Value decision device and value decision method

Abstract

A value decision device according to the present invention comprises an acquisition unit that acquires metadata indicating information related to content provided from a terminal, from among data associated with the content, and a decision unit that decides the value for providing the content on the basis of the reliability related to the content based on the metadata.

Description

Consideration Determination Device and Consideration Determination Method

[0001] This disclosure relates to a technique for determining consideration.

[0002] Patent Document 1 discloses an e-commerce server that dynamically varies the price of content based on the quality of the content, the elapsed time since the content was published, and the number of preview times of the content.

[0003] Japanese Patent Application Laid-Open No. 2010-257024

[0004] The number of fake contents such as fake news is increasing. For this reason, there is growing interest in the reliability regarding content. However, the e-commerce server described in Patent Document 1 cannot reflect the reliability regarding content in the consideration for providing the content.

[0005] An object of this disclosure is to provide a technique capable of reflecting the reliability regarding content in the consideration for providing the content.

[0006] A consideration determination device according to an aspect of this disclosure includes an acquisition unit that acquires metadata indicating information regarding the content from among the data attached to the content provided from a terminal, and a determination unit that determines the consideration for providing the content based on the reliability regarding the content based on the metadata.

[0007] A consideration determination method according to another aspect of this disclosure includes acquiring metadata indicating information regarding the content from among the data attached to the content provided from a terminal, and determining the consideration for providing the content based on the reliability regarding the content based on the metadata.

[0008] According to this disclosure, it is possible to provide a technique capable of reflecting the reliability regarding content in the consideration for providing the content.

[0009] This is a diagram illustrating an example of a content management system CS. This is a diagram illustrating an example of a terminal 10. This is a diagram illustrating an example of terminal data D1. This is a diagram illustrating an example of metadata M1. This is a diagram illustrating an example of a content distribution server 20. This is a diagram illustrating an example of a price determination server 40. This is a diagram illustrating an example of price data D2. This is a diagram illustrating an example of demand determination data D3. This is a diagram illustrating an example of device determination data D4. This is a diagram illustrating an example of user determination data D5. This is a diagram illustrating the operation of the price determination server 40.

[0010] 1: Embodiment 1-1: Content Management System CS Figure 1 shows an example of a content management system CS. The content management system CS manages content. The content management system CS includes a terminal 10, a content distribution server 20, a terminal 30, and a payment determination server 40. The content management system CS may further include other terminals.

[0011] Terminal 10 is, for example, a smartphone. Terminal 10 is not limited to a smartphone. For example, terminal 10 may be a tablet or a laptop computer.

[0012] Terminal 10 generates content. For example, if terminal 10 is a smartphone, the smartphone's camera generates the content. The content generated by the smartphone's camera is, for example, image data showing a still image. The content generated by the smartphone's camera may also be image data showing a video. Terminal 10 is an example of a device that generates content.

[0013] Terminal 10 may also be a detection device having a sensor. The sensor of the detection device may be, for example, a temperature sensor, humidity sensor, pressure sensor, ultrasonic sensor, photoelectric sensor, or fiber sensor. The sensor of the detection device generates content. The sensor of the detection device is an example of a sensor that generates content. The content generated by the sensor of the detection device is, for example, data output from the sensor of the detection device.

[0014] Terminal 10 generates metadata associated with the content when it generates the content. The metadata provides information about the content. Terminal 10 provides the content with metadata to the content distribution server 20.

[0015] The content and associated metadata may be generated by a device other than terminal 10. This other device could be, for example, a smartphone, tablet, laptop, camera, or detection device. Terminal 10 receives the content and associated metadata from the other device. Terminal 10 provides the content and metadata received from the other device to the content distribution server 20.

[0016] Terminal 10 is used by user U1. User U1 is an example of a user of the terminal that provided the content.

[0017] The content distribution server 20 manages the distribution of content. The content distribution server 20 stores the content and metadata provided by the terminal 10. The content distribution server 20 provides the content and metadata provided by the terminal 10 to the terminal 30 in response to a request from the terminal 30.

[0018] Terminal 30 is used by content recipients. Content recipients are, for example, individuals or news organizations.

[0019] The price determination server 40 determines the price for providing content based on the level of confidence in the content. The term "based on the level of confidence in the content" means "at least based on the level of confidence in the content." Also, "price for providing content" includes "incentives for providing content." The level of confidence in the content is based on metadata associated with the content. The price determination server 40 may be included in the content distribution server 20.

[0020] 1-2: Terminal 10 Figure 2 shows an example of terminal 10. The terminal 10 shown in Figure 2 is, for example, a smartphone. Terminal 10 generates content C1. Terminal 10 adds metadata M1 to content C1. Therefore, metadata M1 is attached to content C1. Metadata M1 indicates information about content C1. In response to instructions from user U1, terminal 10 provides content C1 with metadata M1 to content distribution server 20. Terminal 10 may also provide content and metadata received from a device other than terminal 10 to content distribution server 20 in response to instructions from user U1.

[0021] To simplify the explanation, the following description will mainly focus on an example where content C1, accompanied by metadata M1, is provided to the content distribution server 20.

[0022] Terminal 10 includes an input device 11, a camera 12, a display device 13, a GPS (Global Positioning System) module 14, a communication device 15, a storage device 16, a processing device 17, and a bus 18.

[0023] Bus 18 is a wiring configuration for communicating information. Bus 18 interconnects the input device 11, the camera 12, the display device 13, the GPS module 14, the communication device 15, the storage device 16, and the processing device 17. Bus 18 may consist of a single bus or various buses provided between each device.

[0024] The input device 11 is the user interface of the terminal 10. The input device 11 includes a touch panel. In addition to the touch panel, the input device 11 may include a plurality of operation keys. The input device 11 may include a plurality of operation keys without including a touch panel. The input device 11 may include an audio input device such as a microphone. The input device 11 receives various inputs from the user U1. For example, the input device 11 receives subject data input from the user U1. The subject data is data indicating the type of subject in the imaging performed by the camera 12.

[0025] The camera 12 includes an image sensor 12a, such as a CMOS sensor. The camera 12 generates content C1 by performing imaging. For example, the image sensor 12a included in the camera 12 generates content C1. Content C1 is, for example, image data. The image sensor 12a is an example of a sensor that generates content.

[0026] The display device 13 displays various types of information. For example, the display device 13 displays content C1 generated by the camera 12.

[0027] The GPS module 14 generates location data F1 indicating the location of the terminal 10. The GPS module 14 generates location data F1 based on satellite signals from GPS satellites. The GPS module 14 generates location data F1 in response to the camera 12 generating content C1. Therefore, the location data F1 indicates the location of the terminal 10 when content C1 was generated by the terminal 10. In other words, the location data F1 indicates the location where content C1 was generated.

[0028] The communication device 15 can communicate with the content distribution server 20 via the communication network NW. The communication device 15 may also communicate with the content distribution server 20 without using the communication network NW. The communication device 15 may also communicate with the terminal 30 via the communication network NW. The communication device 15 may also communicate with the terminal 30 without using the communication network NW. The communication device 15 may also communicate with the payment determination server 40 via the communication network NW. The communication device 15 may also communicate with the payment determination server 40 without using the communication network NW.

[0029] The storage device 16 is a recording medium that can be read by the processing device 17. The storage device 16 includes at least one memory. The storage device 16 includes, for example, non-volatile memory and volatile memory. Non-volatile memory is, for example, ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory), and EEPROM (Electrically Erasable Programmable Read Only Memory). Volatile memory is, for example, RAM (Random Access Memory) and VRAM (Video Random Access Memory).

[0030] The storage device 16 stores the program PG1 and terminal data D1. The program PG1 includes multiple instructions. The terminal data D1 is data related to terminal 10.

[0031] Figure 3 shows an example of terminal data D1. Terminal data D1 includes part number data D11, sensor type data D12, mode data D13, and user data D14.

[0032] The part number data D11 indicates the part number of terminal 10 and the part numbers of each of the multiple elements installed in terminal 10. The multiple elements include the processor installed in terminal 10. Part number data D11 is an example of information regarding the hardware of the device that generated the content.

[0033] The sensor type data D12 indicates the type of sensor that generates content C1. In terminal 10, the image sensor 12a generates content C1. Therefore, the sensor type data D12 indicates an image sensor.

[0034] Mode data D13 indicates the mode setting status on terminal 10. Mode data D13 indicates, for example, whether developer mode is turned off on terminal 10. When developer mode is off, for example, the risk of installing undesirable apps can be avoided. Therefore, when developer mode is off, the likelihood of the software configuration of terminal 10 being changed by the addition of undesirable apps is lower compared to when developer mode is on terminal 10. Mode data D13 is an example of information regarding the software of the device that generated the content.

[0035] User data D14 is data that identifies user U1 of terminal 10. User data D14 is user ID (Identification) of user U1. User ID of user U1 is, for example, "U1". User data D14 is an example of information about the user of the terminal that provided the content.

[0036] In Figure 2, the processing unit 17 includes at least one CPU (Central Processing Unit). The at least one CPU is an example of at least one processor. The at least one processor is an example of at least one computer. The processing unit 17 reads program PG1 from the storage device 16. By executing program PG1, the processing unit 17 functions as a date and time counter 171, a metadata generation unit 172, and an operation control unit 173. The date and time counter 171, the metadata generation unit 172, and the operation control unit 173 may each be composed of circuits such as a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), or an FPGA (Field Programmable Gate Array).

[0037] The date and time counter 171 counts the current date and time. The date and time counter 171 outputs date and time data F2 indicating the current date and time. A real-time clock may be used instead of the date and time counter 171.

[0038] The metadata generation unit 172 generates metadata M1. The metadata M1 is data associated with the content C1.

[0039] FIG. 4 is a diagram showing an example of the metadata M1. The metadata M1 includes position data F1, date and time data F2, sensor type data D12, subject data F4, and reliability data F5.

[0040] The metadata generation unit 172 acquires the position data F1 generated by the GPS module 14 in response to the camera 12 generating the content C1.

[0041] The metadata generation unit 172 acquires the date and time data F2 output by the date and time counter 171 when the camera 12 generates the content C1. Therefore, the date and time data F2 acquired by the metadata generation unit 172 indicates the generation time of the content.

[0042] The metadata generation unit 172 reads the sensor type data D12 included in the terminal data D1 shown in FIG. 3.

[0043] The metadata generation unit 172 acquires the subject data received by the input device 11 as the subject data F4.

[0044] The metadata generation unit 172 reads the product number data D11, mode data D13, and user data D14 included in the terminal data D1 shown in FIG. 3 as the reliability data F5.

[0045] In FIG. 4, the reliability data F5 includes hardware data F51, software data F52, and user data F53.

[0046] The hardware data F51 includes the product number data D11 read by the metadata generation unit 172. The product number data D11 indicates the product number of the terminal 10 and the product numbers of each of the plurality of elements mounted on the terminal 10.

[0047] Software data F52 includes mode data D13 read by metadata generation unit 172. The mode data D13 indicates, for example, whether the developer mode is off. Note that the software data F52 may include data indicating the hash value of the position data F1. The hash value of the position data F1 is generated, for example, by the metadata generation unit 172. Also, the software data F52 may include data indicating the hash value of the date and time data F2. The hash value of the date and time data F2 is generated, for example, by the metadata generation unit 172.

[0048] User data F53 includes user data D14 read by metadata generation unit 172. The user data D14 is data for identifying the user U1 of the terminal 10.

[0049] The metadata generation unit 172 generates metadata M1 including the position data F1, the date and time data F2, the sensor type data D12, the subject data F4, and the reliability data F5. The metadata generation unit 172 adds the metadata M1 to the content C1.

[0050] Note that when the terminal 10 receives content and metadata different from the image data, the metadata is different from the metadata M1 shown in FIG. 4 in that it does not include the subject data F4.

[0051] In FIG. 2, the operation control unit 173 controls the operation of the terminal 10. For example, the operation control unit 173 provides the content C1 with the metadata M1 from the communication device 15 to the content distribution server 20. Also, the operation control unit 173 receives notification data A1 indicating the amount of consideration for the provision of the content C1 via the communication device 15.

[0052] 1-3: Content Distribution Server 20 Figure 5 shows an example of a content distribution server 20. The content distribution server 20 is a server composed of multiple computers. The content distribution server 20 may also be composed of a single computer. The content distribution server 20 can communicate with terminal 10. The content distribution server 20 can communicate with terminal 30. The content distribution server 20 can communicate with price determination server 40.

[0053] The content distribution server 20 includes a communication device 21, a storage device 22, a processing device 23, and a bus 24.

[0054] Bus 24 is a wiring system for transmitting information. Bus 24 connects the communication device 21, the storage device 22, and the processing device 23. Bus 24 may consist of a single bus, or it may consist of various buses provided between the devices.

[0055] The communication device 21 can communicate with the terminal 10 via the communication network NW. The communication device 21 may also communicate with the terminal 10 without using the communication network NW. The communication device 21 can communicate with the terminal 30 via the communication network NW. The communication device 21 may also communicate with the terminal 30 without using the communication network NW. The communication device 21 can communicate with the payment determination server 40 via the communication network NW. The communication device 21 may also communicate with the payment determination server 40 without using the communication network NW.

[0056] The storage device 22 is a recording medium readable by the processing device 23. The storage device 22 includes at least one memory. The storage device 22 includes, for example, a non-volatile memory and a volatile memory. The storage device 22 stores the program PG2. The program PG2 includes a plurality of instructions. The storage device 22 includes a content storage area 22a. The content storage area 22a stores the content and metadata provided to the content distribution server 20.

[0057] The processing unit 23 includes at least one CPU. The processing unit 23 reads the program PG2 from the storage device 22. The processing unit 23 functions as an operation control unit 231 by executing the program PG2.

[0058] The operation control unit 231 controls the operation of the content distribution server 20. For example, the operation control unit 231 receives content C1 and metadata M1 provided from the terminal 10 via the communication device 21. The operation control unit 231 stores content C1 and metadata M1 in the content storage area 22a. The operation control unit 231 provides the metadata M1, which is part of the data associated with content C1, to the payment determination server 40 via the communication device 21. The operation control unit 231 may also provide content C1 with metadata M1 to the payment determination server 40 via the communication device 21.

[0059] The operation control unit 231 receives notification data A1 provided by the payment determination server 40 via the communication device 21. Notification data A1 indicates the amount of payment for the provision of content. The operation control unit 231 provides notification data A1 to the terminal 10 from the communication device 21. The operation control unit 231 also provides content C1 with metadata M1 to the terminal 30 from the communication device 21, for example, in response to a request from the terminal 30.

[0060] 1-4: Price Determination Server 40 Figure 6 shows an example of a price determination server 40. The price determination server 40 is an example of a price determination device. The price determination server 40 is a server composed of multiple computers. The price determination server 40 may be composed of a single computer. The price determination server 40 is able to communicate with at least the content distribution server 20.

[0061] The price determination server 40 includes a communication device 41, a storage device 42, a processing device 43, and a bus 44.

[0062] Bus 44 is a wiring configuration for transmitting information. Bus 44 connects the communication device 41, the storage device 42, and the processing device 43. Bus 44 may consist of a single bus, or it may consist of various buses provided between the devices.

[0063] The communication device 41 can communicate with the content distribution server 20 via the communication network NW. The communication device 41 may also communicate with the content distribution server 20 without using the communication network NW. The communication device 41 may also communicate with the terminal 10 via the communication network NW. The communication device 41 may also communicate with the terminal 10 without using the communication network NW. The communication device 41 may also communicate with the terminal 30 via the communication network NW. The communication device 41 may also communicate with the terminal 30 without using the communication network NW.

[0064] The storage device 42 is a recording medium that can be read by the processing device 43. The storage device 42 includes at least one memory. The storage device 42 includes, for example, a non-volatile memory and a volatile memory. The storage device 42 stores a program PG3, price data D2, demand determination data D3, device determination data D4, and user determination data D5. The program PG3 includes a plurality of instructions.

[0065] The compensation data D2 is generated when determining the compensation for providing content C1. The compensation data D2 is generated by the compensation determination server 40 based on the metadata M1 associated with content C1. After the compensation for providing content C1 is determined, the compensation data D2 may be deleted. Also, the compensation data D2 does not have to be stored in the storage device 42.

[0066] Figure 7 shows an example of price data D2. The price data D2 shown in Figure 7 is generated, for example, when the sensor type data D12 of the metadata M1 shown in Figure 4 indicates an image sensor. The price data D2 shown in Figure 7 shows scarcity B1, demand B2, and confidence B3.

[0067] Scarcity B1 is the scarcity of content C1. Demand B2 is the demand for content C1. For example, demand B2 is the demand for content C1 during the period before metadata M1 is acquired by the price determination server 40. Confidence B3 is the confidence level regarding content C1. Note that the price data D2 only needs to show confidence B3.

[0068] Scarcity B1, demand B2, and trust level B3 each influence the price paid for providing content C1.

[0069] For example, in a situation where demand B2 and trust level B3 remain unchanged, the higher the scarcity B1, the higher the price paid for providing content C1.

[0070] Assuming that reliability B3 and rarity B1 remain unchanged, the higher the demand B2, the higher the price paid for providing content C1.

[0071] Assuming scarcity B1 and demand B2 remain unchanged, a higher level of trust B3 will result in a higher price for providing content C1.

[0072] Rarity B1 includes the rarity of time B11, the rarity of sensor type B12, the rarity of location B13, the rarity of subject type B14, and the degree of immediacy B15.

[0073] The rarity of time B11 is the rarity of the time when content C1 was created. The rarity of sensor type B12 is the rarity of the type of sensor that created content C1. The rarity of location B13 is the rarity of the location where content C1 was created. The rarity of subject type B14 is the rarity of the type of subject shown in content C1. The immediacy B15 is represented by the elapsed time from the date and time when content C1 was created to the current date and time.

[0074] The rarity of time (B11), the rarity of sensor type (B12), the rarity of location (B13), the rarity of subject type (B14), and the immediacy (B15) each affect rarity (B11).

[0075] In situations where the rarity of sensor type B12, location rarity B13, subject type rarity B14, and immediacy B15 remain unchanged, the higher the rarity of time B11, the higher the rarity B1.

[0076] In situations where the rarity of location B13, the rarity of subject type B14, the immediacy B15, and the rarity of time B11 remain unchanged, the higher the rarity of sensor type B12, the higher the rarity B1.

[0077] In situations where the rarity of the subject type (B14), immediacy (B15), time (B11), and sensor type (B12) remain unchanged, the higher the rarity of the location (B13), the higher the rarity of location (B11).

[0078] In situations where the degree of immediacy B15, the rarity of time B11, the rarity of sensor type B12, and the rarity of location B13 remain unchanged, the higher the rarity of the subject type B14, the higher the rarity B1.

[0079] In situations where the rarity of time (B11), the rarity of sensor type (B12), the rarity of location (B13), and the rarity of subject type (B14) remain unchanged, the higher the immediacy (B15), the higher the rarity (B11).

[0080] Furthermore, rarity B1 is not limited to a configuration that includes time rarity B11, sensor type rarity B12, location rarity B13, subject type rarity B14, and immediacy B15. For example, rarity B1 may include other rarity aspects related to content C1. Also, time rarity B11, sensor type rarity B12, location rarity B13, subject type rarity B14, or immediacy B15 may be omitted.

[0081] Demand B2 is the demand for content C1 during the period prior to the acquisition of metadata M1 by the price determination server 40. Demand B2 includes attention level B21 and the offered incentive B22.

[0082] Attention level B21 is the attention level of content C1. Attention level B21 is, for example, the number of requests for content C1 during the period before metadata M1 is acquired by the payment determination server 40. For example, if the subject of content C1 is a cat, attention level B21 is the number of requests for content whose subject is a cat during the period before metadata M1 is acquired by the payment determination server 40. The number of requests for content C1 may also be referred to as the number of demands for content C1.

[0083] The offered incentive B22 is the amount of incentive offered for content C1 during the period prior to the acquisition of metadata M1 by the compensation determination server 40. For example, if the subject of content C1 is a cat, the offered incentive B22 is the amount of incentive offered for content whose subject is a cat during the period prior to the acquisition of metadata M1 by the compensation determination server 40.

[0084] Attention level B21 and offered incentive B22 each influence demand B2.

[0085] Assuming the offered incentive B22 remains unchanged, the higher the level of attention B21, the higher the demand B2.

[0086] Assuming that attention level B21 remains unchanged, the higher the offered incentive B22, the higher the demand B2 will be.

[0087] Trust level B3 includes hardware trust level B31, software trust level B32, and content provider trust level B33.

[0088] Hardware reliability B31 is the hardware reliability of the device that generated content C1. Software reliability B32 is the software reliability of the device that generated content C1. Content provider reliability B33 is the reliability of user U1 of terminal 10 that provided content C1.

[0089] Hardware reliability B31, software reliability B32, and content provider reliability B33 each influence reliability B3.

[0090] Assuming that the software reliability B32 and the content provider reliability B33 remain unchanged, the higher the hardware reliability B31, the higher the reliability B3.

[0091] Assuming that the reliability of the content provider (B33) and the hardware (B31) remain unchanged, a higher software reliability (B32) will result in a higher reliability (B3).

[0092] Assuming that hardware reliability B31 and software reliability B32 remain unchanged, the higher the content provider's reliability B33, the higher their reliability B3.

[0093] Furthermore, if the sensor type data D12 of the metadata M1 shown in Figure 4 does not indicate an image sensor, the rarity B14 of the subject type and demand B2 of the compensation data D2 shown in Figure 7 will not be generated. Therefore, the elements included in the compensation data D2 can be changed depending on the type of sensor that generated the content.

[0094] Figure 8 shows an example of the demand determination data D3 shown in Figure 6. The demand determination data D3 is used to determine the demand B2 shown in Figure 7. The demand determination data D3 is generated based on requests from users who wish to acquire the content. These requests from users who wish to acquire the content may also be referred to as requests from users who wish to acquire the content.

[0095] Demand determination data D3 is generated, for example, by the price determination server 40. For example, the price determination server 40 generates demand determination data D3 based on a request from user U1 of terminal 10, a request from a user of terminal 30, and a request from a user of another terminal. Demand determination data D3 may be generated by a server other than the price determination server 40. For example, demand determination data D3 may be generated by the content distribution server 20. In this case, demand determination data D3 is provided from the content distribution server 20 to the price determination server 40. Demand determination data D3 may also be generated by the administrator of the price determination server 40.

[0096] The demand determination data D3 shows the subject type D31, the number of requests D32, and the offered incentive D33. Subject type D31 is the type of subject shown in the content. Number of requests D32 is the number of requests for content showing the type of subject indicated by subject type D31. Offered incentive D33 is the amount of incentive offered for content showing the type of subject indicated by subject type D31. If multiple incentive amounts are offered for the content, offered incentive D33 is, for example, the average of the multiple incentive amounts. Offered incentive D33 is not limited to the average of multiple incentive amounts. For example, offered incentive D33 may be the highest amount among the multiple incentive amounts. The demand determination data D3 shown in Figure 8 shows that there are 20 requests for content where the subject type is a cat, and the amount of incentive offered for content where the subject type is a cat is 300 yen.

[0097] Figure 9 shows an example of the device determination data D4 shown in Figure 6. The device determination data D4 is used, for example, when determining the hardware reliability B31 shown in Figure 7.

[0098] Device identification data D4 shows the terminal part number D41 and the element part number D42. Terminal part number D41 indicates the terminal's part number. Element part number D42 indicates the part number of a legitimate element installed by the manufacturer in the terminal determined by terminal part number D41. A legitimate element is, for example, a legitimate processor installed by the manufacturer in the terminal determined by terminal part number D41. The device identification data D4 shown in Figure 9 shows that the terminal part number D41 is "AAA" and the part number of a legitimate element installed in the terminal with part number "AAA" is "BB1". Note that the element part number D42 column may show the individual part numbers of multiple legitimate elements.

[0099] Figure 10 shows an example of user identification data D5 shown in Figure 6. User identification data D5 is used to determine the confidence level B33 of the content provider shown in Figure 7.

[0100] User determination data D5 includes user data D51 and confidence data D52. User data D51 is the user ID. The user ID is data that identifies the user. Confidence data D52 indicates the confidence level of the user as determined by user data D51.

[0101] A user's trustworthiness is determined, for example, based on the reliability of content they have previously provided. For instance, a user who has previously provided tampered content will have a lower trustworthiness score than the first threshold. Additionally, if content provided by a user receives low ratings from those who have acquired it, the trustworthiness of that user will also be lower than the first threshold.

[0102] User determination data D5 is generated, for example, by the payment determination server 40. For example, the payment determination server 40 generates user determination data D5 based on evaluation information from user U1 of terminal 10, evaluation information from users of terminal 30, evaluation information from users of other terminals, and the user's history of providing tampered content. The evaluation information indicates the content acquirer's evaluation of the content provider. User determination data D5 may be generated by a server other than the payment determination server 40. For example, user determination data D5 may be generated by the content distribution server 20. In this case, user determination data D5 is provided from the content distribution server 20 to the payment determination server 40. User determination data D5 may also be generated by the administrator of the payment determination server 40.

[0103] The user determination data D5 shown in Figure 10 indicates that the confidence level of user U1, as shown by user data D51, is "80". The confidence level is indicated by a number within the range of 0 to 100. A confidence level of "0" means the lowest confidence level. A confidence level of "100" means the highest confidence level. Note that the confidence level may also be indicated by a number within a range other than 0 to 100.

[0104] In Figure 6, the processing unit 43 includes at least one CPU. The processing unit 43 reads program PG3 from the storage device 42. By executing program PG3, the processing unit 43 functions as a date and time counter 431, an acquisition unit 432, a determination unit 433, and a transmission unit 434. The processing unit 43 may also function as the date and time counter 431 by executing a program different from program PG3. The processing unit 43 is another example of a price determination device.

[0105] The date and time counter 431 counts the current date and time. A real-time clock may be used instead of the date and time counter 431.

[0106] The acquisition unit 432 acquires at least metadata M1 that indicates information about content C1 from the data associated with content C1 provided by terminal 10. For example, the acquisition unit 432 acquires metadata M1 provided by terminal 10 via content distribution server 20 and communication device 41.

[0107] The decision unit 433 determines the price for providing content C1 based on the confidence level B3 for content C1. The confidence level B3 for content C1 is based on metadata M1. For example, if the sensor type data D12 of the metadata M1 shown in Figure 4 indicates an image sensor, the decision unit 433 determines the price for providing content C1 based on the rarity B1 based on metadata M1, the demand B2 based on metadata M1, and the confidence level B3 based on metadata M1.

[0108] Here, we assume that the rarity B1 based on metadata M1, the demand B2 based on metadata M1, and the confidence level B3 based on metadata M1 are all numerical values. In this case, the determination unit 433 determines the amount of consideration for providing content C1 as the sum of the value obtained by adding the rarity B1 based on metadata M1, the demand B2 based on metadata M1, and the confidence level B3 based on metadata M1.

[0109] The determination unit 433 may determine the amount of compensation for providing content C1 by adding together the rarity B1 based on metadata M1 and the confidence B3 based on metadata M1.

[0110] Alternatively, the determination unit 433 may determine the amount of consideration for providing content C1 as a value obtained by summing the demand B2 based on metadata M1 and the confidence level B3 based on metadata M1.

[0111] Alternatively, the determination unit 433 may determine the confidence level B3 based on the metadata M1 as the amount of consideration for providing the content C1.

[0112] The decision unit 433 determines scarcity B1 based on metadata M1. The decision unit 433 determines demand B2 based on metadata M1. The decision unit 433 determines confidence B3 based on metadata M1. Below, an example of a method by which the decision unit 433 determines scarcity B1 based on metadata M1, an example of a method by which the decision unit 433 determines demand B2 based on metadata M1, and an example of a method by which the decision unit 433 determines confidence B3 based on metadata M1 will be explained.

[0113] 1-4-1: An Example of a Method for Determining Rarity B1 First, an example of a method by which the determination unit 433 determines rarity B1 based on metadata M1 will be explained.

[0114] For example, the determination unit 433 determines the rarity B1 shown in Figure 7 based on the metadata M1. Specifically, the determination unit 433 determines the rarity B11 of time, the rarity B12 of sensor type, the rarity B13 of location, the rarity B14 of subject type, and the immediacy B15 based on the metadata M1.

[0115] Next, the determination unit 433 stores the rarity of time B11, the rarity of sensor type B12, the rarity of location B13, the rarity of subject type B14, and the immediacy B15 in the storage device 42. However, the determination unit 433 does not have to store the rarity of time B11, the rarity of sensor type B12, the rarity of location B13, the rarity of subject type B14, and the immediacy B15 in the storage device 42.

[0116] Next, the determination unit 433 determines the rarity B1 based on the rarity B11 of the time, the rarity B12 of the sensor type, the rarity B13 of the location, the rarity B14 of the subject type, and the immediacy B15. For example, it is assumed that the rarity B11 of the time, the rarity B12 of the sensor type, the rarity B13 of the location, the rarity B14 of the subject type, and the immediacy B15 are all numerical values. In this case, the determination unit 433 determines the rarity B1 by summing the rarity B11 of the time, the rarity B12 of the sensor type, the rarity B13 of the location, the rarity B14 of the subject type, and the immediacy B15.

[0117] 1-4-1-1: An example of a method for determining the rarity B11 of a time The determination unit 433 will explain an example of a method for determining the rarity B11 of a time based on the metadata M1.

[0118] In the following explanation, for the sake of simplicity, the metadata M1 acquired by the acquisition unit 432 will be referred to as "acquired metadata M11". Acquired metadata M11 includes various types of data shown in Figure 4. Furthermore, it is assumed that the content storage area 22a of the content distribution server 20 shown in Figure 5 already stores at least one content item. For example, it is assumed that the content storage area 22a of the content distribution server 20 shown in Figure 5 already stores multiple content items. Each of these multiple content items is accompanied by metadata that includes various types of data shown in Figure 4. Therefore, the content storage area 22a also stores multiple metadata items that correspond one-to-one with multiple content items.

[0119] The determination unit 433 selects a first content from among the multiple contents stored in the content storage area 22a that has metadata indicating the same date and time as the date and time data F2 of the acquired metadata M11 shown in Figure 4.

[0120] The determination unit 433 calculates a first ratio, which is the ratio of the number of first content items to the number of content items stored in the content storage area 22a, as a percentage. For example, if the number of content items stored in the content storage area 22a is "100" and the number of first content items is "5", the determination unit 433 calculates "5%" as the first ratio.

[0121] The determination unit 433 determines a value p1 obtained by subtracting the value of the first percentage from "100". For example, if the first percentage is "5%", the determination unit 433 determines "95" as the value p1.

[0122] If the content storage area 22a does not contain any content, the determination unit 433 determines the value p1 to be "100".

[0123] The determination unit 433 determines the value p1 as the rarity B11 of the time. The determination unit 433 may also determine the value "p1 × k1", obtained by multiplying the value p1 by a weighting coefficient k1, as the rarity B11 of the time. The value of the weighting coefficient k1 is, for example, "1". The value of the weighting coefficient k1 is not limited to "1" and can be changed as appropriate.

[0124] 1-4-1-2: An example of a method for determining the rarity B12 of the sensor type Next, an example of a method by which the determination unit 433 determines the rarity B12 of the sensor type based on the acquired metadata M11 will be explained.

[0125] The determination unit 433 selects a second content from among the multiple contents stored in the content storage area 22a that has metadata indicating the same sensor type as the sensor type shown in the sensor type data D12 of the acquired metadata M11 shown in Figure 4.

[0126] The determination unit 433 calculates a second ratio, which is the ratio of the number of second content items to the number of content items stored in the content storage area 22a, as a percentage. For example, if the number of content items stored in the content storage area 22a is "100" and the number of second content items is "10", the determination unit 433 calculates "10%" as the second ratio.

[0127] The determination unit 433 determines a value p2 obtained by subtracting the value of the second percentage from "100". For example, if the second percentage is "10%", the determination unit 433 determines "90" as the value p2.

[0128] If the content storage area 22a does not contain any content, the determination unit 433 determines the value p2 to be "100".

[0129] The determination unit 433 determines the value p2 as the rarity B12 of the sensor type. The determination unit 433 may also determine the value "p2 × k2", obtained by multiplying the value p2 by a weighting coefficient k2, as the rarity B12 of the sensor type. The value of the weighting coefficient k2 is, for example, "1". The value of the weighting coefficient k2 is not limited to "1" and can be changed as appropriate.

[0130] 1-4-1-3: An example of a method for determining the rarity B13 of a location Next, an example of a method by which the determination unit 433 determines the rarity B13 of a location based on the acquired metadata M11 will be explained.

[0131] The determination unit 433 selects a third content from among the multiple contents stored in the content storage area 22a that has metadata indicating the same location as the location data F1 of the acquired metadata M11 shown in Figure 4.

[0132] The determination unit 433 calculates the third ratio, which is the ratio of the number of third content items to the number of content items stored in the content storage area 22a, as a percentage. For example, if the number of content items stored in the content storage area 22a is "100" and the number of third content items is "6", the determination unit 433 calculates "6%" as the third ratio.

[0133] The determination unit 433 determines a value p3 obtained by subtracting the value of the third percentage from "100". For example, if the third percentage is "6%", the determination unit 433 determines "94" as the value p3.

[0134] If the content storage area 22a does not contain any content, the determination unit 433 determines the value p3 to be "100".

[0135] The determination unit 433 determines the value p3 as the rarity B13 of the location. The determination unit 433 may also determine the value "p3 × k3", obtained by multiplying the value p3 by a weighting coefficient k3, as the rarity B13 of the location. The value of the weighting coefficient k3 is, for example, "1". The value of the weighting coefficient k3 is not limited to "1" and can be changed as appropriate.

[0136] 1-4-1-4: An example of a method for determining the rarity B14 of the subject type Next, an example of a method by which the determination unit 433 determines the rarity B14 of the subject type based on the acquired metadata M11 will be explained.

[0137] The determination unit 433 selects a fourth content from among the multiple contents stored in the content storage area 22a that has metadata indicating the same type of subject as the subject data F4 of the acquired metadata M11 shown in Figure 4.

[0138] The determination unit 433 calculates the fourth ratio, which is the ratio of the number of fourth contents to the number of contents stored in the content storage area 22a, as a percentage. For example, if the number of contents stored in the content storage area 22a is "100" and the number of fourth contents is "3", the determination unit 433 calculates "3%" as the fourth ratio.

[0139] The determination unit 433 determines a value p4 obtained by subtracting the value of the fourth percentage from "100". For example, if the fourth percentage is "3%", the determination unit 433 determines "97" as the value p4.

[0140] If the content storage area 22a does not contain any content, the determination unit 433 determines the value p4 to be "100".

[0141] The determination unit 433 determines the value p4 as the rarity B14 of the subject type. The determination unit 433 may also determine the value "p4 × k4", obtained by multiplying the value p4 by a weighting coefficient k4, as the rarity B14 of the subject type. The value of the weighting coefficient k4 is, for example, "1". The value of the weighting coefficient k4 is not limited to "1" and can be changed as appropriate.

[0142] 1-4-1-5: An example of a method for determining the immediacy B15 Next, an example of a method by which the determination unit 433 determines the immediacy B15 based on the acquired metadata M11 will be explained.

[0143] The determination unit 433 calculates the elapsed time as the difference between the date and time shown in the date and time data F2 of the acquired metadata M11 shown in Figure 4 and the current date and time counted by the date and time counter 431. The elapsed time is the time that has passed from the date and time when content C1 was generated to the current date and time.

[0144] The determination unit 433 determines the value p5 based on the elapsed time. The maximum value of p5 is, for example, "100". The maximum value of p5 is not limited to "100" and can be changed as appropriate.

[0145] If the elapsed time is less than the threshold T1, the determination unit 433 determines the value p5 such that, for example, the value p5 decreases as the elapsed time increases. If the elapsed time is greater than or equal to the threshold T1, the determination unit 433 determines the value p5 as "0". The threshold T1 is, for example, 24 hours. The threshold T1 is not limited to 24 hours and can be changed as appropriate.

[0146] The determination unit 433 determines the value p5 as the degree of immediacy B15. The determination unit 433 may also determine the degree of immediacy B15 as the value "p5 × k5" obtained by multiplying the value p5 by a weighting coefficient k5. The value of the weighting coefficient k5 is, for example, "1". The value of the weighting coefficient k5 is not limited to "1" and can be changed as appropriate.

[0147] 1-4-2: An example of a method for determining demand B2 Next, an example of a method by which the determination unit 433 determines demand B2 based on the acquired metadata M11 will be explained.

[0148] For example, the decision unit 433 determines the demand B2 shown in Figure 7 based on the acquired metadata M11. Specifically, the decision unit 433 determines the level of attention B21 and the offered incentive B22 based on the acquired metadata M11.

[0149] Next, the decision unit 433 stores the attention level B21 and the offered incentive B22 in the storage device 42. However, the decision unit 433 does not necessarily have to store the attention level B21 and the offered incentive B22 in the storage device 42.

[0150] Next, the decision unit 433 determines the demand B2 based on the attention level B21 and the offered incentive B22. For example, let's assume that the attention level B21 and the offered incentive B22 are numerical values. In this case, the decision unit 433 determines the demand B2 by adding up the attention level B21 and the offered incentive B22.

[0151] 1-4-2-1: An example of a method for determining attention level B21. An example of a method in which the determination unit 433 determines attention level B21 based on the acquired metadata M11 is described below.

[0152] The determination unit 433, by referring to the demand determination data D3 shown in Figure 8, determines the number of requests D32 corresponding to the same subject type D31 as the subject type shown in the subject data F4 of the acquired metadata M11 shown in Figure 4, as the value q1. For example, if the subject data F4 of the acquired metadata M11 indicates "cat", the determination unit 433 determines "20" as the value q1. The demand determination data D3 referred to by the determination unit 433 is generated during the period before the acquisition of metadata M1 by the price determination server 40.

[0153] If the demand determination data D3 does not indicate the same type of subject as the subject data F4 of the acquired metadata M11, the determination unit 433 determines the value q1 as "0".

[0154] The determination unit 433 determines the value q1 as the attention level B21. The determination unit 433 may also determine the attention level B21 as the value "q1 × k6" obtained by multiplying the value q1 by the weight coefficient k6. The value of the weight coefficient k6 is, for example, "1". The value of the weight coefficient k6 is not limited to "1" and can be changed as appropriate.

[0155] 1-4-2-2: An example of a method for determining the presented incentive B22. An example of a method in which the determination unit 433 determines the presented incentive B22 based on the acquired metadata M11 is described below.

[0156] The decision unit 433, by referring to the demand determination data D3 shown in Figure 8, determines the value q2 of the presentation incentive D33 that corresponds to the same subject type D31 as the subject type shown in the subject data F4 of the acquired metadata M11 shown in Figure 4. For example, if the subject data F4 of the acquired metadata M11 indicates "cat", the decision unit 433 determines "300" as the value q2.

[0157] If the demand determination data D3 does not indicate the same type of subject as the subject data F4 of the acquired metadata M11, the determination unit 433 determines the value q2 to be "0".

[0158] The determination unit 433 determines the value q2 as the presented incentive B22. The determination unit 433 may also determine the value "q2 × k7", obtained by multiplying the value q2 by a weighting coefficient k7, as the presented incentive B22. The value of the weighting coefficient k7 is, for example, "1". The value of the weighting coefficient k7 is not limited to "1" and can be changed as appropriate.

[0159] 1-4-3: An example of a method for determining confidence level B3 Next, an example of a method by which the determination unit 433 determines confidence level B3 based on the acquired metadata M11 will be explained.

[0160] For example, the decision unit 433 determines the confidence level B3 shown in Figure 7 based on the acquired metadata M11. Specifically, the decision unit 433 determines the hardware confidence level B31, the software confidence level B32, and the content provider confidence level B33 based on the acquired metadata M11.

[0161] Next, the determination unit 433 stores the hardware reliability B31, the software reliability B32, and the content provider reliability B33 in the storage device 42. However, the determination unit 433 does not necessarily have to store the hardware reliability B31, the software reliability B32, and the content provider reliability B33 in the storage device 42.

[0162] Next, the determination unit 433 determines the reliability B3 based on the hardware reliability B31, the software reliability B32, and the content provider reliability B33. For example, it is assumed that the hardware reliability B31, the software reliability B32, and the content provider reliability B33 are all numerical values. In this case, the determination unit 433 determines the reliability B3 by adding up the hardware reliability B31, the software reliability B32, and the content provider reliability B33.

[0163] The determination unit 433 may also determine the reliability B3 by adding the hardware reliability B31 and the software reliability B32.

[0164] Alternatively, the determination unit 433 may determine the reliability B3 by adding the reliability B32 of the software and the reliability B33 of the content provider.

[0165] Alternatively, the determination unit 433 may determine the reliability B3 by adding the reliability B33 of the content provider and the reliability B31 of the hardware.

[0166] Furthermore, the determination unit 433 may determine the reliability B3 to be any of the hardware reliability B31, the software reliability B32, or the content provider reliability B33.

[0167] 1-4-3-1: An example of a method for determining hardware reliability B31 An example of a method in which the determination unit 433 determines hardware reliability B31 based on acquired metadata M11 is described below.

[0168] The determination unit 433, by referring to the device determination data D4 shown in Figure 9, determines the part number D42 of the element corresponding to the part number D41 of the terminal that is the same as the part number of the terminal 10 shown in the hardware data F51 of the acquired metadata M11 shown in Figure 4.

[0169] The determination unit 433 determines whether the part number of the element that is the same as the part number D42 of the determined element is shown in the hardware data F51 of the acquired metadata M11 shown in Figure 4.

[0170] The part number D42 of the element indicated by the device determination data D4 represents the part number of a legitimate element installed by the manufacturer in the terminal, which is determined by the corresponding terminal part number D41. Therefore, if the part number of the same element as the determined part number D42 is shown in the hardware data F51 of the acquired metadata M11, the determination unit 433 determines that the terminal 10 has not been modified. If the terminal 10 has not been modified, the terminal 10 has higher reliability than a modified terminal. If the part number of the same element as the determined part number D42 is shown in the hardware data F51 of the acquired metadata M11, the determination unit 433 determines "100" as the value r1.

[0171] On the other hand, if the part number of the element that is the same as the part number D42 of the determined element is not shown in the hardware data F51 of the acquired metadata M11, the determination unit 433 determines that the terminal 10 has been modified. If the terminal 10 has been modified, the terminal 10 has lower reliability compared to an unmodified terminal. If the determination unit 433 determines that the value r1 is "0" if the part number of the element that is the same as the part number D42 of the determined element is not shown in the hardware data F51 of the acquired metadata M11.

[0172] The determination unit 433 determines the value r1 as the hardware reliability B31. The determination unit 433 may also determine the value "r1 × k8", obtained by multiplying the value r1 by a weighting coefficient k8, as the hardware reliability B31. The value of the weighting coefficient k8 is, for example, "1". The value of the weighting coefficient k8 is not limited to "1" and can be changed as appropriate.

[0173] 1-4-3-2: An example of a method for determining the reliability of software B32 An example of a method in which the determination unit 433 determines the reliability of software B32 based on the acquired metadata M11 is described below.

[0174] The determination unit 433 determines whether the software data F52 of the acquired metadata M11 shown in Figure 4 indicates that developer mode is off.

[0175] When developer mode is off, for example, the risk of installing an undesirable app can be avoided. Therefore, if the software data F52 of the acquired metadata M11 indicates that developer mode is off, it is more likely that the software configuration of terminal 10 has not been changed by the addition of an undesirable app compared to terminal 10 when developer mode is on. Thus, if the software data F52 of the acquired metadata M11 indicates that developer mode is off, the determination unit 433 determines that the reliability of the software of terminal 10 that generated content C1 is higher than the second criterion value. If the software data F52 of the acquired metadata M11 indicates that developer mode is off, the determination unit 433 determines "100" as the value r2.

[0176] On the other hand, if the software data F52 of the acquired metadata M11 does not indicate that developer mode is off, the determination unit 433 determines that the reliability of the software of the terminal 10 that generated content C1 is lower than the second criterion value. If the software data F52 of the acquired metadata M11 does not indicate that developer mode is off, the determination unit 433 determines "0" as the value r2.

[0177] The determination unit 433 determines the value r2 as the software reliability B32. The determination unit 433 may also determine the value "r2 × k9", obtained by multiplying the value r2 by a weighting coefficient k9, as the software reliability B32. The value of the weighting coefficient k9 is, for example, "1". The value of the weighting coefficient k9 is not limited to "1" and can be changed as appropriate.

[0178] Furthermore, if the software data F52 of the acquired metadata M11 includes data indicating the hash value of the location data F1, the determination unit 433 calculates the hash value of the location data F1. If the calculated hash value of the location data F1 differs from the hash value of the location data F1 shown in the software data F52 of the acquired metadata M11, the determination unit 433 determines "0" as the value r2, regardless of the software data F52 of the acquired metadata M11.

[0179] Furthermore, if the software data F52 of the acquired metadata M11 also includes data indicating the hash value of the date and time data F2, the determination unit 433 calculates the hash value of the date and time data F2. If the calculated hash value of the date and time data F2 differs from the hash value of the date and time data F2 shown in the software data F52 of the acquired metadata M11, the determination unit 433 determines "0" as the value r2, regardless of the software data F52 of the acquired metadata M11.

[0180] Furthermore, if the software data F52 of the acquired metadata M11 contains only data indicating the hash value of the location data F1, the determination unit 433 calculates the hash value of the location data F1. If the calculated hash value of the location data F1 matches the hash value of the location data F1 shown in the software data F52 of the acquired metadata M11, the determination unit 433 determines "100" as the value r2. If the calculated hash value of the location data F1 is different from the hash value of the location data F1 shown in the software data F52 of the acquired metadata M11, the determination unit 433 determines "0" as the value r2.

[0181] Furthermore, if the software data F52 of the acquired metadata M11 contains only data indicating the hash value of the date and time data F2, the determination unit 433 calculates the hash value of the date and time data F2. If the calculated hash value of the date and time data F2 matches the hash value of the date and time data F2 shown in the software data F52 of the acquired metadata M11, the determination unit 433 determines "100" as the value r2. If the calculated hash value of the date and time data F2 is different from the hash value of the date and time data F2 shown in the software data F52 of the acquired metadata M11, the determination unit 433 determines "0" as the value r2.

[0182] Furthermore, if the software data F52 of the acquired metadata M11 contains only data indicating the hash value of the location data F1 and the hash value of the date and time data F2, the determination unit 433 calculates the hash value of the location data F1 and the hash value of the date and time data F2. If the calculated hash value of the location data F1 matches the hash value of the location data F1 shown in the software data F52 of the acquired metadata M11, and the calculated hash value of the date and time data F2 matches the hash value of the date and time data F2 shown in the software data F52 of the acquired metadata M11, the determination unit 433 determines "100" as the value r2. If the calculated hash value of the location data F1 is different from the hash value of the location data F1 shown in the software data F52 of the acquired metadata M11, the determination unit 433 determines "0" as the value r2. The determination unit 433 determines "0" as the value r2 if the calculated hash value of the date and time data F2 is different from the hash value of the date and time data F2 shown in the software data F52 of the acquired metadata M11.

[0183] Furthermore, if the calculated hash value of location data F1 matches the hash value of location data F1 shown in the software data F52 of acquired metadata M11, and the calculated hash value of date and time data F2 matches the hash value of date and time data F2 shown in the software data F52 of acquired metadata M11, the determination unit 433 determines that content C1 is authentic.

[0184] 1-4-3-3: An example of a method for determining the reliability B33 of a content provider An example of a method in which the determination unit 433 determines the reliability B33 of a content provider based on the acquired metadata M11 is described.

[0185] The determination unit 433, by referring to the user determination data D5 shown in Figure 10, determines the value q3 to be the value indicated by the confidence data D52 corresponding to the same user ID as the user ID shown in the user data F53 of the acquired metadata M11 shown in Figure 4. For example, if the user data F53 of the acquired metadata M11 shown in Figure 4 indicates "U1" as the user ID, the determination unit 433 determines "80" as the value q3.

[0186] The determination unit 433 determines the value q3 as the confidence level B33 of the content provider. The determination unit 433 may also determine the value "q3 × k10", obtained by multiplying the value q3 by a weighting coefficient k10, as the confidence level B33 of the content provider. The value of the weighting coefficient k10 is, for example, "1". The value of the weighting coefficient k10 is not limited to "1" and can be changed as appropriate.

[0187] If the sensor type data D12 of the metadata M1 shown in Figure 4 does not indicate an image sensor, the determination unit 433 uses the elements of the compensation data D2 shown in Figure 7, excluding the rarity B14 and demand B2 of the subject type, to determine the amount of compensation for providing content C1.

[0188] When the decision unit 433 determines the amount of consideration for providing content C1, it generates notification data A1 indicating the amount of consideration for providing content C1.

[0189] The transmitting unit 434 transmits notification data A1 to the content distribution server 20. The content distribution server 20 provides notification data A1 to the terminal 10. Subsequently, the user U1 of terminal 10 is paid the amount indicated in notification data A1. Also, if the content distribution server 20 receives a request from terminal 30 to acquire content C1, for example, it first provides notification data A1 to terminal 30 as a payment request notification. Once the content distribution server 20 confirms payment in response to the payment request notification, it provides content C1 to terminal 30.

[0190] 1-5: Operation Diagram 11 is a diagram illustrating the operation of the price determination server 40. Below, we will explain the operation when the sensor type data D12 of the metadata M1 shown in Figure 4 indicates an image sensor.

[0191] In step S101, the acquisition unit 432 acquires metadata M1 from the data associated with the content C1 provided from the terminal 10 via the content distribution server 20 and the communication device 41.

[0192] Next, in step S102, the determination unit 433 determines the rarity B1 based on the metadata M1 acquired by the acquisition unit 432. For example, the determination unit 433 determines the rarity B1 as described above.

[0193] Next, in step S103, the determination unit 433 determines the demand B2 based on the metadata M1 acquired by the acquisition unit 432. For example, the determination unit 433 determines the demand B2 as described above.

[0194] Step S103 may be executed during the period from the time when step S101 is completed until the time when step S102 is started.

[0195] Next, in step S104, the determination unit 433 determines the confidence level B3 based on the metadata M1 acquired by the acquisition unit 432. For example, the determination unit 433 determines the confidence level B3 as described above.

[0196] Furthermore, step S104 may be executed during the period from the completion of step S101 to the start of step S102. Also, step S104 may be executed during the period from the completion of step S102 to the start of step S103. Furthermore, if step S103 is executed during the period from the completion of step S101 to the start of step S102, then step S104 may be executed during the period from the completion of step S101 to the start of step S103. Furthermore, if step S103 is executed during the period from the completion of step S101 to the start of step S102, then step S104 may be executed during the period from the completion of step S103 to the start of step S102.

[0197] Next, in step S105, the decision unit 433 determines the price for providing content C1 based on rarity B1, demand B2, and reliability B3. For example, as described above, the decision unit 433 determines the amount of the price for providing content C1.

[0198] Next, in step S106, the determination unit 433 generates notification data A1 indicating the amount of consideration for the provision of content C1.

[0199] Next, in step S107, the transmission unit 434 transmits notification data A1 to the content distribution server 20. The content distribution server 20 provides notification data A1 to the terminal 10. Subsequently, the user U1 of the terminal 10 is paid the amount indicated in notification data A1.

[0200] 1-6: Summary of Embodiments The payment determination server 40 includes an acquisition unit 432 and a determination unit 433. The acquisition unit 432 acquires metadata M1 that indicates information about content C1 from the data associated with content C1 provided from terminal 10. The determination unit 433 determines the payment for the provision of content C1 based on the confidence level B3 regarding content C1 based on the metadata M1.

[0201] Therefore, the confidence level B3 for content C1 can be reflected in the compensation for providing content C1.

[0202] Metadata M1 may indicate information about the hardware of the terminal 10 that generated the content C1. The confidence level B3 for content C1 may include the confidence level B31 of the terminal 10's hardware. In this case, the confidence level B31 of the terminal 10's hardware that generated content C1 can be reflected in the price for providing content C1.

[0203] Metadata M1 may indicate information about the software of the terminal 10 that generated the content C1. The confidence level B3 for content C1 may include the confidence level B32 of the software of the terminal 10. In this case, the confidence level B32 of the software of the terminal 10 that generated content C1 can be reflected in the price for providing content C1.

[0204] Metadata M1 may indicate information about user U1 of terminal 10 that provided content C1. The confidence level B3 for content C1 may include the confidence level B33 of user U1 of terminal 10. In this case, the confidence level B33 of user U1 of terminal 10 that provided content C1 can be reflected in the payment for providing content C1.

[0205] The decision unit 433 may determine the price for providing content C1 based on the confidence level B3 regarding content C1 and the rarity B11 of the time of creation of content C1. In this case, the price for providing content C1 can be determined by taking into account the confidence level B3 regarding content C1 and the rarity B11 of the time of creation of content C1. Therefore, the content can be evaluated more fairly.

[0206] The determination unit 433 may determine the price for providing content C1 based on the confidence level B3 regarding content C1 and the rarity level B12 of the type of sensor that generated content C1. In this case, the price for providing content C1 can be determined by taking into account the confidence level B3 regarding content C1 and the rarity level B12 of the type of sensor that generated content C1. Therefore, the content can be evaluated more fairly.

[0207] The decision unit 433 may determine the price for providing content C1 based on the confidence level B3 regarding content C1 and the rarity B13 of the location where content C1 is generated. In this case, the price for providing content C1 can be determined by taking into account the confidence level B3 regarding content C1 and the rarity B13 of the location where content C1 is generated. Therefore, the content can be evaluated more fairly.

[0208] The decision unit 433 may determine the price for providing content C1 based on the confidence level B3 regarding content C1 and the demand B2 for content C1 during the period before the acquisition of metadata M1. In this case, the price for providing content C1 can be determined by taking into account the confidence level B3 regarding content C1 and the demand B2 for content C1 during the period before the acquisition of metadata M1. Therefore, the content can be evaluated more fairly.

[0209] 2: Modifications The following are examples of modifications to the above-described embodiments. Two or more modifications can be arbitrarily selected from the following examples and combined as appropriate, within the bounds of mutual non-contradictory relationships.

[0210] 2-1: In the first modified embodiment, content C1 may be content that has been determined to be authentic. In this case, terminal 10 provides content C1 that has been determined to be authentic. For example, if metadata M1 indicates the hash value of content C1 at the time of content C1 generation, the operation control unit 173 of terminal 10 may operate as follows. When the operation control unit 173 receives an instruction from input device 11 to provide content C1, it calculates the hash value of content C1. If the calculated hash value of content C1 matches the hash value of content C1 shown in metadata M1, the operation control unit 173 provides content C1 and metadata M1 to the content distribution server 20. If the calculated hash value of content C1 differs from the hash value of content C1 shown in metadata M1, the operation control unit 173 prohibits the provision of content C1 and metadata M1.

[0211] According to the first modified example, content C1 is content that has been determined to be authentic. Therefore, the authenticity of content C1 provided to the content distribution server 20 is guaranteed. Furthermore, appropriate compensation can be offered to the provider of content C1 whose authenticity is guaranteed.

[0212] 2-2: In the second modified embodiment and the first modified embodiment, the weight coefficients may be changed dynamically. For example, if the content of image data taken at a certain time and place is extremely scarce, the determination unit 433 increases the time weight coefficient k1 and the location weight coefficient k3.

[0213] According to the second modification, the compensation for necessary content can be increased by adjusting the weighting coefficients.

[0214] 2-3: In the third modified embodiment, the first modified embodiment, and the second modified embodiment, the determination unit 433 may change the method for calculating the consideration according to the content.

[0215] For example, the determination unit 433 may determine the price for providing content showing an image based on rarity B1 and confidence B3, and the price for providing content based on the temperature measurement result may be determined based solely on confidence B3.

[0216] Furthermore, the decision unit 433 may determine the price for providing content showing a dog as the subject based on rarity B1, demand B2, and confidence B3, and may determine the price for providing content showing clouds as the subject based on rarity B1 and confidence B3.

[0217] According to the third modification, the method for calculating compensation can be changed depending on the content.

[0218] 2-4: Fourth Modified Embodiment, and in the first to third modified embodiments, the consideration data D2 is not limited to the example shown in Figure 7. For example, demand B2 may include the level of attention from content providers. The level of attention from content providers is, for example, the number of requests to the content provider during the period before the acquisition of metadata M1 by the consideration determination server 40. Requests to content providers mean requests for content provided by the content provider. In this case, even if the sensor type data D12 of metadata M1 does not indicate an image sensor, the amount of consideration for the provision of content C1 can be determined by taking demand B2 into account.

[0219] 3. Other (1) Each function illustrated in Figure 2, Figure 5, or Figure 6 can be implemented by any combination of hardware and software. The method of implementing each function is not particularly limited. Each function may be implemented using one physically or logically coupled device, or it may be implemented using a device configured by directly or indirectly connecting two or more physically or logically separated devices (for example, using wired, wireless, etc.). Each function may be implemented by combining the above one device or the above multiple devices with software.

[0220] (2) In this disclosure, the term “apparatus” may be replaced with other terms such as circuit, device or unit.

[0221] (3) In each of the embodiments and the first to fourth modifications, the storage device 16, storage device 22, and storage device 42 may consist of at least one of the following: an optical disc such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., compact disc, digital multipurpose disc, Blu-ray® disc), a smart card, flash memory (e.g., card, stick, key drive), a floppy® disk, a magnetic strip, etc. The program may also be transmitted from a network via a telecommunications line.

[0222] (4) Each of the embodiments and the first to fourth modified examples is LTE (Long Term Evolution), LTE-A (LTA-Advanced), SUPER 3G, IMT-Advanced, 4G (4th generation mobile communication system), 5G (5th generation mobile communication system), 6th generation mobile communication system (6G), xth generation mobile communication system (xG) (where x is, for example, an integer or decimal), FRA (Future Radio Access), NR (new Radio), New radio access (NX), Future generation radio access (FX), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20 may apply to at least one system utilizing UWB (Ultra-WideBand), Bluetooth®, or other appropriate systems, and to next-generation systems extended, modified, created, or defined based thereon. Alternatively, multiple systems may be applied in combination (e.g., a combination of at least one of LTE and LTE-A with 5G).

[0223] (5) The processing procedures, sequences, or flowcharts illustrated in each of the embodiments and the first to fourth modifications may be in any order, as long as they do not contradict each other. For example, the methods described in this disclosure present various step elements in an illustrative order and are not limited to any particular order presented.

[0224] (6) In each of the embodiments and the first to fourth modifications, the input and output information may be stored in a specific location (e.g., memory) or managed using a management table. The input and output information may be overwritten, updated, or appended to. The output information may be deleted. The input information may be transmitted to other devices.

[0225] (7) In each of the embodiments and the first to fourth modifications, the determination may be based on a value represented by one bit (0 or 1), on a Boolean value (true or false), or on a numerical comparison (for example, a comparison with a predetermined value).

[0226] (8) The programs illustrated in each of the embodiments and the first to fourth modifications should be broadly interpreted to mean instructions, instruction sets, code, code segments, program code, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, procedures, or functions, whether they are called software, firmware, middleware, microcode, or hardware description languages ​​or by other names. Furthermore, software, or instructions, etc., may be transmitted or received via a transmission medium. For example, if software is transmitted from a website, server, or other remote source using at least one of wired technology (such as coaxial cable, fiber optic cable, twisted pair, and digital subscriber line (DSL)) and wireless technology (such as infrared, microwave, etc.), at least one of these wired and wireless technologies is included in the definition of a transmission medium.

[0227] (9) The information described in each of the embodiments and the first to fourth modifications may be represented using any of the following different technologies. For example, the data and information that may be referred to throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields, magnetic particles, optical fields, photons, or any combination thereof. Notwithstanding the terms described herein and the terms necessary for understanding this disclosure, terms may be replaced with terms having the same or similar meanings.

[0228] (10) In each of the embodiments and the first to fourth modifications, the terms “system” and “network” are used interchangeably.

[0229] (11) In each of the embodiments and the first to fourth modifications, each of terminals 10 and 10a is, for example, a mobile station. A mobile station may also be referred to by those skilled in the art as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or several other appropriate terms.

[0230] (12) A mobile station may also be called a transmitting device, receiving device, or communication device. A mobile station may also be a device mounted on a mobile body, or the mobile body itself. A mobile body means a movable object. The speed of movement of a mobile body is arbitrary. A mobile body is stoppable. A mobile body includes, but is not limited to, vehicles, transport vehicles, automobiles, motorcycles, bicycles, connected cars, excavators, bulldozers, wheel loaders, dump trucks, forklifts, trains, buses, handcarts, rickshaws, ships and other watercraft, airplanes, rockets, satellites, drones (registered trademark), multicopters, quadcopters, balloons, and things mounted on them. A mobile body may be a mobile body that moves autonomously based on operational commands. A mobile body may be a vehicle (e.g., a car, an airplane), an unmanned mobile body (e.g., a drone, an autonomous vehicle), or a robot (manned or unmanned). A mobile station also includes devices that do not necessarily move during communication operations. For example, the mobile station could be an IoT (Internet of Things) device such as a sensor.

[0231] (13) In each of the embodiments and the first to fourth modifications, the term “decision” may encompass a wide variety of actions. “Decision” may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiry (e.g., searching in a table, database or another data structure), ascertaining, etc. “Decision” may also include, for example, receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in memory), etc. “Decision” may also include, for example, resolving, selecting, choosing, establishing, comparing, etc. In other words, "decision" can include considering that some action has been "decided." Furthermore, "decision" can be reinterpreted as "assuming," "expecting," or "considering."

[0232] (14) In each of the embodiments and the first to fourth modifications, the term “connected,” or any variation thereof, means any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are “connected” or “coupled” to each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, “connection” may be read as “access.” As used in the present disclosure, two elements may be considered to be “connected” or “coupled” to each other using at least one of one or more wires, cables, and printed electrical connections, and, in some non-limiting and non-exclusive examples, electromagnetic energy having wavelengths in the radio frequency domain, microwave domain, and optical (both visible and invisible) domain.

[0233] (15) In each of the first embodiment and the first to fourth modifications, the phrase "based on" does not mean "based solely on" unless otherwise specified. In other words, the phrase "based on" means both "based solely on" and "based at least on".

[0234] (16) Any reference to elements using the designations “first” and “second” as used in this disclosure does not limit the quantity or order of those elements in general. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Accordingly, references to the first and second elements do not imply that only two elements may be adopted or that the first element must precede the second element in any way.

[0235] (17) Where “include,” “including,” and variations thereof are used in each of the embodiments and the first to fourth modifications in this disclosure or in the claims, these terms are intended to be inclusive, as is the term “comprising.” Furthermore, where the term “or” is used in this disclosure or in the claims, it is intended not to be an exclusive OR.

[0236] (18) Where articles are added in the Disclosure by translation, such as a, an, and the in English, the Disclosure may include the fact that the noun following these articles is plural.

[0237] (19) The information, parameters, etc. described in this disclosure may be expressed using absolute values, relative values ​​from a given value, or other corresponding information.

[0238] (20) In this disclosure, the term “A and B are different” may mean “A and B are different from each other.” The term may also mean “A and B are each different from C.” Terms such as “separate” and “combine” may be interpreted in the same way as “different.”

[0239] (21) Each aspect / embodiment described herein may be used individually, in combination, or switched between as needed during implementation. Furthermore, notification of certain information (e.g., notification that "it is X") is not limited to explicit notification, but may also be implicit (e.g., by not providing such notification).

[0240] (22) It will be obvious to those skilled in the art that the present invention is not limited to the embodiments described herein. The present invention can be implemented in modified and altered forms without departing from the spirit and scope of the invention as defined by the claims. Accordingly, the descriptions herein are for illustrative purposes only and are not intended to be restrictive in any way to the present invention. Furthermore, multiple embodiments selected from those illustrated herein may be combined.

[0241] 4. Aspects that can be understood from the above-described forms or modifications The following aspects can be understood from at least one of the above-described forms or modifications.

[0242] 4-1: First Embodiment The consideration determination device according to the first embodiment includes an acquisition unit and a determination unit. The acquisition unit acquires metadata indicating information about the content from among the data accompanying the content provided from the terminal. The determination unit determines the consideration for the provision of the content based on the confidence level regarding the content based on the metadata.

[0243] In this configuration, the level of trust in the content can be reflected in the compensation for providing the content.

[0244] 4-2: Second Embodiment In the example of the first embodiment (second embodiment), the metadata indicates information about the hardware of the device that generated the content, and the reliability of the content includes the reliability of the hardware. According to this embodiment, the reliability of the hardware of the device that generated the content can be reflected in the compensation for the provision of the content.

[0245] 4-3: Third Embodiment In an example of the first or second embodiment (third embodiment), the metadata indicates information about the software of the device that generated the content, and the confidence level of the content includes the confidence level of the software. According to this embodiment, the confidence level of the software of the device that generated the content can be reflected in the compensation for the provision of the content.

[0246] 4-4: Fourth Embodiment In any example of the first to third embodiments (fourth embodiment), the metadata indicates information about the user of the terminal that provided the content, and the confidence level regarding the content includes the confidence level of the user. According to this embodiment, the confidence level of the user of the device that provided the content can be reflected in the compensation for the provision of the content.

[0247] 4-5: Fifth Embodiment In any example of the first to fourth embodiments (fifth embodiment), the content is content that has been determined to be authentic. According to this embodiment, the authenticity of the content is guaranteed. Furthermore, appropriate compensation can be offered to providers of content whose authenticity is guaranteed.

[0248] 4-6: Sixth Embodiment In any example of the first to fifth embodiments (sixth embodiment), the determination unit determines the consideration based on the reliability of the content and the rarity of the time of generation of the content. According to this embodiment, the possibility of tampered content being distributed can be reduced.

[0249] 4-7: Seventh Embodiment In any example of the first to sixth embodiments (seventh embodiment), the determination unit determines the compensation based on the reliability of the content and the rarity of the type of sensor that generated the content. According to this embodiment, the compensation for providing the content can be determined by taking into account the reliability of the content and the rarity of the type of sensor that generated the content.

[0250] 4-8: Eighth Embodiment In any example of the first to seventh embodiments (eighth embodiment), the determination unit determines the compensation based on the reliability of the content and the rarity of the location where the content is generated. According to this embodiment, the compensation for the provision of content can be determined by taking into account the reliability of the content and the rarity of the location where the content is generated.

[0251] 4-9: Ninth Embodiment In any example of the first to eighth embodiments (the ninth embodiment), the determination unit determines the consideration based on the reliability of the content and the demand for the content during the period prior to the acquisition of the metadata. According to this embodiment, the consideration for the provision of content can be determined by taking into account the reliability of the content and the demand for the content during the period prior to the acquisition of the metadata.

[0252] 4-10: Tenth Embodiment The method for determining consideration according to the tenth embodiment includes obtaining metadata indicating information about the content from data accompanying the content provided from the terminal, and determining the consideration for the provision of the content based on the reliability of the content based on the metadata. According to this embodiment, the same effects as the first embodiment can be achieved.

[0253] CS...Content Management System, U1...User, 10...Terminal, 11...Input Device, 12...Camera, 12a...Image Sensor, 13...Display Device, 14...GPS Module, 15...Communication Device, 16...Storage Device, 17...Processing Device, 18...Bus, 20...Content Distribution Server, 21...Communication Device, 22...Storage Device, 22a...Content Storage Area, 23...Processing Device, 231...Operation Control Unit, 24...Bus, 30...Terminal, 40...Price Determination Server, 41...Communication Device, 42...Storage Device, 43...Processing Device, 431...Date and Time Counter, 432...Acquisition Unit, 433...Determination Unit, 434...Transmission Unit, 44...Bus.

Claims

1. A price determination device comprising: an acquisition unit that acquires metadata indicating information about the content from data accompanying the content provided from a terminal; and a determination unit that determines the price for providing the content based on the reliability of the content based on the metadata.

2. The consideration determination device according to claim 1, wherein the metadata indicates information about the hardware of the device that generated the content, and the confidence level with respect to the content includes the confidence level of the hardware.

3. The consideration determination device according to claim 1, wherein the metadata indicates information about the software of the device that generated the content, and the confidence level with respect to the content includes the confidence level of the software.

4. The consideration determination device according to claim 1, wherein the metadata indicates information about the user of the terminal that provided the content, and the confidence level regarding the content includes the confidence level of the user.

5. The consideration determination device according to claim 1, wherein the content is determined to be authentic content.

6. The price determination device according to claim 1, wherein the determination unit determines the price based on the reliability of the content and the rarity of the time of generation of the content.

7. The price determination device according to claim 1, wherein the determination unit determines the price based on the reliability of the content and the rarity of the type of sensor that generated the content.

8. The consideration determination device according to claim 1, wherein the determination unit determines the consideration based on the reliability of the content and the rarity of the location where the content is generated.

9. The consideration determination device according to claim 1, wherein the determination unit determines the consideration based on the reliability of the content and the demand for the content during the period prior to the acquisition of the metadata.

10. A method for determining consideration, which includes: obtaining metadata from data associated with content provided from a terminal that indicates information about the content; and determining consideration for the provision of the content based on the confidence level regarding the content based on the metadata.

Images