system

Abstract

We provide the system. [Solution] A means for receiving desired product information and location information obtained from the user, A means for collecting product price information from multiple sales locations based on the desired product information, A means for generating an optimal purchase plan considering the product price information and location information, A means of notifying the user of the purchase plan, A system that includes this.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When consumers purchase products from a variety of sales outlets, they try to obtain products at a lower price due to soaring prices, but it is not easy to find the most economical purchasing method. Also, it is difficult to grasp coupon and discount information, which causes problems of wasting time and effort. In such a situation, there is a need to provide a means by which consumers can easily and quickly obtain an optimal purchase plan.

Means for Solving the Problems

[0005] The present invention provides a system that includes means for receiving desired product information and location information obtained from a user, means for collecting product price information from multiple sales locations based on the desired product information, means for generating an optimal purchase plan considering the product price information and location information, and means for notifying the user of the purchase plan. This system can acquire information on desired products and coupons, reflect them in the purchase plan, and provide recommended products considering past purchase history. This enables the system to help consumers purchase products in the most economical and efficient way possible.

[0006] A "user" is an individual or legal entity that uses this system to input product information and receive suggestions for the optimal purchasing plan.

[0007] "Desired product information" refers to information such as the name and quantity of the products that the user wishes to purchase.

[0008] "Location information" refers to information about the user's current location or a specified geographical location.

[0009] A "sales outlet" refers to a seller, such as a retail store or online platform, that provides a product.

[0010] "Product pricing information" refers to data that includes product prices at each sales location, as well as related coupons and discount information.

[0011] A "purchase plan" is a plan that proposes the most economical and efficient way to purchase the desired product.

[0012] "Notification means" refers to a method or function for informing the user of the generated purchase plan.

[0013] A "coupon" is an electronic or paper-based voucher used to offer a discount when purchasing goods.

[0014] "Past purchase history" refers to information and records about products that a user has previously purchased.

[0015] A "recommended product" is a product proposed by the system to the user based on past purchase history and current purchase trends.

BRIEF DESCRIPTION OF THE DRAWINGS

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] Embodiments of the present invention will be described in detail below. This system is configured using digital terminals such as smartphones and personal computers, and consists of multiple functional modules in order to provide the user with an optimal product purchase plan.

[0038] The user begins by using a device to enter information about the product they wish to purchase. This information includes the product name, the required quantity, and the desired pickup location or delivery address. The device then transmits this information to the server.

[0039] The server searches a database of relevant sales locations based on the received product and location information. It then uses APIs from online platforms and local stores to collect the latest price information, availability, and available coupons for the relevant products. For example, if a user enters that they want to buy "rice, milk, and eggs," the server will retrieve information about these products from nearby supermarkets and major online stores.

[0040] Next, the server analyzes the acquired information to calculate the most economical purchase method for each product. This takes into account the final price when coupons and point systems are applied. Accessibility from the user's current location is also included in the evaluation. The server generates the most effective purchase plan for the user and compares multiple options.

[0041] The terminal notifies the user of a purchase plan sent from the server. This notification includes detailed information such as the store with the lowest price, the best combination of products to purchase, and the cost after applying coupons. Based on the information provided, the user can choose where to actually purchase the products.

[0042] Furthermore, this system can refer to a user's past purchase history and suggest products based on that history. This further personalizes the user's shopping experience and improves convenience.

[0043] In this way, the system provides a highly effective means of supporting users' purchasing behavior and saving them time and costs.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user uses a terminal to enter a list of items they wish to purchase. This includes specifying the product name, quantity, budget, whether they want any coupons, and their current location. Once the terminal correctly receives the entered information, it prepares to send it to the server.

[0047] Step 2:

[0048] The server retrieves the user's desired product information and location information received from the terminal. Next, it generates a query to look up multiple sales location databases that match the user's desired products. This query includes the necessary information to retrieve current market prices, discount information, and inventory status.

[0049] Step 3:

[0050] The server collects information through APIs and websites of online and local stores. In particular, it retrieves the price, inventory information, and discount information when coupons are applied for each product, and aggregates this information in a central database. The information collection process is performed in real time and is designed to obtain the most up-to-date market information possible.

[0051] Step 4:

[0052] The server performs a detailed analysis based on the collected data and calculates the purchase cost for each product. This includes the final price after applying coupons and a cost comparison when purchasing items from multiple stores in combination. It also evaluates the cost of visiting offline stores based on the distance from the user's current location.

[0053] Step 5:

[0054] The server generates the best possible purchase plan, ranking multiple options based on the user's criteria. The generated plan includes details such as the cheapest store, the best value bundle, and online purchase options.

[0055] Step 6:

[0056] The server notifies the user's device of the generated purchase plan. The device then displays this information clearly to the user, allowing them to review the proposal, including detailed pricing information and applicable coupons.

[0057] Step 7:

[0058] The user reviews the notified plan and selects the most suitable purchase option. After making a selection, the device sends this information back to the server as feedback, which is used to improve future services.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] Modern consumer purchasing behavior is complex due to multiple factors, including the diversity of product prices, fluctuations in inventory, and the availability of coupons. As a result, consumers spend a great deal of time and effort finding the optimal way to purchase products. Furthermore, the lack of personalized recommendations that leverage individual users' purchase history makes it difficult for consumers to find products that suit their preferences.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for receiving desired product information and location information obtained from the user, means for collecting product price information and inventory information from multiple sales locations, and means for generating an optimal purchase plan considering coupon information. This allows the user to be presented with the best options for purchasing products efficiently and economically, saving time and costs. Furthermore, by providing recommended products that take into account past purchase history, the user's purchasing experience is further improved.

[0064] A "user" refers to an individual or group that uses a digital device to input product information and receive a purchase plan.

[0065] "Desired product information" refers to data that includes specific information such as the name, quantity, and pickup location of the product the user wishes to purchase.

[0066] "Location information" refers to data that indicates the geographical location of the user or the place where the product will be received.

[0067] A "sales outlet" refers to a facility that includes online platforms and physical stores where products are sold.

[0068] "Product pricing information" refers to data regarding the selling price of a specific product.

[0069] "Inventory information" refers to data regarding the supply status and available quantities of a particular product.

[0070] "Coupon information" refers to data that includes information about discounts and benefits that apply when purchasing products.

[0071] A "purchase plan" refers to a plan that includes suggestions for how a user can best purchase a product.

[0072] "Notification" refers to the process or action of communicating information to a user via a device.

[0073] "Purchase history" refers to data that records a user's past purchase history and related information.

[0074] "Recommended products" refer to products selected based on the user's past purchase history and preferences.

[0075] This invention is a system that utilizes digital devices such as smartphones and personal computers to provide users with the most suitable product purchase plan. The system is primarily composed of interactions between three parties: the user, the device, and the server.

[0076] The user enters information about the product they wish to purchase via a digital terminal. This information includes the product name, quantity, and pickup location. The terminal transmits the entered information to the server. After receiving the information, the server searches a database of relevant sales locations. The server executes database queries and retrieves the latest price and inventory information for the product via the online platform or local store APIs. This may involve using HTTP requests, for example.

[0077] Next, the server uses the collected information to perform price comparisons and generate the optimal purchase plan. This is done by an algorithm that calculates the most economical way to buy. This process takes into account factors such as the application of coupons and the use of point systems. Furthermore, it also takes into account ease of access based on the user's location information.

[0078] As a concrete example of operation, let's consider a scenario where a user wants to purchase a new smartphone case within a budget. The user enters the product name and budget on their device and presses the submit button. The server uses this information to collect price information and generates the most cost-effective purchase plan. The device then notifies the user of the generated plan, and the user can choose the best option from the presented choices.

[0079] Furthermore, the server analyzes the user's past purchase history to suggest highly relevant products. This functionality is achieved by utilizing a generative AI model that learns the user's purchasing habits.

[0080] Examples of prompt messages include the following:

[0081] "A user is looking for a smartphone case. They have specified the product name, color, material, and budget. Based on this, generate the best purchase plan."

[0082] To implement this system, digital terminals and servers must be connected via the internet, and configured to allow for rapid and accurate information exchange. This system will enable users to efficiently select and purchase products.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The user enters and submits product information. Using a digital device, the user enters the name of the product they wish to purchase, the required quantity, and the desired pickup location or delivery address, and then presses the "Submit" button. This input becomes the dataset necessary for the next processing step.

[0086] Step 2:

[0087] The terminal sends product information to the server. The terminal sends information entered by the user to the server. At this stage, the information is transferred to the server via the network. The input is data entered by the user into the terminal, and as output, it is converted into a data format that the server can use.

[0088] Step 3:

[0089] The server searches the database and collects information. Based on the received product information, the server issues SQL queries to search the databases of related sales locations. As a result of the queries, it collects price information, inventory information, and coupon information from multiple sales locations. The input is the request information from the user, and the output is product price, inventory, and coupon information.

[0090] Step 4:

[0091] The server generates the optimal purchase plan. Based on the collected information, the server calculates the most economical purchase plan, considering price, inventory status, and coupon availability. Here, an algorithm is used to analyze the input data, evaluate the final price and convenience, and generate the optimal option. The output is the optimized purchase plan.

[0092] Step 5:

[0093] The device notifies the user of the generated purchase plan. The device notifies the user of the purchase plan received from the server. Specifically, the device uses push notifications and in-app messages to display details such as the cheapest store information and the cost after applying coupons. The input is the purchase plan from the server, and the output is a display of information that is understandable to the user.

[0094] Step 6:

[0095] The user makes a purchase decision based on the information provided. The user reviews the purchase plan displayed on the device and selects where to actually purchase the product. Specifically, they choose their desired store and price from the presented options and then proceed with the purchase. The input is the purchase plan information presented from the device, and the output is the user's purchase decision.

[0096] Step 7:

[0097] The server analyzes the user's past purchase history to suggest products. The server uses machine learning algorithms to analyze the user's past purchase data and generate relevant product recommendations based on that analysis. Specifically, it sends product information tailored to the user's preferences to the terminal. The input is the user's purchase history data, and the output is recommended product information.

[0098] (Application Example 1)

[0099] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0100] For consumers to make the most economical purchases, there is a significant challenge: they must manually gather and compare a large amount of information, which is very time-consuming and laborious. Furthermore, the lack of product recommendations that take past purchase history into account prevents them from having a more personalized shopping experience.

[0101] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0102] In this invention, the server includes means for receiving desired product information and location information obtained from the user, means for collecting product provision condition information, and means for generating consumer-oriented purchase suggestions. This enables the efficient provision of optimal purchase plans and product recommendations based on past purchase history.

[0103] A "user" is a consumer who uses the system to provide information about product purchases.

[0104] "Desired product information" refers to detailed information such as the name and quantity of the product the user wishes to purchase.

[0105] "Location information" refers to geographical information about the place where the user wants to receive the product or the delivery destination.

[0106] "Product availability information" refers to price, stock, and available discount information for products obtained from multiple sales locations.

[0107] "Consumer-oriented purchasing suggestions" refer to proposals that present users with the most suitable purchasing methods based on collected data.

[0108] "Electronic devices" refer to digital devices such as smartphones and personal computers.

[0109] "Purchase history" refers to records of products a user has purchased in the past and related data.

[0110] The system implementing this invention operates in conjunction with an electronic device such as a smartphone to improve user convenience. The user uses the electronic device to input information about the desired product to purchase and location information. This information is transmitted from the device to the server.

[0111] Upon receiving information, the server collects product availability information from multiple sales locations. Specifically, it retrieves price, inventory, and available discount information for each sales location. This is done using a mobile application framework based on React Native and server-side technology based on Node.js. The database uses MySQL® to organize the information, and Axios is used for API integration.

[0112] The server then generates optimal consumer-oriented purchase suggestions based on the collected information and the user's location. These suggestions calculate the most economical way to purchase each product and also take into account the best access options from the user's current location.

[0113] The generated purchase suggestions are sent back to the electronic terminal and the user is notified. Furthermore, past purchase history is referenced on the electronic terminal, and products are recommended based on that.

[0114] For example, if a user enters "organic coffee beans" as their desired product, the server will collect information from local and online stores and present a plan that includes the cheapest store to purchase them from and available coupon information.

[0115] An example of a prompt message to use with a generative AI model would be: "We have data with information about the product the user wants to buy. Based on this, please provide the store where you can buy it at the lowest price and any available coupons."

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The user enters information about the desired product and location via an electronic terminal. The entered information includes the name of the product the user wishes to purchase, the quantity, and the desired pickup location. This information is sent from the terminal to the server as input data.

[0119] Step 2:

[0120] The server searches databases of multiple sales locations based on the product information received from the user. Here, the server uses the sales location's API to retrieve relevant information, such as price data and inventory information. The databases contain real-time price information and inventory status for each product.

[0121] Step 3:

[0122] The server uses collected price information, inventory information, and current location information to calculate the optimal purchase plan. Specifically, the server analyzes product prices and inventory status, and combines available coupons and point programs to determine the most economical way to purchase. This calculation also takes into account accessibility based on location information.

[0123] Step 4:

[0124] The server sends the calculated optimal purchase plan to the electronic device. The device notifies the user of this plan, visually presenting the best purchase options. This notification also includes information on the cheapest store and the cost after applying coupons.

[0125] Step 5:

[0126] Electronic devices, based on information retrieved from a server, refer to the user's past purchase history and recommend relevant products accordingly. This process analyzes the user's historical data and recommends similar or related new products. This provides more personalized suggestions based on the user's future purchasing behavior.

[0127] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0128] Embodiments of the present invention will be described in detail. This invention is a purchase plan presentation system that employs a user emotion recognition function and is configured using a network of digital terminals and servers.

[0129] The user first uses a device to input information about the product they wish to purchase. During this process, an emotion engine analyzes the user's facial expressions and voice through devices such as the camera and microphone to determine their emotional state. For example, if the user smiles while inputting their desired product, it is recognized as a positive emotion, while a frown or other facial expression is identified as a negative emotion.

[0130] The server receives desired product information, location information, and emotional state data transmitted from the terminal, and collects prices and terms of availability from a database of multiple sales locations. Product suggestions are also adjusted according to the emotional state. For example, if the user is perceived as being in a relaxed state, slightly more expensive options may be suggested.

[0131] Next, the server analyzes price information along with emotional data to generate an optimal purchase plan tailored to the user's emotional state. For example, if the analysis indicates fatigue, it might emphasize the convenience of home delivery. This allows users to enjoy a stress-free shopping experience.

[0132] The device notifies the user of the purchase plan sent from the server. This notification allows the user to choose the purchase option that best suits their emotional state, resulting in a more satisfying purchasing experience.

[0133] Furthermore, the server stores recognized emotional data and uses it to optimize future purchasing plans. This data is used to analyze the purchasing history of users similar to an individual user's purchasing tendencies and emotional state.

[0134] This system enables more personalized product recommendations based on user emotions, supporting purchasing decisions. Thus, the present invention provides an effective method for offering a new purchasing experience that takes consumer emotions into consideration.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] The user uses the device to input the name and quantity of the product they wish to purchase. The device is equipped with a camera and microphone, which are used to capture the user's facial expressions and voice in real time. The emotion engine uses this data to recognize the user's emotions and identify emotional states such as joy, surprise, and anger.

[0138] Step 2:

[0139] The device sends purchase preference information to the server along with the recognized emotional state. This information includes the user's location and past purchase history.

[0140] Step 3:

[0141] Based on the received user data, the server issues queries to databases of multiple sales locations related to the desired product. Here, it retrieves product prices, stock availability, and coupon information. Furthermore, if the user's emotional state is "fatigue," the system prioritizes simpler purchasing methods.

[0142] Step 4:

[0143] The server generates the optimal purchase plan for the user based on feedback from the emotion engine and acquired product information. Here, it adjusts suggestions according to the user's emotions, offering a wider range of options to users in a positive state and a plan that allows users in a negative state to complete the purchase easily and simply.

[0144] Step 5:

[0145] The server sends the generated purchase plan to the device. The device then notifies the user of this information and clearly displays which option is suitable. The plan includes product details, price comparisons, and recommended purchase methods.

[0146] Step 6:

[0147] The user selects the most appealing option based on the provided purchase plans. After making a selection, the device returns feedback to the server regarding the user's choice and their emotional state at that time.

[0148] Step 7:

[0149] The server stores feedback on emotional states and purchase history data, and uses this data to generate future purchasing plans. This data helps optimize future product recommendations and plans.

[0150] (Example 2)

[0151] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0152] Traditional purchasing systems often failed to consider the user's emotional state when providing product information and price suggestions, making it difficult to deliver a shopping experience that was optimal for the user's current situation. Furthermore, because products were suggested without reflecting individual users' emotions or purchase history, personalized experiences could not be provided, posing a challenge to increasing user satisfaction.

[0153] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0154] In this invention, the server includes means for receiving desired product information and location information obtained from the user; means for collecting product price information and terms of service from multiple sales regions based on the desired product information and analyzed emotional state, and adjusting the proposed content; and means for generating an optimal purchase plan considering the product price information, location information, and emotional state. This makes it possible to provide a personalized purchase plan that corresponds to the user's emotional state.

[0155] A "user" is an individual or organization that uses the system to input product information and receive purchase plan suggestions.

[0156] "Product information" refers to detailed information about the product the user wishes to purchase. This information includes the product name, category, and budget.

[0157] "Location information" refers to location data related to the user's current location or the region in which they wish to make a purchase.

[0158] "Emotional state" refers to data that describes the psychological condition of a user, analyzed from their facial expressions and tone of voice. This includes both positive and negative emotions.

[0159] A "server" is a central computer system that aggregates and analyzes data received from users, generates an optimal purchasing plan, and sends it to the terminal.

[0160] A "purchase plan" is a general term for product suggestions optimized based on the user's desired product information, location information, and emotional state, as well as the plan related to their purchase.

[0161] A "sales area" is a range comprised of multiple regions where a product is sold. It is used to select the most suitable supplier based on the user's location information.

[0162] A "personalized experience" is a user-specific and optimized experience that takes into account the individual user's emotions and past behavior.

[0163] This invention is a system that presents a personalized purchasing plan, taking into account the user's emotional state. The system connects digital terminals and a server via a network, aiming to provide users with a more appropriate purchasing experience.

[0164] The user first uses a device to enter information about the product they wish to purchase. The device is equipped with a camera and microphone, and through this hardware, an emotion engine analyzes the user's facial expressions and voice. This determines the user's emotional state. For example, a smile is recognized as a positive emotion, while a frown is judged as a negative emotion.

[0165] The terminal transmits the user's entered product information, analyzed emotional state, and location information to the server. Based on the received data, the server collects product pricing information and terms of availability from a database of multiple sales regions. Crucially, the server adjusts its suggestions according to the user's emotional state. For example, a user identified as relaxed might be offered a slightly more expensive option.

[0166] The server analyzes collected price information, location information, and emotional state data to generate the optimal purchase plan for the user. For example, if past purchase history and current emotional state are analyzed to indicate fatigue, it can highlight convenient home delivery options.

[0167] The generated purchase plan is sent to the device, which then notifies the user. The user receives the notification and can choose options that align with their emotional state, leading to a more satisfying purchase experience.

[0168] Furthermore, the server accumulates recognized emotion data and purchase history data, and uses a generative AI model to optimize future purchasing plans. This allows for the generation of new prompts such as "What products would you recommend to a relaxed customer?", and the system is constantly being improved to provide a better purchasing experience.

[0169] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0170] Step 1:

[0171] The user uses a device to input information about the product they wish to purchase. This data includes product name, category, and budget. This gathers basic information about the user's preferences. The device uses its camera and microphone to record the user's facial expressions and voice in real time. This data is analyzed by an emotion engine to determine the user's emotional state (e.g., positive, negative). The input for this step is the user input and emotional state data, while the output is the analyzed emotional state.

[0172] Step 2:

[0173] The terminal sends user-inputted product information, analyzed emotional state, and location information to the server. The transmitted data is important as material for concretizing the user's request. The server prepares to process the received data. The input for this step is the dataset from the terminal, and the output is the accurate receipt of data to the server.

[0174] Step 3:

[0175] The server analyzes the data received from the terminal and, based on that, collects product pricing information and terms of availability from databases of multiple sales regions. The server then runs an emotion-specific algorithm to tailor suggestions according to the user's emotional state. For example, if the user is relaxed, it will explore premium options. The input for this step is the user's desired product information and emotional state received by the server, and the output is a list of product candidates tailored to that emotional state.

[0176] Step 4:

[0177] The server generates an optimal purchase plan based on collected price and offer information, taking into account the user's desired product information, location information, and analyzed emotional state. This plan is designed to optimize user needs and provide emotionally-adjusted purchase options. The inputs to this step are price information, location information, and emotional state, and the output is the optimized purchase plan.

[0178] Step 5:

[0179] The server sends the generated purchase plan to the terminal. The terminal notifies the user of the received purchase plan and displays it in the user interface. This process allows the user to decide whether to purchase based on the plan. The input for this step is the purchase plan from the server, and the output is the notification and display to the user.

[0180] Step 6:

[0181] The server stores sentiment data and purchase history obtained from all past interactions. This stored data is used as prompts to optimize future purchase plans using a generative AI model. For example, it generates a prompt such as, "What products should I recommend to a relaxed customer?" The input for this step is past sentiment and purchase data, and the output is data for the next optimization opportunity.

[0182] (Application Example 2)

[0183] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0184] In today's shopping experience, users face a wide variety of product options, which can lead to confusion when choosing the best option from many choices. Furthermore, providing uniform recommendations without considering the user's emotional state makes it difficult to maximize individual satisfaction. To address this challenge, there is a need to provide purchase suggestions that reflect the user's real-time emotional state.

[0185] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0186] In this invention, the server includes means for receiving desired item information and location information obtained from the user, means for using an image device and an audio device to analyze the user's emotional state, and means for adjusting the purchase plan based on the analyzed emotional state. This makes it possible to provide a purchase plan that is suitable for the user's emotional state.

[0187] A "user" refers to an individual who intends to purchase goods using the system.

[0188] "Desired item information" refers to information about the items that a user wishes to purchase.

[0189] "Location information" refers to information about the user's current location.

[0190] A "sales location" refers to a physical store or a sales location within a network where goods are sold.

[0191] "Goods price information" refers to information regarding the price of goods at the point of sale.

[0192] A "purchase plan" refers to a proposal for optimal purchasing based on the user's preferences.

[0193] An "imaging device" refers to a device used to analyze a user's facial expressions.

[0194] A "voice device" refers to a device used to analyze a user's voice.

[0195] "Emotional state" refers to the result of an analysis of the user's current emotional state.

[0196] "Benefits" refer to advantages or discounts offered to users in relation to their purchase.

[0197] "Purchase history" refers to records of items that a user has purchased in the past.

[0198] "Recommended items" refer to items that have been deemed suitable for suggestion to users.

[0199] The system for implementing this invention consists of a communication device owned by the user that exchanges information with a cloud server.

[0200] First, the user inputs information about the desired item through a communication device. The communication device is equipped with an image device and an audio device, which are used to analyze the user's emotional state in real time. This analysis uses the user's facial expressions captured by the image device and the tone of their voice collected by the audio device. An emotion recognition engine operating on the cloud is used to analyze the emotional state. Specifically, general image analysis software and audio analysis software are used for emotion recognition.

[0201] The cloud server receives desired item information, location information, and analyzed sentiment data transmitted from communication devices. Based on this, the server executes database queries to collect item price information from various sales locations. It also refers to past purchase history and sentiment data and generates a purchase plan using data processing tools. For example, using Apache® Spark allows for rapid processing of large amounts of data.

[0202] The generated purchase plan is adjusted based on the user's emotional state and communicated to the user via a communication device. This notification includes recommended items and special offers, providing the user with the best possible purchasing experience.

[0203] For example, if a user is looking for a new electronic device and their expression is calm, a slightly more expensive but high-performance product, along with corresponding perks, will be suggested. It is also possible to use a generative AI model to create prompts that recommend the most suitable fashion items when the user is smiling. An example of such a prompt would be, "Recommend the most suitable fashion items when the user is smiling."

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The terminal accepts information about the items the user desires. The user inputs the item's name, type, and desired price range via a communication device. This information is temporarily stored within the terminal. Additionally, the terminal's image and audio devices capture the user's facial expressions and voice, and this data is prepared for emotional state analysis.

[0207] Step 2:

[0208] The device sends captured facial expression and voice data to an emotion recognition engine. The emotion recognition engine analyzes the received data and identifies the user's current emotional state. Specifically, image processing software analyzes facial expressions, and voice analysis software analyzes voice tone. The resulting emotional state data is then sent to the server.

[0209] Step 3:

[0210] The server receives information about desired items, location, and emotional state from the terminal. Based on the received data, it queries the database to collect item price information from multiple sales locations. The database returns price information and availability conditions for each sales location.

[0211] Step 4:

[0212] The server generates an optimal purchase plan based on received item price and location information. Here, data processing tools such as Apache Spark are used to rapidly process large amounts of data. The generated purchase plan is then adjusted to reflect the analyzed sentiment state, for example, including more expensive options if the user is happy.

[0213] Step 5:

[0214] The server sends the final purchase plan to the device. The device then notifies the user of this plan. The plan includes a list of recommended items and perks, and is personalized according to the user's emotional state. The user receives this notification and can choose the purchase option that best suits their emotional state.

[0215] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0216] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0217] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0218] [Second Embodiment]

[0219] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0220] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0221] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0222] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0223] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0224] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0225] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0226] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0227] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0228] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0229] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0230] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0231] Embodiments of the present invention will be described in detail below. This system is configured using digital terminals such as smartphones and personal computers, and consists of multiple functional modules in order to provide the user with an optimal product purchase plan.

[0232] The user begins by using a device to enter information about the product they wish to purchase. This information includes the product name, the required quantity, and the desired pickup location or delivery address. The device then transmits this information to the server.

[0233] The server searches a database of relevant sales locations based on the received product and location information. It then uses APIs from online platforms and local stores to collect the latest price information, availability, and available coupons for the relevant products. For example, if a user enters that they want to buy "rice, milk, and eggs," the server will retrieve information about these products from nearby supermarkets and major online stores.

[0234] Next, the server analyzes the acquired information to calculate the most economical purchase method for each product. This takes into account the final price when coupons and point systems are applied. Accessibility from the user's current location is also included in the evaluation. The server generates the most effective purchase plan for the user and compares multiple options.

[0235] The terminal notifies the user of a purchase plan sent from the server. This notification includes detailed information such as the store with the lowest price, the best combination of products to purchase, and the cost after applying coupons. Based on the information provided, the user can choose where to actually purchase the products.

[0236] Furthermore, this system can refer to a user's past purchase history and suggest products based on that history. This further personalizes the user's shopping experience and improves convenience.

[0237] In this way, the system provides a highly effective means of supporting users' purchasing behavior and saving them time and costs.

[0238] The following describes the processing flow.

[0239] Step 1:

[0240] The user uses a terminal to enter a list of items they wish to purchase. This includes specifying the product name, quantity, budget, whether they want any coupons, and their current location. Once the terminal correctly receives the entered information, it prepares to send it to the server.

[0241] Step 2:

[0242] The server retrieves the user's desired product information and location information received from the terminal. Next, it generates a query to look up multiple sales location databases that match the user's desired products. This query includes the necessary information to retrieve current market prices, discount information, and inventory status.

[0243] Step 3:

[0244] The server collects information through APIs and websites of online and local stores. In particular, it retrieves the price, inventory information, and discount information when coupons are applied for each product, and aggregates this information in a central database. The information collection process is performed in real time and is designed to obtain the most up-to-date market information possible.

[0245] Step 4:

[0246] The server performs a detailed analysis based on the collected data and calculates the purchase cost for each product. This includes the final price after applying coupons and a cost comparison when purchasing items from multiple stores in combination. It also evaluates the cost of visiting offline stores based on the distance from the user's current location.

[0247] Step 5:

[0248] The server generates the best possible purchase plan, ranking multiple options based on the user's criteria. The generated plan includes details such as the cheapest store, the best value bundle, and online purchase options.

[0249] Step 6:

[0250] The server notifies the user's device of the generated purchase plan. The device then displays this information clearly to the user, allowing them to review the proposal, including detailed pricing information and applicable coupons.

[0251] Step 7:

[0252] The user reviews the notified plan and selects the most suitable purchase option. After making a selection, the device sends this information back to the server as feedback, which is used to improve future services.

[0253] (Example 1)

[0254] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0255] Modern consumer purchasing behavior is complex due to multiple factors, including the diversity of product prices, fluctuations in inventory, and the availability of coupons. As a result, consumers spend a great deal of time and effort finding the optimal way to purchase products. Furthermore, the lack of personalized recommendations that leverage individual users' purchase history makes it difficult for consumers to find products that suit their preferences.

[0256] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0257] In this invention, the server includes means for receiving desired product information and location information obtained from the user, means for collecting product price information and inventory information from multiple sales locations, and means for generating an optimal purchase plan considering coupon information. This allows the user to be presented with the best options for purchasing products efficiently and economically, saving time and costs. Furthermore, by providing recommended products that take into account past purchase history, the user's purchasing experience is further improved.

[0258] A "user" refers to an individual or group that uses a digital device to input product information and receive a purchase plan.

[0259] "Desired product information" refers to data that includes specific information such as the name, quantity, and pickup location of the product the user wishes to purchase.

[0260] "Location information" refers to data that indicates the geographical location of the user or the place where the product will be received.

[0261] A "sales outlet" refers to a facility that includes online platforms and physical stores where products are sold.

[0262] "Product pricing information" refers to data regarding the selling price of a specific product.

[0263] "Inventory information" refers to data regarding the supply status and available quantities of a particular product.

[0264] "Coupon information" refers to data that includes information about discounts and benefits that apply when purchasing products.

[0265] A "purchase plan" refers to a plan that includes suggestions for how a user can best purchase a product.

[0266] "Notification" refers to the process or action of communicating information to a user via a device.

[0267] "Purchase history" refers to data that records a user's past purchase history and related information.

[0268] "Recommended products" refer to products selected based on the user's past purchase history and preferences.

[0269] This invention is a system that utilizes digital devices such as smartphones and personal computers to provide users with the most suitable product purchase plan. The system is primarily composed of interactions between three parties: the user, the device, and the server.

[0270] The user enters information about the product they wish to purchase via a digital terminal. This information includes the product name, quantity, and pickup location. The terminal transmits the entered information to the server. After receiving the information, the server searches a database of relevant sales locations. The server executes database queries and retrieves the latest price and inventory information for the product via the online platform or local store APIs. This may involve using HTTP requests, for example.

[0271] Next, the server uses the collected information to perform price comparisons and generate the optimal purchase plan. This is done by an algorithm that calculates the most economical way to buy. This process takes into account factors such as the application of coupons and the use of point systems. Furthermore, it also takes into account ease of access based on the user's location information.

[0272] As a concrete example of operation, let's consider a scenario where a user wants to purchase a new smartphone case within a budget. The user enters the product name and budget on their device and presses the submit button. The server uses this information to collect price information and generates the most cost-effective purchase plan. The device then notifies the user of the generated plan, and the user can choose the best option from the presented choices.

[0273] Furthermore, the server analyzes the user's past purchase history to suggest highly relevant products. This functionality is achieved by utilizing a generative AI model that learns the user's purchasing habits.

[0274] Examples of prompt messages include the following:

[0275] "The user is looking for a smartphone case. The user has specified the product name, color, material, and budget. Based on this, please generate an optimal purchase plan."

[0276] To implement this system, a digital terminal and a server need to be connected via the Internet and configured so that information can be exchanged quickly and accurately. With this system, users can efficiently select and purchase products.

[0277] The flow of specific processing in Example 1 will be described using Fig. 11.

[0278] Step 1:

[0279] The user inputs and sends product information. The user uses a digital terminal to input the name of the product to be purchased, the required quantity, the desired receiving location, and the location information of the delivery destination, etc., and presses the "Send" button. This input becomes the data set required for the next process.

[0280] Step 2:

[0281] The terminal sends the product information to the server. The terminal sends the information input by the user to the server. At this stage, the procedure for transferring the information to the server through the network is performed. The input is the data entered by the user into the terminal, and as its output, it is converted into a data format that can be used by the server.

[0282] Step 3:

[0283] The server searches the database and collects information. The server issues an SQL query to search the database of relevant sales bases based on the received product information. As a result of the query, price information, inventory information, and coupon information from multiple sales bases are collected. The input is the request information from the user, and the output is the product price, inventory, and coupon information.

[0284] Step 4:

[0285] The server generates an optimal purchase plan. Based on the collected information, the server calculates the most economical purchase plan considering price, inventory status, and coupon availability. Here, an algorithm is used to analyze the input data, evaluate the final price and convenience, and generate an optimal option. The output is an optimized purchase plan.

[0286] Step 5:

[0287] The terminal notifies the user of the generated purchase plan. The terminal notifies the user of the purchase plan received from the server. Specifically, the terminal uses push notifications or in-app messages to display details such as the information of the cheapest store and the cost after coupon application. The input is the purchase plan from the server, and the output is the display of information understandable to the user.

[0288] Step 6:

[0289] The user makes a purchase selection based on the provided information. The user checks the purchase plan displayed on the terminal and selects where to actually purchase the product. Specifically, the user selects the desired store and price from the presented options and performs the action of executing the purchase procedure. The input is the presented information of the purchase plan from the terminal, and the output is the user's purchase decision.

[0290] Step 7:

[0291] The server analyzes the user's past purchase history and presents recommended products. The server uses a machine learning algorithm to analyze the user's past purchase data and generates relevant recommended products based on it. Specifically, it transmits product information suitable for the user's preferences to the terminal. The input is the user's purchase history data, and the output is the recommended product information.

[0292] (Application Example 1)

[0293] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0294] For consumers to make the most economical purchases, there is a significant challenge: they must manually gather and compare a large amount of information, which is very time-consuming and laborious. Furthermore, the lack of product recommendations that take past purchase history into account prevents them from having a more personalized shopping experience.

[0295] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0296] In this invention, the server includes means for receiving desired product information and location information obtained from the user, means for collecting product provision condition information, and means for generating consumer-oriented purchase suggestions. This enables the efficient provision of optimal purchase plans and product recommendations based on past purchase history.

[0297] A "user" is a consumer who uses the system to provide information about product purchases.

[0298] "Desired product information" refers to detailed information such as the name and quantity of the product the user wishes to purchase.

[0299] "Location information" refers to geographical information about the place where the user wants to receive the product or the delivery destination.

[0300] "Product availability information" refers to price, stock, and available discount information for products obtained from multiple sales locations.

[0301] "Consumer-oriented purchasing suggestions" refer to proposals that present users with the most suitable purchasing methods based on collected data.

[0302] "Electronic devices" refer to digital devices such as smartphones and personal computers.

[0303] The "purchase history" refers to the records of the products purchased by the user in the past and the related data.

[0304] The system for implementing this invention operates in cooperation with an electronic terminal such as a smartphone in order to improve the convenience of the user. The user inputs the desired product information and location information that they want to purchase using the electronic terminal. These pieces of information are transmitted from the terminal to the server.

[0305] When the server receives the information, it collects product offering condition information from a plurality of sales bases. Specifically, it obtains the price, inventory, and available discount information for each sales base. For this, a mobile application framework using React Native and server-side technology using Node.js are used. The database organizes the information using MySQL, and Axios is used for API cooperation.

[0306] The server further generates an optimal purchase proposal for consumers based on the collected information and the user's location information. This proposal calculates the most economical purchase method for each product and also takes into account the optimal access from the user's current location.

[0307] The generated purchase proposal is transmitted to the electronic terminal again and notified to the user. Furthermore, the past purchase history is referenced on the electronic terminal, and products are recommended based on it.

[0308] As a specific example, when the user inputs "organic coffee beans" as the desired product, the server collects information on stores within the region and online stores, and presents the store where it can be purchased at the lowest price and the available coupon information as a plan.

[0309] As an example of the prompt sentence when using the generation AI model, a format such as "There is data in which the information of the product that the user wants to purchase is input. Based on this, please provide the store where it can be purchased at the lowest price and the available coupon information." is used.

[0310] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0311] Step 1:

[0312] The user enters information about the desired product and location via an electronic terminal. The entered information includes the name of the product the user wishes to purchase, the quantity, and the desired pickup location. This information is sent from the terminal to the server as input data.

[0313] Step 2:

[0314] The server searches databases of multiple sales locations based on the product information received from the user. Here, the server uses the sales location's API to retrieve relevant information, such as price data and inventory information. The databases contain real-time price information and inventory status for each product.

[0315] Step 3:

[0316] The server uses collected price information, inventory information, and current location information to calculate the optimal purchase plan. Specifically, the server analyzes product prices and inventory status, and combines available coupons and point programs to determine the most economical way to purchase. This calculation also takes into account accessibility based on location information.

[0317] Step 4:

[0318] The server sends the calculated optimal purchase plan to the electronic device. The device notifies the user of this plan, visually presenting the best purchase options. This notification also includes information on the cheapest store and the cost after applying coupons.

[0319] Step 5:

[0320] Electronic devices, based on information retrieved from a server, refer to the user's past purchase history and recommend relevant products accordingly. This process analyzes the user's historical data and recommends similar or related new products. This provides more personalized suggestions based on the user's future purchasing behavior.

[0321] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0322] Embodiments of the present invention will be described in detail. This invention is a purchase plan presentation system that employs a user emotion recognition function and is configured using a network of digital terminals and servers.

[0323] The user first uses a device to input information about the product they wish to purchase. During this process, an emotion engine analyzes the user's facial expressions and voice through devices such as the camera and microphone to determine their emotional state. For example, if the user smiles while inputting their desired product, it is recognized as a positive emotion, while a frown or other facial expression is identified as a negative emotion.

[0324] The server receives desired product information, location information, and emotional state data transmitted from the terminal, and collects prices and terms of availability from a database of multiple sales locations. Product suggestions are also adjusted according to the emotional state. For example, if the user is perceived as being in a relaxed state, slightly more expensive options may be suggested.

[0325] Next, the server analyzes price information along with emotional data to generate an optimal purchase plan tailored to the user's emotional state. For example, if the analysis indicates fatigue, it might emphasize the convenience of home delivery. This allows users to enjoy a stress-free shopping experience.

[0326] The device notifies the user of the purchase plan sent from the server. This notification allows the user to choose the purchase option that best suits their emotional state, resulting in a more satisfying purchasing experience.

[0327] Furthermore, the server stores recognized emotional data and uses it to optimize future purchasing plans. This data is used to analyze the purchasing history of users similar to an individual user's purchasing tendencies and emotional state.

[0328] This system enables more personalized product recommendations based on user emotions, supporting purchasing decisions. Thus, the present invention provides an effective method for offering a new purchasing experience that takes consumer emotions into consideration.

[0329] The following describes the processing flow.

[0330] Step 1:

[0331] The user uses the device to input the name and quantity of the product they wish to purchase. The device is equipped with a camera and microphone, which are used to capture the user's facial expressions and voice in real time. The emotion engine uses this data to recognize the user's emotions and identify emotional states such as joy, surprise, and anger.

[0332] Step 2:

[0333] The device sends purchase preference information to the server along with the recognized emotional state. This information includes the user's location and past purchase history.

[0334] Step 3:

[0335] Based on the received user data, the server issues queries to databases of multiple sales locations related to the desired product. Here, it retrieves product prices, stock availability, and coupon information. Furthermore, if the user's emotional state is "fatigue," the system prioritizes simpler purchasing methods.

[0336] Step 4:

[0337] The server generates the optimal purchase plan for the user based on feedback from the emotion engine and acquired product information. Here, it adjusts suggestions according to the user's emotions, offering a wider range of options to users in a positive state and a plan that allows users in a negative state to complete the purchase easily and simply.

[0338] Step 5:

[0339] The server sends the generated purchase plan to the device. The device then notifies the user of this information and clearly displays which option is suitable. The plan includes product details, price comparisons, and recommended purchase methods.

[0340] Step 6:

[0341] The user selects the most appealing option based on the provided purchase plans. After making a selection, the device returns feedback to the server regarding the user's choice and their emotional state at that time.

[0342] Step 7:

[0343] The server stores feedback on emotional states and purchase history data, and uses this data to generate future purchasing plans. This data helps optimize future product recommendations and plans.

[0344] (Example 2)

[0345] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0346] Traditional purchasing systems often failed to consider the user's emotional state when providing product information and price suggestions, making it difficult to deliver a shopping experience that was optimal for the user's current situation. Furthermore, because products were suggested without reflecting individual users' emotions or purchase history, personalized experiences could not be provided, posing a challenge to increasing user satisfaction.

[0347] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0348] In this invention, the server includes means for receiving desired product information and location information obtained from the user; means for collecting product price information and terms of service from multiple sales regions based on the desired product information and analyzed emotional state, and adjusting the proposed content; and means for generating an optimal purchase plan considering the product price information, location information, and emotional state. This makes it possible to provide a personalized purchase plan that corresponds to the user's emotional state.

[0349] A "user" is an individual or organization that uses the system to input product information and receive purchase plan suggestions.

[0350] "Product information" refers to detailed information about the product the user wishes to purchase. This information includes the product name, category, and budget.

[0351] "Location information" refers to location data related to the user's current location or the region in which they wish to make a purchase.

[0352] "Emotional state" refers to data that describes the psychological condition of a user, analyzed from their facial expressions and tone of voice. This includes both positive and negative emotions.

[0353] A "server" is a central computer system that aggregates and analyzes data received from users, generates an optimal purchasing plan, and sends it to the terminal.

[0354] A "purchase plan" is a general term for product suggestions optimized based on the user's desired product information, location information, and emotional state, as well as the plan related to their purchase.

[0355] A "sales area" is a range comprised of multiple regions where a product is sold. It is used to select the most suitable supplier based on the user's location information.

[0356] A "personalized experience" is a user-specific and optimized experience that takes into account the individual user's emotions and past behavior.

[0357] This invention is a system that presents a personalized purchasing plan, taking into account the user's emotional state. The system connects digital terminals and a server via a network, aiming to provide users with a more appropriate purchasing experience.

[0358] The user first uses a device to enter information about the product they wish to purchase. The device is equipped with a camera and microphone, and through this hardware, an emotion engine analyzes the user's facial expressions and voice. This determines the user's emotional state. For example, a smile is recognized as a positive emotion, while a frown is judged as a negative emotion.

[0359] The terminal transmits the user's entered product information, analyzed emotional state, and location information to the server. Based on the received data, the server collects product pricing information and terms of availability from a database of multiple sales regions. Crucially, the server adjusts its suggestions according to the user's emotional state. For example, a user identified as relaxed might be offered a slightly more expensive option.

[0360] The server analyzes collected price information, location information, and emotional state data to generate the optimal purchase plan for the user. For example, if past purchase history and current emotional state are analyzed to indicate fatigue, it can highlight convenient home delivery options.

[0361] The generated purchase plan is sent to the device, which then notifies the user. The user receives the notification and can choose options that align with their emotional state, leading to a more satisfying purchase experience.

[0362] Furthermore, the server accumulates recognized emotion data and purchase history data, and uses a generative AI model to optimize future purchasing plans. This allows for the generation of new prompts such as "What products would you recommend to a relaxed customer?", and the system is constantly being improved to provide a better purchasing experience.

[0363] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0364] Step 1:

[0365] The user uses a device to input information about the product they wish to purchase. This data includes product name, category, and budget. This gathers basic information about the user's preferences. The device uses its camera and microphone to record the user's facial expressions and voice in real time. This data is analyzed by an emotion engine to determine the user's emotional state (e.g., positive, negative). The input for this step is the user input and emotional state data, while the output is the analyzed emotional state.

[0366] Step 2:

[0367] The terminal sends user-inputted product information, analyzed emotional state, and location information to the server. The transmitted data is important as material for concretizing the user's request. The server prepares to process the received data. The input for this step is the dataset from the terminal, and the output is the accurate receipt of data to the server.

[0368] Step 3:

[0369] The server analyzes the data received from the terminal and, based on that, collects product pricing information and terms of availability from databases of multiple sales regions. The server then runs an emotion-specific algorithm to tailor suggestions according to the user's emotional state. For example, if the user is relaxed, it will explore premium options. The input for this step is the user's desired product information and emotional state received by the server, and the output is a list of product candidates tailored to that emotional state.

[0370] Step 4:

[0371] The server generates an optimal purchase plan based on collected price and offer information, taking into account the user's desired product information, location information, and analyzed emotional state. This plan is designed to optimize user needs and provide emotionally-adjusted purchase options. The inputs to this step are price information, location information, and emotional state, and the output is the optimized purchase plan.

[0372] Step 5:

[0373] The server sends the generated purchase plan to the terminal. The terminal notifies the user of the received purchase plan and displays it in the user interface. This process allows the user to decide whether to purchase based on the plan. The input for this step is the purchase plan from the server, and the output is the notification and display to the user.

[0374] Step 6:

[0375] The server stores sentiment data and purchase history obtained from all past interactions. This stored data is used as prompts to optimize future purchase plans using a generative AI model. For example, it generates a prompt such as, "What products should I recommend to a relaxed customer?" The input for this step is past sentiment and purchase data, and the output is data for the next optimization opportunity.

[0376] (Application Example 2)

[0377] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0378] In today's shopping experience, users face a wide variety of product options, which can lead to confusion when choosing the best option from many choices. Furthermore, providing uniform recommendations without considering the user's emotional state makes it difficult to maximize individual satisfaction. To address this challenge, there is a need to provide purchase suggestions that reflect the user's real-time emotional state.

[0379] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0380] In this invention, the server includes means for receiving desired item information and location information obtained from the user, means for using an image device and an audio device to analyze the user's emotional state, and means for adjusting the purchase plan based on the analyzed emotional state. This makes it possible to provide a purchase plan that is suitable for the user's emotional state.

[0381] A "user" refers to an individual who intends to purchase goods using the system.

[0382] "Desired item information" refers to information about the items that a user wishes to purchase.

[0383] "Location information" refers to information about the user's current location.

[0384] A "sales location" refers to a physical store or a sales location within a network where goods are sold.

[0385] "Goods price information" refers to information regarding the price of goods at the point of sale.

[0386] A "purchase plan" refers to a proposal for optimal purchasing based on the user's preferences.

[0387] An "imaging device" refers to a device used to analyze a user's facial expressions.

[0388] A "voice device" refers to a device used to analyze a user's voice.

[0389] "Emotional state" refers to the result of an analysis of the user's current emotional state.

[0390] "Benefits" refer to advantages or discounts offered to users in relation to their purchase.

[0391] "Purchase history" refers to records of items that a user has purchased in the past.

[0392] "Recommended items" refer to items that have been deemed suitable for suggestion to users.

[0393] The system for implementing this invention consists of a communication device owned by the user that exchanges information with a cloud server.

[0394] First, the user inputs information about the desired item through a communication device. The communication device is equipped with an image device and an audio device, which are used to analyze the user's emotional state in real time. This analysis uses the user's facial expressions captured by the image device and the tone of their voice collected by the audio device. An emotion recognition engine operating on the cloud is used to analyze the emotional state. Specifically, general image analysis software and audio analysis software are used for emotion recognition.

[0395] The cloud server receives desired item information, location information, and analyzed sentiment data transmitted from communication devices. Based on this, the server executes database queries to collect item price information from various sales locations. It also refers to past purchase history and sentiment data and generates a purchase plan using data processing tools. For example, Apache Spark can be used to process large amounts of data quickly.

[0396] The generated purchase plan is adjusted based on the user's emotional state and communicated to the user via a communication device. This notification includes recommended items and special offers, providing the user with the best possible purchasing experience.

[0397] For example, if a user is looking for a new electronic device and their expression is calm, a slightly more expensive but high-performance product, along with corresponding perks, will be suggested. It is also possible to use a generative AI model to create prompts that recommend the most suitable fashion items when the user is smiling. An example of such a prompt would be, "Recommend the most suitable fashion items when the user is smiling."

[0398] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0399] Step 1:

[0400] The terminal accepts information about the items the user desires. The user inputs the item's name, type, and desired price range via a communication device. This information is temporarily stored within the terminal. Additionally, the terminal's image and audio devices capture the user's facial expressions and voice, and this data is prepared for emotional state analysis.

[0401] Step 2:

[0402] The device sends captured facial expression and voice data to an emotion recognition engine. The emotion recognition engine analyzes the received data and identifies the user's current emotional state. Specifically, image processing software analyzes facial expressions, and voice analysis software analyzes voice tone. The resulting emotional state data is then sent to the server.

[0403] Step 3:

[0404] The server receives information about desired items, location, and emotional state from the terminal. Based on the received data, it queries the database to collect item price information from multiple sales locations. The database returns price information and availability conditions for each sales location.

[0405] Step 4:

[0406] The server generates an optimal purchase plan based on received item price and location information. Here, data processing tools such as Apache Spark are used to rapidly process large amounts of data. The generated purchase plan is then adjusted to reflect the analyzed sentiment state, for example, including more expensive options if the user is happy.

[0407] Step 5:

[0408] The server sends the final purchase plan to the device. The device then notifies the user of this plan. The plan includes a list of recommended items and perks, and is personalized according to the user's emotional state. The user receives this notification and can choose the purchase option that best suits their emotional state.

[0409] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0410] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0411] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0412] [Third Embodiment]

[0413] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0414] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0415] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0416] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0417] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0418] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0419] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0420] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0421] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0422] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0423] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0424] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0425] Embodiments of the present invention will be described in detail below. This system is configured using digital terminals such as smartphones and personal computers, and consists of multiple functional modules in order to provide the user with an optimal product purchase plan.

[0426] The user begins by using a device to enter information about the product they wish to purchase. This information includes the product name, the required quantity, and the desired pickup location or delivery address. The device then transmits this information to the server.

[0427] The server searches a database of relevant sales locations based on the received product and location information. It then uses APIs from online platforms and local stores to collect the latest price information, availability, and available coupons for the relevant products. For example, if a user enters that they want to buy "rice, milk, and eggs," the server will retrieve information about these products from nearby supermarkets and major online stores.

[0428] Next, the server analyzes the acquired information to calculate the most economical purchase method for each product. This takes into account the final price when coupons and point systems are applied. Accessibility from the user's current location is also included in the evaluation. The server generates the most effective purchase plan for the user and compares multiple options.

[0429] The terminal notifies the user of a purchase plan sent from the server. This notification includes detailed information such as the store with the lowest price, the best combination of products to purchase, and the cost after applying coupons. Based on the information provided, the user can choose where to actually purchase the products.

[0430] Furthermore, this system can refer to a user's past purchase history and suggest products based on that history. This further personalizes the user's shopping experience and improves convenience.

[0431] In this way, the system provides a highly effective means of supporting users' purchasing behavior and saving them time and costs.

[0432] The following describes the processing flow.

[0433] Step 1:

[0434] The user uses a terminal to enter a list of items they wish to purchase. This includes specifying the product name, quantity, budget, whether they want any coupons, and their current location. Once the terminal correctly receives the entered information, it prepares to send it to the server.

[0435] Step 2:

[0436] The server retrieves the user's desired product information and location information received from the terminal. Next, it generates a query to look up multiple sales location databases that match the user's desired products. This query includes the necessary information to retrieve current market prices, discount information, and inventory status.

[0437] Step 3:

[0438] The server collects information through APIs and websites of online and local stores. In particular, it retrieves the price, inventory information, and discount information when coupons are applied for each product, and aggregates this information in a central database. The information collection process is performed in real time and is designed to obtain the most up-to-date market information possible.

[0439] Step 4:

[0440] The server performs a detailed analysis based on the collected data and calculates the purchase cost for each product. This includes the final price after applying coupons and a cost comparison when purchasing items from multiple stores in combination. It also evaluates the cost of visiting offline stores based on the distance from the user's current location.

[0441] Step 5:

[0442] The server generates the best possible purchase plan, ranking multiple options based on the user's criteria. The generated plan includes details such as the cheapest store, the best value bundle, and online purchase options.

[0443] Step 6:

[0444] The server notifies the user's device of the generated purchase plan. The device then displays this information clearly to the user, allowing them to review the proposal, including detailed pricing information and applicable coupons.

[0445] Step 7:

[0446] The user reviews the notified plan and selects the most suitable purchase option. After making a selection, the device sends this information back to the server as feedback, which is used to improve future services.

[0447] (Example 1)

[0448] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0449] Modern consumer purchasing behavior is complex due to multiple factors, including the diversity of product prices, fluctuations in inventory, and the availability of coupons. As a result, consumers spend a great deal of time and effort finding the optimal way to purchase products. Furthermore, the lack of personalized recommendations that leverage individual users' purchase history makes it difficult for consumers to find products that suit their preferences.

[0450] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0451] In this invention, the server includes means for receiving desired product information and location information obtained from the user, means for collecting product price information and inventory information from multiple sales locations, and means for generating an optimal purchase plan considering coupon information. This allows the user to be presented with the best options for purchasing products efficiently and economically, saving time and costs. Furthermore, by providing recommended products that take into account past purchase history, the user's purchasing experience is further improved.

[0452] A "user" refers to an individual or group that uses a digital device to input product information and receive a purchase plan.

[0453] "Desired product information" refers to data that includes specific information such as the name, quantity, and pickup location of the product the user wishes to purchase.

[0454] "Location information" refers to data that indicates the geographical location of the user or the place where the product will be received.

[0455] A "sales outlet" refers to a facility that includes online platforms and physical stores where products are sold.

[0456] "Product pricing information" refers to data regarding the selling price of a specific product.

[0457] "Inventory information" refers to data regarding the supply status and available quantities of a particular product.

[0458] "Coupon information" refers to data that includes information about discounts and benefits that apply when purchasing products.

[0459] A "purchase plan" refers to a plan that includes suggestions for how a user can best purchase a product.

[0460] "Notification" refers to the process or action of communicating information to a user via a device.

[0461] "Purchase history" refers to data that records a user's past purchase history and related information.

[0462] "Recommended products" refer to products selected based on the user's past purchase history and preferences.

[0463] This invention is a system that utilizes digital devices such as smartphones and personal computers to provide users with the most suitable product purchase plan. The system is primarily composed of interactions between three parties: the user, the device, and the server.

[0464] The user enters information about the product they wish to purchase via a digital terminal. This information includes the product name, quantity, and pickup location. The terminal transmits the entered information to the server. After receiving the information, the server searches a database of relevant sales locations. The server executes database queries and retrieves the latest price and inventory information for the product via the online platform or local store APIs. This may involve using HTTP requests, for example.

[0465] Next, the server uses the collected information to perform price comparisons and generate the optimal purchase plan. This is done by an algorithm that calculates the most economical way to buy. This process takes into account factors such as the application of coupons and the use of point systems. Furthermore, it also takes into account ease of access based on the user's location information.

[0466] As a concrete example of operation, let's consider a scenario where a user wants to purchase a new smartphone case within a budget. The user enters the product name and budget on their device and presses the submit button. The server uses this information to collect price information and generates the most cost-effective purchase plan. The device then notifies the user of the generated plan, and the user can choose the best option from the presented choices.

[0467] Furthermore, the server analyzes the user's past purchase history to suggest highly relevant products. This functionality is achieved by utilizing a generative AI model that learns the user's purchasing habits.

[0468] Examples of prompt messages include the following:

[0469] "A user is looking for a smartphone case. They have specified the product name, color, material, and budget. Based on this, generate the best purchase plan."

[0470] To implement this system, digital terminals and servers must be connected via the internet, and configured to allow for rapid and accurate information exchange. This system will enable users to efficiently select and purchase products.

[0471] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0472] Step 1:

[0473] The user enters and submits product information. Using a digital device, the user enters the name of the product they wish to purchase, the required quantity, and the desired pickup location or delivery address, and then presses the "Submit" button. This input becomes the dataset necessary for the next processing step.

[0474] Step 2:

[0475] The terminal sends product information to the server. The terminal sends information entered by the user to the server. At this stage, the information is transferred to the server via the network. The input is data entered by the user into the terminal, and as output, it is converted into a data format that the server can use.

[0476] Step 3:

[0477] The server searches the database and collects information. Based on the received product information, the server issues SQL queries to search the databases of related sales locations. As a result of the queries, it collects price information, inventory information, and coupon information from multiple sales locations. The input is the request information from the user, and the output is product price, inventory, and coupon information.

[0478] Step 4:

[0479] The server generates the optimal purchase plan. Based on the collected information, the server calculates the most economical purchase plan, considering price, inventory status, and coupon availability. Here, an algorithm is used to analyze the input data, evaluate the final price and convenience, and generate the optimal option. The output is the optimized purchase plan.

[0480] Step 5:

[0481] The device notifies the user of the generated purchase plan. The device notifies the user of the purchase plan received from the server. Specifically, the device uses push notifications and in-app messages to display details such as the cheapest store information and the cost after applying coupons. The input is the purchase plan from the server, and the output is a display of information that is understandable to the user.

[0482] Step 6:

[0483] The user makes a purchase decision based on the information provided. The user reviews the purchase plan displayed on the device and selects where to actually purchase the product. Specifically, they choose their desired store and price from the presented options and then proceed with the purchase. The input is the purchase plan information presented from the device, and the output is the user's purchase decision.

[0484] Step 7:

[0485] The server analyzes the user's past purchase history to suggest products. The server uses machine learning algorithms to analyze the user's past purchase data and generate relevant product recommendations based on that analysis. Specifically, it sends product information tailored to the user's preferences to the terminal. The input is the user's purchase history data, and the output is recommended product information.

[0486] (Application Example 1)

[0487] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0488] For consumers to make the most economical purchases, there is a significant challenge: they must manually gather and compare a large amount of information, which is very time-consuming and laborious. Furthermore, the lack of product recommendations that take past purchase history into account prevents them from having a more personalized shopping experience.

[0489] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0490] In this invention, the server includes means for receiving desired product information and location information obtained from the user, means for collecting product provision condition information, and means for generating consumer-oriented purchase suggestions. This enables the efficient provision of optimal purchase plans and product recommendations based on past purchase history.

[0491] A "user" is a consumer who uses the system to provide information about product purchases.

[0492] "Desired product information" refers to detailed information such as the name and quantity of the product the user wishes to purchase.

[0493] "Location information" refers to geographical information about the place where the user wants to receive the product or the delivery destination.

[0494] "Product availability information" refers to price, stock, and available discount information for products obtained from multiple sales locations.

[0495] "Consumer-oriented purchasing suggestions" refer to proposals that present users with the most suitable purchasing methods based on collected data.

[0496] "Electronic devices" refer to digital devices such as smartphones and personal computers.

[0497] "Purchase history" refers to records of products a user has purchased in the past and related data.

[0498] The system implementing this invention operates in conjunction with an electronic device such as a smartphone to improve user convenience. The user uses the electronic device to input information about the desired product to purchase and location information. This information is transmitted from the device to the server.

[0499] Upon receiving information, the server collects product availability information from multiple sales locations. Specifically, it retrieves price, inventory, and available discount information for each sales location. This is done using a mobile application framework based on React Native and server-side technology based on Node.js. MySQL is used to organize the information in the database, and Axios is used for API integration.

[0500] The server then generates optimal consumer-oriented purchase suggestions based on the collected information and the user's location. These suggestions calculate the most economical way to purchase each product and also take into account the best access options from the user's current location.

[0501] The generated purchase suggestions are sent back to the electronic terminal and the user is notified. Furthermore, past purchase history is referenced on the electronic terminal, and products are recommended based on that.

[0502] For example, if a user enters "organic coffee beans" as their desired product, the server will collect information from local and online stores and present a plan that includes the cheapest store to purchase them from and available coupon information.

[0503] An example of a prompt message to use with a generative AI model would be: "We have data with information about the product the user wants to buy. Based on this, please provide the store where you can buy it at the lowest price and any available coupons."

[0504] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0505] Step 1:

[0506] The user enters information about the desired product and location via an electronic terminal. The entered information includes the name of the product the user wishes to purchase, the quantity, and the desired pickup location. This information is sent from the terminal to the server as input data.

[0507] Step 2:

[0508] The server searches databases of multiple sales locations based on the product information received from the user. Here, the server uses the sales location's API to retrieve relevant information, such as price data and inventory information. The databases contain real-time price information and inventory status for each product.

[0509] Step 3:

[0510] The server uses collected price information, inventory information, and current location information to calculate the optimal purchase plan. Specifically, the server analyzes product prices and inventory status, and combines available coupons and point programs to determine the most economical way to purchase. This calculation also takes into account accessibility based on location information.

[0511] Step 4:

[0512] The server sends the calculated optimal purchase plan to the electronic device. The device notifies the user of this plan, visually presenting the best purchase options. This notification also includes information on the cheapest store and the cost after applying coupons.

[0513] Step 5:

[0514] Electronic devices, based on information retrieved from a server, refer to the user's past purchase history and recommend relevant products accordingly. This process analyzes the user's historical data and recommends similar or related new products. This provides more personalized suggestions based on the user's future purchasing behavior.

[0515] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0516] Embodiments of the present invention will be described in detail. This invention is a purchase plan presentation system that employs a user emotion recognition function and is configured using a network of digital terminals and servers.

[0517] The user first uses a device to input information about the product they wish to purchase. During this process, an emotion engine analyzes the user's facial expressions and voice through devices such as the camera and microphone to determine their emotional state. For example, if the user smiles while inputting their desired product, it is recognized as a positive emotion, while a frown or other facial expression is identified as a negative emotion.

[0518] The server receives desired product information, location information, and emotional state data transmitted from the terminal, and collects prices and terms of availability from a database of multiple sales locations. Product suggestions are also adjusted according to the emotional state. For example, if the user is perceived as being in a relaxed state, slightly more expensive options may be suggested.

[0519] Next, the server analyzes price information along with emotional data to generate an optimal purchase plan tailored to the user's emotional state. For example, if the analysis indicates fatigue, it might emphasize the convenience of home delivery. This allows users to enjoy a stress-free shopping experience.

[0520] The device notifies the user of the purchase plan sent from the server. This notification allows the user to choose the purchase option that best suits their emotional state, resulting in a more satisfying purchasing experience.

[0521] Furthermore, the server stores recognized emotional data and uses it to optimize future purchasing plans. This data is used to analyze the purchasing history of users similar to an individual user's purchasing tendencies and emotional state.

[0522] This system enables more personalized product recommendations based on user emotions, supporting purchasing decisions. Thus, the present invention provides an effective method for offering a new purchasing experience that takes consumer emotions into consideration.

[0523] The following describes the processing flow.

[0524] Step 1:

[0525] The user uses the device to input the name and quantity of the product they wish to purchase. The device is equipped with a camera and microphone, which are used to capture the user's facial expressions and voice in real time. The emotion engine uses this data to recognize the user's emotions and identify emotional states such as joy, surprise, and anger.

[0526] Step 2:

[0527] The device sends purchase preference information to the server along with the recognized emotional state. This information includes the user's location and past purchase history.

[0528] Step 3:

[0529] Based on the received user data, the server issues queries to databases of multiple sales locations related to the desired product. Here, it retrieves product prices, stock availability, and coupon information. Furthermore, if the user's emotional state is "fatigue," the system prioritizes simpler purchasing methods.

[0530] Step 4:

[0531] The server generates the optimal purchase plan for the user based on feedback from the emotion engine and acquired product information. Here, it adjusts suggestions according to the user's emotions, offering a wider range of options to users in a positive state and a plan that allows users in a negative state to complete the purchase easily and simply.

[0532] Step 5:

[0533] The server sends the generated purchase plan to the device. The device then notifies the user of this information and clearly displays which option is suitable. The plan includes product details, price comparisons, and recommended purchase methods.

[0534] Step 6:

[0535] The user selects the most appealing option based on the provided purchase plans. After making a selection, the device returns feedback to the server regarding the user's choice and their emotional state at that time.

[0536] Step 7:

[0537] The server stores feedback on emotional states and purchase history data, and uses this data to generate future purchasing plans. This data helps optimize future product recommendations and plans.

[0538] (Example 2)

[0539] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0540] Traditional purchasing systems often failed to consider the user's emotional state when providing product information and price suggestions, making it difficult to deliver a shopping experience that was optimal for the user's current situation. Furthermore, because products were suggested without reflecting individual users' emotions or purchase history, personalized experiences could not be provided, posing a challenge to increasing user satisfaction.

[0541] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0542] In this invention, the server includes means for receiving desired product information and location information obtained from the user; means for collecting product price information and terms of service from multiple sales regions based on the desired product information and analyzed emotional state, and adjusting the proposed content; and means for generating an optimal purchase plan considering the product price information, location information, and emotional state. This makes it possible to provide a personalized purchase plan that corresponds to the user's emotional state.

[0543] A "user" is an individual or organization that uses the system to input product information and receive purchase plan suggestions.

[0544] "Product information" refers to detailed information about the product the user wishes to purchase. This information includes the product name, category, and budget.

[0545] "Location information" refers to location data related to the user's current location or the region in which they wish to make a purchase.

[0546] "Emotional state" refers to data that describes the psychological condition of a user, analyzed from their facial expressions and tone of voice. This includes both positive and negative emotions.

[0547] A "server" is a central computer system that aggregates and analyzes data received from users, generates an optimal purchasing plan, and sends it to the terminal.

[0548] A "purchase plan" is a general term for product suggestions optimized based on the user's desired product information, location information, and emotional state, as well as the plan related to their purchase.

[0549] A "sales area" is a range comprised of multiple regions where a product is sold. It is used to select the most suitable supplier based on the user's location information.

[0550] A "personalized experience" is a user-specific and optimized experience that takes into account the individual user's emotions and past behavior.

[0551] This invention is a system that presents a personalized purchasing plan, taking into account the user's emotional state. The system connects digital terminals and a server via a network, aiming to provide users with a more appropriate purchasing experience.

[0552] The user first uses a device to enter information about the product they wish to purchase. The device is equipped with a camera and microphone, and through this hardware, an emotion engine analyzes the user's facial expressions and voice. This determines the user's emotional state. For example, a smile is recognized as a positive emotion, while a frown is judged as a negative emotion.

[0553] The terminal transmits the user's entered product information, analyzed emotional state, and location information to the server. Based on the received data, the server collects product pricing information and terms of availability from a database of multiple sales regions. Crucially, the server adjusts its suggestions according to the user's emotional state. For example, a user identified as relaxed might be offered a slightly more expensive option.

[0554] The server analyzes collected price information, location information, and emotional state data to generate the optimal purchase plan for the user. For example, if past purchase history and current emotional state are analyzed to indicate fatigue, it can highlight convenient home delivery options.

[0555] The generated purchase plan is sent to the device, which then notifies the user. The user receives the notification and can choose options that align with their emotional state, leading to a more satisfying purchase experience.

[0556] Furthermore, the server accumulates recognized emotion data and purchase history data, and uses a generative AI model to optimize future purchasing plans. This allows for the generation of new prompts such as "What products would you recommend to a relaxed customer?", and the system is constantly being improved to provide a better purchasing experience.

[0557] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0558] Step 1:

[0559] The user uses a device to input information about the product they wish to purchase. This data includes product name, category, and budget. This gathers basic information about the user's preferences. The device uses its camera and microphone to record the user's facial expressions and voice in real time. This data is analyzed by an emotion engine to determine the user's emotional state (e.g., positive, negative). The input for this step is the user input and emotional state data, while the output is the analyzed emotional state.

[0560] Step 2:

[0561] The terminal sends user-inputted product information, analyzed emotional state, and location information to the server. The transmitted data is important as material for concretizing the user's request. The server prepares to process the received data. The input for this step is the dataset from the terminal, and the output is the accurate receipt of data to the server.

[0562] Step 3:

[0563] The server analyzes the data received from the terminal and, based on that, collects product pricing information and terms of availability from databases of multiple sales regions. The server then runs an emotion-specific algorithm to tailor suggestions according to the user's emotional state. For example, if the user is relaxed, it will explore premium options. The input for this step is the user's desired product information and emotional state received by the server, and the output is a list of product candidates tailored to that emotional state.

[0564] Step 4:

[0565] The server generates an optimal purchase plan based on collected price and offer information, taking into account the user's desired product information, location information, and analyzed emotional state. This plan is designed to optimize user needs and provide emotionally-adjusted purchase options. The inputs to this step are price information, location information, and emotional state, and the output is the optimized purchase plan.

[0566] Step 5:

[0567] The server sends the generated purchase plan to the terminal. The terminal notifies the user of the received purchase plan and displays it in the user interface. This process allows the user to decide whether to purchase based on the plan. The input for this step is the purchase plan from the server, and the output is the notification and display to the user.

[0568] Step 6:

[0569] The server stores sentiment data and purchase history obtained from all past interactions. This stored data is used as prompts to optimize future purchase plans using a generative AI model. For example, it generates a prompt such as, "What products should I recommend to a relaxed customer?" The input for this step is past sentiment and purchase data, and the output is data for the next optimization opportunity.

[0570] (Application Example 2)

[0571] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0572] In today's shopping experience, users face a wide variety of product options, which can lead to confusion when choosing the best option from many choices. Furthermore, providing uniform recommendations without considering the user's emotional state makes it difficult to maximize individual satisfaction. To address this challenge, there is a need to provide purchase suggestions that reflect the user's real-time emotional state.

[0573] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0574] In this invention, the server includes means for receiving desired item information and location information obtained from the user, means for using an image device and an audio device to analyze the user's emotional state, and means for adjusting the purchase plan based on the analyzed emotional state. This makes it possible to provide a purchase plan that is suitable for the user's emotional state.

[0575] A "user" refers to an individual who intends to purchase goods using the system.

[0576] "Desired item information" refers to information about the items that a user wishes to purchase.

[0577] "Location information" refers to information about the user's current location.

[0578] A "sales location" refers to a physical store or a sales location within a network where goods are sold.

[0579] "Goods price information" refers to information regarding the price of goods at the point of sale.

[0580] A "purchase plan" refers to a proposal for optimal purchasing based on the user's preferences.

[0581] An "imaging device" refers to a device used to analyze a user's facial expressions.

[0582] A "voice device" refers to a device used to analyze a user's voice.

[0583] "Emotional state" refers to the result of an analysis of the user's current emotional state.

[0584] "Benefits" refer to advantages or discounts offered to users in relation to their purchase.

[0585] "Purchase history" refers to records of items that a user has purchased in the past.

[0586] "Recommended items" refer to items that have been deemed suitable for suggestion to users.

[0587] The system for implementing this invention consists of a communication device owned by the user that exchanges information with a cloud server.

[0588] First, the user inputs information about the desired item through a communication device. The communication device is equipped with an image device and an audio device, which are used to analyze the user's emotional state in real time. This analysis uses the user's facial expressions captured by the image device and the tone of their voice collected by the audio device. An emotion recognition engine operating on the cloud is used to analyze the emotional state. Specifically, general image analysis software and audio analysis software are used for emotion recognition.

[0589] The cloud server receives desired item information, location information, and analyzed sentiment data transmitted from communication devices. Based on this, the server executes database queries to collect item price information from various sales locations. It also refers to past purchase history and sentiment data and generates a purchase plan using data processing tools. For example, Apache Spark can be used to process large amounts of data quickly.

[0590] The generated purchase plan is adjusted based on the user's emotional state and communicated to the user via a communication device. This notification includes recommended items and special offers, providing the user with the best possible purchasing experience.

[0591] For example, if a user is looking for a new electronic device and their expression is calm, a slightly more expensive but high-performance product, along with corresponding perks, will be suggested. It is also possible to use a generative AI model to create prompts that recommend the most suitable fashion items when the user is smiling. An example of such a prompt would be, "Recommend the most suitable fashion items when the user is smiling."

[0592] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0593] Step 1:

[0594] The terminal accepts information about the items the user desires. The user inputs the item's name, type, and desired price range via a communication device. This information is temporarily stored within the terminal. Additionally, the terminal's image and audio devices capture the user's facial expressions and voice, and this data is prepared for emotional state analysis.

[0595] Step 2:

[0596] The device sends captured facial expression and voice data to an emotion recognition engine. The emotion recognition engine analyzes the received data and identifies the user's current emotional state. Specifically, image processing software analyzes facial expressions, and voice analysis software analyzes voice tone. The resulting emotional state data is then sent to the server.

[0597] Step 3:

[0598] The server receives information about desired items, location, and emotional state from the terminal. Based on the received data, it queries the database to collect item price information from multiple sales locations. The database returns price information and availability conditions for each sales location.

[0599] Step 4:

[0600] The server generates an optimal purchase plan based on received item price and location information. Here, data processing tools such as Apache Spark are used to rapidly process large amounts of data. The generated purchase plan is then adjusted to reflect the analyzed sentiment state, for example, including more expensive options if the user is happy.

[0601] Step 5:

[0602] The server sends the final purchase plan to the device. The device then notifies the user of this plan. The plan includes a list of recommended items and perks, and is personalized according to the user's emotional state. The user receives this notification and can choose the purchase option that best suits their emotional state.

[0603] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0604] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0605] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0606] [Fourth Embodiment]

[0607] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0608] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0609] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0610] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0611] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0612] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0613] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0614] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0615] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0616] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0617] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0618] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0619] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0620] Embodiments of the present invention will be described in detail below. This system is configured using digital terminals such as smartphones and personal computers, and consists of multiple functional modules in order to provide the user with an optimal product purchase plan.

[0621] The user begins by using a device to enter information about the product they wish to purchase. This information includes the product name, the required quantity, and the desired pickup location or delivery address. The device then transmits this information to the server.

[0622] The server searches a database of relevant sales locations based on the received product and location information. It then uses APIs from online platforms and local stores to collect the latest price information, availability, and available coupons for the relevant products. For example, if a user enters that they want to buy "rice, milk, and eggs," the server will retrieve information about these products from nearby supermarkets and major online stores.

[0623] Next, the server analyzes the acquired information to calculate the most economical purchase method for each product. This takes into account the final price when coupons and point systems are applied. Accessibility from the user's current location is also included in the evaluation. The server generates the most effective purchase plan for the user and compares multiple options.

[0624] The terminal notifies the user of a purchase plan sent from the server. This notification includes detailed information such as the store with the lowest price, the best combination of products to purchase, and the cost after applying coupons. Based on the information provided, the user can choose where to actually purchase the products.

[0625] Furthermore, this system can refer to a user's past purchase history and suggest products based on that history. This further personalizes the user's shopping experience and improves convenience.

[0626] In this way, the system provides a highly effective means of supporting users' purchasing behavior and saving them time and costs.

[0627] The following describes the processing flow.

[0628] Step 1:

[0629] The user uses a terminal to enter a list of items they wish to purchase. This includes specifying the product name, quantity, budget, whether they want any coupons, and their current location. Once the terminal correctly receives the entered information, it prepares to send it to the server.

[0630] Step 2:

[0631] The server retrieves the user's desired product information and location information received from the terminal. Next, it generates a query to look up multiple sales location databases that match the user's desired products. This query includes the necessary information to retrieve current market prices, discount information, and inventory status.

[0632] Step 3:

[0633] The server collects information through APIs and websites of online and local stores. In particular, it retrieves the price, inventory information, and discount information when coupons are applied for each product, and aggregates this information in a central database. The information collection process is performed in real time and is designed to obtain the most up-to-date market information possible.

[0634] Step 4:

[0635] The server performs a detailed analysis based on the collected data and calculates the purchase cost for each product. This includes the final price after applying coupons and a cost comparison when purchasing items from multiple stores in combination. It also evaluates the cost of visiting offline stores based on the distance from the user's current location.

[0636] Step 5:

[0637] The server generates the best possible purchase plan, ranking multiple options based on the user's criteria. The generated plan includes details such as the cheapest store, the best value bundle, and online purchase options.

[0638] Step 6:

[0639] The server notifies the user's device of the generated purchase plan. The device then displays this information clearly to the user, allowing them to review the proposal, including detailed pricing information and applicable coupons.

[0640] Step 7:

[0641] The user reviews the notified plan and selects the most suitable purchase option. After making a selection, the device sends this information back to the server as feedback, which is used to improve future services.

[0642] (Example 1)

[0643] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0644] Modern consumer purchasing behavior is complex due to multiple factors, including the diversity of product prices, fluctuations in inventory, and the availability of coupons. As a result, consumers spend a great deal of time and effort finding the optimal way to purchase products. Furthermore, the lack of personalized recommendations that leverage individual users' purchase history makes it difficult for consumers to find products that suit their preferences.

[0645] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0646] In this invention, the server includes means for receiving desired product information and location information obtained from the user, means for collecting product price information and inventory information from multiple sales locations, and means for generating an optimal purchase plan considering coupon information. This allows the user to be presented with the best options for purchasing products efficiently and economically, saving time and costs. Furthermore, by providing recommended products that take into account past purchase history, the user's purchasing experience is further improved.

[0647] A "user" refers to an individual or group that uses a digital device to input product information and receive a purchase plan.

[0648] "Desired product information" refers to data that includes specific information such as the name, quantity, and pickup location of the product the user wishes to purchase.

[0649] "Location information" refers to data that indicates the geographical location of the user or the place where the product will be received.

[0650] A "sales outlet" refers to a facility that includes online platforms and physical stores where products are sold.

[0651] "Product pricing information" refers to data regarding the selling price of a specific product.

[0652] "Inventory information" refers to data regarding the supply status and available quantities of a particular product.

[0653] "Coupon information" refers to data that includes information about discounts and benefits that apply when purchasing products.

[0654] A "purchase plan" refers to a plan that includes suggestions for how a user can best purchase a product.

[0655] "Notification" refers to the process or action of communicating information to a user via a device.

[0656] "Purchase history" refers to data that records a user's past purchase history and related information.

[0657] "Recommended products" refer to products selected based on the user's past purchase history and preferences.

[0658] This invention is a system that utilizes digital devices such as smartphones and personal computers to provide users with the most suitable product purchase plan. The system is primarily composed of interactions between three parties: the user, the device, and the server.

[0659] The user enters information about the product they wish to purchase via a digital terminal. This information includes the product name, quantity, and pickup location. The terminal transmits the entered information to the server. After receiving the information, the server searches a database of relevant sales locations. The server executes database queries and retrieves the latest price and inventory information for the product via the online platform or local store APIs. This may involve using HTTP requests, for example.

[0660] Next, the server uses the collected information to perform price comparisons and generate the optimal purchase plan. This is done by an algorithm that calculates the most economical way to buy. This process takes into account factors such as the application of coupons and the use of point systems. Furthermore, it also takes into account ease of access based on the user's location information.

[0661] As a concrete example of operation, let's consider a scenario where a user wants to purchase a new smartphone case within a budget. The user enters the product name and budget on their device and presses the submit button. The server uses this information to collect price information and generates the most cost-effective purchase plan. The device then notifies the user of the generated plan, and the user can choose the best option from the presented choices.

[0662] Furthermore, the server analyzes the user's past purchase history to suggest highly relevant products. This functionality is achieved by utilizing a generative AI model that learns the user's purchasing habits.

[0663] Examples of prompt messages include the following:

[0664] "A user is looking for a smartphone case. They have specified the product name, color, material, and budget. Based on this, generate the best purchase plan."

[0665] To implement this system, digital terminals and servers must be connected via the internet, and configured to allow for rapid and accurate information exchange. This system will enable users to efficiently select and purchase products.

[0666] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0667] Step 1:

[0668] The user enters and submits product information. Using a digital device, the user enters the name of the product they wish to purchase, the required quantity, and the desired pickup location or delivery address, and then presses the "Submit" button. This input becomes the dataset necessary for the next processing step.

[0669] Step 2:

[0670] The terminal sends product information to the server. The terminal sends information entered by the user to the server. At this stage, the information is transferred to the server via the network. The input is data entered by the user into the terminal, and as output, it is converted into a data format that the server can use.

[0671] Step 3:

[0672] The server searches the database and collects information. Based on the received product information, the server issues SQL queries to search the databases of related sales locations. As a result of the queries, it collects price information, inventory information, and coupon information from multiple sales locations. The input is the request information from the user, and the output is product price, inventory, and coupon information.

[0673] Step 4:

[0674] The server generates the optimal purchase plan. Based on the collected information, the server calculates the most economical purchase plan, considering price, inventory status, and coupon availability. Here, an algorithm is used to analyze the input data, evaluate the final price and convenience, and generate the optimal option. The output is the optimized purchase plan.

[0675] Step 5:

[0676] The device notifies the user of the generated purchase plan. The device notifies the user of the purchase plan received from the server. Specifically, the device uses push notifications and in-app messages to display details such as the cheapest store information and the cost after applying coupons. The input is the purchase plan from the server, and the output is a display of information that is understandable to the user.

[0677] Step 6:

[0678] The user makes a purchase decision based on the information provided. The user reviews the purchase plan displayed on the device and selects where to actually purchase the product. Specifically, they choose their desired store and price from the presented options and then proceed with the purchase. The input is the purchase plan information presented from the device, and the output is the user's purchase decision.

[0679] Step 7:

[0680] The server analyzes the user's past purchase history to suggest products. The server uses machine learning algorithms to analyze the user's past purchase data and generate relevant product recommendations based on that analysis. Specifically, it sends product information tailored to the user's preferences to the terminal. The input is the user's purchase history data, and the output is recommended product information.

[0681] (Application Example 1)

[0682] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0683] For consumers to make the most economical purchases, there is a significant challenge: they must manually gather and compare a large amount of information, which is very time-consuming and laborious. Furthermore, the lack of product recommendations that take past purchase history into account prevents them from having a more personalized shopping experience.

[0684] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0685] In this invention, the server includes means for receiving desired product information and location information obtained from the user, means for collecting product provision condition information, and means for generating consumer-oriented purchase suggestions. This enables the efficient provision of optimal purchase plans and product recommendations based on past purchase history.

[0686] A "user" is a consumer who uses the system to provide information about product purchases.

[0687] "Desired product information" refers to detailed information such as the name and quantity of the product the user wishes to purchase.

[0688] "Location information" refers to geographical information about the place where the user wants to receive the product or the delivery destination.

[0689] "Product availability information" refers to price, stock, and available discount information for products obtained from multiple sales locations.

[0690] "Consumer-oriented purchasing suggestions" refer to proposals that present users with the most suitable purchasing methods based on collected data.

[0691] "Electronic devices" refer to digital devices such as smartphones and personal computers.

[0692] "Purchase history" refers to records of products a user has purchased in the past and related data.

[0693] The system implementing this invention operates in conjunction with an electronic device such as a smartphone to improve user convenience. The user uses the electronic device to input information about the desired product to purchase and location information. This information is transmitted from the device to the server.

[0694] Upon receiving information, the server collects product availability information from multiple sales locations. Specifically, it retrieves price, inventory, and available discount information for each sales location. This is done using a mobile application framework based on React Native and server-side technology based on Node.js. MySQL is used to organize the information in the database, and Axios is used for API integration.

[0695] The server then generates optimal consumer-oriented purchase suggestions based on the collected information and the user's location. These suggestions calculate the most economical way to purchase each product and also take into account the best access options from the user's current location.

[0696] The generated purchase suggestions are sent back to the electronic terminal and the user is notified. Furthermore, past purchase history is referenced on the electronic terminal, and products are recommended based on that.

[0697] For example, if a user enters "organic coffee beans" as their desired product, the server will collect information from local and online stores and present a plan that includes the cheapest store to purchase them from and available coupon information.

[0698] An example of a prompt message to use with a generative AI model would be: "We have data with information about the product the user wants to buy. Based on this, please provide the store where you can buy it at the lowest price and any available coupons."

[0699] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0700] Step 1:

[0701] The user enters information about the desired product and location via an electronic terminal. The entered information includes the name of the product the user wishes to purchase, the quantity, and the desired pickup location. This information is sent from the terminal to the server as input data.

[0702] Step 2:

[0703] The server searches databases of multiple sales locations based on the product information received from the user. Here, the server uses the sales location's API to retrieve relevant information, such as price data and inventory information. The databases contain real-time price information and inventory status for each product.

[0704] Step 3:

[0705] The server uses collected price information, inventory information, and current location information to calculate the optimal purchase plan. Specifically, the server analyzes product prices and inventory status, and combines available coupons and point programs to determine the most economical way to purchase. This calculation also takes into account accessibility based on location information.

[0706] Step 4:

[0707] The server sends the calculated optimal purchase plan to the electronic device. The device notifies the user of this plan, visually presenting the best purchase options. This notification also includes information on the cheapest store and the cost after applying coupons.

[0708] Step 5:

[0709] Electronic devices, based on information retrieved from a server, refer to the user's past purchase history and recommend relevant products accordingly. This process analyzes the user's historical data and recommends similar or related new products. This provides more personalized suggestions based on the user's future purchasing behavior.

[0710] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0711] Embodiments of the present invention will be described in detail. This invention is a purchase plan presentation system that employs a user emotion recognition function and is configured using a network of digital terminals and servers.

[0712] The user first uses a device to input information about the product they wish to purchase. During this process, an emotion engine analyzes the user's facial expressions and voice through devices such as the camera and microphone to determine their emotional state. For example, if the user smiles while inputting their desired product, it is recognized as a positive emotion, while a frown or other facial expression is identified as a negative emotion.

[0713] The server receives desired product information, location information, and emotional state data transmitted from the terminal, and collects prices and terms of availability from a database of multiple sales locations. Product suggestions are also adjusted according to the emotional state. For example, if the user is perceived as being in a relaxed state, slightly more expensive options may be suggested.

[0714] Next, the server analyzes price information along with emotional data to generate an optimal purchase plan tailored to the user's emotional state. For example, if the analysis indicates fatigue, it might emphasize the convenience of home delivery. This allows users to enjoy a stress-free shopping experience.

[0715] The device notifies the user of the purchase plan sent from the server. This notification allows the user to choose the purchase option that best suits their emotional state, resulting in a more satisfying purchasing experience.

[0716] Furthermore, the server stores recognized emotional data and uses it to optimize future purchasing plans. This data is used to analyze the purchasing history of users similar to an individual user's purchasing tendencies and emotional state.

[0717] This system enables more personalized product recommendations based on user emotions, supporting purchasing decisions. Thus, the present invention provides an effective method for offering a new purchasing experience that takes consumer emotions into consideration.

[0718] The following describes the processing flow.

[0719] Step 1:

[0720] The user uses the device to input the name and quantity of the product they wish to purchase. The device is equipped with a camera and microphone, which are used to capture the user's facial expressions and voice in real time. The emotion engine uses this data to recognize the user's emotions and identify emotional states such as joy, surprise, and anger.

[0721] Step 2:

[0722] The device sends purchase preference information to the server along with the recognized emotional state. This information includes the user's location and past purchase history.

[0723] Step 3:

[0724] Based on the received user data, the server issues queries to databases of multiple sales locations related to the desired product. Here, it retrieves product prices, stock availability, and coupon information. Furthermore, if the user's emotional state is "fatigue," the system prioritizes simpler purchasing methods.

[0725] Step 4:

[0726] The server generates the optimal purchase plan for the user based on feedback from the emotion engine and acquired product information. Here, it adjusts suggestions according to the user's emotions, offering a wider range of options to users in a positive state and a plan that allows users in a negative state to complete the purchase easily and simply.

[0727] Step 5:

[0728] The server sends the generated purchase plan to the device. The device then notifies the user of this information and clearly displays which option is suitable. The plan includes product details, price comparisons, and recommended purchase methods.

[0729] Step 6:

[0730] The user selects the most appealing option based on the provided purchase plans. After making a selection, the device returns feedback to the server regarding the user's choice and their emotional state at that time.

[0731] Step 7:

[0732] The server stores feedback on emotional states and purchase history data, and uses this data to generate future purchasing plans. This data helps optimize future product recommendations and plans.

[0733] (Example 2)

[0734] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0735] Traditional purchasing systems often failed to consider the user's emotional state when providing product information and price suggestions, making it difficult to deliver a shopping experience that was optimal for the user's current situation. Furthermore, because products were suggested without reflecting individual users' emotions or purchase history, personalized experiences could not be provided, posing a challenge to increasing user satisfaction.

[0736] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0737] In this invention, the server includes means for receiving desired product information and location information obtained from the user; means for collecting product price information and terms of service from multiple sales regions based on the desired product information and analyzed emotional state, and adjusting the proposed content; and means for generating an optimal purchase plan considering the product price information, location information, and emotional state. This makes it possible to provide a personalized purchase plan that corresponds to the user's emotional state.

[0738] A "user" is an individual or organization that uses the system to input product information and receive purchase plan suggestions.

[0739] "Product information" refers to detailed information about the product the user wishes to purchase. This information includes the product name, category, and budget.

[0740] "Location information" refers to location data related to the user's current location or the region in which they wish to make a purchase.

[0741] "Emotional state" refers to data that describes the psychological condition of a user, analyzed from their facial expressions and tone of voice. This includes both positive and negative emotions.

[0742] A "server" is a central computer system that aggregates and analyzes data received from users, generates an optimal purchasing plan, and sends it to the terminal.

[0743] A "purchase plan" is a general term for product suggestions optimized based on the user's desired product information, location information, and emotional state, as well as the plan related to their purchase.

[0744] A "sales area" is a range comprised of multiple regions where a product is sold. It is used to select the most suitable supplier based on the user's location information.

[0745] A "personalized experience" is a user-specific and optimized experience that takes into account the individual user's emotions and past behavior.

[0746] This invention is a system that presents a personalized purchasing plan, taking into account the user's emotional state. The system connects digital terminals and a server via a network, aiming to provide users with a more appropriate purchasing experience.

[0747] The user first uses a device to enter information about the product they wish to purchase. The device is equipped with a camera and microphone, and through this hardware, an emotion engine analyzes the user's facial expressions and voice. This determines the user's emotional state. For example, a smile is recognized as a positive emotion, while a frown is judged as a negative emotion.

[0748] The terminal transmits the user's entered product information, analyzed emotional state, and location information to the server. Based on the received data, the server collects product pricing information and terms of availability from a database of multiple sales regions. Crucially, the server adjusts its suggestions according to the user's emotional state. For example, a user identified as relaxed might be offered a slightly more expensive option.

[0749] The server analyzes collected price information, location information, and emotional state data to generate the optimal purchase plan for the user. For example, if past purchase history and current emotional state are analyzed to indicate fatigue, it can highlight convenient home delivery options.

[0750] The generated purchase plan is sent to the device, which then notifies the user. The user receives the notification and can choose options that align with their emotional state, leading to a more satisfying purchase experience.

[0751] Furthermore, the server accumulates recognized emotion data and purchase history data, and uses a generative AI model to optimize future purchasing plans. This allows for the generation of new prompts such as "What products would you recommend to a relaxed customer?", and the system is constantly being improved to provide a better purchasing experience.

[0752] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0753] Step 1:

[0754] The user uses a device to input information about the product they wish to purchase. This data includes product name, category, and budget. This gathers basic information about the user's preferences. The device uses its camera and microphone to record the user's facial expressions and voice in real time. This data is analyzed by an emotion engine to determine the user's emotional state (e.g., positive, negative). The input for this step is the user input and emotional state data, while the output is the analyzed emotional state.

[0755] Step 2:

[0756] The terminal sends user-inputted product information, analyzed emotional state, and location information to the server. The transmitted data is important as material for concretizing the user's request. The server prepares to process the received data. The input for this step is the dataset from the terminal, and the output is the accurate receipt of data to the server.

[0757] Step 3:

[0758] The server analyzes the data received from the terminal and, based on that, collects product pricing information and terms of availability from databases of multiple sales regions. The server then runs an emotion-specific algorithm to tailor suggestions according to the user's emotional state. For example, if the user is relaxed, it will explore premium options. The input for this step is the user's desired product information and emotional state received by the server, and the output is a list of product candidates tailored to that emotional state.

[0759] Step 4:

[0760] The server generates an optimal purchase plan based on collected price and offer information, taking into account the user's desired product information, location information, and analyzed emotional state. This plan is designed to optimize user needs and provide emotionally-adjusted purchase options. The inputs to this step are price information, location information, and emotional state, and the output is the optimized purchase plan.

[0761] Step 5:

[0762] The server sends the generated purchase plan to the terminal. The terminal notifies the user of the received purchase plan and displays it in the user interface. This process allows the user to decide whether to purchase based on the plan. The input for this step is the purchase plan from the server, and the output is the notification and display to the user.

[0763] Step 6:

[0764] The server stores sentiment data and purchase history obtained from all past interactions. This stored data is used as prompts to optimize future purchase plans using a generative AI model. For example, it generates a prompt such as, "What products should I recommend to a relaxed customer?" The input for this step is past sentiment and purchase data, and the output is data for the next optimization opportunity.

[0765] (Application Example 2)

[0766] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0767] In today's shopping experience, users face a wide variety of product options, which can lead to confusion when choosing the best option from many choices. Furthermore, providing uniform recommendations without considering the user's emotional state makes it difficult to maximize individual satisfaction. To address this challenge, there is a need to provide purchase suggestions that reflect the user's real-time emotional state.

[0768] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0769] In this invention, the server includes means for receiving desired item information and location information obtained from the user, means for using an image device and an audio device to analyze the user's emotional state, and means for adjusting the purchase plan based on the analyzed emotional state. This makes it possible to provide a purchase plan that is suitable for the user's emotional state.

[0770] A "user" refers to an individual who intends to purchase goods using the system.

[0771] "Desired item information" refers to information about the items that a user wishes to purchase.

[0772] "Location information" refers to information about the user's current location.

[0773] A "sales location" refers to a physical store or a sales location within a network where goods are sold.

[0774] "Goods price information" refers to information regarding the price of goods at the point of sale.

[0775] A "purchase plan" refers to a proposal for optimal purchasing based on the user's preferences.

[0776] An "imaging device" refers to a device used to analyze a user's facial expressions.

[0777] A "voice device" refers to a device used to analyze a user's voice.

[0778] "Emotional state" refers to the result of an analysis of the user's current emotional state.

[0779] "Benefits" refer to advantages or discounts offered to users in relation to their purchase.

[0780] "Purchase history" refers to records of items that a user has purchased in the past.

[0781] "Recommended items" refer to items that have been deemed suitable for suggestion to users.

[0782] The system for implementing this invention consists of a communication device owned by the user that exchanges information with a cloud server.

[0783] First, the user inputs information about the desired item through a communication device. The communication device is equipped with an image device and an audio device, which are used to analyze the user's emotional state in real time. This analysis uses the user's facial expressions captured by the image device and the tone of their voice collected by the audio device. An emotion recognition engine operating on the cloud is used to analyze the emotional state. Specifically, general image analysis software and audio analysis software are used for emotion recognition.

[0784] The cloud server receives desired item information, location information, and analyzed sentiment data transmitted from communication devices. Based on this, the server executes database queries to collect item price information from various sales locations. It also refers to past purchase history and sentiment data and generates a purchase plan using data processing tools. For example, Apache Spark can be used to process large amounts of data quickly.

[0785] The generated purchase plan is adjusted based on the user's emotional state and communicated to the user via a communication device. This notification includes recommended items and special offers, providing the user with the best possible purchasing experience.

[0786] For example, if a user is looking for a new electronic device and their expression is calm, a slightly more expensive but high-performance product, along with corresponding perks, will be suggested. It is also possible to use a generative AI model to create prompts that recommend the most suitable fashion items when the user is smiling. An example of such a prompt would be, "Recommend the most suitable fashion items when the user is smiling."

[0787] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0788] Step 1:

[0789] The terminal accepts information about the items the user desires. The user inputs the item's name, type, and desired price range via a communication device. This information is temporarily stored within the terminal. Additionally, the terminal's image and audio devices capture the user's facial expressions and voice, and this data is prepared for emotional state analysis.

[0790] Step 2:

[0791] The device sends captured facial expression and voice data to an emotion recognition engine. The emotion recognition engine analyzes the received data and identifies the user's current emotional state. Specifically, image processing software analyzes facial expressions, and voice analysis software analyzes voice tone. The resulting emotional state data is then sent to the server.

[0792] Step 3:

[0793] The server receives information about desired items, location, and emotional state from the terminal. Based on the received data, it queries the database to collect item price information from multiple sales locations. The database returns price information and availability conditions for each sales location.

[0794] Step 4:

[0795] The server generates an optimal purchase plan based on received item price and location information. Here, data processing tools such as Apache Spark are used to rapidly process large amounts of data. The generated purchase plan is then adjusted to reflect the analyzed sentiment state, for example, including more expensive options if the user is happy.

[0796] Step 5:

[0797] The server sends the final purchase plan to the device. The device then notifies the user of this plan. The plan includes a list of recommended items and perks, and is personalized according to the user's emotional state. The user receives this notification and can choose the purchase option that best suits their emotional state.

[0798] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0799] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0800] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0801] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0802] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0803] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0804] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0805] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0806] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0807] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0808] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0809] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0810] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0811] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0812] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0813] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0814] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0815] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0816] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0817] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0818] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0819] The following is further disclosed regarding the embodiments described above.

[0820] (Claim 1)

[0821] A means for receiving desired product information and location information obtained from the user,

[0822] A means for collecting product price information from multiple sales locations based on the desired product information,

[0823] A means for generating an optimal purchase plan considering the product price information and location information,

[0824] A means of notifying the user of the purchase plan,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, comprising means for obtaining information regarding desired products and coupons, and means for reflecting this information in a purchase plan.

[0828] (Claim 3)

[0829] The system according to claim 1, further comprising means for providing recommended products taking into account the user's past purchase history.

[0830] "Example 1"

[0831] (Claim 1)

[0832] A means for receiving desired product information and location information obtained from the user,

[0833] A means for collecting product price information and inventory information from multiple sales locations based on the desired product information,

[0834] A means for generating an optimal purchase plan while considering coupon information based on the product price information and location information,

[0835] A means of notifying the user of the purchase plan,

[0836] A method for suggesting products based on the user's past purchase history,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, which obtains information regarding desired products and coupons and reflects said information in a purchase plan.

[0840] (Claim 3)

[0841] The system according to claim 1, further comprising means for presenting a user with multiple options for selecting where to purchase a product.

[0842] "Application Example 1"

[0843] (Claim 1)

[0844] A means for receiving desired product information and location information obtained from the user,

[0845] A means for collecting information on product provision conditions from multiple sales locations based on the desired product information,

[0846] A means for generating optimal consumer purchase suggestions considering the product provision conditions information and location information,

[0847] A means of notifying the user of the purchase proposal,

[0848] A method for recommending products based on past purchase history on electronic devices,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, comprising means for obtaining information regarding desired products and discount information, and means for reflecting this information in a purchase proposal.

[0852] (Claim 3)

[0853] The system according to claim 1, further comprising means for providing recommended products taking into account the user's past purchase history.

[0854] "Example 2 of combining an emotion engine"

[0855] (Claim 1)

[0856] A means for receiving desired product information and location information obtained from the user,

[0857] A means for collecting product pricing information and terms of offer from multiple sales regions based on the desired product information and analyzed emotional state, and adjusting the proposed content.

[0858] A means for generating an optimal purchase plan considering the product price information, location information, and emotional state,

[0859] A means of notifying the user of the purchase plan,

[0860] A system that includes this.

[0861] (Claim 2)

[0862] The system according to claim 1, comprising means for obtaining desired products and discount information and reflecting them in a purchase plan.

[0863] (Claim 3)

[0864] The system according to claim 1, further comprising means for providing recommended products in consideration of the user's previous purchase history and perceived emotional state.

[0865] "Application example 2 when combining with an emotional engine"

[0866] (Claim 1)

[0867] A means for receiving desired item information and location information obtained from the user,

[0868] A means for collecting price information for goods from multiple sales locations based on the desired goods information,

[0869] A means for generating an optimal purchasing plan considering the price information and location information of the item,

[0870] A means of using an image device and an audio device to analyze the emotional state of a user,

[0871] Means for adjusting the purchase plan based on the analyzed emotional state,

[0872] Means for notifying the user of the purchase plan,

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, comprising means for obtaining information regarding desired goods and benefits, and means for reflecting this information in a purchasing plan.

[0876] (Claim 3)

[0877] The system according to claim 1, further comprising means for providing recommended items taking into account the user's past purchase history and emotional state. [Explanation of Symbols]

[0878] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving desired product information and location information obtained from the user, A means for collecting product price information from multiple sales locations based on the desired product information, A means for generating an optimal purchase plan considering the product price information and location information, Means for notifying the user of the purchase plan, A system that includes this.

2. The system according to claim 1, comprising means for obtaining information regarding desired products and coupons, and means for reflecting this information in a purchase plan.

3. The system according to claim 1, further comprising means for providing recommended products taking into account the user's past purchase history.

Images