system

Abstract

We provide the system. [Solution] A speech recognition means for converting speech information into text information, A natural language processing means that generates a response based on the aforementioned text information, A speech synthesis means that outputs the generated response as audio information, A management system for managing customer orders and controlling the serving of food and beverages, A service delivery system that includes an automated payment method for processing customer payment information.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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 in 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] In the food and beverage industry, the labor shortage is intensifying, and it is required to reduce the manpower while maintaining the quality of customer service. However, conventional food delivery robots can only perform simple food delivery operations and cannot realize natural conversations with customers. Therefore, it is an issue to provide a high-quality customer experience while efficiently automating the process from order reception to food delivery and settlement in a consistent manner.

Means for Solving the Problems

[0005] The present invention provides a service provision system that includes speech recognition means for converting speech information into text information, natural language processing means for generating a response based on the text information, speech synthesis means for outputting the generated response as speech information, management means for managing customer orders and controlling the serving of food and beverages, and an automated payment means for processing customer payment information. This enables serving robots to engage in natural conversations with customers, efficiently automate the process from ordering to payment, and improve the quality of customer service.

[0006] A "speech recognition means" is a technological device that receives speech information as digital data and converts it into corresponding text information.

[0007] "Natural language processing means" refers to a technical device that analyzes text information and performs appropriate semantic understanding and response generation.

[0008] A "speech synthesis means" is a technological device that converts text information into speech information and outputs it as speech through a speaker or similar device.

[0009] A "management device" is a technical device that processes and manages order information received from customers and instructs the serving of food and beverages based on that information.

[0010] An "automated payment system" is a technological device that electronically processes customer payment information and completes the payment.

[0011] The "service provision system" is a system that combines the above-mentioned speech recognition means, natural language processing means, speech synthesis means, management means, and automated payment means, and automates the entire process from customer service to payment. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential 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 the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, the 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), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the 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.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention provides a system that integrates speech recognition, natural language processing, speech synthesis, customer management, and automated payment methods to fully automate the customer service process in the food service industry.

[0034] The server uses speech recognition to convert the voice of a user in the store into text data. This data is then analyzed by the server's natural language processing system to provide information for understanding the order details and the intent of the question.

[0035] Next, the server generates a response. This response is a crucial step, including order confirmation, prompting for additional questions, and service instructions. The generated text response is then converted into speech by a speech synthesis system and delivered to the user via an in-store terminal.

[0036] The terminal also plays a role in receiving user input and has the ability to adjust orders or suggest additional items as needed. Once the order is confirmed, the terminal sends instructions to the cooking system, which then automatically controls serving after cooking is complete.

[0037] After the order is served, the server's automated payment system activates and processes the payment to the user's account. This process ensures that customer information is kept secure and that payments are processed quickly and accurately.

[0038] For example, if a user at a restaurant says, "I'd like one miso ramen, please," the terminal captures the voice and converts it to text using speech recognition. This text data is sent to a server, where natural language processing understands the order. Based on this, a response is generated, such as, "One miso ramen, correct? Would you like something to drink?", and this response is output to the user via voice from the terminal. The entire process proceeds seamlessly according to any additional requests from the user, ultimately resulting in automated serving and payment.

[0039] In this way, we can provide the restaurant industry with an efficient and humane customer service experience while also addressing the challenge of labor shortages.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user begins speaking to place an order. The terminal acquires voice data through the microphone and converts it into text data using speech recognition technology.

[0043] Step 2:

[0044] The terminal sends the converted text data to the server. The server receives the data and uses natural language processing to analyze the order details and intent.

[0045] Step 3:

[0046] The server generates response messages based on the analysis results. For example, it might prepare messages to confirm the order details or prompt for additional information.

[0047] Step 4:

[0048] The generated response message is sent from the server to the terminal and converted into speech by the terminal's speech synthesis system. The terminal then communicates the response to the user through the speech output.

[0049] Step 5:

[0050] The user makes additional orders or confirmations. The terminal collects this new voice data and repeats the process from step 1.

[0051] Step 6:

[0052] Once all orders are confirmed, the terminal sends the order details to the cooking system and relays cooking instructions to the appropriate department.

[0053] Step 7:

[0054] Once cooking is complete, the terminal begins the necessary movements and arrangements to serve the ordered items.

[0055] Step 8:

[0056] After the meal is served, the server processes the user's payment information using an automated payment system and completes the payment. At this time, the user receives a notification that the payment has been completed.

[0057] (Example 1)

[0058] 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."

[0059] Traditional food services heavily rely on human labor, making it difficult to streamline operations and provide prompt customer service. Furthermore, the fragmentation of processes such as ordering, serving, and payment resulted in a less-than-smooth overall customer experience. Especially with the worsening labor shortage, there is a growing need to provide efficient yet humane customer service.

[0060] 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.

[0061] In this invention, the server includes recognition means for converting voice information into information, natural processing means for generating a response based on the information, and synthesis means for outputting the generated response as information. This enables the automation of the entire process and the provision of an integrated customer service experience.

[0062] "Audio information" refers to data and signals obtained using sound.

[0063] "Recognition means" refers to systems and devices for converting audio information into text or digital data.

[0064] "Natural processing methods" refer to technologies that analyze text data, understand its intent and content, and generate appropriate responses.

[0065] "Synthesis means" refers to a device or system for converting generated text data into speech and outputting it.

[0066] "Management means" refers to methods and systems for managing the entire service based on received data and instructions, and for providing the optimal service.

[0067] "Automated means" refers to devices and technologies that process transaction information and other data mechanically rather than manually.

[0068] A "generative model" refers to an algorithm or framework that uses machine learning or AI technology to generate responses based on data.

[0069] "Request information" refers to data and information related to orders and requests obtained from customers.

[0070] "Cooking equipment" refers to tools and machines used for preparing food.

[0071] "Timing" refers to the appropriate time or timeframe for a particular action or process to take place.

[0072] A "system" refers to a collection of multiple means or devices that work together to achieve a specific function or purpose.

[0073] This invention is a system for completely automating the customer service process in food and beverage services. This system integrates speech recognition, natural language processing, speech synthesis, customer management, and automated payment methods.

[0074] The server uses speech recognition software (e.g., cloud-based speech recognition technology) to convert the user's voice in the store into text data. This text data is then analyzed by a natural language processing system (e.g., a machine learning model) on the server. This allows the server to understand the order details and the user's intent. The server also utilizes a generative AI model to automatically generate appropriate responses. An example of a prompt would be, "Generate a confirmation response based on the user's order details."

[0075] The generated response is converted into speech using speech synthesis technology (e.g., text-to-speech synthesis) and communicated to the user via an in-store terminal. The terminal acts as the user interface, allowing for order adjustments and additional service suggestions. Once the user confirms the order, the terminal sends specific instructions to the cooking system, which then automatically prepares and serves the food.

[0076] After the order has been served, the server securely completes the transaction from the user's account through an automated payment system. This automated payment system utilizes a secure payment platform (e.g., online payment service) to ensure fast payments while protecting customer information.

[0077] As a concrete example, when a user orders "One miso ramen, please" at a restaurant, the terminal captures this voice and converts it into text using a recognition system. This is sent to a server, where it undergoes analysis using natural language processing to generate a response such as "One miso ramen, correct? Would you like something to drink?", which is then delivered to the user via speech synthesis. This improves the efficiency of food service and enhances the customer experience.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The user places an order by voice within the store. The terminal captures this voice through the microphone and sends it to the server as digital audio data. The input is the user's voice data, and the output is the server receiving the digital audio data. At this stage, the microphone's capture function is active.

[0081] Step 2:

[0082] The server converts received digital audio data into text data using speech recognition technology. The input is audio data, and the output is text information that reflects the content of that audio. In the conversion process, speech recognition software operates, and natural language processing generates text for the next step.

[0083] Step 3:

[0084] The server inputs text data into a natural language processing system. The system analyzes this data to understand the order details and the user's intent. Here, the input is text data, and the output is information that identifies the user's intent and order details. A generative AI model is used in the analysis, and the prompt "Generate a confirmation response based on the user's order details" is applied.

[0085] Step 4:

[0086] Based on the analysis results, the server generates an appropriate response, which is then converted into speech data using a speech synthesis system. The input is the analyzed order information, and the output is the speech data of the response. A generative AI model is used to generate the response, and speech synthesis technology makes the response available as speech.

[0087] Step 5:

[0088] The generated response audio data is transmitted to the user via the terminal. The terminal uses its voice output function to ask the user for confirmation or additional questions. The input is audio data, and the output is the actual voice message to the user.

[0089] Step 6:

[0090] The user reviews the response and adds or modifies the order as needed. This information is sent back to the server, and the process from step 2 is repeated as necessary. The input is the user's new order or modification, and the output is the sending of the updated information to the server.

[0091] Step 7:

[0092] Once an order is confirmed, the terminal sends that information as instructions to the cooking system. The input is the final order details, and the output is the instructions to the cooking system. Here, the start of the cooking process is controlled manually or automatically.

[0093] Step 8:

[0094] After cooking is complete, the terminal uses an automated serving system to deliver the ordered items to the user. The input is information about the cooked items, and the output is the delivery of the items to the user. At this stage, the serving function is operational.

[0095] Step 9:

[0096] The server activates the automated payment system once the order is completed. The input is the user's payment information, and the output is a confirmation of the transaction completion. A secure online payment platform operates during the payment process, ensuring the payment is completed safely.

[0097] (Application Example 1)

[0098] 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."

[0099] Current mobile food delivery services require users to manually place orders, a process that is cumbersome and time-consuming. Furthermore, it is difficult to quickly respond to the individual needs of each user, highlighting the need for improved service quality. Additionally, misdeliveries and time losses due to order issues hinder efficient operation.

[0100] 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.

[0101] In this invention, the server includes speech recognition means for converting voice information into text information, natural language processing means for generating a response based on the text information, speech synthesis means for outputting the generated response as voice information, automatic payment means for processing the user's payment information, and linking means for linking information with specific mobility services. As a result, users can place orders intuitively using their voice, the system can process them quickly and accurately, and an automated payment and efficient service flow can be realized.

[0102] "Speech recognition means" refers to a technology that analyzes speech signals and converts them into corresponding textual information.

[0103] "Natural language processing means" refers to technologies for analyzing textual information into a format that a computer can understand and generating appropriate responses.

[0104] "Speech synthesis means" refers to a technology that has the function of converting text information into speech data and outputting it as natural-sounding speech.

[0105] "Management means" refers to the technology for receiving orders from users, transmitting information to an external preparation system based on those orders, and providing goods at the appropriate time.

[0106] An "automated payment method" is a technology that has the function of securely processing users' payment information and completing payments quickly and accurately.

[0107] "Interlocking means" refers to technologies that enable efficient order processing and logistics by sharing information and coordinating with specific mobility-related services.

[0108] The system for realizing this invention combines speech recognition means, natural language processing means, speech synthesis means, management means, automatic payment means, and interlocking means.

[0109] The server converts voice commands from the user into text data using a speech recognition API (e.g., Google® Cloud Speech-to-Text). This data is then analyzed by a natural language processing library (e.g., NLTK or spaCy) to understand the user's intent. After analysis, an appropriate response is generated in voice format using a speech synthesis API (e.g., Google Cloud Text-to-Speech) and output to the user via their device.

[0110] Order and payment information is transferred to an external cooking system via a management system at the appropriate time. Furthermore, user payments are processed quickly and securely using an automated payment platform (e.g., Stripe). In addition, data is shared with other transportation-related services through integration mechanisms to improve workflow efficiency.

[0111] For example, if a user voice-inputs "I want to order a sushi set," the server recognizes the speech, converts it to text, and analyzes the order using natural language processing. Based on the analysis, it outputs a message via speech synthesis, such as "Please choose the toppings for your sushi set." When using a generative AI model, a prompt such as "Please suggest some toppings that are good for someone eating sushi for the first time" can be used to get appropriate advice from the AI.

[0112] This type of system allows users to enjoy an intuitive and seamless ordering experience.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The user places an order by voice into their smartphone. The device receives the voice input through its microphone and sends the voice data to the server.

[0116] Step 2:

[0117] The server passes the received audio data to a speech recognition API (e.g., Google Cloud Speech-to-Text) to convert the audio into text. In this step, the input is audio data and the output is the corresponding text data.

[0118] Step 3:

[0119] The server processes the obtained text data using natural language processing tools (e.g., NLTK or spaCy) to analyze the user's order intent. The input is text data, and the analysis result outputs structured order information.

[0120] Step 4:

[0121] The server generates voice responses for confirmation and suggestions using speech synthesis (e.g., Google Cloud Text-to-Speech) based on the parsed order information. The input is structured order information, and the output is voice data.

[0122] Step 5:

[0123] The terminal receives audio data from the server and responds to the user using an audio output device. Here, it performs specific actions such as confirming information using speech synthesis and communicating the next instructions to the user.

[0124] Step 6:

[0125] Once the user has confirmed the order, the server transmits the order information to an external cooking system via a management mechanism. The input is the final order information, and the output is the commands for cooking instructions.

[0126] Step 7:

[0127] The server uses an automated payment method (e.g., Stripe) to process the user's payment information and complete the payment. The input is the user's payment information, and the output is the payment completion status.

[0128] Step 8:

[0129] The server uses interoperability to share information with mobile services and support smooth distribution. Here, order delivery information is entered, and optimized delivery instructions are output.

[0130] 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.

[0131] This invention is a system for analyzing customer voices and emotions in the service process of restaurants to provide a higher quality customer service experience. The system includes speech recognition means, natural language processing means, speech synthesis means, management means, automatic payment means, and an emotion engine for analyzing user emotions.

[0132] The device collects the user's speech using a microphone and converts it into text data using speech recognition technology. In this process, an emotion engine analyzes the speech signal to infer the user's emotional state. For example, it analyzes whether the user is anxious or calm based on the intonation and speed of their speech.

[0133] After receiving text data, the server uses natural language processing to understand the order and generates an appropriate response along with the analyzed sentiment information. The sentiment information is used to adjust the content and tone of the response. For example, if the sentiment engine determines that the user is dissatisfied, the server will generate a polite response such as, "Sorry for the wait."

[0134] The generated response is sent from the server to the terminal and output as speech by the terminal's speech synthesis system. This allows the user to experience a natural and friendly service.

[0135] Once the user confirms their order, the terminal transmits the order information to an external cooking system via a management mechanism, enabling efficient food delivery. Furthermore, after the order has been served, the server completes the payment process using an automated payment system.

[0136] For example, if a customer at a restaurant says, "I'm in a big hurry, please prepare my food quickly," the terminal converts the audio information into text, and an emotion engine analyzes the emotion conveying the user's urgency. Based on this information, the server generates a quick and reassuring response such as, "We will start preparing your food immediately, please wait a moment." This process improves the customer experience.

[0137] This system enables immediate feedback on customer emotions, leading to personalized service and improved customer satisfaction.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] The user begins speaking to place an order. The device uses the microphone to capture the voice and collects the audio data.

[0141] Step 2:

[0142] The device uses speech recognition to collect audio data and converts it into text data. During this process, an emotion engine analyzes the intonation and speed of the speech to determine the user's emotional state.

[0143] Step 3:

[0144] The terminal sends the converted text data and recognized emotion information to the server.

[0145] Step 4:

[0146] The server analyzes the received text data using natural language processing to understand the order. Simultaneously, it utilizes emotional information to generate an appropriate response message, customizing its tone and content to reflect the user's emotions.

[0147] Step 5:

[0148] The server generates a response message and sends it to the terminal, which then uses its speech synthesis capabilities to output it as speech. Through this response, the terminal confirms the order with the user and prompts for additional questions as needed.

[0149] Step 6:

[0150] After the user receives a response, they will speak again if they have any additional orders or changes. This information will be processed again through steps 1 to 3.

[0151] Step 7:

[0152] Once all orders are confirmed, the terminal transmits the order details to an external cooking system via a management mechanism. The system then monitors the completion time of cooking and prepares for serving.

[0153] Step 8:

[0154] After cooking is complete, the device autonomously moves and delivers the food to the designated table.

[0155] Step 9:

[0156] After serving is complete, the server processes the user's payment information via an automated payment system and completes the payment. The user receives a payment completion notification, ensuring a safe and swift transaction.

[0157] (Example 2)

[0158] 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".

[0159] In recent years, restaurants have been required to provide fast and personalized service that meets the diverse needs of their customers. However, traditional systems have faced challenges in recognizing customer emotions and providing appropriate service. Furthermore, efficiently managing the entire process from ordering to serving and payment remains a challenge.

[0160] 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.

[0161] In this invention, the server includes information recognition means for converting voice information into text information, emotion analysis means for estimating an emotional state from the voice information, and natural language processing means for generating a response based on the text information and the emotional state. This makes it possible to provide individually optimized services that respond to the user's emotions.

[0162] "Audio information" refers to the data of sounds emitted by a speaker through a microphone, which is then processed and converted into text.

[0163] "Text information" refers to data in which audio information is represented as characters by speech recognition technology, and is in a format that can be analyzed by natural language processing.

[0164] "Information recognition means" refers to a technological device that receives audio information and converts it into text information, and this includes speech recognition software.

[0165] An "emotion analysis device" is a technological device that improves the quality of dialogue by determining the speaker's emotional state from audio and text information.

[0166] "Natural language processing means" refers to a technical device for understanding dialogue content and generating appropriate responses based on text information and emotional states.

[0167] A "speech synthesis device" is a technological device that outputs a generated response as speech, and is a device for conveying information in a way that is easy for people to understand.

[0168] An "information management system" is a technological device that processes order information received from users and manages and transmits that information in cooperation with external systems.

[0169] An "automated payment method" is a technological device that processes the user's payment information and completes payments quickly and without contact.

[0170] This invention is a system for interacting with users through voice and providing more personalized services. The following details each component of this system and its operation.

[0171] The device collects the user's speech using a high-performance microphone. The collected audio information is converted into text in real time using speech recognition software (for example, a cloud-based speech recognition service).

[0172] Furthermore, the device is equipped with emotion detection software as a means of emotion analysis, which infers the user's emotional state by analyzing the voice signal. This emotion analysis is used to determine whether the user is calm, in a hurry, or otherwise, based on characteristics such as voice tone and speaking speed.

[0173] The server receives text and sentiment information sent from the terminal and uses natural language processing software (for example, natural language analysis libraries and generative AI models) to understand the user's order and generate an appropriate response based on their emotions. For example, if the sentiment analysis tool determines that the user is irritated, the server designs a calming response such as, "I'm sorry if this has caused you any discomfort."

[0174] The generated response is sent from the server to the terminal and output as speech using the terminal's speech synthesis software (for example, a text-to-speech API). This allows the user to receive a natural and user-friendly service.

[0175] As a concrete example, if a customer at a restaurant says, "I'm busy, please prepare my food quickly," the terminal converts this into text and uses sentiment analysis to identify the emotion associated with the customer's urgency. The server then generates a reassuring response such as, "We are working quickly to prepare your order, please wait a moment." In this way, the customer experience is improved, and customer satisfaction can be increased.

[0176] An example of a prompt would be, "Please tell me the procedure for analyzing speech and generating the best response based on the user's emotions." In this example, the generative AI model determines emotions from the speech data and provides a process to improve the response.

[0177] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0178] Step 1:

[0179] The device collects the user's speech using a microphone and inputs it as audio information. The collected audio information is analyzed using speech recognition software and converted into text information. Specifically, spectral analysis of the audio signal is performed to extract sound features and convert them into string data. The output is text information including the user's requests and orders.

[0180] Step 2:

[0181] The terminal passes the converted text information to an emotion analysis system, which estimates the user's emotional state based on their voice characteristics. This process analyzes voice features such as tone, intonation, and speed, and the emotion engine outputs the user's emotion tags (e.g., joy, frustration, calmness). This identifies the emotional state and allows for the deriving of a response that reflects the user's mood.

[0182] Step 3:

[0183] The server receives text information and sentiment tags from the terminal as input and uses natural language processing software to understand the user's request. This process involves data processing to analyze the order details and intentions from the text. The output is information for generating a response based on the user's order details and intent.

[0184] Step 4:

[0185] The server inputs the analysis results into a generating AI model, which then creates an appropriate response based on the text information and emotional state. The model uses this information to construct a response with a tone that corresponds to the user's emotions. The generated output is a natural conversational sentence as a response to the user.

[0186] Step 5:

[0187] The terminal receives the response sent from the server and converts it into speech information using speech synthesis software. Specifically, it converts the text response into speech data and performs a process to play it back in a natural human voice. The output is a speech response that the user can hear.

[0188] Step 6:

[0189] Once a user confirms an order, the terminal passes the order information to a management system, which then processes the order in conjunction with an external cooking system. The input is the confirmed order details, and the output is the instruction to be transmitted to the cooking system. This ensures efficient supply and smooth service delivery.

[0190] Step 7:

[0191] The server processes payments using an automated payment system after the order is fulfilled. Inputs are the user's payment information and the order amount, and output is a secure payment confirmation. This process ensures fast and accurate payments.

[0192] (Application Example 2)

[0193] 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".

[0194] Traditional food delivery systems process orders based solely on the user's spoken information, making it difficult to provide optimal service tailored to the user's emotions and urgency. Furthermore, responses to users tend to be formulaic, hindering improvements in customer satisfaction. Additionally, delivery times are often not adequately optimized, sometimes failing to meet customer expectations.

[0195] 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.

[0196] In this invention, the server includes emotion analysis means for analyzing emotional states from voice signals, response adjustment means for adjusting response content according to the emotional state, and delivery optimization means for presenting the response adjusted by the response adjustment means and the optimal delivery option. This enables the provision of personalized responses that take into account the user's emotional state and the provision of fast and optimal delivery services.

[0197] "Speech recognition means" refers to a technology that converts speech information into text information, and is a device that makes the user's speech into a format that a machine can understand.

[0198] "Natural language processing means" refers to technologies that generate responses based on text information, and is a system that enables machines to understand natural human language and return appropriate responses.

[0199] A "speech synthesis means" is a technology that outputs generated text information as speech information, and is a device that converts text information back into natural-sounding speech for transmission.

[0200] "Management means" refers to technologies for managing customer orders and controlling the provision of food and beverages, as well as systems for appropriately processing order information and coordinating with external systems.

[0201] An "automated payment method" is a system that automatically processes a user's payment information, and is a technology for efficiently completing transactions.

[0202] "Emotional analysis means" refers to a technology that analyzes emotional states from audio signals, and is a system for reading the emotions contained in speech.

[0203] A "response adjustment mechanism" is a technology that adjusts the content of responses based on the results of an analysis of the emotional state, and is a system for further personalizing the user's experience.

[0204] "Delivery optimization means" refers to a technology that presents the optimal delivery option based on adjusted responses, and is a method for building a delivery plan that meets the user's needs.

[0205] This invention is a system that provides personalized services to users in a food delivery service by using voice and sentiment analysis. The server and terminal work together to generate responses that meet the user's needs through voice recognition, natural language processing, and sentiment analysis.

[0206] The server primarily uses cloud services to process audio data. The terminal collects audio data using the microphone on the user's smart device (such as a smartphone or tablet). The Google Cloud Speech-to-Text API is used to convert the audio information acquired by the terminal into text information. This text information is then processed using natural language processing via the Google Cloud Natural Language API. Based on the results, sentiment analysis is performed by IBM Watson® Tone Analyzer. This allows the server to determine the user's emotional state based on the intonation and tone of their voice.

[0207] The results of sentiment analysis are reflected in response generation. For example, if a user says, "I'm busy, please deliver the food quickly," the system recognizes the urgency through sentiment analysis and presents expedited delivery options. These delivery options are then processed quickly using an automated payment method via the Stripe API. Furthermore, the generated response is synthesized into speech on the terminal and presented as a friendly and approachable response to the user.

[0208] For example, if a user says, "I have a movie tonight, so I'd like dinner delivered early," the device collects this as voice information, and after the aforementioned process, the server presents the shortest possible delivery option. This response is delivered in a reassuring, adjusted tone.

[0209] As an example of a prompt to input into the generating AI model, a user might say, "Please give me a regular pizza as quickly as possible," and the system will generate and present a quick response and specific delivery options.

[0210] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0211] Step 1:

[0212] The user places an order by voice through a smart device. The device collects the voice using a microphone and processes it as digital voice data. The input for this step is a voice signal, and the output is voice data in digital format. This includes operations to properly collect and digitize the voice signal.

[0213] Step 2:

[0214] The device uses the Google Cloud Speech-to-Text API to convert digital audio data into text data. The input is digital audio data, and the output is text data. This conversion process involves analyzing the audio signal and processing the data for phoneme recognition.

[0215] Step 3:

[0216] The server receives text data and uses the Google Cloud Natural Language API to perform natural language processing and understand the order details. The input is text data, and the output is structured order data. This step involves semantic analysis of the text and extraction of order information.

[0217] Step 4:

[0218] The server uses IBM Watson Tone Analyzer to analyze emotional states derived from speech intonation. Input is text data, and output is data related to emotional states. This includes actions to evaluate the emotional elements of speech and determine the user's mood and urgency.

[0219] Step 5:

[0220] The server uses a generative AI model to generate a response based on the obtained sentiment and order details, and leverages Stripe API business logic to determine the optimal delivery option for the order. The input is structured order details and sentiment data, and the output is a refined response and delivery options. Response refinement and rapid decision-making processes are performed.

[0221] Step 6:

[0222] The server sends an optimized response to the terminal, and the terminal uses speech synthesis to output voice to the user. The input is the adjusted response and delivery option data, and the output is a natural voice response to the user. Voice data is generated and feedback is provided to the user.

[0223] Step 7:

[0224] After an order is confirmed, the server uses automated payment methods to process the payment and initiate delivery. The input is the confirmed order information, and the output is confirmation data of the completed transaction. Fast and accurate payment data processing is performed.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] [Second Embodiment]

[0229] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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).

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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".

[0241] This invention provides a system that integrates speech recognition, natural language processing, speech synthesis, customer management, and automated payment methods to fully automate the customer service process in the food service industry.

[0242] The server uses speech recognition to convert the voice of a user in the store into text data. This data is then analyzed by the server's natural language processing system to provide information for understanding the order details and the intent of the question.

[0243] Next, the server generates a response. This response is a crucial step, including order confirmation, prompting for additional questions, and service instructions. The generated text response is then converted into speech by a speech synthesis system and delivered to the user via an in-store terminal.

[0244] The terminal also plays a role in receiving user input and has the ability to adjust orders or suggest additional items as needed. Once the order is confirmed, the terminal sends instructions to the cooking system, which then automatically controls serving after cooking is complete.

[0245] After the order is served, the server's automated payment system activates and processes the payment to the user's account. This process ensures that customer information is kept secure and that payments are processed quickly and accurately.

[0246] For example, if a user at a restaurant says, "I'd like one miso ramen, please," the terminal captures the voice and converts it to text using speech recognition. This text data is sent to a server, where natural language processing understands the order. Based on this, a response is generated, such as, "One miso ramen, correct? Would you like something to drink?", and this response is output to the user via voice from the terminal. The entire process proceeds seamlessly according to any additional requests from the user, ultimately resulting in automated serving and payment.

[0247] In this way, we can provide the restaurant industry with an efficient and humane customer service experience while also addressing the challenge of labor shortages.

[0248] The following describes the processing flow.

[0249] Step 1:

[0250] The user begins speaking to place an order. The terminal acquires voice data through the microphone and converts it into text data using speech recognition technology.

[0251] Step 2:

[0252] The terminal sends the converted text data to the server. The server receives the data and uses natural language processing to analyze the order details and intent.

[0253] Step 3:

[0254] The server generates response messages based on the analysis results. For example, it might prepare messages to confirm the order details or prompt for additional information.

[0255] Step 4:

[0256] The generated response message is sent from the server to the terminal and converted into speech by the terminal's speech synthesis system. The terminal then communicates the response to the user through the speech output.

[0257] Step 5:

[0258] The user makes additional orders or confirmations. The terminal collects this new voice data and repeats the process from step 1.

[0259] Step 6:

[0260] Once all orders are confirmed, the terminal sends the order details to the cooking system and relays cooking instructions to the appropriate department.

[0261] Step 7:

[0262] Once cooking is complete, the terminal begins the necessary movements and arrangements to serve the ordered items.

[0263] Step 8:

[0264] After the meal is served, the server processes the user's payment information using an automated payment system and completes the payment. At this time, the user receives a notification that the payment has been completed.

[0265] (Example 1)

[0266] 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."

[0267] Traditional food services heavily rely on human labor, making it difficult to streamline operations and provide prompt customer service. Furthermore, the fragmentation of processes such as ordering, serving, and payment resulted in a less-than-smooth overall customer experience. Especially with the worsening labor shortage, there is a growing need to provide efficient yet humane customer service.

[0268] 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.

[0269] In this invention, the server includes recognition means for converting voice information into information, natural processing means for generating a response based on the information, and synthesis means for outputting the generated response as information. This enables the automation of the entire process and the provision of an integrated customer service experience.

[0270] "Audio information" refers to data and signals obtained using sound.

[0271] "Recognition means" refers to systems and devices for converting audio information into text or digital data.

[0272] "Natural processing methods" refer to technologies that analyze text data, understand its intent and content, and generate appropriate responses.

[0273] "Synthesis means" refers to a device or system for converting generated text data into speech and outputting it.

[0274] "Management means" refers to methods and systems for managing the entire service based on received data and instructions, and for providing the optimal service.

[0275] "Automated means" refers to devices and technologies that process transaction information and other data mechanically rather than manually.

[0276] A "generative model" refers to an algorithm or framework that uses machine learning or AI technology to generate responses based on data.

[0277] "Request information" refers to data and information related to orders and requests obtained from customers.

[0278] "Cooking equipment" refers to tools and machines used for preparing food.

[0279] "Timing" refers to the appropriate time or timeframe for a particular action or process to take place.

[0280] A "system" refers to a collection of multiple means or devices that work together to achieve a specific function or purpose.

[0281] This invention is a system for completely automating the customer service process in food and beverage services. This system integrates speech recognition, natural language processing, speech synthesis, customer management, and automated payment methods.

[0282] The server uses speech recognition software (e.g., cloud-based speech recognition technology) to convert the user's voice in the store into text data. This text data is then analyzed by a natural language processing system (e.g., a machine learning model) on the server. This allows the server to understand the order details and the user's intent. The server also utilizes a generative AI model to automatically generate appropriate responses. An example of a prompt would be, "Generate a confirmation response based on the user's order details."

[0283] The generated response is converted into speech using speech synthesis technology (e.g., synthesis technology that converts text into speech) and transmitted to the user via the in-store terminal. The terminal functions as the user interface and can adjust orders and propose additional services. When the user confirms the order, the terminal sends specific instructions to the cooking system, and cooking and serving are automatically performed.

[0284] After the order has been served, the server completes the transaction securely from the user's account through the automatic payment system. For automatic payments, a secure payment platform (e.g., online payment service) is used to enable quick payments while protecting customer information.

[0285] As a specific example, when a user places an order for "one miso ramen" at a restaurant, the terminal captures this voice through the microphone and converts it into text using the recognition system. This is sent to the server, and after analysis by natural language processing means, a response such as "One miso ramen, right? How about a drink?" is generated and delivered to the user through speech synthesis. This achieves an improvement in the efficiency of food services and the customer experience.

[0286] The flow of the specific process in Example 1 will be described using FIG. 11.

[0287] Step 1:

[0288] The user places an order by voice within the store. The terminal captures this voice through the microphone and transmits it to the server as digital voice data. The input is the user's voice data, and the output is the server's reception of the digital voice data. At this stage, the microphone's capture function operates.

[0289] Step 2:

[0290] The server converts received digital audio data into text data using speech recognition technology. The input is audio data, and the output is text information that reflects the content of that audio. In the conversion process, speech recognition software operates, and natural language processing generates text for the next step.

[0291] Step 3:

[0292] The server inputs text data into a natural language processing system. The system analyzes this data to understand the order details and the user's intent. Here, the input is text data, and the output is information that identifies the user's intent and order details. A generative AI model is used in the analysis, and the prompt "Generate a confirmation response based on the user's order details" is applied.

[0293] Step 4:

[0294] Based on the analysis results, the server generates an appropriate response, which is then converted into speech data using a speech synthesis system. The input is the analyzed order information, and the output is the speech data of the response. A generative AI model is used to generate the response, and speech synthesis technology makes the response available as speech.

[0295] Step 5:

[0296] The generated response audio data is transmitted to the user via the terminal. The terminal uses its voice output function to ask the user for confirmation or additional questions. The input is audio data, and the output is the actual voice message to the user.

[0297] Step 6:

[0298] The user reviews the response and adds or modifies the order as needed. This information is sent back to the server, and the process from step 2 is repeated as necessary. The input is the user's new order or modification, and the output is the sending of the updated information to the server.

[0299] Step 7:

[0300] When the order is confirmed, the terminal sends the information to the cooking system as an instruction. The input is the final order content, and the output is the instruction to the cooking system. Here, the start of the cooking process is controlled manually or automatically.

[0301] Step 8:

[0302] After the cooking is completed, the terminal uses the automatic food delivery system to deliver the ordered products to the user. The input is the information of the cooked products, and the output is the provision of the products to the user. At this stage, the food delivery function operates.

[0303] Step 9:

[0304] The server activates the automatic settlement system when the order is completed. The input is the payment information of the user, and the output is the confirmation of the completion of the transaction. In the settlement process, a secure online settlement platform operates to complete the payment safely.

[0305] (Application Example 1)

[0306] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0307] In the current mobile food delivery service, manual operations are required when users place orders, and the process is complicated and time-consuming. Also, it is difficult to quickly respond to the needs of individual users, and an improvement in service quality is required. Furthermore, problems with orders lead to misdeliveries and time losses, and the issue is that efficient operation is hindered.

[0308] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0309] In this invention, the server includes speech recognition means for converting voice information into text information, natural language processing means for generating a response based on the text information, speech synthesis means for outputting the generated response as voice information, automatic payment means for processing the user's payment information, and linking means for linking information with specific mobility services. As a result, users can place orders intuitively using their voice, the system can process them quickly and accurately, and an automated payment and efficient service flow can be realized.

[0310] "Speech recognition means" refers to a technology that analyzes speech signals and converts them into corresponding textual information.

[0311] "Natural language processing means" refers to technologies for analyzing textual information into a format that a computer can understand and generating appropriate responses.

[0312] "Speech synthesis means" refers to a technology that has the function of converting text information into speech data and outputting it as natural-sounding speech.

[0313] "Management means" refers to the technology for receiving orders from users, transmitting information to an external preparation system based on those orders, and providing goods at the appropriate time.

[0314] An "automated payment method" is a technology that has the function of securely processing users' payment information and completing payments quickly and accurately.

[0315] "Interlocking means" refers to technologies that enable efficient order processing and logistics by sharing information and coordinating with specific mobility-related services.

[0316] The system for realizing this invention combines speech recognition means, natural language processing means, speech synthesis means, management means, automatic payment means, and interlocking means.

[0317] The server converts voice commands from the user into text data using a speech recognition API (e.g., Google Cloud Speech-to-Text). This data is then analyzed by a natural language processing library (e.g., NLTK or spaCy) to understand the user's intent. After analysis, an appropriate response is generated in voice format using a speech synthesis API (e.g., Google Cloud Text-to-Speech) and output to the user via their device.

[0318] Order and payment information is transferred to an external cooking system via a management system at the appropriate time. Furthermore, user payments are processed quickly and securely using an automated payment platform (e.g., Stripe). In addition, data is shared with other transportation-related services through integration mechanisms to improve workflow efficiency.

[0319] For example, if a user voice-inputs "I want to order a sushi set," the server recognizes the speech, converts it to text, and analyzes the order using natural language processing. Based on the analysis, it outputs a message via speech synthesis, such as "Please choose the toppings for your sushi set." When using a generative AI model, a prompt such as "Please suggest some toppings that are good for someone eating sushi for the first time" can be used to get appropriate advice from the AI.

[0320] This type of system allows users to enjoy an intuitive and seamless ordering experience.

[0321] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0322] Step 1:

[0323] The user places an order by voice into their smartphone. The device receives the voice input through its microphone and sends the voice data to the server.

[0324] Step 2:

[0325] The server passes the received audio data to a speech recognition API (e.g., Google Cloud Speech-to-Text) to convert the audio into text. In this step, the input is audio data and the output is the corresponding text data.

[0326] Step 3:

[0327] The server processes the obtained text data using natural language processing tools (e.g., NLTK or spaCy) to analyze the user's order intent. The input is text data, and the analysis result outputs structured order information.

[0328] Step 4:

[0329] The server generates voice responses for confirmation and suggestions using speech synthesis (e.g., Google Cloud Text-to-Speech) based on the parsed order information. The input is structured order information, and the output is voice data.

[0330] Step 5:

[0331] The terminal receives audio data from the server and responds to the user using an audio output device. Here, it performs specific actions such as confirming information using speech synthesis and communicating the next instructions to the user.

[0332] Step 6:

[0333] Once the user has confirmed the order, the server transmits the order information to an external cooking system via a management mechanism. The input is the final order information, and the output is the commands for cooking instructions.

[0334] Step 7:

[0335] The server processes the user's payment information and completes the payment using an automated payment method (e.g., Stripe). The input is the user's payment information, and the output is the payment completion status.

[0336] Step 8:

[0337] The server uses interoperability to share information with mobile services and support smooth distribution. Here, order delivery information is entered, and optimized delivery instructions are output.

[0338] 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.

[0339] This invention is a system for analyzing customer voices and emotions in the service process of restaurants to provide a higher quality customer service experience. The system includes speech recognition means, natural language processing means, speech synthesis means, management means, automatic payment means, and an emotion engine for analyzing user emotions.

[0340] The device collects the user's speech using a microphone and converts it into text data using speech recognition technology. In this process, an emotion engine analyzes the speech signal to infer the user's emotional state. For example, it analyzes whether the user is anxious or calm based on the intonation and speed of their speech.

[0341] After receiving text data, the server uses natural language processing to understand the order and generates an appropriate response along with the analyzed sentiment information. The sentiment information is used to adjust the content and tone of the response. For example, if the sentiment engine determines that the user is dissatisfied, the server will generate a polite response such as, "Sorry for the wait."

[0342] The generated response is sent from the server to the terminal and output as speech by the terminal's speech synthesis system. This allows the user to experience a natural and friendly service.

[0343] Once the user confirms their order, the terminal transmits the order information to an external cooking system via a management mechanism, enabling efficient food delivery. Furthermore, after the order has been served, the server completes the payment process using an automated payment system.

[0344] For example, if a customer at a restaurant says, "I'm in a big hurry, please prepare my food quickly," the terminal converts the audio information into text, and an emotion engine analyzes the emotion conveying the user's urgency. Based on this information, the server generates a quick and reassuring response such as, "We will start preparing your food immediately, please wait a moment." This process improves the customer experience.

[0345] This system enables immediate feedback on customer emotions, leading to personalized service and improved customer satisfaction.

[0346] The following describes the processing flow.

[0347] Step 1:

[0348] The user begins speaking to place an order. The device uses the microphone to capture the voice and collects the audio data.

[0349] Step 2:

[0350] The device uses speech recognition to collect audio data and converts it into text data. During this process, an emotion engine analyzes the intonation and speed of the speech to determine the user's emotional state.

[0351] Step 3:

[0352] The terminal sends the converted text data and recognized emotion information to the server.

[0353] Step 4:

[0354] The server analyzes the received text data using natural language processing to understand the order. Simultaneously, it utilizes emotional information to generate appropriate response messages, customizing them to reflect the user's emotions in tone and content.

[0355] Step 5:

[0356] The server generates a response message and sends it to the terminal, which then uses its speech synthesis capabilities to output it as speech. Through this response, the terminal confirms the order with the user and prompts for additional questions as needed.

[0357] Step 6:

[0358] After the user receives a response, they will speak again if they have any additional orders or changes. This information will be processed again through steps 1 to 3.

[0359] Step 7:

[0360] Once all orders are confirmed, the terminal transmits the order details to an external cooking system via a management mechanism. The system then monitors the completion time of cooking and prepares for serving.

[0361] Step 8:

[0362] After cooking is complete, the device autonomously moves and delivers the food to the designated table.

[0363] Step 9:

[0364] After serving is complete, the server processes the user's payment information via an automated payment system and completes the payment. The user receives a payment completion notification, ensuring a safe and swift transaction.

[0365] (Example 2)

[0366] 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".

[0367] In recent years, restaurants have been required to provide fast and personalized service that meets the diverse needs of their customers. However, traditional systems have faced challenges in recognizing customer emotions and providing appropriate service. Furthermore, efficiently managing the entire process from ordering to serving and payment remains a challenge.

[0368] 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.

[0369] In this invention, the server includes information recognition means for converting voice information into text information, emotion analysis means for estimating an emotional state from the voice information, and natural language processing means for generating a response based on the text information and the emotional state. This makes it possible to provide individually optimized services that respond to the user's emotions.

[0370] "Audio information" refers to the data of sounds emitted by a speaker through a microphone, which is then processed and converted into text.

[0371] "Text information" refers to data in which audio information is represented as characters by speech recognition technology, and is in a format that can be analyzed by natural language processing.

[0372] "Information recognition means" refers to a technological device that receives audio information and converts it into text information, and this includes speech recognition software.

[0373] An "emotion analysis device" is a technological device that improves the quality of dialogue by determining the speaker's emotional state from audio and text information.

[0374] "Natural language processing means" refers to a technical device for understanding dialogue content and generating appropriate responses based on text information and emotional states.

[0375] A "speech synthesis device" is a technological device that outputs a generated response as speech, and is a device for conveying information in a way that is easy for people to understand.

[0376] An "information management system" is a technological device that processes order information received from users and manages and transmits that information in cooperation with external systems.

[0377] An "automated payment method" is a technological device that processes the user's payment information and completes payments quickly and without contact.

[0378] This invention is a system for interacting with users through voice and providing more personalized services. The following details each component of this system and its operation.

[0379] The device collects the user's speech using a high-performance microphone. The collected audio information is converted into text in real time using speech recognition software (for example, a cloud-based speech recognition service).

[0380] Furthermore, the device is equipped with emotion detection software as a means of emotion analysis, which infers the user's emotional state by analyzing the voice signal. This emotion analysis is used to determine whether the user is calm, in a hurry, or otherwise, based on characteristics such as voice tone and speaking speed.

[0381] The server receives text and sentiment information sent from the terminal and uses natural language processing software (for example, natural language analysis libraries and generative AI models) to understand the user's order and generate an appropriate response based on their emotions. For example, if the sentiment analysis tool determines that the user is irritated, the server designs a calming response such as, "I'm sorry if this has caused you any discomfort."

[0382] The generated response is sent from the server to the terminal and output as speech using the terminal's speech synthesis software (for example, a text-to-speech API). This allows the user to receive a natural and user-friendly service.

[0383] As a concrete example, if a customer at a restaurant says, "I'm busy, please prepare my food quickly," the terminal converts this into text and uses sentiment analysis to identify the emotion associated with the customer's urgency. The server then generates a reassuring response such as, "We are working quickly to prepare your order, please wait a moment." In this way, the customer experience is improved, and customer satisfaction can be increased.

[0384] An example of a prompt would be, "Please tell me the procedure for analyzing speech and generating the best response based on the user's emotions." In this example, the generative AI model determines emotions from the speech data and provides a process to improve the response.

[0385] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0386] Step 1:

[0387] The device collects the user's speech using a microphone and inputs it as audio information. The collected audio information is analyzed using speech recognition software and converted into text information. Specifically, spectral analysis of the audio signal is performed to extract sound features and convert them into string data. The output is text information including the user's requests and orders.

[0388] Step 2:

[0389] The terminal passes the converted text information to an emotion analysis system, which estimates the user's emotional state based on their voice characteristics. This process analyzes voice features such as tone, intonation, and speed, and the emotion engine outputs the user's emotion tags (e.g., joy, frustration, calmness). This identifies the emotional state and allows for the deriving of a response that reflects the user's mood.

[0390] Step 3:

[0391] The server receives text information and sentiment tags from the terminal as input and uses natural language processing software to understand the user's request. This process involves data processing to analyze the order details and intentions from the text. The output is information for generating a response based on the user's order details and intent.

[0392] Step 4:

[0393] The server inputs the analysis results into a generating AI model, which then creates an appropriate response based on the text information and emotional state. The model uses this information to construct a response with a tone that corresponds to the user's emotions. The generated output is a natural conversational sentence as a response to the user.

[0394] Step 5:

[0395] The terminal receives the response sent from the server and converts it into speech information using speech synthesis software. Specifically, it converts the text response into speech data and performs a process to play it back in a natural human voice. The output is a speech response that the user can hear.

[0396] Step 6:

[0397] Once a user confirms an order, the terminal passes the order information to a management system, which then processes the order in conjunction with an external cooking system. The input is the confirmed order details, and the output is the instruction to be transmitted to the cooking system. This ensures efficient supply and smooth service delivery.

[0398] Step 7:

[0399] The server processes payments using an automated payment system after the order is fulfilled. Inputs are the user's payment information and the order amount, and output is a secure payment confirmation. This process ensures fast and accurate payments.

[0400] (Application Example 2)

[0401] 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."

[0402] Traditional food delivery systems process orders based solely on the user's spoken information, making it difficult to provide optimal service tailored to the user's emotions and urgency. Furthermore, responses to users tend to be formulaic, hindering improvements in customer satisfaction. Additionally, delivery times are often not adequately optimized, sometimes failing to meet customer expectations.

[0403] 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.

[0404] In this invention, the server includes emotion analysis means for analyzing emotional states from voice signals, response adjustment means for adjusting response content according to the emotional state, and delivery optimization means for presenting the response adjusted by the response adjustment means and the optimal delivery option. This enables the provision of personalized responses that take into account the user's emotional state and the provision of fast and optimal delivery services.

[0405] "Speech recognition means" refers to a technology that converts speech information into text information, and is a device that makes the user's speech into a format that a machine can understand.

[0406] "Natural language processing means" refers to technologies that generate responses based on text information, and is a system that enables machines to understand natural human language and return appropriate responses.

[0407] A "speech synthesis means" is a technology that outputs generated text information as speech information, and is a device that converts text information back into natural-sounding speech for transmission.

[0408] "Management means" refers to technologies for managing customer orders and controlling the provision of food and beverages, as well as systems for appropriately processing order information and coordinating with external systems.

[0409] An "automated payment method" is a system that automatically processes a user's payment information, and is a technology for efficiently completing transactions.

[0410] "Emotional analysis means" refers to a technology that analyzes emotional states from audio signals, and is a system for reading the emotions contained in speech.

[0411] A "response adjustment mechanism" is a technology that adjusts the content of responses based on the results of an analysis of the emotional state, and is a system for further personalizing the user's experience.

[0412] "Delivery optimization means" refers to a technology that presents the optimal delivery option based on a tailored response, and is a method for building a delivery plan that meets the user's needs.

[0413] This invention is a system that provides personalized services to users in a food delivery service by using voice and sentiment analysis. The server and terminal work together to generate responses that meet the user's needs through voice recognition, natural language processing, and sentiment analysis.

[0414] The server primarily uses cloud services to process audio data. The terminal collects audio data using the microphone on the user's smart device (such as a smartphone or tablet). The Google Cloud Speech-to-Text API is used to convert the audio information acquired by the terminal into text information. This text information is then processed using natural language processing via the Google Cloud Natural Language API. Based on the results, sentiment analysis is performed by IBM Watson Tone Analyzer. This allows the server to determine the user's emotional state based on the intonation and tone of their voice.

[0415] The results of sentiment analysis are reflected in response generation. For example, if a user says, "I'm busy, please deliver the food quickly," the system recognizes the urgency through sentiment analysis and presents expedited delivery options. These delivery options are then processed quickly using an automated payment method via the Stripe API. Furthermore, the generated response is synthesized into speech on the terminal and presented as a friendly and approachable response to the user.

[0416] For example, if a user says, "I have a movie tonight, so I'd like dinner delivered early," the device collects this as voice information, and after the aforementioned process, the server presents the shortest possible delivery option. This response is delivered in a reassuring, adjusted tone.

[0417] As an example of a prompt to input into the generating AI model, a user might say, "Please give me a regular pizza as quickly as possible," and the system will generate and present a quick response and specific delivery options.

[0418] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0419] Step 1:

[0420] The user places an order by voice through a smart device. The device collects the voice using a microphone and processes it as digital voice data. The input to this step is an audio signal, and the output is audio data in digital format. This includes operations to properly collect and digitize the audio signal.

[0421] Step 2:

[0422] The device uses the Google Cloud Speech-to-Text API to convert digital audio data into text data. The input is digital audio data, and the output is text data. This conversion process involves analyzing the audio signal and processing the data through phoneme recognition.

[0423] Step 3:

[0424] The server receives text data and uses the Google Cloud Natural Language API to perform natural language processing and understand the order details. The input is text data, and the output is structured order data. This step involves semantic analysis of the text and extraction of order information.

[0425] Step 4:

[0426] The server uses IBM Watson Tone Analyzer to analyze emotional states derived from speech intonation. Input is text data, and output is data related to emotional states. The process includes evaluating the emotional elements of speech and determining the user's mood and urgency.

[0427] Step 5:

[0428] The server uses a generative AI model to generate a response based on the obtained sentiment and order details, and leverages Stripe API business logic to determine the optimal delivery option for the order. The input is structured order details and sentiment data, and the output is a refined response and delivery options. Response refinement and rapid decision-making processes are performed.

[0429] Step 6:

[0430] The server sends an optimized response to the terminal, and the terminal uses speech synthesis to output voice to the user. The input is the adjusted response and delivery option data, and the output is a natural voice response to the user. Voice data is generated and feedback is provided to the user.

[0431] Step 7:

[0432] After an order is confirmed, the server uses automated payment methods to process the payment and initiate delivery. The input is the confirmed order information, and the output is confirmation data of the completed transaction. Fast and accurate payment data processing is performed.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] [Third Embodiment]

[0437] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0438] 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.

[0439] 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).

[0440] 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.

[0441] 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.

[0442] 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).

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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".

[0449] This invention provides a system that integrates speech recognition, natural language processing, speech synthesis, customer management, and automated payment methods to fully automate the customer service process in the food service industry.

[0450] The server uses speech recognition to convert the voice of a user in the store into text data. This data is then analyzed by the server's natural language processing system to provide information for understanding the order details and the intent of the question.

[0451] Next, the server generates a response. This response is a crucial step, including order confirmation, prompting for additional questions, and service instructions. The generated text response is then converted into speech by a speech synthesis system and delivered to the user via an in-store terminal.

[0452] The terminal also plays a role in receiving user input and has the ability to adjust orders or suggest additional items as needed. Once the order is confirmed, the terminal sends instructions to the cooking system, which then automatically controls serving after cooking is complete.

[0453] After the order is served, the server's automated payment system activates and processes the payment to the user's account. This process ensures that customer information is kept secure and that payments are processed quickly and accurately.

[0454] For example, if a user at a restaurant says, "I'd like one miso ramen, please," the terminal captures the voice and converts it to text using speech recognition. This text data is sent to a server, where natural language processing understands the order. Based on this, a response is generated, such as, "One miso ramen, correct? Would you like something to drink?", and this response is output to the user via voice from the terminal. The entire process proceeds seamlessly according to any additional requests from the user, ultimately resulting in automated serving and payment.

[0455] In this way, we can provide the restaurant industry with an efficient and humane customer service experience while also addressing the challenge of labor shortages.

[0456] The following describes the processing flow.

[0457] Step 1:

[0458] The user begins speaking to place an order. The terminal acquires voice data through the microphone and converts it into text data using speech recognition technology.

[0459] Step 2:

[0460] The terminal sends the converted text data to the server. The server receives the data and uses natural language processing to analyze the order details and intent.

[0461] Step 3:

[0462] The server generates response messages based on the analysis results. For example, it might prepare messages to confirm the order details or prompt for additional information.

[0463] Step 4:

[0464] The generated response message is sent from the server to the terminal and converted into speech by the terminal's speech synthesis system. The terminal then communicates the response to the user through the speech output.

[0465] Step 5:

[0466] The user makes additional orders or confirmations. The terminal collects this new voice data and repeats the process from step 1.

[0467] Step 6:

[0468] Once all orders are confirmed, the terminal sends the order details to the cooking system and relays cooking instructions to the appropriate department.

[0469] Step 7:

[0470] Once cooking is complete, the terminal begins the necessary movements and arrangements to serve the ordered items.

[0471] Step 8:

[0472] After the meal is served, the server processes the user's payment information using an automated payment system and completes the payment. At this time, the user receives a notification that the payment has been completed.

[0473] (Example 1)

[0474] 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."

[0475] Traditional food services heavily rely on human labor, making it difficult to streamline operations and provide prompt customer service. Furthermore, the fragmentation of processes such as ordering, serving, and payment resulted in a less-than-smooth overall customer experience. Especially with the worsening labor shortage, there is a growing need to provide efficient yet humane customer service.

[0476] 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.

[0477] In this invention, the server includes recognition means for converting voice information into information, natural processing means for generating a response based on the information, and synthesis means for outputting the generated response as information. This enables the automation of the entire process and the provision of an integrated customer service experience.

[0478] "Audio information" refers to data and signals obtained using sound.

[0479] "Recognition means" refers to systems and devices for converting audio information into text or digital data.

[0480] "Natural processing methods" refer to technologies that analyze text data, understand its intent and content, and generate appropriate responses.

[0481] "Synthesis means" refers to a device or system for converting generated text data into speech and outputting it.

[0482] "Management means" refers to methods and systems for managing the entire service based on received data and instructions, and for providing the optimal service.

[0483] "Automated means" refers to devices and technologies that process transaction information and other data mechanically rather than manually.

[0484] A "generative model" refers to an algorithm or framework that uses machine learning or AI technology to generate responses based on data.

[0485] "Request information" refers to data and information related to orders and requests obtained from customers.

[0486] "Cooking equipment" refers to tools and machines used for preparing food.

[0487] "Timing" refers to the appropriate time or timeframe for a particular action or process to take place.

[0488] A "system" refers to a collection of multiple means or devices that work together to achieve a specific function or purpose.

[0489] This invention is a system for completely automating the customer service process in food and beverage services. This system integrates speech recognition, natural language processing, speech synthesis, customer management, and automated payment methods.

[0490] The server uses speech recognition software (e.g., cloud-based speech recognition technology) to convert the user's voice in the store into text data. This text data is then analyzed by a natural language processing system (e.g., a machine learning model) on the server. This allows the server to understand the order details and the user's intent. The server also utilizes a generative AI model to automatically generate appropriate responses. An example of a prompt would be, "Generate a confirmation response based on the user's order details."

[0491] The generated response is converted into speech using speech synthesis technology (e.g., text-to-speech synthesis) and communicated to the user via an in-store terminal. The terminal acts as the user interface, allowing for order adjustments and additional service suggestions. Once the user confirms the order, the terminal sends specific instructions to the cooking system, which then automatically prepares and serves the food.

[0492] After the order has been served, the server securely completes the transaction from the user's account through an automated payment system. This automated payment system utilizes a secure payment platform (e.g., online payment service) to ensure fast payments while protecting customer information.

[0493] As a concrete example, when a user orders "One miso ramen, please" at a restaurant, the terminal captures this voice and converts it into text using a recognition system. This is sent to a server, where it undergoes analysis using natural language processing to generate a response such as "One miso ramen, correct? Would you like something to drink?", which is then delivered to the user via speech synthesis. This improves the efficiency of food service and enhances the customer experience.

[0494] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0495] Step 1:

[0496] The user places an order by voice within the store. The terminal captures this voice through the microphone and sends it to the server as digital audio data. The input is the user's voice data, and the output is the server receiving the digital audio data. At this stage, the microphone's capture function is active.

[0497] Step 2:

[0498] The server converts received digital audio data into text data using speech recognition technology. The input is audio data, and the output is text information that reflects the content of that audio. In the conversion process, speech recognition software operates, and natural language processing generates text for the next step.

[0499] Step 3:

[0500] The server inputs text data into a natural language processing system. The system analyzes this data to understand the order details and the user's intent. Here, the input is text data, and the output is information that identifies the user's intent and order details. A generative AI model is used in the analysis, and the prompt "Generate a confirmation response based on the user's order details" is applied.

[0501] Step 4:

[0502] Based on the analysis results, the server generates an appropriate response, which is then converted into speech data using a speech synthesis system. The input is the analyzed order information, and the output is the speech data of the response. A generative AI model is used to generate the response, and speech synthesis technology makes the response available as speech.

[0503] Step 5:

[0504] The generated response audio data is transmitted to the user via the terminal. The terminal uses its voice output function to ask the user for confirmation or additional questions. The input is audio data, and the output is the actual voice message to the user.

[0505] Step 6:

[0506] The user reviews the response and adds or modifies the order as needed. This information is sent back to the server, and the process from step 2 is repeated as necessary. The input is the user's new order or modification, and the output is the sending of the updated information to the server.

[0507] Step 7:

[0508] Once an order is confirmed, the terminal sends that information as instructions to the cooking system. The input is the final order details, and the output is the instructions to the cooking system. Here, the start of the cooking process is controlled manually or automatically.

[0509] Step 8:

[0510] After cooking is complete, the terminal uses an automated serving system to deliver the ordered items to the user. The input is information about the cooked items, and the output is the delivery of the items to the user. At this stage, the serving function is operational.

[0511] Step 9:

[0512] The server activates the automated payment system once the order is completed. The input is the user's payment information, and the output is a confirmation of the transaction completion. A secure online payment platform operates during the payment process, ensuring the payment is completed safely.

[0513] (Application Example 1)

[0514] 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."

[0515] Current mobile food delivery services require users to manually place orders, a process that is cumbersome and time-consuming. Furthermore, it is difficult to quickly respond to the individual needs of each user, highlighting the need for improved service quality. Additionally, misdeliveries and time losses due to order issues hinder efficient operation.

[0516] 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.

[0517] In this invention, the server includes speech recognition means for converting voice information into text information, natural language processing means for generating a response based on the text information, speech synthesis means for outputting the generated response as voice information, automatic payment means for processing the user's payment information, and linking means for linking information with specific mobility services. As a result, users can place orders intuitively using their voice, the system can process them quickly and accurately, and an automated payment and efficient service flow can be realized.

[0518] "Speech recognition means" refers to a technology that analyzes speech signals and converts them into corresponding textual information.

[0519] "Natural language processing means" refers to technologies for analyzing textual information into a format that a computer can understand and generating appropriate responses.

[0520] "Speech synthesis means" refers to a technology that has the function of converting text information into speech data and outputting it as natural-sounding speech.

[0521] "Management means" refers to the technology for receiving orders from users, transmitting information to an external preparation system based on those orders, and providing goods at the appropriate time.

[0522] An "automated payment method" is a technology that has the function of securely processing users' payment information and completing payments quickly and accurately.

[0523] "Interlocking means" refers to technologies that enable efficient order processing and logistics by sharing information and coordinating with specific mobility-related services.

[0524] The system for realizing this invention combines speech recognition means, natural language processing means, speech synthesis means, management means, automatic payment means, and interlocking means.

[0525] The server converts voice commands from the user into text data using a speech recognition API (e.g., Google Cloud Speech-to-Text). This data is then analyzed by a natural language processing library (e.g., NLTK or spaCy) to understand the user's intent. After analysis, an appropriate response is generated in voice format using a speech synthesis API (e.g., Google Cloud Text-to-Speech) and output to the user via their device.

[0526] Order and payment information is transferred to an external cooking system via a management system at the appropriate time. Furthermore, user payments are processed quickly and securely using an automated payment platform (e.g., Stripe). In addition, data is shared with other transportation-related services through integration mechanisms to improve workflow efficiency.

[0527] For example, if a user voice-inputs "I want to order a sushi set," the server recognizes the speech, converts it to text, and analyzes the order using natural language processing. Based on the analysis, it outputs a message via speech synthesis, such as "Please choose the toppings for your sushi set." When using a generative AI model, a prompt such as "Please suggest some toppings that are good for someone eating sushi for the first time" can be used to get appropriate advice from the AI.

[0528] This type of system allows users to enjoy an intuitive and seamless ordering experience.

[0529] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0530] Step 1:

[0531] The user places an order by voice into their smartphone. The device receives the voice input through its microphone and sends the voice data to the server.

[0532] Step 2:

[0533] The server passes the received audio data to a speech recognition API (e.g., Google Cloud Speech-to-Text) to convert the audio into text. In this step, the input is audio data and the output is the corresponding text data.

[0534] Step 3:

[0535] The server processes the obtained text data using natural language processing tools (e.g., NLTK or spaCy) to analyze the user's order intent. The input is text data, and the analysis result outputs structured order information.

[0536] Step 4:

[0537] The server generates voice responses for confirmation and suggestions using speech synthesis (e.g., Google Cloud Text-to-Speech) based on the parsed order information. The input is structured order information, and the output is voice data.

[0538] Step 5:

[0539] The terminal receives audio data from the server and responds to the user using an audio output device. Here, it performs specific actions such as confirming information using speech synthesis and communicating the next instructions to the user.

[0540] Step 6:

[0541] Once the user has confirmed the order, the server transmits the order information to an external cooking system via a management mechanism. The input is the final order information, and the output is the commands for cooking instructions.

[0542] Step 7:

[0543] The server uses an automated payment method (e.g., Stripe) to process the user's payment information and complete the payment. The input is the user's payment information, and the output is the payment completion status.

[0544] Step 8:

[0545] The server uses interoperability to share information with mobile services and support smooth distribution. Here, order delivery information is entered, and optimized delivery instructions are output.

[0546] 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.

[0547] This invention is a system for analyzing customer voices and emotions in the service process of restaurants to provide a higher quality customer service experience. The system includes speech recognition means, natural language processing means, speech synthesis means, management means, automatic payment means, and an emotion engine for analyzing user emotions.

[0548] The device collects the user's speech using a microphone and converts it into text data using speech recognition technology. In this process, an emotion engine analyzes the speech signal to infer the user's emotional state. For example, it analyzes whether the user is anxious or calm based on the intonation and speed of their speech.

[0549] After receiving text data, the server uses natural language processing to understand the order and generates an appropriate response along with the analyzed sentiment information. The sentiment information is used to adjust the content and tone of the response. For example, if the sentiment engine determines that the user is dissatisfied, the server will generate a polite response such as, "Sorry for the wait."

[0550] The generated response is sent from the server to the terminal and output as speech by the terminal's speech synthesis system. This allows the user to experience a natural and friendly service.

[0551] Once the user confirms their order, the terminal transmits the order information to an external cooking system via a management mechanism, enabling efficient food delivery. Furthermore, after the order has been served, the server completes the payment process using an automated payment system.

[0552] For example, if a customer at a restaurant says, "I'm in a big hurry, please prepare my food quickly," the terminal converts the audio information into text, and an emotion engine analyzes the emotion conveying the user's urgency. Based on this information, the server generates a quick and reassuring response such as, "We will start preparing your food immediately, please wait a moment." This process improves the customer experience.

[0553] This system enables immediate feedback on customer emotions, leading to personalized service and improved customer satisfaction.

[0554] The following describes the processing flow.

[0555] Step 1:

[0556] The user begins speaking to place an order. The device uses the microphone to capture the voice and collects the audio data.

[0557] Step 2:

[0558] The device uses speech recognition to collect audio data and converts it into text data. During this process, an emotion engine analyzes the intonation and speed of the speech to determine the user's emotional state.

[0559] Step 3:

[0560] The terminal sends the converted text data and recognized emotion information to the server.

[0561] Step 4:

[0562] The server analyzes the received text data using natural language processing to understand the order. Simultaneously, it utilizes emotional information to generate an appropriate response message, customizing its tone and content to reflect the user's emotions.

[0563] Step 5:

[0564] The server generates a response message and sends it to the terminal, which then uses its speech synthesis capabilities to output it as speech. Through this response, the terminal confirms the order with the user and prompts for additional questions as needed.

[0565] Step 6:

[0566] After the user receives a response, they will speak again if they have any additional orders or changes. This information will be processed again through steps 1 to 3.

[0567] Step 7:

[0568] Once all orders are confirmed, the terminal transmits the order details to an external cooking system via a management mechanism. The system then monitors the completion time of cooking and prepares for serving.

[0569] Step 8:

[0570] After cooking is complete, the device autonomously moves and delivers the food to the designated table.

[0571] Step 9:

[0572] After serving is complete, the server processes the user's payment information via an automated payment system and completes the payment. The user receives a payment completion notification, ensuring a safe and swift transaction.

[0573] (Example 2)

[0574] 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."

[0575] In recent years, restaurants have been required to provide fast and personalized service that meets the diverse needs of their customers. However, traditional systems have faced challenges in recognizing customer emotions and providing appropriate service. Furthermore, efficiently managing the entire process from ordering to serving and payment remains a challenge.

[0576] 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.

[0577] In this invention, the server includes information recognition means for converting voice information into text information, emotion analysis means for estimating an emotional state from the voice information, and natural language processing means for generating a response based on the text information and the emotional state. This makes it possible to provide individually optimized services that respond to the user's emotions.

[0578] "Audio information" refers to the data of sounds emitted by a speaker through a microphone, which is then processed and converted into text.

[0579] "Text information" refers to data in which audio information is represented as characters by speech recognition technology, and is in a format that can be analyzed by natural language processing.

[0580] "Information recognition means" refers to a technological device that receives audio information and converts it into text information, and this includes speech recognition software.

[0581] An "emotion analysis device" is a technological device that improves the quality of dialogue by determining the speaker's emotional state from audio and text information.

[0582] "Natural language processing means" refers to a technical device for understanding dialogue content and generating appropriate responses based on text information and emotional states.

[0583] A "speech synthesis device" is a technological device that outputs a generated response as speech, and is a device for conveying information in a way that is easy for people to understand.

[0584] An "information management system" is a technological device that processes order information received from users and manages and transmits that information in cooperation with external systems.

[0585] An "automated payment method" is a technological device that processes the user's payment information and completes payments quickly and without contact.

[0586] This invention is a system for interacting with users through voice and providing more personalized services. The following details each component of this system and its operation.

[0587] The device collects the user's speech using a high-performance microphone. The collected audio information is converted into text in real time using speech recognition software (for example, a cloud-based speech recognition service).

[0588] Furthermore, the device is equipped with emotion detection software as a means of emotion analysis, which infers the user's emotional state by analyzing the voice signal. This emotion analysis is used to determine whether the user is calm, in a hurry, or otherwise, based on characteristics such as voice tone and speaking speed.

[0589] The server receives text and sentiment information sent from the terminal and uses natural language processing software (for example, natural language analysis libraries and generative AI models) to understand the user's order and generate an appropriate response based on their emotions. For example, if the sentiment analysis tool determines that the user is irritated, the server designs a calming response such as, "I'm sorry if this has caused you any discomfort."

[0590] The generated response is sent from the server to the terminal and output as speech using the terminal's speech synthesis software (for example, a text-to-speech API). This allows the user to receive a natural and user-friendly service.

[0591] As a concrete example, if a customer at a restaurant says, "I'm busy, please prepare my food quickly," the terminal converts this into text and uses sentiment analysis to identify the emotion associated with the customer's urgency. The server then generates a reassuring response such as, "We are working quickly to prepare your order, please wait a moment." In this way, the customer experience is improved, and customer satisfaction can be increased.

[0592] An example of a prompt would be, "Please tell me the procedure for analyzing speech and generating the best response based on the user's emotions." In this example, the generative AI model determines emotions from the speech data and provides a process to improve the response.

[0593] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0594] Step 1:

[0595] The device collects the user's speech using a microphone and inputs it as audio information. The collected audio information is analyzed using speech recognition software and converted into text information. Specifically, spectral analysis of the audio signal is performed to extract sound features and convert them into string data. The output is text information including the user's requests and orders.

[0596] Step 2:

[0597] The terminal passes the converted text information to an emotion analysis system, which estimates the user's emotional state based on their voice characteristics. This process analyzes voice features such as tone, intonation, and speed, and the emotion engine outputs the user's emotion tags (e.g., joy, frustration, calmness). This identifies the emotional state and allows for the deriving of a response that reflects the user's mood.

[0598] Step 3:

[0599] The server receives text information and sentiment tags from the terminal as input and uses natural language processing software to understand the user's request. This process involves data processing to analyze the order details and intentions from the text. The output is information for generating a response based on the user's order details and intent.

[0600] Step 4:

[0601] The server inputs the analysis results into a generating AI model, which then creates an appropriate response based on the text information and emotional state. The model uses this information to construct a response with a tone that corresponds to the user's emotions. The generated output is a natural conversational sentence as a response to the user.

[0602] Step 5:

[0603] The terminal receives the response sent from the server and converts it into speech information using speech synthesis software. Specifically, it converts the text response into speech data and performs a process to play it back in a natural human voice. The output is a speech response that the user can hear.

[0604] Step 6:

[0605] Once a user confirms an order, the terminal passes the order information to a management system, which then processes the order in conjunction with an external cooking system. The input is the confirmed order details, and the output is the instruction to be transmitted to the cooking system. This ensures efficient supply and smooth service delivery.

[0606] Step 7:

[0607] The server processes payments using an automated payment system after the order is fulfilled. Inputs are the user's payment information and the order amount, and output is a secure payment confirmation. This process ensures fast and accurate payments.

[0608] (Application Example 2)

[0609] 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."

[0610] Traditional food delivery systems process orders based solely on the user's spoken information, making it difficult to provide optimal service tailored to the user's emotions and urgency. Furthermore, responses to users tend to be formulaic, hindering improvements in customer satisfaction. Additionally, delivery times are often not adequately optimized, sometimes failing to meet customer expectations.

[0611] 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.

[0612] In this invention, the server includes emotion analysis means for analyzing emotional states from voice signals, response adjustment means for adjusting response content according to the emotional state, and delivery optimization means for presenting the response adjusted by the response adjustment means and the optimal delivery option. This enables the provision of personalized responses that take into account the user's emotional state and the provision of fast and optimal delivery services.

[0613] "Speech recognition means" refers to a technology that converts speech information into text information, and is a device that makes the user's speech into a format that a machine can understand.

[0614] "Natural language processing means" refers to technologies that generate responses based on text information, and is a system that enables machines to understand natural human language and return appropriate responses.

[0615] A "speech synthesis means" is a technology that outputs generated text information as speech information, and is a device that converts text information back into natural-sounding speech for transmission.

[0616] "Management means" refers to technologies for managing customer orders and controlling the provision of food and beverages, as well as systems for appropriately processing order information and coordinating with external systems.

[0617] An "automated payment method" is a system that automatically processes a user's payment information, and is a technology for efficiently completing transactions.

[0618] "Emotional analysis means" refers to a technology that analyzes emotional states from audio signals, and is a system for reading the emotions contained in speech.

[0619] A "response adjustment mechanism" is a technology that adjusts the content of responses based on the results of an analysis of the emotional state, and is a system for further personalizing the user's experience.

[0620] "Delivery optimization means" refers to a technology that presents the optimal delivery option based on adjusted responses, and is a method for building a delivery plan that meets the user's needs.

[0621] This invention is a system that provides personalized services to users in a food delivery service by using voice and sentiment analysis. The server and terminal work together to generate responses that meet the user's needs through voice recognition, natural language processing, and sentiment analysis.

[0622] The server primarily uses cloud services to process audio data. The terminal collects audio data using the microphone on the user's smart device (such as a smartphone or tablet). The Google Cloud Speech-to-Text API is used to convert the audio information acquired by the terminal into text information. This text information is then processed using natural language processing via the Google Cloud Natural Language API. Based on the results, sentiment analysis is performed by IBM Watson Tone Analyzer. This allows the server to determine the user's emotional state based on the intonation and tone of their voice.

[0623] The results of sentiment analysis are reflected in response generation. For example, if a user says, "I'm busy, please deliver the food quickly," the system recognizes the urgency through sentiment analysis and presents expedited delivery options. These delivery options are then processed quickly using an automated payment method via the Stripe API. Furthermore, the generated response is synthesized into speech on the terminal and presented as a friendly and approachable response to the user.

[0624] For example, if a user says, "I have a movie tonight, so I'd like dinner delivered early," the device collects this as voice information, and after the aforementioned process, the server presents the shortest possible delivery option. This response is delivered in a reassuring, adjusted tone.

[0625] As an example of a prompt to input into the generating AI model, a user might say, "Please give me a regular pizza as quickly as possible," and the system will generate and present a quick response and specific delivery options.

[0626] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0627] Step 1:

[0628] The user places an order by voice through a smart device. The device collects the voice using a microphone and processes it as digital voice data. The input for this step is a voice signal, and the output is voice data in digital format. This includes operations to properly collect and digitize the voice signal.

[0629] Step 2:

[0630] The device uses the Google Cloud Speech-to-Text API to convert digital audio data into text data. The input is digital audio data, and the output is text data. This conversion process involves analyzing the audio signal and processing the data for phoneme recognition.

[0631] Step 3:

[0632] The server receives text data and uses the Google Cloud Natural Language API to perform natural language processing and understand the order details. The input is text data, and the output is structured order data. This step involves semantic analysis of the text and extraction of order information.

[0633] Step 4:

[0634] The server uses IBM Watson Tone Analyzer to analyze emotional states derived from speech intonation. Input is text data, and output is data related to emotional states. This includes actions to evaluate the emotional elements of speech and determine the user's mood and urgency.

[0635] Step 5:

[0636] The server uses a generative AI model to generate a response based on the obtained sentiment and order details, and leverages Stripe API business logic to determine the optimal delivery option for the order. The input is structured order details and sentiment data, and the output is a refined response and delivery options. Response refinement and rapid decision-making processes are performed.

[0637] Step 6:

[0638] The server sends an optimized response to the terminal, and the terminal uses speech synthesis to output voice to the user. The input is the adjusted response and delivery option data, and the output is a natural voice response to the user. Voice data is generated and feedback is provided to the user.

[0639] Step 7:

[0640] After an order is confirmed, the server uses automated payment methods to process the payment and initiate delivery. The input is the confirmed order information, and the output is confirmation data of the completed transaction. Fast and accurate payment data processing is performed.

[0641] 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.

[0642] 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.

[0643] 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.

[0644] [Fourth Embodiment]

[0645] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0646] 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.

[0647] 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).

[0648] 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.

[0649] 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.

[0650] 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).

[0651] 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.

[0652] 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.

[0653] 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.

[0654] 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.

[0655] 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.

[0656] 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.

[0657] 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".

[0658] This invention provides a system that integrates speech recognition, natural language processing, speech synthesis, customer management, and automated payment methods to fully automate the customer service process in the food service industry.

[0659] The server uses speech recognition to convert the voice of a user in the store into text data. This data is then analyzed by the server's natural language processing system to provide information for understanding the order details and the intent of the question.

[0660] Next, the server generates a response. This response is a crucial step, including order confirmation, prompting for additional questions, and service instructions. The generated text response is then converted into speech by a speech synthesis system and delivered to the user via an in-store terminal.

[0661] The terminal also plays a role in receiving user input and has the ability to adjust orders or suggest additional items as needed. Once the order is confirmed, the terminal sends instructions to the cooking system, which then automatically controls serving after cooking is complete.

[0662] After the order is served, the server's automated payment system activates and processes the payment to the user's account. This process ensures that customer information is kept secure and that payments are processed quickly and accurately.

[0663] For example, if a user at a restaurant says, "I'd like one miso ramen, please," the terminal captures the voice and converts it to text using speech recognition. This text data is sent to a server, where natural language processing understands the order. Based on this, a response is generated, such as, "One miso ramen, correct? Would you like something to drink?", and this response is output to the user via voice from the terminal. The entire process proceeds seamlessly according to any additional requests from the user, ultimately resulting in automated serving and payment.

[0664] In this way, we can provide the restaurant industry with an efficient and humane customer service experience while also addressing the challenge of labor shortages.

[0665] The following describes the processing flow.

[0666] Step 1:

[0667] The user begins speaking to place an order. The terminal acquires voice data through the microphone and converts it into text data using speech recognition technology.

[0668] Step 2:

[0669] The terminal sends the converted text data to the server. The server receives the data and uses natural language processing to analyze the order details and intent.

[0670] Step 3:

[0671] The server generates response messages based on the analysis results. For example, it might prepare messages to confirm the order details or prompt for additional information.

[0672] Step 4:

[0673] The generated response message is sent from the server to the terminal and converted into speech by the terminal's speech synthesis system. The terminal then communicates the response to the user through the speech output.

[0674] Step 5:

[0675] The user makes additional orders or confirmations. The terminal collects this new voice data and repeats the process from step 1.

[0676] Step 6:

[0677] Once all orders are confirmed, the terminal sends the order details to the cooking system and relays cooking instructions to the appropriate department.

[0678] Step 7:

[0679] Once cooking is complete, the terminal begins the necessary movements and arrangements to serve the ordered items.

[0680] Step 8:

[0681] After the meal is served, the server processes the user's payment information using an automated payment system and completes the payment. At this time, the user receives a notification that the payment has been completed.

[0682] (Example 1)

[0683] 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".

[0684] Traditional food services heavily rely on human labor, making it difficult to streamline operations and provide prompt customer service. Furthermore, the fragmentation of processes such as ordering, serving, and payment resulted in a less-than-smooth overall customer experience. Especially with the worsening labor shortage, there is a growing need to provide efficient yet humane customer service.

[0685] 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.

[0686] In this invention, the server includes recognition means for converting voice information into information, natural processing means for generating a response based on the information, and synthesis means for outputting the generated response as information. This enables the automation of the entire process and the provision of an integrated customer service experience.

[0687] "Audio information" refers to data and signals obtained using sound.

[0688] "Recognition means" refers to systems and devices for converting audio information into text or digital data.

[0689] "Natural processing methods" refer to technologies that analyze text data, understand its intent and content, and generate appropriate responses.

[0690] "Synthesis means" refers to a device or system for converting generated text data into speech and outputting it.

[0691] "Management means" refers to methods and systems for managing the entire service based on received data and instructions, and for providing the optimal service.

[0692] "Automated means" refers to devices and technologies that process transaction information and other data mechanically rather than manually.

[0693] A "generative model" refers to an algorithm or framework that uses machine learning or AI technology to generate responses based on data.

[0694] "Request information" refers to data and information related to orders and requests obtained from customers.

[0695] "Cooking equipment" refers to tools and machines used for preparing food.

[0696] "Timing" refers to the appropriate time or timeframe for a particular action or process to take place.

[0697] A "system" refers to a collection of multiple means or devices that work together to achieve a specific function or purpose.

[0698] This invention is a system for completely automating the customer service process in food and beverage services. This system integrates speech recognition, natural language processing, speech synthesis, customer management, and automated payment methods.

[0699] The server uses speech recognition software (e.g., cloud-based speech recognition technology) to convert the user's voice in the store into text data. This text data is then analyzed by a natural language processing system (e.g., a machine learning model) on the server. This allows the server to understand the order details and the user's intent. The server also utilizes a generative AI model to automatically generate appropriate responses. An example of a prompt would be, "Generate a confirmation response based on the user's order details."

[0700] The generated response is converted into speech using speech synthesis technology (e.g., text-to-speech synthesis) and communicated to the user via an in-store terminal. The terminal acts as the user interface, allowing for order adjustments and additional service suggestions. Once the user confirms the order, the terminal sends specific instructions to the cooking system, which then automatically prepares and serves the food.

[0701] After the order has been served, the server securely completes the transaction from the user's account through an automated payment system. This automated payment system utilizes a secure payment platform (e.g., online payment service) to ensure fast payments while protecting customer information.

[0702] As a concrete example, when a user orders "One miso ramen, please" at a restaurant, the terminal captures this voice and converts it into text using a recognition system. This is sent to a server, where it undergoes analysis using natural language processing to generate a response such as "One miso ramen, correct? Would you like something to drink?", which is then delivered to the user via speech synthesis. This improves the efficiency of food service and enhances the customer experience.

[0703] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0704] Step 1:

[0705] The user places an order by voice within the store. The terminal captures this voice through the microphone and sends it to the server as digital audio data. The input is the user's voice data, and the output is the server receiving the digital audio data. At this stage, the microphone's capture function is active.

[0706] Step 2:

[0707] The server converts received digital audio data into text data using speech recognition technology. The input is audio data, and the output is text information that reflects the content of that audio. In the conversion process, speech recognition software operates, and natural language processing generates text for the next step.

[0708] Step 3:

[0709] The server inputs text data into a natural language processing system. The system analyzes this data to understand the order details and the user's intent. Here, the input is text data, and the output is information that identifies the user's intent and order details. A generative AI model is used in the analysis, and the prompt "Generate a confirmation response based on the user's order details" is applied.

[0710] Step 4:

[0711] Based on the analysis results, the server generates an appropriate response, which is then converted into speech data using a speech synthesis system. The input is the analyzed order information, and the output is the speech data of the response. A generative AI model is used to generate the response, and speech synthesis technology makes the response available as speech.

[0712] Step 5:

[0713] The generated response audio data is transmitted to the user via the terminal. The terminal uses its voice output function to ask the user for confirmation or additional questions. The input is audio data, and the output is the actual voice message to the user.

[0714] Step 6:

[0715] The user reviews the response and adds or modifies the order as needed. This information is sent back to the server, and the process from step 2 is repeated as necessary. The input is the user's new order or modification, and the output is the sending of the updated information to the server.

[0716] Step 7:

[0717] Once an order is confirmed, the terminal sends that information as instructions to the cooking system. The input is the final order details, and the output is the instructions to the cooking system. Here, the start of the cooking process is controlled manually or automatically.

[0718] Step 8:

[0719] After cooking is complete, the terminal uses an automated serving system to deliver the ordered items to the user. The input is information about the cooked items, and the output is the delivery of the items to the user. At this stage, the serving function is operational.

[0720] Step 9:

[0721] The server activates the automated payment system once the order is completed. The input is the user's payment information, and the output is a confirmation of the transaction completion. A secure online payment platform operates during the payment process, ensuring the payment is completed safely.

[0722] (Application Example 1)

[0723] 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".

[0724] Current mobile food delivery services require users to manually place orders, a process that is cumbersome and time-consuming. Furthermore, it is difficult to quickly respond to the individual needs of each user, highlighting the need for improved service quality. Additionally, misdeliveries and time losses due to order issues hinder efficient operation.

[0725] 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.

[0726] In this invention, the server includes speech recognition means for converting voice information into text information, natural language processing means for generating a response based on the text information, speech synthesis means for outputting the generated response as voice information, automatic payment means for processing the user's payment information, and linking means for linking information with specific mobility services. As a result, users can place orders intuitively using their voice, the system can process them quickly and accurately, and an automated payment and efficient service flow can be realized.

[0727] "Speech recognition means" refers to a technology that analyzes speech signals and converts them into corresponding textual information.

[0728] "Natural language processing means" refers to technologies for analyzing textual information into a format that a computer can understand and generating appropriate responses.

[0729] "Speech synthesis means" refers to a technology that has the function of converting text information into speech data and outputting it as natural-sounding speech.

[0730] "Management means" refers to the technology for receiving orders from users, transmitting information to an external preparation system based on those orders, and providing goods at the appropriate time.

[0731] An "automated payment method" is a technology that has the function of securely processing users' payment information and completing payments quickly and accurately.

[0732] "Interlocking means" refers to technologies that enable efficient order processing and logistics by sharing information and coordinating with specific mobility-related services.

[0733] The system for realizing this invention combines speech recognition means, natural language processing means, speech synthesis means, management means, automatic payment means, and interlocking means.

[0734] The server converts voice commands from the user into text data using a speech recognition API (e.g., Google Cloud Speech-to-Text). This data is then analyzed by a natural language processing library (e.g., NLTK or spaCy) to understand the user's intent. After analysis, an appropriate response is generated in voice format using a speech synthesis API (e.g., Google Cloud Text-to-Speech) and output to the user via their device.

[0735] Order and payment information is transferred to an external cooking system via a management system at the appropriate time. Furthermore, user payments are processed quickly and securely using an automated payment platform (e.g., Stripe). In addition, data is shared with other transportation-related services through integration mechanisms to improve workflow efficiency.

[0736] For example, if a user voice-inputs "I want to order a sushi set," the server recognizes the speech, converts it to text, and analyzes the order using natural language processing. Based on the analysis, it outputs a message via speech synthesis, such as "Please choose the toppings for your sushi set." When using a generative AI model, a prompt such as "Please suggest some toppings that are good for someone eating sushi for the first time" can be used to get appropriate advice from the AI.

[0737] This type of system allows users to enjoy an intuitive and seamless ordering experience.

[0738] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0739] Step 1:

[0740] The user places an order by voice into their smartphone. The device receives the voice input through its microphone and sends the voice data to the server.

[0741] Step 2:

[0742] The server passes the received audio data to a speech recognition API (e.g., Google Cloud Speech-to-Text) to convert the audio into text. In this step, the input is audio data and the output is the corresponding text data.

[0743] Step 3:

[0744] The server processes the obtained text data using natural language processing tools (e.g., NLTK or spaCy) to analyze the user's order intent. The input is text data, and the analysis result outputs structured order information.

[0745] Step 4:

[0746] The server generates voice responses for confirmation and suggestions using speech synthesis (e.g., Google Cloud Text-to-Speech) based on the parsed order information. The input is structured order information, and the output is voice data.

[0747] Step 5:

[0748] The terminal receives audio data from the server and responds to the user using an audio output device. Here, it performs specific actions such as confirming information using speech synthesis and communicating the next instructions to the user.

[0749] Step 6:

[0750] Once the user has confirmed the order, the server transmits the order information to an external cooking system via a management mechanism. The input is the final order information, and the output is the commands for cooking instructions.

[0751] Step 7:

[0752] The server uses an automated payment method (e.g., Stripe) to process the user's payment information and complete the payment. The input is the user's payment information, and the output is the payment completion status.

[0753] Step 8:

[0754] The server uses interoperability to share information with mobile services and support smooth distribution. Here, order delivery information is entered, and optimized delivery instructions are output.

[0755] 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.

[0756] This invention is a system for analyzing customer voices and emotions in the service process of restaurants to provide a higher quality customer service experience. The system includes speech recognition means, natural language processing means, speech synthesis means, management means, automatic payment means, and an emotion engine for analyzing user emotions.

[0757] The device collects the user's speech using a microphone and converts it into text data using speech recognition technology. In this process, an emotion engine analyzes the speech signal to infer the user's emotional state. For example, it analyzes whether the user is anxious or calm based on the intonation and speed of their speech.

[0758] After receiving text data, the server uses natural language processing to understand the order and generates an appropriate response along with the analyzed sentiment information. The sentiment information is used to adjust the content and tone of the response. For example, if the sentiment engine determines that the user is dissatisfied, the server will generate a polite response such as, "Sorry for the wait."

[0759] The generated response is sent from the server to the terminal and output as speech by the terminal's speech synthesis system. This allows the user to experience a natural and friendly service.

[0760] Once the user confirms their order, the terminal transmits the order information to an external cooking system via a management mechanism, enabling efficient food delivery. Furthermore, after the order has been served, the server completes the payment process using an automated payment system.

[0761] For example, if a customer at a restaurant says, "I'm in a big hurry, please prepare my food quickly," the terminal converts the audio information into text, and an emotion engine analyzes the emotion conveying the user's urgency. Based on this information, the server generates a quick and reassuring response such as, "We will start preparing your food immediately, please wait a moment." This process improves the customer experience.

[0762] This system enables immediate feedback on customer emotions, leading to personalized service and improved customer satisfaction.

[0763] The following describes the processing flow.

[0764] Step 1:

[0765] The user begins speaking to place an order. The device uses the microphone to capture the voice and collects the audio data.

[0766] Step 2:

[0767] The device uses speech recognition to collect audio data and converts it into text data. During this process, an emotion engine analyzes the intonation and speed of the speech to determine the user's emotional state.

[0768] Step 3:

[0769] The terminal sends the converted text data and recognized emotion information to the server.

[0770] Step 4:

[0771] The server analyzes the received text data using natural language processing to understand the order. Simultaneously, it utilizes emotional information to generate an appropriate response message, customizing its tone and content to reflect the user's emotions.

[0772] Step 5:

[0773] The server generates a response message and sends it to the terminal, which then uses its speech synthesis capabilities to output it as speech. Through this response, the terminal confirms the order with the user and prompts for additional questions as needed.

[0774] Step 6:

[0775] After the user receives a response, they will speak again if they have any additional orders or changes. This information will be processed again through steps 1 to 3.

[0776] Step 7:

[0777] Once all orders are confirmed, the terminal transmits the order details to an external cooking system via a management mechanism. The system then monitors the completion time of cooking and prepares for serving.

[0778] Step 8:

[0779] After cooking is complete, the device autonomously moves and delivers the food to the designated table.

[0780] Step 9:

[0781] After serving is complete, the server processes the user's payment information via an automated payment system and completes the payment. The user receives a payment completion notification, ensuring a safe and swift transaction.

[0782] (Example 2)

[0783] 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".

[0784] In recent years, restaurants have been required to provide fast and personalized service that meets the diverse needs of their customers. However, traditional systems have faced challenges in recognizing customer emotions and providing appropriate service. Furthermore, efficiently managing the entire process from ordering to serving and payment remains a challenge.

[0785] 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.

[0786] In this invention, the server includes information recognition means for converting voice information into text information, emotion analysis means for estimating an emotional state from the voice information, and natural language processing means for generating a response based on the text information and the emotional state. This makes it possible to provide individually optimized services that respond to the user's emotions.

[0787] "Audio information" refers to the data of sounds emitted by a speaker through a microphone, which is then processed and converted into text.

[0788] "Text information" refers to data in which audio information is represented as characters by speech recognition technology, and is in a format that can be analyzed by natural language processing.

[0789] "Information recognition means" refers to a technological device that receives audio information and converts it into text information, and this includes speech recognition software.

[0790] An "emotion analysis device" is a technological device that improves the quality of dialogue by determining the speaker's emotional state from audio and text information.

[0791] "Natural language processing means" refers to a technical device for understanding dialogue content and generating appropriate responses based on text information and emotional states.

[0792] A "speech synthesis device" is a technological device that outputs a generated response as speech, and is a device for conveying information in a way that is easy for people to understand.

[0793] An "information management system" is a technological device that processes order information received from users and manages and transmits that information in cooperation with external systems.

[0794] An "automated payment method" is a technological device that processes the user's payment information and completes payments quickly and without contact.

[0795] This invention is a system for interacting with users through voice and providing more personalized services. The following details each component of this system and its operation.

[0796] The device collects the user's speech using a high-performance microphone. The collected audio information is converted into text in real time using speech recognition software (for example, a cloud-based speech recognition service).

[0797] Furthermore, the device is equipped with emotion detection software as a means of emotion analysis, which infers the user's emotional state by analyzing the voice signal. This emotion analysis is used to determine whether the user is calm, in a hurry, or otherwise, based on characteristics such as voice tone and speaking speed.

[0798] The server receives text and sentiment information sent from the terminal and uses natural language processing software (for example, natural language analysis libraries and generative AI models) to understand the user's order and generate an appropriate response based on their emotions. For example, if the sentiment analysis tool determines that the user is irritated, the server designs a calming response such as, "I'm sorry if this has caused you any discomfort."

[0799] The generated response is sent from the server to the terminal and output as speech using the terminal's speech synthesis software (for example, a text-to-speech API). This allows the user to receive a natural and user-friendly service.

[0800] As a concrete example, if a customer at a restaurant says, "I'm busy, please prepare my food quickly," the terminal converts this into text and uses sentiment analysis to identify the emotion associated with the customer's urgency. The server then generates a reassuring response such as, "We are working quickly to prepare your order, please wait a moment." In this way, the customer experience is improved, and customer satisfaction can be increased.

[0801] An example of a prompt would be, "Please tell me the procedure for analyzing speech and generating the best response based on the user's emotions." In this example, the generative AI model determines emotions from the speech data and provides a process to improve the response.

[0802] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0803] Step 1:

[0804] The device collects the user's speech using a microphone and inputs it as audio information. The collected audio information is analyzed using speech recognition software and converted into text information. Specifically, spectral analysis of the audio signal is performed to extract sound features and convert them into string data. The output is text information including the user's requests and orders.

[0805] Step 2:

[0806] The terminal passes the converted text information to an emotion analysis system, which estimates the user's emotional state based on their voice characteristics. This process analyzes voice features such as tone, intonation, and speed, and the emotion engine outputs the user's emotion tags (e.g., joy, frustration, calmness). This identifies the emotional state and allows for the deriving of a response that reflects the user's mood.

[0807] Step 3:

[0808] The server receives text information and sentiment tags from the terminal as input and uses natural language processing software to understand the user's request. This process involves data processing to analyze the order details and intentions from the text. The output is information for generating a response based on the user's order details and intent.

[0809] Step 4:

[0810] The server inputs the analysis results into a generating AI model, which then creates an appropriate response based on the text information and emotional state. The model uses this information to construct a response with a tone that corresponds to the user's emotions. The generated output is a natural conversational sentence as a response to the user.

[0811] Step 5:

[0812] The terminal receives the response sent from the server and converts it into speech information using speech synthesis software. Specifically, it converts the text response into speech data and performs a process to play it back in a natural human voice. The output is a speech response that the user can hear.

[0813] Step 6:

[0814] Once a user confirms an order, the terminal passes the order information to a management system, which then processes the order in conjunction with an external cooking system. The input is the confirmed order details, and the output is the instruction to be transmitted to the cooking system. This ensures efficient supply and smooth service delivery.

[0815] Step 7:

[0816] The server processes payments using an automated payment system after the order is fulfilled. Inputs are the user's payment information and the order amount, and output is a secure payment confirmation. This process ensures fast and accurate payments.

[0817] (Application Example 2)

[0818] 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".

[0819] Traditional food delivery systems process orders based solely on the user's spoken information, making it difficult to provide optimal service tailored to the user's emotions and urgency. Furthermore, responses to users tend to be formulaic, hindering improvements in customer satisfaction. Additionally, delivery times are often not adequately optimized, sometimes failing to meet customer expectations.

[0820] 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.

[0821] In this invention, the server includes emotion analysis means for analyzing emotional states from voice signals, response adjustment means for adjusting response content according to the emotional state, and delivery optimization means for presenting the response adjusted by the response adjustment means and the optimal delivery option. This enables the provision of personalized responses that take into account the user's emotional state and the provision of fast and optimal delivery services.

[0822] "Speech recognition means" refers to a technology that converts speech information into text information, and is a device that makes the user's speech into a format that a machine can understand.

[0823] "Natural language processing means" refers to technologies that generate responses based on text information, and is a system that enables machines to understand natural human language and return appropriate responses.

[0824] A "speech synthesis means" is a technology that outputs generated text information as speech information, and is a device that converts text information back into natural-sounding speech for transmission.

[0825] "Management means" refers to technologies for managing customer orders and controlling the provision of food and beverages, as well as systems for appropriately processing order information and coordinating with external systems.

[0826] An "automated payment method" is a system that automatically processes a user's payment information, and is a technology for efficiently completing transactions.

[0827] "Emotional analysis means" refers to a technology that analyzes emotional states from audio signals, and is a system for reading the emotions contained in speech.

[0828] A "response adjustment mechanism" is a technology that adjusts the content of responses based on the results of an analysis of the emotional state, and is a system for further personalizing the user's experience.

[0829] "Delivery optimization means" refers to a technology that presents the optimal delivery option based on adjusted responses, and is a method for building a delivery plan that meets the user's needs.

[0830] This invention is a system that provides personalized services to users in a food delivery service by using voice and sentiment analysis. The server and terminal work together to generate responses that meet the user's needs through voice recognition, natural language processing, and sentiment analysis.

[0831] The server primarily uses cloud services to process audio data. The terminal collects audio data using the microphone on the user's smart device (such as a smartphone or tablet). The Google Cloud Speech-to-Text API is used to convert the audio information acquired by the terminal into text information. This text information is then processed using natural language processing via the Google Cloud Natural Language API. Based on the results, sentiment analysis is performed by IBM Watson Tone Analyzer. This allows the server to determine the user's emotional state based on the intonation and tone of their voice.

[0832] The results of sentiment analysis are reflected in response generation. For example, if a user says, "I'm busy, please deliver the food quickly," the system recognizes the urgency through sentiment analysis and presents expedited delivery options. These delivery options are then processed quickly using an automated payment method via the Stripe API. Furthermore, the generated response is synthesized into speech on the terminal and presented as a friendly and approachable response to the user.

[0833] For example, if a user says, "I have a movie tonight, so I'd like dinner delivered early," the device collects this as voice information, and after the aforementioned process, the server presents the shortest possible delivery option. This response is delivered in a reassuring, adjusted tone.

[0834] As an example of a prompt to input into the generating AI model, a user might say, "Please give me a regular pizza as quickly as possible," and the system will generate and present a quick response and specific delivery options.

[0835] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0836] Step 1:

[0837] The user places an order by voice through a smart device. The device collects the voice using a microphone and processes it as digital voice data. The input for this step is a voice signal, and the output is voice data in digital format. This includes operations to properly collect and digitize the voice signal.

[0838] Step 2:

[0839] The device uses the Google Cloud Speech-to-Text API to convert digital audio data into text data. The input is digital audio data, and the output is text data. This conversion process involves analyzing the audio signal and processing the data for phoneme recognition.

[0840] Step 3:

[0841] The server receives text data and uses the Google Cloud Natural Language API to perform natural language processing and understand the order details. The input is text data, and the output is structured order data. This step involves semantic analysis of the text and extraction of order information.

[0842] Step 4:

[0843] The server uses IBM Watson Tone Analyzer to analyze emotional states derived from speech intonation. Input is text data, and output is data related to emotional states. This includes actions to evaluate the emotional elements of speech and determine the user's mood and urgency.

[0844] Step 5:

[0845] The server uses a generative AI model to generate a response based on the obtained sentiment and order details, and leverages Stripe API business logic to determine the optimal delivery option for the order. The input is structured order details and sentiment data, and the output is a refined response and delivery options. Response refinement and rapid decision-making processes are performed.

[0846] Step 6:

[0847] The server sends an optimized response to the terminal, and the terminal uses speech synthesis to output voice to the user. The input is the adjusted response and delivery option data, and the output is a natural voice response to the user. Voice data is generated and feedback is provided to the user.

[0848] Step 7:

[0849] After an order is confirmed, the server uses automated payment methods to process the payment and initiate delivery. The input is the confirmed order information, and the output is confirmation data of the completed transaction. Fast and accurate payment data processing is performed.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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."

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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 as being incorporated by reference.

[0871] The following is further disclosed regarding the embodiments described above.

[0872] (Claim 1)

[0873] A speech recognition means for converting speech information into text information,

[0874] A natural language processing means that generates a response based on the aforementioned text information,

[0875] A speech synthesis means that outputs the generated response as audio information,

[0876] A management system for managing customer orders and controlling the serving of food and beverages,

[0877] A service delivery system that includes an automated payment method for processing customer payment information.

[0878] (Claim 2)

[0879] The service provision system according to claim 1, wherein the speech recognition means and the natural language processing means are configured to engage in natural conversation with customers using a generative model.

[0880] (Claim 3)

[0881] The service provision system according to claim 1, wherein the management means is configured to transmit received order information to an external cooking system and optimize the timing of serving.

[0882] "Example 1"

[0883] (Claim 1)

[0884] A recognition means for converting audio information into information,

[0885] A natural processing means that generates a response based on the aforementioned information,

[0886] A synthesis means that outputs the generated response as information,

[0887] A management system for managing customer requests and controlling food delivery,

[0888] A system that includes automated means for processing customer transaction information.

[0889] (Claim 2)

[0890] The system according to claim 1, wherein the recognition means and the natural processing means are configured to engage in natural dialogue with the customer using a generative model.

[0891] (Claim 3)

[0892] The system according to claim 1, wherein the management means is configured to transmit the received request information to an external cooking device and optimize the timing of serving.

[0893] "Application Example 1"

[0894] (Claim 1)

[0895] A speech recognition means for converting speech information into text information,

[0896] A natural language processing means that generates a response based on the aforementioned character information,

[0897] A speech synthesis means that outputs the generated response as audio information,

[0898] A management system for managing customer orders and controlling the serving of food and beverages,

[0899] An automated payment method that processes the user's payment information,

[0900] A system that includes means for linking specific mobility services and information.

[0901] (Claim 2)

[0902] The system according to claim 1, wherein the speech recognition means and the natural language processing means are configured to perform natural dialogue with the user using a generative model.

[0903] (Claim 3)

[0904] The system according to claim 1, wherein the management means is configured to transmit received order information to an external manufacturing system and optimize the timing of serving.

[0905] "Example 2 of combining an emotion engine"

[0906] (Claim 1)

[0907] Information recognition means for converting audio information into text information,

[0908] An emotion analysis means for estimating an emotional state from the aforementioned audio information,

[0909] A natural language processing means that generates a response based on the aforementioned text information and emotional state,

[0910] A speech synthesis means that outputs the generated response as audio information,

[0911] An information management system for managing user orders and controlling the supply of food and beverages,

[0912] A system that includes an automated payment method for processing user payment information.

[0913] (Claim 2)

[0914] The system according to claim 1, wherein the speech recognition means and the natural language processing means are configured to engage in natural dialogue with the user using a generative model and adjust the response based on emotional information.

[0915] (Claim 3)

[0916] The system according to claim 1, wherein the information management means is configured to transmit received order information to an external manufacturing system and optimize the supply plan.

[0917] "Application example 2 when combining with an emotional engine"

[0918] (Claim 1)

[0919] A speech recognition means for converting speech information into text information,

[0920] A natural language processing means that generates a response based on the aforementioned text information,

[0921] A speech synthesis means that outputs the generated response as audio information,

[0922] A management system for managing customer orders and controlling the provision of food and beverages,

[0923] An automated payment method that processes the user's payment information,

[0924] An emotion analysis method that analyzes emotional states from audio signals,

[0925] A response adjustment means that adjusts the content of the response according to the aforementioned emotional state,

[0926] A system including a delivery optimization means that presents a response adjusted by the response adjustment means and an optimal delivery option.

[0927] (Claim 2)

[0928] The system according to claim 1, wherein the speech recognition means and the natural language processing means are configured to perform natural dialogue using a generative model and to present rapid delivery options through sentiment analysis.

[0929] (Claim 3)

[0930] The system according to claim 1, wherein the management means is configured to transmit received order information to an external supply system and to optimize the timing of delivery. [Explanation of symbols]

[0931] 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 speech recognition means for converting speech information into text information, A natural language processing means that generates a response based on the aforementioned text information, A speech synthesis means that outputs the generated response as audio information, A management system for managing customer orders and controlling the serving of food and beverages, A service delivery system that includes an automated payment method for processing customer payment information.

2. The service provision system according to claim 1, wherein the speech recognition means and the natural language processing means are configured to engage in natural conversation with customers using a generative model.

3. The service provision system according to claim 1, wherein the management means is configured to transmit received order information to an external cooking system and optimize the timing of serving.

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