system

Abstract

The system according to the embodiment aims to protect privacy while utilizing location information. [Solution] A system according to an embodiment includes a collection unit, a generation unit, a mixing unit, and an analysis unit. The collection unit collects location information based on a user's behavior history. The generation unit generates a behavior path based on the location information collected by the collection unit. The mixing unit mixes the behavior path generated by the generation unit with an actual behavior path. The analysis unit analyzes the data generated by the mixing unit.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately protect privacy when utilizing location information, and there is a risk that individuals may be identified.

[0005] The system according to the embodiment aims to protect privacy while utilizing location information. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, a mixing unit, and an analysis unit. The collection unit collects location information based on a user's behavior history. The generation unit generates a behavior path based on the location information collected by the collection unit. The mixing unit mixes the behavior path generated by the generation unit with an actual behavior path. The analysis unit analyzes the data generated by the mixing unit. [Effects of the Invention]

[0007] The system according to the embodiment can protect privacy while utilizing location information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A system according to an embodiment of the present invention is a system for performing analysis while ensuring privacy in geotargeting using location information. This system collects location information based on a user's behavioral history and mixes in statistically indistinguishable behavioral paths, making it difficult to identify individuals. This allows for analysis based on location information while ensuring privacy. This system collects location information based on a user's behavioral history and mixes in randomly generated behavioral paths, allowing for analysis while ensuring privacy. For example, in geotargeting, it is possible to analyze a user's behavioral patterns and deliver optimal advertisements. Furthermore, analysis utilizing location information is possible in fields such as urban planning and traffic management.

[0029] A geotargeting system according to an embodiment includes a collection unit, a generation unit, a mixing unit, and an analysis unit. The collection unit collects location information based on a user's behavioral history. For example, the collection unit can collect detailed data such as places visited by the user and travel routes. The collection unit can collect location information using GPS data, Wi-Fi location information, beacon data, etc. The generation unit generates a behavioral path based on the location information collected by the collection unit. For example, the generation unit can add a randomly generated behavioral path. The generation unit can generate a behavioral path using a shortest path algorithm, a random walk algorithm, or the like. The mixing unit mixes the behavioral path generated by the generation unit with an actual behavioral path. For example, the mixing unit can add a randomly generated behavioral path to the actual behavioral path. The mixing unit can set parameters to provide randomness. The analysis unit analyzes the data generated by the mixing unit. For example, the analysis unit can include an evaluation index for balancing privacy protection and analysis accuracy. As a result, the geotargeting system according to the embodiment can collect location information based on a user's behavioral history and mix it with randomly generated behavioral paths, thereby performing analysis while ensuring privacy.

[0030] The generation unit may include an algorithm for generating a behavioral path. The generation unit may generate a behavioral path using, for example, a shortest path algorithm. For example, the generation unit may calculate the shortest path from locations visited by the user and generate the path as the behavioral path. The generation unit may also generate a behavioral path using a random walk algorithm. For example, the generation unit may randomly select a next location to visit from locations visited by the user and generate the path as the behavioral path. Furthermore, the generation unit may also generate a behavioral path using a Markov model. For example, the generation unit may probabilistically select a next location to visit based on the user's past behavior history and generate the path as the behavioral path. This allows for the generation of realistic behavioral paths, thereby providing more reliable data.

[0031] The mixing unit can set parameters. The mixing unit can set parameters to, for example, impart randomness. For example, the mixing unit can adjust the number of activity routes to be generated. The mixing unit can also adjust the distance of the activity route to be generated. Furthermore, the mixing unit can randomly select visited locations for the activity route to be generated. This makes it difficult to identify individuals by imparting randomness.

[0032] The analysis unit can be provided with an index for evaluating privacy protection and analysis accuracy. The analysis unit is provided with, for example, an evaluation index for balancing privacy protection and analysis accuracy. For example, when prioritizing privacy protection, the analysis unit can set an evaluation index by slightly sacrificing analysis accuracy. Furthermore, when prioritizing analysis accuracy, the analysis unit can set an evaluation index by slightly sacrificing privacy protection. Furthermore, the analysis unit can set an appropriate evaluation index to balance privacy protection and analysis accuracy. This enables appropriate analysis by balancing privacy protection and analysis accuracy.

[0033] The collection unit can collect data on places visited by the user and routes traveled. The collection unit collects, for example, detailed data on places visited by the user and routes traveled. For example, the collection unit can use GPS data to identify places visited by the user and collect that data. The collection unit can also use Wi-Fi location information to identify routes traveled by the user and collect that data. Furthermore, the collection unit can use beacon data to identify the length of time the user stayed and collect that data. By collecting detailed data, more accurate analysis is possible.

[0034] The generation unit can generate a behavioral route that combines location information. The generation unit generates, for example, a behavioral route that combines multiple pieces of location information. For example, the generation unit can generate a new behavioral route by combining location information of multiple stores visited by the user. The generation unit can also generate a behavioral route that combines multiple pieces of location information based on the user's travel route. Furthermore, the generation unit can generate a behavioral route that combines multiple pieces of location information, taking into account the user's stay time. In this way, by combining multiple pieces of location information, it becomes more difficult to identify an individual.

[0035] The mixing unit can mix actual behavioral paths and generated behavioral paths. The mixing unit, for example, mixes actual behavioral paths and randomly generated behavioral paths. For example, when a user moves from visited store A to store B, the mixing unit can add a behavioral path indicating that the user moved from store A to store C. Furthermore, when a user moves from visited store A to store B, the mixing unit can also add a behavioral path indicating that the user moved from store A to store D. Furthermore, when a user moves from visited store A to store B, the mixing unit can also add a behavioral path indicating that the user moved from store A to store E. In this way, by mixing actual behavioral paths and randomly generated behavioral paths, it becomes difficult to identify individuals.

[0036] The analysis unit can include application examples such as geotargeting, urban planning, and traffic management. For example, in geotargeting, the analysis unit can analyze user behavior patterns and deliver optimal advertisements. In urban planning, the analysis unit can also perform optimal urban design based on user behavior data. Furthermore, in traffic management, the analysis unit can also propose optimal transportation routes based on user movement data. This allows for support for a variety of application examples, enabling use in a wide range of fields.

[0037] The collection unit can analyze the user's past behavioral history and select a collection method. The collection unit, for example, analyzes the user's past behavioral history and selects the optimal collection method. For example, the collection unit can prioritize collection of places that the user frequently visits. The collection unit can also analyze the user's past movement patterns and select an efficient collection method. Furthermore, the collection unit can concentrate collection during specific time periods based on the user's behavioral history. This enables efficient data collection by selecting the optimal collection method based on the user's past behavioral history.

[0038] The collection unit can perform filtering based on the user's current activity status and areas of interest when collecting location information. For example, the collection unit performs filtering based on the user's current activity status and areas of interest when collecting location information. For example, when the user is shopping, the collection unit can prioritize collecting location information of stores visited. Furthermore, when the user is sightseeing, the collection unit can prioritize collecting location information of tourist spots. Furthermore, when the user is commuting, the collection unit can prioritize collecting location information of the commuting route. In this way, highly relevant data can be collected by filtering based on the user's activity status and areas of interest.

[0039] The collection unit can select a collection means according to the user's input method when collecting location information. For example, when collecting location information, the collection unit selects a collection means according to the user's input method. For example, when the user uses voice input, the collection unit can collect location information using voice recognition technology. Also, when the user uses text input, the collection unit can collect location information using text analysis technology. Furthermore, when the user uses image input, the collection unit can collect location information using image recognition technology. This enables efficient data collection by selecting the optimal collection means according to the user's input method.

[0040] When collecting location information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, when collecting location information, the collection unit prioritizes collecting highly relevant information based on the user's geographical location information. For example, when the user is in an urban area, the collection unit can prioritize collecting location information of surrounding stores and facilities. Furthermore, when the user is in a tourist destination, the collection unit can prioritize collecting location information of tourist spots. Furthermore, when the user is in a residential area, the collection unit can prioritize collecting location information of nearby public facilities. This enables efficient data collection by collecting highly relevant information taking into account the user's geographical location information.

[0041] The collection unit can analyze the user's social media activities and collect related information when collecting location information. For example, the collection unit can analyze the user's social media activities and collect related information when collecting location information. For example, the collection unit can collect location information of locations where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect location information of related locations. Furthermore, the collection unit can also collect location information of related locations by referring to the activities of the user's friends on social media. In this way, highly relevant information can be collected by analyzing the user's social media activities.

[0042] The collection unit can customize the collection method based on the user's past feedback when collecting location information. For example, the collection unit customizes the collection method based on the user's past feedback when collecting location information. For example, the collection unit can adjust the type of location information to be collected based on feedback provided by the user in the past. The collection unit can also adjust the collection frequency based on the user's past feedback. Furthermore, the collection unit can customize the collection means by referring to the user's past feedback. In this way, the collection method can be customized by reflecting the user's past feedback.

[0043] The generation unit can apply an algorithm for imitating a behavior pattern when generating a behavior route. The generation unit can apply an algorithm for imitating a behavior pattern when generating a behavior route. For example, the generation unit can generate a realistic behavior pattern based on the user's past behavior history. The generation unit can also generate a realistic behavior route by taking into account the user's movement speed and means of transportation. Furthermore, the generation unit can generate a realistic behavior route by taking into account the order of the places visited by the user. In this way, by imitating a realistic behavior pattern, a more reliable behavior route can be generated.

[0044] The generation unit can improve the accuracy of generation based on the user's past behavior history when generating a behavior route. For example, the generation unit can improve the accuracy of generation by referring to the user's past behavior history when generating a behavior route. For example, the generation unit can generate a highly accurate behavior route based on places the user has visited in the past. The generation unit can also analyze the user's past movement patterns and generate a highly accurate behavior route. Furthermore, the generation unit can generate an optimal behavior route from the user's past behavior history. In this way, a highly accurate behavior route can be generated by referring to the user's past behavior history.

[0045] The generation unit can generate different behavior patterns according to different time periods and days of the week when generating a behavior route. For example, the generation unit generates different behavior patterns based on different time periods and days of the week when generating a behavior route. For example, the generation unit can generate weekday behavior patterns and weekend behavior patterns separately. The generation unit can also generate daytime behavior patterns and nighttime behavior patterns separately. Furthermore, the generation unit can generate behavior patterns based on specific events or holidays. In this way, by generating behavior patterns based on different time periods and days of the week, it is possible to provide a more realistic behavior route.

[0046] The generation unit can determine the range of generation based on the geographical range of movement of the user when generating a movement route. For example, the generation unit can determine the range of generation based on the geographical range of movement of the user when generating a movement route. For example, if the user is in an urban area, the generation unit can generate a movement route within the city. Also, if the user is in the suburbs, the generation unit can generate a movement route in the suburbs. Furthermore, if the user is traveling, the generation unit can generate a movement route to a travel destination. In this way, by determining the range of generation based on the geographical range of movement of the user, it is possible to provide a more realistic movement route.

[0047] The generation unit can improve the accuracy of generation when generating a behavioral route by referring to the user's related activity data. For example, the generation unit can improve the accuracy of generation when generating a behavioral route by referring to the user's related activity data. For example, the generation unit can generate a highly accurate behavioral route based on the user's past activity data. The generation unit can also generate a highly accurate behavioral route by referring to the user's current activity data. Furthermore, the generation unit can analyze the user's related activity data and generate an optimal behavioral route. In this way, a highly accurate behavioral route can be generated by referring to the user's related activity data.

[0048] The generation unit can customize the generation pattern based on the user's lifestyle when generating a behavioral route. For example, the generation unit customizes the generation pattern based on the user's lifestyle when generating a behavioral route. For example, the generation unit can generate a daily behavioral route based on the user's lifestyle. The generation unit can also generate a behavioral route suitable for a specific time period according to the user's lifestyle. Furthermore, the generation unit can generate behavioral routes for weekends and holidays taking the user's lifestyle into consideration. In this way, by customizing the generation pattern according to the user's lifestyle, a more realistic behavioral route can be provided.

[0049] The mixing unit can set parameters when mixing. For example, the mixing unit sets parameters to impart randomness when mixing. For example, the mixing unit can adjust the number of activity routes to be generated. The mixing unit can also adjust the distance of the activity route to be generated. Furthermore, the mixing unit can randomly select visited locations for the activity route to be generated. This makes it difficult to identify individuals by imparting randomness.

[0050] The mixing unit can apply an algorithm for balancing the actual behavioral paths and the generated behavioral paths during mixing. The mixing unit can, for example, apply an algorithm for balancing the actual behavioral paths and the randomly generated behavioral paths during mixing. For example, the mixing unit can apply an algorithm for adjusting the ratio between the actual behavioral paths and the randomly generated behavioral paths. The mixing unit can also apply an algorithm for adjusting the distance between the actual behavioral paths and the randomly generated behavioral paths. Furthermore, the mixing unit can also apply an algorithm for adjusting the visited locations of the actual behavioral paths and the randomly generated behavioral paths. In this way, by balancing the actual behavioral paths and the randomly generated behavioral paths, it is possible to make it difficult to identify an individual.

[0051] The mixing unit can apply different mixing patterns based on different time periods or days of the week during mixing. For example, the mixing unit can apply different mixing patterns based on different time periods or days of the week during mixing. For example, the mixing unit can apply a weekday mixing pattern and a weekend mixing pattern separately. The mixing unit can also apply a daytime mixing pattern and a nighttime mixing pattern separately. Furthermore, the mixing unit can apply a mixing pattern based on a specific event or holiday. In this way, more realistic data can be provided by applying mixing patterns based on different time periods or days of the week.

[0052] The mixing unit can determine the range of mixing based on the geographical range of movement of the user when mixing. The mixing unit, for example, determines the range of mixing based on the geographical range of movement of the user when mixing. For example, when the user is in an urban area, the mixing unit can preferentially mix activity routes within the city. Furthermore, when the user is in the suburbs, the mixing unit can also preferentially mix activity routes in the suburbs. Furthermore, when the user is traveling, the mixing unit can also preferentially mix activity routes to the travel destination. In this way, by determining the range of mixing based on the geographical range of movement of the user, more realistic data can be provided.

[0053] The mixing unit can improve the accuracy of mixing by referring to the user's related activity data when mixing. The mixing unit can improve the accuracy of mixing by referring to the user's related activity data when mixing, for example. For example, the mixing unit can mix highly accurate activity routes based on the user's past activity data. The mixing unit can also mix highly accurate activity routes by referring to the user's current activity data. Furthermore, the mixing unit can analyze the user's related activity data and mix the optimal activity routes. In this way, by referring to the user's related activity data, it is possible to mix highly accurate activity routes.

[0054] The mixing unit can customize the mixing pattern according to the user's lifestyle when mixing. For example, the mixing unit customizes the mixing pattern based on the user's lifestyle when mixing. For example, the mixing unit can mix daily activity routes based on the user's lifestyle. The mixing unit can also mix activity routes suitable for specific time periods according to the user's lifestyle. Furthermore, the mixing unit can also mix activity routes for weekends and holidays, taking the user's lifestyle into consideration. In this way, by customizing the mixing pattern according to the user's lifestyle, more realistic data can be provided.

[0055] The analysis unit can apply an index for evaluating privacy protection and analysis accuracy during analysis. For example, the analysis unit applies an evaluation index for balancing privacy protection and analysis accuracy during analysis. For example, when prioritizing privacy protection, the analysis unit can set an evaluation index by slightly sacrificing analysis accuracy. Furthermore, when prioritizing analysis accuracy, the analysis unit can set an evaluation index by slightly sacrificing privacy protection. Furthermore, the analysis unit can set an appropriate evaluation index to balance privacy protection and analysis accuracy. This enables appropriate analysis by balancing privacy protection and analysis accuracy.

[0056] The analysis unit can apply different analysis methods based on different time periods or days of the week during analysis. For example, the analysis unit can apply different analysis methods based on different time periods or days of the week during analysis. For example, the analysis unit can apply a weekday analysis method and a weekend analysis method separately. The analysis unit can also apply a daytime analysis method and a nighttime analysis method separately. Furthermore, the analysis unit can apply an analysis method based on specific events or holidays. This allows for more realistic analysis by applying analysis methods based on different time periods or days of the week.

[0057] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past behavioral history. During analysis, the analysis unit, for example, improves the accuracy of the analysis by referring to the user's past behavioral history. For example, the analysis unit can perform a highly accurate analysis based on the user's past behavioral history. The analysis unit can also analyze the user's past movement patterns and perform a highly accurate analysis. Furthermore, the analysis unit can apply the optimal analysis method based on the user's past behavioral history. This makes it possible to perform a highly accurate analysis by referring to the user's past behavioral history.

[0058] The analysis unit can determine the scope of analysis based on the geographical range of the user during analysis. The analysis unit, for example, determines the scope of analysis based on the geographical range of the user during analysis. For example, if the user is in an urban area, the analysis unit can prioritize analyzing behavioral data within the city. Also, if the user is in the suburbs, the analysis unit can prioritize analyzing behavioral data in the suburbs. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing behavioral data at the travel destination. Thus, by determining the scope of analysis based on the geographical range of the user, more realistic analysis is possible.

[0059] The analysis unit can improve the accuracy of the analysis by referring to the user's related activity data during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's related activity data during analysis, for example. For example, the analysis unit can perform a highly accurate analysis based on the user's past activity data. The analysis unit can also perform a highly accurate analysis by referring to the user's current activity data. Furthermore, the analysis unit can analyze the user's related activity data and apply an optimal analysis method. In this way, by referring to the user's related activity data, a highly accurate analysis is possible.

[0060] The analysis unit can customize the analysis pattern based on the user's lifestyle during analysis. For example, the analysis unit customizes the analysis pattern based on the user's lifestyle during analysis. For example, the analysis unit can analyze daily behavioral data based on the user's lifestyle. The analysis unit can also apply an analysis method suitable for a specific time period according to the user's lifestyle. Furthermore, the analysis unit can analyze behavioral data on weekends and holidays, taking the user's lifestyle into consideration. This allows for more realistic analysis by customizing the analysis pattern according to the user's lifestyle.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] When collecting location information based on a user's behavior history, the collection unit can adjust the collection frequency taking into account the remaining battery power of the user's device. For example, when the remaining battery power of the user's device is low, the collection unit can reduce the collection frequency to reduce battery consumption. In addition, when the user's device is charging, the collection unit can increase the collection frequency to collect more detailed data. Furthermore, when the remaining battery power of the user's device is medium, the collection unit can set a balanced collection frequency. This allows for efficient data collection by adjusting the collection frequency according to the remaining battery power of the user's device.

[0063] When generating a behavioral route, the generation unit can generate a behavioral pattern related to a specific event or holiday based on the user's past behavior history. For example, the generation unit can generate a similar behavioral pattern based on places the user visited on a specific holiday in the past. The generation unit can also generate a behavioral route related to an event based on data on events the user participated in in the past. Furthermore, the generation unit can generate a behavioral pattern related to a specific season or holiday from the user's past behavior history. This makes it possible to provide a more realistic behavioral route by generating a behavioral pattern related to a specific event or holiday.

[0064] When mixing the generated activity path and the actual activity path, the mixing unit can adjust the mixing ratio based on the user's current activity status. For example, when the user is actively engaged, the mixing unit can increase the ratio of the actual activity path to be mixed. Also, when the user is resting, the mixing unit can increase the ratio of the generated activity path to be mixed. Furthermore, when the user is moving, the mixing unit can perform a balanced mixing of the actual activity path and the generated activity path. In this way, by adjusting the mixing ratio according to the user's current activity status, more appropriate data can be provided.

[0065] When analyzing the data generated by the mixing unit, the analysis unit can adjust the analysis method by taking into account the user's past feedback. For example, the analysis unit can apply an analysis method that emphasizes privacy protection based on feedback provided by the user in the past. The analysis unit can also select a method that emphasizes analysis accuracy based on the user's feedback. Furthermore, the analysis unit can apply a method that balances privacy protection and analysis accuracy by referring to the user's feedback. This enables more appropriate analysis by reflecting the user's past feedback.

[0066] When collecting location information based on a user's behavioral history, the collection unit can improve the collection accuracy by utilizing sensor information from the user's device. For example, the collection unit can use an acceleration sensor in the user's device to identify the user's movement speed and collect that data. The collection unit can also use a gyro sensor in the user's device to identify the user's movement direction and collect that data. Furthermore, the collection unit can use a barometric pressure sensor in the user's device to identify the user's altitude and collect that data. This makes it possible to collect data with higher accuracy by utilizing the device's sensor information.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The collection unit collects location information based on the user's behavioral history. For example, the collection unit can collect detailed data such as the places the user visited and the route they took. The collection unit can collect location information using GPS data, Wi-Fi location information, beacon data, etc. Step 2: The generation unit generates a behavioral path based on the location information collected by the collection unit. For example, the generation unit can add a randomly generated behavioral path. The generation unit can generate a behavioral path using a shortest path algorithm, a random walk algorithm, or the like. Step 3: The mixing unit mixes the behavioral path generated by the generation unit with the actual behavioral path. For example, the mixing unit can add a randomly generated behavioral path to the actual behavioral path. The mixing unit can set parameters to add randomness. Step 4: The analysis unit analyzes the data generated by the mixer. For example, the analysis unit may be provided with an evaluation metric for balancing privacy protection and analysis accuracy.

[0069] (Example 2) A system according to an embodiment of the present invention is a system for performing analysis while ensuring privacy in geotargeting using location information. This system collects location information based on a user's behavioral history and mixes in statistically indistinguishable behavioral paths, making it difficult to identify individuals. This allows for analysis based on location information while ensuring privacy. This system collects location information based on a user's behavioral history and mixes in randomly generated behavioral paths, allowing for analysis while ensuring privacy. For example, in geotargeting, it is possible to analyze a user's behavioral patterns and deliver optimal advertisements. Furthermore, analysis utilizing location information is possible in fields such as urban planning and traffic management.

[0070] A geotargeting system according to an embodiment includes a collection unit, a generation unit, a mixing unit, and an analysis unit. The collection unit collects location information based on a user's behavioral history. For example, the collection unit can collect detailed data such as places visited by the user and travel routes. The collection unit can collect location information using GPS data, Wi-Fi location information, beacon data, etc. The generation unit generates a behavioral path based on the location information collected by the collection unit. For example, the generation unit can add a randomly generated behavioral path. The generation unit can generate a behavioral path using a shortest path algorithm, a random walk algorithm, or the like. The mixing unit mixes the behavioral path generated by the generation unit with an actual behavioral path. For example, the mixing unit can add a randomly generated behavioral path to the actual behavioral path. The mixing unit can set parameters to provide randomness. The analysis unit analyzes the data generated by the mixing unit. For example, the analysis unit can include an evaluation index for balancing privacy protection and analysis accuracy. As a result, the geotargeting system according to the embodiment can collect location information based on a user's behavioral history and mix it with randomly generated behavioral paths, thereby performing analysis while ensuring privacy.

[0071] The generation unit may include an algorithm for generating a behavioral path. The generation unit may generate a behavioral path using, for example, a shortest path algorithm. For example, the generation unit may calculate the shortest path from locations visited by the user and generate the path as the behavioral path. The generation unit may also generate a behavioral path using a random walk algorithm. For example, the generation unit may randomly select a next location to visit from locations visited by the user and generate the path as the behavioral path. Furthermore, the generation unit may also generate a behavioral path using a Markov model. For example, the generation unit may probabilistically select a next location to visit based on the user's past behavior history and generate the path as the behavioral path. This allows for the generation of realistic behavioral paths, thereby providing more reliable data.

[0072] The mixing unit can set parameters. The mixing unit can set parameters to, for example, impart randomness. For example, the mixing unit can adjust the number of activity routes to be generated. The mixing unit can also adjust the distance of the activity route to be generated. Furthermore, the mixing unit can randomly select visited locations for the activity route to be generated. This makes it difficult to identify individuals by imparting randomness.

[0073] The analysis unit can be provided with an index for evaluating privacy protection and analysis accuracy. The analysis unit is provided with, for example, an evaluation index for balancing privacy protection and analysis accuracy. For example, when prioritizing privacy protection, the analysis unit can set an evaluation index by slightly sacrificing analysis accuracy. Furthermore, when prioritizing analysis accuracy, the analysis unit can set an evaluation index by slightly sacrificing privacy protection. Furthermore, the analysis unit can set an appropriate evaluation index to balance privacy protection and analysis accuracy. This enables appropriate analysis by balancing privacy protection and analysis accuracy.

[0074] The collection unit can collect data on places visited by the user and routes traveled. The collection unit collects, for example, detailed data on places visited by the user and routes traveled. For example, the collection unit can use GPS data to identify places visited by the user and collect that data. The collection unit can also use Wi-Fi location information to identify routes traveled by the user and collect that data. Furthermore, the collection unit can use beacon data to identify the length of time the user stayed and collect that data. By collecting detailed data, more accurate analysis is possible.

[0075] The generation unit can generate a behavioral route that combines location information. The generation unit generates, for example, a behavioral route that combines multiple pieces of location information. For example, the generation unit can generate a new behavioral route by combining location information of multiple stores visited by the user. The generation unit can also generate a behavioral route that combines multiple pieces of location information based on the user's travel route. Furthermore, the generation unit can generate a behavioral route that combines multiple pieces of location information, taking into account the user's stay time. In this way, by combining multiple pieces of location information, it becomes more difficult to identify an individual.

[0076] The mixing unit can mix actual behavioral paths and generated behavioral paths. The mixing unit, for example, mixes actual behavioral paths and randomly generated behavioral paths. For example, when a user moves from visited store A to store B, the mixing unit can add a behavioral path indicating that the user moved from store A to store C. Furthermore, when a user moves from visited store A to store B, the mixing unit can also add a behavioral path indicating that the user moved from store A to store D. Furthermore, when a user moves from visited store A to store B, the mixing unit can also add a behavioral path indicating that the user moved from store A to store E. In this way, by mixing actual behavioral paths and randomly generated behavioral paths, it becomes difficult to identify individuals.

[0077] The analysis unit can include application examples such as geotargeting, urban planning, and traffic management. For example, in geotargeting, the analysis unit can analyze user behavior patterns and deliver optimal advertisements. In urban planning, the analysis unit can also perform optimal urban design based on user behavior data. Furthermore, in traffic management, the analysis unit can also propose optimal transportation routes based on user movement data. This allows for support for a variety of application examples, enabling use in a wide range of fields.

[0078] The collection unit can estimate the user's emotions and adjust the timing of collecting location information based on the user's emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of collecting location information based on the user's emotions. For example, the collection unit can periodically collect location information when the user is relaxed. Furthermore, the collection unit can reduce the collection frequency to prioritize privacy when the user is feeling stressed. Furthermore, the collection unit can collect location information in real time when the user is in a hurry. This enables privacy-focused collection by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] The collection unit can analyze the user's past behavioral history and select a collection method. The collection unit, for example, analyzes the user's past behavioral history and selects the optimal collection method. For example, the collection unit can prioritize collection of places that the user frequently visits. The collection unit can also analyze the user's past movement patterns and select an efficient collection method. Furthermore, the collection unit can concentrate collection during specific time periods based on the user's behavioral history. This enables efficient data collection by selecting the optimal collection method based on the user's past behavioral history.

[0080] The collection unit can perform filtering based on the user's current activity status and areas of interest when collecting location information. For example, the collection unit performs filtering based on the user's current activity status and areas of interest when collecting location information. For example, when the user is shopping, the collection unit can prioritize collecting location information of stores visited. Furthermore, when the user is sightseeing, the collection unit can prioritize collecting location information of tourist spots. Furthermore, when the user is commuting, the collection unit can prioritize collecting location information of the commuting route. In this way, highly relevant data can be collected by filtering based on the user's activity status and areas of interest.

[0081] The collection unit can select a collection means according to the user's input method when collecting location information. For example, when collecting location information, the collection unit selects a collection means according to the user's input method. For example, when the user uses voice input, the collection unit can collect location information using voice recognition technology. Also, when the user uses text input, the collection unit can collect location information using text analysis technology. Furthermore, when the user uses image input, the collection unit can collect location information using image recognition technology. This enables efficient data collection by selecting the optimal collection means according to the user's input method.

[0082] The collection unit can estimate the user's emotions and determine the priority of location information to be collected based on the user's emotions. The collection unit, for example, estimates the user's emotions and determines the priority of location information to be collected based on the user's emotions. For example, when the user is relaxed, the collection unit can collect all location information equally. Furthermore, when the user is stressed, the collection unit can limit the location information to be collected with an emphasis on privacy. Furthermore, when the user is in a hurry, the collection unit can prioritize the collection of important location information. In this way, by determining the priority of location information to be collected according to the user's emotions, collection with an emphasis on privacy is possible. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] When collecting location information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, when collecting location information, the collection unit prioritizes collecting highly relevant information based on the user's geographical location information. For example, when the user is in an urban area, the collection unit can prioritize collecting location information of surrounding stores and facilities. Furthermore, when the user is in a tourist destination, the collection unit can prioritize collecting location information of tourist spots. Furthermore, when the user is in a residential area, the collection unit can prioritize collecting location information of nearby public facilities. This enables efficient data collection by collecting highly relevant information taking into account the user's geographical location information.

[0084] The collection unit can analyze the user's social media activities and collect related information when collecting location information. For example, the collection unit can analyze the user's social media activities and collect related information when collecting location information. For example, the collection unit can collect location information of locations where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect location information of related locations. Furthermore, the collection unit can also collect location information of related locations by referring to the activities of the user's friends on social media. In this way, highly relevant information can be collected by analyzing the user's social media activities.

[0085] The collection unit can customize the collection method based on the user's past feedback when collecting location information. For example, the collection unit customizes the collection method based on the user's past feedback when collecting location information. For example, the collection unit can adjust the type of location information to be collected based on feedback provided by the user in the past. The collection unit can also adjust the collection frequency based on the user's past feedback. Furthermore, the collection unit can customize the collection means by referring to the user's past feedback. In this way, the collection method can be customized by reflecting the user's past feedback.

[0086] The generation unit can estimate the user's emotions and adjust the pattern of the behavioral path to be generated based on the user's emotions. The generation unit, for example, estimates the user's emotions and adjusts the pattern of the behavioral path to be generated based on the user's emotions. For example, if the user is relaxed, the generation unit can generate a leisurely behavioral path. Also, if the user is in a hurry, the generation unit can generate a behavioral path that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a visually stimulating behavioral path. In this way, by adjusting the pattern of the behavioral path according to the user's emotions, a more appropriate behavioral path can be generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The generation unit can apply an algorithm for imitating a behavior pattern when generating a behavior route. The generation unit can apply an algorithm for imitating a behavior pattern when generating a behavior route. For example, the generation unit can generate a realistic behavior pattern based on the user's past behavior history. The generation unit can also generate a realistic behavior route by taking into account the user's movement speed and means of transportation. Furthermore, the generation unit can generate a realistic behavior route by taking into account the order of the places visited by the user. In this way, by imitating a realistic behavior pattern, a more reliable behavior route can be generated.

[0088] The generation unit can improve the accuracy of generation based on the user's past behavior history when generating a behavior route. For example, the generation unit can improve the accuracy of generation by referring to the user's past behavior history when generating a behavior route. For example, the generation unit can generate a highly accurate behavior route based on places the user has visited in the past. The generation unit can also analyze the user's past movement patterns and generate a highly accurate behavior route. Furthermore, the generation unit can generate an optimal behavior route from the user's past behavior history. In this way, a highly accurate behavior route can be generated by referring to the user's past behavior history.

[0089] The generation unit can generate different behavior patterns according to different time periods and days of the week when generating a behavior route. For example, the generation unit generates different behavior patterns based on different time periods and days of the week when generating a behavior route. For example, the generation unit can generate weekday behavior patterns and weekend behavior patterns separately. The generation unit can also generate daytime behavior patterns and nighttime behavior patterns separately. Furthermore, the generation unit can generate behavior patterns based on specific events or holidays. In this way, by generating behavior patterns based on different time periods and days of the week, it is possible to provide a more realistic behavior route.

[0090] The generation unit can estimate the user's emotions and adjust the length of the generated behavioral path based on the user's emotions. The generation unit, for example, estimates the user's emotions and adjusts the length of the generated behavioral path based on the user's emotions. For example, the generation unit can generate a longer behavioral path when the user is relaxed. The generation unit can also generate a shorter behavioral path when the user is in a hurry. Furthermore, the generation unit can generate a visually stimulating behavioral path when the user is excited. In this way, by adjusting the length of the behavioral path according to the user's emotions, a more appropriate behavioral path can be generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] The generation unit can determine the range of generation based on the geographical range of movement of the user when generating a movement route. For example, the generation unit can determine the range of generation based on the geographical range of movement of the user when generating a movement route. For example, if the user is in an urban area, the generation unit can generate a movement route within the city. Also, if the user is in the suburbs, the generation unit can generate a movement route in the suburbs. Furthermore, if the user is traveling, the generation unit can generate a movement route to a travel destination. In this way, by determining the range of generation based on the geographical range of movement of the user, it is possible to provide a more realistic movement route.

[0092] The generation unit can improve the accuracy of generation when generating a behavioral route by referring to the user's related activity data. For example, the generation unit can improve the accuracy of generation when generating a behavioral route by referring to the user's related activity data. For example, the generation unit can generate a highly accurate behavioral route based on the user's past activity data. The generation unit can also generate a highly accurate behavioral route by referring to the user's current activity data. Furthermore, the generation unit can analyze the user's related activity data and generate an optimal behavioral route. In this way, a highly accurate behavioral route can be generated by referring to the user's related activity data.

[0093] The generation unit can customize the generation pattern based on the user's lifestyle when generating a behavioral route. For example, the generation unit customizes the generation pattern based on the user's lifestyle when generating a behavioral route. For example, the generation unit can generate a daily behavioral route based on the user's lifestyle. The generation unit can also generate a behavioral route suitable for a specific time period according to the user's lifestyle. Furthermore, the generation unit can generate behavioral routes for weekends and holidays taking the user's lifestyle into consideration. In this way, by customizing the generation pattern according to the user's lifestyle, a more realistic behavioral route can be provided.

[0094] The mixing unit can estimate the user's emotions and adjust the proportion of action paths to be mixed based on the user's emotions. The mixing unit, for example, estimates the user's emotions and adjusts the proportion of action paths to be mixed based on the user's emotions. For example, if the user is relaxed, the mixing unit can evenly mix actual action paths and randomly generated action paths. Also, if the user is stressed, the mixing unit can increase the proportion of actual action paths to be mixed. Furthermore, if the user is in a hurry, the mixing unit can increase the proportion of randomly generated action paths to be mixed. This allows for adjusting the proportion of action paths to be mixed according to the user's emotions, thereby providing more appropriate data. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] The mixing unit can set parameters when mixing. For example, the mixing unit sets parameters to impart randomness when mixing. For example, the mixing unit can adjust the number of activity routes to be generated. The mixing unit can also adjust the distance of the activity route to be generated. Furthermore, the mixing unit can randomly select visited locations for the activity route to be generated. This makes it difficult to identify individuals by imparting randomness.

[0096] The mixing unit can apply an algorithm for balancing the actual behavioral paths and the generated behavioral paths during mixing. The mixing unit can, for example, apply an algorithm for balancing the actual behavioral paths and the randomly generated behavioral paths during mixing. For example, the mixing unit can apply an algorithm for adjusting the ratio between the actual behavioral paths and the randomly generated behavioral paths. The mixing unit can also apply an algorithm for adjusting the distance between the actual behavioral paths and the randomly generated behavioral paths. Furthermore, the mixing unit can also apply an algorithm for adjusting the visited locations of the actual behavioral paths and the randomly generated behavioral paths. In this way, by balancing the actual behavioral paths and the randomly generated behavioral paths, it is possible to make it difficult to identify an individual.

[0097] The mixing unit can apply different mixing patterns based on different time periods or days of the week during mixing. For example, the mixing unit can apply different mixing patterns based on different time periods or days of the week during mixing. For example, the mixing unit can apply a weekday mixing pattern and a weekend mixing pattern separately. The mixing unit can also apply a daytime mixing pattern and a nighttime mixing pattern separately. Furthermore, the mixing unit can apply a mixing pattern based on a specific event or holiday. In this way, more realistic data can be provided by applying mixing patterns based on different time periods or days of the week.

[0098] The mixing unit can estimate the user's emotions and adjust the display method of the action paths to be mixed based on the user's emotions. For example, the mixing unit can estimate the user's emotions and adjust the display method of the action paths to be mixed based on the user's emotions. For example, the mixing unit can provide a visually easy-to-understand display method when the user is relaxed. Furthermore, the mixing unit can provide a simple and highly visible display method when the user is stressed. Furthermore, the mixing unit can provide a display method that focuses on the main points when the user is in a hurry. This allows for adjusting the display method according to the user's emotions to provide more appropriate data. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0099] The mixing unit can determine the range of mixing based on the geographical range of movement of the user when mixing. The mixing unit, for example, determines the range of mixing based on the geographical range of movement of the user when mixing. For example, when the user is in an urban area, the mixing unit can preferentially mix activity routes within the city. Furthermore, when the user is in the suburbs, the mixing unit can also preferentially mix activity routes in the suburbs. Furthermore, when the user is traveling, the mixing unit can also preferentially mix activity routes to the travel destination. In this way, by determining the range of mixing based on the geographical range of movement of the user, more realistic data can be provided.

[0100] The mixing unit can improve the accuracy of mixing by referring to the user's related activity data when mixing. The mixing unit can improve the accuracy of mixing by referring to the user's related activity data when mixing, for example. For example, the mixing unit can mix highly accurate activity routes based on the user's past activity data. The mixing unit can also mix highly accurate activity routes by referring to the user's current activity data. Furthermore, the mixing unit can analyze the user's related activity data and mix the optimal activity routes. In this way, by referring to the user's related activity data, it is possible to mix highly accurate activity routes.

[0101] The mixing unit can customize the mixing pattern according to the user's lifestyle when mixing. For example, the mixing unit customizes the mixing pattern based on the user's lifestyle when mixing. For example, the mixing unit can mix daily activity routes based on the user's lifestyle. The mixing unit can also mix activity routes suitable for specific time periods according to the user's lifestyle. Furthermore, the mixing unit can also mix activity routes for weekends and holidays, taking the user's lifestyle into consideration. In this way, by customizing the mixing pattern according to the user's lifestyle, more realistic data can be provided.

[0102] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the display method of the analysis results based on the user's emotions. For example, the analysis unit can display detailed analysis results when the user is relaxed. The analysis unit can also display simple, highly visible analysis results when the user is stressed. Furthermore, the analysis unit can display analysis results that focus on the main points when the user is in a hurry. This allows for more appropriate analysis results to be provided by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] The analysis unit can apply an index for evaluating privacy protection and analysis accuracy during analysis. For example, the analysis unit applies an evaluation index for balancing privacy protection and analysis accuracy during analysis. For example, when prioritizing privacy protection, the analysis unit can set an evaluation index by slightly sacrificing analysis accuracy. Furthermore, when prioritizing analysis accuracy, the analysis unit can set an evaluation index by slightly sacrificing privacy protection. Furthermore, the analysis unit can set an appropriate evaluation index to balance privacy protection and analysis accuracy. This enables appropriate analysis by balancing privacy protection and analysis accuracy.

[0104] The analysis unit can apply different analysis methods based on different time periods or days of the week during analysis. For example, the analysis unit can apply different analysis methods based on different time periods or days of the week during analysis. For example, the analysis unit can apply a weekday analysis method and a weekend analysis method separately. The analysis unit can also apply a daytime analysis method and a nighttime analysis method separately. Furthermore, the analysis unit can apply an analysis method based on specific events or holidays. This allows for more realistic analysis by applying analysis methods based on different time periods or days of the week.

[0105] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past behavioral history. During analysis, the analysis unit, for example, improves the accuracy of the analysis by referring to the user's past behavioral history. For example, the analysis unit can perform a highly accurate analysis based on the user's past behavioral history. The analysis unit can also analyze the user's past movement patterns and perform a highly accurate analysis. Furthermore, the analysis unit can apply the optimal analysis method based on the user's past behavioral history. This makes it possible to perform a highly accurate analysis by referring to the user's past behavioral history.

[0106] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the user's emotions. The analysis unit, for example, estimates the user's emotions and prioritizes the analysis results based on the user's emotions. For example, when the user is relaxed, the analysis unit can display all analysis results evenly. Furthermore, when the user is stressed, the analysis unit can prioritize displaying important analysis results. Furthermore, when the user is in a hurry, the analysis unit can prioritize displaying analysis results that highlight the main points. This allows for more appropriate analysis results to be provided by prioritizing the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0107] The analysis unit can determine the scope of analysis based on the geographical range of the user during analysis. The analysis unit, for example, determines the scope of analysis based on the geographical range of the user during analysis. For example, if the user is in an urban area, the analysis unit can prioritize analyzing behavioral data within the city. Also, if the user is in the suburbs, the analysis unit can prioritize analyzing behavioral data in the suburbs. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing behavioral data at the travel destination. Thus, by determining the scope of analysis based on the geographical range of the user, more realistic analysis is possible.

[0108] The analysis unit can improve the accuracy of the analysis by referring to the user's related activity data during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's related activity data during analysis, for example. For example, the analysis unit can perform a highly accurate analysis based on the user's past activity data. The analysis unit can also perform a highly accurate analysis by referring to the user's current activity data. Furthermore, the analysis unit can analyze the user's related activity data and apply an optimal analysis method. In this way, by referring to the user's related activity data, a highly accurate analysis is possible.

[0109] The analysis unit can customize the analysis pattern based on the user's lifestyle during analysis. For example, the analysis unit customizes the analysis pattern based on the user's lifestyle during analysis. For example, the analysis unit can analyze daily behavioral data based on the user's lifestyle. The analysis unit can also apply an analysis method suitable for a specific time period according to the user's lifestyle. Furthermore, the analysis unit can analyze behavioral data on weekends and holidays, taking the user's lifestyle into consideration. This allows for more realistic analysis by customizing the analysis pattern according to the user's lifestyle. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned collection unit, generation unit, mixing unit, and analysis unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects location information using GPS data, Wi-Fi location information, and beacon data of the smart device 14. The generation unit is realized by the identification processing unit 290 of the data processing device 12 and randomly generates activity routes based on the collected location information. The mixing unit is realized by the identification processing unit 290 of the data processing device 12 and mixes the generated activity routes with actual activity routes. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the mixed data. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, generation unit, mixing unit, and analysis unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects location information using GPS data, Wi-Fi location information, and beacon data of the smart glasses 214. The generation unit is realized by the identification processing unit 290 of the data processing device 12 and randomly generates activity routes based on the collected location information. The mixing unit is realized by the identification processing unit 290 of the data processing device 12 and mixes the generated activity routes with actual activity routes. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the mixed data. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, generation unit, mixing unit, and analysis unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects location information using GPS data, Wi-Fi location information, and beacon data of the headset type terminal 314. The generation unit is realized by the identification processing unit 290 of the data processing device 12 and randomly generates activity routes based on the collected location information. The mixing unit is realized by the identification processing unit 290 of the data processing device 12 and mixes the generated activity routes with actual activity routes. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the mixed data. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, generation unit, mixing unit, and analysis unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects location information using GPS data, Wi-Fi location information, and beacon data of the robot 414. The generation unit is realized by the identification processing unit 290 of the data processing device 12 and randomly generates a movement path based on the collected location information. The mixing unit is realized by the identification processing unit 290 of the data processing device 12 and mixes the generated movement path with the actual movement path. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the mixed data.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] When collecting location information based on a user's behavior history, the collection unit can adjust the collection frequency taking into account the remaining battery power of the user's device. For example, when the remaining battery power of the user's device is low, the collection unit can reduce the collection frequency to reduce battery consumption. In addition, when the user's device is charging, the collection unit can increase the collection frequency to collect more detailed data. Furthermore, when the remaining battery power of the user's device is medium, the collection unit can set a balanced collection frequency. This allows for efficient data collection by adjusting the collection frequency according to the remaining battery power of the user's device.

[0112] When generating a behavioral route, the generation unit can generate a behavioral pattern related to a specific event or holiday based on the user's past behavior history. For example, the generation unit can generate a similar behavioral pattern based on places the user visited on a specific holiday in the past. The generation unit can also generate a behavioral route related to an event based on data on events the user participated in in the past. Furthermore, the generation unit can generate a behavioral pattern related to a specific season or holiday from the user's past behavior history. This makes it possible to provide a more realistic behavioral route by generating a behavioral pattern related to a specific event or holiday.

[0113] When mixing the generated activity path and the actual activity path, the mixing unit can adjust the mixing ratio based on the user's current activity status. For example, when the user is actively engaged, the mixing unit can increase the ratio of the actual activity path to be mixed. Also, when the user is resting, the mixing unit can increase the ratio of the generated activity path to be mixed. Furthermore, when the user is moving, the mixing unit can perform a balanced mixing of the actual activity path and the generated activity path. In this way, by adjusting the mixing ratio according to the user's current activity status, more appropriate data can be provided.

[0114] When analyzing the data generated by the mixing unit, the analysis unit can adjust the analysis method by taking into account the user's past feedback. For example, the analysis unit can apply an analysis method that emphasizes privacy protection based on feedback provided by the user in the past. The analysis unit can also select a method that emphasizes analysis accuracy based on the user's feedback. Furthermore, the analysis unit can apply a method that balances privacy protection and analysis accuracy by referring to the user's feedback. This enables more appropriate analysis by reflecting the user's past feedback.

[0115] When collecting location information based on a user's behavioral history, the collection unit can improve the collection accuracy by utilizing sensor information from the user's device. For example, the collection unit can use an acceleration sensor in the user's device to identify the user's movement speed and collect that data. The collection unit can also use a gyro sensor in the user's device to identify the user's movement direction and collect that data. Furthermore, the collection unit can use a barometric pressure sensor in the user's device to identify the user's altitude and collect that data. This makes it possible to collect data with higher accuracy by utilizing the device's sensor information.

[0116] The generation unit can estimate the user's emotions and adjust the pattern of the generated behavioral path based on the user's emotions. For example, if the user is relaxed, the generation unit can generate a leisurely behavioral path. If the user is in a hurry, the generation unit can also generate a behavioral path that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a visually stimulating behavioral path. In this way, by adjusting the behavioral path pattern according to the user's emotions, a more appropriate behavioral path can be generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0117] The mixing unit can estimate the user's emotions and adjust the proportion of action paths to be mixed based on the user's emotions. For example, if the user is relaxed, the mixing unit can evenly mix actual action paths and randomly generated action paths. Furthermore, if the user is stressed, the mixing unit can also increase the proportion of actual action paths to be mixed. Furthermore, if the user is in a hurry, the mixing unit can also increase the proportion of randomly generated action paths to be mixed. This allows for adjusting the proportion of action paths to be mixed according to the user's emotions, thereby providing more appropriate data. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0118] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the user's emotions. For example, the analysis unit can display detailed analysis results when the user is relaxed. Furthermore, the analysis unit can display simple, highly visible analysis results when the user is stressed. Furthermore, the analysis unit can display analysis results that focus on the main points when the user is in a hurry. This allows for more appropriate analysis results to be provided by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0119] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the user's emotions. For example, if the user is relaxed, the analysis unit can display all analysis results equally. Furthermore, if the user is feeling stressed, the analysis unit can prioritize displaying important analysis results. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying analysis results that focus on the main points. This allows for more appropriate analysis results to be provided by prioritizing the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0120] The collection unit can estimate the user's emotions and adjust the timing of collecting location information based on the user's emotions. For example, the collection unit can periodically collect location information when the user is relaxed. Furthermore, the collection unit can reduce the collection frequency to prioritize privacy when the user is stressed. Furthermore, the collection unit can collect location information in real time when the user is in a hurry. This allows for privacy-focused collection by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The collection unit collects location information based on the user's behavioral history. For example, the collection unit can collect detailed data such as the places the user visited and the route they took. The collection unit can collect location information using GPS data, Wi-Fi location information, beacon data, etc. Step 2: The generation unit generates a behavioral path based on the location information collected by the collection unit. For example, the generation unit can add a randomly generated behavioral path. The generation unit can generate a behavioral path using a shortest path algorithm, a random walk algorithm, or the like. Step 3: The mixing unit mixes the behavioral path generated by the generation unit with the actual behavioral path. For example, the mixing unit can add a randomly generated behavioral path to the actual behavioral path. The mixing unit can set parameters to add randomness. Step 4: The analysis unit analyzes the data generated by the mixer. For example, the analysis unit may be provided with an evaluation metric for balancing privacy protection and analysis accuracy.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 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.

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

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

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

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0137] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0146] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0166] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0167] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0168] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0170] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0178] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0179] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0180] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0184] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0185] 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.

[0186] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0187] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0188] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0189] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0191] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0192] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0193] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0194] [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects location information based on a user's behavior history; a generation unit that generates a movement path based on the location information collected by the collection unit; a mixing unit that mixes the activity path generated by the generation unit with an actual activity path; an analysis unit that analyzes the data generated by the mixing unit; Equipped with A system characterized by:

2. The generation unit Equipped with an algorithm for generating behavioral paths 2. The system of claim 1.

3. The mixing section Set parameters 2. The system of claim 1.

4. The analysis unit Equipped with metrics to evaluate privacy protection and analytical accuracy 2. The system of claim 1.

5. The collecting unit Collect data on the places users visit and the routes they take 2. The system of claim 1.

6. The generation unit Generate a route combining location information 2. The system of claim 1.

7. The mixing section Mixing real and generated paths 2. The system of claim 1.

8. The analysis unit Applications include geotargeting, urban planning, and traffic management 2. The system of claim 1.

Images