to be executed on the client, the server device will send a task execution instruction to the client device. The server device determines the response to the task request based on the task execution result, which is then provided to the user by the client device.

[0045] In a typical processing flow, the client device can send a user-inputted task request to the server device. The server device can then send a task execution instruction to the client device based on the received task request. The client device can execute the task request based on the received instruction and send the execution result back to the server device. The server device can respond to the received execution result, determine a response to the task request based on the result, and send that response to the client device. The client device, upon receiving the response, provides it to the user.

[0046] It can be observed that traditional task processing methods have a relatively long chain. After the client device executes the task execution command, it needs to provide the execution result to the server device and then provide the user with a response to the execution result sent back by the server device. Therefore, even after the client device completes the task execution command, it still needs to wait for a period of time (e.g., tens or hundreds of milliseconds) to obtain a response, which affects the efficiency of the user obtaining a response. In addition, communication between the client device and the server device usually relies on the network. In the case of poor network communication capabilities, even if the client device completes the task execution command, it may still fail to obtain the response sent by the server device, and thus be unable to provide a response to the user. This results in the user not knowing the execution status of the task request, affecting the user's response experience.

[0047] In view of the above, according to embodiments of the present disclosure, an improved task processing scheme is provided. According to the scheme of the embodiments of the present disclosure, on the client device side, in response to sending a task request to the server device, a task execution instruction corresponding to the task request is received from the server device for execution. Prediction information for the task request is received from the server device, the prediction information indicating at least one predicted execution result of the task execution instruction and a predicted response corresponding to each of the at least one predicted execution result. After the execution of the task execution instruction is completed, a response to the task request is provided based on the matching between the target execution result of the task execution instruction and at least one predicted execution result.

[0048] According to the scheme of this disclosure embodiment, on the server side, in response to receiving a task request sent by the client device, a task execution instruction corresponding to the task request is determined based on the task request. Prediction information for the task request is determined based on the task execution instruction. The prediction information is then sent to the client device.

[0049] In this way, the server device can determine prediction information based on the task execution instruction and send it to the client device. The prediction information indicates at least one predicted execution result of the task execution instruction and a prediction response corresponding to each of the at least one predicted execution result. The client device can determine the matching result between the target execution result of the task execution instruction and at least one predicted execution result, and in response to the inclusion of a target predicted execution result that matches the target execution result among the at least one predicted execution result, determine the prediction response corresponding to the target predicted execution result as the response to the task request. In this case, the client device does not need to send the target execution result to the server device again. This can improve the stability and efficiency of the response provided by the client device.

[0050] The following description will continue with reference to the accompanying drawings, which will provide some exemplary embodiments of this disclosure.

[0051] Figure 2A flowchart of a signaling flow 200 for task processing according to some embodiments of the present disclosure is shown. For ease of discussion, refer to... Figure 1 To describe signaling flow 200. For example... Figure 2 As shown, the signaling flow 200 involves client device 110 and server device 120, wherein server device 120 includes voice service 201 and model service 202.

[0052] Speech service 201 can provide speech processing services using trained speech processing models. Speech processing services may include, for example, text-to-speech (TTS) services (also known as text-to-speech services) and automatic speech recognition (ASR) services (also known as speech-to-text services). Accordingly, the speech processing models may include machine learning models for performing TTS (which may be simply referred to as TTS models) and machine learning models for performing ASR (which may be simply referred to as ASR models). The input to an ASR model is speech, and its output is text. The input to a TTS model is text, and its output is the corresponding speech.

[0053] Model service 202 can leverage trained machine learning models to provide task processing services, response services, etc. It is understood that, depending on the specific service, model service 202 can utilize different models to provide the corresponding service. As an example, model service 202 can leverage a question-answering model to provide a question-answering service, where the input of the question-answering model is the question text, and the output is the corresponding response text. It is understood that the machine learning models used by voice service 201 and model service 202 can be based on any suitable model architecture, including but not limited to Transformer models, convolutional neural networks (CNNs), recurrent neural networks (RNNs), deep neural networks (DNNs), etc. In some embodiments, the machine learning model can also be based on a language model (LM).

[0054] In some embodiments, client device 110 may receive task requests from a user (e.g., user 130) in any suitable manner. For example, client device 110 may receive task requests input by the user via a microphone, input field, etc. In some embodiments, a task request may include a user question to the digital assistant. Client device 110 receives user questions during user interaction with the digital assistant. For example, client device 110 may receive user questions from the user in the interactive interface of application 112 and / or digital assistant 114, and determine the user question as a task request in response to the user question indicating a task. The task request may be presented in the interactive interface, for example, in the form of a conversation message from the user. It is understood that during user interaction with the digital assistant, client device 110 may receive multiple task requests, which correspond to multiple tasks.

[0055] Client device 110 can send the received task request to server device 120. It is understood that the task request can be any appropriate type of request, such as text or voice. In some embodiments, if the task request is a voice request (which can be referred to as a task request voice), client device 110 can send the task request voice (211) to the voice service 201 in server device 120. Voice service 201 can use an ASR model to determine the task request text corresponding to the task request voice and send the task request text (212) to model service 202. It is understood that if the task request is directly a text request (which can be directly referred to as a task request text), client device 110 can directly send the task request text (213) to model service 202 in server device 120.

[0056] In response to receiving a task request from a client device, server device 120 may determine (214) a task execution instruction corresponding to the task request. It is understood that server device 120 may determine the task execution instruction in any appropriate manner. In some embodiments, if model service 202 receives a task request, model service 202 may determine the task execution instruction corresponding to the task request using a trained machine learning model.

[0057] Server device 120 can send (215) a defined task execution instruction to client device 110. Client device 110 can receive the task execution instruction and determine the task execution result for the task request by executing (217) the task execution instruction. For example, if the task request is "turn on Bluetooth", server device 120 can send an instruction to client device 110 to turn on Bluetooth based on the task request. Client device 110 can turn on its own Bluetooth by executing the instruction.

[0058] In some embodiments, the server device 120 may also determine (216) prediction information for the task request based on the task execution instruction. Regarding the timing of determining the prediction information, to avoid the situation where the client device 110 has already completed the task execution instruction and sent the execution result to the server device 120 when the server device 120 determines the prediction information, the server device 120 may determine the prediction information even without receiving the execution result of the task execution instruction. In this case, if the server device 120 has already received the execution result, the server device 120 may stop determining the prediction information and directly determine the response to the execution result.

[0059] In some embodiments, the server device 120 can also determine the type of the task execution instruction / task request, and in response to the type being a predetermined type, determine prediction information based on the task execution instruction. For example, the server device 120 can obtain a set of predetermined types, which can be user-set or determined by the server device 120 / client device 110 itself. For example, the server device 120 can determine prediction information in response to an instruction of a predetermined type, and not determine prediction information in response to an instruction that is not a predetermined type.

[0060] Regarding the specific method of determining the prediction information, the server device 120 may, for example, determine at least one predicted execution result corresponding to the task execution instruction based at least on the task execution instruction. The server device 120 may also utilize a trained language model to determine the prediction response corresponding to each of the at least one predicted execution result. Furthermore, the server device 120 may determine the prediction information based at least one predicted execution result and the prediction response corresponding to each of the at least one predicted execution result. For example, the server device 120 may also determine at least one prediction result identifier (e.g., a prediction code) corresponding to the at least one predicted execution result, which can be any suitable identifier such as code, number, text, or image.

[0061] The server device 120 can determine the prediction result identifier in any suitable manner. For example, the server device 120 can obtain a correspondence table between result identifiers and execution results, and determine the prediction result identifier corresponding to each prediction execution result by looking up the table. The server device 120 can then determine prediction information based on at least one prediction execution result, at least one prediction result identifier, and prediction responses corresponding to at least one prediction execution result.

[0062] The server device 120 may determine at least one predicted execution result using any suitable method. In some embodiments, if the task request corresponds to a target user (i.e., the task request is a request from the target user), the server device 120 may determine at least one predicted execution result based on the task execution instructions and historical interaction information related to the target user. Historical interaction information may, for example, indicate user attribute information of the target user, the target user's historical task requests, historical execution results for those historical task requests, and historical responses, etc.

[0063] In some embodiments, the model service 202 in the server device 120 can determine at least one predicted execution result using a trained prediction model. For example, the model service 202 can determine a prompt for the prediction model based at least on task execution instructions and historical interaction information related to the target user. The model service 202 can also, for example, obtain a prompt template and determine the prompt input by filling the prompt template with task execution instructions and historical interaction information. The model service 202 can then provide this prompt input to the prediction model to determine at least one predicted execution result based on the task execution instructions and historical interaction information.

[0064] Model service 202 can, for example, utilize a trained language model to determine the predictive response for each predicted execution result. Model service 202 can, for example, construct a prompt word input for the language model based on the predicted execution result. This prompt word input can guide the language model to determine a response to the predicted execution result, which is also the predictive response to the predicted execution result. Model service 202 can further determine predictive information based on at least one predicted execution result and the predictive responses corresponding to each of the at least one predicted execution result.

[0065] In some embodiments, the predicted response may default to a text-type response (which may be referred to as response text). If the client device 110 can present the response text to the target user, the model service 202 may directly send the prediction information to the client device 110. In some embodiments, if the client device 110 can present the response to the target user in the form of audio / speech (which may be referred to as response audio), the server device 120 may also convert the text-type predicted response to the audio type. As an example, the model service 202 may send the prediction information to (219) the speech service 201 to instruct the speech service 201 to convert the predicted response to the audio type.

[0066] After receiving the prediction information, the voice service 201 can determine (220) the corresponding response audio based on the response text in the prediction information. For example, the voice service 201 can use a TTS model to convert the response text in the prediction information into response audio. The server device 120 then sends (221) the prediction information including the response audio to the client device 110.

[0067] In some embodiments, the server device 120 may also acquire predetermined conditions, which may indicate a predetermined number of predicted execution results. If at least one predicted execution result includes multiple predicted execution results, and the number of predicted execution results included in the multiple predicted execution results exceeds the predetermined number, the server device 120 may determine, based on historical interaction information related to the target user, a predetermined number of predicted execution results that best match the target user from the multiple predicted execution results. For example, if a task execution instruction instructs to turn on Bluetooth at the client device 110, the predicted execution results for the task execution instruction may include two predicted execution results: Bluetooth successfully turned on and Bluetooth not turned on. If historical interaction information indicates that Bluetooth can usually be turned on successfully, and the predetermined number is 1, then the server device 120 may determine, from these two predicted execution results, the single predicted execution result that best matches the target user: Bluetooth successfully turned on. The server device 120 may then determine only a set of predicted responses corresponding to this set of predicted execution results, and determine prediction information based on this set of predicted execution results and the set of predicted responses.

[0068] In some embodiments, before sending the prediction information, the server device 120 may also send a prediction indication (218) to the client device 110 in response to the determination of the prediction information. After receiving the prediction indication, the client device 110 can know that the server device 120 is about to send the prediction information to it. The server device 120 may send the prediction information to the client device 110 after sending the prediction indication. It is understood that although step 218 is located before step 219 in the figure, in reality, step 218 may occur after step 220 and before step 221, or step 218 may occur after step 216 and before step 222.

[0069] Client device 110 can receive prediction information for a task request from server device 120. The prediction information indicates at least one predicted execution result of the task execution instruction and a prediction response corresponding to each of the at least one predicted execution result. In some embodiments, client device 110 can receive the prediction information from server device 120 before the execution of the task execution instruction is completed. That is, even if server device 120 sends prediction information to client device 110 after the execution of the task execution instruction has been completed, client device 110 may still not receive the prediction information.

[0070] Alternatively or additionally, in some embodiments, the client device 110 may receive prediction information from the server device 120 before a preset time period expires after the completion of the task execution instruction. Taking a prediction time period of 1 second as an example, if the server device 120 sends prediction information to itself within 1 second after the task execution instruction has been completed, the client device 110 may receive the prediction information. If the server device 120 sends prediction information after 1 second (e.g., the second second after execution), the client device 110 may choose not to receive the prediction information.

[0071] After the task execution instruction is completed, the client device 110 can determine (222) the matching result between the target execution result of the task execution instruction and at least one predicted execution result. It is understood that if the client device 110 receives prediction information before the task execution instruction is completed, the client device 110 can cache the prediction information so that a response can be determined subsequently based on the prediction information. For example, the client device 110 can compare the target execution result with at least one predicted execution result in the prediction information, and in response to determining that a predicted execution result from the at least one predicted execution result is the same as the target execution result, determine that predicted execution result as the target predicted execution result that matches the target execution result.

[0072] In some embodiments, if the prediction information also indicates prediction result identifiers corresponding to each prediction execution result, the client device 110 may also compare the result identifier corresponding to the target execution result (also referred to as the result code, which may include any appropriate identifier such as code, number, text, or image) with the prediction result identifiers contained in the prediction information, and search for a prediction result identifier that is the same as the result identifier from the prediction result identifiers contained in the prediction information. The client device 110 may determine the prediction result identifier as the target prediction result identifier that matches the result identifier. The client device 110 may then determine the prediction execution result corresponding to the target prediction result identifier as the target prediction execution result that matches the target execution result.

[0073] The client device 110 may then determine the prediction response corresponding to the target prediction execution result as a response to the task request in response to determining the target prediction execution result from at least one prediction execution result (i.e., at least one prediction execution result includes a prediction execution result that is the same as the target execution result, or at least one prediction result identifier corresponding to at least one prediction execution result includes a prediction result identifier that is the same as the result identifier corresponding to the target execution result).

[0074] Client device 110 may provide (223) the response to the target user. For example, if the prediction information includes response text, client device 110 may display the response text via a screen. If the prediction information includes response audio, client device 110 may play the response audio via a speaker.

[0075] In some embodiments, the client device 110 may also receive feedback on the provided response (i.e., the predicted response corresponding to the target predicted execution result) and send the feedback (224) to the server device 120. The feedback may, for example, indicate the target user's satisfaction or preference level with the response / predictive information. In response to receiving feedback on the predicted information, the server device 120 may adjust the determination of predicted information for subsequent task requests based on the feedback. For example, if the server device 120 determines at least one predicted execution result using a predictive model and determines the predicted response for each predicted execution result using a language model, the server device 120 may fine-tune the predictive model and / or language model based on the feedback (225). This can improve the accuracy of the determination of subsequent predicted information, making the user more satisfied with the subsequently determined predicted information.

[0076] In some embodiments, the client device 110 may also send (226) the target execution result to the server device 120 in response to determining that the target execution result does not match at least one predicted execution result (i.e., at least one predicted execution result does not include a predicted execution result that is the same as the target execution result, or at least one predicted result identifier does not include a predicted result identifier that is the same as the result identifier), or in response to not receiving prediction information (including the server device 120 not sending prediction information and the client device 110 not receiving prediction information. It can be understood that the client device 110 will not receive prediction information after the task execution instruction is completed or after the preset time period expires after the task execution instruction is completed).

[0077] Server device 120 may, in response to receiving an execution result sent by client device 110, determine (227) a target response for the target execution result based on the execution result. Server device 120 may determine the response in any appropriate manner. For example, model service 202 in server device 120 may determine the target response based on the target execution result using a trained machine learning model. Server device 120 may, in response to determining the target response, send the target response to client device 110.

[0078] Similar to the previous example, after model service 202 determines the target response using a language model, it can directly send the text-type target response to client device 110, or it can send the target response to voice service 201 (228) to instruct voice service 201 to convert the target response to an audio type. Voice service 201 can, in response to receiving the target response, determine (229) the corresponding response audio based on the text-type target response. For example, voice service 201 can use a TTS model to convert the target response to response audio. Server device 120 then sends (230) the response audio corresponding to the target response to client device 110. Client device 110 can then provide (231) the target response to the target user.

[0079] In summary, according to embodiments of this disclosure, the server device can determine prediction information based on task execution instructions and send the prediction information to the client device. The prediction information indicates at least one predicted execution result of the task execution instructions and a prediction response corresponding to each of the at least one predicted execution result. The client device can determine the matching result between the target execution result of the task execution instructions and the at least one predicted execution result, and in response to the inclusion of a target predicted execution result matching the target execution result among the at least one predicted execution result, determine the prediction response corresponding to the target predicted execution result as a response to the task request. In this case, the client device does not need to send the target execution result to the server device again. This can improve the stability and efficiency of the response provided by the client device.

[0080] Figure 3 A flowchart of a method 300 for task processing according to some embodiments of the present disclosure is shown. Method 300 can be implemented at a client device 110. Reference is made below. Figure 1 Description method 300.

[0081] In box 310, the client device 110 responds to sending a task request to the server device 120 and receives a task execution instruction corresponding to the task request from the server device 120 for execution.

[0082] In box 320, client device 110 receives prediction information for a task request from server device 120. The prediction information indicates at least one predicted execution result of the task execution instruction and a prediction response corresponding to each of the at least one predicted execution result.

[0083] In box 330, after the task execution instruction is completed, the client device 110 provides a response to the task request based on the matching between the target execution result of the task execution instruction and at least one predicted execution result.

[0084] In some embodiments, receiving prediction information for a task request from the server device 120 includes: receiving prediction information from the server device before the task execution instruction is completed; or receiving prediction information from the server device before a preset time period expires after the task execution instruction is completed.

[0085] In some embodiments, receiving prediction information for a task request from the server device 120 includes: receiving a prediction indication for providing prediction information from the server device 120; and receiving prediction information from the server device 120.

[0086] In some embodiments, providing a response to a task request based on the matching between a target execution result of a task execution instruction and at least one predicted execution result includes: comparing the target execution result with at least one predicted execution result; and in response to determining a target predicted execution result that matches the target execution result from at least one predicted execution result, providing a target predicted response corresponding to the target predicted execution result.

[0087] In some embodiments, the prediction information further indicates at least one prediction result identifier corresponding to at least one prediction execution result, and comparing the target execution result with at least one prediction execution result includes: comparing the result identifier corresponding to the target execution result with at least one prediction result identifier; and in response to determining a target prediction result identifier that matches the result identifier from at least one prediction result identifier, determining that the target prediction execution result corresponding to the target prediction result identifier matches the execution result.

[0088] In some embodiments, method 300 further includes: in response to determining that the target execution result does not match at least one predicted execution result, or in response to not receiving prediction information, sending the target execution result to server device 120; receiving a target response for the target execution result from server device 120; and providing a target response for the target execution result.

[0089] In some embodiments, method 300 further includes: receiving feedback on the provided response; and sending the feedback to server device 120.

[0090] Figure 4 A flowchart of a method 400 for task processing according to some embodiments of the present disclosure is shown. Method 400 may be implemented at server device 120. Reference is made below. Figure 1 Description method 400.

[0091] In box 410, server device 120 responds to receiving a task request sent by client device 110 and determines the task execution instruction corresponding to the task request based on the task request.

[0092] In box 420, server device 120 determines prediction information for the task request based on the task execution instructions.

[0093] In box 430, server device 120 sends prediction information to client device 110.

[0094] In some embodiments, determining prediction information for a task request based on a task execution instruction includes: determining prediction information when no execution result of the task execution instruction is received.

[0095] In some embodiments, determining prediction information for a task request based on a task execution instruction includes: determining prediction information based on a task execution instruction in response to an instruction of a predetermined type.

[0096] In some embodiments, determining prediction information for a task request based on a task execution instruction includes: determining at least one predicted execution result corresponding to the task execution instruction based at least on the task execution instruction; determining prediction responses corresponding to the at least one predicted execution result using a trained language model; and determining prediction information based at least one predicted execution result and prediction responses corresponding to the at least one predicted execution result.

[0097] In some embodiments, determining prediction information based at least on at least one prediction execution result and prediction responses corresponding to at least one prediction execution result includes: determining at least one prediction result identifier corresponding to at least one prediction execution result; and determining prediction information based on at least one prediction execution result, at least one prediction result identifier, and prediction responses corresponding to at least one prediction execution result.

[0098] In some embodiments, the task request corresponds to a target user, and determining at least one predicted execution result corresponding to the task execution instruction includes: determining at least one predicted execution result based on the task execution instruction and historical interaction information related to the target user.

[0099] In some embodiments, sending prediction information to client device 110 includes: in response to determining the prediction information, sending a prediction instruction to client device 110 regarding the provision of the prediction information; and sending the prediction information to client device 110.

[0100] In some embodiments, method 400 further includes: in response to receiving an execution result sent by client device 110, determining a target response for the execution result based on the execution result; and sending the target response to client device 110.

[0101] In some embodiments, method 400 further includes: in response to receiving feedback on prediction information sent by a client device, adjusting the determination of prediction information for subsequent task requests based on the feedback.

[0102] Embodiments of this disclosure also provide corresponding apparatus for implementing the above methods or processes.

[0103] Figure 5 An exemplary structural block diagram of an apparatus 500 for task processing according to some embodiments of the present disclosure is shown. The apparatus 500 may be implemented as or included in a client device 110. Various modules / components in the apparatus 500 may be implemented by hardware, software, firmware, or any combination thereof.

[0104] like Figure 5 As shown, the apparatus 500 includes a task request sending module 510, configured to receive a task execution instruction corresponding to the task request from the server device for execution in response to sending a task request to the server device. The apparatus 500 also includes a prediction information receiving module 520, configured to receive prediction information for the task request from the server device, the prediction information indicating at least one predicted execution result of the task execution instruction and a prediction response corresponding to each of the at least one predicted execution result. The apparatus 500 further includes a response providing module 530, configured to provide a response to the task request after the execution of the task execution instruction is completed, based on the matching between the target execution result of the task execution instruction and at least one predicted execution result.

[0105] In some embodiments, the prediction information receiving module 520 is further configured to: receive prediction information from the server device before the task execution instruction is completed; or receive prediction information from the server device before a preset time period expires after the task execution instruction is completed.

[0106] In some embodiments, the prediction information receiving module 520 is further configured to: receive a prediction indication of the provision of prediction information from the server device 120; and receive prediction information from the server device 120.

[0107] In some embodiments, the response providing module 530 is further configured to: compare the target execution result with at least one predicted execution result; and in response to determining a target predicted execution result that matches the target execution result from the at least one predicted execution result, provide a target predicted response corresponding to the target predicted execution result.

[0108] In some embodiments, the prediction information further indicates at least one prediction result identifier corresponding to at least one prediction execution result, and the response providing module 530 is further configured to: compare the result identifier corresponding to the target execution result with at least one prediction result identifier; and in response to determining a target prediction result identifier that matches the result identifier from at least one prediction result identifier, determine that the target prediction execution result corresponding to the target prediction result identifier matches the execution result.

[0109] In some embodiments, the apparatus 500 further includes: an execution result sending module configured to send the target execution result to the server device 120 in response to determining that the target execution result does not match at least one predicted execution result, or in response to not receiving prediction information; receive a target response for the target execution result from the server device 120; and a target response providing module configured to provide a target response for the target execution result.

[0110] In some embodiments, the apparatus 500 further includes: a feedback receiving module configured to receive feedback in response to the provided response; and a feedback sending module configured to send the feedback to the server device 120.

[0111] Figure 6 An exemplary structural block diagram of an apparatus 600 for task processing according to some embodiments of the present disclosure is shown. The apparatus 600 may be implemented as or included in a server device 120. Various modules / components in the apparatus 600 may be implemented by hardware, software, firmware, or any combination thereof.

[0112] like Figure 6 As shown, the device 600 includes a task request receiving module 610, configured to determine a task execution instruction corresponding to the task request based on the task request in response to receiving a task request sent by a client device. The device 600 also includes a prediction information determining module 620, configured to determine prediction information for the task request based on the task execution instruction. The device 600 further includes a prediction information sending module 630, configured to send the prediction information to the client device.

[0113] In some embodiments, determining prediction information for a task request based on a task execution instruction includes: determining prediction information when no execution result of the task execution instruction is received.

[0114] In some embodiments, the prediction information determination module 620 is further configured to: determine prediction information based on the task execution instruction in response to an instruction of a predetermined type.

[0115] In some embodiments, the prediction information determination module 620 is further configured to: determine at least one predicted execution result corresponding to the task execution instruction based at least on the task execution instruction; determine the prediction response corresponding to each of the at least one predicted execution result using a trained language model; and determine prediction information based at least on the at least one predicted execution result and the prediction response corresponding to each of the at least one predicted execution result.

[0116] In some embodiments, the prediction information determination module 620 is further configured to: determine at least one prediction result identifier corresponding to at least one prediction execution result; and determine prediction information based on at least one prediction execution result, at least one prediction result identifier, and prediction responses corresponding to at least one prediction execution result.

[0117] In some embodiments, the task request corresponds to a target user, and the prediction information determination module 620 is further configured to determine at least one predicted execution result based on the task execution instruction and historical interaction information related to the target user.

[0118] In some embodiments, the prediction information sending module 630 is further configured to: in response to the determination of prediction information, send a prediction instruction to the client device 110 regarding the provision of prediction information; and send prediction information to the client device 110.

[0119] In some embodiments, the apparatus 600 further includes: a target response determination module configured to determine a target response to the execution result based on the execution result received from the client device 110; and a target response sending module configured to send the target response to the client device 110.

[0120] In some embodiments, the apparatus 600 further includes an adjustment module configured to adjust the determination of prediction information for subsequent task requests based on feedback received from a client device regarding prediction information.

[0121] The modules included in device 500 and / or device 600 can be implemented in various ways, including software, hardware, firmware, or any combination thereof. In some embodiments, one or more modules can be implemented using software and / or firmware, such as machine-executable instructions stored on a storage medium. In addition to or as an alternative to machine-executable instructions, some or all of the modules in device 500 and / or device 600 can be implemented at least partially by one or more hardware logic components. By way of example and not limitation, exemplary types of hardware logic components that can be used include field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-chips (SoCs), complex programmable logic devices (CPLDs), and so on.

[0122] It should be understood that one or more steps in the above methods can be performed by suitable electronic devices or combinations of electronic devices. Such electronic devices or combinations of electronic devices may include, for example, […]. Figure 1 The client device 110 and / or server device 120 in the middle.

[0123] Figure 7 A block diagram of an electronic device 700 in which one or more embodiments of the present disclosure may be implemented is shown. It should be understood that... Figure 7 The electronic device 700 shown is merely exemplary and should not be construed as limiting the functionality and scope of the embodiments described herein. Figure 7 The electronic device 700 shown can be used to achieve Figure 1 The client device 110 and / or the server device 120.

[0124] like Figure 7 As shown, electronic device 700 is in the form of a general-purpose electronic device. Components of electronic device 700 may include, but are not limited to, one or more processors or processing units 710, memory 720, storage device 730, one or more communication units 740, one or more input devices 750, and one or more output devices 760. Processing unit 710 may be a physical or virtual processor and is capable of performing various processes according to programs stored in memory 720. In a multiprocessor system, multiple processing units execute computer-executable instructions in parallel to improve the parallel processing capability of electronic device 700.

[0125] Electronic device 700 typically includes multiple computer storage media. Such media can be any accessible media that is accessible to electronic device 700, including but not limited to volatile and non-volatile media, removable and non-removable media. Memory 720 can be volatile memory (e.g., registers, cache, random access memory (RAM)), non-volatile memory (e.g., read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. Storage device 730 can be removable or non-removable media and can include machine-readable media, such as flash drives, disks, or any other media that can be used to store information and / or data and can be accessed within electronic device 700.

[0126] Electronic device 700 may further include additional removable / non-removable, volatile / non-volatile storage media. Although not explicitly stated... Figure 7 As shown, disk drives for reading from or writing to removable, non-volatile disks (e.g., "floppy disks") and optical disk drives for reading from or writing to removable, non-volatile optical disks can be provided. In these cases, each drive can be connected to a bus (not shown) via one or more data media interfaces. Memory 720 may include computer program product 725 having one or more program modules configured to perform various methods or actions of various embodiments of this disclosure.

[0127] The communication unit 740 enables communication with other electronic devices via a communication medium. Additionally, the functionality of the components of the electronic device 700 can be implemented using a single computing cluster or multiple computing machines capable of communicating via communication connections. Therefore, the electronic device 700 can operate in a networked environment using logical connections to one or more other servers, network personal computers (PCs), or another network node.

[0128] Input device 750 can be one or more input devices, such as a mouse, keyboard, trackball, etc. Output device 760 can be one or more output devices, such as a monitor, speaker, printer, etc. Electronic device 700 can also communicate with one or more external devices (not shown) via communication unit 740 as needed. These external devices include storage devices, display devices, etc., and can communicate with one or more devices that enable user interaction with electronic device 700, or with any device that enables electronic device 700 to communicate with one or more other electronic devices (e.g., network card, modem, etc.). Such communication can be performed via input / output (I / O) interface (not shown).

[0129] According to an exemplary implementation of this disclosure, a computer-readable storage medium is provided that stores computer-executable instructions thereon, wherein the computer-executable instructions are executed by a processor to implement the methods described above. According to an exemplary implementation of this disclosure, a computer program product is also provided, which is tangibly stored on a non-transitory computer-readable medium and includes computer-executable instructions, which are executed by a processor to implement the methods described above.

[0130] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, devices, and computer program products implemented according to this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0131] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processing unit of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0132] Computer-readable program instructions can be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions that execute on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0133] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some, as newer, implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0134] Various implementations of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed implementations. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations. The terminology used herein is chosen to best explain the principles, practical applications, or improvements to technology in the market, or to enable others skilled in the art to understand the various implementations disclosed herein.

Claims

1. A method for task processing, implemented at a client device, comprising: In response to sending a task request to the server device, the system receives a task execution instruction corresponding to the task request from the server device for execution. The server receives prediction information for the task request, the prediction information indicating at least one predicted execution result of the task execution instruction and a prediction response corresponding to each of the at least one predicted execution result; as well as After the task execution instruction is completed, a response to the task request is provided based on the matching between the target execution result of the task execution instruction and the at least one predicted execution result.

2. The method of claim 1, wherein receiving prediction information for the task request from the server device includes: The prediction information is received from the server device before the task execution instruction is completed. or Before the preset time period expires after the completion of the task execution instruction, the prediction information is received from the server device.

3. The method of claim 1, wherein receiving prediction information for the task request from the server device includes: Receive a prediction instruction provided for the prediction information from the server device; as well as The prediction information is received from the server device.

4. The method of claim 1, wherein providing a response to the task request based on the matching between the target execution result of the task execution instruction and the at least one predicted execution result comprises: The target execution result is compared with the at least one predicted execution result; as well as In response to determining a target prediction execution result that matches the target execution result from the at least one prediction execution result, a target prediction response corresponding to the target prediction execution result is provided.

5. The method of claim 4, wherein the prediction information further indicates at least one prediction result identifier corresponding to the at least one prediction execution result, and comparing the target execution result with the at least one prediction execution result includes: Compare the result identifier corresponding to the target execution result with the at least one prediction result identifier; as well as In response to determining a target prediction result identifier that matches the result identifier from the at least one prediction result identifier, it is determined that the target prediction execution result corresponding to the target prediction result identifier matches the execution result.

6. The method according to claim 1, further comprising: In response to determining that the target execution result does not match any of the at least one predicted execution result, or in response to not receiving the prediction information, the target execution result is sent to the server device; Receive the target response for the target execution result from the server device; as well as Provide the target response for the execution result of the target.

7. The method according to claim 1, further comprising: Receive feedback in response to the provided response; as well as The feedback is sent to the server device.

8. A method for task processing, implemented at a server-side device, comprising: In response to receiving a task request from a client device, determine the task execution instruction corresponding to the task request based on the task request; Based on the task execution instructions, predictive information for the task request is determined; as well as The prediction information is sent to the client device.

9. The method of claim 8, wherein determining the prediction information for the task request based on the task execution instruction comprises: The prediction information is determined when no execution result of the task execution instruction is received.

10. The method of claim 8, wherein determining the prediction information for the task request based on the task execution instruction comprises: In response to the task execution instruction being a predetermined type of instruction, the prediction information is determined based on the task execution instruction.

11. The method of claim 8, wherein determining the prediction information for the task request based on the task execution instruction comprises: At least one predicted execution result corresponding to the task execution instruction is determined based on the task execution instruction; Using a trained language model, determine the prediction response corresponding to each of the at least one prediction execution result; as well as The prediction information is determined based at least on the at least one prediction execution result and the prediction response corresponding to the at least one prediction execution result.

12. The method of claim 11, wherein determining the prediction information based at least on the at least one prediction execution result and prediction responses corresponding to the at least one prediction execution result comprises: Determine at least one prediction result identifier corresponding to the at least one prediction execution result; as well as The prediction information is determined based on the at least one prediction execution result, the at least one prediction result identifier, and the prediction response corresponding to each of the at least one prediction execution result.

13. The method of claim 11, wherein the task request corresponds to a target user, and determining at least one predicted execution result corresponding to the task execution instruction comprises: Based on the task execution instructions and historical interaction information related to the target user, the at least one predicted execution result is determined.

14. The method of claim 8, wherein sending the prediction information to the client device comprises: In response to the determination of the prediction information, a prediction instruction for providing the prediction information is sent to the client device; as well as The prediction information is sent to the client device.

15. The method of claim 8, further comprising: In response to receiving the execution result sent by the client device, a target response is determined based on the execution result; as well as Send the target response to the client device.

16. The method of claim 8, further comprising: In response to receiving feedback from the client device regarding the prediction information, the determination of prediction information for subsequent task requests is adjusted based on the feedback.

17. An apparatus for task processing, implemented at a client device, comprising: The task request sending module is configured to receive a task execution instruction corresponding to the task request from the server device in response to sending a task request to the server device for execution; A prediction information receiving module is configured to receive prediction information for the task request from the server device, wherein the prediction information indicates at least one predicted execution result of the task execution instruction and a prediction response corresponding to each of the at least one predicted execution result. as well as A response providing module is configured to provide a response to the task request after the task execution instruction has been executed, based on the matching between the target execution result of the task execution instruction and the at least one predicted execution result.

18. An apparatus for task processing, implemented at a server device, comprising: The task request receiving module is configured to, in response to receiving a task request sent by a client device, determine the task execution instruction corresponding to the task request based on the task request; The prediction information determination module is configured to determine prediction information for the task request based on the task execution instruction; as well as The prediction information sending module is configured to send the prediction information to the client device.

19. An electronic device comprising: At least one processing unit; as well as At least one memory, coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions causing the electronic device to perform the method according to any one of claims 1 to 7, and / or, the method according to any one of claims 8 to 16, when executed by the at least one processing unit.

20. A computer-readable storage medium having a computer program stored thereon, the computer program being executable by a processor to implement the method according to any one of claims 1 to 7, and / or, the method according to any one of claims 8 to 16.

21. A computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements the method according to any one of claims 1 to 7, and / or, the method according to any one of claims 8 to 16.

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