System

Abstract

An object of a system according to an embodiment is to provide optimized information to a customer who is holding a call.SOLUTION: A system according to an embodiment includes a voice recognizer, a tendency analyzer, an information generator, and a distributor. The voice recognition unit recognizes a customer's voice using a voice recognition technique. The tendency analysis unit analyzes a tendency based on the interest or needs of the customer recognized by the voice recognition unit. The information generation unit generates information on a new product or service on the basis of the tendency analyzed by the tendency analysis unit. The distribution unit distributes the information generated by the information generation unit to the customer who is holding the call in real time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology did not provide effective information to customers on hold, making it difficult to improve customer engagement.

[0005] The system according to the embodiment aims to provide optimized information to customers while their calls are on hold. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice recognition unit, a trend analysis unit, an information generation unit, and a distribution unit. The voice recognition unit recognizes the voice of a customer using voice recognition technology. The trend analysis unit analyzes trends based on the customer's interests and needs recognized by the voice recognition unit. The information generation unit generates information about new products and services based on the trends analyzed by the trend analysis unit. The distribution unit distributes the information generated by the information generation unit in real time to customers whose calls are on hold. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimized information to customers while their calls are on hold. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices 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), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, 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), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An advertisement delivery system according to an embodiment of the present invention provides an optimized advertising experience to customers on hold. This advertisement delivery system utilizes voice recognition technology and trend analysis to generate and deliver information on new products and services tailored to the interests and needs of customers in real time. This enables the advertisement delivery system to transform waiting time into an innovative touchpoint, improving customer engagement and generating revenue.

[0029] An advertisement delivery system according to an embodiment includes a voice recognition unit, a trend analysis unit, an information generation unit, and a delivery unit. The voice recognition unit recognizes a customer's voice using voice recognition technology. For example, the voice recognition unit analyzes the customer's voice data in real time and converts the customer's speech into text data. The voice recognition unit can also improve the quality of the voice data using noise reduction technology. The voice recognition unit can analyze the customer's speech with high accuracy using a voice recognition algorithm. The trend analysis unit analyzes trends based on the customer's interests and needs recognized by the voice recognition unit. For example, the trend analysis unit extracts keywords from the customer's speech and identifies the customer's interests based on past data and trends. The trend analysis unit can also analyze customer behavior patterns using data mining technology. The trend analysis unit can predict the customer's interests and needs using statistical techniques. The information generation unit generates information on new products and services based on the trends analyzed by the trend analysis unit. For example, the information generation unit references a product information database to generate product information that matches the customer's interests. The information generation unit can also generate advertising content optimal for the customer using a generation algorithm. Furthermore, the information generation unit can generate customized advertisements according to customer needs. The distribution unit distributes the information generated by the information generation unit to customers on hold in real time. For example, the distribution unit transmits the generated advertisements to the customer's device using a distribution protocol. The distribution unit can also distribute advertisements in real time using technology to minimize latency. Furthermore, the distribution unit can distribute advertisements in an optimal format according to the customer's device. As a result, the advertisement distribution system according to the embodiment can provide optimal advertisements in real time based on the customer's interests and needs. For example, if a customer is interested in a new smartphone, information and special offers about the latest smartphones can be distributed in real time. Furthermore, if a customer is interested in a particular service, information about the service can be provided in real time.This allows customers to obtain information tailored to their interests and needs while they wait, and allows companies to significantly improve the precision of their marketing.

[0030] The speech recognition unit analyzes not only specific keywords from a customer's voice but also their speaking style and tone, allowing for more accurate identification of their interests and needs. For example, the speech recognition unit analyzes the customer's voice data and identifies their interests and needs by taking into account not only specific keywords but also their speaking style and tone. For example, if a customer speaks in an excited tone, it can be determined that the customer has a strong interest. The speech recognition unit can also analyze the customer's speaking style and tone using speech waveform feature extraction technology. Furthermore, the speech recognition unit can analyze the customer's emotions and intentions with high accuracy using acoustic feature analysis technology. Thus, by analyzing the customer's speaking style and tone, it can be more accurate to identify their interests and needs. For example, if a customer speaks in a calm tone, it can be determined that their interest is sustained. Furthermore, if a customer speaks in a hurried tone, it can be determined that their interest is temporary.

[0031] The voice recognition unit can analyze a customer's past call history to understand long-term changes in their interests and needs. The voice recognition unit, for example, analyzes a customer's past call history to build a system that understands long-term changes in their interests and needs. For example, it analyzes how a customer's interests are changing based on the content of past calls. The voice recognition unit can also convert past call data into text data using call content text conversion technology. Furthermore, the voice recognition unit can understand changes in a customer's interests and needs with high accuracy using time-series data analysis technology. Thus, by analyzing a customer's past call history, it is possible to understand changes in their long-term interests and needs. For example, if a customer has talked about the same product multiple times in the past, it can be determined that their interest in that product is sustained. Furthermore, if a customer's past interests have recently changed, it is possible to understand the change and respond to the new interest.

[0032] The voice recognition unit can analyze other activities the customer is engaged in during the call and provide relevant advertisements. For example, the voice recognition unit can analyze data on web browsing the customer is engaged in during the call and provide relevant advertisements based on the content of the analysis. For example, if a customer is searching for a specific product, advertisements related to that product can be delivered. The voice recognition unit can also analyze data on app usage and provide advertisements related to the app the customer is using. Furthermore, the voice recognition unit can analyze the customer's device usage in real time and provide advertisements according to the situation. This allows relevant advertisements to be provided by analyzing other activities the customer is engaged in during the call. For example, if a customer is browsing a travel-related website during the call, travel-related advertisements can be delivered. Also, if a customer is using a shopping app during the call, advertisements for products related to that app can be delivered.

[0033] The speech recognition unit can simultaneously analyze the conversations of multiple customers, identify common interests and needs, and provide group advertising. The speech recognition unit, for example, simultaneously analyzes the conversation data of multiple customers to build a system that identifies common interests and needs. For example, a common advertisement is delivered to a group of customers who are interested in the same product. The speech recognition unit can also individually analyze the conversations of multiple customers using speech separation technology. Furthermore, the speech recognition unit can simultaneously analyze the conversations of multiple customers with high accuracy using a parallel processing algorithm. This makes it possible to simultaneously analyze the conversations of multiple customers, identify common interests and needs, and provide group advertising. For example, advertisements related to those hobbies can be delivered to a group of customers who share the same hobby. Furthermore, advertisements related to the event can be provided to a group of customers attending the same event.

[0034] The trend analysis unit also integrates the customer's purchase history and web browsing history, allowing for more accurate identification of interests and needs. The trend analysis unit, for example, integrates the customer's purchase history and web browsing history to build a system that performs trend analysis. For example, the customer's interests and needs are identified based on past purchase data. The trend analysis unit can also refer to a database of purchase history to analyze the customer's purchasing patterns. Furthermore, the trend analysis unit can analyze web browsing history data to identify products and services in which the customer is interested. In this way, by integrating the customer's purchase history and web browsing history, it is possible to more accurately identify interests and needs. For example, it is possible to suggest new products related to products the customer has purchased in the past. It is also possible to provide services related to websites the customer frequently visits.

[0035] The trend analysis unit can estimate a customer's lifestyle and values ​​based on the results of the trend analysis and generate advertisements accordingly. The trend analysis unit, for example, builds a system that estimates a customer's lifestyle and values ​​based on the results of the trend analysis. For example, advertisements for health-related products and services are delivered to health-conscious customers. The trend analysis unit can also estimate a customer's lifestyle with high accuracy using behavioral pattern analysis technology. Furthermore, the trend analysis unit can identify a customer's values ​​using value classification criteria and generate advertisements accordingly. This makes it possible to generate more personalized advertisements by estimating a customer's lifestyle and values. For example, advertisements for environmentally friendly products can be delivered to eco-conscious customers. Furthermore, advertisements for the latest fashion items can be provided to fashion-conscious customers.

[0036] The trend analysis unit can also take into account trends according to seasons and events when conducting trend analysis. The trend analysis unit, for example, builds a system that takes into account trends according to seasons and events when conducting trend analysis. For example, during the Christmas season, advertisements for Christmas-related products and services are distributed. The trend analysis unit can also collect seasonal trend data and generate advertisements based on that data. Furthermore, the trend analysis unit can evaluate the influence of events and generate advertisements related to the events. This allows for the generation of more effective advertisements by taking into account trends according to seasons and events. For example, distributing Black Friday sale information can increase customer purchasing motivation. Furthermore, travel-related advertisements can be provided during the summer vacation season.

[0037] The trend analysis unit can integrate the results of the trend analysis with behavioral data from different devices to provide cross-device advertising. For example, the trend analysis unit can integrate the results of the trend analysis with behavioral data from different devices to build a system for providing cross-device advertising. For example, data from smartphones and tablets can be integrated to deliver advertisements. The trend analysis unit can also centrally manage customer behavioral data using data synchronization technology between devices. Furthermore, the trend analysis unit can maintain consistency of advertising content using an advertising delivery protocol between devices. This enables cross-device advertising by integrating behavioral data from different devices. For example, an advertisement related to a product a customer searched for on a smartphone can be displayed on a tablet. Furthermore, an advertisement related to a website a customer viewed on a PC can be delivered to a smartphone.

[0038] The trend analysis unit can compare the results of the trend analysis with data from different regions or cultural spheres to identify trends specific to the region. The trend analysis unit, for example, compares the results of the trend analysis with data from different regions or cultural spheres to build a system for identifying trends specific to the region. For example, the trend analysis unit analyzes purchasing trends by region and delivers advertisements. The trend analysis unit can also collect consumption trend data by region and generate advertisements based on the data. Furthermore, the trend analysis unit can take into account differences in values ​​between cultural spheres and generate advertisements specific to the region. This makes it possible to identify trends specific to the region by comparing data from different regions or cultural spheres. For example, advertisements for products and services specific to Asia can be delivered to customers in Asia. Furthermore, advertisements based on trends specific to Europe and the United States can be provided to customers in Europe and the United States.

[0039] The information generation unit can provide more personalized information by taking into account customers' past feedback and reviews when generating information about new products and services. The information generation unit, for example, analyzes customers' past feedback and reviews to build a system that generates information about new products and services. For example, it can provide information related to products that customers have given high ratings to. The information generation unit can also classify customer feedback using review evaluation criteria and generate advertisements based on the results. Furthermore, the information generation unit can analyze the content of the feedback and provide information customized according to the customer's needs. This allows more personalized information to be provided by taking into account customers' past feedback and reviews. For example, it can provide information about new products related to products that customers have given high ratings to in the past. Furthermore, if a customer has given positive feedback about a specific service, it can provide information related to that service.

[0040] When generating information about new products and services, the information generation unit can also refer to information about competitors and provide differentiated advertisements. The information generation unit, for example, references information about competitors and builds a system for generating information about new products and services. For example, an advertisement is provided that emphasizes the advantages of the company's products compared to those of competitors. The information generation unit can also analyze the features of competitors' products and generate advertisements that highlight the superiority of the company's products. Furthermore, the information generation unit can analyze competitors' marketing strategies and generate advertisements to counter them. In this way, differentiated advertisements can be provided by referring to information about competitors. For example, an advertisement can be delivered that emphasizes that the company's products are cheaper than those of competitors. An advertisement can also be provided that highlights that the company's products have better functionality than those of competitors.

[0041] When generating information about new products and services, the information generation unit can propose bundles of related products and services based on a customer's purchase history. The information generation unit, for example, analyzes a customer's purchase history and builds a system for proposing bundles of related products and services. For example, the information generation unit proposes accessories and services related to products purchased by the customer. The information generation unit can also propose optimal bundles to the customer using a bundle proposal generation algorithm. Furthermore, the information generation unit can propose bundles at attractive prices for the customer using a bundle pricing method. This allows for more effective advertising by proposing bundles of related products and services based on the customer's purchase history. For example, if a customer purchases a smartphone, accessories and services related to the smartphone can be proposed as a bundle. Furthermore, if a customer purchases a travel package, optional tours and insurance related to the trip can be proposed.

[0042] When generating information about new products and services, the information generation unit can analyze customers' social media activities and provide content that will interest them. For example, the information generation unit builds a system that analyzes customers' social media activities and generates information about new products and services. For example, it provides information related to content that customers have "liked" or shared. The information generation unit can also analyze the content of social media posts to identify customers' interests. Furthermore, the information generation unit can analyze follower response data to identify topics that customers are interested in. This makes it possible to provide content that will interest customers by analyzing their social media activities. For example, if a customer has "liked" a particular brand, it can provide information about new products from that brand. Also, if a customer has shared a particular event, it can provide information related to that event.

[0043] The information generation unit can provide information about nearby stores and events by taking into account the customer's location information when generating information about new products and services. The information generation unit, for example, analyzes the customer's location information and builds a system that provides information about nearby stores and events. For example, the information generation unit can provide information about special offers at stores close to the customer's current location. The information generation unit can also use location information acquisition technology to identify the customer's current location with high accuracy. Furthermore, the information generation unit can refer to a store information database and provide optimal store information based on the customer's location. This makes it possible to provide information about nearby stores and events by taking the customer's location information into consideration. For example, if a customer is in a shopping mall, it can provide information about special offers at stores in the mall. Also, if a customer is at a specific event venue, it can provide information related to the event.

[0044] The distribution unit can analyze the customer's current situation during real-time distribution and provide advertisements accordingly. The distribution unit, for example, builds a system that analyzes the customer's current situation and provides advertisements accordingly. For example, advertisements that can be viewed in a short time are delivered to customers commuting. The distribution unit can also acquire customer situation data in real time and generate advertisements based on that data. Furthermore, the distribution unit can grasp the customer's current situation with high accuracy using situation data analysis technology. This allows the distribution unit to analyze the customer's current situation and provide advertisements accordingly. For example, if the customer is taking a break, an advertisement for a product with a relaxing effect can be delivered. Also, if the customer is exercising, an advertisement for an energetic product can be provided.

[0045] During real-time delivery, the delivery unit can deliver advertisements at optimal timing based on past customer response data. The delivery unit, for example, analyzes past customer response data and builds a system for delivering advertisements at optimal timing. For example, the delivery unit delivers advertisements during time periods when customers are most likely to respond. The delivery unit can also obtain past customer response data with high accuracy using response data collection technology. Furthermore, the delivery unit can analyze customer responses using response evaluation criteria and generate advertisements based on the results. This allows advertisements to be delivered at optimal timing based on past customer response data. For example, if a customer has responded to an advertisement during a specific time period in the past, the advertisement can be delivered during that time period. Furthermore, if a customer has responded to an advertisement under specific circumstances, an advertisement appropriate for that situation can be provided.

[0046] The distribution unit can provide advertisements in the optimal format during real-time distribution, taking into account the status of the customer's device. The distribution unit, for example, analyzes the status of the customer's device and builds a system that provides advertisements in the optimal format. For example, when the battery level is low, an advertisement that can be viewed in a short time is delivered. The distribution unit can also acquire device status data in real time and generate advertisements based on that data. Furthermore, the distribution unit can analyze the network connection status and provide advertisements in the optimal format. This allows advertisements to be provided in the optimal format by taking into account the status of the customer's device. For example, when the network connection is unstable, an advertisement that can be viewed with low bandwidth can be delivered. Furthermore, the optimal advertisement format can be provided depending on the screen size of the device.

[0047] The distribution unit can refer to the customer's calendar information during real-time distribution and provide advertisements related to the schedule. The distribution unit, for example, analyzes the customer's calendar information and builds a system that provides advertisements related to the schedule. For example, if the customer has a meeting scheduled, business-related advertisements are delivered. The distribution unit can also grasp the customer's schedule with high accuracy using data acquisition technology from a calendar app. Furthermore, the distribution unit can generate advertisements according to the customer's schedule using schedule classification criteria. This makes it possible to provide advertisements related to the schedule by referring to the customer's calendar information. For example, if the customer has travel plans, travel-related advertisements can be delivered. Also, if the customer has plans to attend a sporting event, advertisements related to the event can be provided.

[0048] The distribution unit can analyze a customer's music or video playback history during real-time distribution and provide relevant advertisements. The distribution unit, for example, builds a system that analyzes a customer's music or video playback history and provides relevant advertisements. For example, if a customer is playing music by a specific artist, information about that artist's new songs can be provided. The distribution unit can also use playback history collection technology to obtain a customer's playback history with high accuracy. Furthermore, the distribution unit can identify a customer's interests using playback frequency evaluation criteria and generate advertisements based on the results. This makes it possible to provide relevant advertisements by analyzing a customer's music or video playback history. For example, if a customer frequently plays music of a specific genre, advertisements related to that genre can be delivered. Also, if a customer is watching a specific movie, advertisements for products related to that movie can be delivered.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The advertisement distribution system may further include a purchase history analysis unit that analyzes a customer's purchase history. The purchase history analysis unit, for example, analyzes data on products and services purchased by the customer in the past to identify the customer's purchasing patterns. This makes it possible to provide advertisements for new products and services related to products purchased by the customer in the past. For example, if a customer has previously purchased cosmetics from a specific brand, it is possible to provide information on new products from that brand. Also, if a customer has previously purchased travel-related products, it is possible to provide information on new travel packages. Furthermore, it is possible to analyze reviews of products purchased by the customer in the past and suggest products that suit the customer's preferences.

[0051] The ad delivery system can further include a social media analysis unit that analyzes the customer's social media activities. For example, the social media analysis unit analyzes the content that the customer has "liked" or shared to identify the customer's interests. As a result, if the customer has "liked" a particular brand, it can provide information about new products from that brand. Also, if the customer has shared information about a particular event, it can provide information related to that event. Furthermore, the social media analysis unit can analyze follower response data to identify topics in which the customer is interested. For example, if the customer has shared music by a particular artist, it can provide information about new songs by that artist.

[0052] The advertising distribution system can further include a location information analysis unit that analyzes customer location information. The location information analysis unit, for example, identifies the customer's current location with high accuracy and provides information on nearby stores and events. This makes it possible to provide the customer with information on special offers at stores close to their current location. Also, if the customer is at a specific event venue, it can provide information related to that event. Furthermore, the location information analysis unit can analyze the customer's movement patterns and provide advertisements related to places the customer frequently visits. For example, it can provide information on special offers at cafes that the customer frequently visits.

[0053] The advertisement distribution system may further include a calendar analysis unit that analyzes the customer's calendar information. The calendar analysis unit, for example, accurately grasps the customer's schedule and provides advertisements related to the schedule. As a result, if the customer has a meeting scheduled, it is possible to provide business-related advertisements. Also, if the customer has a travel schedule, it is possible to provide travel-related advertisements. Furthermore, the calendar analysis unit may use schedule classification criteria to generate advertisements according to the customer's schedule. For example, if the customer has plans to participate in a sporting event, it is possible to provide advertisements related to that event.

[0054] The advertisement delivery system may further include a playback history analysis unit that analyzes the music and video playback history of a customer. The playback history analysis unit, for example, analyzes data on music and videos played by a customer to identify the customer's interests. As a result, if a customer plays music by a particular artist, it is possible to provide information on new songs by that artist. Also, if a customer frequently plays music of a particular genre, it is possible to provide advertisements related to that genre. Furthermore, the playback history analysis unit may analyze data on movies and dramas watched by a customer and provide advertisements for related products based on the content. For example, if a customer watches a particular movie, it is possible to provide advertisements for products related to that movie.

[0055] The advertisement distribution system may further include a device analysis unit that analyzes the status of the customer's device. The device analysis unit may, for example, analyze the remaining battery level and network connection status of the customer's device and provide advertisements in the optimal format. This makes it possible to provide advertisements that can be viewed in a short time when the battery level is low. Also, when the network connection is unstable, it is possible to provide advertisements that can be viewed with low bandwidth. Furthermore, the device analysis unit may also provide the optimal advertisement format depending on the screen size of the device. For example, it may be possible to provide advertisements that are suitable for the small screen of a smartphone.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The voice recognition unit recognizes the customer's voice using voice recognition technology. For example, the voice recognition unit analyzes the customer's voice data in real time and converts the customer's speech into text data. The voice recognition unit can also improve the quality of the voice data using noise reduction technology. Furthermore, the voice recognition unit can analyze the customer's speech with high accuracy using a voice recognition algorithm. Step 2: The trend analysis unit analyzes trends based on the customer's interests and needs recognized by the voice recognition unit. For example, the trend analysis unit extracts keywords from the customer's speech and identifies the customer's interests based on past data and trends. The trend analysis unit can also use data mining technology to analyze customer behavior patterns. Furthermore, the trend analysis unit can predict customer interests and needs using statistical methods. Step 3: The information generation unit generates information about new products and services based on the trends analyzed by the trend analysis unit. For example, the information generation unit references a database of product information and generates product information that matches the customer's interests. The information generation unit can also use a generation algorithm to generate advertising content that is optimal for the customer. Furthermore, the information generation unit can generate customized advertisements according to the customer's needs. Step 4: The distribution unit distributes the information generated by the information generation unit to the customer on hold in real time. For example, the distribution unit transmits the generated advertisement to the customer's device using a distribution protocol. The distribution unit can also distribute the advertisement in real time using technology to minimize latency. Furthermore, the distribution unit can distribute the advertisement in an optimal format depending on the customer's device.

[0058] (Example 2) An advertisement delivery system according to an embodiment of the present invention provides an optimized advertising experience to customers on hold. This advertisement delivery system utilizes voice recognition technology and trend analysis to generate and deliver information on new products and services tailored to the interests and needs of customers in real time. This enables the advertisement delivery system to transform waiting time into an innovative touchpoint, improving customer engagement and generating revenue.

[0059] An advertisement delivery system according to an embodiment includes a voice recognition unit, a trend analysis unit, an information generation unit, and a delivery unit. The voice recognition unit recognizes a customer's voice using voice recognition technology. For example, the voice recognition unit analyzes the customer's voice data in real time and converts the customer's speech into text data. The voice recognition unit can also improve the quality of the voice data using noise reduction technology. The voice recognition unit can analyze the customer's speech with high accuracy using a voice recognition algorithm. The trend analysis unit analyzes trends based on the customer's interests and needs recognized by the voice recognition unit. For example, the trend analysis unit extracts keywords from the customer's speech and identifies the customer's interests based on past data and trends. The trend analysis unit can also analyze customer behavior patterns using data mining technology. The trend analysis unit can predict the customer's interests and needs using statistical techniques. The information generation unit generates information on new products and services based on the trends analyzed by the trend analysis unit. For example, the information generation unit references a product information database to generate product information that matches the customer's interests. The information generation unit can also generate advertising content optimal for the customer using a generation algorithm. Furthermore, the information generation unit can generate customized advertisements according to customer needs. The distribution unit distributes the information generated by the information generation unit to customers on hold in real time. For example, the distribution unit transmits the generated advertisements to the customer's device using a distribution protocol. The distribution unit can also distribute advertisements in real time using technology to minimize latency. Furthermore, the distribution unit can distribute advertisements in an optimal format according to the customer's device. As a result, the advertisement distribution system according to the embodiment can provide optimal advertisements in real time based on the customer's interests and needs. For example, if a customer is interested in a new smartphone, information and special offers about the latest smartphones can be distributed in real time. Furthermore, if a customer is interested in a particular service, information about the service can be provided in real time.This allows customers to obtain information tailored to their interests and needs while they wait, and allows companies to significantly improve the precision of their marketing.

[0060] The voice recognition unit can analyze a customer's emotions in real time and dynamically change the advertising content according to the emotions. For example, the voice recognition unit analyzes a customer's voice in real time to detect changes in emotions. For example, if a customer is excited, an energetic advertisement is delivered, and if a customer is relaxed, a calm advertisement is delivered. The voice recognition unit can also analyze a customer's emotions with high accuracy using voice tone analysis technology. Furthermore, the voice recognition unit can calculate a customer's emotion score using an emotion analysis algorithm and dynamically change the advertising content based on the score. This allows for a more effective advertising experience by providing advertisements according to the customer's emotions. For example, if a customer is feeling stressed, an advertisement for a product with a relaxing effect can be delivered. Furthermore, if a customer is happy, an advertisement that elicits even more positive emotions can be delivered.

[0061] The speech recognition unit analyzes not only specific keywords from a customer's voice but also their speaking style and tone, allowing for more accurate identification of their interests and needs. For example, the speech recognition unit analyzes the customer's voice data and identifies their interests and needs by taking into account not only specific keywords but also their speaking style and tone. For example, if a customer speaks in an excited tone, it can be determined that the customer has a strong interest. The speech recognition unit can also analyze the customer's speaking style and tone using speech waveform feature extraction technology. Furthermore, the speech recognition unit can analyze the customer's emotions and intentions with high accuracy using acoustic feature analysis technology. Thus, by analyzing the customer's speaking style and tone, it can be more accurate to identify their interests and needs. For example, if a customer speaks in a calm tone, it can be determined that their interest is sustained. Furthermore, if a customer speaks in a hurried tone, it can be determined that their interest is temporary.

[0062] The voice recognition unit can analyze a customer's past call history to understand long-term changes in their interests and needs. The voice recognition unit, for example, analyzes a customer's past call history to build a system that understands long-term changes in their interests and needs. For example, it analyzes how a customer's interests are changing based on the content of past calls. The voice recognition unit can also convert past call data into text data using call content text conversion technology. Furthermore, the voice recognition unit can understand changes in a customer's interests and needs with high accuracy using time-series data analysis technology. Thus, by analyzing a customer's past call history, it is possible to understand changes in their long-term interests and needs. For example, if a customer has talked about the same product multiple times in the past, it can be determined that their interest in that product is sustained. Furthermore, if a customer's past interests have recently changed, it is possible to understand the change and respond to the new interest.

[0063] The voice recognition unit can analyze other activities the customer is engaged in during the call and provide relevant advertisements. For example, the voice recognition unit can analyze data on web browsing the customer is engaged in during the call and provide relevant advertisements based on the content of the analysis. For example, if a customer is searching for a specific product, advertisements related to that product can be delivered. The voice recognition unit can also analyze data on app usage and provide advertisements related to the app the customer is using. Furthermore, the voice recognition unit can analyze the customer's device usage in real time and provide advertisements according to the situation. This allows relevant advertisements to be provided by analyzing other activities the customer is engaged in during the call. For example, if a customer is browsing a travel-related website during the call, travel-related advertisements can be delivered. Also, if a customer is using a shopping app during the call, advertisements for products related to that app can be delivered.

[0064] The speech recognition unit can simultaneously analyze the conversations of multiple customers, identify common interests and needs, and provide group advertising. The speech recognition unit, for example, simultaneously analyzes the conversation data of multiple customers to build a system that identifies common interests and needs. For example, a common advertisement is delivered to a group of customers who are interested in the same product. The speech recognition unit can also individually analyze the conversations of multiple customers using speech separation technology. Furthermore, the speech recognition unit can simultaneously analyze the conversations of multiple customers with high accuracy using a parallel processing algorithm. This makes it possible to simultaneously analyze the conversations of multiple customers, identify common interests and needs, and provide group advertising. For example, advertisements related to those hobbies can be delivered to a group of customers who share the same hobby. Furthermore, advertisements related to the event can be provided to a group of customers attending the same event.

[0065] The voice recognition unit can use the emotion estimation function to provide voice feedback based on the customer's emotions and generate advertisements that elicit positive emotions. The voice recognition unit, for example, uses the emotion estimation function to build a system that provides voice feedback based on the customer's emotions. For example, if the customer is happy, an advertisement that elicits even more positive emotions is delivered. The voice recognition unit can also use voice synthesis technology to provide feedback according to the customer's emotions. Furthermore, the voice recognition unit can use emotion labeling technology to analyze the customer's emotions with high accuracy and generate advertisements based on the results. In this way, by providing voice feedback based on the customer's emotions, it is possible to generate advertisements that elicit positive emotions. For example, if the customer is feeling stressed, an advertisement for a product with a relaxing effect can be delivered. Also, if the customer is happy, an advertisement that elicits even more positive emotions can be delivered.

[0066] The trend analysis unit also integrates the customer's purchase history and web browsing history, allowing for more accurate identification of interests and needs. The trend analysis unit, for example, integrates the customer's purchase history and web browsing history to build a system that performs trend analysis. For example, the customer's interests and needs are identified based on past purchase data. The trend analysis unit can also refer to a database of purchase history to analyze the customer's purchasing patterns. Furthermore, the trend analysis unit can analyze web browsing history data to identify products and services in which the customer is interested. In this way, by integrating the customer's purchase history and web browsing history, it is possible to more accurately identify interests and needs. For example, it is possible to suggest new products related to products the customer has purchased in the past. It is also possible to provide services related to websites the customer frequently visits.

[0067] The trend analysis unit can estimate a customer's lifestyle and values ​​based on the results of the trend analysis and generate advertisements accordingly. The trend analysis unit, for example, builds a system that estimates a customer's lifestyle and values ​​based on the results of the trend analysis. For example, advertisements for health-related products and services are delivered to health-conscious customers. The trend analysis unit can also estimate a customer's lifestyle with high accuracy using behavioral pattern analysis technology. Furthermore, the trend analysis unit can identify a customer's values ​​using value classification criteria and generate advertisements accordingly. This makes it possible to generate more personalized advertisements by estimating a customer's lifestyle and values. For example, advertisements for environmentally friendly products can be delivered to eco-conscious customers. Furthermore, advertisements for the latest fashion items can be provided to fashion-conscious customers.

[0068] The trend analysis unit can also take into account trends according to seasons and events when conducting trend analysis. The trend analysis unit, for example, builds a system that takes into account trends according to seasons and events when conducting trend analysis. For example, during the Christmas season, advertisements for Christmas-related products and services are distributed. The trend analysis unit can also collect seasonal trend data and generate advertisements based on that data. Furthermore, the trend analysis unit can evaluate the influence of events and generate advertisements related to the events. This allows for the generation of more effective advertisements by taking into account trends according to seasons and events. For example, distributing Black Friday sale information can increase customer purchasing motivation. Furthermore, travel-related advertisements can be provided during the summer vacation season.

[0069] The trend analysis unit can integrate the results of the trend analysis with behavioral data from different devices to provide cross-device advertising. For example, the trend analysis unit can integrate the results of the trend analysis with behavioral data from different devices to build a system for providing cross-device advertising. For example, data from smartphones and tablets can be integrated to deliver advertisements. The trend analysis unit can also centrally manage customer behavioral data using data synchronization technology between devices. Furthermore, the trend analysis unit can maintain consistency of advertising content using an advertising delivery protocol between devices. This enables cross-device advertising by integrating behavioral data from different devices. For example, an advertisement related to a product a customer searched for on a smartphone can be displayed on a tablet. Furthermore, an advertisement related to a website a customer viewed on a PC can be delivered to a smartphone.

[0070] The trend analysis unit can compare the results of the trend analysis with data from different regions or cultural spheres to identify trends specific to the region. The trend analysis unit, for example, compares the results of the trend analysis with data from different regions or cultural spheres to build a system for identifying trends specific to the region. For example, the trend analysis unit analyzes purchasing trends by region and delivers advertisements. The trend analysis unit can also collect consumption trend data by region and generate advertisements based on the data. Furthermore, the trend analysis unit can take into account differences in values ​​between cultural spheres and generate advertisements specific to the region. This makes it possible to identify trends specific to the region by comparing data from different regions or cultural spheres. For example, advertisements for products and services specific to Asia can be delivered to customers in Asia. Furthermore, advertisements based on trends specific to Europe and the United States can be provided to customers in Europe and the United States.

[0071] The trend analysis unit can use the emotion estimation function to perform trend analysis based on customer emotions and generate advertisements that are likely to resonate emotionally. The trend analysis unit, for example, uses the emotion estimation function to build a system that performs trend analysis based on customer emotions. For example, it identifies trends with a high proportion of positive emotions and delivers advertisements. The trend analysis unit can also use emotion labeling technology to analyze customer emotions with high accuracy and generate advertisements based on the results. Furthermore, the trend analysis unit can generate advertisements that are likely to resonate emotionally using advertising copy and visual elements that appeal to emotions. In this way, by performing trend analysis based on customer emotions, it is possible to generate advertisements that are likely to resonate emotionally. For example, if a customer is happy, it is possible to deliver an advertisement that elicits even more positive emotions. Furthermore, if a customer is moved, it is possible to provide an advertisement that shares that emotion.

[0072] The information generation unit can provide more personalized information by taking into account customers' past feedback and reviews when generating information about new products and services. The information generation unit, for example, analyzes customers' past feedback and reviews to build a system that generates information about new products and services. For example, it can provide information related to products that customers have given high ratings to. The information generation unit can also classify customer feedback using review evaluation criteria and generate advertisements based on the results. Furthermore, the information generation unit can analyze the content of the feedback and provide information customized according to the customer's needs. This allows more personalized information to be provided by taking into account customers' past feedback and reviews. For example, it can provide information about new products related to products that customers have given high ratings to in the past. Furthermore, if a customer has given positive feedback about a specific service, it can provide information related to that service.

[0073] When generating information about new products and services, the information generation unit can also refer to information about competitors and provide differentiated advertisements. The information generation unit, for example, references information about competitors and builds a system for generating information about new products and services. For example, an advertisement is provided that emphasizes the advantages of the company's products compared to those of competitors. The information generation unit can also analyze the features of competitors' products and generate advertisements that highlight the superiority of the company's products. Furthermore, the information generation unit can analyze competitors' marketing strategies and generate advertisements to counter them. In this way, differentiated advertisements can be provided by referring to information about competitors. For example, an advertisement can be delivered that emphasizes that the company's products are cheaper than those of competitors. An advertisement can also be provided that highlights that the company's products have better functionality than those of competitors.

[0074] When generating information about new products and services, the information generation unit can propose bundles of related products and services based on a customer's purchase history. The information generation unit, for example, analyzes a customer's purchase history and builds a system for proposing bundles of related products and services. For example, the information generation unit proposes accessories and services related to products purchased by the customer. The information generation unit can also propose optimal bundles to the customer using a bundle proposal generation algorithm. Furthermore, the information generation unit can propose bundles at attractive prices for the customer using a bundle pricing method. This allows for more effective advertising by proposing bundles of related products and services based on the customer's purchase history. For example, if a customer purchases a smartphone, accessories and services related to the smartphone can be proposed as a bundle. Furthermore, if a customer purchases a travel package, optional tours and insurance related to the trip can be proposed.

[0075] When generating information about new products and services, the information generation unit can analyze customers' social media activities and provide content that will interest them. For example, the information generation unit builds a system that analyzes customers' social media activities and generates information about new products and services. For example, it provides information related to content that customers have "liked" or shared. The information generation unit can also analyze the content of social media posts to identify customers' interests. Furthermore, the information generation unit can analyze follower response data to identify topics that customers are interested in. This makes it possible to provide content that will interest customers by analyzing their social media activities. For example, if a customer has "liked" a particular brand, it can provide information about new products from that brand. Also, if a customer has shared a particular event, it can provide information related to that event.

[0076] The information generation unit can provide information about nearby stores and events by taking into account the customer's location information when generating information about new products and services. The information generation unit, for example, analyzes the customer's location information and builds a system that provides information about nearby stores and events. For example, the information generation unit can provide information about special offers at stores close to the customer's current location. The information generation unit can also use location information acquisition technology to identify the customer's current location with high accuracy. Furthermore, the information generation unit can refer to a store information database and provide optimal store information based on the customer's location. This makes it possible to provide information about nearby stores and events by taking the customer's location information into consideration. For example, if a customer is in a shopping mall, it can provide information about special offers at stores in the mall. Also, if a customer is at a specific event venue, it can provide information related to the event.

[0077] The information generation unit can use the emotion estimation function to propose new products and services based on the customer's emotions and generate advertisements that elicit positive emotions. The information generation unit, for example, uses the emotion estimation function to build a system that proposes new products and services based on the customer's emotions. For example, if the customer is happy, an advertisement that elicits even more positive emotions is delivered. The information generation unit can also use emotion labeling technology to analyze the customer's emotions with high accuracy and generate advertisements based on the results. Furthermore, the information generation unit can generate advertisements that elicit positive emotions using advertising copy and visual elements that appeal to emotions. This makes it possible to generate advertisements that elicit positive emotions by proposing new products and services based on the customer's emotions. For example, if the customer is feeling stressed, an advertisement for a product with a relaxing effect can be delivered. Also, if the customer is happy, an advertisement that elicits even more positive emotions can be delivered.

[0078] The distribution unit can analyze the customer's current situation during real-time distribution and provide advertisements accordingly. The distribution unit, for example, builds a system that analyzes the customer's current situation and provides advertisements accordingly. For example, advertisements that can be viewed in a short time are delivered to customers commuting. The distribution unit can also acquire customer situation data in real time and generate advertisements based on that data. Furthermore, the distribution unit can grasp the customer's current situation with high accuracy using situation data analysis technology. This allows the distribution unit to analyze the customer's current situation and provide advertisements accordingly. For example, if the customer is taking a break, an advertisement for a product with a relaxing effect can be delivered. Also, if the customer is exercising, an advertisement for an energetic product can be provided.

[0079] During real-time delivery, the delivery unit can deliver advertisements at optimal timing based on past customer response data. The delivery unit, for example, analyzes past customer response data and builds a system for delivering advertisements at optimal timing. For example, the delivery unit delivers advertisements during time periods when customers are most likely to respond. The delivery unit can also obtain past customer response data with high accuracy using response data collection technology. Furthermore, the delivery unit can analyze customer responses using response evaluation criteria and generate advertisements based on the results. This allows advertisements to be delivered at optimal timing based on past customer response data. For example, if a customer has responded to an advertisement during a specific time period in the past, the advertisement can be delivered during that time period. Furthermore, if a customer has responded to an advertisement under specific circumstances, an advertisement appropriate for that situation can be provided.

[0080] The distribution unit can provide advertisements in the optimal format during real-time distribution, taking into account the status of the customer's device. The distribution unit, for example, analyzes the status of the customer's device and builds a system that provides advertisements in the optimal format. For example, when the battery level is low, an advertisement that can be viewed in a short time is delivered. The distribution unit can also acquire device status data in real time and generate advertisements based on that data. Furthermore, the distribution unit can analyze the network connection status and provide advertisements in the optimal format. This allows advertisements to be provided in the optimal format by taking into account the status of the customer's device. For example, when the network connection is unstable, an advertisement that can be viewed with low bandwidth can be delivered. Furthermore, the optimal advertisement format can be provided depending on the screen size of the device.

[0081] The distribution unit can refer to the customer's calendar information during real-time distribution and provide advertisements related to the schedule. The distribution unit, for example, analyzes the customer's calendar information and builds a system that provides advertisements related to the schedule. For example, if the customer has a meeting scheduled, business-related advertisements are delivered. The distribution unit can also grasp the customer's schedule with high accuracy using data acquisition technology from a calendar app. Furthermore, the distribution unit can generate advertisements according to the customer's schedule using schedule classification criteria. This makes it possible to provide advertisements related to the schedule by referring to the customer's calendar information. For example, if the customer has travel plans, travel-related advertisements can be delivered. Also, if the customer has plans to attend a sporting event, advertisements related to the event can be provided.

[0082] The distribution unit can analyze a customer's music or video playback history during real-time distribution and provide relevant advertisements. The distribution unit, for example, builds a system that analyzes a customer's music or video playback history and provides relevant advertisements. For example, if a customer is playing music by a specific artist, information about that artist's new songs can be provided. The distribution unit can also use playback history collection technology to obtain a customer's playback history with high accuracy. Furthermore, the distribution unit can identify a customer's interests using playback frequency evaluation criteria and generate advertisements based on the results. This makes it possible to provide relevant advertisements by analyzing a customer's music or video playback history. For example, if a customer frequently plays music of a specific genre, advertisements related to that genre can be delivered. Also, if a customer is watching a specific movie, advertisements for products related to that movie can be delivered.

[0083] The distribution unit can use the emotion estimation function to provide real-time advertisements based on customer emotions and generate advertisements that elicit positive emotions. The distribution unit, for example, uses the emotion estimation function to build a system for providing real-time advertisements based on customer emotions. For example, if a customer is happy, the distribution unit can deliver advertisements that elicit even more positive emotions. The distribution unit can also use emotion labeling technology to analyze customer emotions with high accuracy and generate advertisements based on the results. Furthermore, the distribution unit can generate advertisements that elicit positive emotions using advertising copy and visual elements that appeal to emotions. In this way, by providing real-time advertisements based on customer emotions, advertisements that elicit positive emotions can be generated. For example, if a customer is feeling stressed, an advertisement for a product with a relaxing effect can be delivered. Furthermore, if a customer is happy, an advertisement that elicits even more positive emotions can be delivered.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The advertisement distribution system may further include a purchase history analysis unit that analyzes a customer's purchase history. The purchase history analysis unit, for example, analyzes data on products and services purchased by the customer in the past to identify the customer's purchasing patterns. This makes it possible to provide advertisements for new products and services related to products purchased by the customer in the past. For example, if a customer has previously purchased cosmetics from a specific brand, it is possible to provide information on new products from that brand. Also, if a customer has previously purchased travel-related products, it is possible to provide information on new travel packages. Furthermore, it is possible to analyze reviews of products purchased by the customer in the past and suggest products that suit the customer's preferences.

[0086] The advertisement distribution system may further include an emotion analysis unit that estimates the customer's emotions and generates advertisements based on the estimated emotions. The emotion analysis unit, for example, analyzes the customer's voice data and estimates the customer's emotions in real time. As a result, if the customer is feeling stressed, it is possible to provide advertisements for products with a relaxing effect. Also, if the customer is happy, it is possible to provide advertisements that elicit more positive emotions. Furthermore, the emotion analysis unit may calculate an emotion score for the customer and dynamically change the advertisement content based on the score. For example, if the customer is excited, it is possible to provide an energetic advertisement.

[0087] The ad delivery system can further include a social media analysis unit that analyzes the customer's social media activities. For example, the social media analysis unit analyzes the content that the customer has "liked" or shared to identify the customer's interests. As a result, if the customer has "liked" a particular brand, it can provide information about new products from that brand. Also, if the customer has shared information about a particular event, it can provide information related to that event. Furthermore, the social media analysis unit can analyze follower response data to identify topics in which the customer is interested. For example, if the customer has shared music by a particular artist, it can provide information about new songs by that artist.

[0088] The advertising distribution system can further include a location information analysis unit that analyzes customer location information. The location information analysis unit, for example, identifies the customer's current location with high accuracy and provides information on nearby stores and events. This makes it possible to provide the customer with information on special offers at stores close to their current location. Also, if the customer is at a specific event venue, it can provide information related to that event. Furthermore, the location information analysis unit can analyze the customer's movement patterns and provide advertisements related to places the customer frequently visits. For example, it can provide information on special offers at cafes that the customer frequently visits.

[0089] The advertisement delivery system may further include an emotion estimation unit that estimates the customer's emotion and generates an advertisement based on the estimated emotion. The emotion estimation unit, for example, analyzes the customer's voice data and estimates the customer's emotion in real time. As a result, if the customer is feeling stressed, an advertisement for a product with a relaxing effect can be provided. Also, if the customer is happy, an advertisement that elicits even more positive emotions can be provided. Furthermore, the emotion estimation unit can calculate the customer's emotion score and dynamically change the advertisement content based on the score. For example, if the customer is excited, an energetic advertisement can be provided.

[0090] The advertisement distribution system may further include a calendar analysis unit that analyzes the customer's calendar information. The calendar analysis unit, for example, accurately grasps the customer's schedule and provides advertisements related to the schedule. As a result, if the customer has a meeting scheduled, it is possible to provide business-related advertisements. Also, if the customer has a travel schedule, it is possible to provide travel-related advertisements. Furthermore, the calendar analysis unit may use schedule classification criteria to generate advertisements according to the customer's schedule. For example, if the customer has plans to participate in a sporting event, it is possible to provide advertisements related to that event.

[0091] The advertisement delivery system may further include an emotion estimation unit that estimates the customer's emotion and generates an advertisement based on the estimated emotion. The emotion estimation unit, for example, analyzes the customer's voice data and estimates the customer's emotion in real time. As a result, if the customer is feeling stressed, an advertisement for a product with a relaxing effect can be provided. Also, if the customer is happy, an advertisement that elicits even more positive emotions can be provided. Furthermore, the emotion estimation unit can calculate the customer's emotion score and dynamically change the advertisement content based on the score. For example, if the customer is excited, an energetic advertisement can be provided.

[0092] The advertisement delivery system may further include a playback history analysis unit that analyzes the music and video playback history of a customer. The playback history analysis unit, for example, analyzes data on music and videos played by a customer to identify the customer's interests. As a result, if a customer plays music by a particular artist, it is possible to provide information on new songs by that artist. Also, if a customer frequently plays music of a particular genre, it is possible to provide advertisements related to that genre. Furthermore, the playback history analysis unit may analyze data on movies and dramas watched by a customer and provide advertisements for related products based on the content. For example, if a customer watches a particular movie, it is possible to provide advertisements for products related to that movie.

[0093] The advertisement delivery system may further include an emotion estimation unit that estimates the customer's emotion and generates an advertisement based on the estimated emotion. The emotion estimation unit, for example, analyzes the customer's voice data and estimates the customer's emotion in real time. As a result, if the customer is feeling stressed, an advertisement for a product with a relaxing effect can be provided. Also, if the customer is happy, an advertisement that elicits even more positive emotions can be provided. Furthermore, the emotion estimation unit can calculate the customer's emotion score and dynamically change the advertisement content based on the score. For example, if the customer is excited, an energetic advertisement can be provided.

[0094] The advertisement distribution system may further include a device analysis unit that analyzes the status of the customer's device. The device analysis unit may, for example, analyze the remaining battery level and network connection status of the customer's device and provide advertisements in the optimal format. This makes it possible to provide advertisements that can be viewed in a short time when the battery level is low. Also, when the network connection is unstable, it is possible to provide advertisements that can be viewed with low bandwidth. Furthermore, the device analysis unit may also provide the optimal advertisement format depending on the screen size of the device. For example, it may be possible to provide advertisements that are suitable for the small screen of a smartphone.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The voice recognition unit recognizes the customer's voice using voice recognition technology. For example, the voice recognition unit analyzes the customer's voice data in real time and converts the customer's speech into text data. The voice recognition unit can also improve the quality of the voice data using noise reduction technology. Furthermore, the voice recognition unit can analyze the customer's speech with high accuracy using a voice recognition algorithm. Step 2: The trend analysis unit analyzes trends based on the customer's interests and needs recognized by the voice recognition unit. For example, the trend analysis unit extracts keywords from the customer's speech and identifies the customer's interests based on past data and trends. The trend analysis unit can also use data mining technology to analyze customer behavior patterns. Furthermore, the trend analysis unit can predict customer interests and needs using statistical methods. Step 3: The information generation unit generates information about new products and services based on the trends analyzed by the trend analysis unit. For example, the information generation unit references a database of product information and generates product information that matches the customer's interests. The information generation unit can also use a generation algorithm to generate advertising content that is optimal for the customer. Furthermore, the information generation unit can generate customized advertisements according to the customer's needs. Step 4: The distribution unit distributes the information generated by the information generation unit to the customer on hold in real time. For example, the distribution unit transmits the generated advertisement to the customer's device using a distribution protocol. The distribution unit can also distribute the advertisement in real time using technology to minimize latency. Furthermore, the distribution unit can distribute the advertisement in an optimal format depending on the customer's device.

[0097] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0098] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 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.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, a 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.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0138] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] 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.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0141] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0147] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0148] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0149] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0150] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0151] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0153] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0154] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0155] 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.

[0156] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0157] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0158] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0159] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0160] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0161] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0162] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0163] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0164] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a voice recognition unit that recognizes the voice of a customer using voice recognition technology; a trend analysis unit that analyzes trends based on the interests and needs of customers recognized by the voice recognition unit; an information generation unit that generates information on new products and services based on the trends analyzed by the trend analysis unit; a distribution unit that distributes the information generated by the information generation unit to customers whose calls are on hold in real time. A system characterized by:

2. The voice recognition unit Analyze not only specific keywords but also speaking style and tone from the customer's voice to identify the customer's interests and needs with greater precision 2. The system of claim 1.

3. The trend analysis unit The customer's purchasing history and web browsing history are also integrated to more accurately identify the customer's interests and needs.

2. The system of claim 1.

4. The information generation unit When generating information about new products and services, the company takes into account the customer's past feedback and reviews to provide more personalized information.

2. The system of claim 1.

5. The distribution unit During real-time delivery, analyze the current situation of the customer and provide advertisements accordingly.

2. The system of claim 1.

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