Display control device, display control method, and display control program

Abstract

A display control device (100) generates a trained model through learning detection of information that is obtained when each of a plurality of content items is displayed by a display device (210), and indicates the situation when the display is performed and the length of the time during which the displayed content is visually recognized. The display control device: infers, by using the trained model, the time during which the plurality of content items are visually recognized for respective inference patterns indicating the situation when the display is performed; and determines display ratios of the plurality of content items for the respective inference patterns on the basis of the inference result.

Description

Display control device, display control method, and display control program

[0001] The present disclosure relates to techniques for effectively displaying information on a display medium.

[0002] BACKGROUND ART In recent years, information has been provided in various places such as stores, train stations, airports, and hotels using electronic video media such as digital signage.

[0003] Furthermore, a technique for effectively displaying information by switching display content is known. Patent Document 1 discloses a technique for acquiring information about nearby users from camera images or the like and switching display content.

[0004] In the technology of Patent Document 1, attributes and environments are linked to display content manually. Therefore, the effect of switching display content depends on the skill of the person who links them. Furthermore, if there are multiple display contents to be linked to attributes and environments, the display allocation of the multiple display contents must be manually adjusted. Therefore, the effect of switching display content depends on the skill of the person who adjusts them. Furthermore, an operation that evenly distributes the display of multiple display contents does not sufficiently improve the effect.

[0005] Japanese Patent Application Laid-Open No. 2019-159468

[0006] An object of the present disclosure is to enable the display of multiple pieces of content at a display ratio suited to the situation when the content is displayed.

[0007] The display control device of the present disclosure includes: a learning unit that learns detection information obtained each time a plurality of contents is displayed by the display device, the detection information indicating the situation when the display was performed and the length of time the displayed content was viewed, and generates a learned model; and an inference unit that uses the learned model to infer the time when each of the plurality of contents is viewed for each inference pattern indicating the situation when the display was performed, and determines the display ratio of each of the plurality of contents for each inference pattern based on the inference result.

[0008] According to the present disclosure, it is possible to determine a display ratio appropriate for each situation when content is displayed, thereby enabling multiple pieces of content to be displayed at a display ratio appropriate for the situation when the content is displayed.

[0009] 1 is a configuration diagram of an information providing system 200 according to the first embodiment. A configuration diagram of a display control device 100 according to the first embodiment. A functional configuration diagram of the display control device 100 according to the first embodiment. A configuration diagram of a display device 210 according to the first embodiment. A flowchart of a display control method according to the first embodiment. A flowchart of step S110 according to the first embodiment. A diagram showing an example of detection information 181 according to the first embodiment. A diagram showing an example of display performance information 182 according to the first embodiment. A diagram showing an example of content information 183 according to the first embodiment. A diagram showing an example of learning data 184 according to the first embodiment. A flowchart of step S120 according to the first embodiment. A diagram showing an example of inference pattern information 185 according to the first embodiment. A diagram showing an example of inference result 186 according to the first embodiment. A diagram showing an example of display ratio information 187 according to the first embodiment. A hardware configuration diagram of the display control device 100 according to the first embodiment. A hardware configuration diagram of a display device 210 according to the first embodiment.

[0010] In the embodiments and drawings, the same or corresponding elements are denoted by the same reference numerals. The description of elements denoted by the same reference numerals as those already described will be omitted or simplified as appropriate. Arrows in the drawings primarily indicate the flow of data or the flow of processing.

[0011] First Embodiment An information providing system 200 will be described with reference to FIGS.

[0012] ***Description of Configuration*** The configuration of the information providing system 200 will be described with reference to Fig. 1. The information providing system 200 includes a display device 210 and a display control device 100. The display device 210 includes a display 211 and a camera 212.

[0013] The display 211 displays content. The content may be, for example, an advertisement introducing a product or service. The content may also be information such as facility information, traffic information, or tourist information. The content displayed on the display 211 is referred to as "display content." A person who views the display content is referred to as a "viewer."

[0014] The camera 212 captures an image in front of the display 211. The image obtained by capturing the image is referred to as a "camera image." The camera image is used to detect the viewer and the attributes of the viewer.

[0015] The display device 210 and the display control device 100 communicate with each other via a network 201. An example of the network 201 is the Internet.

[0016] The information providing system 200 may include multiple display devices 210 located at different locations.

[0017] The configuration of the display control device 100 will be described with reference to Fig. 2. The display control device 100 is a computer including hardware such as a processor 101, a memory 102, an auxiliary storage device 103, a communication device 104, and an input / output interface 105. These pieces of hardware are connected to each other via signal lines.

[0018] Examples of computers include personal computers, stationary computers such as server computers, portable computers such as smartphones and tablet computers, microcomputers embedded in devices, and SoCs (SoC is an abbreviation for System on Chip).

[0019] The processor 101 is an IC that performs arithmetic processing and controls other hardware. For example, the processor 101 is a CPU, a DSP, or an FPGA. The processor 101 may be a single processor or a multiprocessor. IC is an abbreviation for Integrated Circuit. CPU is an abbreviation for Central Processing Unit. DSP is an abbreviation for Digital Signal Processor. FPGA is an abbreviation for Field Programmable Gate Array.

[0020] The memory 102 is a volatile or non-volatile storage device. The memory 102 is also called a primary storage device or a main memory. For example, the memory 102 is a RAM. Data stored in the memory 102 is saved in the secondary storage device 103 as needed. RAM is an abbreviation for Random Access Memory.

[0021] The auxiliary storage device 103 is a non-volatile storage device. For example, the auxiliary storage device 103 is a ROM, HDD, SSD, flash memory, or a combination of these. Data stored in the auxiliary storage device 103 is loaded into the memory 102 as needed. ROM is an abbreviation for Read Only Memory. HDD is an abbreviation for Hard Disk Drive. SSD is an abbreviation for Solid State Drive.

[0022] The communication device 104 is a receiver and a transmitter. For example, the communication device 104 is a communication chip or a NIC. The display control device 100 performs communication using the communication device 104. NIC is an abbreviation for Network Interface Card.

[0023] The input / output interface 105 is a port to which an input device and an output device are connected. For example, the input / output interface 105 is a USB terminal. Input and output of the display control device 100 is performed using the input / output interface 105. USB is an abbreviation for Universal Serial Bus.

[0024] The display control device 100 includes elements such as a learning unit 110 and an inference unit 120. These elements are realized by software.

[0025] The auxiliary storage device 103 stores a display control program for causing the computer to function as the learning unit 110 and the inference unit 120. The display control program is loaded into the memory 102 and executed by the processor 101. The auxiliary storage device 103 also stores an OS. At least a portion of the OS is loaded into the memory 102 and executed by the processor 101. The processor 101 executes the display control program while executing the OS. OS is an abbreviation for Operating System.

[0026] Data (input data, output data, etc.) of the display control program is stored in the storage unit 190. The auxiliary storage device 103 functions as the storage unit 190. However, a storage device such as the memory 102, a register in the processor 101, or a cache memory in the processor 101 may function as the storage unit 190 instead of or together with the auxiliary storage device 103.

[0027] The display control device 100 may include a plurality of processors that replace the processor 101 .

[0028] The display control program can be recorded (stored) in a computer-readable manner on a non-volatile recording medium such as an optical disk or a flash memory, etc. The display control program may be provided as a program product.

[0029] 3 shows the functional configuration of the display control device 100. The learning unit 110 includes elements such as an acquisition unit 111, a learning data generation unit 112, and a learning processing unit 113. The inference unit 120 includes elements such as an acquisition unit 121, an inference processing unit 122, a display ratio determination unit 123, and an output unit 124. The storage unit 190 includes a detection information storage unit 191, a display record storage unit 192, a content information storage unit 193, a learned model storage unit 194, and an inference pattern storage unit 195.

[0030] The configuration of the display device 210 will be described with reference to Fig. 4. The display device 210 is a computer equipped with hardware such as a processor 213, a memory 214, an auxiliary storage device 215, a display 211, a camera 212, and a communication device 216. These pieces of hardware are connected to one another via signal lines.

[0031] The processor 213 is an IC that performs arithmetic processing and controls other hardware. For example, the processor 213 is a CPU. The memory 214 is a volatile or non-volatile storage device. The memory 214 is also called a primary storage device or main memory. For example, the memory 214 is a RAM. Data stored in the memory 214 is saved in the secondary storage device 215 as needed. The secondary storage device 215 is a non-volatile storage device. The secondary storage device 215 is also called storage. For example, the secondary storage device 215 is a ROM, a HDD, a flash memory, or a combination thereof. Data stored in the secondary storage device 215 is loaded into the memory 214 as needed. The display 211 may be a flat display or a curved display. For example, the display 211 is a liquid crystal display or an LED display. LED is an abbreviation for Light Emission Diode. The camera 212 is an imaging device that captures images and outputs them. The communication device 216 is a receiver and a transmitter. For example, the communication device 216 is a communication chip or a NIC.

[0032] The display device 210 may include a projector instead of or in addition to the display 211. The projector displays content by projecting the content, for example, the content is displayed as a three-dimensional hologram.

[0033] The display device 210 includes an element called a control unit 217. This element is realized by software.

[0034] The auxiliary storage device 215 stores a display program for causing the computer to function as the control unit 217. The display program is loaded into the memory 214 and executed by the processor 213. The auxiliary storage device 215 also stores an OS. At least a portion of the OS is loaded into the memory 214 and executed by the processor 213. The processor 213 executes the display program while executing the OS.

[0035] The data of the display program is stored in the storage unit 218. The auxiliary storage device 215 functions as the storage unit 218. However, a storage device such as the memory 214, a register in the processor 213, or a cache memory in the processor 213 may function as the storage unit 218 instead of or together with the auxiliary storage device 215.

[0036] The display program can be recorded (stored) in a computer-readable manner on a non-volatile recording medium such as an optical disk or a flash memory.

[0037] ***Explanation of Operation*** The operational procedure of the information providing system 200 corresponds to an information providing method. The operational procedure of the information providing system 200 corresponds to a processing procedure by an information providing program. The information providing program includes a display control program and a display program.

[0038] The operation procedure of the display control device 100 corresponds to a display control method, and also corresponds to a processing procedure according to a display control program.

[0039] An overview of the display control method will be described with reference to Fig. 5. In step S110, the learning unit 110 learns the detection information 181 obtained each time a plurality of contents are displayed by the display device 210, and generates a learned model.

[0040] The detection information 181 indicates the circumstances under which the display was performed and the length of time the displayed content was viewed.

[0041] In step S120, the inference unit 120 uses the trained model to infer the viewing time of each of the plurality of content items for each inference pattern. The inference pattern indicates the circumstances under which the content items are displayed. Then, based on the inference results, the inference unit 120 determines the display ratio of each of the plurality of content items for each inference pattern.

[0042] Details of step S110 will be described with reference to Fig. 6. In step S111, the acquisition unit 111 acquires the detection information 181, the display record information 182, and the content information 183 from the storage unit 190. The detection information 181 is acquired from the detection information storage unit 191. The display record information 182 is acquired from the display record storage unit 192. The content information 183 is acquired from the content information storage unit 193.

[0043] The detection information 181 will be described with reference to FIG. 7 . The detection information 181 indicates a display status and a "viewing time." The display status is the status when content is displayed. The display status includes date and time information, attributes of the viewer, and the like. In FIG. 7 , the detection information 181 indicates a "person ID," a "date and time," a "gender," and an "age" as the display status. The "person ID" is an identifier that identifies the viewer. The viewer is detected using camera footage. The "person ID" is assigned to each detected viewer. The "date and time" is the date and time when the viewer began to view the displayed content. The "date and time" corresponds to the capture time of the camera footage in which the viewer was detected. The "gender" is the gender of the viewer. The "gender" is detected using the camera footage in which the viewer was detected. The "age" is the age (or age category) of the viewer. The "age" is detected using the camera footage in which the viewer was detected. The "viewing time" is the length of time the viewer viewed the displayed content. The “viewing time” corresponds to the time during which the viewer continues to be detected. The detection information 181 is, for example, generated by the display device 210, transmitted from the display device 210, received by the acquisition unit 111, and stored in the storage unit 190.

[0044] The display performance information 182 will be described with reference to FIG. 8 . The display performance information 182 indicates the performance of displaying each of a plurality of pieces of content. In FIG. 8 , the display performance information 182 indicates a "content ID," a "display start date and time," a "display end date and time," and a "display time." The "content ID" is an identifier that identifies the content. A "content ID" is assigned to each piece of content. The "display start date and time" is the date and time when the display of the content started. If the content is a video, the "display start date and time" corresponds to the date and time when playback of the content started. The "display end date and time" is the date and time when display of the content ended. If the content is a video, the "display end date and time" corresponds to the date and time when playback of the content ended. The "display time" is the length of time the content was displayed. The "display time" corresponds to the length of time from the display start date and time to the display end date and time. If the content is a video, the "display time" corresponds to the playback time of the content. The display performance information 182 is, for example, generated by the display device 210 , transmitted from the display device 210 , received by the acquisition unit 111 , and stored in the storage unit 190 .

[0045] The content information 183 will be described with reference to FIG. 9 . The content information 183 indicates various information about each of a plurality of pieces of content. In FIG. 9 , the content information 183 indicates a "content ID," a "content type," a "display time," a "posting start date," and a "posting end date." The "content ID" is an identifier for identifying the content. The "content type" is the type of content. Examples of the "content type" are (still) images and videos. The "playback time" is the duration of video content. The "posting start date" is the date on which information provision by displaying the content began. The "posting end date" is the date on which information provision by displaying the content will end. The content information 183 is, for example, registered in the display device 210, transmitted from the display device 210, received by the acquisition unit 111, and stored in the storage unit 190.

[0046] 6, the description will continue from step S112. In step S112, the training data generation unit 112 generates training data 184 using the information acquired in step S111.

[0047] The training data 184 is data used for training to generate a trained model.

[0048] The learning data generating unit 112 mainly combines the detection information 181 and the display performance information 182 to generate learning data 184 .

[0049] The learning data 184 will be described with reference to FIG. 10 . The learning data 184 indicates a display situation and viewing information. The display situation is the situation when the content is displayed. The display situation includes information on date and time, attributes of the viewer, and the like. In FIG. 10 , the learning data 184 indicates, as display situations, a "person ID," "date and time," "gender," "age," "time period segment," and "day segment." The "person ID" is an identifier that identifies the viewer. The "person ID" is obtained from the detection information 181. The "date and time" is the date and time when the viewer began viewing the displayed content. The "date and time" is obtained from the detection information 181. The "gender" is the gender of the viewer. The "gender" is obtained from the detection information 181. The "age" is the age of the viewer. The "age" is obtained from the detection information 181. The "time period segment" is the segment of the time period when the displayed content is viewed by the viewer. The time period is divided into, for example, morning, afternoon, and night. The "time period segment" is determined based on the "date and time." The "day segment" is the segment of the day on which the display content is viewed by the viewer. Days are, for example, divided into weekdays and holidays. The "day segment" is determined based on the "date and time."

[0050] The viewing information is information related to the viewing of the display content by the viewer. In FIG. 10 , the learning data 184 indicates, as viewing information, a "viewing time," a "content ID," and a "time breakdown." The "viewing time" is the length of time the viewer views the display content. The "viewing time" is obtained from the detection information 181. The "content ID" is an identifier for identifying the viewed display content. The "content ID" is determined based on the "date and time" and the display performance information 182. In the display performance information 182, the period from the "display start date and time" to the "display end date and time" is referred to as the display time slot. The "content ID" in the display performance information 182 whose display time slot includes the same date and time as the "date and time" of the learning data 184 is the "content ID" of the learning data 184. The "time breakdown" is the "viewing time" for each display content. The "time breakdown" is determined based on the "date and time," "viewing time," and the display performance information 182. In the learning data 184, a time period from the "date and time" until the "viewing time" has elapsed is referred to as a viewing time period. The length of the time period that overlaps between the display time period and the viewing time period is the "time breakdown."

[0051] A further explanation of the learning data 184 is provided. The learning data 184 consists of information indicating how long a user's viewing time was when a particular piece of content was displayed under a particular attribute and in a particular environment. In FIG. 10 , viewing time is the objective variable. However, it is conceivable that users tend to view content such as videos with long playback times for longer periods of time. Therefore, when there is content with a long playback time, it may be difficult to perform fair learning. Therefore, the ratio of viewing time to playback time may be used as the objective variable. In other words, a relative indicator, such as the percentage of playback time during which the content was viewed, may be used as the objective variable.

[0052] 6, the description will continue from step S113. In step S113, the learning processing unit 113 executes learning using the learning data 184. As a result, a learned model is generated.

[0053] The generated trained model estimates the amount of time each piece of content is viewed for each display situation.

[0054] For example, a trained model is a supervised model that is generated by learning the relationship between objective variables such as viewing time and number of views, using the viewer's attributes, environmental information (such as date and time), and content as explanatory variables.

[0055] The trained model can be generated using, for example, a bandit algorithm, which is a type of reinforcement learning. In this case, attribute information and environmental information can be used as context information, display content can be selected, and viewing time and number of viewings can be used as rewards.

[0056] The trained model can be generated using a regression method such as multiple regression. In this case, the learning processing unit 113 uses the multiple regression formula of Equation (1) to optimize the constant term γ by the least squares method or the like to generate the trained model. "y" is the objective variable. "x １ ~x ｉ " indicates attribute information, environmental information, and display content. １ ~α ｉ " and "β １ ~β ｊ " is a correction coefficient. The correction coefficient α is a parameter of the model. y = α １ x １ +α ２ x ２ +...+α ｉ x ｉ +γ (1)

[0057] A further explanation of the generation of trained models follows. The trained models may be generated using any learning algorithm. For example, known statistical methods or known machine learning algorithms may be used. Examples of known statistical methods include simple regression and multiple regression. Examples of known machine learning algorithms include neural networks, support vector machines (SVMs), gradient boosting decision trees (GBDTs), clustering, and reinforcement learning. Alternatively, a contextual bandit algorithm, which is a type of reinforcement learning, may be used. The contextual bandit algorithm determines which content to display using three concepts: "options," "context," and "reward." "Options" are content IDs. "Context" is attribute information and environmental information. "Reward" is the viewing time and number of viewings. As a result, optimal content for the input "context" is selected based on past "options" and "rewards." Examples of contextual bandit algorithms include Linear Upper Confidence found and Linear Thompson Sampling.

[0058] In step S114, the learning processing unit 113 stores the learned model in the learned model storage unit 194.

[0059] Step S120 will be described in detail with reference to FIG. 11. In step S121, the acquisition unit 121 acquires content information 183, a trained model, and inference pattern information 185 from the storage unit 190. The content information 183 is acquired from the content information storage unit 193. The trained model is acquired from the trained model storage unit 194. The inference pattern information 185 is acquired from the inference pattern storage unit 195.

[0060] The inference pattern information 185 will be described with reference to FIG. 12. The inference pattern information 185 indicates an inference pattern. In FIG. 12, the inference pattern information 185 indicates an inference pattern by a set of "pattern No.", "gender", "age", "time period segment", and "day segment". "Pattern No." is an identifier that identifies an inference pattern. "Gender" is the gender of the viewer. "Age segment" is the age segment of the viewer. "Time period segment" is the segment of the time period during which the display content was viewed by the viewer. "Day segment" is the segment of the day during which the display content was viewed by the viewer.

[0061] Each inference pattern is defined in advance. Defining many inference patterns makes it possible to optimize the content display ratio under various conditions (situations).

[0062] 11 , the description will continue from step S122. In step S122, the inference processing unit 122 executes inference for each inference pattern using the trained model. As a result, an inference result 186 is obtained.

[0063] Specifically, the inference processing unit 122 receives the inference pattern information 185 as an input and uses the learned model to infer objective variables such as the viewing time and the number of viewings for the content information 183. The inference processing unit 122 may refer to the content information 183 to determine which content has passed its end date of publication and has stopped being published, and may not perform inference on that content.

[0064] The inference result 186 will be explained based on Figure 13. The inference result 186 indicates, for each inference pattern, an estimated value of the time that each content will be viewed when displayed on the display 211. In Figure 13, the inference result 186 indicates an "inference pattern" and a "viewing time." The "inference pattern" is the same as the inference pattern indicated in the inference pattern information 185. The "viewing time" is an estimated value of the time that each content will be viewed.

[0065] 11 , the description will continue from step S123. In step S123, the display ratio determination unit 123 determines the display ratio of each piece of content for each inference pattern based on the inference result 186. Information indicating the determined display ratio is referred to as display ratio information 187.

[0066] For example, the display ratio determination unit 123 calculates the display ratio for each piece of content using a softmax function. The softmax function is expressed by the following equation (2). "i" indicates the content ID. ｉ " indicates the display rate (probability) of content whose content ID is "i". "V" indicates an inference value. "n" indicates the number of contents.

[0067]

[0068] If the trained model is not a model that performs point prediction but a model that performs interval prediction, such as Gaussian process regression, a predicted value and an upper bound or lower bound may be used when determining the display percentage. For example, the upper bound is used, and the inference results for three pieces of content are revised to 17±3 seconds, 7±3 seconds, and 9±1 seconds at a 95% confidence interval. In this case, the upper bounds of 20 seconds, 10 seconds, and 10 seconds are used, and the display percentages of the three pieces of content are determined to be 50%, 25%, and 25%.

[0069] When a trained model is generated using Linear Thompson Sampling, the trained model holds, as an internal parameter, a probability distribution indicating the probability with which content should be displayed during training. Therefore, the display ratio may be determined using the probability distribution held within the trained model as is.

[0070] If the learning data 184 is not sufficiently accumulated and a learned model has not been constructed, the display ratio determination unit 123 determines the display ratio of each content in an equal distribution.

[0071] The display ratio information 187 will be explained based on Fig. 14. The display ratio information 187 indicates the display ratio of each content for each inference pattern. In Fig. 14, the display ratio information 187 indicates an "inference pattern" and a "display ratio." The "inference pattern" is the same as the inference pattern indicated in the inference pattern information 185. The "display ratio" is the display ratio of each content.

[0072] 11, step S124 will be described. In step S124, the output unit 124 outputs the display ratio information 187.

[0073] Specifically, the output unit 124 transmits the display ratio information 187 to the display device 210. The control unit 217 receives the display ratio information 187 and stores it in the storage unit 218. The control unit 217 then uses the display ratio information 187 to display content as follows: First, the control unit 217 determines the situation when the content is displayed. For example, the control unit 217 uses camera footage to detect the "gender" and "age" of the person in front of the display 211. The control unit 217 also determines the "time period segment" and "day segment" based on the current date and time. Next, the control unit 217 selects an "inference pattern" that matches the determined situation from the display ratio information 187, and obtains a "display ratio" associated with the selected "inference pattern" from the display ratio information 187. The control unit 217 then displays each piece of content on the display 211 according to the obtained "display ratio."

[0074] By periodically executing the flow of the display control method and periodically delivering the display ratio information 187 to the display device 210, the display device 210 can display the optimum content according to the attributes and environment.

[0075] ***Effects of First Embodiment*** The first embodiment relates to an apparatus and method for effectively displaying information on a display medium.

[0076] If the model for determining the content display ratio is placed on the server side, it is necessary to query the server to determine the content display ratio. This results in a time lag before the content is displayed, which could cause people to walk away from the signage. If the model for determining the content display ratio is placed on the edge side, computational resources for model training are required on the edge side, which incurs costs on the edge side. If the model is distributed from the server side to the edge side, issues arise regarding the communication costs and communication load for distributing the model. Furthermore, computational resources for using the model to determine the display ratio are required on the edge side, which incurs costs on the edge side.

[0077] The first embodiment has been made in consideration of these circumstances, and aims to display content at an optimal display ratio according to attribute information and environmental information while reducing the time lag until content is displayed by distributing display ratio information generated in advance to offline video media such as signage.

[0078] The display control device 100 constructs a model for determining the optimal display ratio based on accumulated past data, and distributes information on the display ratio determined based on the inference results of the trained model.

[0079] According to the first embodiment, it is possible to display content at an optimal display ratio according to attributes and environment while reducing the time lag until content is displayed on offline video media such as signage. Furthermore, since the model can be centrally located on the server side, data management and model learning can be made more efficient. Furthermore, even if connection to the server is temporarily lost due to network problems or other reasons, the edge-side signage can switch the display of content that is optimal for each combination of attributes and environment based on display ratio information distributed in advance.

[0080] *** Supplementary Note to First Embodiment *** The hardware configuration of the display control device 100 will be described with reference to Fig. 15 . The display control device 100 includes a processing circuit 109. The processing circuit 109 is hardware that realizes a learning unit 110 and an inference unit 120. The processing circuit 109 may be dedicated hardware, or may be a processor 101 that executes a program stored in a memory 102.

[0081] When the processing circuit 109 is dedicated hardware, the processing circuit 109 may be, for example, a single circuit, a multiple circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, or a combination thereof. ASIC is an abbreviation for Application Specific Integrated Circuit. FPGA is an abbreviation for Field Programmable Gate Array.

[0082] The display control device 100 may include a plurality of processing circuits that replace the processing circuit 109 .

[0083] In the processing circuit 109, some functions may be realized by dedicated hardware, and the remaining functions may be realized by software or firmware.

[0084] In this way, the functions of the display control device 100 can be realized by hardware, software, firmware, or a combination of these.

[0085] The hardware configuration of the display device 210 will be described with reference to Fig. 16. The display device 210 includes a processing circuit 219. The processing circuit 219 is hardware that realizes the control unit 217. The processing circuit 219 may be dedicated hardware, or may be a processor 213 that executes a program stored in the memory 214.

[0086] When processing circuitry 219 is dedicated hardware, processing circuitry 219 may be, for example, a single circuit, multiple circuits, a programmed processor, parallel programmed processors, an ASIC, an FPGA, or a combination thereof.

[0087] The display device 210 may include a plurality of processing circuits that replace the processing circuit 219 .

[0088] In the processing circuit 219, some functions may be realized by dedicated hardware, and the remaining functions may be realized by software or firmware.

[0089] Thus, the functionality of the display device 210 can be realized by hardware, software, firmware, or a combination thereof.

[0090] The first embodiment is an example of a preferred embodiment and is not intended to limit the technical scope of the present disclosure. The first embodiment may be implemented in part or in combination with other embodiments. The procedures described using flowcharts and the like may be modified as appropriate.

[0091] The "part" of each element of the display control device 100 and the display device 210 may be read as "processing," "step," "circuit," or "circuitry."

[0092] 100 Display control device, 101 Processor, 102 Memory, 103 Auxiliary storage device, 104 Communication device, 105 Input / output interface, 109 Processing circuit, 110 Learning unit, 111 Acquisition unit, 112 Learning data generation unit, 113 Learning processing unit, 120 Inference unit, 121 Acquisition unit, 122 Inference processing unit, 123 Display ratio determination unit, 124 Output unit, 181 Detection information, 182 Display performance information, 183 Content information, 184 Learning data, 185 Inference pattern information, 186 Inference result, 187 Display ratio information, 190 Storage unit, 191 Detection information storage unit, 192 Display performance storage unit, 193 Content information storage unit, 194 Learned model storage unit, 195 Inference pattern storage unit, 200 Information provision system, 201 Network, 210 Display device, 211 Display, 212 camera, 213 processor, 214 memory, 215 auxiliary storage device, 216 communication device, 217 control unit, 218 storage unit, 219 processing circuit.

Claims

1. A display control device comprising: a learning unit that learns detection information obtained each time a plurality of contents is displayed by a display device, the detection information indicating the situation when the display was performed and the length of time the displayed content was viewed, and generates a trained model; and an inference unit that uses the trained model to infer the time when each of the plurality of contents is viewed for each inference pattern indicating the situation when the display was performed, and determines the display ratio of each of the plurality of contents for each inference pattern based on the inference results.

2. The display control device according to claim 1, wherein the inference unit transmits display ratio information indicating the determined display ratio to a display device that displays each of the plurality of contents at a display ratio corresponding to an inference pattern that matches the situation when the display is performed.

3. A display control method that generates a trained model by learning detection information obtained each time a plurality of contents is displayed by a display device, the detection information indicating the circumstances under which the display was performed and the length of time the displayed content was viewed; uses the trained model to infer the amount of time that each of the plurality of contents is viewed for each inference pattern indicating the circumstances under which the display was performed; and determines the display ratio of each of the plurality of contents for each inference pattern based on the inference results.

4. A display control program for causing a computer to execute the following steps: a learning process for generating a trained model by learning detection information obtained each time a plurality of contents is displayed by a display device, the detection information indicating the situation when the display was performed and the length of time the displayed content was viewed; and an inference process for inferring the time when each of the plurality of contents is viewed using the trained model for each inference pattern indicating the situation when the display was performed, and determining the display ratio of each of the plurality of contents for each inference pattern based on the inference results.

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