Information processing method and apparatus, device and storage medium

Abstract

Embodiments of the present disclosure provide an information processing method and apparatus, a device and a storage medium. The method comprises: in response to receiving a user input, determining a set of structured objects from among a plurality of structured objects on the basis of a corresponding semantic relevance between the plurality of structured objects and the user input (210); on the basis of the user input and configuration information of at least some of the structured objects in the set of structured objects, using a target model to determine a target structured object from the set of structured objects (220); and on the basis of the target structured object, generating a response to the user input (230).

Description

Information processing methods, apparatus, equipment and storage media

[0001] This application claims priority to Chinese Patent Application No. 202410667960.7, filed on May 27, 2024, entitled "Information Processing Method, Apparatus, Device and Storage Medium", the entire contents of which are incorporated herein by reference. Technical Field

[0002] The exemplary embodiments disclosed herein generally relate to the field of computers, and particularly to information processing methods, apparatus, devices, and computer-readable storage media. Background Technology

[0003] With the development of information technology, various terminal devices can provide people with a variety of services in work and life. Applications providing these services can be deployed on these terminal devices. The terminal devices present relevant content and interact with users through the application's user interface to meet various user needs. In some cases, users may initiate task processing requests within the application. Therefore, improving the efficiency and accuracy of task processing is a key concern. Summary of the Invention

[0004] In a first aspect of this disclosure, an information processing method is provided. The method includes: in response to receiving user input, determining a set of structured objects from a plurality of structured objects based on the semantic relevance of the structured objects to the user input; determining a target structured object from the set of structured objects using a target model based on the user input and configuration information of at least a portion of the structured objects in the set; and generating a response to the user input based on the target structured object.

[0005] In a second aspect of this disclosure, an apparatus for information processing is provided, comprising: a first object determination module configured to, in response to receiving user input, determine a set of structured objects from a plurality of structured objects based on the corresponding semantic relevance of the user input to the structured objects; a second object determination module configured to, based on the user input and configuration information of at least a portion of the structured objects in the set of structured objects, determine a target structured object from the set of structured objects using a target model; and a response generation module configured to generate a response to the user input based on the target structured object.

[0006] In a third aspect of this disclosure, an electronic device is provided. The device includes at least one processor; and at least one memory coupled to the at least one processor and storing instructions for execution by the at least one processor. When executed by the at least one processor, the instructions cause the electronic device to perform the method of the first aspect.

[0007] In a fourth aspect of this disclosure, a computer-readable storage medium is provided. The medium stores computer-executable instructions that, when executed by a processor, implement the method of the first aspect.

[0008] According to a fifth aspect of this disclosure, a computer program product is provided, including computer-executable instructions, the computer program product being tangibly stored in a computer storage medium and including computer-executable instructions that, when executed by a device, cause the device to perform the method of the first aspect of this disclosure.

[0009] It should be understood that the description in this 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

[0010] 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:

[0011] Figure 1 shows a schematic diagram of an example environment in which embodiments of the present disclosure can be implemented;

[0012] Figure 2 shows a flowchart of an information processing procedure according to some embodiments of the present disclosure;

[0013] Figure 3 illustrates a schematic diagram of an example of information processing according to some embodiments of the present disclosure;

[0014] Figure 4 shows a schematic structural block diagram of an apparatus for information processing according to some embodiments of the present disclosure; and

[0015] Figure 5 shows a block diagram of an electronic device that can implement one or more embodiments of the present disclosure. Detailed Implementation

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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.

[0020] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, relevant users should be informed of the type, scope of use, and usage scenarios of the information involved in this disclosure through appropriate means in accordance with relevant laws and regulations, and authorization should be obtained from the relevant users. Among them, relevant users may include any type of rights holder, such as individuals, enterprises, and groups.

[0021] For example, in response to receiving an active request from a user, a prompt message is sent to the relevant user to clearly inform the user that the requested operation will require obtaining and using the user's information, thereby enabling the relevant user to choose whether to provide information to the software or hardware such as the electronic device, application, server, or storage medium that performs the operation of the technical solution disclosed herein based on the prompt message.

[0022] 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 information to the electronic device.

[0023] 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. The activation of digital assistant-related functions, the acquisition of data, the processing and storage of data, etc., in the embodiments of this disclosure shall all require prior authorization from the user and other rights holders associated with the user, and shall comply with the agreements and rules between relevant laws and regulations and rights holders.

[0024] 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.

[0025] Figure 1 illustrates a schematic diagram of an example environment 100 in which embodiments of the present disclosure can be implemented. Environment 100 relates to an application management platform 110, which can support application creation and / or application execution. In some embodiments, the portion of the application management platform 110 used to support application creation may also be referred to as an application creation portion. In some embodiments, the portion of the application management platform 110 used to support application execution may also be referred to as an application execution portion.

[0026] As shown in the figure, the application creation section provides an environment for user 105 to create and publish applications. User 105 can be referred to as the application creation user or creator. In some embodiments, the application creation section can be a low-code platform that provides a collection of tools for application creation. The application creation section can support visual development of various types of applications, allowing developers to skip the manual coding process and accelerate the application development cycle and reduce costs. The application creation section can support any suitable platform for users to develop one or more types of applications, such as an application platform as a service (aPaaS) based platform. Such a platform enables users to efficiently develop applications, enabling operations such as application creation and application function adjustment.

[0027] The application creation component can be deployed locally on user 105's terminal device and / or supported by a server-side device. For example, user 105's terminal device can run a client with the application creation component, which can support interaction between the user and the application creation component provided by the server. When the application creation component runs locally on the user's terminal device, user 105 can directly interact with the local application creation component using the terminal device. When the application creation component runs on a server-side device, the server-side device can provide services to the client running on the terminal device based on the communication connection with the terminal device. The application creation component can present a corresponding page 130 to user 105 based on user 105's actions, to output and / or receive application creation-related information from user 105.

[0028] In some embodiments, the application creation section may be associated with a corresponding database, which stores the data or information required for the application creation process supported by the application creation section. For example, the database may store the code and description information corresponding to the various functional modules that make up the application. The application creation section can also perform operations such as calling, adding, deleting, and updating the functional modules in the database. The database may also store operations that can be performed on different functional blocks. For example, in a scenario where an application needs to be created, the application creation section can call the corresponding functional blocks from the database to build the application.

[0029] In embodiments of this disclosure, user 105 can create and publish target application 120 as needed in the application creation section. Target application 120 can be published to any suitable application runtime section, as long as the application runtime section can support the operation of target application 120. After publication, target application 120 can be operated by one or more end users 145. End user 145 can operate target application 120 through an associated terminal device 146 and thereby interact with application management platform 110. End user 145 can be referred to as the end user of target application 120. In some embodiments, target application 120 may include or be implemented as digital assistant 122.

[0030] Digital assistant 122 can be configured to have intelligent conversational capabilities. In the example shown, digital assistant 122 can be integrated into target application 120, serving as part of target application 120 to assist in task processing within target application 120. In other examples, digital assistant 122 can be configured as a standalone application, such as a web application or other type of application. In such examples, digital assistant 122 and target application 120 can be considered as the same application. Digital assistant 122 is provided to assist users with various task processing needs in different applications and scenarios. During interaction with digital assistant 122, the user inputs interactive messages, and digital assistant 122 responds to the user's input by providing reply messages. Typically, digital assistant 122 can support users inputting questions in natural language and performs tasks and provides replies based on its understanding of natural language input and logical reasoning capabilities.

[0031] In some embodiments, the digital assistant 122 can interact with the end user 145 as a contact. For example, the digital assistant 122 can be implemented in an instant messaging (IM) application. The digital assistant 122 can interact with the end user 145 in a one-on-one chat session. In some embodiments, the digital assistant 122 can interact with multiple users in a group chat session that includes multiple users.

[0032] For each end user 145, the client of the application runtime portion can present an interaction window 142 of the target application 120 or digital assistant 122 in the client interface, such as a conversation window with the digital assistant 122. The end user 145 can enter conversation messages in the conversation window, and the target application 120 can determine the response message from the digital assistant 122 based on the created configuration information and present it to the user in the interaction window 142. In some embodiments, depending on the configuration of the target application 120, the interaction messages with the target application 120 can include multimodal messages, such as text messages (e.g., natural language text), voice messages, image messages, video messages, and so on.

[0033] Similar to the application creation component, the application runtime component can be deployed locally on each end user's (145's) terminal device and / or supported by a server device. For example, the end user's (145's) terminal device can run a client with the application runtime component, which can support interaction between the user and the application runtime component provided by the server. When the application runtime component runs locally on the user's terminal device, the end user (145) can directly interact with the local application runtime component using the terminal device. When the application runtime component runs on a server device, the server device can provide services to the client running on the terminal device based on the communication connection with the terminal device. The application runtime component can present corresponding application pages to the end user (145) based on the user's (145's) actions, outputting and / or receiving application-related information from the user (145).

[0034] In some embodiments, the implementation of at least some functions of the target application 120, and / or the implementation of at least some functions of the digital assistant 122 within the target application 120, may be based on models. During the creation or operation of the target application 120, one or more models 155 may be invoked, such as the capabilities of model 155. In the target application 120, the digital assistant 122 may utilize model 155 to understand user input and provide responses to the user based on the output of model 155.

[0035] During the creation process, the application management platform 110 needs to use model 155 to test the target application 120 to determine whether the running results of the target application 120 meet expectations. During operation, in response to different operation requests from users of the target application 120, the application operation part may need to use model 155 to determine the response results to users.

[0036] Although shown as independent of the application management platform 110, one or more models 155 may run on the application management platform 110 or other remote servers. In some embodiments, model 155 may be a machine learning model, a deep learning model, a learning model, a neural network, etc. In some embodiments, the model may be based on a language model (LM). A language model, by learning from a large corpus, is capable of question answering. Model 155 may also be based on other suitable models.

[0037] The application management platform 110 can run on suitable electronic devices. These electronic devices can be any type of computing-capable device, including terminal devices or server devices. Terminal devices 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 of the foregoing, including accessories and peripherals of these devices or any combination thereof. Server devices can include, for example, computing systems / servers, such as mainframes, edge computing nodes, computing devices in cloud environments, and so on. In some embodiments, the management platform 110 can be implemented based on cloud services.

[0038] It should be understood that the structure and function of environment 100 are described for illustrative purposes only and do not imply any limitation on the scope of this disclosure. For example, although a single user interacting with the application creation section and a single user interacting with the application running section are illustrated, in reality multiple users can access application management platform 110 to each create a digital assistant, and each digital assistant can be used to interact with multiple users.

[0039] As mentioned earlier, users can initiate task processing requests within the application to process information. Applications can leverage models to process information. Traditionally, taking information retrieval as an example, applications can retrieve information based on predefined query templates or by manually matching query tables. This limits the flexibility and efficiency of information retrieval. Furthermore, since the input capacity of the target model (also known as the model window size) is limited, when the target application uses the model to perform information processing tasks involving large amounts of data, the limited input capacity restricts the amount of data the model can process, resulting in poor efficiency in information processing. This can easily lead to information loss, thereby affecting the accuracy of information processing.

[0040] In view of this, an improved information processing scheme is provided in this embodiment. In this scheme, in response to receiving user input, a set of structured objects is determined from the multiple structured objects based on the semantic relevance of the user input to the corresponding structured objects. Based on the user input and configuration information of at least a portion of the structured objects in the set, a target structured object is determined from the set of structured objects using a target model. Based on the target structured object, a response to the user input is generated.

[0041] This approach allows the model to perform two stages of screening—coarse and fine—on multiple structured objects. This helps reduce information loss and improves the efficiency and accuracy of structured object selection. Furthermore, it enhances the model's ability to handle complex and large volumes of data.

[0042] Some exemplary embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0043] The task management process described in the embodiments of this disclosure can be implemented on an application management platform, a terminal device with the application management platform installed, and / or a server corresponding to the application management platform. In the examples below, for the sake of discussion, the description is from the perspective of the application management platform, such as the application management platform 110 shown in FIG1. ​​The user interface presented by the application management platform 110 can be presented via the terminal device of user 145, and the application management platform 110 can receive user input via the terminal device of user 145. In some embodiments of this disclosure, user 145 is the terminal user of the target application 120. It should be understood that the user interface presented by the application management platform 110 can also be presented via the terminal device of user 105, and the application management platform 110 can also receive user input via the terminal device of user 105. In some embodiments of this disclosure, user 105 is the creator, manager, or maintainer of the target application 120.

[0044] Figure 2 shows a flowchart of an information processing procedure 200 according to some embodiments of the present disclosure. Procedure 200 can be implemented in the application management platform 110, for example, by the application runtime portion of the application management platform 110. The task processing procedure shown in Figure 2 will be described below with reference to Figure 1.

[0045] In box 210, in response to receiving user input, the application management platform 110 determines a set of structured objects from the multiple structured objects based on the semantic relevance of the user input to the multiple structured objects. The process of determining a set of structured objects from the multiple structured objects can also be regarded as a coarse screening of the multiple structured objects.

[0046] Structured objects can be any suitable type of object capable of storing or representing information in a structured manner, including but not limited to data tables, databases, APIs, etc. User input can be input from any suitable user, for example, it can be user input from user 145. User input can be of any suitable type, such as text, voice, gesture, etc. Application management platform 110 can receive user input in any suitable manner, for example, it can receive text-type user input via an input box, audio-type user input via a microphone, etc. User input can be presented, for example, in an interactive window (e.g., interactive window 142). In some embodiments, when the user input is non-text type, application management platform 110 can process the user input to determine the corresponding text. For example, if the user input is audio type, application management platform 110 can convert the corresponding audio into text. Application management platform 110 can then determine the semantic relevance between the user input and the structured object based on the corresponding text.

[0047] Application management platform 110 can determine the semantic relevance of multiple structured objects to user input using any appropriate method. For example, application management platform 110 can use a model to determine the semantic relevance of each structured object and user input. In some embodiments, application management platform 110 can determine the semantics of each structured object and user input, and determine the semantic relevance of each structured object to user input by comparing the semantics of the multiple structured objects with the semantics of the user input.

[0048] Alternatively or additionally, in some embodiments, for a given structured object among a plurality of structured objects, the application management platform 110 may obtain a first encoded representation of the given structured object. The application management platform 110 may determine the first encoded representation of the given structured object in any suitable manner. For example, the application management platform 110 may determine the first encoded representation of the given structured object based on predetermined rules or algorithms. As another example, the application management platform 110 may determine the first encoded representation of the given structured object using any suitable model / encoder.

[0049] In some embodiments, the application management platform 110 can divide the summary information of a given structured object into multiple information chunks, and generate a first encoded representation by encoding each of the multiple information chunks. The summary information of the structured object may include, for example, the name of the structured object, a description of the structured object, etc. The size of these multiple information chunks can be predefined, or it can be determined by the application management platform 110 based on the number of texts in the summary information of the given structured object. Similarly, the application management platform 110 can also determine a second encoded representation input by the user.

[0050] The application management platform 110 can then determine the semantic relevance between a given structured object and the user input based on the similarity between the first encoded representation and the second encoded representation of the user input. For example, the application management platform 110 can treat the first and second encoded representations as two vectors respectively, and determine the similarity between the first and second encoded representations by calculating the distance between these two vectors. It can be understood that the smaller the distance between the two vectors, the greater the similarity between the corresponding first and second encoded representations, and the higher the semantic relevance between the given structured object and the user input. The application management platform 110 can use this method to determine the semantic relevance between multiple structured objects and the user input.

[0051] The following describes an example embodiment for determining a set of structured objects from multiple structured objects based on the semantic relevance of these structured objects to user input. In some embodiments, the application management platform 110 may determine at least one structured object among the multiple structured objects whose corresponding semantic relevance is higher than a predetermined threshold as a set of structured objects. Alternatively or additionally, in some embodiments, the application management platform 110 may sort the multiple structured objects in descending order based on the semantic relevance of these multiple structured objects to user input. The application management platform 110 may determine at least one structured object that ranks first by a predetermined number among the multiple structured objects as a set of structured objects.

[0052] In box 220, the application management platform 110, based on user input and configuration information of at least a portion of the structured objects in a set of structured objects, uses a target model to determine a target structured object from the set of structured objects. The process of determining the target structured object from a set of structured objects can also be viewed as a fine-tuning of the set of structured objects.

[0053] In some embodiments, the configuration information of a given structured object among multiple structured objects may include at least one of the following: the name of the given structured object, the description of the given structured object, one or more fields contained in the given structured object, or corresponding descriptions of one or more fields. Taking a data table as an example, the configuration information of each data table among multiple data tables may include the name, description, fields contained in the data table, and descriptions of the fields contained in the data table.

[0054] In some embodiments, the application management platform 110 can sort a set of candidate structured objects based on the semantic relevance between a set of structured objects and the corresponding user input. For example, the application management platform 110 can sort the set of candidate structured objects in descending order based on the semantic relevance between a set of structured objects and the corresponding user input. It is understood that the structured object ranked higher in the sorting corresponds to a higher semantic relevance. The application management platform 110 can determine a set of candidate structured objects from a set of structured objects according to the sorting, based on the input capacity of the target model and the corresponding input consumption of the set of structured objects. Here, the input capacity of the target model represents the maximum capacity of data that the target model can receive. Here, the input consumption of a structured object represents the amount of data used as input to the target model based on the configuration information of that structured object. The application management platform 110 can, for example, determine whether each structured object should be added to the set of candidate structured objects sequentially according to the sorting.

[0055] For example, the application management platform 110 can determine the first structured object to be added to the candidate structured object set from a set of structured objects according to a sorting order. It should be noted that if the first structured object is not the first structured object in the sorting, at least one structured object preceding the first structured object should be added to the candidate structured object set. The application management platform 110 can determine a first total consumption (token) between the input consumption of the structured objects already added to the candidate structured object set and the input consumption of the first structured object. The first total consumption can, for example, be the sum of the input consumption of the structured objects already added to the candidate structured object set and the input consumption of the first structured object.

[0056] The application management platform 110 can compare the first total consumption with the input capacity of the target model to determine whether the first total consumption exceeds the input capacity. If the first total consumption does not exceed the input capacity, the application management platform 110 can add the first structured object to the candidate structured object set. If the first total consumption exceeds the input capacity, the application management platform 110 does not need to add the first structured object to the candidate structured object set, and the application management platform 110 can determine that the determination of the candidate structured object set has been completed.

[0057] For example, if the first structured object is the third structured object in a group of structured objects, then the two structured objects preceding the first structured object are added to the candidate structured object set. The application management platform 110 can determine the input consumption of the first two structured objects and the first total input consumption of the first structured object (for example, the sum of the input consumption of the three structured objects). If the input capacity of the target model is 50 and the first total consumption is 40, then the application management platform 110 can determine that the first total consumption does not exceed the input capacity, and thus can add the first structured object to the candidate structured object set. At this time, the candidate structured object set includes the first three structured objects in a group of structured objects.

[0058] Furthermore, for the fourth structured object in a set of structured objects, if it is determined to be the second structured object, the application management platform 110 can determine the input consumption of the first three structured objects and the second total input consumption of the second structured object (for example, it can be the sum of the input consumption of the first four structured objects in a set of structured objects). If the second total consumption is 55, the application management platform 110 can complete the determination of the candidate structured object set in response to the second total consumption exceeding the input capacity. The determined candidate structured object set includes the first three structured objects in a set of structured objects.

[0059] Once the candidate structured object set is determined, the application management platform 110 can select a target structured object from the candidate structured object set based on user input and the configuration information of the candidate structured objects in the candidate structured object set, using the target model. This target model can be a model deployed locally on the application management platform 110 or a model deployed on other electronic devices. The target model can be based on any suitable model structure, including but not limited to Transformer models, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Neural Networks (DNNs), and any other suitable model.

[0060] Application management platform 110 can, for example, determine prompt input for the target model based on user input and configuration information of candidate structured objects in the candidate structured object set. Application management platform 110 can, for example, obtain a prompt template for the target model and determine the prompt input for the target model by filling the prompt template with user input and configuration information of candidate structured objects in the candidate structured object set. Application management platform 110 can provide this prompt input to the target model. After receiving the prompt input, the target model can generate corresponding model output based on the prompt input. The model output can, for example, indicate which target structured objects to select from the candidate structured object set. Application management platform 110 can obtain the model output from the target model and select target structured objects from the candidate structured object set based on the model output. It is understood that the target structured object may include one or more structured objects.

[0061] The above describes an example process 200 for selecting structured objects. An example implementation of structured object selection will now be described with reference to FIG3. FIG3 shows a schematic diagram of an example 300 of information processing according to some embodiments of the present disclosure. Example 300 may include a coarse screening stage 310 and a fine screening stage 320. In the coarse screening stage 310, the application management platform 110 can determine a set of structured objects from a plurality of structured objects. In the fine screening stage 320, the application management platform 110 can determine a target structured object from the set of structured objects. The number of structured objects included in the target structured object should be less than or equal to the number of structured objects included in the set of structured objects.

[0062] For example, in the coarse screening stage 310, after the application management platform 110 obtains multiple structured objects, it can determine the summary information 311 of each structured object. Based on the summary information 311, the application management platform 110 can determine (312) the first encoded representation of each structured object. Based on the similarity between the first encoded representation of each structured object and the second encoded representation of the user input, the application management platform 110 can determine the semantic relevance 313 between each structured object and the user input. Based on the semantic relevance 313 between each structured object and the user input, the application management platform 110 can sort (314) the multiple structured objects, for example, by sorting them in descending order. The application management platform 110 can then determine multiple structured objects ranked before a predetermined position as a group of structured objects 315.

[0063] In the fine screening stage 320, the application management platform 110 can sort a set of candidate structured objects 315 based on the semantic relevance of a set of structured objects to the user input, and determine the total input consumption of the target structured object and the input consumption of the previous structured objects in turn (321). The application management platform 110 can determine (322) whether the total input consumption of the target structured object and the input consumption of the previous structured objects exceeds the input capacity of the model. If the total input consumption of the target structured object and the input consumption of the previous structured objects does not exceed the input capacity of the model, the application management platform 110 can add (323) the target structured object to the candidate structured object set. If the total input consumption of the target structured object and the input consumption of the previous structured objects exceeds the input capacity of the model, the application management platform 110 can determine that the determination of the candidate structured object set has been completed (i.e., the target structured object is not added to the candidate structured object set).

[0064] Application management platform 110 can determine (324) the prompt word input for the target model based on the configuration information of the candidate structured objects in the candidate structured object set and user input. Application management platform 110 can provide (325) the prompt word input to the target model to obtain the model output for the prompt word input from the target model. Application management platform 110 can determine the target structured object 326 from a set of structured objects based on the model output.

[0065] Referring back to Figure 2, in box 230, the application management platform 110 generates a response to user input based on a target structured object. In some embodiments, the application management platform 110 can directly determine the response to user input based on the target structured object. For example, the application management platform 110 can directly provide the target structured object to the user. Alternatively or additionally, in some embodiments, to improve the granularity and accuracy of the response, the application management platform 110 can also select at least one field from multiple fields based on the semantic similarity between multiple fields contained in the target structured object and the corresponding user input. The application management platform 110 can determine the target field from the at least one field based on the user input and the summary information of the at least one field, using a target model. The application management platform 110 can then generate a response to user input based on the target field and the field values ​​under the target field. That is, the application management platform 110 can also perform coarse and fine screening on the multiple fields included in the target structured object. The way the application management platform 110 performs coarse and fine screening on multiple fields is similar to the way it performs coarse and fine screening on multiple structured objects, and will not be described in detail here.

[0066] It should be noted that, in some embodiments, to improve the accuracy of fine-tuning multiple fields, the configuration information of candidate structured objects in the prompt word input determined by the application management platform 110 during the fine-tuning stage of multiple fields can be richer in information than the configuration information of candidate structured objects in the prompt word input determined during the fine-tuning stage of multiple structured objects. For example, if the configuration information of candidate structured objects in the prompt word input determined during the fine-tuning stage of multiple structured objects can include the name of the structured object, the description of the structured object, one or more fields contained in the structured object, and a brief description of one or more fields, then the configuration information of candidate structured objects in the prompt word input determined during the fine-tuning stage of multiple fields can include the name of the structured object, the description of the structured object, one or more fields contained in the structured object, and a detailed description of one or more fields.

[0067] In summary, according to the embodiments of this disclosure, a model can be used to perform two stages of screening: coarse screening and fine screening, on multiple structured objects. Furthermore, in some embodiments, the model can be used to perform two stages of screening: coarse screening and fine screening, on multiple fields in the target structured object obtained through fine screening. This helps reduce information loss and improve the efficiency and accuracy of information processing. In addition, it enhances the model's ability to process complex information and large volumes of data.

[0068] Embodiments of this disclosure also provide corresponding apparatus for implementing the methods or processes described above. Figure 4 shows a schematic structural block diagram of an apparatus 400 for information processing according to some embodiments of this disclosure. The apparatus 400 may be implemented in or included in an application management platform 110, for example. The various modules / components in the apparatus 400 may be implemented by hardware, software, firmware, or any combination thereof.

[0069] As shown in the figure, the device 400 includes a first object determination module 410, configured to, in response to receiving user input, determine a set of structured objects from a plurality of structured objects based on the semantic relevance of the corresponding structured objects to the user input. The device 400 also includes a second object determination module 420, configured to, based on the user input and configuration information of at least a portion of the structured objects in the set, determine a target structured object from the set of structured objects using a target model. The device 400 also includes a response generation module 430, configured to generate a response to the user input based on the target structured object.

[0070] In some embodiments, the second object determination module 420 includes: a sorting module configured to sort a set of candidate structured objects based on the semantic relevance of a set of structured objects to user input; an object set determination module configured to determine a set of candidate structured objects from the set of structured objects according to the sorting based on the input capacity of the target model and the corresponding input consumption of the set of structured objects, wherein the input consumption of a structured object represents the amount of data in which the configuration information of the structured object is used as input to the target model; and an object selection module configured to select a target structured object from the set of candidate structured objects based on user input and the configuration information of the candidate structured objects in the candidate structured object set, using the target model.

[0071] In some embodiments, the object set determination module includes: a first determination module configured to determine, in order of sorting, a first structured object to be added to a candidate structured object set; a second determination module configured to determine whether a first total consumption of the input consumption of the structured objects already added to the candidate structured object set and the input consumption of the first structured object exceeds the input capacity; and an addition module configured to add the first structured object to the candidate structured object set in response to the first total consumption not exceeding the input capacity.

[0072] In some embodiments, the apparatus 400 includes: a third determining module configured to determine, in order, a second structured object to be added to a candidate structured object set from a set of structured objects, the second structured object being after a first structured object; a fourth determining module configured to determine whether a second total consumption of the input consumption of the structured objects already added to the candidate structured object set and the input consumption of the second structured object exceeds the input capacity; and a completing module configured to complete the determination of the candidate structured object set in response to the second total consumption exceeding the input capacity.

[0073] In some embodiments, the semantic relevance of a plurality of structured objects to the user input is determined as follows: for a given structured object among a plurality of structured objects, a first encoded representation of the given structured object is obtained; and the semantic relevance of the given structured object to the user input is determined based on the similarity between the first encoded representation and a second encoded representation of the user input.

[0074] In some embodiments, the first encoded representation is generated by dividing the summary information of a given structured object into multiple information blocks; and by encoding the multiple information blocks respectively to generate the first encoded representation.

[0075] In some embodiments, the response generation module 430 includes: a field selection module configured to select at least one field from multiple fields based on the semantic similarity between multiple fields contained in the target structured object and the corresponding user input; a field determination module configured to determine a target field from the at least one field based on the user input and summary information of the at least one field, using a target model; and a response determination module configured to generate a response to the user input based on the target field and the field values ​​under the target field.

[0076] In some embodiments, the configuration information of a given structured object among a plurality of structured objects includes at least one of the following: the name of the given structured object, the description of the given structured object, one or more fields contained in the given structured object, or the corresponding description of one or more fields.

[0077] The units and / or modules included in device 400 can be implemented in various ways, including software, hardware, firmware, or any combination thereof. In some embodiments, one or more units and / or 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 units and / or modules in device 400 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.

[0078] Figure 5 shows a block diagram of an electronic device 500 in which one or more embodiments of the present disclosure may be implemented. It should be understood that the electronic device 500 shown in Figure 5 is merely exemplary and should not constitute any limitation on the functionality and scope of the embodiments described herein. The electronic device 500 shown in Figure 5 may include or be implemented as the application management platform 110 of Figure 1, or the device 400 of Figure 4.

[0079] As shown in Figure 5, electronic device 500 is in the form of a general-purpose electronic device. Components of electronic device 500 may include, but are not limited to, one or more processors 510 or processing units, memory 520, storage device 530, one or more communication units 540, one or more input devices 550, and one or more output devices 560. Processor 510 may be a physical or virtual processor and is capable of performing various processes according to programs stored in memory 520. In a multiprocessor system, multiple processors execute computer-executable instructions in parallel to improve the parallel processing capability of electronic device 500.

[0080] Electronic device 500 typically includes multiple computer storage media. Such media can be any accessible media that is accessible to electronic device 500, including but not limited to volatile and non-volatile media, removable and non-removable media. Memory 520 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 530 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 500.

[0081] Electronic device 500 may further include additional removable / non-removable, volatile / non-volatile storage media. Although not shown in FIG. 5, 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 may be provided. In these cases, each drive may be connected to a bus (not shown) via one or more data media interfaces. Memory 520 may include computer program product 525 having one or more program modules configured to perform various methods or actions of various embodiments of the present disclosure.

[0082] Communication unit 540 enables communication with other electronic devices via a communication medium. Additionally, the functionality of components of electronic device 500 can be implemented using a single computing cluster or multiple computing machines capable of communicating via communication connections. Therefore, electronic device 500 can operate in a networked environment using logical connections to one or more other servers, network personal computers (PCs), or another network node.

[0083] Input device 550 can be one or more input devices, such as a mouse, keyboard, trackball, etc. Output device 560 can be one or more output devices, such as a monitor, speaker, printer, etc. Electronic device 500 can also communicate with one or more external devices (not shown) via communication unit 540 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 500, or with any device that enables electronic device 500 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).

[0084] 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.

[0085] 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.

[0086] These computer-readable program instructions can be provided to a processor 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 processor 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.

[0087] 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.

[0088] 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 alternative 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.

[0089] 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. An information processing method comprising: in response to receiving a user input, determining a set of structured objects from a plurality of structured objects based on respective semantic relevancies of the plurality of structured objects to the user input; determining a target structured object from the set of structured objects based on a configuration information of at least a portion of the set of structured objects and the user input, using a target model; and generating a response to the user input based on the target structured object.

2. The method of claim 1, wherein determining a target structured object from the set of structured objects comprises: ordering the set of candidate structured objects based on respective semantic relevancies of the set of structured objects to the user input; and determining a set of candidate structured objects from the set of structured objects according to the ordering, based on an input capacity of the target model and respective input consumptions of the set of structured objects, wherein an input consumption of a structured object represents an amount of data to be used as input of the target model for the configuration information of the structured object; and selecting the target structured object from the set of candidate structured objects based on the user input and the configuration information of the candidate structured objects in the set of candidate structured objects, using the target model.

3. The method of claim 2, wherein determining a set of candidate structured objects from the set of structured objects according to the ordering comprises: determining a first structured object from the set of structured objects to be added to the set of candidate structured objects according to the ordering; determining whether a first total consumption of input consumptions of structured objects already added to the set of candidate structured objects and an input consumption of the first structured object exceeds the input capacity; and in response to the first total consumption not exceeding the input capacity, adding the first structured object to the set of candidate structured objects.

4. The method of claim 2 or 3, further comprising: determining a second structured object from the set of structured objects to be added to the set of candidate structured objects according to the ordering, the second structured object being after the first structured object; determining whether a second total consumption of input consumptions of structured objects already added to the set of candidate structured objects and an input consumption of the second structured object exceeds the input capacity; and in response to the second total consumption exceeding the input capacity, completing the determination of the set of candidate structured objects.

5. The method of claim 1, wherein the respective semantic relevancies of the plurality of structured objects to the user input are determined as follows: for a given structured object in the plurality of structured objects, obtaining a first encoded representation of the given structured object; and determining a semantic relevance of the given structured object to the user input based on a similarity between the first encoded representation and a second encoded representation of the user input.

6. The method of claim 5, wherein the first encoded representation is generated as follows: ​ ​ ​ ​ ​ ​ dividing the summary information of the given structured object into a plurality of information blocks; and generating the first encoded representation by encoding the plurality of information blocks respectively. 7.The method of claim 1, wherein generating the response to the user input comprises: selecting at least one field from a plurality of fields contained in the target structured object based on a semantic similarity of the respective fields to the user input; determining a target field from the at least one field based on the user input and summary information of the at least one field using the target model; and generating the response to the user input based on the target field and a field value under the target field. 8.The method of claim 1, wherein the configuration information of a given structured object from the plurality of structured objects comprises at least one of: a name of the given structured object, a description of the given structured object, one or more fields contained in the given structured object, or a respective description of the one or more fields. 9.An apparatus for information processing, comprising: a first object determining module configured to determine a set of structured objects from a plurality of structured objects based on a respective semantic relevance of the structured objects to a user input in response to receiving the user input; a second object determining module configured to determine a target structured object from the set of structured objects based on a configuration information of at least a portion of the set of structured objects and the user input using a target model; and a response generating module configured to generate a response to the user input based on the target structured object. 10.An electronic device, comprising: at least one processor; and at least one memory coupled to the at least one processor and storing instructions for execution by the at least one processor, which when executed by the at least one processor cause the electronic device to perform the method according to any one of claims 1-8. 11.A computer-readable storage medium having computer-executable instructions stored thereon that are executable by a processor to implement the method according to any one of claims 1-8. 12.A computer program product tangibly stored in a computer storage medium and comprising computer-executable instructions that, when executed by a device, cause the device to perform the method according to any one of claims 1-8. ​ ​

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