Information Processing Method, Information Processing Apparatus, and Program

Abstract

This information processing method: identifies, on the basis of gene analysis data of a user, a disease with which a user is likely to become ill; analyzes behavior history data of the user and operation history data of an apparatus; generates first lifestyle pattern data that indicates a lifestyle pattern of the user until the current point in time; executes, on the basis of the first lifestyle pattern data, standard lifestyle pattern data, and the operation history data, a simulation in which a digital twin of the user and a digital twin of the apparatus are operated in cyber space; generates, from the simulation execution results, second lifestyle pattern data in which a future lifestyle pattern of the user is predicted; and calculates, on the basis of the second lifestyle pattern data, a future illness risk of the user with respect to the identified disease.

Description

Information processing method, information processing device, and program

[0001] The present disclosure relates to a technology for simulating a user's lifestyle patterns using a digital twin.

[0002] In recent years, attention has been focused on technology that performs various simulations using digital twins, which are realistic representations of real-world objects or people in cyberspace. For example, Patent Literature 1 discloses a technology that generates a digital twin of a vehicle, executes one or more simulations based on the generated digital twin, and generates evaluation data that describes the price of an insurance policy for the vehicle based on the execution results of the one or more simulations.

[0003] Furthermore, with the recent development of genetic analysis technology, a technique has become known in which trait information such as a user's constitution is analyzed from the user's genetic information and the analysis results are notified to the user. For example, Patent Literature 2 discloses a technique for referencing the user's constitution information determined according to the user's genetic test results, generating an avatar image of the user having a form corresponding to the constitution information, and displaying the generated avatar image.

[0004] However, the above-mentioned conventional technology does not take into consideration the user's future risk of illness, and therefore further improvement is needed.

[0005] JP 2020-13557 A JP 2016-71721 A

[0006] The present disclosure has been made to solve the above-mentioned problems, and aims to provide a technology for predicting a user's future disease risk.

[0007] An information processing method according to one aspect of the present disclosure includes a computer: generating, in cyberspace, digital twins of a user and of devices installed in the user's residence based on real-world data; acquiring behavioral history data indicating the user's behavioral history and operation history data indicating the operation history of the devices; identifying a disease that the user may suffer from based on the user's genetic analysis data; analyzing the behavioral history data and the operation history data; generating first lifestyle pattern data that indicates the user's lifestyle pattern up to the present; running a simulation in cyberspace to operate the user's digital twin and the digital twin of the devices based on the first lifestyle pattern data, standard lifestyle pattern data that indicates a standard lifestyle pattern according to a future life stage, and the operation history data; generating second lifestyle pattern data that predicts the user's future lifestyle pattern from the results of running the simulation; calculating the user's future disease risk for the identified disease based on the second lifestyle pattern data; and outputting the disease risk.

[0008] According to the present disclosure, it is possible to predict a user's future disease risk.

[0009] 1 is an overall configuration diagram of an information processing system according to an embodiment of the present disclosure. FIG. 1 is a block diagram showing an example of the configuration of a server shown in FIG. 1. FIG. 2 is a block diagram showing an example of the configuration of a sensor device. FIG. 3 is a block diagram showing an example of the configuration of an appliance. FIG. 4 is a block diagram showing an example of the configuration of a terminal device. FIG. 5 is a sequence diagram showing data transmission and reception in a sensor device, appliance, and server. FIG. 6 is a flowchart showing an example of processing by the server shown in FIG. 1. FIG. 7 is a diagram showing an example of a digital twin of a residence. FIG. 8 is a diagram showing an example of a digital twin of a residence. FIG. 9 is a diagram showing an example of a digital twin of a region. FIG. 10 is an explanatory diagram of processing for generating first lifestyle pattern data. FIG. 11 is a diagram showing an example of the data configuration of lifestyle pattern data for each day. FIG. 12 is a diagram showing an example of the data configuration of multiple lifestyle pattern data. FIG. 13 is an explanatory diagram of a simulation. FIG. 14 is an explanatory diagram of processing for calculating future disease risk. FIG. 15 is a diagram showing a presentation screen. FIG. 16 is a diagram showing a presentation screen according to another example. FIG. 17 is a diagram showing a presentation screen according to yet another example.

[0010] (Findings underlying the present disclosure) In recent years, the speed and cost of technologies for analyzing human genes have been increasing. As a result, users can easily undergo genetic testing in the comfort of their own homes. Such genetic testing can determine whether a user has a constitution that makes them susceptible to developing a particular disease, such as a lifestyle-related disease.

[0011] However, just because a user has a predisposition to a particular disease does not necessarily mean that they will develop that disease. For example, by improving their future lifestyle patterns, it is possible to reduce the risk of developing a particular disease. To do this, it is effective to predict the user's future lifestyle patterns.

[0012] However, there was no technology that could predict a user's future lifestyle patterns and predict the user's future disease risk based on the predicted future lifestyle patterns and the results of genetic analysis.

[0013] For example, in the above-mentioned Patent Document 1, only a digital twin of a vehicle is generated, but not a digital twin of a user. In the above-mentioned Patent Document 2, only an avatar image having a form corresponding to the user's physical constitution information at the time of genetic testing is generated, but future disease risk is not predicted.

[0014] The present inventors have therefore discovered that if a digital twin of a user and devices in the user's home is generated in cyberspace (computer space) and the generated digital twin is operated in cyberspace, the user's future lifestyle pattern can be predicted. The present inventors have also discovered that if the predicted future lifestyle pattern and the user's genetic analysis results are used, the user's future disease risk can be predicted, and have come up with the following aspects.

[0015] An information processing method according to one aspect of the present disclosure includes a computer: generating, in cyberspace, digital twins of a user and of devices installed in the user's residence based on real-world data; acquiring behavioral history data indicating the user's behavioral history and operation history data indicating the operation history of the devices; identifying a disease that the user may suffer from based on the user's genetic analysis data; analyzing the behavioral history data and the operation history data; generating first lifestyle pattern data that indicates the user's lifestyle pattern up to the present; running a simulation in cyberspace to operate the user's digital twin and the digital twin of the devices based on the first lifestyle pattern data, standard lifestyle pattern data that indicates a standard lifestyle pattern according to a future life stage, and the operation history data; generating second lifestyle pattern data that predicts the user's future lifestyle pattern from the results of running the simulation; calculating the user's future disease risk for the identified disease based on the second lifestyle pattern data; and outputting the disease risk.

[0016] According to this configuration, diseases that the user may contract are identified from the user's genetic analysis data. First lifestyle pattern data indicating the user's lifestyle patterns up to the present are generated from the user's behavioral history data and the device's operation history data. A simulation is performed in cyberspace to operate the user's digital twin and the device's digital twin based on the generated first lifestyle pattern data, standard lifestyle pattern data corresponding to a future life stage, and the operation history data. Second lifestyle pattern data predicting the user's future lifestyle pattern is generated from the results of the simulation. A future disease risk of contracting the identified disease is calculated based on the generated second lifestyle pattern data, and the calculated disease risk is output. Therefore, this configuration can predict the disease risk of diseases that the user may contract in the future. Furthermore, by presenting the future disease risk to the user, the user can be given an opportunity to review their current lifestyle pattern. This allows the user to reduce their future disease risk.

[0017] In the above information processing method, the cyberspace may include a digital twin of the residence.

[0018] According to this configuration, since a digital twin of the residence is included, it is possible to simulate the user's behavior within the residence, thereby improving the accuracy of predictions of the user's future lifestyle patterns.

[0019] In the above information processing method, an improvement plan for the lifestyle pattern of the user may be generated based on the second lifestyle pattern data and the disease risk, and the output may further include outputting the improvement plan.

[0020] According to this configuration, a lifestyle pattern for reducing the risk of disease can be presented to the user because suggestions for improving the user's lifestyle pattern are output.

[0021] In the above information processing method, the improvement plan may include exercise information indicating exercise recommended to reduce the disease risk.

[0022] According to this configuration, it is possible to present to the user suitable exercises for reducing the risk of disease.

[0023] In the information processing method, the disease risk may be calculated by calculating the disease risk within one or more future periods.

[0024] According to this configuration, it is possible to present to the user the degree of risk of disease at any point in the future.

[0025] In the above information processing method, the disease may be a lifestyle-related disease.

[0026] According to this configuration, the disease risk of lifestyle-related diseases can be presented to the user.

[0027] In the information processing method, generating the second lifestyle pattern data may include predicting a lifestyle pattern for each day from the present to a predetermined time point in the future.

[0028] According to this configuration, future lifestyle patterns are predicted on a day-by-day basis, allowing for detailed prediction of future lifestyle patterns.

[0029] In the above information processing method, the real-world data may include attribute data of the user and location data of the device.

[0030] According to this configuration, the real-world data includes user attribute data and device location data, making it possible to accurately generate a user's digital twin and accurately position the device's digital twin.

[0031] In the above information processing method, the execution of the simulation may include operating the user's digital twin in cyberspace based on the first lifestyle pattern data and the standard lifestyle pattern data, and executing a simulation in which the device's digital twin is operated in cyberspace based on operation history data.

[0032] According to this configuration, a simulation is performed in which the user's digital twin is operated in cyberspace based on the first lifestyle pattern data and the standard lifestyle pattern data, and the digital twin of the device is operated in cyberspace based on the device's operation history data, thereby making it possible to accurately predict the user's future lifestyle patterns.

[0033] The present disclosure can also be realized as a program that causes a computer to execute each of the characteristic configurations included in such an information processing method, or as an information processing system operated by this program. Needless to say, such a computer program can be distributed on a computer-readable non-transitory recording medium such as a CD-ROM or via a communication network such as the Internet.

[0034] Note that each of the embodiments described below represents a specific example of the present disclosure. The numerical values, shapes, components, steps, and step orders shown in the following embodiments are merely examples and are not intended to limit the present disclosure. Furthermore, among the components in the following embodiments, components that are not described in the independent claims that represent the highest concept are described as optional components. Furthermore, in all of the embodiments, the respective contents can be combined.

[0035] 1 is an overall configuration diagram of an information processing system 1 according to an embodiment of the present disclosure. The information processing system 1 includes a server 10, a sensor device 20, equipment 30, and a terminal device 40. The server 10, the sensor device 20, the equipment 30, and the terminal device 40 are connected to each other so as to be able to communicate with each other via a network 50. The network 50 is, for example, a wide area communication network including the Internet and a mobile phone communication network. Furthermore, the network 50 may also include a local area network.

[0036] The server 10 is, for example, a cloud server configured with one or more computers. The server 10 receives sensing data from the sensor device 20. The server 10 receives operation data from the equipment 30. The server 10 transmits presentation data to the terminal device 40, the presentation data including future disease risks and lifestyle pattern improvement proposals for the disease of the user at risk.

[0037] The sensor device 20 detects sensing data required to detect the user's behavior. The sensor device 20 is, for example, a mobile terminal such as a smartwatch, a smartphone, or a tablet terminal. The sensing data includes, for example, user location data, user biometric data, user image data, and measurement time. The sensor device 20 may be a camera installed in a residence. Furthermore, the sensor device 20 may be an odor sensor installed in a residence.

[0038] The device 30 is an electrical device installed in the user's residence. The electrical device is, for example, a household electrical device such as an air conditioner, a cooking appliance such as an oven, a refrigerator, a washing machine, a television, a smart speaker, an audio device, a DVD recorder, or a Blu-ray recorder. For example, when the device 30 is turned on, the device 30 transmits operation data to the server 10 at a predetermined sampling period.

[0039] The terminal device 40 is a device that outputs the presentation data transmitted from the server 10. The terminal device 40 is, for example, a desktop computer installed in the user's home, a mobile terminal (smartphone, tablet terminal) carried by the user, or the like. The presentation data may be displayed on a device 30 having a display. When the terminal device 40 is configured as a mobile terminal, this mobile terminal may include the functions of the sensor device 20 and the terminal device 40.

[0040] Fig. 2 is a block diagram showing an example of the configuration of the server 10 shown in Fig. 1. The server 10 includes a communication unit 110, a processor 120, and a memory 130. The communication unit 110 is configured with a communication circuit that connects the server 10 to the network 50. The communication unit 110 receives sensing data from the sensor device 20, receives operation data from the equipment 30, and transmits presentation data to the terminal device 40.

[0041] The processor 120 is composed of a processor such as a CPU, and includes a digital twin generation unit 121, an acquisition unit 122, an identification unit 123, a first generation unit 124, a simulation execution unit 125, a second generation unit 126, a disease risk calculation unit 127, and an output unit 128. Each block included in the processor 120 may be realized by the CPU executing a predetermined program, or may be composed of a dedicated hardware circuit.

[0042] The digital twin generation unit 121 generates a digital twin of a user in cyberspace using user data. The user data includes user attribute data such as age, gender, height, and weight. This attribute data is basic data required to generate a digital twin of a user.

[0043] The digital twin generation unit 121 uses the device data to generate in cyberspace a digital twin of the device 30. The device data includes, for example, the type of device 30, data indicating the installation position of the device 30 in the home, data indicating the model of the device 30, and functions indicating the input / output relationship between operations input to the device and the output corresponding to those operations.

[0044] The digital twin generation unit 121 generates a digital twin of a residence in cyberspace using structural data that three-dimensionally represents the structure of the residence. The structural data is, for example, Computer-Aided-Design (CAD) data and Building Information Modeling (BIM) data. The structural data of a residence is data for reproducing a three-dimensional model of a real residence in cyberspace. The structural data of a residence includes structural data of the residence's exterior, floor plan, garden, etc. The digital twin generation unit 121 may also generate a digital twin of a certain area that includes the user's residence. In this case, the digital twin generation unit 121 may generate a digital twin of this area using the structural data of this area.

[0045] In addition, since Dymola, MapleSim, Simulink, etc. are known as software for generating digital twins, the digital twin generation unit 121 may generate digital twins using these software.

[0046] When the communication unit 110 receives sensing data transmitted from the sensor device 20, the acquisition unit 122 acquires the sensing data from the communication unit 110 and stores the acquired sensing data as behavior history data in the memory 130. The behavior history data is data in which, for example, the sensor value included in the sensing data, the type of the sensor device 20 that transmitted the sensing data, and a timestamp are associated with each other. The sensor value includes, for example, user position data, user biometric data, etc.

[0047] When the communication unit 110 receives operation data transmitted from the device 30, the acquisition unit 122 acquires the operation data from the communication unit 110 and stores the acquired operation data in the memory 130 as operation history data. The operation history data is, for example, data in which the operation values ​​indicated by the operation data, the type of device 30 that transmitted the operation data, and a timestamp are associated with each other. The operation values ​​include, for example, power on, power off, and setting contents. For example, in the case of an air conditioning device, the setting contents include the set temperature and the operation mode, such as cooling or heating.

[0048] The acquisition unit 122 acquires the behavior history data and the operation history data from the memory 130 when the simulation execution unit 125 executes a simulation.

[0049] The identification unit 123 acquires genetic analysis data from the memory 130 and identifies diseases for which the user is at risk based on the acquired genetic analysis data. The genetic analysis data includes disease-associated SNPs (Single Nucleotide Polymorphisms), which are SNPs associated with specific diseases, and the types of the disease-associated SNPs.

[0050] Human base sequences are 99.9% identical, with 0.1% differences present. These differences result in differences in appearance, abilities, constitution, and so on. When a difference in base sequence occurs at a frequency of 1% or more in a human population, the difference in base sequence is called a polymorphism. There are various types of polymorphisms, but a SNP is one in which one base is replaced with another. There are many SNPs, and it has been shown that certain SNPs are associated with specific diseases. Such SNPs are called disease-associated SNPs.

[0051] The SNP type is a combination of SNPs inherited from the father and SNPs inherited from the mother, such as AA, AG, and GG. This SNP type can be used to identify the user's risk of developing a certain disease in the future.

[0052] Therefore, the identification unit 123 identifies diseases that the user may suffer from based on the disease-associated SNPs and the types of disease-associated SNPs. Furthermore, the identification unit 123 identifies diseases that the user may suffer from and also identifies the disease risk. The disease risk indicates the probability of contracting a specific disease and is expressed as a numerical value between 0 and 100, for example. The specific disease is, for example, a lifestyle-related disease. Examples of lifestyle-related diseases include arteriosclerosis, hypertension, diabetes, osteoporosis, and dementia.

[0053] The server 10 may acquire the user's genetic analysis data in advance and store it in the memory 130. The genetic analysis data may be generated based on test results from an external institution, or may be measured at the user's home. Methods for measuring SNPs and SNP types include, for example, Restriction Fragment Length Polymorphism (RFLP), Single Strand Conformation Polymorphism (SSCP), TaqMan PCR, SNaP Shot, Invader, mass spectrometry, and methods using DNA microarrays.

[0054] The first generation unit 124 analyzes the behavior history data and operation history data acquired by the acquisition unit 122 from the memory 130, and generates first lifestyle pattern data indicating the user's lifestyle pattern up to the present. In the present embodiment, the first generation unit 124 generates lifestyle pattern data for each day during the period for which the behavior history data was acquired. For example, if the period for acquiring the behavior history data is five years, lifestyle pattern data for 365 days x five years is generated. The first generation unit 124 then generates the first lifestyle pattern data by grouping the lifestyle pattern data for each day, for example, by day of the week.

[0055] The simulation execution unit 125 operates the user's digital twin in cyberspace using the first lifestyle pattern data and standard lifestyle pattern data that indicates a standard lifestyle pattern according to a future life stage, and also performs a simulation to operate the digital twin of the device 30 in cyberspace based on the operation history data.

[0056] The standard lifestyle pattern data is data showing the lifestyle patterns of a typical person for each age. FIG. 14 is a diagram showing an example of a life stage. A life stage refers to a stage of life that changes with age. For example, the life stages include fetus, infant, elementary school student, junior high school student, high school student, working adult, and elderly.

[0057] For example, Japanese people graduate from university at 22, start working at 23, and retire at 65. As people get older, they tend to sleep less, eat less, and burn fewer calories for each activity due to a decrease in their basal metabolic rate.

[0058] Therefore, the standard lifestyle pattern data is composed of lifestyle pattern data of a general person created for each age group in consideration of such life stages. The standard lifestyle pattern data may be composed of lifestyle pattern data for each day of the week according to age, for example. Furthermore, the standard lifestyle pattern data may include a basal metabolic rate and a standard daily calorie intake according to age.

[0059] Since the first lifestyle pattern data described above indicates the user's lifestyle pattern up to the present, in order to predict the user's future lifestyle pattern, it is necessary to operate the user's digital twin in cyberspace according to the predicted future lifestyle pattern. Therefore, the simulation execution unit 125 uses the standard lifestyle pattern data when executing the simulation.

[0060] The simulation execution unit 125 executes a simulation during a simulation period, for example, from the present (the time of execution of the simulation) to a certain point in the future (for example, five years from now). For example, the simulation execution unit 125 may execute a simulation on a daily basis during the simulation period. For example, when executing a simulation for a certain day two years from now, the simulation execution unit 125 may modify the first lifestyle pattern data for the day of the week corresponding to that day using the standard lifestyle pattern data for the corresponding day of the week two years from now. Then, the simulation execution unit 125 may operate the user's digital twin in cyberspace using the modified first lifestyle pattern data.

[0061] Furthermore, the simulation execution unit 125 uses the operation history data compiled by day of the week to operate the digital twin of the device 30. For example, when executing a simulation for a certain day two years from now, the simulation execution unit 125 can operate the digital twin of the device 30 in cyberspace using the operation history data corresponding to that day of the week.

[0062] The second generation unit 126 generates second lifestyle pattern data that predicts the user's future lifestyle pattern from the results of the simulation. The second lifestyle pattern data is, for example, configured from lifestyle pattern data that chronologically lists the activities of the user's digital twin for each day of the simulation period. The lifestyle pattern data for each day is data in which the user's activities for each day are arranged in chronological order, such as sleeping from midnight to 6:00 and eating from 5:30 to 7:00. Here, similar to the first generation unit 124, the second generation unit 126 uses the behavior history data and operation history data included in the results of the simulation execution to identify the user's activities and record the identified activities in chronological order, thereby generating lifestyle pattern data for each day of the simulation period.

[0063] The disease risk calculation unit 127 calculates the user's future disease risk for the disease identified by the identification unit 123 based on the second lifestyle pattern data. For example, the disease risk calculation unit 127 identifies one or more candidate causes of the disease identified by the identification unit 123 by referring to a candidate cause database in which a plurality of diseases are previously associated with candidate causes that cause each disease. The disease risk calculation unit 127 then calculates evaluation values ​​of the one or more candidate causes of the identified disease from the second lifestyle pattern data, and calculates the future disease risk using the calculated evaluation values ​​of the one or more candidate causes.

[0064] Furthermore, the disease risk calculation unit 127 may calculate the disease risk within one or more future periods, such as one year, three years, or five years from the present.

[0065] Furthermore, the disease risk calculation unit 127 generates a lifestyle pattern improvement plan based on the evaluation value of the cause candidate.

[0066] The output unit 128 outputs the future disease risk calculated by the disease risk calculation unit 127. For example, the output unit 128 may generate presentation data including the future disease risk and transmit the presentation data to the terminal device 40 using the communication unit 110, thereby causing the presentation data to be output to the terminal device 40.

[0067] The memory 130 is configured with a non-volatile storage device such as a flash memory, and stores user data, behavior history data, device data, operation history data, standard life pattern data, structural data, gene analysis data, and a cause candidate database.

[0068] Next, a description will be given of the details of the sensor device 20. Fig. 3 is a block diagram showing an example of the configuration of the sensor device 20. The sensor device 20 includes a sensor unit 210, a control unit 220, and a communication unit 230.

[0069] The sensor unit 210 is composed of, for example, a GPS sensor, a biometric sensor, an image sensor, etc., and measures sensing data at a predetermined sampling period. The biometric sensor measures the user's biometric data. The biometric data includes heart rate, amount of exercise, calories burned, calories ingested, whether or not the user smokes, and amount of alcohol consumed. The biometric sensor is, for example, a heart rate sensor, an acceleration sensor, a gyro sensor, an image sensor, and an odor sensor. The GPS sensor measures the location data of the user carrying the sensor device 20. The heart rate sensor measures the user's heart rate. The acceleration sensor and three-axis gyro sensor measure the user's amount of exercise and calories burned. The image sensor measures the user's calorie intake and alcohol intake. The odor sensor detects the odor of tobacco.

[0070] The control unit 220 is configured with, for example, a processor such as a CPU, and is responsible for overall control of the sensor device 20. For example, the control unit 220 transmits sensing data measured by the sensor unit 210 at a predetermined sampling period to the server 10 using the communication unit 230.

[0071] The communication unit 230 is configured with a communication circuit that connects the sensor device 20 to the network 50. The communication unit 230 transmits the sensing data measured by the sensor unit 210 to the server 10 under the control of the control unit 220.

[0072] Next, a description will be given of the configuration of the device 30. Fig. 4 is a block diagram showing an example of the configuration of the device 30. The device 30 includes a sensor unit 310, a control unit 320, a communication unit 330, and an operation unit 340.

[0073] The sensor unit 310 differs depending on the type of device 30. For example, if the device 30 is an air conditioner, the sensor unit 310 includes a temperature sensor that measures the temperature of the surrounding environment and a temperature sensor that measures the temperature of a refrigerant. If the device 30 is a cooking appliance or a refrigerator, the sensor unit 310 includes a temperature sensor that measures the temperature inside the appliance.

[0074] The control unit 320 is configured with a processor such as a CPU, and is responsible for overall control of the device 30. For example, the control unit 320 controls the device 30 based on sensing data measured by the sensor unit 310 and user operations input via the operation unit 340. The control unit 320 also generates operation data of the device 30 at a predetermined sampling period based on the state of the device 30, and transmits the generated operation data to the server 10 using the communication unit 330.

[0075] The communication unit 330 is a communication circuit for connecting the device 30 to a network. The communication unit 330 transmits operation data generated by the control unit 320 to the server 10. The operation unit 340 is configured with an operation device such as a touch panel or input buttons, and accepts operations from the user. The operation data includes operation values ​​such as power on, power off, and setting contents.

[0076] Next, a description will be given of the terminal device 40. Fig. 5 is a block diagram showing an example of the configuration of the terminal device 40. The terminal device 40 includes a control unit 410, a display unit 420, an operation unit 440, and a communication unit 430.

[0077] The control unit 410 is configured with a processor such as a CPU, and is responsible for overall control of the terminal device 40. When the communication unit 430 receives presentation data transmitted from the server 10, the control unit 410 causes the display unit 420 to display the presentation data.

[0078] The display unit 420 is configured with a display device such as a liquid crystal display panel or an organic EL panel, and displays the presented data under the control of the control unit 410 .

[0079] The communication unit 430 is a communication circuit for connecting the terminal device 40 to the network 50. The communication unit 430 receives presentation data transmitted from the server 10.

[0080] The operation unit 440 is configured with operation devices such as a touch panel, a keyboard, and a mouse, and receives operations from the user.

[0081] FIG. 6 is a sequence diagram showing data transmission and reception between the sensor device 20, the device 30, and the server 10. As shown in FIG. 6, the sensor device 20 generates sensing data at a predetermined sampling period and transmits it to the server 10. The device 30 generates operational data at a predetermined sampling period and transmits it to the server 10. Here, the operational data has been described as being transmitted at a predetermined sampling period, but the present disclosure is not limited to this. The operational data may also be transmitted when a predetermined event occurs. Examples of predetermined events include turning the power of the device 30 on and off, a change in the state of the device 30, etc. In this way, the server 10 can store the sensing data in the memory 130 as user behavior history data and store the operational data in the memory 130 as operation history data of the device 30.

[0082] FIG. 7 is a flowchart showing an example of processing by the server 10 shown in FIG. 1 . First, in step S301, the digital twin generation unit 121 generates a digital twin of a user in cyberspace using the user data stored in the memory 130, and also generates a digital twin of the user's residence in cyberspace using the structural data stored in the memory 130. FIG. 8 is a diagram showing an example of the exterior of a digital twin of a residence. FIG. 9 is a diagram showing an example of the floor plan of a digital twin of a residence. As shown in FIG. 8 , the digital twin of a residence is three-dimensional modeling data generated using the structural data of the user's residence. Therefore, the residential building has windows and doors arranged in the same way as an actual building, and the exterior is realistically reproduced. Furthermore, the digital twin of a residence realistically reproduces not only the residential building itself, but also the residential grounds, plants planted on the grounds, and fences surrounding the grounds.

[0083] Furthermore, the digital twin of a residence also reproduces the layout of the residence in three dimensions, as shown in Figure 9. In the example of Figure 9, the interior spaces of the residence, such as the living room, toilet, bathroom, kitchen, and closet, are reproduced, as well as the furniture arranged within the residence.

[0084] In step S302, the digital twin generation unit 121 generates a digital twin of the area using the structural data of the area including the user's residence stored in the memory 130. FIG. 10 is a diagram showing an example of a digital twin of the area. This area may be, for example, an area within a certain range that includes the user's residence, or the town and city to which the residence belongs. As shown in FIG. 10 , the digital twin of the area includes, for example, the residences within the area, as well as roads, commercial facilities, streetlights, and other structures that exist in the actual area.

[0085] In step S303, the acquisition unit 122 acquires, from the memory 130, behavior history data for a certain period of time in the past that is stored in the memory 130. In the following description, the certain period of time in the past is, for example, 3 years, 5 years, 7 years, 10 years, etc., and is not particularly limited.

[0086] In step S304, the acquisition unit 122 acquires operation history data for a certain period of time in the past stored in the memory 130 from the memory 130. The certain period of time in the operation history data is the same period of time as the certain period of time in the behavior history data.

[0087] In step S305, the identification unit 123 identifies diseases for which the user is at risk from the genetic analysis data stored in the memory 130. This identifies whether the user who provided the genetic analysis data is prone to each disease, such as arteriosclerosis, hypertension, diabetes, osteoporosis, and dementia.

[0088] In step S306, the first generation unit 124 analyzes the behavior history data and operation history data for a certain period of time in the past to generate first lifestyle pattern data that indicates the user's lifestyle patterns up to the present. FIG. 11 is an explanatory diagram of the first lifestyle pattern data generation process. First, the first generation unit 124 divides the behavior history data and operation history data for a certain period of time in chronological order into predetermined periods, thereby dividing the behavior history data and operation history data into initial segments 1301. The predetermined periods are appropriate lengths of time, such as 30 seconds, 1 minute, 10 minutes, and 30 minutes, and are not particularly limited. Next, the first generation unit 124 clusters each initial segment 1301 and assigns a symbol indicating the user's behavior to each initial segment 1301.

[0089] The first generation unit 124 inputs the behavior history data and operation history data included in each initial segment 1301 into a machine learning model, for example, which uses behavior history data and operation history data as explanatory variables and user behavior as a target variable and is obtained in advance by machine learning, and identifies user behavior. The first generation unit 124 then assigns a symbol indicating the identified behavior to each initial segment 1301. Alternatively, the first generation unit 124 may perform clustering using a method such as the k-means method or random forest, and assign a symbol to each initial segment 1301 based on the clustering results. A list of symbols assigned to each initial segment 1301 is shown on the right side of FIG. 11.

[0090] The symbols are data indicating the user's behavior, such as sleeping, walking, working (tasks), working (meetings), resting, eating and drinking, smoking, going out, using the toilet, and bathing. In addition to the symbols, symbol supplemental information is also assigned to each initial segment 1301. The symbol supplemental information for sleep indicates sleep patterns such as REM sleep and non-REM sleep. Information indicating the sleep pattern is identified, for example, from sensor values ​​(e.g., exercise amount, heart rate, etc.) included in the initial segment 1301 to which the sleep symbol is assigned. The symbol supplemental information for each of walking, working (tasks), working (meetings), resting, going out, using the toilet, and bathing is, for example, the amount of exercise. The amount of exercise is identified, for example, from sensor values ​​(e.g., angular velocity measured by a gyro sensor or acceleration measured by an acceleration sensor) included in the initial segment 1301 to which a symbol related to the amount of exercise is assigned. The amount of exercise may be the user's angular velocity or the user's acceleration, or may be the value obtained by multiplying the user's weight by speed, or may be the calories burned.

[0091] The symbol supplemental information for eating and drinking is the amount of food eaten and the amount of alcohol consumed. The amount of food eaten is identified from a sensor value (e.g., calorie intake) included in the initial segment 1301 to which the eating and drinking symbol is assigned. The calorie intake can be obtained, for example, by analyzing the food eaten by the user from image data of the user while eating. The amount of alcohol consumed can be identified, for example, from a sensor value (e.g., alcohol intake) included in the initial segment 1301 to which the eating and drinking symbol is assigned.

[0092] The symbol supplemental information for smoking is the frequency of smoking. Whether or not a user smokes is detected by linking the results of the odor sensor's detection of cigarette odor with the user's location data. The frequency of smoking is, for example, the number of cigarettes smoked.

[0093] Next, the first generation unit 124 combines one or more chronologically consecutive initial segments 1301 assigned with the same symbol. In this example, two initial segments 1301 assigned with the symbol "A" are combined to generate segment 1310, three initial segments 1301 assigned with the symbol "B" are combined to generate segment 1310, and a segment 1310 consisting of one initial segment 1301 assigned with the symbol "C" is generated.

[0094] 12 is a diagram showing an example of the data configuration of daily lifestyle pattern data 1201. Daily lifestyle pattern data 1201 is made up of one or more segments 1310 arranged in chronological order in a 24-hour time slot from midnight to midnight (midnight). In this example, a segment 1310 having a sleeping symbol is arranged in the time slot from midnight to 6:30, and a segment 1310 having an eating and drinking symbol is arranged in the time slot from 6:30 to 7:30.

[0095] Furthermore, each day's lifestyle pattern data 1201 is associated with date data including a year (YYYY), a month (MM), and a day (DD). As such, it can be seen that each day's lifestyle pattern data 1201 is data that represents the user's activities on each day over a certain period of time in the past in chronological order.

[0096] Fig. 13 is a diagram showing an example of the data configuration of lifestyle pattern data generated by the first generating unit 124. As shown in Fig. 13 , the first generating unit 124 generates lifestyle pattern data 1201 for each day of a certain period in the past, such as lifestyle pattern data 1201 for May 13, 2019 and lifestyle pattern data 1201 for May 14, 2019.

[0097] Next, first generation unit 124 generates lifestyle pattern data for each day of the week by grouping lifestyle pattern data 1201 from a certain period of time in the past by day of the week. In this case, first generation unit 124 divides a time period, for example, from midnight to midnight into initial segments 1301, votes for symbols constituting the lifestyle pattern data classified by day of the week for the initial segments 1301 that correspond to the time period, and determines a representative activity for each initial segment 1301 based on the voting results. First generation unit 124 then generates segment 1310 by combining one or more initial segments 1301 that have the same symbol and are consecutive in time series, thereby generating lifestyle pattern data for each day of the week. In this way, first lifestyle pattern data composed of lifestyle pattern data for each day of the week is generated.

[0098] 7. In step S307, the first generation unit 124 organizes the operation history data by day of the week. For example, as shown in FIG. 11 , the first generation unit 124 may divide the operation history data into a plurality of initial segments 1301 and generate operation history data for each day of the week by calculating the average value of each type of sensor value in each initial segment 1301.

[0099] In step S308, the simulation execution unit 125 uses the first lifestyle pattern data and the standard lifestyle pattern data to execute the above-mentioned simulation in which the digital twin of the user and the digital twin of the device 30 are operated in cyberspace.

[0100] FIG. 15 is an explanatory diagram of a simulation. FIG. 15 shows an example of a simulation of Mr. A, who is 22 years old and a fourth-year university student as of 2024. In this simulation, first lifestyle pattern data of Mr. A for the past five years from 2019 to 2023 is used. This first lifestyle pattern data is corrected according to the standard lifestyle pattern data, and Mr. A's digital twin is operated in cyberspace according to the corrected first lifestyle pattern data. At this time, the device 30 is also operated according to the device history data for the past five years. Here, a simulation is performed for the future five years from 2025 to 2029, that is, the period from the first year to the fifth year of working life.

[0101] For example, when running a simulation of one day when Person A is 25 years old, the simulation execution unit 125 modifies the first lifestyle pattern data for the day of the week corresponding to the relevant day (e.g., Tuesday) using the standard lifestyle pattern data for Tuesday when Person A is 25. The simulation execution unit 125 then operates the user's digital twin in cyberspace using the modified first lifestyle pattern data.

[0102] For example, if the sleep time indicated by the standard lifestyle pattern data of a 25-year-old is x% shorter than the sleep time indicated by the first lifestyle pattern data, the simulation execution unit 125 corrects the first lifestyle pattern data so that the sleep time is shortened by x%.

[0103] For example, if the standard lifestyle pattern data states that the basal metabolic rate of a 25-year-old is y% lower than that of a 22-year-old, the simulation execution unit 125 modifies the first lifestyle pattern data so that the calories burned or the amount of exercise for each action is reduced by y%.

[0104] For example, if the simulation execution unit 125 indicates that the daily calorie intake of a 25-year-old is reduced by z% compared to the daily calorie intake of a 25-year-old, the simulation execution unit 125 modifies the first lifestyle pattern data so that the calorie intake shown in the standard lifestyle pattern data for a 25-year-old is reduced by z%.

[0105] Furthermore, the simulation execution unit 125 uses the operation history data compiled by day of the week to operate the digital twin of the device 30. For example, when executing a simulation for a certain day, the simulation execution unit 125 operates the digital twin of the device 30 in cyberspace using the operation history data corresponding to that day of the week.

[0106] Furthermore, the simulation execution unit 125 monitors the behavioral content of the user's digital twin and the operation content of the digital twin of the device 30, and generates the execution results of the simulation by recording behavior history data indicating the monitored behavioral content and operation history data indicating the operation content in chronological order at a predetermined sampling period. The monitored behavioral content includes, for example, location data of the user's digital twin and the user's biometric data. The monitored operation content includes, for example, the operation values ​​of the digital twin of the device 30.

[0107] Returning to Fig. 7, in step S309, the second generation unit 126 generates second lifestyle pattern data from the results of the simulation. In the example of Fig. 15, lifestyle pattern data for each day for the next five years is generated.

[0108] Here, similar to the first generation unit 124, the second generation unit 126 may generate the second lifestyle pattern data using the method shown in FIG. 11 . That is, the second generation unit 126 divides the behavior history data and operation history data included in the execution result of the simulation into initial segments 1301, and assigns a symbol to each initial segment 1301 by clustering each initial segment 1301. Here, the assigned symbol is the same as that of the first lifestyle pattern data. Then, the second generation unit 126 combines the initial segments 1301 assigned the same symbol. The second generation unit 126 performs this process on the behavior history data and operation history data for each future day, thereby generating lifestyle pattern data for each day for the next five years.

[0109] Returning to Fig. 7, in step S310, the disease risk calculation unit 127 calculates the future disease risk for the disease identified in step S305 based on the second lifestyle pattern data. Fig. 16 is an explanatory diagram of the process for calculating the future disease risk. The following description will be given assuming that the disease identified in step S305 is arteriosclerosis. The disease risk calculation unit 127 refers to the cause candidate database stored in memory 130 and identifies cause candidate associated with arteriosclerosis.

[0110] Here, the possible causes of arteriosclerosis are the amount of exercise and smoking habits. In this case, the disease risk calculation unit 127 calculates an exercise evaluation value, which is an evaluation value related to the exercise of the user's digital twin, and a smoking evaluation value related to the smoking habits from the second lifestyle pattern data, calculates an overall evaluation value from both evaluation values, and calculates this overall evaluation value as the future disease risk. Here, the exercise evaluation value takes a value between 0 and 1, for example, and increases as the average calories burned per day or the amount of exercise of the user's digital twin increases. The smoking evaluation value takes a value between 0 and 1, for example, and increases as the average number of cigarettes smoked per day of the user's digital twin decreases. The overall evaluation value is, for example, the average of the exercise evaluation value and the smoking evaluation value.

[0111] Furthermore, the disease risk calculation unit 127 calculates the disease risk within one or more future periods based on the overall evaluation value. For example, the disease risk calculation unit 127 may calculate the disease risk within one or more future periods by correcting the overall evaluation value using a predetermined arithmetic formula that increases the overall evaluation value as time passes.

[0112] Although arteriosclerosis is used as an example here, disease risks can be calculated in a similar manner for other diseases (hypertension, diabetes, osteoporosis, dementia, etc.) That is, the disease risk calculation unit 127 can refer to the cause candidate database to identify cause candidates corresponding to the disease, calculate an evaluation value for each of the identified cause candidates, and calculate an overall evaluation value from each evaluation value.

[0113] Return to FIG. 7. In step S311, the disease risk calculation unit 127 generates an improvement plan based on the evaluation value for each candidate cause. See FIG. 16. In the case of FIG. 16, the smoking evaluation value was higher than the threshold, but the exercise evaluation value was lower than the threshold, so the amount of exercise is identified as an improvement target. In this way, the disease risk calculation unit 127 compares the evaluation value for each candidate cause with the threshold, and identifies candidate causes whose evaluation value is lower than the threshold as lifestyle pattern improvement targets. The threshold is, for example, the evaluation value for each candidate cause of an average person of the same generation.

[0114] Returning to Fig. 7, in step S312, the disease risk calculation unit 127 generates presentation data including disease risks and improvement proposals within one or more future periods.

[0115] In step S313, the output unit 128 outputs the presentation data. Here, the output unit 128 may transmit the presentation data to the terminal device 40 using the communication unit 110. Upon receiving the presentation data, the terminal device 40 displays the presentation data on the display unit 420.

[0116] 17 is a diagram showing a presentation screen 1700. The presentation screen 1700 is a display screen for presentation data. This is the same for the presentation screens in FIGS. 18 to 20. Here, the presentation screen 1700 for Taro Matsushita, who is 56 years old, is displayed. The presentation screen 1700 includes a disease display field 1701, a disease risk display field 1702, and an improvement proposal display field 1703.

[0117] The disease display field 1701 displays diseases that have been determined to be potentially fatal among multiple diseases. In this example, arteriosclerosis has been identified as a disease that has a potential to be fatal among arteriosclerosis, hypertension, diabetes, osteoporosis, and dementia, and therefore the outline of arteriosclerosis is displayed thicker than the outlines of the other diseases.

[0118] The disease risk display field 1702 displays future disease risks. Here, disease risks for the relevant user and for a typical person within three and five years are displayed. In this example, the user's disease risk within three years is displayed as 0.63, and the disease risk within five years is displayed as 0.87. On the other hand, the disease risks for a typical person aged 56 within three and five years are displayed as 0.35 and 0.59, respectively. This allows the user to recognize that he or she has a higher risk of developing arteriosclerosis than a typical person.

[0119] The improvement suggestion display field 1703 displays improvement suggestions for lifestyle patterns. The exercise evaluation score of this user was lower than the evaluation score (threshold) of an average person of the same generation. Therefore, advice to encourage exercise habits is displayed in the improvement suggestion display field 1703.

[0120] 18 is a diagram showing another example of a presentation screen 1800. The presentation screen 1800 includes a disease display field 1801, a disease risk display field 1802, an improvement plan display field 1803, and a details display field 1804.

[0121] The illness display field 1801 and the improvement plan display field 1803 are the same as the illness display field 1701 and the improvement plan display field 1703. While the illness risk display field 1702 also displayed the future illness risk of an average person of the same generation as the user, the illness risk display field 1802 displays only the user's future illness risk. Here, the user's illness risks within the next three years and within the next five years are displayed. The details display field 1804 displays supplementary explanations of the improvement plans listed in the improvement plan display field 1803. Here, an exterior view of the residence is displayed, and the details display field 1804 displays advice recommending taking walks around the residence. Furthermore, the floor plan of the residence is displayed, and advice recommending taking walks every hour because the user spends a lot of time sitting in a chair is displayed in the details display field 1804. The exterior view and floor plan of the residence displayed in the details display field 1804 are generated based on the digital twin of the user's residence generated by the digital twin generation unit 121.

[0122] 19 is a diagram showing a presentation screen 1900 according to yet another example. The presentation screen 1900 includes a disease display field 1901, a disease risk display field 1902, and an improvement plan display field 1903. The disease display field 1901 and the disease risk display field 1902 are the same as the disease display field 1801 and the disease risk display field 1802.

[0123] The improvement suggestion display field 1903 displays advice recommending exercise habits as well as cautions regarding lifestyle habits. In this case, because smoking increases the risk of arteriosclerosis, advice to refrain from smoking is displayed in the improvement suggestion display field 1903. Note that, since this user does not smoke, the advice also includes wording that takes this into consideration.

[0124] 20 is a diagram showing a presentation screen 2000 according to yet another example. The presentation screen 2000 includes a schedule display field 2001. The schedule display field 2001 displays the user's schedule for the past week on a daily basis. This schedule may be generated based on the second lifestyle pattern data, or a schedule generated by external schedule software may be used, for example.

[0125] In this example, the disease risk calculation unit 127 has determined that the amount of exercise for this user is lower than that of an average person of the same generation. Therefore, walking is incorporated into the schedule to encourage exercise habits. For example, the disease risk calculation unit 127 acquires the user's schedule for the past week and detects free time of a predetermined duration or more from the acquired user schedule. Then, the disease risk calculation unit 127 generates the schedule display field 2001 by incorporating a walking schedule into the detected free time. In this example, a 30-minute or 45-minute walking time is scheduled for each day of the week from Sunday, May 12, 2024 to Saturday, May 18, 2024.

[0126] This allows the user to easily improve their lifestyle patterns by taking walks according to the schedule display field 2001.

[0127] As described above, the information processing system 1 according to this embodiment identifies diseases that the user may contract from the user's genetic analysis data. First lifestyle pattern data indicating the user's lifestyle patterns up to the present are generated from the user's behavioral history data and the device's operation history data. A simulation is performed in cyberspace to operate the user's digital twin and the device's digital twin based on the generated first lifestyle pattern data, standard lifestyle pattern data corresponding to a future life stage, and the operation history data. Second lifestyle pattern data predicting the user's future lifestyle pattern is generated from the results of the simulation. A future disease risk for the identified disease is calculated based on the generated second lifestyle pattern data, and the calculated disease risk is output. Therefore, this configuration can predict the disease risk of a disease that the user may contract in the future. Furthermore, presenting the user with the future disease risk can provide the user with an opportunity to reconsider their current lifestyle pattern. This allows the user to reduce their future disease risk.

[0128] The present disclosure can employ the following modifications.

[0129] (1) In the flowchart of Fig. 7, step S305 of identifying a disease associated with a risk of illness is provided after steps S301 and S302 of generating a digital twin, but the order of steps S305 and S302 may be arbitrary as long as they are provided before the execution of a simulation. For example, step S305 may be provided before steps S301 and S302.

[0130] (2) In the lifestyle pattern data illustrated in Fig. 12 and other figures, it has been described that one symbol is assigned to segment 1310, but the present disclosure is not limited to this, and multiple symbols may be assigned. Note that when multiple symbols are assigned, the first generator 124 may vote for multiple symbols as one set to identify representative behaviors when generating lifestyle pattern data for each day of the week.

[0131] According to the present disclosure, the future disease risk of a user is calculated, which is useful in the healthcare industry.

Claims

1. An information processing method in which a computer generates, in cyberspace, digital twins of a user and devices installed in the user's residence based on real-world data, acquires behavioral history data indicating the user's behavioral history and operation history data indicating the operation history of the devices, identifies diseases that the user may have based on the user's genetic analysis data, analyzes the behavioral history data and the operation history data to generate first lifestyle pattern data that indicates the user's lifestyle pattern up to the present, runs a simulation in cyberspace to operate the user's digital twin and the digital twin of the devices based on the first lifestyle pattern data, standard lifestyle pattern data that indicates a standard lifestyle pattern according to a future life stage, and the operation history data, generates second lifestyle pattern data that predicts the user's future lifestyle pattern from the results of running the simulation, calculates the user's future disease risk for the identified disease based on the second lifestyle pattern data, and outputs the disease risk.

2. The information processing method according to claim 1, wherein the cyberspace includes a digital twin of the residence.

3. An information processing method according to claim 1 or 2, further comprising generating an improvement plan for the user's lifestyle pattern based on the second lifestyle pattern data and the disease risk, and outputting the improvement plan.

4. The information processing method according to claim 3, wherein the improvement plan includes exercise information indicating recommended exercises to reduce the disease risk.

5. An information processing method according to any one of claims 1 to 4, wherein the disease risk calculation calculates the disease risk within one or more future periods.

6. The information processing method according to any one of claims 1 to 5, wherein the disease is a lifestyle-related disease.

7. The information processing method according to any one of claims 1 to 6, wherein the generation of the second lifestyle pattern data includes predicting a lifestyle pattern for each day from the present to a predetermined time point in the future.

8. The information processing method according to any one of claims 1 to 7, wherein the real-world data includes attribute data of the user and location data of the device.

9. An information processing method according to any one of claims 1 to 8, wherein the execution of the simulation involves operating the user's digital twin in cyberspace based on the first lifestyle pattern data and the standard lifestyle pattern data, and also running a simulation in cyberspace of the device's digital twin based on operation history data.

10. An information processing device comprising: a digital twin generation unit that generates in cyberspace digital twins of a user and devices installed in the user's residence based on real-world data; an acquisition unit that acquires behavioral history data that indicates the user's behavioral history and operation history data that indicates the operation history of the devices; an identification unit that identifies diseases that the user may suffer from based on the user's genetic analysis data; a first generation unit that analyzes the behavioral history data and the operation history data and generates first lifestyle pattern data that indicates the user's lifestyle pattern up to the present; a simulation execution unit that executes a simulation to operate the user's digital twin and the digital twin of the devices in cyberspace based on the first lifestyle pattern data, standard lifestyle pattern data that indicates a standard lifestyle pattern according to a future life stage, and the operation history data; a second generation unit that generates second lifestyle pattern data that predicts the user's future lifestyle pattern from the results of executing the simulation; a disease risk calculation unit that calculates the user's future disease risk for the identified disease based on the second lifestyle pattern data; and an output unit that outputs the disease risk.

11. A program that causes a computer to: generate in cyberspace digital twins of a user and of devices installed in the user's residence based on real-world data; acquire behavioral history data that indicates the user's behavioral history and operation history data that indicates the operation history of the devices; identify diseases that the user may suffer from based on the user's genetic analysis data; analyze the behavioral history data and the operation history data to generate first lifestyle pattern data that indicates the user's lifestyle pattern up to the present; run a simulation in cyberspace to operate the user's digital twin and the digital twin of the devices based on the first lifestyle pattern data, standard lifestyle pattern data that indicates a standard lifestyle pattern according to a future life stage, and the operation history data; generate second lifestyle pattern data that predicts the user's future lifestyle pattern from the results of running the simulation; calculate the user's future disease risk for the identified disease based on the second lifestyle pattern data; and output the disease risk.

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