Natural language information processing method and system

Abstract

The invention relates to a natural language information processing method and system, the system is provided with a cloud server, in the natural language information processing method executed in the cloud server, after an online dialogue program is started, content input by a user is received through a dialogue interface, and semantic features of the content input by the user are obtained by using an intelligent method; the method comprises the following steps: acquiring semantic features, acquiring user preferences and real-time environment information, judging contents conforming to the semantic features, the user preferences and the real-time environment information, processing the obtained contents through a natural language model to generate a dialogue content, and importing the dialogue content to an online dialogue program. And the dialogue content which conforms to the semantic meaning of the user, the preference of the user and the real-time environment information can be output through the dialogue interface.

Description

Technical Field

[0001] The disclosed book is about a chat robot, and more particularly, it relates to a natural language information processing method and system that can conduct conversations based on user meaning, user preferences and real-time environmental information. Background Art

[0002] Among the artificial intelligence (AI) that is currently developing rapidly in various fields, one type is a natural language chatbot that can process natural language and automatically generate content, such as the Chat Generative Pre-trained Transformer (ChatGPT) developed by OpenAI. This type of natural language chatbot uses generative artificial intelligence technology to train a large amount of data first, and then generate new data related to the original data, and build an intelligent model after deep learning (such as generative adversarial networks (GAN)).

[0003] Taking ChatGPT as an example, ChatGPT is trained by learning a large amount of online information and can communicate with users in natural language. However, the content it commonly responds to users is standard answers learned through learning, and it cannot adapt in real time to provide answers related to the user's current status. Although it is a natural language chatbot, it lacks content that is relevant to the user and conforms to the real-time situation. Summary of the invention

[0004] The public book proposes a natural language information processing method and system. In the process of conversation between a chat robot and a user, which is implemented using natural language processing (NLP) and generative artificial intelligence (generative AI) technology, it further refers to the user's preferences and real-time environmental information to provide conversation content that meets personal needs and the current situation.

[0005] In a natural language information processing system implemented by a computer system, a cloud server is provided, and a processing circuit is used to execute a natural language information processing method. The cloud server provides a user with a user interface to start an online dialogue program to receive user input content through the dialogue interface, and then obtain the semantic features of the content input by the user, as well as user data and real-time environmental information, so as to determine the content that matches the semantic features of the content input by the user, the user preferences obtained from the user data, and the real-time environmental information, and then generate dialogue content after being processed by a natural language model running in the online dialogue program, and finally import the dialogue content into the online dialogue program and output it.

[0006] The content received by the system through the dialogue interface may be text, voice or audio / video content. If the content received is voice or audio / video content, it may be converted into text through a text conversion program, and then the semantic features of the text may be obtained after semantic analysis.

[0007] According to an embodiment, the natural language model running in the cloud server can generate dialogue content by using a transformer to perform machine translation, document summarization, document generation and other procedures. Furthermore, a vector algorithm can be executed on the content input by the user, the user's preferences and the real-time environment information to annotate the obtained text, calculate the vector of each word, and then obtain the content with relevance according to the vector distance between the words, thereby generating dialogue content that meets the user's preferences and the real-time environment information.

[0008] Furthermore, a vector algorithm is further executed on the historical conversation records recorded in the database or memory of the cloud server to generate conversation content that can match the user's current emotions.

[0009] Preferably, the real-time environmental information may include one or any combination of real-time weather, real-time traffic conditions, real-time news and real-time location-related network information obtained from one or more external systems in real time.

[0010] Preferably, the obtained conversation content includes text generated by natural language model processing and audio and video content obtained by querying an audio and video database.

[0011] In one embodiment, a conversation link icon is displayed through a user interface provided by the cloud server, and the online conversation program is started after clicking the conversation link icon.

[0012] In the dialogue interface, an input field for user input content and a dialogue display area for displaying the dialogue content output by the chat robot and the content input by the user are provided.

[0013] Furthermore, a chatbot is implemented using an online conversation program, wherein the natural language model running therein generates conversation content based on the semantic features of the content input by the user, the user's preferences, and real-time environmental information, which may include providing multiple recommendation options, multiple recommended audio and video content, and / or multiple recommended friend links.

[0014] To further understand the features and technical contents of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are only for reference and description and are not intended to limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A diagram showing an embodiment of a system architecture for executing a natural language information processing method;

[0016] Figure 2 A diagram showing an embodiment of a data structure for running a natural language information processing method;

[0017] Figure 3 One of the flow charts of the embodiment showing the method for processing natural language information;

[0018] Figure 4 A second flow chart showing an embodiment of a method for processing natural language information; and

[0019] Figures 5 to 9 A diagram showing an example of a graphical user interface provided by a natural language information processing system. DETAILED DESCRIPTION

[0020] The following is an explanation of the embodiments of the present invention through specific embodiments. Those skilled in the art can understand the advantages and effects of the present invention from the contents disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and the details in this specification can also be modified and changed in various ways based on different viewpoints and applications without departing from the concept of the present invention. In addition, the drawings of the present invention are only simple schematic illustrations and are not depicted according to actual sizes. It is stated in advance. The following embodiments will further explain the relevant technical contents of the present invention in detail, but the disclosed contents are not intended to limit the scope of protection of the present invention.

[0021] It should be understood that, although the terms "first", "second", "third", etc. may be used herein to describe various components or signals, these components or signals should not be limited by these terms. These terms are mainly used to distinguish one component from another component, or one signal from another signal. In addition, the term "or" used herein may include any one or more combinations of the associated listed items depending on the actual situation.

[0022] The specification discloses a natural language information processing method and system. The natural language information processing method runs in a cloud server to realize a natural language information processing system. The cloud server can provide social media services through the network, allowing users to share text, pictures and audio and video content after joining. The cloud server also provides chat robots in their respective fields, which can provide users with conversations through the services provided by the cloud server. Artificial intelligence technology is used, including the chatbot trained by learning data from various fields using machine learning algorithms and natural language processing (NLP) technology to provide conversation services, and the user's preferences are obtained by learning the user's activities in social media, so that the chatbot can provide conversation content that is more in line with the user's personal needs and current environmental characteristics based on the user's conversation meaning, user preferences, and real-time environmental information.

[0023] The above system implemented by the cloud server can refer to Figure 1 A diagram of an embodiment of a system architecture for running a natural language information processing method is shown.

[0024] The figure shows a cloud server 100 implemented by a computer system, a database and a network, wherein various functional modules are implemented by software and hardware collaboration, such as a natural language processing module 101 for executing a natural language information processing method as shown in the figure, wherein the natural language processing module 101 implements a chat robot with natural language processing capabilities; a machine learning module 103 runs a machine learning algorithm, and in addition to training a natural language model, can also learn the user's activities on the network through a deep learning method to obtain information about the user's preferences, so that the chat robot can provide conversation content that meets the user's preferences; the cloud server 100 provides an external system interface module 105, wherein circuits and related application software for connecting to an external system (such as an external system 1 111 and an external system 2 112) (such as via a network 10) and obtaining data through an application programming interface (API) are run; the cloud server 100 provides a user interface module 107, and the network connection function in the user interface module 107 allows the user device 150 to connect to the cloud server 100, and can run a server (webserver) that provides network services, so that the user device 150 can execute an application program corresponding to the service to obtain the service provided by the cloud server 100.

[0025] According to the architecture shown in the figure, the cloud server 100 is provided with a built-in or externally connected database, and data services are provided through the cloud server 100, such as the audio and video database 110 shown in the figure, which provides the user device 150 with access via the network 10 to store the audio and video content uploaded and shared by users at various ends, and may include text and graphics; the user database 120 stores user data, including user personal data, uploaded text, graphics and audio and video content, and obtains user activity data in the network service provided by the cloud server 100, such as browsed content, tracking, liking, sharing and subscription network activities, which can form a user profile, and as the conversation content continues to be generated over time, the user database 120 can store and update the user data according to the time dimension, including recording the user's historical conversation record, which becomes a conversation record for the machine learning algorithm in the natural language model; the vector database 130 is a structured information that records various texts, graphics and audio and video content after vectorization calculation, which can be used to compare various data that conform to the user's personalization.

[0026] According to the system architecture diagram shown in the figure, the cloud server 100 obtains data from the external system through the network 10 or the connection under a specific protocol, such as the external system 1 111 and the external system 2 112 schematically shown in the figure, which are, for example, servers established by the government or enterprises to provide open data, so that the cloud server 100 can obtain demand-compliant and real-time information through the external system interface module 105 using the application programming interface provided by each external system, such as real-time weather, real-time traffic conditions, real-time news and real-time location-related network information.

[0027] The user device 150 executes an application that can obtain services provided by the cloud server 100. For example, if the cloud server 100 provides social media services, the user device 150 executes the corresponding social media application and obtains the social media services through the user interface module 107. In particular, the cloud server 100 provides a natural language chat robot through the natural language processing module 101, so that the user can have a conversation with the chat robot through the dialogue interface 115. On the other hand, the cloud server 100 can learn the activity data of the user using various services of the cloud server 100 through the machine learning module 103, including obtaining the activity data of the user using the social media application and the dialogue interface 115, so that the machine learning module 103 can learn the user's interest characteristics and establish user data.

[0028] It is worth mentioning that the various texts, images and audio-visual contents obtained by the cloud server 100 are unstructured information, which can be converted into vectorized data through encoding to facilitate the acquisition of the meaning, which is conducive to data search. Furthermore, the vectorized data can be used to compare the user's search keywords, and a distance function is used to calculate the distance between the search keywords and the vectorized data in the database. The closer the distance, the closer the data, so that the user can search for data through the vector database 130.

[0029] According to an embodiment, the vector database 130 of the cloud server 100 can support multi-modal search services such as text and images, wherein structured information is provided, such as various texts, patterns and audio-visual contents are converted into text, and vectorized data is obtained by vector algorithm calculation, which can be applied to search services and can be used in natural language processing programs. The natural language processing program will use a natural language model to map the vectorized data to a vector space. Taking the words input by the user as an example, a word vector is obtained after vector calculation.

[0030] According to the embodiment, the natural language information processing method proposed in the disclosure is implemented as a chat robot running on the cloud server 100. The chat robot can communicate with the user in natural language, including text and voice. In addition to responding to the information input by the user, it can also obtain the user data through the cloud server before the conversation, from which the user's personality and habits can be derived. In addition, the real-time status can be obtained from the external system (111, 112), for example, the local weather and news can be obtained according to the user's location, so that the reply content can not only be targeted at the user's preferences, but also reflect the actual status.

[0031] For example, when a user mentions his or her needs during a chat with the chatbot proposed in the open book, such as mentioning that it is time to eat, the chatbot will provide recommendations for meals and restaurants based on the user's favorite meals learned in the past, as well as the user's current location or the location information mentioned by the user in the conversation, rather than just obtaining answers from the database that has already been learned.

[0032] Furthermore, trained chatbots in various fields can be set up in the cloud server running the natural language information processing method. When the user expresses the need for further information in the conversation, the chatbot can introduce chatbots in related fields (such as business / product / field robots in a restaurant, food court, or night market), so that the chatbots in related fields can continue to communicate with the user in natural language and provide more professional and accurate conversation content.

[0033] The conversation service provided by the above system can be a function running in social media. The user's activities in social media are also the data for the system to learn the user's preferences and form structured data in the system. Figure 2 The data structure embodiment diagram of the natural language information processing method is shown, wherein the data is divided into social media platform data (social media platform) 21, user data (such as user profile) 23 and user activity data (user profile data) 25.

[0034] The social media platform data 21 is non-public data in the system. The system running the natural language information processing method obtains the viewer data (viewers) 211 of users accessing various contents provided by the cloud server, and the creator data (creators) 212 of users providing various contents in the system. It also includes business data (business) 213 provided by the system to enterprises to establish corporate data for advertising, and, because the system can provide localized services, various location data (locations) 214 related to geographic locations will be obtained.

[0035] User data 23 is publicly available data in the system, covering data edited by users themselves, including viewer data 231 obtained by the system from various user activity data, which may include the user's interest data as a viewer obtained through machine learning, such as recent interest data, historical interest data, and location-related interest data.

[0036] The creator data 232 in the user data 23 is the relevant data of the user as a creator, covering the data related to the creator's preference types and the creator's location learned by the system through machine learning. For example, it may include the data of the user as a creator, and the types and locations of the creator's preferences learned through learning, including geographic location, or a specific location of a place.

[0037] When the user is an enterprise, the business data 233 in the user data 23 includes the business type of the enterprise and its product features obtained by the system through machine learning.

[0038] The user activity data 25 is non-public data in the system, which includes statistical data of the user's activities in various services provided by the cloud server, and includes data obtained through machine learning, mainly including viewer data 251, creator data 252 and business data 253.

[0039] Viewer data 251 refers to the browsing rate, browsing time, and activity data such as following, liking, commenting, and subscribing of users using the services provided in the cloud server; creator data 252 refers to the statistical data when the user acts as a creator, such as the followers of the channel or account, the number of views of the invented content and the account browsing rate; business data 253 refers to the followers, content views, and overall impression data obtained when the user is a business.

[0040] The social media platform data 21, user data 23 and user activity data 25 collected and learned by the cloud server above become the basis for the dialogue service proposed in the open book using natural language processing and generative artificial intelligence technology. The cloud server calculates the above data through processing circuits to provide a chat robot that meets the personalized and real-time needs of users.

[0041] According to an embodiment, in a cloud server, the natural language model running therein can first perform a vector algorithm on the content input by the user through the dialogue interface, the user's preferences, and the real-time environmental information to annotate the obtained text, calculate the vector of each word, and obtain the relevant content after querying the database based on the vector distance between the words, and generate the dialogue content that conforms to the user's preferences and the real-time environmental information. In the process of online dialogue, the transformer model can be used to perform machine translation, document summarization, and document generation on the textual data. Please refer to Figure 4 The description of the process of the illustrated embodiment is used to derive the user's semantics in the conversation so that the chat robot can generate the content of the conversation.

[0042] The conversation program obtained by implementing the natural language information processing method described in the above embodiment can actually be run by starting a graphical user interface (GUI) through an application (such as a social media application) executed in the user's device to display the conversation content between the chat robot and the user. The natural language information processing method executed by the system using the cloud server architecture can be referred to Figure 3 The embodiment flow chart shown in FIG. Figures 5 to 9 The diagram shows an example of a graphical user interface provided by the natural language information processing system. The example shown in the diagram is not intended to limit the actual operating method.

[0043] exist Figure 3 In the process shown, at the beginning, the user uses the application running in the user device to connect to the cloud server, use the services provided therein, and browse the content therein. The cloud server provides (such as through Figure 1 The graphical user interface module 107 of the display can be used to browse text, graphics and audio and video content. Figure 5 As shown, after the user device executes the application, a map interface 50 with an electronic map as the background is started, wherein a schematic diagram shows that a link icon is marked on the map interface 50 according to the geographic information associated with each piece of audio and video content. For example, the multiple link points associated with the geographic location in the figure may include one or more audio and video connection points, and the attached figure uses audio and video connection points 501, 502 and 503 as examples.

[0044] According to the embodiment, Figure 5 As shown, while browsing the content on the map interface 50, there are several icons below indicating link icons such as play 511, dialogue 512, assistant 513, search 514 and user homepage 515. At this time, the user can touch or use a specific gesture to select the dialogue link icon 512, or click on a link point marked somewhere on the map interface 80 to provide a dialogue, thereby starting the online dialogue program (step S301).

[0045] on the other hand, Figure 6 Another schematic diagram of a link method for starting an online dialogue program is provided. For example, when a user selects any audio and video link point (501, 502 or 503) on the map interface 50, the screen starts as shown in FIG. Figure 6 The video playback page 60 is displayed for playing a video shared or created by a user. The figure also shows the geographic location 601 of the video. The sidebar displays several link icons, such as favorite 603, conversation 604, collection 605 and share 606. The user can also start the online conversation program by clicking the conversation link icon of conversation 604 (step S301).

[0046] Then, a dialogue interface is started, allowing the user to input text, graphics or specific audio and video content (for example, input a link to share audio and video content) through the dialogue interface, so that the cloud server receives the content input by the user through the user interface module (step S303). According to the embodiment, the online dialogue program is implemented as a chat robot using a natural language model, which can communicate with the user through the dialogue interface and execute the natural language information processing method for the content input by the user each time. Figure 7 Displayed dialogue interface 70, Figure 8 The displayed dialog interface 80 and Fig. 9 The displayed dialogue interface 90, etc., each example displayed dialogue interface will provide an input field for the user to input content, and a dialogue display area for displaying the dialogue content output by the chat robot and the content input by the user.

[0047] At this time, the cloud server obtains the content input by the user through the user interface module, wherein the content received through the dialogue interface can be text, voice or audio and video content. If the received content is voice or audio and video content, it can be converted into text through a text conversion program, and semantic analysis can be performed to obtain semantic features (step S305). During the execution of the above program, the cloud server obtains user data from the user database, and the external system (which can be Figure 1 The external system interface module 105 displayed obtains real-time environment information (step S307).

[0048] Afterwards, the semantic features of the content that matches the user input, the user preferences obtained from the user data, and the content of the real-time environment information can be determined (or screened out after querying the database) (step S309), and processed by the natural language model running in the online dialogue program to generate dialogue content (step S311). Afterwards, the dialogue content is imported into the online dialogue program and output in the dialogue interface (step S313).

[0049] Furthermore, when the natural language model of the cloud server is running, it uses a database or system memory to record information of multiple dimensions, which may include historical conversation records under the same online conversation program, so that before the chat robot generates a conversation, such as step S309, in addition to considering the user's semantic features, user preferences and real-time environmental information in the conversation, it can also consider the user's historical conversation records in this online conversation program (step S315), so that the conversation content generated by the natural language model (step S311) is a conversation content that is in line with the current situation.

[0050] For example, the historical conversation records of users in the same online conversation program often contain the current context and can reflect the user's current emotions and needs. Figure 1 In the embodiment shown, the natural language processing model 101 in the cloud server 100 can use the natural language model to simultaneously consider the user's meaning, user preferences, real-time environmental information, and historical conversation records, and when generating conversation content, the same conversation context can be continued. For example, the same conversation topic can be continued, and when generating a conversation in natural language, the language can have the same tone as the previous conversation content (reflecting the user's emotions: joy, anger, sadness, happiness, etc.), so that the chat robot can learn the user's emotional expression through historical conversation records.

[0051] Related illustrations can be found in Figure 7 , Figure 7The dialogue interface 70 shows several dialogue contents 701, 702, and 703 between the user and the chat robot, and the chat robot can also query the database based on the user's semantic features obtained from the dialogue content 702 to provide recommended audio and video content 704. An input field 705 is provided below the dialogue interface 70 for the user to further input the dialogue content.

[0052] Another mode is, Figure 8 The example of the conversation interface 80 shown in the figure shows that when the online conversation process is started, the system directly provides natural language conversation content 801, 802, 804 based on the user's preferences and real-time information, and directly provides recommended audio and video content 803. The user can then use the input field 805 in the conversation interface 80 to respond to the above conversation content.

[0053] According to another embodiment, the natural language model running in the chatbot generates the conversation content based on the semantic features of the content input by the user, the user's preferences and the real-time environment information, and may also include providing multiple recommendation options, multiple recommended audio and video content, and / or multiple recommended friend links. Fig. 9 Display example.

[0054] In the online chat program, Fig. 9 The dialogue interface 90 shown includes a chat robot generating dialogue content 901 based on the semantic features of the user, and the semantics in this example allow the chat robot to determine that the user is deciding on a specific matter, so it provides several recommended options 902. In particular, the chat robot provides the user with recommended options based on the real-time environmental information obtained by the system from an external system. For example, the chat robot can provide recommended options 902 based on the real-time climate, traffic conditions, time, and the user's location. If the time is exactly meal time, and referring to the user's eating habits, the chat robot can provide meal options based on restaurants that are open near the user's location.

[0055] Accordingly, if the user expresses a desire to watch video content, the recommendation options 902 may be a plurality of recommended video content; if the user expresses a desire to find friends with similar interests, the recommendation options 902 may be a plurality of recommended friend links.

[0056] Furthermore, the user then uses the input field 906 to respond to the recommended options 902 and inputs the dialogue content 903, so that the chatbot responds to the dialogue content 904 according to the semantics of the dialogue content 903, and proposes a plurality of recommended contents 905 according to the semantics of the above dialogue content. Continuing with the above example, when the user responds that he wants one of the meals, the chatbot can provide restaurant options corresponding to the meal the user wants to eat based on the real-time weather, road conditions and user location obtained by the system from the external system. If the weather is bad and there is a traffic jam on the road, the chatbot should recommend restaurant options that are convenient for the user to go to.

[0057] According to the above embodiments, it can be seen that, unlike the current generative artificial intelligence-implemented chatbot technology that can only respond to user questions with information obtained through past data training, the chatbot implemented by the natural language information processing method proposed in the public book can generate conversation content that meets the user's needs based on the semantic features analyzed from the user's conversation, the user's preferences obtained by the system learning the user's activity data, and real-time environmental information.

[0058] According to yet another embodiment, reference may be made to Figure 4 A flow chart of another embodiment of a natural language information processing method is shown.

[0059] exist Figure 4 In the display process, the user starts the online dialogue program through the application (step S401) and talks with the chat robot, so that the system receives the dialogue content input by the user (step S403) to further obtain the user's semantic features. According to the embodiment, the cloud server can use the natural language processing module to perform transformation calculation (transformer) and vector operation (vector operation) to obtain semantic features (step S405).

[0060] It is worth mentioning that according to the embodiment of the natural language information processing method proposed in the open book, artificial intelligence technology is used to learn natural language, and after natural language understanding, text classification and grammar analysis are performed. When processing the dialogue content input by the user, a transformation model (transformer model, which was developed by Google in 2017) can be used. TMThe deep learning method proposed by the Brain Team processes the natural language content input by users in a time sequence. If the input content is not text, it needs to be converted into text first. In this way, in the online dialogue program, this conversion model can be used to perform machine translation, document summarization, document generation, etc.

[0061] After obtaining the semantic features of the user's conversation content, the system combines the user's preferences and the user's current location obtained by the system or analyzes the location of interest to the user from the conversation content, and obtains real-time environmental information from an external system in real time based on this location (step S407). The real-time environmental information may include one or any combination of real-time weather, real-time traffic conditions, real-time news, and real-time location-related network information (such as POI on a map, POI evaluation, etc.) obtained from one or more external systems in real time.

[0062] Afterwards, the system will use the vector database to calculate the closest answer based on the user's semantic features, user preferences and real-time environmental information, or add historical conversation records (step S409). It should be mentioned here that the data in the vector database is structured information obtained by using vector algorithms, so that the system can obtain words with similar semantics from the obtained content based on vector distance. For example, the vector distance between the two words "computer" appearing in the conversation content and the two words "calculation" in the database is closer, while the vector distance between "computer" and "running" is farther.

[0063] In this embodiment, the content input by the user, the content that the user is interested in, and the real-time environment information, and the historical conversation records can be added as needed, and the obtained text is annotated after executing the vector algorithm, and the vector of each word is calculated. The content with relevance is obtained according to the vector distance between the words, and the conversation content that meets the user's preferences and real-time environment information is generated accordingly. Further, according to the embodiment, when the vector algorithm is executed on the historical conversation records recorded in the cloud server, the conversation content that can meet the user's current emotions can be generated, such as continuing the same topic in the historical conversation records, and using words that are equivalent to the emotions obtained by analysis.

[0064] Furthermore, the system also searches the video database based on the above information to obtain the corresponding video content (step S411), and the chat robot generates the dialogue content by natural language processing and generative artificial intelligence technology (step S413), and outputs the dialogue content on the dialogue interface (step S415). In addition, in one embodiment, during the chat process, the system will continue the above steps, so that the chat robot can communicate with the user through natural language (text or sound), and provide the user with interesting and real-time content (video, text).

[0065] In summary, according to the embodiments of the above-mentioned natural language information processing method and system, the cloud server proposed by the system executes the natural language information processing method to provide natural language dialogue services, and also refers to the user preferences obtained through deep learning and obtains real-time environmental information during the dialogue process with the user, so as to realize a chat robot that can adapt to the user's preferences and real-time information and the dialogue program that is carried out.

[0066] The contents disclosed above are only preferred feasible embodiments of the present invention, and are not intended to limit the claims of the present invention. Therefore, all equivalent technical changes made using the contents of the present invention's specification and drawings are included in the claims of the present invention.

Claims

1. A natural language information processing method, executed in a cloud server, Features The method includes: Starting an online dialogue program and receiving content input by a user through a dialogue interface; Obtaining semantic features of the content input by the user; Obtain user data and real-time environmental information; Determine the semantic features of the content input by the user, the user preferences obtained from the user data, and the content of the real-time environment information, and generate a dialogue content after processing by a natural language model running in the online dialogue program; and The conversation content is imported into the online conversation program, and the conversation content is outputted on the conversation interface.

2. The natural language information processing method according to claim 1, It is characterized in that The content received through the dialogue interface is text, voice or video content. If the content received is voice or video content, it is converted into text through a text conversion program, and then the semantic features of the text are obtained through semantic analysis.

3. The natural language information processing method according to claim 2, It is characterized in that The natural language model running in the cloud server generates the conversation content by using a conversion model to perform machine translation, document summarization and document generation.

4. The natural language information processing method according to claim 3, It is characterized in that In the cloud server, a vector algorithm is further executed on the content input by the user, the user's preferences and the real-time environmental information to annotate the obtained text, calculate the vector of each word, obtain related content based on the vector distance between words, and generate the conversation content that conforms to the user's preferences and the real-time environmental information.

5. The natural language information processing method according to claim 4, Features The vector algorithm is further executed on the historical conversation records recorded in the cloud server to generate the conversation content.

6. The natural language information processing method according to claim 1, Features The real-time environmental information includes one or any combination of real-time weather, real-time traffic conditions, real-time news and real-time location-related network information obtained from one or more external systems in real time.

7. The natural language information processing method according to claim 1, Features The conversation content includes text generated by processing the natural language model and audio-visual content obtained by querying an audio-visual database.

8. The natural language information processing method according to claim 1, It is characterized in that A conversation link pattern is displayed through a user interface provided by the cloud server, and the online conversation program is started after clicking the conversation link pattern.

9. The natural language information processing method according to any one of claims 1 to 8, Features The online dialogue program implements a chat robot using the natural language model to communicate with the user through the dialogue interface and execute the natural language information processing method for the content input by the user each time.

10. The natural language information processing method according to claim 9, Features The dialogue interface provides an input field for the user to input content, and a dialogue display area for displaying the dialogue content output by the chat robot and the content input by the user.

11. The natural language information processing method according to claim 9, Features The natural language model running in the chat robot generates the conversation content based on the semantic features of the content input by the user, the user's preferences and the real-time environmental information, including providing multiple recommendation options, multiple recommended audio and video content, and / or multiple recommended friend links.

12. A natural language information processing system implemented by a computer system, Features The system comprises: A cloud server uses a processing circuit to execute a natural language information processing method, including: Starting an online dialogue program and receiving content input by a user through a dialogue interface; Obtaining semantic features of the content input by the user; Obtain user data and real-time environmental information; Determine the semantic features of the content input by the user, the user preferences obtained from the user data, and the content of the real-time environment information, and generate a dialogue content after processing by a natural language model running in the online dialogue program; and The conversation content is imported into the online conversation program, and the conversation content is outputted on the conversation interface.

13. The natural language information processing system according to claim 12, Features The cloud server provides an external system interface for connecting to one or more external systems to obtain the real-time environmental information, including one or any combination of real-time weather, real-time traffic conditions, real-time news and real-time location-related network information.

14. The natural language information processing system according to claim 12, Features The cloud server provides a user interface, in which a dialogue link pattern is displayed. After clicking the dialogue link pattern, the online dialogue program is started and the dialogue interface is opened.

15. The natural language information processing system according to claim 14, It is characterized in that The natural language model running in the cloud server generates the conversation content by using a conversion model to perform machine translation, document summarization and document generation; A vector algorithm is further executed on the content input by the user, the user's preferences and the real-time environmental information to annotate the obtained text, calculate the vector of each word, obtain related content based on the vector distance between words, and generate the conversation content that conforms to the user's preferences and the real-time environmental information.

16. The natural language information processing system according to claim 15, Features The vector algorithm is further executed on the historical conversation records recorded in the cloud server to generate the conversation content.

17. The natural language information processing system according to any one of claims 12 to 16, Features The online dialogue program implements a chat robot using the natural language model to communicate with the user through the dialogue interface and execute the natural language information processing method for the content input by the user each time.

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