Information processing device, information processing method and information processing program

Abstract

To facilitate selecting an appropriate article to wear.SOLUTION: An information processing device 1 includes an action schedule information estimation unit, an environment information estimation unit, an attribute information estimation unit and a proposal article estimation unit. The action schedule information estimation unit estimates an action schedule including a user's destination and a movement path. The environment information estimation unit estimates environment information corresponding to the action schedule. The attribute information estimation unit estimates attribute information unique to the user including constitution of the user corresponding to a weather condition. The proposal article estimation unit estimates preferable articles to wear the user wears according to the action schedule on the basis of the action schedule, the environment information and the attribute information unique to the user, and proposes the estimated articles to wear to the user.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an information processing device, an information processing method, and an information processing program. [Background technology]

[0002] Conventionally, there are systems that suggest appropriate clothing according to weather conditions such as temperature, humidity, and weather. As such a system, for example, a system that suggests clothing according to the user's behavior and preferences has been proposed. In addition, a system that suggests comfortable clothing taking into account individual differences in how people feel about the weather has also been proposed. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2002-215770 A [Patent Document 2] JP 2019-71027 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the conventional technology, appropriate clothing suggestions for each individual are not made in consideration of individual attributes such as sweating patterns and preferences in addition to weather conditions during travel and at the destination. Therefore, in the conventional technology, the selection of appropriate clothing according to the overall activities of the day and individual attributes is ultimately left to the individual, and the burden of selecting wearable items is large. Therefore, it is difficult for users to select appropriate wearable items that suit them.

[0005] In view of the above, it is an object of the present invention to provide an information processing device, an information processing method, and an information processing program that facilitate the selection of an appropriate worn article. [Means for solving the problem]

[0006] The information processing device according to the present application includes an action plan information estimation unit, an environmental information estimation unit, an attribute information estimation unit, and a suggested item estimation unit. The action plan information estimation unit estimates an action plan including a user's destination and a travel route. The environmental information estimation unit estimates environmental information according to the action plan. The attribute information estimation unit estimates user-specific attribute information including the user's constitution according to weather conditions. The suggested item estimation unit estimates wearable items that are preferable for the user to wear according to the action plan based on the action plan, environmental information, and user-specific attribute information, and suggests the estimated wearable items to the user. Effect of the Invention

[0007] In one aspect, the present invention can facilitate the selection of an appropriate wearing article. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram showing a process executed by an information processing device according to an embodiment. [Diagram 2] FIG. 2 is a system configuration diagram illustrating an example of an information processing system according to the embodiment. [Diagram 3] FIG. 3 is a block diagram of an information processing device. [Figure 4] FIG. 4 is a flowchart of a wearing item suggestion process performed by the information processing device according to the embodiment. [Diagram 5] FIG. 5 is a diagram illustrating an example of a hardware configuration. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, the information processing device, the information processing method, and the information processing program according to the present application will be described in detail with reference to the drawings. Note that the information processing device, the information processing method, and the information processing program according to the present application are not limited to the embodiments. In addition, the same parts in the following embodiments are given the same reference numerals, and duplicated descriptions are omitted.

[0010] (Embodiment) Fig. 1 is a diagram showing a process executed by an information processing device according to an embodiment. The process executed by the information processing device 1 according to the embodiment will be described with reference to Fig. 1. Note that Fig. 1 shows an operation example of an information processing system 100 including the information processing device 1 according to the embodiment.

[0011] As shown in FIG. 1, an information processing system 100 according to the embodiment includes at least an information processing device 1 and a user terminal 2.

[0012] The information processing device 1 receives an operation that triggers the wearable article suggestion process and acquires the user's planned activity information from the time of receiving the operation (step S1). The operation that triggers the wearable article suggestion process is not particularly limited, and may be, for example, an operation such as checking the weather for the day using a predetermined weather forecast application running on the user terminal 2, or an input of a command to start the wearable article suggestion process to the information processing device 1. The information processing device 1 may provide a service of notifying the weather forecast application running on the user terminal 2 of information. The user's planned activity information is information indicating what actions the user will take, such as the user's destination, travel route, and vehicle to be used for travel.

[0013] Next, the information processing device 1 acquires environmental information such as weather information and average indoor temperature after the operation is received according to the user's action schedule information (step S2). For example, the information processing device 1 acquires weather information according to the user's position for each hour in accordance with the user's movement. The weather information includes temperature, humidity, wind speed, etc.

[0014] Next, the information processing device 1 estimates attribute information of the user (step S3). The attribute information of the user includes, for example, attributes related to physical condition such as sweating or poor circulation, attributes related to temperature sensation such as sensitivity to heat or cold, and attributes related to hobbies and interests such as favorite colors and patterns.

[0015] Next, the information processing device 1 estimates a suggested article to be worn from the attribute information, the action schedule information, and the environmental information (step S4). For example, the information processing device 1 can estimate a suggested article to be worn using a trained machine learning model.

[0016] After that, the information processing device 1 transmits information on the estimated suggested article to be worn to the user terminal 2 (step S5). The user terminal 2 displays the suggested article to be worn on, for example, a predetermined weather forecast application, and makes a specific clothing suggestion to the user.

[0017] In this way, the information processing device 1 according to the embodiment can suggest clothes according to the user's behavior, environmental information according to the behavior, and the user's attributes. That is, according to the information processing device 1 according to the embodiment, the user can receive suggestions of appropriate clothes according to his / her situation, and can easily select appropriate clothes.

[0018] Fig. 2 is a system configuration diagram showing an example of an information processing system according to the embodiment. Next, a configuration example of the information processing system 100 according to the embodiment will be described with reference to Fig. 2.

[0019] For example, the information processing system 100 includes, in addition to an information processing device 1 and a user terminal 2, a weather information providing system 3, a weather forecast system 4, and a product sales system 5. The information processing device 1 is connected to the user terminal 2, the weather information providing system 3, the weather forecast system 4, and the product sales system 5 via a network such as the Internet. In addition, the user has a wearable device 6 capable of communicating with the user terminal 2.

[0020] The information processing device 1 is a server device that executes an information processing method according to the embodiment. The information processing device 1 cooperates with a weather information providing system 3, a weather forecast system 4, and a product sales system 5 to provide API (Application Programming Interface) services and the like for various applications and various data to a user terminal 2. The information processing device 1 may be realized by a cloud system or the like.

[0021] The information processing device 1 may also be an information processing device that provides some kind of online Web service to a plurality of user terminals 2. For example, the information processing device 1 may provide services such as Internet connection, search service, SNS (Social Networking Service), Electronic Commerce (EC), electronic payment, online games, online banking, online trading, hotel and ticket reservations, video and music distribution, news, maps, route search, route guidance, line information, operation information, and weather forecasts as Web services. In practice, the information processing device 1 may cooperate with various servers that provide the above-mentioned Web services and act as an intermediary for the Web services or be responsible for processing the Web services.

[0022] The user terminal 2 is a terminal device owned by a user who is the distribution destination of distribution information in the information processing device 1. The user terminal 2 may be any type of terminal device such as a smartphone, a desktop PC (Personal Computer), a notebook PC, or a tablet PC. The user terminal 2 transmits various types of information to the information processing device 1 or the like, and receives information provided by the information processing device 1 or the like.

[0023] For example, the user terminal 2 runs a schedule management application. Next, the user terminal 2 receives input of the user's schedule. Then, the user terminal 2 transmits the input information of the user's schedule to the information processing device 1, and stores it in the storage unit 15 as schedule information 151.

[0024] The user terminal 2 also communicates with a wearable device 6 such as a smart watch. The user terminal 2 then acquires the user's location information throughout the day and information on the user's physical condition, such as heart rate and body temperature, acquired by the wearable device 6. The user terminal 2 then transmits the user's location information throughout the day together with the date to the information processing device 1, which stores the information as action history information 152 in the storage unit 15. The user terminal 2 also transmits information on the user's physical condition throughout the day together with the date and time to the information processing device 1, which stores the information as physical condition history information 153 in the storage unit 15 of the information processing device 1.

[0025] The weather information providing system 3 is a system owned by the Japan Meteorological Agency, a company that provides other weather information providing services, etc. The weather information providing system 3 provides information such as temperature, humidity, and wind speed at each point.

[0026] The weather forecast system 4 forecasts the weather based on weather information obtained from the weather information providing system 3, etc. Then, when the weather forecast system 4 receives an input from the user terminal 2 for an operation such as checking the weather for that day from a user using a weather forecast application, the weather forecast system 4 transmits the forecast result for the location where the user is located for that day and displays it on the weather forecast application. At the same time, the weather forecast system 4 instructs the information processing device 1 to execute a process for suggesting wearable items.

[0027] The weather forecast system 4 can also use a weather forecast application running on the user terminal 2 to give a survey to the user along with the weather forecast. For example, the weather forecast system 4 may ask the user to select whether the user feels hot or cold under the weather conditions at the time and place by displaying a screen on the user terminal 2 to ask the user to select whether the user is sweating under the weather conditions at the time and place by displaying a screen on the user terminal 2 to ask the user to select whether the user is sweating under the weather conditions at the time and place. The weather forecast system 4 acquires the input of the user's answer to the survey from the user terminal 2. The weather forecast system 4 then transmits the acquired survey result to the information processing device 1 together with the weather information for the time and place where the survey was answered, and stores it in the storage unit 15 as survey result information 154.

[0028] The product sales system 5 provides information on products for sale to an application running on the user terminal 2. Then, the product sales system 5 receives an input for purchasing a product from the user terminal 2 and confirms the purchase of the specified product by the user. The product sales system 5 transmits the user's purchase history to the information processing device 1 and stores it in the storage unit 15 as purchase history information 155.

[0029] 2, the weather forecast system 4 and the product sales system 5 are each a separate device from the information processing device 1, but the system configuration is not limited to this. For example, the information processing device 1 may have the functions of either the weather forecast system 4 or the product sales system 5, or both.

[0030] Fig. 3 is a block diagram of the information processing device. Next, details of the clothing suggestion process by the information processing device 1 according to the present embodiment will be described with reference to Fig. 3.

[0031] As shown in FIG. 3, the information processing device 1 according to this embodiment has an environmental information estimating unit 11, a proposed item estimating unit 12, an activity schedule information estimating unit 13, an attribute information estimating unit 14, and a storage unit 15.

[0032] The activity schedule information estimation unit 13 receives an instruction to execute a process of suggesting a wearable item from the weather forecast system 4. Alternatively, the activity schedule information estimation unit 13 may directly receive an instruction to execute a process of suggesting a wearable item from the user using an input unit (not shown).

[0033] Next, the activity schedule information estimation unit 13 acquires the user's schedule from the time point onward from the schedule information 151. Then, the activity schedule information estimation unit 13 identifies the destination in case of movement from the user's schedule. Here, when movement is performed multiple times in one day and there are multiple destinations, the destinations for each movement are acquired in order, and each is considered as one movement.

[0034] Furthermore, when the schedule information 151 does not include the user's schedule for that day, the activity plan information estimation unit 13 may estimate the destination for that day from the user's daily activity history included in the activity history information 152. For example, the activity plan information estimation unit 13 may estimate the presence or absence of the user's movement for that day and the destination, etc., using a machine learning model that has been trained using the date, time, day of the week, etc. as input. In this case, the machine learning model performs learning in advance using learning data that includes information on the date, time, day of the week, etc. and the user's daily activity history corresponding thereto.

[0035] Next, the behavior schedule information estimation unit 13 estimates the user's movement route to the destination. Furthermore, the behavior schedule information estimation unit 13 estimates the movement mode according to the movement route, that is, whether the user moves by walking, bicycle, train, taxi, bus, etc. In this case, the behavior schedule information estimation unit 13 may combine a plurality of types of movement modes. For example, the behavior schedule information estimation unit 13 may estimate the movement route and movement mode using a machine learning model that has been trained using the user's current location and destination as input. In this case, the machine learning model is trained in advance using training data including the departure points and destinations of many people and the movement routes and movement modes in those cases.

[0036] Next, the behavior schedule information estimation unit 13 estimates the user's exercise level for that day, such as how far the user will walk when moving. For example, the behavior schedule information estimation unit 13 may estimate the exercise level using a machine learning model that has been trained using the user's movement route and movement form as input. In this case, the machine learning model performs learning in advance using learning data including the movement routes of many people and the exercise movement forms corresponding to the movement routes. Furthermore, the behavior schedule information estimation unit 13 may estimate the exercise level by taking into account information such as the user's heart rate acquired by the wearable device 6 stored in the physical condition history information 153 in addition to the movement route and movement form.

[0037] Thereafter, the activity schedule information estimation unit 13 outputs the movement destination, movement route, movement mode, and activity level, etc., together as activity schedule information to the proposed item estimation unit 12. In addition, the activity schedule information estimation unit 13 outputs the movement destination and movement route of the activity schedule information to the environmental information estimation unit 11. In addition, the activity schedule information estimation unit 13 instructs the attribute information estimation unit 14 to acquire attribute information unique to the user.

[0038] The environmental information estimation unit 11 receives an input of the user's destination and travel route for that day from the activity schedule information estimation unit 13. Then, the environmental information estimation unit 11 estimates the area in which the user will be active at each time from the destination and travel route.

[0039] Next, when the area where the user is active is outdoors, the environmental information estimation unit 11 acquires outdoor weather information corresponding to the time of each estimated area from the weather information providing system 3. The outdoor weather information includes temperature, humidity, wind speed, and the like. When the area where the user is active is indoors, the environmental information estimation unit 11 estimates the average temperature in the building. For example, the environmental information estimation unit 11 uses information on the building where the user is located as input and estimates the average temperature while the user is there using a machine learning model that has been trained. In this case, the machine learning model is trained in advance using training data including information on the structure and usage of various buildings at each point, as well as room temperature by hour.

[0040] Thereafter, the environmental information estimation unit 11 outputs environmental information including weather information when the user is outdoors and average temperature when the user is indoors, according to the behavior schedule information, to the proposed item estimation unit 12. Here, the environmental information estimation unit 11 estimates the average temperature as indoor environmental information, but may also acquire other information that may affect the user's physical condition, such as average humidity.

[0041] The attribute information estimation unit 14 receives an instruction to acquire user-specific attribute information from the behavior schedule information estimation unit 13. Next, the attribute information estimation unit 14 refers to physical condition history information 153, questionnaire result information 154, and purchase history information 155 stored in the storage unit 15.

[0042] The attribute information estimation unit 14 acquires information on the user's physical condition, including the user's heart rate and body temperature, corresponding to the user's past date and time, contained in the physical condition history information 153. Next, the attribute information estimation unit 14 acquires weather information for each date and time from a database or the like held by the weather information providing system 3. Then, the attribute information estimation unit 14 associates the weather information for each date and time with the user's physical condition, and acquires information on the user's physical condition corresponding to weather conditions, such as weather and temperature. The attribute information estimation unit 14 also acquires a questionnaire result corresponding to the user's past weather conditions, contained in the questionnaire result information 143. The attribute information estimation unit 14 can obtain information on the user's physical constitution, such as the user's perceived temperature and the degree of sweating in response to temperature, from the questionnaire result corresponding to the weather conditions. The attribute information estimation unit 14 also acquires the user's purchase history from the purchase history information 155. The attribute information estimation unit 14 can determine, from the user's purchase history, whether the user often purchases thick clothes or short-sleeved clothes, and can determine, from the determination result, whether the user is sensitive to heat or cold.

[0043] The attribute information estimation unit 14 estimates physical attribute information representing the user's constitution, such as whether the user is a sweaty person or has poor circulation, and the user's sensitivity to temperature, from the information on the user's physical condition corresponding to the weather conditions, the questionnaire results, and the purchase history information. For example, the attribute information estimation unit 14 can estimate the user's physical attribute information using a trained machine learning model with the input of the user's physical condition information corresponding to the weather and temperature and the questionnaire results. In this case, the machine learning model performs machine learning in advance using training data including the physical condition information and questionnaire results of a large number of people for each weather condition, and the attribute information of each person.

[0044] In addition, the attribute information estimation unit 14 estimates attribute information related to the user's hobbies and preferences, such as the user's favorite colors and clothing shapes, from the purchase history information 155, etc. After that, the attribute information estimation unit 14 outputs user-specific attribute information including attribute information related to the body and hobbies and preferences, etc. to the proposed item estimation unit 12.

[0045] Here, the attribute information estimation unit 14 estimates the attribute information on the user's body using the information on the user's physical condition corresponding to the weather conditions, the questionnaire results, and the purchase history information, but it is not necessary to use all of this information for the estimation. For example, the attribute information estimation unit 14 may estimate the attribute information on the body using the questionnaire results without using the information on the user's physical condition corresponding to the weather conditions or the purchase history. In addition, the attribute information estimation unit 14 may use other information as long as it is information that can be used to estimate the user's constitution.

[0046] The proposed item estimation unit 12 receives activity plan information from the activity plan information estimation unit 13. In addition, the proposed item estimation unit 12 receives input of environmental information corresponding to the activity plan from the environmental information estimation unit 11. In addition, the proposed item estimation unit 12 receives input of user-specific attribute information from the attribute information estimation unit 14.

[0047] The suggested item estimation unit 12 then estimates the items suggested to be worn by the user on that day based on the planned activity information, the environmental information, and the user's unique attribute information. For example, the suggested item estimation unit 12 broadly selects candidates for suggested items to be worn based on the environmental information and the attribute information. The suggested item estimation unit 12 then narrows down the suggested items to be worn from the selected candidates based on the planned activity information. At this time, the suggested item estimation unit 12 determines the suggested items to be worn by taking into consideration the shape of the clothing, such as short sleeves or long sleeves, the thickness of the clothing, the material of the clothing, and the like.

[0048] Furthermore, the suggested item estimation unit 12 may select suggested items to wear by prioritizing a color according to the user's tastes and preferences with regard to the color of clothing, or may select suggested items to wear by prioritizing a general coordination such as white clothes if it is sunny and hot. Furthermore, when the amount of clouds and the like are obtained from the environmental information, the suggested item estimation unit 12 may select accessories according to the amount of clouds as suggested items to wear. For example, when there are few clouds and the user is easily dazzled, the suggested item estimation unit 12 may suggest sunglasses, or a hat, a parasol, etc.

[0049] For example, the suggested item estimation unit 12 can estimate suggested items to wear using a trained machine learning model with the action plan information, environmental information, and user-specific attribute information as input. In this case, the machine learning model performs machine learning in advance using learning data including past action plan information, environmental information, and attribute information of a large number of people, as well as information on the worn items selected therefor.

[0050] Here, the suggested item estimation unit 12 may not use all of the information used in the present embodiment to estimate the suggested item to be worn. For example, the suggested item estimation unit 12 may estimate the suggested item to be worn without using attribute information related to hobbies and preferences or the exercise level included in the planned action information.

[0051] Thereafter, the suggested item estimation unit 12 transmits the determined suggested wearing item to the user terminal 2 to suggest the wearing item to the user. For example, the suggested item estimation unit 12 may provide the user with information on the suggested wearing item by displaying it together with weather information in a weather forecast application.

[0052] Fig. 4 is a flowchart of the wearing article suggestion process by the information processing device according to the embodiment. Next, the flow of the wearing article suggestion process by the information processing device 1 according to the embodiment will be described with reference to Fig. 4. Here, the information processing device will be described as a case where the information processing device executes the wearing article suggestion process triggered by an operation of a weather forecast application and displays the suggestion result on the weather forecast application.

[0053] The user performs an operation to check the weather using a weather forecast application running on the user terminal 2 (step S1). By this operation, the activity schedule information estimation unit 13 receives an instruction from the weather forecast system 4 to execute an outfit suggestion process.

[0054] Next, the activity schedule information estimation unit 13 acquires the user's schedule for the day and thereafter from the schedule information 151. Then, the activity schedule information estimation unit 13 identifies the destination of the user in case of movement from the user's schedule. Also, if the schedule information 151 does not include the user's schedule for that day, the activity schedule information estimation unit 13 estimates the destination of that day from the user's daily activity history included in the activity history information 152, and identifies the user's destination (step S2).

[0055] Next, the activity schedule information estimation unit 13 estimates the user's movement route to the destination. Furthermore, the activity schedule information estimation unit 13 estimates a movement mode according to the movement route (step S3).

[0056] Next, the activity schedule information estimation unit 13 estimates the user's exercise level for that day, such as how far the user will walk based on the destination and the travel route (step S4).

[0057] The environmental information estimation unit 11 receives an input of the user's destination and travel route for that day from the activity schedule information estimation unit 13. Then, the environmental information estimation unit 11 estimates the area in which the user will be active at each time from the destination and travel route (step S5).

[0058] Next, the environmental information estimation unit 11 estimates environmental information including weather information when the user is outdoors and average temperature when the user is indoors, according to the behavior schedule information (step S6).

[0059] The attribute information estimation unit 14 receives an instruction to acquire user-specific attribute information from the behavior schedule information estimation unit 13. Next, the attribute information estimation unit 14 refers to the physical condition history information 153, the questionnaire result information 154, and the purchase history information 155 stored in the storage unit 15, and acquires the user's physical condition and questionnaire results corresponding to the weather conditions, as well as the purchase history (step S7).

[0060] Next, the attribute information estimation unit 14 estimates attribute information related to the user's body from the information on the user's physical condition corresponding to the weather conditions, the questionnaire results, and the purchase history information. The attribute information estimation unit 14 estimates attribute information related to the user's hobbies and preferences, such as the user's favorite colors and clothing shapes, from the purchase history information 155, etc. (step S8).

[0061] The suggested item estimation unit 12 receives planned behavior information from the behavior schedule information estimation unit 13. The suggested item estimation unit 12 also receives input of environmental information according to the planned behavior from the environmental information estimation unit 11. The suggested item estimation unit 12 also receives input of user-specific attribute information from the attribute information estimation unit 14. Then, the suggested item estimation unit 12 estimates suggested items to be worn by the user on that day from the planned behavior information, environmental information, and user-specific attribute information (step S9).

[0062] After that, the suggested item estimation unit 12 transmits the determined suggested item to be worn to the user terminal 2 (step S10).

[0063] The user checks the wearing article suggested by the information processing device 1 using a weather forecast application running on the user terminal 2 (step S11).

[0064] (effect) As described above, the information processing device 1 according to the present embodiment estimates a travel route from the user's destination and acquires environmental information according to the user's behavior. Furthermore, the information processing device 1 acquires user-specific attribute information including attribute information related to the user's body, such as the user's perceived temperature, sweating, or sensitivity to cold, and attribute information related to hobbies and preferences. Then, the information processing device 1 estimates suggested articles to wear based on the environmental information according to the user's behavior and the user-specific attribute information, and provides the article to the user. This makes it possible to appropriately suggest clothes and accessories according to the user's behavior and attributes. Therefore, the user can easily know the wearable articles that suit his / her schedule and constitution, and it becomes possible to easily select appropriate wearable articles.

[0065] (program) The above-mentioned processing by the information processing device 1 is realized by an information processing program according to the present application. For example, the environmental information estimation unit 11, the proposed item estimation unit 12, the action schedule information estimation unit 13, and the attribute information estimation unit 14, etc., of the information processing device 1 are realized by a CPU (Central Processing Unit) or an MPU (Micro Processing Unit) of the information processing device 1, in which an information processing program included in, for example, a range specification application uses a RAM (Random Access Memory) as a working area, and executes an information processing procedure related to the information processing program.

[0066] It should be noted that all of the processes executed by the information processing device 1 according to the present application do not necessarily have to be realized by an information processing program. For example, information outside the information processing device 1 may be acquired by an OS (Operating System) of the information processing device 1. In other words, the information processing program itself may not execute the processes executed by the information processing device 1 as described above, but may realize the processes of the information processing device 1 described above by receiving data acquired by the OS (for example, data used to display (play) content such as images).

[0067] (Hardware configuration) An information processing device such as the information processing device 1 according to the above-described embodiment is realized, for example, by a computer 1000 as shown in Fig. 5. Fig. 5 is a diagram showing an example of a hardware configuration. The computer 1000 has a CPU 1100, a RAM 1200, a ROM (Read Only Memory) 1300, a HDD (Hard Disk Drive) 1400, a communication interface (I / F) 1500, an input / output interface (I / F) 1600, and a media interface (I / F) 1700.

[0068] The arithmetic device 1030 operates based on programs stored in the primary storage device 1040 and the secondary storage device 1050, programs read from the input device 1020, and the like, and executes various processes. The arithmetic device 1030 is realized by, for example, a CPU, an MPU, an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or the like.

[0069] The CPU 1100 operates and controls each unit based on a program stored in the ROM 1300 or the HDD 1400. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 is started up, programs that depend on the hardware of the computer 1000, and the like.

[0070] The HDD 1400 stores programs executed by the CPU 1100, data used by such programs, etc. The communication interface 1500 receives data from other devices via a predetermined network N and sends it to the CPU 1100, and transmits data generated by the CPU 1100 to other devices via the predetermined network N.

[0071] The CPU 1100 controls output devices such as a display and a printer, and input devices such as a keyboard and a mouse, via the input / output interface 1600. The CPU 1100 acquires data from the input devices via the input / output interface 1600. The CPU 1100 also outputs generated data to the output devices via the input / output interface 1600.

[0072] The media interface 1700 reads a program or data stored in the storage medium 1800 and provides it to the CPU 1100 via the RAM 1200. The CPU 1100 loads the program from the storage medium 1800 onto the RAM 1200 via the media interface 1700 and executes the loaded program. The storage medium 1800 is, for example, an optical storage medium such as a DVD (Digital Versatile Disc) or a PD (Phase change rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

[0073] For example, when the computer 1000 functions as the information processing device 1 according to the embodiment, the CPU 1100 of the computer 1000 executes a program loaded on the RAM 1200 to realize the functions of an environmental information estimation unit 11, a proposed item estimation unit 12, an action schedule information estimation unit 13, and an attribute information estimation unit 14. The CPU 1100 of the computer 1000 reads and executes these programs from the storage medium 1800, but as another example, these programs may be obtained from another device via a predetermined network N.

[0074] The above describes the embodiments of the present application in detail based on the drawings, but these are merely examples, and the present invention can be implemented in other forms that include the aspects described in the Disclosure of the Invention and that have been modified and improved in various ways based on the knowledge of those skilled in the art.

[0075] (others) Furthermore, among the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically by a known method. In addition, the information including the processing procedures, specific names, various data and parameters shown in the above documents and drawings can be changed arbitrarily unless otherwise specified. For example, the various information shown in each drawing is not limited to the illustrated information.

[0076] In addition, each component of each device shown in the figure is a functional concept, and does not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit according to various loads, usage conditions, etc.

[0077] Furthermore, the above-described embodiments and modifications can be appropriately combined as long as the processing contents are not contradictory.

[0078] Moreover, the above-mentioned "section, module, unit" can be read as "means" or "circuit", etc. For example, the estimation unit can be read as an acquisition means or an acquisition circuit. [Explanation of symbols]

[0079] 1. Information processing device 2. User terminal 3. Weather Information System 4. Weather Forecast System 5 Product sales system 11 Environmental Information Estimation Department 12 Proposed product estimation department 13 Action Plan Information Estimation Department 14 Attribute information estimation section 15 Storage section 151 Schedule Information 152 Behavioral History Information 153 Health History Information 154 Survey result information 155 Purchase history information

Claims

1. an activity plan information estimation unit that estimates an activity plan including a destination and a travel route of a user; an environmental information estimation unit that estimates environmental information according to the behavior schedule; an attribute information estimation unit that estimates user-specific attribute information including a constitution of the user according to weather conditions; a suggested item estimation unit that estimates a wearable item that is preferable for the user to wear according to the action plan, based on the action plan, the environmental information, and the user-specific attribute information, and suggests the estimated wearable item to the user; An information processing device comprising:

2. The information processing apparatus according to claim 1 , wherein the attribute information estimation unit estimates the attribute information specific to the user based on a result of a questionnaire regarding the user's physical condition according to weather conditions.

3. The information processing apparatus according to claim 1 , wherein the attribute information estimation unit estimates the attribute information specific to the user based on information about the user's physical condition obtained from a wearable device worn by the user.

4. The information processing apparatus according to claim 1 , wherein the environmental information estimation unit identifies an activity area of ​​the user for each time period from the destination and the travel route, and acquires environmental information corresponding to the identified activity area.

5. The information processing device according to claim 1, characterized in that the environmental information estimation unit acquires at least the temperature, humidity, and wind speed at the user's location as the environmental information when the user's location is outdoors, and estimates the average temperature at the user's location as the environmental information when the user's location is indoors.

6. An information processing method executed by an information processing device, An acquisition step of acquiring a lifestyle from a user attribute of a user; an action plan information estimation step of estimating an action plan including a destination and a moving route of the user; an environmental information estimation step of estimating environmental information according to the behavior schedule; an attribute information estimation step of estimating user-specific attribute information including a constitution of the user according to weather conditions; a suggested item estimation step of estimating a worn item that is preferable for the user to wear according to the action plan, based on the action plan, the environmental information, and the user-specific attribute information, and suggesting the estimated worn item to the user; 13. An information processing method comprising:

7. An acquisition procedure for acquiring a lifestyle from a user's user attributes; An action plan information estimation step of estimating an action plan including a destination and a moving route of a user; an environmental information estimation step of estimating environmental information according to the behavior schedule; an attribute information estimation step of estimating user-specific attribute information including a constitution of the user according to weather conditions; a suggested item estimation step of estimating a wearable item that is preferable for the user to wear according to the action plan, based on the action plan, the environmental information, and the user-specific attribute information, and suggesting the estimated wearable item to the user; An information processing program characterized by causing a computer to execute the above.

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