TWI841410B - Intelligent marketing system and method - Google Patents

Abstract

The present invention provides an intelligent marketing system and method, including: an input-output information conversion module, which recognizes and converts the input information provided by the client into an identifiable signal; a semantic data conversion module, which converts the information available for Transformation of identified signals into semantic data; conversion of semantic data into intelligent judgment commands and database search through an intelligent judgment instruction conversion module; comparison, judgment and learning of an intelligent learning analysis module; feedback and error detection The module is responsible for feeding back and updating the database and debugging, and finally confirming an order information, and then further completing the transaction through a trading platform, applying the current intelligent AI technology to make the marketing system more convenient and humanized.

Description

Intelligent marketing system and method

The present invention relates to a marketing system and method, and more particularly to an intelligent marketing system and method.

With the change of consumption habits and the demand for modernization, more and more beverage shops, snack bars, restaurants, service industries and retail industries adopt POS systems (Point of sale) for marketing processes. Although it can save personnel costs and simplify processes, extended problems also arise. For example, the risk of contact infection will bring new challenges to epidemic prevention policies, as well as problems such as construction costs and machine program updates. Not only does it take up space, but it is also likely to cause the entire POS system to be inconvenient to use because the system data needs to be updated at any time.

Even though cloud marketing systems have emerged at this stage, there are still many inconveniences in their use. For example, they are limited to screen or voice input functions, which still causes difficulties for some visually impaired, language-impaired or illiterate people. In addition, whether the cloud marketing system has a confidentiality mechanism is still a major issue.

The present invention is to solve the problem of inconvenience in using the previous technology in the marketing system, and proposes an intelligent marketing system and method. Through the present invention, it can achieve the effect of facilitating the use of a wide range of consumers and the intelligent learning and analysis of the marketing system.

In the present invention, a smart marketing system is provided, comprising: a client interface, which operates on an electronic device; an input/output information conversion module, which is informationally connected to the client interface, comprising: an input information conversion unit, which recognizes and converts one of a first image signal, a first voice signal and a first text signal provided by the client interface into an input message; and an output information conversion unit, which converts an output message into one of a second image signal, a second voice signal and a second text signal to the client interface; A semantic data conversion module, which is informationally connected to the input/output information conversion module, has the function of analyzing human input information and emotions, and includes: a natural language input processing unit, which converts the input information into a first semantic data; and a natural language output processing unit, which converts a second semantic data into the output message; A smart judgment instruction conversion module, which is informationally connected to the semantic data conversion module, includes: an intelligent judgment instruction input conversion unit, which converts the first semantic data into a first intelligent judgment instruction; and an intelligent judgment instruction output conversion unit, which converts a second intelligent judgment instruction into one of the second semantic data; A database module, informationally connected to the intelligent judgment instruction conversion module, comprising: an instruction database unit, which receives the first intelligent judgment instruction and searches for the corresponding first intelligent judgment instruction in the instruction database unit and feeds back the second intelligent judgment instruction and updates it in the instruction database unit; An intelligent learning analysis module, informationally connected to the database module, compares, judges and learns the first intelligent judgment instruction with the instructions stored in the database to form the second intelligent judgment instruction; An order module, which is connected to the intelligent learning analysis module and a store end information, includes: an order processing unit, which confirms the order data formed by the second intelligent judgment instruction with the store end, and outputs an order form after confirmation. The store end can modify and confirm and update the database through the order processing unit and the intelligent learning analysis module; and A transaction platform module, which connects the order module with the store end and the client end interface, includes: a transaction processing server unit, which performs payment transactions with the order form and the client end interface, and sends the order form that confirms the completion of the transaction to the client end interface and the store end for processing.

Furthermore, the embodiment further includes a feedback and debugging module, which information is connected to the intelligent learning analysis module and the database module, and updates the database and debugs the second intelligent judgment instruction through feedback, and responds to the client interface through the second intelligent judgment instruction, and generates an order form after confirmation by the client interface.

Furthermore, in one embodiment of the present invention, the input/output information conversion module further includes a location information unit that can provide the client interface location information to provide reference information to the intelligent learning analysis module.

Specifically, the electronic device is one of a mobile phone, a computer, a website and a POS machine. Nowadays, tablet computers are popular and any electronic device that can connect to the Internet can become an electronic device of the smart marketing system.

Preferably, in actual use, the semantic data conversion module further includes a multi-language support unit that can identify and translate multiple languages, thereby reducing language barriers in today's diversified society.

Specifically, in one embodiment of the present invention, the database module further includes a customer data unit, which can store the client member data, including personal preferences, consumer groups, ordering cycles, etc.

More specifically, in actual use, the database module further includes a store data unit, which can store the store data, such as advertisements, promotional activities, and inventory data.

In an embodiment, the order module further includes an evaluation unit that can analyze and compile the evaluation data of the client and the store, update the database and provide learning for the intelligent learning analysis module.

Furthermore, the order module further includes a delivery unit that can match the order data with the client and the order data with the store to find the best delivery route and the most economical delivery cost.

In one embodiment of the present invention, the transaction platform module further includes a blockchain encryption unit to ensure that the order data will not be arbitrarily modified during the transaction, and send the order form confirming the completion of the transaction to the client interface and the merchant end for processing.

Furthermore, the trading platform module further includes a wallet recharge unit, which can store the points or currencies pre-charged by the client for consumption by the client interface.

The present invention also discloses an intelligent marketing method, which operates each module in the above-mentioned intelligent marketing system, and includes the following steps: (1) an input message conversion step, which is to identify and convert one of a first image signal, a first voice signal and a first text signal provided by a client interface into an input message; (2) a first semantic data conversion step, in which the input message is converted into a first semantic data; (3) a first intelligent judgment instruction conversion and recognition step, which is to convert the first semantic data into a first intelligent judgment instruction; (4) a database search step, which is to receive the first intelligent judgment instruction and search in a database; (5) An intelligent learning and analysis step is to compare, judge and learn the first intelligent judgment instruction with the instructions stored in the database to form a second intelligent judgment instruction; (6) A feedback and debugging step is to respond to the client interface through feedback and debugging and generate an order data, including the following steps: (6.1) A database update step is to feedback the second intelligent judgment instruction and update it in the database; (6.2) A second intelligent judgment instruction conversion and recognition step is to convert the second intelligent judgment instruction into a second semantic data; (6.3) A second semantic data conversion step is to convert the second semantic data into an output message; and (6.4) An output message conversion step converts the output message into one of a second image signal, a second voice signal and a second text signal to the client interface. Before the client confirms the order, the client returns to the input message conversion step to repeatedly confirm the order data with the client interface; (7) An order confirmation step, after the feedback and debugging step confirms the order data, an order module is used to confirm with a merchant terminal and the client interface. After confirmation, an order form is output and provided to a transaction platform module for payment transaction; (8) A transaction platform step is the act of using the order form with the client interface to make payment using a transaction platform module; and (9) 1. Form an order step. After the transaction platform step is completed, the completed order form is sent to the client interface and the store end for processing. Based on the above, the intelligent marketing system and method of the present invention have the following effects: (1) Multiple input methods: Consumers can use multiple input methods such as images, voice and text, so that the use is not restricted by age and physiology. (2) Multiple communication methods: Multi-language mode, so that communication is not restricted by nationality and region. (3) Intelligent learning analysis mode: Repeated use and communication allow the intelligent learning analysis module to continuously optimize internal instructions and correctly respond to users' questions and needs.

(4) Real-time data update: The database content can be continuously updated during use, so that the communication between the store and the client can maintain the latest information.

(5) Transaction confidentiality: Blockchain encryption technology is introduced to keep transaction information between the store and the client confidential, and to protect the customer's personal information and the store's business secrets during the transaction process.

As mentioned above, the intelligent marketing system and method of the present invention have the following effects:

(1) Multiple input methods: Consumers can use multiple input methods such as image, voice and text, so that usage is not restricted by age or physiology.

(2) Multiple communication methods: Multilingual mode allows communication to be unrestricted by nationality or region.

(3) Intelligent learning analysis mode: Repeated use and communication allow the intelligent learning analysis module to continuously optimize internal instructions and correctly respond to users’ questions and needs.

(4) Real-time data update: The database content can be continuously updated during use, so that the communication between the store and the client can maintain the latest information.

(5) Transaction confidentiality: Blockchain encryption technology is introduced to keep transaction information between the store and the client confidential, and to protect the customer's personal information and the store's business secrets during the transaction process.

In order to enable those with ordinary knowledge in the relevant technical field to understand the content of the present invention and to implement the content of the present invention accordingly, the following is explained with appropriate embodiments in conjunction with diagrams. Equivalent replacements and modifications based on the content of the present invention are all included in the scope of the rights of the present invention. In addition, it is stated that the quantifiers "one" or "an" used throughout the present invention are used to express the usual meaning of the scope of the present invention, and should be interpreted in the present invention as including one or at least one, and a single concept also includes plural situations, unless other meanings are clearly intended in the present invention.

Please refer to FIG1 , which is a schematic diagram of the intelligent marketing system of the present invention.

The first embodiment of the present invention is the smart marketing system as described in claim 1, comprising: a client interface C, which operates on an electronic device, which can be one of a mobile phone, a computer, a website, and a POS machine. Nowadays, tablet computers are popular and any electronic device that can connect to the Internet can become an electronic device of the smart marketing system. An input/output information conversion module 10 is informationally connected to the client interface C. The information connection can be wired or wireless to transmit and exchange information, including: an input information conversion unit 11, which converts a first image signal provided by the client interface C, such as a picture or image of a meal that the customer wants to buy; a first voice signal, in which the customer directly speaks the name of the product, such as: I want a cup of black tea latte; and a first text signal, in which the customer directly inputs text through a computer keyboard or a mobile phone, such as: a steak; the input information conversion unit 11 converts the input signal into the first image signal provided by the client interface C, such as: a picture or image of a meal that the customer wants to buy; a first voice signal, in which the customer directly speaks the name of the product, such as: I want a cup of black tea latte; and a first text signal, in which the customer directly inputs text through a computer keyboard or a mobile phone, such as: a steak; One of the first image signal, the first voice signal and the first text signal is identified and converted into an input message; and the message that the intelligent marketing system replies to the client is presented to the client interface C through an output message conversion unit 12, and the output message conversion unit 12 converts an output message into one of a second image signal, a second voice signal and a second text signal to the client interface C; the output message is a message in response to the client, and the presented signal is, for example: apple pattern (image signal), black tea latte without ice? (voice signal), medium rare? (text presentation).

Next, it is a processing stage of a semantic data conversion module 20. The module information is connected to the input/output information conversion module 10, and has the function of analyzing human input information and emotions, including: A natural language input processing unit 21, which converts the input information into a first semantic data. The processing method includes the following steps: 1. Input message recognition: convert the input message into a text form for subsequent processing and analysis. 2. Lexical analysis: Text analysis can identify words, such as nouns, verbs, adjectives, pronouns, etc. For example: I like to eat apples, "I" is a pronoun, "like to eat" is a verb, and "apple" is a noun. 3. Syntactic analysis: Analyze the structure and grammatical relationship of the sentence to understand the relationship between words in the sentence, for example: I want an apple, "one" is the qualifier, and the dependent word is "apple". 4. Semantic analysis: Determine the meaning of each word by analyzing the context and context, and understand the meaning of the input message at the same time, for example: "Please help me find a hot pot restaurant nearby, the price is not too expensive.", the semantics is "find" as the action, the object is "hot pot restaurant", and the qualifier is "the price is not too expensive". 5. Named entity recognition: Determine the special entities such as names of people, places, and organizations in the input message, for example: Guanmiao pineapple, extract "Guanmiao". 6. Keyword extraction: Extract the words that appear frequently or are key words in the input message, for example: I want a cup of pearl milk tea, the keyword is "pearl milk tea". 7. Sentiment analysis: Perform sentiment analysis on the input message to determine the emotional tendency of the text; for example: Healing black tea, extract "healing", and further subdivide the emotional categories, such as joy, worry, and anger. The natural language input processing unit 21 can convert the input message into a first semantic data through the above 7 processing methods; and a natural language output processing unit 22 converts a second semantic data into the output message, and the processing method includes the following steps: 1. Analyze the input semantic data: Determine the structure, grammar and semantics of the semantic data by analyzing the input second semantic data, such as "coffee", "latte", "two cups" and "hot". 2. Determine the output target: Determine what the output target is, such as generating a dialogue reply, translating text, generating a summary, etc., for example: the reply object is an adult, a child or an American. 3. Generate output: Generate output information according to the target, and use machine processing unit rules to generate signal output such as image, voice or text through in-depth analysis, for example: the output is an image of a green apple or a cup of pearl milk tea voice. 4. Optimize output: Based on the above generated output results, optimize and improve the output information to achieve better performance and client experience, for example: "A cup of milk tea with pearls, sugar-free and ice-free" is optimized to "A cup of sugar-free and ice-free pearl milk tea".

It is worth emphasizing that the first semantic data must pass through an intelligent judgment instruction input conversion unit 31 before being converted into a first intelligent judgment instruction for storage in a database module 40; and the second intelligent judgment instruction output from the database module 40 must be converted into the second semantic data through the intelligent judgment instruction output conversion unit 32 and output to the natural language output processing unit 22. The first intelligent judgment instruction and the second intelligent judgment instruction are in a computer storage format, which is convenient for storage in the database module 40.

Next, the database module 40 is informationally connected to the intelligent judgment instruction conversion module 30, and includes: an instruction database unit 41, which receives the first intelligent judgment instruction, searches for the corresponding first intelligent judgment instruction in the instruction database unit 41, and feeds back the second intelligent judgment instruction and updates one of the instruction database units 41; the instruction database unit 41 has at least one order structure data, and the order structure data includes an item field, a quantity field, and a feature field, and each field has a corresponding key data instruction for comparison.

In addition, an intelligent learning analysis module 50 is connected to the database module 40 information, and compares, judges, machine learns and processes deep learning on the first intelligent judgment instruction with the key data instruction/order structure data stored in the database module 40 to form the second intelligent judgment instruction, and then feeds back and updates the instruction database unit 41, and then outputs the second semantic data through the intelligent judgment instruction output conversion unit 32, and converts the second semantic data into the output message through the natural language output processing unit 22, and finally converts the output message into one of a second image signal, a second voice signal and a second text signal to the client interface C through the output message conversion unit 12.

In another embodiment, a customer wants to enjoy chicken but does not want the burden of fat. At this time, the client interface C inputs "I want a portion of chicken but I am losing weight. Do you have any suggestions?" At this time, through the conversion and semantic analysis of the input message by the intelligent marketing system, the intelligent learning analysis module 50 can obtain the information of "portion", "chicken", and "weight loss". From the comparison of the key data instruction/order structure data stored in the database module 40, the key words such as "low fat", "boiled", "low calories", and "chicken breast" can be obtained. At this time, the intelligent learning analysis module 50 generates the second intelligent judgment instruction, which is first stored in the database module 40, and then converted into the second semantic data through the intelligent judgment instruction output conversion unit 32 and output to the natural language output processing unit 22, and the second semantic data is converted into the output message, and then the output message conversion unit 12 converts the output message into one of a second image signal, a second voice signal and a second text signal to the client interface C, and the output message is "I recommend you a small portion of boiled chicken breast, how about it?"

In another embodiment, three customers want to order food at the same time, one of whom is a vegetarian and the other does not eat beef. They enter "three meal sets, one vegetarian, one does not eat beef, please recommend" in the client interface C. As in the analysis method described in the previous embodiment, "three", "one vegetarian", "one does not contain beef", and "one combination" can be obtained. The message presented in the client interface C is as follows: Based on your request, I recommend the following three meal set options: 1. Vegetarian meal set: Main course: fresh salad with seasonal vegetables and homemade sauce Side dish: whole wheat bread Snack: fruit platter 2. Non-beef meal set: Main course: grilled chicken leg Side dish: French fries Snack: fresh fruit slices 3. Vegetarian meal set: Main course: grilled pork Side dish: brown rice Snack: small cheesecake We provide vegetarian and non-beef options upon request. Is there anything that needs to be added or modified?

After the above-mentioned continuous and repeated communication with the customer, when the customer confirms the required content, it will enter an order module 70 stage. The order module 70 is connected to the intelligent learning analysis module 50 and a store end B information, including: an order processing unit 71, which confirms the order data formed by the second intelligent judgment instruction with the store end B, and outputs an order form after confirmation. The store end B can modify and confirm and update the database module 40 through the order processing unit 71 and the intelligent learning analysis module 50.

When the order form is confirmed to be correct, it enters a transaction platform module 80, and the information connects the order module 70 with the store end B and the client interface C, including: a transaction processing server unit 81, which is used to perform payment transactions between the order form and the client interface C, such as credit card transactions or cash on delivery and other payment methods, and sends the order form confirming the completion of the transaction to the client interface C and the store end B for processing. The above is the complete operating mode of the first embodiment of the present invention.

Please refer to FIG. 2, which is a schematic diagram of the intelligent marketing system of the present invention combined with a feedback and debugging module; another embodiment of the present invention is to combine the first intelligent marketing system with a feedback and debugging module 60. Specifically, the feedback and debugging module 60 is connected to the intelligent learning analysis module 50 and the database module 40, and can further debug and confirm the second intelligent judgment instruction before feeding it back to the instruction database unit 41 to update its data and feedback to the client; for example: there is an order data of "Hot Jade Lemonade without sugar and with less ice". At this time, the feedback and debugging module 60 determines that the sweetness of the Jade Lemonade set at the store end B is not adjustable, and will give feedback to the client end "The sweetness of the Jade Lemonade is not adjustable. Do you want to order other drinks?" The customer specifies that the drink must be heated but with less ice, which is obviously illogical. Therefore, the feedback is given to the client end, "Do you want hot Jade Lemonade? Or Jade Lemonade with less ice?" Only one of the two options, heating or less ice, can be selected to obtain the correct order information.

Preferably, please refer to FIG. 3 , which is a schematic diagram of the internal unit of the input/output information conversion module of the present invention; in another embodiment, the input/output information conversion module 10 further includes a location information unit 13, which can provide the client interface C location information to provide the intelligent learning analysis module 50 with reference information.

Please refer to Figure 4, which is a schematic diagram of the internal units of the semantic data conversion module of the present invention. In another embodiment, the semantic data conversion module 20 further includes a multi-language support unit 23, which can identify and translate multiple languages. For example, when the input message received is in English, it can be identified by the multi-language support unit 23 as the first semantic data for the intelligent judgment command input conversion unit 31 to process, or the second semantic data output by the intelligent judgment command output conversion unit 32 can be used to convert the original Mandarin response into an English reply to the client message. Please refer to Figure 5, which is a schematic diagram of the internal units of the intelligent judgment command conversion module of the present invention.

In addition, please refer to Figure 6, which is a schematic diagram of the internal units of the database module of the present invention. The database module 40 further includes a customer data unit 42, which can store the client interface C member data, such as customer age, preferences, etc.; and a store data unit 43, which can store the store end B data, such as promotional plans, advertisements and evaluations, etc.

Please refer to FIG. 7, which is a schematic diagram of the internal units of the order module of the present invention. The order module 70 further includes an evaluation unit 72, which can analyze and compile the evaluation data of the client interface C and the store interface B, and update the database module 40 and provide the intelligent learning analysis module 50 with learning. For example, the client can fill out a satisfaction questionnaire after each order consumption. The results presented in the questionnaire are analyzed and compiled to provide a basis for future reference for customers and provide suggestions for improvement for the store. The data analyzed by the evaluation unit 72 The data is fed back to the store data unit 43 and the customer data unit 42 for storage respectively; and a delivery unit 73 can match the order data with the client interface C and the order data with the store end B for the best delivery route and the most economical delivery cost. When the client orders a product, the required delivery time can be immediately presented to provide the customer with real-time suggestions; for example: Mr. A needs a drink that can be delivered to his home within 15 minutes and the shipping cost is less than 30 yuan. At this time, the delivery unit 73 will provide Mr. A with the information of drink stores that meet this condition for Mr. A to choose.

Please refer to FIG8, which is a schematic diagram of the internal unit of the order module of the present invention combined with the feedback and debugging module. In another embodiment, when the client orders two or more commodities at the same time, although the respective delivery time and cost provided by the delivery unit 73 meet the customer's needs, the feedback and debugging module 60 can provide the customer with another more economical option. For example, if a certain person orders 100 yuan of goods and a certain person orders 100 yuan of goods, the customer can choose a more economical option. The burger and the 50-dollar cold drink are in different stores, but because they are from different stores, the shipping fee needs to be paid for two items, assuming they are 50 yuan and 30 yuan respectively. However, if the burger is ordered from the same beverage store, although the burger is 55 yuan, which is 5 yuan more than the other store, the shipping fee only needs to be paid for one item. Originally, the customer had to pay 230 yuan, but after analysis by the feedback and debugging module 60, the customer only needs to pay 185 yuan, which is the most beneficial to the customer overall.

In another embodiment of the present invention, the order module 70 further includes a recommendation unit, which can receive a product information provided by the store end B, and then update the order structure data in the instruction database unit 41. The product information is one of the current products, promotional products and recommended products. When the client wants the intelligent marketing system to recommend products, the intelligent learning analysis module 50 will preferentially extract the product information provided by the store end B from the order structure data. For example, when a certain C asks for any recommended meals, the intelligent marketing system preferentially extracts the recommended products provided by the store end B, because the recommended products can maximize the profit of the store end B. When a certain C replies that my budget is limited and please recommend suitable meals, the intelligent marketing system will preferentially recommend promotional products, which can help customers Save money; when the customer asks for any recommended drinks, the intelligent marketing system will give priority to recommending drink options among the expiring products, so as to reduce the waste of expired products on the store side B, reduce the cost of handling expired products, and achieve the effect of environmental protection and energy saving; further, in other embodiments, when the store side B prompts that one or more raw materials are in short supply or scarce, when the client wants the intelligent marketing system to recommend products, the intelligent learning analysis module 50 will remove the product information provided by the store side B that uses the raw materials in short supply from the order structure data. In other embodiments, when the client purchases products that use these raw materials in short supply, the intelligent learning analysis module 50 prompts the client that the products will not have these raw materials in short supply or will be replaced by other raw materials.

On the other hand, please refer to FIG. 9, which is a schematic diagram of the internal unit of the trading platform module of the present invention. The present invention discloses a blockchain encryption unit 83 in the trading platform module 80, which can provide a secure, transparent and traceable trading environment and ensure that the order data will not be arbitrarily tampered with during the transaction, including: (1) Identity verification: ensuring the authenticity and security of the user's identity. This can include the use of passwords, multi-factor authentication (MFA), biometric technology and other methods. (2) Blockchain network: used to store and verify transaction data, which can be a public blockchain (such as Bitcoin or Ethereum) or a private blockchain (such as an enterprise's internal blockchain); the blockchain network ensures the security, immutability and consensus mechanism of the transaction. (3) Decentralized trading environment: The trading platform module 80 is combined with the blockchain encryption unit 83, so that customers can trade directly on the trading platform without relying on the intermediary service of a third-party organization. Therefore, the blockchain encryption technology is applied to the trading platform module 80, and the order form confirming the completion of the transaction is sent to the client interface C and the merchant end B for processing.

In addition, the trading platform module 80 further includes a wallet recharge unit 82, which has a recharge function and can store the points or currency pre-charged by the client interface C for consumption by the client interface C. Customers can check the balance in their wallet at any time to understand the scope of transactions or purchases they can make, and provide transaction records for customers to check at any time. 82 is not only applicable to the recharge of physical currency, but also supports the current virtual currency transactions, allowing clients to choose multiple payment methods, such as: credit card, Jiekou Pay, PayPal, etc., so that users can easily recharge their wallets, and in each consumption, the reward amount received can be stored in the wallet recharge unit 82 for the next consumption.

On the other hand, the present invention discloses an intelligent marketing method, which operates in an intelligent marketing system as described in any of the embodiments, and includes the following steps: (1) an input message conversion step S10, which is to identify and convert one of a first image signal, a first voice signal and a first text signal provided by a client interface C into an input message; (2) a first semantic data conversion step S20, in which the input message is converted into a first semantic data; (3) a first intelligent judgment instruction conversion and recognition step S30, which is to convert the first semantic data into a first intelligent judgment instruction; (4) a database search step S40, which is to receive the first intelligent judgment instruction and search in a database; (5) An intelligent learning analysis step S50 is to compare, judge and learn the first intelligent judgment instruction with the instructions stored in the database to form a second intelligent judgment instruction; (6) A feedback and debugging step S60 is a step of responding to the client interface C through feedback and debugging and generating an order data, including the following steps: (6.1) An update database step S41 is to feedback the second intelligent judgment instruction and update it in the database; (6.2) A second intelligent judgment instruction conversion recognition step S31 is to convert the second intelligent judgment instruction into a second semantic data; (6.3) A second semantic data conversion step S21 is to convert the second semantic data into an output message; and (6.4) An output message conversion step S11 converts the output message into one of a second image signal, a second voice signal and a second text signal to the client interface C. Before the client confirms, the client returns to the input message conversion step S10 to repeatedly confirm the order data with the client interface C; (7) An order confirmation step S70, after the feedback and debugging step S60 confirms the order data, an order module 70 is used to confirm with a store end B and the client interface C. After confirmation, an order form is output and provided to a transaction platform module 80 for payment transaction; (8) A transaction platform step S80 is to use a transaction platform module 80 to make payment with the order form and the client interface C; and (9) an order generation step S90, after the transaction platform step S80 is completed, the completed order form is sent to the client interface C and the merchant end B for processing.

The above disclosed embodiments are only for illustrative purposes to illustrate the principles, features and effects of the present invention, and are not intended to limit the scope of the present invention. Any person skilled in the art or skilled in the art may modify and alter the above embodiments without violating the spirit and scope of the present invention. Any equivalent changes and modifications made by using the contents disclosed in the present invention include but are not limited to disassembling some modules of the present invention. Or after disassembly, the assembly is connected in series through third-party software or hardware, which should still be included in the scope of the patent application below. A person with ordinary knowledge in the relevant field should understand that each module, each unit and each device described in each embodiment of the present invention can be implemented by a special circuit or coding. The circuit may include but is not limited to transistors or other circuit elements. The coding process can be completed by a specific encoder and is implemented through the coordinated operation of software, hardware and firmware.

B: Store side C: Client interface 10: Input and output information conversion module 11: Input information conversion unit 12: Output information conversion unit 13: Location information unit 20: Semantic data conversion module 21: Natural language input processing unit 22: Natural language output processing unit 23: Multi-language support unit 30: Intelligent judgment command conversion module 31: Intelligent judgment command input conversion unit 32: Intelligent judgment command output conversion unit 40: Database module 41: Command database unit 42: Customer data unit 43: Store data unit 50: Intelligent learning analysis module 60: Feedback and debugging module 70: Order module 71: Order processing unit 72: Evaluation unit 73: Distribution unit 80: Trading platform module 81: Transaction processing server unit 82: Wallet recharge unit 83: Blockchain encryption unit S10: Input message conversion step S11: Output message conversion step S20: First semantic data conversion step S21: Second semantic data conversion step S30: First intelligent judgment instruction conversion recognition step S31: Second intelligent judgment instruction conversion recognition step S40: Database search step S41: Database update step S50: Intelligent learning analysis step S60: Feedback and debugging step S70: Order confirmation step S80: Trading platform step S90: Order formation step

Figure 1 is a schematic diagram of the intelligent marketing system of the present invention; Figure 2 is a schematic diagram of the intelligent marketing system combined with feedback and debugging module of the present invention; Figure 3 is a schematic diagram of the internal unit of the input and output message conversion module of the present invention; Figure 4 is a schematic diagram of the internal unit of the creative semantic data conversion module; Figure 5 is a schematic diagram of the internal unit of the intelligent judgment instruction conversion module of the present invention; Figure 6 is a schematic diagram of the internal unit of the database module of the present invention; Figure 7 is a schematic diagram of the internal unit of the order module of the present invention; Figure 8 is a schematic diagram of the internal unit of the order module of the present invention combined with feedback and debugging module; Figure 9 is a schematic diagram of the internal unit of the trading platform module of the present invention; Figure 10 is a step flow chart of the intelligent marketing method of the present invention

B: Store side

C: Client

10: Input and output information conversion module

20:Semantic data conversion module

30: Intelligent judgment command conversion module

40: Database module

50: Intelligent learning analysis module

70: Order module

80: Trading platform module

Claims (9)

A smart marketing system includes: a client interface, which operates on an electronic device; an input/output information conversion module, which is informationally connected to the client interface, and includes: an input information conversion unit, which recognizes and converts one of a first image signal, a first voice signal, and a first text signal provided by the client interface into an input message; and an output information conversion unit, which converts an output message into one of a second image signal, a second voice signal, and a second text signal to the client interface; a semantic data conversion module, which is informationally connected to the input/output information conversion module, and has the function of analyzing human input messages and emotions, including : a natural language input processing unit, which converts the input message into a first semantic data; and a natural language output processing unit, which converts a second semantic data into the output message; an intelligent judgment instruction conversion module, which is informationally connected to the semantic data conversion module, including: an intelligent judgment instruction input conversion unit, which converts the first semantic data into a first intelligent judgment instruction; and an intelligent judgment instruction output conversion unit, which converts a second intelligent judgment instruction into one of the second semantic data; a database module, which is informationally connected to the intelligent judgment instruction conversion module, including: an instruction database unit, which receives the first intelligent judgment instruction in the instruction The intelligent learning analysis module is connected to the database module information, and compares, judges and learns the first intelligent judgment instruction with the instruction stored in the database to form the second intelligent judgment instruction; an order module is connected to the intelligent learning analysis module and a store end information, including: an order processing unit, which confirms the order data formed by the second intelligent judgment instruction with the store end, and outputs an order form after confirming that there is no error. The store end can use the order processing unit and the intelligent learning analysis module to conduct Modify and confirm and update the database module; a transaction platform module, which information connects the order module with the store end and the client end interface, including: a transaction processing server unit, which is to conduct payment transactions between the order form and the client end interface, and send the order form that confirms the completion of the transaction to the client end interface and the store end for processing; and a feedback and debugging module, which information connects the intelligent learning analysis module and the database module, updates the database module and debugs the second intelligent judgment instruction through feedback, and responds to the client end interface through the second intelligent judgment instruction, and generates the order form after confirmation by the client end interface. As described in claim 1, the input/output information conversion module further includes a location information unit that can provide the client interface location information to provide reference information for the intelligent learning analysis module. The intelligent marketing system as described in claim 1, wherein the electronic device is one of a mobile phone, a computer, a website and a POS machine. As described in claim 1, the database module further includes a customer data unit, which can store the client interface member data; and a store data unit, which can store the store data. As described in claim 1, the order module further includes an evaluation unit that can analyze and compile the evaluation data of the client interface and the store, update the database module, and provide learning for the intelligent learning analysis module. As described in claim 1, the order module further includes a delivery unit that can match the order data with the client interface and the order data with the best delivery route and the most economical delivery cost of the store. As described in the smart marketing system of claim 1, the transaction platform module further includes a blockchain encryption unit to ensure that the order data will not be tampered with during the transaction, and to send the order form confirming the completion of the transaction to the client interface and the merchant end for processing. As described in claim 1, the trading platform module further includes a wallet recharge unit that can store points or currencies pre-charged by the client interface for consumption by the client interface. A smart marketing method is operated in a smart marketing system as described in any one of claims 1 to 8, comprising the following steps: an input message conversion step, in which one of a first image signal, a first voice signal and a first text signal provided by a client interface is recognized and converted into an input message; a first semantic data conversion step, in which the input message is converted into a first semantic data; a first intelligent judgment instruction conversion recognition step, in which the first semantic data is converted into a first intelligent judgment instruction; A step of searching a database, receiving the first intelligent judgment instruction and searching in a database; an intelligent learning and analysis step, comparing, judging and learning the first intelligent judgment instruction with the instructions stored in the database to form a second intelligent judgment instruction; a feedback and debugging step, responding to the client interface through feedback and debugging and generating an order data step, including the following steps: an update database step, feeding back the second intelligent judgment instruction and updating the database ; A second intelligent judgment instruction conversion and recognition step is to convert the second intelligent judgment instruction into a second semantic data; a second semantic data conversion step is to convert the second semantic data into an output message; and an output message conversion step is to convert the output message into one of a second image signal, a second voice signal and a second text signal to the client interface. Before the customer confirms, the customer returns to the input message conversion step to repeatedly confirm the order data with the client interface; a confirmation order A single step, after the feedback and debugging step confirms the order data, an order module is used to confirm with a merchant end and the client end interface. After confirmation, an order form is output and provided to a trading platform module for payment transaction; a trading platform step is the act of using a trading platform module to make payment with the order form and the client end interface; and a forming order step, after the trading platform step is completed, the completed order form is sent to the client end interface and the merchant end for processing.

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