system

Abstract

The system according to this embodiment aims to analyze user usage data and customer information and propose effective cross-selling and upselling strategies. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects user usage data and customer information. The analysis unit analyzes the data collected by the collection unit to analyze the usage status of each service and customer behavior patterns. The proposal unit proposes effective cross-selling and upselling strategies based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that cross-sell and upsell strategies have not been sufficiently proposed by effectively utilizing user usage data and customer information.

[0005] The system according to the embodiment aims to analyze user usage data and customer information and propose effective cross-sell and upsell strategies.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects user usage data and customer information. The analysis unit analyzes the data collected by the data collection unit to analyze the usage status of each service and customer behavior patterns. The proposal unit proposes effective cross-selling and upselling strategies based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze user usage data and customer information and propose effective cross-selling and upselling strategies. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​analysis platform according to an embodiment of the present invention is a system that analyzes usage across the entire SoftBank Group and identifies which services will create the best synergy. This system collects user usage data and customer information, and the AI ​​analyzes it to propose optimal cross-sell and up-sell strategies. For example, when collecting user usage data and customer information, this includes usage history of each service, purchase history, and customer attribute information. Specifically, this includes usage history of electronic payment services and contract information for communication services. Next, the AI ​​analyzes the collected data. The AI ​​analyzes the usage status of each service and customer behavior patterns to identify which services will create synergy with other services. For example, if a user who frequently uses electronic payment services also uses communication services, a specific promotion can be proposed to that user. Furthermore, based on the analysis results, the AI ​​proposes optimal cross-sell and up-sell strategies. For example, possible strategies include proposing a new communication plan to a user who uses electronic payment services, or offering benefits for electronic payment services to a user who uses communication services. In this way, the AI ​​analysis platform utilizes user usage data and customer information to propose optimal cross-sell and up-sell strategies and maximize the synergy of services across the entire SoftBank Group. This allows the AI ​​analytics platform to leverage user usage data and customer information to propose optimal cross-sell and up-sell strategies.

[0029] The AI ​​analysis platform according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects user usage data and customer information. User usage data includes, but is not limited to, access logs, click history, and purchase history. Customer information includes, but is not limited to, age, gender, address, and purchase history. The data collection unit can collect, for example, usage history of electronic payment services and contract information for communication services. The analysis unit analyzes the data collected by the data collection unit to analyze the usage status of each service and customer behavior patterns. The analysis is performed using, for example, statistical analysis and machine learning algorithms, but is not limited to, such methods. The analysis unit can identify which services create synergies with other services using, for example, frequency analysis and sequence analysis. The proposal unit proposes optimal cross-sell and up-sell strategies based on the analysis results obtained by the analysis unit. The proposals include, for example, strategies such as suggesting related products and discount campaigns, but are not limited to, such methods. The proposal unit can, for example, propose a new communication plan to a user using an electronic payment service. Furthermore, the proposal unit can offer benefits related to electronic payment services to users of communication services. This allows the AI ​​analysis platform according to the embodiment to utilize user usage data and customer information to propose optimal cross-sell and up-sell strategies.

[0030] The proposal department can offer promotions for other related services to users who use a specific service multiple times within a certain period. For example, the proposal department might offer promotions for other related services to users who use a specific service three or more times within a month. Related services include, but are not limited to, products in the same category or complementary services. For example, the proposal department could offer promotions for communication services to users who frequently use electronic payment services. The proposal department could also offer benefits for electronic payment services to users who frequently use communication services. This allows for increased effectiveness of cross-selling and upselling by offering promotions for other related services to users who frequently use a specific service.

[0031] The data collection unit can collect service usage history, purchase history, and customer attribute information. For example, the data collection unit collects service usage history. Usage history includes, but is not limited to, access date and time, and frequency of use. The data collection unit also collects purchase history. Purchase history includes, but is not limited to, purchase date and time, purchased items, and purchase amount. Furthermore, the data collection unit collects customer attribute information. Customer attribute information includes, but is not limited to, age, gender, address, and occupation. By collecting service usage history, purchase history, and customer attribute information for each service, more detailed data analysis becomes possible.

[0032] The analytics department can analyze the usage status of each service and customer behavior patterns to identify which services create synergistic effects with other services. For example, the analytics department analyzes the usage status of each service. Usage status includes, but is not limited to, frequency of use, usage time, and location of use. The analytics department also analyzes customer behavior patterns. Behavior patterns include, but is not limited to, purchase frequency, purchase timing, and purchase location. For example, the analytics department can use statistical analysis and machine learning algorithms to identify which services create synergistic effects with other services. By analyzing the usage status of each service and customer behavior patterns and identifying services that create synergy, it is possible to propose optimal cross-selling and upselling strategies.

[0033] The proposal department can propose effective cross-sell and up-sell strategies based on the analysis results. For example, the proposal department can suggest related products based on the analysis results. Related products include, but are not limited to, products in the same category or complementary services. The proposal department can also propose discount campaigns. Discount campaigns include, but are not limited to, discounts on specific products or discounts for purchases made within a certain period. For example, the proposal department can propose new communication plans to users of electronic payment services. The proposal department can also offer benefits for electronic payment services to users of communication services. This enables effective proposals to users by suggesting optimal cross-sell and up-sell strategies based on the analysis results.

[0034] Furthermore, the AI ​​analysis platform includes a feedback unit that evaluates the effectiveness of suggestions and improves their accuracy. The feedback unit evaluates the effectiveness of suggestions and improves their accuracy. The effectiveness of suggestions includes, but is not limited to, increased sales and improved customer satisfaction. The feedback unit can evaluate the accuracy of suggestions based, for example, on the success rate of suggestions and customer reactions. This allows the system to be more effective by evaluating the effectiveness of suggestions and improving their accuracy.

[0035] Furthermore, the AI ​​analysis platform includes a notification unit that informs users of the most suitable promotions. These promotions include, but are not limited to, discount coupons and special offers. The notification unit can notify users of promotions via email or push notifications, for example. It can also notify users of promotions at the optimal time based on their behavior patterns and usage. This allows for increased effectiveness of recommendations by notifying users of the most suitable promotions.

[0036] The data collection unit can analyze a user's past usage history and select the optimal data collection method. The optimal data collection method includes, but is not limited to, surveys, log analysis, and sensor data collection. For example, the data collection unit prioritizes collecting data from services that the user has frequently used in the past. Furthermore, the data collection unit can collect data at specific time periods based on the user's past usage history. In addition, the data collection unit can analyze the user's past behavior patterns and select the most efficient data collection method. This allows for efficient data collection by analyzing the user's past usage history and selecting the optimal data collection method.

[0037] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. The user's current lifestyle includes, but is not limited to, work, family, and hobbies. Areas of interest include, but is not limited to, sports, music, and travel. For example, the data collection unit prioritizes collecting data in areas the user is currently interested in. Furthermore, the data collection unit can collect highly relevant data based on the user's lifestyle. In addition, the data collection unit can filter and collect optimal data considering the user's current activities. This allows for the collection of highly relevant data by filtering data based on the user's current lifestyle and areas of interest.

[0038] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. Geographical location information includes, but is not limited to, countries, regions, and cities. For example, if a user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also prioritize the collection of data from locations close to the user's current location. Furthermore, the data collection unit can collect the most relevant data based on the user's geographical location information. This allows for the priority collection of highly relevant data by considering the user's geographical location information.

[0039] The data collection unit can analyze users' social media activity and collect relevant data during data collection. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. For example, the data collection unit can collect relevant data based on information shared by users on social media. The data collection unit can also analyze users' social media activity patterns and collect the most relevant data. Furthermore, the data collection unit can prioritize the collection of data related to topics that users have shown interest in on social media. This allows for the efficient collection of relevant data by analyzing users' social media activity.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. Data importance includes, but is not limited to, business impact and customer interest. For example, the analysis unit performs a detailed analysis on high-importance data, and a simplified analysis on low-importance data. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail based on data importance.

[0041] The analysis unit can apply different analysis algorithms depending on the data category during analysis. Data categories include, but are not limited to, text data, numerical data, and image data. Analysis algorithms include, but are not limited to, regression analysis, clustering, and deep learning. For example, the analysis unit can apply a purchase behavior analysis algorithm to purchase history data. It can also apply a usage pattern analysis algorithm to usage history data. Furthermore, it can apply a customer segmentation algorithm to customer attribute data. By applying different analysis algorithms depending on the data category, more accurate data analysis becomes possible.

[0042] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. The data submission date includes, but is not limited to, the submission date and time. For example, the analysis unit prioritizes the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit can adjust the order of analysis based on the submission date. This enables efficient data analysis by prioritizing analysis based on the data submission date.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The relevance of the data includes, but is not limited to, common attributes and correlations. For example, the analysis unit prioritizes analyzing highly relevant data. It can also postpone analyzing less relevant data. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data.

[0044] The proposal department can adjust the level of detail in a proposal based on the importance of the product. Product importance includes, but is not limited to, factors such as sales contribution and customer interest. For example, the proposal department can provide detailed proposals for highly important products, and concise proposals for less important products. Furthermore, the proposal department can adjust the depth of the proposal according to the product's importance. This allows for more efficient proposals by adjusting the level of detail based on product importance.

[0045] The proposal unit can apply different proposal algorithms depending on the product category when making a proposal. Product categories include, but are not limited to, electronic devices, clothing, and food. Proposal algorithms include, but are not limited to, recommendation systems and collaborative filtering. For example, the proposal unit can apply a usage promotion proposal algorithm to electronic payment services. It can also apply a contract renewal proposal algorithm to communication services. Furthermore, it can apply a content proposal algorithm to entertainment services. By applying different proposal algorithms depending on the product category, more accurate proposals become possible.

[0046] The proposal department can prioritize proposals based on the timing of product submission. This timing includes, but is not limited to, the submission date and time. For example, the proposal department might prioritize the most recent products. It can also postpone older products. Furthermore, the proposal department can adjust the order of proposals based on their submission timing. This allows for more efficient proposals by prioritizing proposals based on their submission timing.

[0047] The proposal department can adjust the order of proposals based on the relevance of the products when making a proposal. This relevance includes, but is not limited to, common attributes and correlations. For example, the proposal department will prioritize proposing highly relevant products. Furthermore, it can postpone proposing less relevant products. In addition, the proposal department can adjust the order of proposals based on the relevance of the products. This allows for more efficient proposals by adjusting the order of proposals based on product relevance.

[0048] The feedback unit can select the optimal feedback method by referring to past feedback data when providing feedback. Past feedback data includes, but is not limited to, past evaluations, comments, and suggestions for improvement. For example, the feedback unit may prioritize providing feedback formats that the user has previously preferred. Furthermore, the feedback unit can select the optimal feedback method based on past feedback data. In addition, the feedback unit can analyze the user's past feedback history to provide the most effective feedback method. This allows for efficient feedback by selecting the optimal feedback method through the referencing of past feedback data.

[0049] The feedback unit can select the optimal feedback method when providing feedback, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, countries, regions, and cities. For example, if the user is in a specific region, the feedback unit will provide feedback relevant to that region. Furthermore, the feedback unit can prioritize providing feedback from locations close to the user's current location. In addition, the feedback unit can provide the most relevant feedback based on the user's geographical location information. This allows for efficient feedback by selecting the optimal feedback method while considering the user's geographical location information.

[0050] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. This past notification history includes, but is not limited to, the content of the notification, the timing of the notification, and the user's response. For example, the notification unit may prioritize providing notification formats that the user has previously preferred. Furthermore, the notification unit can select the optimal notification method based on the past notification history. In addition, the notification unit can analyze the user's past notification history and provide the most effective notification method. This allows for efficient notifications by selecting the optimal notification method based on the user's past notification history.

[0051] The notification unit can select the optimal notification method when sending a notification, taking into account the user's device information. Device information includes, but is not limited to, the device type, OS, and browser. For example, if the user is using a smartphone, the notification unit can provide a notification method adapted to the screen size. If the user is using a tablet, the notification unit can provide a notification method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the notification unit can provide a concise and highly visible notification method. This allows for efficient notifications by selecting the optimal notification method based on the user's device information.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The analysis unit can also adjust the level of detail of the analysis based on the importance of the data. For example, it can perform detailed analysis on data with high business impact and concise analysis on data of low importance. Furthermore, it can perform in-depth analysis on data of high customer interest and superficial analysis on data of low interest. By adjusting the level of detail of the analysis based on the importance of the data, efficient data analysis becomes possible.

[0054] The proposal department can also apply different proposal algorithms depending on the product category. For example, a usage promotion proposal algorithm can be applied to electronic devices, a trend proposal algorithm to clothing, a health proposal algorithm to food products, and a content proposal algorithm to entertainment services. By applying different proposal algorithms depending on the product category, more accurate proposals can be made.

[0055] The data collection unit can also prioritize the collection of highly relevant data by considering the user's geographical location. For example, if a user is in a specific region, it will prioritize the collection of data related to that region and data from locations close to the user's current location. It can also collect the most relevant data based on the user's geographical location. In this way, by considering the user's geographical location, it is possible to prioritize the collection of highly relevant data.

[0056] The feedback system can also select the optimal feedback method by referring to past feedback data. For example, it can prioritize providing feedback formats that users have previously preferred and select the most suitable feedback method based on past feedback data. Furthermore, it can analyze the user's past feedback history to provide the most effective feedback method. This allows for efficient feedback by selecting the optimal method through the referencing of past feedback data.

[0057] The notification unit can also select the optimal notification method by considering the user's device information. For example, if the user is using a smartphone, it can provide a notification method that matches the screen size; if using a tablet, it can provide a notification method optimized for larger screens. Furthermore, if using a smartwatch, it can provide a concise and highly visible notification method. This allows for efficient notifications by selecting the optimal notification method based on the user's device information.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The collection unit collects user usage data and customer information. User usage data includes access logs, click history, and purchase history. Customer information includes age, gender, address, and purchase history. The collection unit can also collect usage history of electronic payment services and contract information for communication services. Step 2: The analysis unit analyzes the data collected by the data collection unit to analyze the usage status of each service and customer behavior patterns. The analysis is performed using statistical analysis and machine learning algorithms. The analysis unit can use frequency analysis and sequence analysis to identify which services create synergies with other services. Step 3: Based on the analysis results obtained by the analysis department, the proposal department proposes the optimal cross-sell and up-sell strategies. The proposal may include strategies such as suggesting related products or discount campaigns. The proposal department may also propose new communication plans to users of electronic payment services or offer benefits related to electronic payment services to users of communication services.

[0060] (Example of form 2) The AI ​​analysis platform according to an embodiment of the present invention is a system that analyzes usage across the entire SoftBank Group and identifies which services will create the best synergy. This system collects user usage data and customer information, and the AI ​​analyzes it to propose optimal cross-sell and up-sell strategies. For example, when collecting user usage data and customer information, this includes usage history of each service, purchase history, and customer attribute information. Specifically, this includes usage history of electronic payment services and contract information for communication services. Next, the AI ​​analyzes the collected data. The AI ​​analyzes the usage status of each service and customer behavior patterns to identify which services will create synergy with other services. For example, if a user who frequently uses electronic payment services also uses communication services, a specific promotion can be proposed to that user. Furthermore, based on the analysis results, the AI ​​proposes optimal cross-sell and up-sell strategies. For example, possible strategies include proposing a new communication plan to a user who uses electronic payment services, or offering benefits for electronic payment services to a user who uses communication services. In this way, the AI ​​analysis platform utilizes user usage data and customer information to propose optimal cross-sell and up-sell strategies and maximize the synergy of services across the entire SoftBank Group. This allows the AI ​​analytics platform to leverage user usage data and customer information to propose optimal cross-sell and up-sell strategies.

[0061] The AI ​​analysis platform according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects user usage data and customer information. User usage data includes, but is not limited to, access logs, click history, and purchase history. Customer information includes, but is not limited to, age, gender, address, and purchase history. The data collection unit can collect, for example, usage history of electronic payment services and contract information for communication services. The analysis unit analyzes the data collected by the data collection unit to analyze the usage status of each service and customer behavior patterns. The analysis is performed using, for example, statistical analysis and machine learning algorithms, but is not limited to, such methods. The analysis unit can identify which services create synergies with other services using, for example, frequency analysis and sequence analysis. The proposal unit proposes optimal cross-sell and up-sell strategies based on the analysis results obtained by the analysis unit. The proposals include, for example, strategies such as suggesting related products and discount campaigns, but are not limited to, such methods. The proposal unit can, for example, propose a new communication plan to a user using an electronic payment service. Furthermore, the proposal unit can offer benefits related to electronic payment services to users of communication services. This allows the AI ​​analysis platform according to the embodiment to utilize user usage data and customer information to propose optimal cross-sell and up-sell strategies.

[0062] The proposal department can offer promotions for other related services to users who use a specific service multiple times within a certain period. For example, the proposal department might offer promotions for other related services to users who use a specific service three or more times within a month. Related services include, but are not limited to, products in the same category or complementary services. For example, the proposal department could offer promotions for communication services to users who frequently use electronic payment services. The proposal department could also offer benefits for electronic payment services to users who frequently use communication services. This allows for increased effectiveness of cross-selling and upselling by offering promotions for other related services to users who frequently use a specific service.

[0063] The data collection unit can collect service usage history, purchase history, and customer attribute information. For example, the data collection unit collects service usage history. Usage history includes, but is not limited to, access date and time, and frequency of use. The data collection unit also collects purchase history. Purchase history includes, but is not limited to, purchase date and time, purchased items, and purchase amount. Furthermore, the data collection unit collects customer attribute information. Customer attribute information includes, but is not limited to, age, gender, address, and occupation. By collecting service usage history, purchase history, and customer attribute information for each service, more detailed data analysis becomes possible.

[0064] The analytics department can analyze the usage status of each service and customer behavior patterns to identify which services create synergistic effects with other services. For example, the analytics department analyzes the usage status of each service. Usage status includes, but is not limited to, frequency of use, usage time, and location of use. The analytics department also analyzes customer behavior patterns. Behavior patterns include, but is not limited to, purchase frequency, purchase timing, and purchase location. For example, the analytics department can use statistical analysis and machine learning algorithms to identify which services create synergistic effects with other services. By analyzing the usage status of each service and customer behavior patterns and identifying services that create synergy, it is possible to propose optimal cross-selling and upselling strategies.

[0065] The proposal department can propose effective cross-sell and up-sell strategies based on the analysis results. For example, the proposal department can suggest related products based on the analysis results. Related products include, but are not limited to, products in the same category or complementary services. The proposal department can also propose discount campaigns. Discount campaigns include, but are not limited to, discounts on specific products or discounts for purchases made within a certain period. For example, the proposal department can propose new communication plans to users of electronic payment services. The proposal department can also offer benefits for electronic payment services to users of communication services. This enables effective proposals to users by suggesting optimal cross-sell and up-sell strategies based on the analysis results.

[0066] Furthermore, the AI ​​analysis platform includes a feedback unit that evaluates the effectiveness of suggestions and improves their accuracy. The feedback unit evaluates the effectiveness of suggestions and improves their accuracy. The effectiveness of suggestions includes, but is not limited to, increased sales and improved customer satisfaction. The feedback unit can evaluate the accuracy of suggestions based, for example, on the success rate of suggestions and customer reactions. This allows the system to be more effective by evaluating the effectiveness of suggestions and improving their accuracy.

[0067] Furthermore, the AI ​​analysis platform includes a notification unit that informs users of the most suitable promotions. These promotions include, but are not limited to, discount coupons and special offers. The notification unit can notify users of promotions via email or push notifications, for example. It can also notify users of promotions at the optimal time based on their behavior patterns and usage. This allows for increased effectiveness of recommendations by notifying users of the most suitable promotions.

[0068] Furthermore, the AI ​​analysis platform's data collection unit estimates the user's emotions and adjusts the timing of data collection based on those emotions. These emotions include, but are not limited to, joy, sadness, and anger. For example, if the user is stressed, the data collection unit will refrain from collecting data and collect it when the user is relaxed. Conversely, if the user is relaxed, the data collection unit will actively collect data to obtain more detailed information. Additionally, if the user is in a hurry, the data collection unit can collect minimal data to reduce the user's burden. This allows for efficient data collection by adjusting the timing of data collection according to the user's emotions, thereby reducing the user's burden.

[0069] The data collection unit can analyze a user's past usage history and select the optimal data collection method. The optimal data collection method includes, but is not limited to, surveys, log analysis, and sensor data collection. For example, the data collection unit prioritizes collecting data from services that the user has frequently used in the past. Furthermore, the data collection unit can collect data at specific time periods based on the user's past usage history. In addition, the data collection unit can analyze the user's past behavior patterns and select the most efficient data collection method. This allows for efficient data collection by analyzing the user's past usage history and selecting the optimal data collection method.

[0070] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. The user's current lifestyle includes, but is not limited to, work, family, and hobbies. Areas of interest include, but is not limited to, sports, music, and travel. For example, the data collection unit prioritizes collecting data in areas the user is currently interested in. Furthermore, the data collection unit can collect highly relevant data based on the user's lifestyle. In addition, the data collection unit can filter and collect optimal data considering the user's current activities. This allows for the collection of highly relevant data by filtering data based on the user's current lifestyle and areas of interest.

[0071] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. User emotions include, but are not limited to, joy, sadness, and anger. For example, if the user is stressed, the data collection unit will postpone collecting less important data. Conversely, if the user is relaxed, the data collection unit can prioritize collecting detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting only the most important data. This allows for efficient data collection by prioritizing data collection according to the user's emotions.

[0072] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. Geographical location information includes, but is not limited to, countries, regions, and cities. For example, if a user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also prioritize the collection of data from locations close to the user's current location. Furthermore, the data collection unit can collect the most relevant data based on the user's geographical location information. This allows for the priority collection of highly relevant data by considering the user's geographical location information.

[0073] The data collection unit can analyze users' social media activity and collect relevant data during data collection. Social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. For example, the data collection unit can collect relevant data based on information shared by users on social media. The data collection unit can also analyze users' social media activity patterns and collect the most relevant data. Furthermore, the data collection unit can prioritize the collection of data related to topics that users have shown interest in on social media. This allows for the efficient collection of relevant data by analyzing users' social media activity.

[0074] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. User emotions include, but are not limited to, joy, sadness, and anger. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is stressed, the analysis unit can provide concise and to-the-point analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results in a format that can be quickly understood. By adjusting the presentation of the analysis according to the user's emotions, the system can provide analysis results that are easy for the user to understand.

[0075] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. Data importance includes, but is not limited to, business impact and customer interest. For example, the analysis unit performs a detailed analysis on high-importance data, and a simplified analysis on low-importance data. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail based on data importance.

[0076] The analysis unit can apply different analysis algorithms depending on the data category during analysis. Data categories include, but are not limited to, text data, numerical data, and image data. Analysis algorithms include, but are not limited to, regression analysis, clustering, and deep learning. For example, the analysis unit can apply a purchase behavior analysis algorithm to purchase history data. It can also apply a usage pattern analysis algorithm to usage history data. Furthermore, it can apply a customer segmentation algorithm to customer attribute data. By applying different analysis algorithms depending on the data category, more accurate data analysis becomes possible.

[0077] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. These emotions include, but are not limited to, joy, sadness, and anger. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is stressed, it can provide concise and to-the-point analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results in a format that can be quickly understood. By adjusting the length of the analysis according to the user's emotions, the analysis results can be made easier for the user to understand.

[0078] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. The data submission date includes, but is not limited to, the submission date and time. For example, the analysis unit prioritizes the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit can adjust the order of analysis based on the submission date. This enables efficient data analysis by prioritizing analysis based on the data submission date.

[0079] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The relevance of the data includes, but is not limited to, common attributes and correlations. For example, the analysis unit prioritizes analyzing highly relevant data. It can also postpone analyzing less relevant data. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data.

[0080] The suggestion function can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. These emotions include, but are not limited to, joy, sadness, and anger. For example, if the user is relaxed, the suggestion function can provide detailed suggestions. If the user is stressed, it can provide concise and to-the-point suggestions. Furthermore, if the user is in a hurry, it can present suggestions in a format that can be quickly understood. By adjusting the presentation of suggestions according to the user's emotions, the system can provide suggestions that are easy for the user to understand.

[0081] The proposal department can adjust the level of detail in a proposal based on the importance of the product. Product importance includes, but is not limited to, factors such as sales contribution and customer interest. For example, the proposal department can provide detailed proposals for highly important products, and concise proposals for less important products. Furthermore, the proposal department can adjust the depth of the proposal according to the product's importance. This allows for more efficient proposals by adjusting the level of detail based on product importance.

[0082] The proposal unit can apply different proposal algorithms depending on the product category when making a proposal. Product categories include, but are not limited to, electronic devices, clothing, and food. Proposal algorithms include, but are not limited to, recommendation systems and collaborative filtering. For example, the proposal unit can apply a usage promotion proposal algorithm to electronic payment services. It can also apply a contract renewal proposal algorithm to communication services. Furthermore, it can apply a content proposal algorithm to entertainment services. By applying different proposal algorithms depending on the product category, more accurate proposals become possible.

[0083] The suggestion function can estimate the user's emotions and adjust the length of the suggestion based on those emotions. These emotions include, but are not limited to, joy, sadness, and anger. For example, if the user is relaxed, the suggestion function can provide a detailed suggestion. If the user is stressed, it can provide a concise and to-the-point suggestion. Furthermore, if the user is in a hurry, it can provide a suggestion in a format that can be quickly understood. By adjusting the length of the suggestion according to the user's emotions, the system can provide suggestions that are easy for the user to understand.

[0084] The proposal department can prioritize proposals based on the timing of product submission. This timing includes, but is not limited to, the submission date and time. For example, the proposal department might prioritize the most recent products. It can also postpone older products. Furthermore, the proposal department can adjust the order of proposals based on their submission timing. This allows for more efficient proposals by prioritizing proposals based on their submission timing.

[0085] The proposal department can adjust the order of proposals based on the relevance of the products when making a proposal. This relevance includes, but is not limited to, common attributes and correlations. For example, the proposal department will prioritize proposing highly relevant products. Furthermore, it can postpone proposing less relevant products. In addition, the proposal department can adjust the order of proposals based on the relevance of the products. This allows for more efficient proposals by adjusting the order of proposals based on product relevance.

[0086] The feedback unit can estimate the user's emotions and adjust the feedback method based on those emotions. These emotions include, but are not limited to, joy, sadness, and anger. For example, if the user is relaxed, the feedback unit can provide detailed feedback. If the user is stressed, it can provide concise and to-the-point feedback. Furthermore, if the user is in a hurry, it can provide feedback in a format that can be quickly understood. This allows the feedback method to be adjusted according to the user's emotions, making the feedback easier for the user to understand.

[0087] The feedback unit can select the optimal feedback method by referring to past feedback data when providing feedback. Past feedback data includes, but is not limited to, past evaluations, comments, and suggestions for improvement. For example, the feedback unit may prioritize providing feedback formats that the user has previously preferred. Furthermore, the feedback unit can select the optimal feedback method based on past feedback data. In addition, the feedback unit can analyze the user's past feedback history to provide the most effective feedback method. This allows for efficient feedback by selecting the optimal feedback method through the referencing of past feedback data.

[0088] The feedback unit can estimate the user's emotions and prioritize feedback based on those emotions. These emotions include, but are not limited to, joy, sadness, and anger. For example, if the user is relaxed, the feedback unit will prioritize providing detailed feedback. If the user is stressed, it will prioritize providing concise, to-the-point feedback. Furthermore, if the user is in a hurry, it will prioritize providing feedback in a format that can be quickly understood. This allows for efficient feedback by prioritizing feedback according to the user's emotions.

[0089] The feedback unit can select the optimal feedback method when providing feedback, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, countries, regions, and cities. For example, if the user is in a specific region, the feedback unit will provide feedback relevant to that region. Furthermore, the feedback unit can prioritize providing feedback from locations close to the user's current location. In addition, the feedback unit can provide the most relevant feedback based on the user's geographical location information. This allows for efficient feedback by selecting the optimal feedback method while considering the user's geographical location information.

[0090] The notification unit can estimate the user's emotions and adjust the notification method based on those emotions. These emotions include, but are not limited to, joy, sadness, and anger. For example, if the user is relaxed, the notification unit can provide a detailed notification. If the user is stressed, it can provide a concise and to-the-point notification. Furthermore, if the user is in a hurry, it can provide a notification in a format that can be quickly understood. By adjusting the notification method according to the user's emotions, notifications can be made easier for the user to understand.

[0091] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. This past notification history includes, but is not limited to, the content of the notification, the timing of the notification, and the user's response. For example, the notification unit may prioritize providing notification formats that the user has previously preferred. Furthermore, the notification unit can select the optimal notification method based on the past notification history. In addition, the notification unit can analyze the user's past notification history and provide the most effective notification method. This allows for efficient notifications by selecting the optimal notification method based on the user's past notification history.

[0092] The notification unit can estimate the user's emotions and prioritize notifications based on those emotions. These emotions include, but are not limited to, joy, sadness, and anger. For example, if the user is relaxed, the notification unit will prioritize detailed notifications. If the user is stressed, it will prioritize concise, to-the-point notifications. Furthermore, if the user is in a hurry, it will prioritize notifications in a format that can be quickly understood. This allows for efficient notifications by prioritizing them according to the user's emotions.

[0093] The notification unit can select the optimal notification method when sending a notification, taking into account the user's device information. Device information includes, but is not limited to, the device type, OS, and browser. For example, if the user is using a smartphone, the notification unit can provide a notification method adapted to the screen size. If the user is using a tablet, the notification unit can provide a notification method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the notification unit can provide a concise and highly visible notification method. This allows for efficient notifications by selecting the optimal notification method based on the user's device information. === Hard Collateral 1-1 === Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, feedback unit, notification unit, and emotion estimation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 38B of the smart device 14 to collect user usage data and customer information, and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes cross-sell and up-sell strategies based on the analysis results. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and evaluates the effectiveness of the proposal and improves its accuracy. The notification unit is implemented, for example, by the control unit 46A of the smart device 14, and notifies the user of the promotion. The emotion estimation unit estimates the user's emotions using the camera 42 and microphone 38B of the smart device 14, and adjusts the timing of data collection via the control unit 46A. === Hard Collateral 1-2 === Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, feedback unit, notification unit, and emotion estimation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect user usage data and customer information, and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and proposes cross-sell and up-sell strategies based on the analysis results. The feedback unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and evaluates the effectiveness of the proposal and improves its accuracy. The notification unit is implemented, for example, by the control unit 46A of the smart glasses 214, and notifies the user of promotions. The emotion estimation unit estimates the user's emotions using the camera 42 and microphone 238 of the smart glasses 214, and adjusts the timing of data collection via the control unit 46A. === Hard Collateral 1-3 === Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, feedback unit, notification unit, and sentiment estimation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect user usage data and customer information, and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes cross-sell and up-sell strategies based on the analysis results. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and evaluates the effectiveness of the proposal and improves its accuracy. The notification unit is implemented, for example, by the control unit 46A of the headset terminal 314, and notifies the user of the promotion. The emotion estimation unit estimates the user's emotions using, for example, the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A adjusts the timing of data collection. === Hard Collateral 1-4 === Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, feedback unit, notification unit, and emotion estimation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the robot 414 to collect user usage data and customer information, and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes cross-sell and up-sell strategies based on the analysis results. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and evaluates the effectiveness of the proposal and improves its accuracy. The notification unit is implemented, for example, by the control unit 46A of the robot 414, and notifies the user of the promotion. The emotion estimation unit estimates the user's emotions using the camera 42 and microphone 238 of the robot 414, and adjusts the timing of data collection via the control unit 46A.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The analysis unit can also estimate the user's emotions and determine the priority of analysis based on those emotions. For example, if the user is stressed, the analysis of less important data will be postponed, while if the user is relaxed, detailed analysis will be prioritized. Furthermore, if the user is in a hurry, only the most important data can be quickly analyzed. This allows for efficient data analysis by adjusting the analysis priority according to the user's emotions.

[0096] The suggestion function can also estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is relaxed, it can provide detailed suggestions; if they are stressed, it can provide concise suggestions. Furthermore, if the user is in a hurry, it can provide suggestions in a format that can be quickly understood. By adjusting the timing of suggestions according to the user's emotions, it can provide the most suitable suggestions for the user.

[0097] The data collection unit can also estimate the user's emotions and adjust the data collection method based on that estimation. For example, if the user is relaxed, a detailed questionnaire can be conducted; if they are stressed, concise questions can be asked. Furthermore, if the user is in a hurry, only the most important data can be collected as a priority. This allows for efficient data collection by adjusting the data collection method according to the user's emotions.

[0098] The feedback unit can also estimate the user's emotions and adjust the content of the feedback based on those emotions. For example, if the user is relaxed, it can provide detailed feedback, while if they are stressed, it can provide concise feedback. Furthermore, if the user is in a hurry, it can provide feedback in a format that can be quickly understood. This allows the feedback to be adjusted according to the user's emotions, making it easier for the user to understand.

[0099] The notification unit can also estimate the user's emotions and adjust the content of the notification based on those emotions. For example, if the user is relaxed, it can provide a detailed notification, while if they are stressed, it can provide a concise notification. Furthermore, if the user is in a hurry, it can provide a notification in a format that can be quickly understood. This allows for notifications to be tailored to the user's emotions, making them easier for the user to understand.

[0100] The analysis unit can also adjust the level of detail of the analysis based on the importance of the data. For example, it can perform detailed analysis on data with high business impact and concise analysis on data of low importance. Furthermore, it can perform in-depth analysis on data of high customer interest and superficial analysis on data of low interest. By adjusting the level of detail of the analysis based on the importance of the data, efficient data analysis becomes possible.

[0101] The proposal department can also apply different proposal algorithms depending on the product category. For example, a usage promotion proposal algorithm can be applied to electronic devices, a trend proposal algorithm to clothing, a health proposal algorithm to food products, and a content proposal algorithm to entertainment services. By applying different proposal algorithms depending on the product category, more accurate proposals can be made.

[0102] The data collection unit can also prioritize the collection of highly relevant data by considering the user's geographical location. For example, if a user is in a specific region, it will prioritize the collection of data related to that region and data from locations close to the user's current location. It can also collect the most relevant data based on the user's geographical location. In this way, by considering the user's geographical location, it is possible to prioritize the collection of highly relevant data.

[0103] The feedback system can also select the optimal feedback method by referring to past feedback data. For example, it can prioritize providing feedback formats that users have previously preferred and select the most suitable feedback method based on past feedback data. Furthermore, it can analyze the user's past feedback history to provide the most effective feedback method. This allows for efficient feedback by selecting the optimal method through the referencing of past feedback data.

[0104] The notification unit can also select the optimal notification method by considering the user's device information. For example, if the user is using a smartphone, it can provide a notification method that matches the screen size; if using a tablet, it can provide a notification method optimized for larger screens. Furthermore, if using a smartwatch, it can provide a concise and highly visible notification method. This allows for efficient notifications by selecting the optimal notification method based on the user's device information.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The collection unit collects user usage data and customer information. User usage data includes access logs, click history, and purchase history. Customer information includes age, gender, address, and purchase history. The collection unit can also collect usage history of electronic payment services and contract information for communication services. Step 2: The analysis unit analyzes the data collected by the data collection unit to analyze the usage status of each service and customer behavior patterns. The analysis is performed using statistical analysis and machine learning algorithms. The analysis unit can use frequency analysis and sequence analysis to identify which services create synergies with other services. Step 3: Based on the analysis results obtained by the analysis department, the proposal department proposes the optimal cross-sell and up-sell strategies. The proposal may include strategies such as suggesting related products or discount campaigns. The proposal department may also propose new communication plans to users of electronic payment services or offer benefits related to electronic payment services to users of communication services.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0109] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0110] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0117] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0118] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0119] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0120] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0121] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0125] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0151] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0160] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0162] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0163] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0164] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0168] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0169] 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.

[0170] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0171] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0172] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0173] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0175] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0176] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0177] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0178] [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user usage data and customer information, An analysis unit analyzes the data collected by the aforementioned collection unit to analyze the usage status of each service and customer behavior patterns, The system includes a proposal unit that proposes effective cross-selling and up-selling strategies based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned proposal section is, Propose promotions for other related services to users who use a specific service multiple times within a certain period. The system according to feature 1.

3. The aforementioned collection unit is We collect service usage history, purchase history, and customer attribute information. The system according to feature 1.

4. The aforementioned analysis unit, We analyze the usage patterns and customer behavior patterns of each service to identify which services can create synergistic effects with others. The system according to feature 1.

5. The aforementioned proposal section is, Based on the analysis results, we propose effective cross-selling and upselling strategies. The system according to feature 1.

6. It also includes a feedback section to evaluate the effectiveness of proposals and improve their accuracy. The system according to feature 1.

7. The aforementioned proposal section is, It features a notification unit that informs users of the most suitable promotions. The system according to feature 1.

8. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze the user's past usage history and select the optimal data collection method. The system according to feature 1.

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