Simulation interaction method, device and equipment of financial product, medium and product

Abstract

The invention provides a financial product simulation interaction method and device, equipment, a medium and a product, and relates to the field of artificial intelligence. According to the method, a virtual scene containing a financial product three-dimensional model is constructed; receiving multi-modal interaction input of a user, wherein the multi-modal interaction input comprises one or more combinations of gesture operation, a voice instruction and a watching focus; and determining feedback information in the interaction intention based on the multi-modal interaction input. The feedback information is transmitted to the virtual scene, interaction feedback is generated, and the interaction feedback comprises explanation information and risk fluctuation information of the financial product. According to the method, the user can carry out financial product learning and resource allocation experience in the virtual scene, the risk information of the financial product is clarified, the user interaction efficiency and immersion are improved, meanwhile, the user can experience different resource allocation schemes under the condition that the user does not undertake the risk, and the resource allocation decision-making ability of the user is improved.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence, and in particular to a method, apparatus, device, medium, and product for simulating interaction of financial products. Background Technology

[0002] Bank financial products come in various types, with significant differences in risk characteristics, return models, and resource allocation thresholds. Customers lacking understanding of these products are prone to making incorrect resource allocation decisions due to inaccurate information, potentially leading to financial losses. Furthermore, regulators require financial institutions to fulfill their obligations to manage financial products, ensuring that customers, through learning, can choose products that match their risk tolerance. Therefore, acquiring knowledge of financial products has become essential for customers to safeguard their assets.

[0003] Currently, banks primarily use digital channels such as apps to present financial product information to customers, mainly in the form of text descriptions, table parameters, and static charts. Customers are required to actively read the risk disclosure statement and product terms. Online customer service interaction mainly consists of text-based Q&A or voice calls, with video explanations provided in some scenarios. Offline, they rely on one-on-one communication with account managers, recommending products through face-to-face explanations and material demonstrations.

[0004] However, existing presentation methods are insufficient for displaying structured information about complex financial products, requiring customers to repeatedly compare terms to understand product details and risk information. Offline services are limited by the distribution of branch outlets, the number of account managers, and service hours, making it difficult to cover a large number of users, and high-frequency communication is costly. Online interaction relies on a single mode, resulting in low customer interaction efficiency and low efficiency in financial product risk assessment. Summary of the Invention

[0005] This application provides a simulated interactive method, apparatus, device, medium, and product for financial products, in order to solve the problems of limited interaction, high customer comprehension threshold, and insufficient personalized service in the education and resource allocation experience of financial products in the prior art.

[0006] Firstly, this application provides a method for simulating interaction with financial products, including:

[0007] Construct a virtual scene containing a 3D model of a financial product, the virtual scene being used to display the data information of the financial product;

[0008] Receive multimodal interactive input from the user, the multimodal interactive input including one or more combinations of gesture operations, voice commands, and gaze focus;

[0009] Based on the multimodal interactive input, the feedback information in the interactive intent is determined, and the feedback information includes at least one of visual feedback, tactile feedback, and verbal feedback;

[0010] The feedback information is transmitted to the virtual scene to generate interactive feedback, which includes explanatory information about the financial product and risk fluctuation information.

[0011] Secondly, this application provides a simulation interaction device for financial products, comprising:

[0012] A construction module is used to construct a virtual scene containing a 3D model of a financial product, the virtual scene being used to display the data information of the financial product;

[0013] The receiving module is used to receive multimodal interactive input from the user, wherein the multimodal interactive input includes one or more combinations of gesture operations, voice commands, and gaze focus;

[0014] The determining module is used to determine feedback information in the interaction intent based on the multimodal interactive input, wherein the feedback information includes at least one of visual feedback, tactile feedback, and verbal feedback;

[0015] The generation module is used to transmit the feedback information to the virtual scene and generate interactive feedback, which includes explanatory information about the financial product and risk fluctuation information.

[0016] Thirdly, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0017] The memory stores computer-executed instructions;

[0018] The processor executes computer execution instructions stored in the memory to implement the simulated interaction method of the financial product as described in the first aspect and various possible implementations of the first aspect above.

[0019] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions thereon, which, when executed by a processor, are used to implement the simulated interaction method of a financial product as described in the first aspect and various possible implementations of the first aspect.

[0020] Fifthly, this application provides a program product, including a computer program, which, when executed by a processor, implements the simulated interaction method of the financial product as described above.

[0021] The financial product simulation interaction method, device, equipment, medium, and product provided in this application construct virtual scenarios that conform to user personalities, receive interactive information input by users in the virtual scenarios through various forms such as voice and actions, accurately analyze user intentions, extract feedback information including explanations of financial product characteristics and risk fluctuations from massive data and knowledge bases, and present the feedback information in the virtual scenario, enabling users to intuitively understand products and risks, simulate market scenarios under different resource allocation schemes, enhance users' cognition of financial products and risk perception, help users make accurate resource allocation decisions, and improve the efficiency and effectiveness of financial learning and resource allocation experience. Attached Figure Description

[0022] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0023] Figure 1 A flowchart illustrating a simulated interaction method for a financial product provided in this application embodiment. Figure 1 ;

[0024] Figure 2 A flowchart illustrating a simulated interaction method for a financial product provided in this application embodiment. Figure 2 ;

[0025] Figure 3 A flowchart illustrating a simulated interaction method for a financial product provided in this application embodiment. Figure 3 ;

[0026] Figure 4 A flowchart illustrating a simulated interaction method for a financial product provided in this application embodiment. Figure 4 ;

[0027] Figure 5 A flowchart illustrating a simulated interaction method for a financial product provided in this application embodiment. Figure 5 ;

[0028] Figure 6 A schematic diagram of the structure of a simulation interaction device for a financial product provided in this application;

[0029] Figure 7 This is a schematic diagram of the structure of an electronic device provided in this application.

[0030] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0031] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0032] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of the relevant data all comply with relevant laws, regulations, and standards, necessary confidentiality measures have been taken, they do not violate public order and good morals, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0033] Furthermore, the technical solution involved in this application, which involves big data analysis of user information (including but not limited to personal biometrics, identity data, consumption data, asset data, electronic terminal operation data, etc.) and the use of artificial intelligence technology for automated decision-making, and makes decisions that have a significant impact on personal rights based on the results of automated decision-making, provides users with corresponding operation entry points for users to choose to agree to or reject the results of automated decision-making; if the user chooses to reject, the process will proceed to the expert decision-making process.

[0034] It should be noted that the simulation interaction method, device, equipment, medium and product of financial products provided in this application can be used in the field of artificial intelligence, or in any field other than artificial intelligence. The application field of the simulation interaction method, device, equipment, medium and product of financial products in this application is not limited.

[0035] Bank financial products come in various types, with significant differences in risk characteristics, return models, and resource allocation thresholds. Customers lacking understanding of these products are prone to making incorrect resource allocation decisions due to inaccurate information, potentially leading to financial losses. Furthermore, regulators require financial institutions to fulfill their obligations to manage financial products, ensuring that customers, through learning, can choose products that match their risk tolerance. Therefore, acquiring knowledge of financial products has become essential for customers to safeguard their assets.

[0036] In existing technologies, bank customers typically rely on bank apps and online customer service to learn about and experience financial products. Bank apps display financial product information through text, tables, and static charts, requiring customers to read the risk disclosure statement and product terms independently. Online customer service primarily uses text or language to explain the products. Offline learning mainly involves financial managers recommending products to customers in a one-on-one manner.

[0037] However, for complex financial products, bank customers often find it difficult to intuitively understand product information through bank apps. Offline financial advisor recommendations are limited by time, location, and the number of customers, resulting in low service frequency and information asymmetry risks. This one-way interaction with the bank lacks flexibility, cannot dynamically adjust to customer behavior, and has low efficiency. Because customers struggle to intuitively understand the structure and risks of financial products, they may rely on historical returns in resource allocation, potentially leading to blind choices, misallocation of funds, and returns that do not meet expectations.

[0038] To address the aforementioned issues, this application proposes a simulated interaction method for financial products. By constructing a virtual scene containing a 3D model of the financial product and integrating multimodal interaction technologies such as gestures and voice, it receives user behavior data in real time and dynamically generates interactive feedback related to the characteristics of the financial product and market fluctuations. This method decomposes the elements of the financial product, explains risk clauses, and simulates risk intensity, thereby enhancing users' risk awareness of the financial product. It solves the problems of existing technologies in the education and resource allocation experience of financial products, such as simplistic interaction, high user comprehension threshold, and insufficient personalized service.

[0039] This application can be applied to learning scenarios involving financial products. Customers can intuitively understand the structure, risk terms, and return logic of various bank financial products through VR (Virtual Reality) systems, especially providing 3D visual breakdowns of complex terms. It can also be applied to resource allocation decision-making training scenarios, where customers can simulate asset allocation and portfolio adjustments in a virtual trading hall under real market conditions, and perceive the risk impact of market fluctuations through force feedback gloves, thereby improving their risk management capabilities.

[0040] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0041] Figure 1 A flowchart illustrating a simulated interaction method for a financial product provided in this application embodiment. Figure 1 .like Figure 1As shown, the simulated interaction method for financial products provided in this embodiment is applied to a financial product experience system, including:

[0042] S101: Construct a virtual scene containing 3D models of financial products.

[0043] Virtual scenes are used to display data and information about financial products.

[0044] Understandably, before users can experience a financial product, the system can extract information from financial institutions, such as financial product parameters (e.g., interest rates, risk levels), business processes (e.g., usage steps), customer profiles (e.g., risk preferences, asset size), and geographic information of the virtual scene (e.g., bank branch layout).

[0045] Building virtual scenarios refers to using digital technology to transform information about financial products into interactive 3D models and embedding them into a virtual environment. This allows users to immerse themselves in the product features, risk information, and usage steps of financial products through the constructed virtual scenarios, improving their learning efficiency, decision-making efficiency, and interactive experience.

[0046] Digital technologies could include 3D modeling, VR (Virtual Reality) / AR (Augmented Reality), and metaverse platforms.

[0047] Virtual environments can include virtual exhibition halls and holographic projection spaces.

[0048] S102: Receive multimodal interactive input from the user.

[0049] Multimodal interactive input includes one or more combinations of gesture operations, voice commands, and gaze focus.

[0050] Understandably, the experience system integrates intelligent interactive technology, capable of receiving multimodal interactive input from users. Multimodal interactive input includes various forms. Gesture operation refers to users interacting with the experience system in a virtual environment through gestures, touch, and other actions. For example, touching a financial product on the screen to choose to learn more about that product.

[0051] Voice commands refer to users being able to directly ask the experience system various questions about learning and experiencing financial products using natural and fluent voice. Focus tracking allows users to gaze at a specific financial product; once the experience system detects that the user's gaze has lasted for a preset duration (e.g., 3 seconds), it can automatically display the corresponding financial product information. Alternatively, when a user is reading risk clauses, the experience system can determine the user's gaze duration through focus tracking; until a preset duration (e.g., 1 minute) is reached, the user cannot turn the page to ensure complete reading.

[0052] The gaze focus can be captured by the VR display device, which can be a lightweight head-mounted display with a resolution of 4K or higher. This device supports a high refresh rate of 120Hz and eye-tracking technology, enabling high-definition rendering of virtual scenes and capture of gaze focus.

[0053] S103: Based on multimodal interactive input, determine the feedback information in the interactive intent.

[0054] The feedback information includes at least one of visual feedback, tactile feedback, and verbal feedback.

[0055] Understandably, after receiving multimodal interactive input from users, the experience system can use efficient algorithms and models to analyze the input information and determine whether the user wants to learn about the basic information of financial products or is concerned about the risks of financial products, thereby obtaining the user's interaction intent.

[0056] It is important to note that when multiple multimodal interactive inputs occur simultaneously, they can be processed according to the priority of gaze focus - voice command - gesture operation.

[0057] Once the interaction intent is determined, the experience system can extract relevant feedback information from a vast database of financial data and professional knowledge. This information is practical and targeted, accurately answering the user's questions. Visual feedback involves displaying the feedback information in a virtual scene for the inventor to view. Voice feedback means the system can answer the user's questions or explain financial products to the user through spoken announcements.

[0058] Haptic feedback refers to the ability of users to perceive the intensity of risk when experiencing the risk type of a financial product through touch. For example, vibration can be used to provide feedback on the risk intensity of a financial product. Vibration frequency and intensity are positively correlated; strong vibration represents high risk, and weak vibration represents low risk. Haptic feedback can be implemented using devices such as force feedback interactive controllers or interactive gloves. Force feedback interactive controllers or interactive gloves support 20+ haptic feedback points, enabling differentiated tactile simulation.

[0059] The above feedback information can appear individually or in combination to help users fully understand the relevant information about financial products.

[0060] S104: Transmit feedback information to the virtual scene to generate interactive feedback.

[0061] The interactive feedback includes explanatory information about financial products and information on risk fluctuations.

[0062] Understandably, after generating feedback information, the system can convey this information to the virtual scene in a vivid, intuitive, and easy-to-understand way, providing users with comprehensive interactive feedback. Explanations of financial products can be presented through 3D models, animated demonstrations, or lectures.

[0063] Historical data for financial products can be displayed using formats such as line charts and tables. Risk volatility information can be simulated under different market scenarios, allowing users to intuitively experience risk volatility under varying conditions.

[0064] This embodiment provides a simulated interaction method for financial products by constructing a virtual scene containing a 3D model of the financial product. It receives multimodal interactive input from the user, including one or more combinations of gestures, voice commands, and gaze focus. Based on the multimodal interactive input, it determines feedback information in the interaction intent, including at least one of visual, tactile, and verbal feedback. This feedback information is then transmitted to the virtual scene to generate interactive feedback, which includes explanatory information about the financial product and information on risk fluctuations. This method allows users to learn about financial products and experience resource allocation in a virtual scene, clearly understanding the risk information of financial products, improving user interaction efficiency and immersion, and enabling users to experience different resource allocation schemes without bearing risk, thus enhancing their resource allocation decision-making ability.

[0065] Figure 2 A flowchart illustrating a simulated interaction method for a financial product provided in this application embodiment. Figure 2 .like Figure 2 As shown, in Figure 1 Based on the examples, the process of constructing a virtual scene is described in detail, including:

[0066] S201: Obtain historical transaction data of financial products and user information.

[0067] Understandably, historical trading data for financial products can include price changes over historical periods, reflecting the product's volatility. It can also include trading volume, trading time, and other information to help users understand market trends.

[0068] User information refers to the specific information of the user experiencing the virtual scene. When a user is experiencing the virtual scene, they can authorize the experience system to obtain relevant information through means such as logging into the system, so as to ensure that the generated virtual scene meets the user's needs.

[0069] User information can include user asset information, historical resource information, and historical experience behavior data. This data can help the experience system analyze users' past resource allocation decisions and resource analysis behaviors, understand users' focus on financial products and services, and provide financial institutions with more accurate service directions.

[0070] S202: Generate the first user risk profile based on user information.

[0071] Understandably, user information refers to a user's historical resource allocation information and historical behavioral data. Generating a user's initial risk profile can provide the user with a financial product portfolio and services that better meet their needs. The risk profile can determine whether the user prefers a high-risk or low-risk financial product portfolio. Risk profile generation can be based on a risk preference model.

[0072] When constructing a risk preference model, historical resource allocation information is an important reference factor. For example, if a user has tended to allocate resources to high-risk products in past resource allocation processes, and maintains a high proportion of resource allocation even during periods of significant market volatility, it may indicate that the user has a high risk tolerance and a preference for high-risk resource allocation.

[0073] Conversely, if a user primarily allocates resources to products with lower risk and adjusts the resource allocation ratio when the duration fluctuates, reducing the resource allocation ratio to high-risk products, it may indicate that the user prefers to allocate resources to low-risk products.

[0074] User behavior data reveals users' historical interaction information. Transaction frequency and the time taken to make resource allocation decisions can reflect users' preferred behaviors. High transaction frequency and short resource allocation decision time can indicate that users are more willing to take on high-risk products and have a strong psychological tolerance.

[0075] By inputting users' historical resource allocation information and behavioral data into a risk preference model, and through algorithmic analysis and calculation, a user risk profile can be obtained. This user risk profile can be represented by a risk level. The higher the level, the more the user tends to favor a high-risk product portfolio.

[0076] S203: Construct a virtual scenario based on the first user's risk profile and historical transaction data.

[0077] The virtual scenario includes the user's risk level and the product status of the financial product.

[0078] Understandably, the purpose of building virtual scenarios is to create a virtual environment that aligns with user preferences, allowing users to immerse themselves in the real financial world, learn about financial products, and simulate the resource allocation risks associated with those products.

[0079] When modeling virtual scenes, the Unity engine and ProBuilder tool can be used, for example. The Unity engine is a development engine with abundant resources and powerful rendering capabilities, capable of creating realistic 3D scenes. ProBuilder, on the other hand, is a tool for rapid modeling, allowing scene building and editing within the Unity engine. By combining these two tools, the physical space of a bank can be recreated with an error margin of less than 5cm. This means that the layout and other elements in the virtual scene are consistent with the actual bank scene, providing users with a more immersive experience.

[0080] The virtual environment includes multiple functional areas. For example, the product display area can showcase matching financial products based on the user's initial risk profile and historical transaction data. For instance, for users with a high risk tolerance, high-yield, high-risk products can be displayed; for users with a low risk tolerance, low-risk products can be displayed.

[0081] The resource allocation sandbox area simulates resource allocation. Users can perform virtual resource allocation operations here, adjusting their resource allocation combinations according to their risk preferences and market conditions, and observing the results. This method allows users to accumulate resource allocation experience and improve their resource allocation capabilities without bearing the risk of resource allocation. The historical scenario area showcases the historical changes in financial scenarios, helping users understand the chain reactions in the financial market.

[0082] Meanwhile, the virtual scene utilizes distributed rendering with a GPU (Graphics Processing Unit) cluster, supporting real-time updates of scene elements. The news scrolling screen connects to an external event interface, allowing it to retrieve external events, extract keywords, and generate titles for display on the scrolling screen. Users can also view their own risk profile through a personalized panel. This risk profile can be displayed as a 3D graph, with the radar chart radius representing risk tolerance and color depth representing knowledge acquisition.

[0083] The construction of the virtual scenario also includes a data isolation mechanism, which separates the user's virtual funds from their real accounts. This process can utilize Docker container technology to create independent simulation spaces, and transaction data can be transmitted encrypted via HTTPS (Hypertext Transfer Protocol Secure). These independent simulation spaces can simulate various financial products offered by banks, each with its own corresponding trading rules.

[0084] Within this simulation space, users can adjust the displayed data and customize its parameters. Furthermore, the simulation space includes an extreme scenario generator with multiple preset classic scenarios, allowing users to experience extreme situations.

[0085] The simulated interaction method for financial products provided in this embodiment constructs a virtual scenario similar to the actual banking scenario, allowing users to immerse themselves in a virtual environment that matches their actual needs, thereby enhancing the interactive experience.

[0086] Figure 3 A flowchart illustrating a simulated interaction method for a financial product provided in this application embodiment. Figure 3 .like Figure 3 As shown, in Figure 1 Based on the embodiments, the gesture recognition process is described in detail, including:

[0087] S301: Determine the gesture type for the gesture operation.

[0088] Understandably, the experience system can monitor the system's interactive area. After the user's hand enters this area and makes a movement, the system can capture key information such as the user's hand movement trajectory and finger state. Different gestures have different parameter templates. After capturing the relevant parameters of the user's hand, the system can match them with preset gesture templates to confirm the type of gesture operation.

[0089] For example, when a user briefly touches the screen with their finger, the system can analyze data such as the location of the touch point and the duration of the touch to determine whether it matches the characteristics of a click gesture. If the collected data matches the preset click gesture parameters, the system can determine that the gesture operation is a click.

[0090] For more complex gestures, the system can simultaneously acquire the movements of two fingers, recording the changes in distance between them and the synchronicity of their movements. When a user simultaneously slides two fingers apart from the same position on the screen, the system can determine the distance of the slide and compare it with a preset distance change threshold for a two-finger zoom gesture. If it is within a reasonable range, the system can determine that the user has performed a two-finger zoom gesture.

[0091] Similarly, when two fingers move from the sides towards the center, the system can identify it as a two-finger contraction gesture. A clenched fist gesture, however, requires the system to detect and determine the degree of bending of the hand's joints and the overall shape changes of the hand.

[0092] S302: Based on the gesture type and the preset gesture library, determine the user's interaction intent and generate feedback information corresponding to the interaction intent.

[0093] Understandably, the preset gesture library records various gesture types and their corresponding interaction intentions, and the gesture library supports the storage of various custom interaction actions. After the system determines the type of gesture input by the user, it can search and match it in the gesture library.

[0094] For example, when the gesture type is two-finger zoom, the gesture library can clearly identify the intent of this gesture type as adjusting the resource allocation ratio. At this time, the system can adjust the ratio of the corresponding product based on the specific position of the user's two-finger zoom. Simultaneously, the adjustment range is displayed on the screen panel, allowing the user to clearly understand the change. If the risk of the corresponding product increases after the ratio adjustment, haptic feedback can be triggered. Based on the feedback requirement of high risk and strong vibration, stronger vibration is applied to the user through the gloves worn on the user's hand or the handle, alerting the user to the increased risk.

[0095] Similarly, the gesture library clearly identifies click and pinch gestures as corresponding to grasping or disassembling financial products; a clenched fist gesture corresponds to emergency liquidation, etc. Users can customize gestures to suit their individual needs.

[0096] The simulated interaction method for financial products provided in this embodiment accurately determines the type of user input gesture by capturing various data and information of the user's hand movements, such as clicking, zooming in / out with two fingers, and clenching a fist. Then, based on a preset gesture library and considering factors such as the location and context of the gesture, the method determines the user's interaction intent. Finally, it generates matching feedback information, such as sound prompts and animation effects. This method improves the accuracy and smoothness of user-system interaction, enabling the system to accurately understand user needs and respond accordingly, greatly enhancing the user's interactive experience.

[0097] Figure 4A flowchart illustrating a simulated interaction method for a financial product provided in this application embodiment. Figure 4 .like Figure 4 As shown, in Figure 1 Based on the embodiments, the speech recognition process is described in detail, including:

[0098] S401: Parse and process the voice commands to obtain the interaction intent.

[0099] Interaction intents include: consultation intent, operation intent, and feature intent.

[0100] Understandably, after receiving a user's voice command, the system first uses advanced speech recognition and natural language processing technologies to parse the command. By integrating interfaces that support multiple speech types, including Mandarin, English, and dialects, the system transforms the user's ambiguous, colloquial speech into a structure that the computer can understand and recognize.

[0101] The system uses parsing to identify key information and semantic logic within voice commands, thereby determining the user's interaction intent. The consultation intent refers to the user's desire to obtain relevant knowledge about financial products through voice commands. In real-world scenarios, users may be unclear about certain terms, restrictions on the use of financial products, risk levels, etc., for example, asking, "What is the subscription fee rate for financial product A?" The system needs to identify the consultation intent to provide specific answers and help the user understand the financial product.

[0102] Operational intent refers to the user's desire to have the system perform a specific financial operation via voice commands. In real-world scenarios, this operation may involve various financial transactions such as redemption and transfer. Characteristic intent refers to the emotional and attitudinal needs expressed by the user in their voice commands. Characteristic intent focuses more on the user's psychological and emotional level.

[0103] S402: Determine whether the interaction intent is a consultation intent and / or an operation intent. If yes, proceed to step S403; otherwise, proceed to step S405.

[0104] S403: Parse the interaction intent and obtain the parsing result.

[0105] Understandably, once the system determines that the interaction intent is a consultation intent and / or an operational intent, it can perform a deeper analysis of the interaction intent to obtain specific analysis results. That is, it can decompose ambiguous intents into clear and explicit instructions or questions.

[0106] For example, for a consultation intent such as "I want to know what the PE valuation method is," the system analyzes and clarifies that the core of the question is a specific explanation of the PE valuation method; for an operational intent such as "buy product X," the system analyzes and clarifies the specific operation.

[0107] S404: Based on the analysis results and the financial knowledge graph, determine the feedback information corresponding to the interaction intent.

[0108] Financial knowledge graphs are generated based on data from various financial products. A financial knowledge graph is a vast knowledge system created by collecting, organizing, and analyzing data from multiple financial products, covering various aspects such as product terms, market dynamics, and classic cases.

[0109] Understandably, after obtaining the analysis results, the system can utilize a financial knowledge graph to determine the corresponding feedback information. The system can first perform a preliminary search in the product knowledge base, which stores a large amount of basic information about financial products. If the product knowledge base does not find a sufficiently detailed or accurate answer, the system can further utilize the financial knowledge graph to determine the answer. The financial knowledge graph is rich in information and supports semantic association recommendations, which can find similar product cases while providing the answer, helping users better understand and apply the relevant knowledge.

[0110] Upon receiving an operational intent, the system analyzes it and then executes the relevant operation in the background. The process and result of this operation are dynamically displayed in the virtual environment, with feedback information detailing the process and outcome. After the operation, if the risk profile of the financial product changes, the user's force feedback gloves or handle will vibrate accordingly. This tactile feedback serves as a reminder of the risk level. Higher risk results in stronger vibrations, while lower risk leads to gentler vibrations.

[0111] S405: Based on the speech knowledge base, determine the feedback information corresponding to the feature intent.

[0112] The voice knowledge base stores multiple response and reassurance statements for emotional questions.

[0113] Understandably, once the system determines that the interaction intent is a feature intent, it parses the feature intent and obtains the specific content expressed by the feature intent, and then searches for the corresponding response statement in the speech knowledge base based on the specific content.

[0114] For example, when a user expresses concerns such as "I'm worried about losing money" while learning about financial products and experiencing resource allocation schemes in a virtual scenario, the system can find corresponding reassuring voice messages, such as: "Don't worry, this is a simulation. We can analyze the financial products together and find a resource allocation scheme that suits you." These reassuring statements alleviate the user's anxiety, boost their confidence, and help them focus on learning about financial products and resource allocation skills, preventing them from getting caught up in negative emotions.

[0115] Optionally, the feedback information for determining the feature intent can specifically include:

[0116] The intention of the features is parsed and processed to obtain speech features.

[0117] Understandably, speech features, such as intonation and speech rate variations in voice commands, can express the user's emotional information. Intonation reflects the rise and fall of the voice; different intonations can convey different emotions and attitudes. A rising intonation may indicate doubt or excitement, while a falling intonation usually indicates seriousness. The system can determine the changes in intonation through spectral analysis of the speech signal and quantify them into specific data.

[0118] Speech rate refers to the number of words a user utters per unit of time, reflecting their thought process and emotional fluctuations. A faster speech rate may indicate tension or excitement, while a slower rate may suggest hesitation or a more stable emotional state. The system can track the number of words a user utters in real time per minute to measure changes in speech rate.

[0119] In addition to variations in tone and rate of speech, speech features can also include volume, frequency of pauses, and other characteristics that can provide a baseline for the system to understand user intent.

[0120] Based on voice features and preset feature thresholds, user behavior characteristics are determined.

[0121] Understandably, after acquiring speech features, the system can compare these features with pre-set feature thresholds. These pre-set feature thresholds are derived through extensive experiments and data analysis, and represent the range of speech features corresponding to different behavioral characteristics.

[0122] During the comparison process, the system can consider the degree of matching between multiple voice features and preset thresholds. When multiple features all meet the threshold range corresponding to a certain behavioral feature, the system can accurately determine the user's behavioral characteristics. Alternatively, when a certain feature significantly exceeds the preset feature threshold, the system can still determine the user's behavioral characteristics, thereby ensuring the accuracy of the behavioral characteristics.

[0123] For example, under normal circumstances, a user's speaking speed usually remains within a relatively stable range. However, when a user is anxious, they often unconsciously speed up their speech. Therefore, a speaking speed exceeding 200 words per minute can be used as an important threshold for identifying a user's anxiety, as this data has high accuracy and reliability. When the system detects that a user speaks more than 200 words per minute, it can determine that the user's behavioral characteristic is anxiety.

[0124] Based on behavioral characteristics and a speech knowledge base, the feedback information corresponding to the feature intent is determined.

[0125] Understandably, once the system determines a user's behavioral characteristics, such as whether the user is experiencing anxiety, it can activate the voice knowledge base's query function. The voice knowledge base stores a large number of statements tailored to different behavioral characteristics and emotional states. These statements cover various expressions, including comforting, encouraging, and guiding, aiming to effectively communicate with users and alleviate their negative emotions.

[0126] The feedback information corresponding to the feature intent is the reassuring statements identified in the voice knowledge base. The system can promptly broadcast these reassuring statements to alleviate the user's tension and anxiety and provide guidance.

[0127] The simulated interaction method for financial products provided in this embodiment parses the user's voice commands to clarify interaction intentions such as consultation, operation, or features. If the intention is consultation or operation, the specific question or instruction is further parsed, and then, using a financial knowledge graph generated from data of various financial products, the corresponding feedback information is accurately retrieved and determined. For feature-based intentions, the corresponding feedback information is determined from the voice knowledge base. This method can identify diverse user voice intentions and obtain accurate feedback information. By utilizing the rich data and semantic association recommendations of the financial knowledge graph and the specific content of the knowledge base, it provides users with comprehensive and detailed information, enhancing their understanding and decision-making abilities regarding financial products.

[0128] Figure 5 A flowchart illustrating a simulated interaction method for a financial product provided in this application embodiment. Figure 5 .like Figure 5 As shown, in Figure 1 Based on the examples, the learning process following the virtual experience is described in detail, including:

[0129] S501: Determine the user's interaction diagnosis results based on the user's multimodal interaction input.

[0130] The interactive diagnostic results are used to indicate incorrect operations by the user in the virtual scene.

[0131] Understandably, during a virtual experience, users can interact with the system in multiple ways. The system can comprehensively collect and integrate these multimodal inputs from different channels. Through in-depth analysis of this data, the system can identify various user actions within the virtual environment.

[0132] Based on pre-defined standards for correct user behavior, the system can determine whether various user actions meet the requirements. Actions that do not meet the standards are considered erroneous, and these erroneous actions can be combined to generate interactive diagnostic results. Examples include: a user incorrectly selecting a financial product during a virtual experience; a user entering incorrect monetary data; or a user executing incorrect steps in the resource allocation process.

[0133] S502: Generate a financial knowledge learning path based on the interactive diagnostic results.

[0134] The financial knowledge learning path is used to remind users to perform the correct operations in the virtual scenario in order to correct any errors.

[0135] Understandably, after receiving the interactive diagnostic results, the experience system can use the incorrect operations in the results to find the correct operations in various knowledge areas, such as financial knowledge and financial products, and create a personalized financial knowledge learning path for the user.

[0136] This financial literacy learning path can include the characteristics of financial products, risk assessment methods, resource allocation strategies, and the order in which users learn. For example, if a user is confused about different types of financial products in a virtual scenario, the differences between the products and their respective characteristics can be explained first to help the user distinguish between them. Finally, the user can be guided on how to choose the financial product that suits them best.

[0137] Through the financial knowledge learning path, the experience system can use prompts and guidance to remind users to follow the correct steps and methods in subsequent virtual scenario operations, gradually correcting previous errors and helping users master financial knowledge and operational skills.

[0138] Optionally, after a customer's experience is complete, the customer information can be revised based on the results of that experience to ensure that the generated virtual scene will meet the customer's needs for the next experience. This includes:

[0139] Based on multimodal interactive input, determine the accuracy of user operations in the virtual scene.

[0140] Understandably, when collecting multimodal interaction input data from users, the experience system can record and analyze the user's answers and choices in virtual scenarios for various questions and tasks. For example, in a virtual financial knowledge quiz scenario, users may face multiple questions about financial markets, financial products, etc., and need to select the correct steps from multiple options to complete the transaction. The system can compare these user answers and choices with pre-set correct answers and standard operations.

[0141] The accuracy rate of a user's actions in a virtual scenario is determined by calculating the proportion of correctly answered questions out of the total number of questions, or the proportion of correctly selected options out of the total number of selections. This accuracy rate directly reflects the user's mastery of the knowledge involved in the virtual scenario and their proficiency in operational skills.

[0142] Based on the operation accuracy rate and accuracy rate threshold, the user's first user risk profile is adjusted to obtain a second user risk profile. The second user risk profile is used to indicate the difficulty of the financial product content in the user's corresponding virtual scenario.

[0143] Understandably, the accuracy threshold is used to measure a user's familiarity with operating in a virtual scenario and their level of financial knowledge. After determining the user's operational accuracy based on multimodal interactive input, the system can compare this accuracy with a preset threshold.

[0144] For example, if a user's operation accuracy rate is greater than 80%, the system can assume that the user has gained a certain understanding of the operations in the virtual scenario and has a relatively clear understanding of the relevant knowledge of financial products. In this case, the system can adjust the user's initial risk profile, reduce the involvement of the virtual advisor in the user's subsequent virtual scenario experience, and open up higher-level scenarios to increase the difficulty of the user experience content, making the user experience more difficult to understand financial products.

[0145] In other words, virtual advisors may no longer provide detailed explanations and prompts for every step of the user's operation, but instead allow the user to complete more operations independently, and only provide appropriate assistance when the user encounters obvious difficulties or makes a clear request.

[0146] Conversely, if a user's operational accuracy rate is below 60%, the system can determine that the user is not yet familiar enough with the virtual scenario and financial knowledge. In this case, the system can adjust the first user risk profile, increasing the participation of the virtual advisor while only opening low-risk modules and reducing content difficulty. The virtual advisor can appear throughout the user's virtual scenario, providing detailed operational guidance, knowledge explanations, and risk warnings to help the user gradually improve their operational accuracy and understanding of financial knowledge. This results in a second user risk profile, which more accurately reflects the level of guidance and support the user needs in the virtual scenario.

[0147] Meanwhile, if a user's accuracy rate is in the middle range, between 60% and 80%, the system can determine that the user has a good operational skill level and relatively complete financial knowledge. The system can then monitor the user's entire operation and provide timely guidance and explanations when needed. In other words, by analyzing the user's erroneous operations, the system can identify the user's weaknesses in financial knowledge, providing guidance only in those areas and reducing guidance for other parts of the process.

[0148] The simulated interaction method for financial products provided in this embodiment collects multimodal input from users in a virtual scenario, determines interactive diagnostic results that can indicate user errors, and generates financial knowledge learning paths to correct these errors. Simultaneously, it determines the user's operation accuracy rate and establishes a risk profile based on this rate. This method can pinpoint user errors, develop learning paths to help users master correct operations and financial knowledge, and flexibly adjust the level of virtual advisor intervention based on the user's knowledge acquisition level, effectively improving the user's learning outcomes and operational capabilities in the virtual environment.

[0149] Figure 6 This is a schematic diagram of the structure of a simulated interactive device for a financial product provided in this application. Figure 6 As shown, this application provides a simulation interaction device for financial products. The simulation interaction device 600 for financial products includes:

[0150] Module 601 is used to construct a virtual scene containing a 3D model of a financial product. The virtual scene is used to display the data information of the financial product.

[0151] The receiving module 602 is used to receive multimodal interactive input from the user, which includes one or more combinations of gesture operations, voice commands, and gaze focus.

[0152] The determination module 603 is used to determine the feedback information in the interaction intent based on the multimodal interactive input. The feedback information includes at least one of visual feedback, tactile feedback, and verbal feedback.

[0153] The generation module 604 is used to transmit feedback information to the virtual scene and generate interactive feedback, which includes explanatory information about financial products and risk fluctuation information.

[0154] Optionally, the determining module 603 is specifically used to determine the gesture type of the gesture operation; based on the gesture type and the preset gesture library, it determines the user's interaction intent and generates feedback information corresponding to the interaction intent.

[0155] Optionally, the determining module 603 is specifically used to parse and process the voice commands to obtain the interaction intent, which includes: consultation intent, operation intent, and feature intent;

[0156] The determination module 603 is specifically used to parse the interaction intent if the interaction intent is a consultation intent and / or an operation intent, and obtain the parsing result; based on the parsing result and the financial knowledge graph, determine the feedback information corresponding to the interaction intent. The financial knowledge graph is generated based on data information from various financial products.

[0157] The determination module 603 is specifically used to determine the feedback information corresponding to the feature intent based on the speech knowledge base if the interaction intent is a feature intent.

[0158] Optionally, the determination module 603 is specifically used to parse and process the feature intent to obtain speech features; determine the user's behavior features based on the speech features and preset feature thresholds; and determine the feedback information corresponding to the feature intent based on the behavior features and the speech knowledge base.

[0159] Optionally, module 601 is constructed, specifically used to obtain historical transaction data of financial products and user information; based on the user information, generate a first user risk profile of the user; based on the first user risk profile and historical transaction data, construct a virtual scenario, which includes the user's risk level and the product status of the financial products.

[0160] Optionally, the determining module 603 is also used to determine the user's interaction diagnosis result based on the user's multimodal interaction input, and the interaction diagnosis result is used to indicate the user's erroneous operation in the virtual scene;

[0161] The generation module 604 is also used to generate a financial knowledge learning path based on the interactive diagnostic results. The financial knowledge learning path is used to remind users to perform the correct operations in the virtual scenario in order to correct the wrong operations.

[0162] Optionally, the determining module 603 is also used to determine the accuracy of the user's operation in the virtual scene based on the multimodal interactive input;

[0163] The determination module 603 is also used to adjust the user's first user risk profile based on the operation accuracy rate and the accuracy rate threshold to obtain a second user risk profile. The second user risk profile is used to indicate the difficulty of the financial product content in the corresponding virtual scenario of the user.

[0164] The implementation principle and technical effects of the simulated interaction device for financial products provided in this application are similar to the implementation methods of the aforementioned simulated interaction methods for financial products, and will not be repeated here.

[0165] Figure 7 This is a schematic diagram of the structure of an electronic device provided in this application. Figure 7This application provides an electronic device 700, which includes a receiver 701, a transmitter 702, a processor 703, and a memory 704.

[0166] Receiver 701 is used to receive commands and data;

[0167] Transmitter 702 is used to send commands and data;

[0168] Memory 704 is used to store instructions executed by the computer;

[0169] The processor 703 is used to execute computer execution instructions stored in the memory 704 to implement the various steps performed by the simulated interaction method for financial products in the above embodiments. For details, please refer to the relevant descriptions in the aforementioned embodiments of the simulated interaction method for financial products.

[0170] Optionally, the memory 704 can be either standalone or integrated with the processor 703.

[0171] When the memory 704 is set up independently, the electronic device also includes a bus for connecting the memory 704 and the processor 703.

[0172] The implementation principle and technical effects of the electronic device provided in this embodiment can be found in the foregoing embodiments, and will not be repeated here.

[0173] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the method of any of the foregoing embodiments.

[0174] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the method of any of the foregoing embodiments.

[0175] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0176] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0177] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0178] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0179] When integrated units / modules are implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor can be any suitable hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC, etc. Unless otherwise specified, the storage unit can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc.

[0180] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0181] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0182] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0183] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for simulating interaction with financial products, characterized in that, include: Construct a virtual scene containing a 3D model of a financial product, the virtual scene being used to display the data information of the financial product; Receive multimodal interactive input from the user, the multimodal interactive input including one or more combinations of gesture operations, voice commands, and gaze focus; Based on the multimodal interactive input, the feedback information in the interactive intent is determined, and the feedback information includes at least one of visual feedback, tactile feedback, and verbal feedback; The feedback information is transmitted to the virtual scene to generate interactive feedback, which includes explanatory information about the financial product and risk fluctuation information.

2. The method according to claim 1, characterized in that, When the multimodal interaction input is a gesture operation, determining the feedback information in the interaction intent based on the multimodal interaction input includes: Determine the gesture type of the gesture operation; Based on the gesture type and the preset gesture library, the user's interaction intent is determined, and feedback information corresponding to the interaction intent is generated.

3. The method according to claim 1, characterized in that, When the multimodal interactive input is a voice command, determining the feedback information in the interactive intent based on the multimodal interactive input includes: The voice commands are parsed and processed to obtain the interaction intent, which includes: consultation intent, operation intent, and feature intent; If the interaction intent is the consultation intent and / or the operation intent, then the interaction intent is parsed to obtain the parsing result; Based on the analysis results and the financial knowledge graph, the feedback information corresponding to the interaction intent is determined. The financial knowledge graph is generated based on data information from various financial products. If the interaction intent is the feature intent, then the feedback information corresponding to the feature intent is determined based on the speech knowledge base.

4. The method according to claim 3, characterized in that, The step of determining the feedback information corresponding to the feature intent based on the speech knowledge base includes: The intended features are parsed to obtain speech features; Based on the voice features and preset feature thresholds, the user's behavioral characteristics are determined; Based on the behavioral features and the speech knowledge base, the feedback information corresponding to the feature intent is determined.

5. The method according to claim 1, characterized in that, The construction of the virtual scene containing a 3D model of the financial product includes: Obtain the historical transaction data of the financial product and the user's user information; Based on the user information, a first user risk profile is generated for the user; Based on the first user risk profile and the historical transaction data, the virtual scenario is constructed, and the virtual scenario includes the user's risk level and the product status of the financial product.

6. The method according to claim 1, characterized in that, The method further includes: Based on the user's multimodal interaction input, the user's interaction diagnosis result is determined, and the interaction diagnosis result is used to indicate the user's erroneous operation in the virtual scene; Based on the interactive diagnostic results, a financial knowledge learning path is generated. This path guides users to perform correct operations in a virtual scenario to correct errors.

7. The method according to claim 1, characterized in that, The method further includes: Based on the multimodal interactive input, determine the user's operation accuracy in the virtual scene; Based on the operation accuracy rate and accuracy rate threshold, the first user risk profile of the user is adjusted to obtain a second user risk profile. The second user risk profile is used to indicate the difficulty of the financial product content in the virtual scenario corresponding to the user.

8. A simulation interaction device for a financial product, characterized in that, include: A construction module is used to construct a virtual scene containing a 3D model of a financial product, the virtual scene being used to display the data information of the financial product; The receiving module is used to receive multimodal interactive input from the user, wherein the multimodal interactive input includes one or more combinations of gesture operations, voice commands, and gaze focus; The determining module is used to determine feedback information in the interaction intent based on the multimodal interactive input, wherein the feedback information includes at least one of visual feedback, tactile feedback, and verbal feedback; The generation module is used to transmit the feedback information to the virtual scene and generate interactive feedback, which includes explanatory information about the financial product and risk fluctuation information.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 7.

11. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 7.

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