Information processing device, information processing method, and program

Abstract

The challenge is to enable individual predictions of battery degradation based on actual user usage patterns, rather than relying on empirical rules. [Solution] The survey result acquisition unit 102 acquires survey result information regarding the use of each of the n mobile terminals 2, which includes at least the battery capacity of each of the n mobile terminals 2, reported by each of the n terminal users C who use each of the n mobile terminals 2. Based on the acquired survey result information, the SOH analysis unit 103 generates a multiple regression analysis curve graph that shows the correlation between the usage period t of each of the n mobile terminals 2 and the SOH, which is used to estimate the battery state of other target devices.

Description

Technical Field

[0005]

[0001] The present invention relates to an information processing apparatus, an information processing method, and a program.

Background Art

[0002] It is said that the deterioration of the battery of a mobile terminal such as a smartphone can be correlated with the performance of the battery itself and the usage pattern of the user of the terminal. In particular, it is said that the environment in which the mobile terminal is placed affects the deterioration of the battery. On the other hand, in the mobile terminal market, the state of the battery has not been emphasized, and it has been recommended to replace the mobile terminal according to the replacement cycle after a predetermined period (for example, two years). However, the aging deterioration of the battery in a device that is driven using power from a battery, including mobile terminals, is an important issue. In addition, there has been a technique for diagnosing battery deterioration. For example, Patent Document 1 describes a technique for calculating the open circuit voltage in a fully charged state of a lead-acid battery and the internal resistance of the lead-acid battery, and calculating the discharge capacity of the lead-acid battery from the open circuit voltage and the internal resistance in the fully charged state.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, there is a situation where there is a demand for the development of a technology that can individually predict the state of battery deterioration in accordance with the actual usage of the user, rather than a method that relies on an empirical rule such as the technology described in Patent Document 1.

[0005] This invention was made in view of the above circumstances, and aims to provide a method that can individually predict battery degradation in accordance with the actual usage of users, without relying on rules of thumb. [Means for solving the problem]

[0006] An acquisition means for acquiring first information regarding the use of each of the one or more target devices, which is powered by a battery, and which includes at least information regarding the battery of each of the one or more target devices, as notified by each of the one or more users who use each of the one or more target devices; Based on the first information acquired by the acquisition means, a generation means generates second information that shows the correlation between the usage period of each of the one or more target devices and the battery state, which is used to estimate the battery state of one or more target devices or other target devices. An information processing device equipped with the following features. [Effects of the Invention]

[0007] According to the present invention, battery degradation can be individually predicted in accordance with the actual usage patterns of the user, without relying on empirical rules. [Brief explanation of the drawing]

[0008] [Figure 1] This figure shows an overview of the service that can be realized from an information processing system to which a server according to one embodiment of the information processing device of the present invention is applied. [Figure 2] This diagram illustrates the results of a survey conducted to understand the actual situation. [Figure 3] This figure shows an example of a multiple regression analysis curve graph. [Figure 4] This figure shows an example of the configuration of an information processing system, including a server according to one embodiment of the information processing device of the present invention. [Figure 5] Figure 4 is a block diagram showing an example of the server hardware configuration in the information processing system. [Figure 6]Figure 5 is a functional block diagram showing the functional configuration of the server for executing battery degradation prediction processing and purchase price calculation processing. [Figure 7] This is an example of a screen from a dedicated application displayed on a mobile device, showing the evaluation of each item that is a factor in battery degradation as a result of a battery degradation diagnosis of the mobile device. [Figure 8] This is an example of a screen from a dedicated app displayed on a mobile device, which suggests ways to use the mobile device based on the results (Report) of a battery degradation diagnosis of the mobile device. [Figure 9] This is a screen of a dedicated app displayed on a mobile device, indicating that the alert settings have been completed as shown in Figure 8. [Figure 10] This is an example of a screen from a dedicated application displayed on a mobile device, which suggests how to handle the mobile device in the future based on the results of a battery degradation diagnosis. [Figure 11] This is an example of a screen displayed in a dedicated app on a mobile device, which suggests how to handle the mobile device going forward after a malfunction check of the device has been completed. [Figure 12] This is an example of a screen from a dedicated app that appears when a malfunction occurs on a mobile device, and which includes a software button to activate the malfunction check function. [Figure 13] This is an example of a screen displayed in a dedicated application on a mobile device, which appears when an abnormality is detected during a malfunction check of the mobile device, and which suggests how to handle the mobile device going forward. [Figure 14] This is an example of a screen displayed on a mobile device using a dedicated application, which is shown when a service provider assesses the value of a mobile device for trade-in, and proposes how the mobile device will be handled in the future. [Modes for carrying out the invention]

[0009] Hereinafter, embodiments of the present invention will be described with reference to the drawings.

[0010] First, referring to FIGS. 1 to 3, an overview of a service (hereinafter referred to as "this service") that can be realized by an information processing system (see FIG. 4 described later) to which a server 1 according to an embodiment of the information processing apparatus of the present invention is applied will be described.

[0011] FIG. 1 is a diagram showing an overview of this service that can be realized by an information processing system to which a server according to an embodiment of the information processing apparatus of the present invention is applied. FIG. 2 is a diagram showing an image of the result of a fact-finding questionnaire. FIG. 3 is a diagram showing an image of a multiple regression analysis curve graph.

[0012] This service is a service that can be realized by an information processing system to which a server 1 managed by a service provider G is applied. The service provider G provides services to the terminal user C and the store staff S of a predetermined store. The terminal user C is a person who uses a mobile terminal 2 driven by a battery. The store staff S is a window staff of a predetermined store that handles the mobile terminal 2 as a business. The "predetermined store" includes sales agents such as smartphone dealers, repairers, buyers, etc.

[0013] When using this service, the terminal user C performs operations on a dedicated application software (hereinafter referred to as "dedicated app") installed on the mobile terminal 2 that enables the use of this service. Also, the terminal user C can use this service by accessing a dedicated website (hereinafter referred to as "dedicated site") that enables the use of this service and is displayed by the browser function of the mobile terminal 2. Hereinafter, "the terminal user C operates the mobile terminal 2" shall mean that the terminal user C uses this service from the dedicated app or the dedicated site. Furthermore, when using this service, store staff member S will operate a dedicated application installed on terminal 3 (hereinafter referred to as "store terminal 3") deployed in the store. Store staff member S can also use this service by accessing a dedicated website displayed using the browser function of store terminal 3. Hereinafter, "Store staff S operates store terminal 3" means that store staff S uses this service via the dedicated app or website.

[0014] This service will conduct a questionnaire survey (hereinafter referred to as the "actual situation survey questionnaire") regarding the use of n mobile devices 2 for each of the n terminal users C (where n is any integer value greater than or equal to 1). Note that if one terminal user C uses multiple mobile devices 2, in this case, terminal user C will be counted as one person per mobile device 2. Also, if one mobile device 2 is shared by multiple terminal users C, in this case as well, one representative will be designated, and terminal user C will be counted as one person per mobile device 2.

[0015] The fact-finding survey questionnaire is conducted by displaying a user interface (UI) consisting of one or more questions and corresponding input fields on mobile device 2. Device user C answers the fact-finding survey questionnaire by operating mobile device 2. However, the method of conducting the fact-finding survey questionnaire is not limited to this. For example, the fact-finding survey questionnaire may be attached to an email addressed to device user C, and device user C may answer it.

[0016] The survey questionnaire includes general questions about the actual usage of mobile device 2, as well as questions about battery life. Figure 2 shows a concrete example of a survey questionnaire. In other words, as shown in Figure 2, the survey questionnaire conducted on n terminal users C includes questions No. 1 through 19. Specifically, for example, questions such as "Please tell us your age," "Please tell us your gender," "Please enter your postal code," "Please tell us your occupation," "Please tell us your family structure," "When did you purchase it?", "Where did you purchase it?", "Please tell us the model name," "Do you use a cover?", "What type is it?", "Have you had the battery repaired since purchase?", "Where did you have it repaired?", "Please tell us your average usage time," "Where do you use it most often?", "Do you turn on Wi-Fi?", "Please tell us your purpose for using it other than making phone calls," "Is the charger an original product?", "When do you charge it?", and "Please tell us your current battery capacity" are included.

[0017] Terminal user C will input answers to these questions as shown in Figure 2, for example. Specifically, as shown in Figure 2, age is "35 years old", gender is "male", postal code is "XXX-XXXX", occupation is "company employee", family structure is "married, 2 children", purchase date of mobile terminal 2 is "YYYY / MM / DD", place of purchase of mobile terminal 2 is "authorized store (new)", model name of mobile terminal 2 is "〇〇〇", use of cover is "yes", type of cover is "notebook type", repair history after purchase of mobile terminal 2 is "yes", repair request destination is "authorized store", mobile terminal 2 The user's daily usage time is "1 hour," the main place of use for mobile device 2 is "home," whether Wi-Fi is used is "yes," the purpose of using mobile device 2 other than calls is "news, social media (real name registration), video sites, music, payments," whether the charger is an original product is "yes," when they charge it is "I charge it when the battery is about to run out (topping up)," and the current battery capacity is "85% of maximum capacity."

[0018] The above-described fact-finding questionnaire is conducted on n terminal users C. The input from each of the n terminal users C is then associated with the questions and retrieved as survey results information by server 1. In addition to the fact-finding questionnaire, server 1 also retrieves the results of independently conducted internet surveys as survey results information. As a concrete example of the survey system, for instance, a survey questionnaire is conducted with terminal user C each time a mobile terminal 2 is purchased at a designated store. By conducting this for a predetermined period (e.g., 3 months), a large amount of survey results information is accumulated on server 1. In addition, an internet survey based on diverse perspectives by multiple staff members (e.g., 30 people) is conducted for a predetermined period (e.g., 3 months), and the survey results information is accumulated on server 1 (e.g., 5000 plots).

[0019] In this service, regression analysis is performed based on multiple survey results collected on Server 1. Specifically, Server 1 generates a multiple regression analysis curve graph. The multiple regression analysis curve graph shows the correlation between the usage period t of each of the n mobile terminals 2 and SOH (State of Health), an indicator representing the degradation state or health of the battery. SOH is calculated using the formula "Full charge capacity at degradation (Ah) / Initial full charge capacity (Ah) × 100".

[0020] Figure 3 shows an image of a multiple regression analysis curve graph. In other words, the multiple regression analysis curve graph is a graph with the horizontal axis representing the usage period t of mobile device 2 by user C, and the vertical axis representing the State of Health (SOH). The multiple plotted circles show the relationship between the usage period t and SOH for each mobile device 2. Note that one circle may represent one mobile device 2, or a group of multiple mobile devices 2. Curve L represents the regression curve. As shown in Figure 3, the State of Health (SOH) tends to be higher for users with shorter usage periods (left side of the graph) and higher for users with longer usage periods (right side of the graph). Furthermore, as indicated by the arrows, values ​​that deviate significantly from the regression curve (curve L) can be presumed to be degradation caused by the usage patterns of terminal user C.

[0021] Once a multiple regression analysis curve graph is generated, machine learning by an AI (artificial intelligence) is performed for a predetermined period (e.g., 3 months) based on the generated multiple regression analysis curve graph, using the accumulated multiple survey results as input data. By performing this type of machine learning, it becomes possible to output information showing the relationship between the multiple regression analysis curve graph in Figure 3 and the responses to the survey questionnaire in Figure 2. Specifically, for example, it is possible to output information showing the relationship between the age of terminal user C and SOH, or the relationship between the environment in which terminal user C is located (for example, temperature derived from the postal code) and SOH. Furthermore, for example, it is possible to output information showing the relationship between the purchase location of mobile terminal 2 (for example, a new product purchased through official channels, or a used product purchased overseas) and SOH.

[0022] Furthermore, for example, using a multiple regression analysis curve graph, it is possible to estimate the SOH value from the usage period t, even if the current battery capacity is unknown. Furthermore, multiple regression analysis curve graphs can be generated for each operating system (OS) of mobile terminal 2 (for example, OS1 and OS2 in Figure 1). Although not shown in the diagram, multiple regression analysis curve graphs can also be generated for each manufacturer and model. For example, multiple regression analysis curve graphs can be generated for each of manufacturers A through C. For example, multiple regression analysis curve graphs can also be generated for each of models a1 through c3 of manufacturer A. It is also possible to generate a multiple regression analysis curve graph that is not limited by OS, manufacturer, or model. In this case, for example, a correction is applied to normalize the multiple regression analysis curve graphs generated for each OS (for example, the multiple regression analysis curve graphs shown by curves L1 and L2 in Figure 1), thereby generating a common multiple regression analysis curve graph (for example, the multiple regression analysis curve graph shown by curve L in Figure 1).

[0023] Once the machine learning results enable the output of information showing the relationship between a multiple regression analysis curve graph and the responses to the survey questionnaire in Figure 2, it becomes possible to diagnose battery degradation for each mobile device 2 and calculate a buyback price that takes into account the battery degradation status. For example, the battery degradation diagnosis may be performed by store staff S based on a request from device user C, or it may be performed automatically at predetermined intervals as a function of a dedicated application. Furthermore, if the dedicated app includes a function to diagnose battery degradation, information indicating the degree of degradation may be notified to the terminal user C as a "notification." This allows terminal user C to become aware of the degradation of the mobile device 2's battery, and at the same time, provides an indicator for deciding when to replace mobile device 2. As a result, terminal user C can replace mobile device 2 at the best possible time. Furthermore, based on the battery degradation diagnosis results, correction data to mitigate the rate of battery degradation may be sent to the mobile terminal 2. This can extend the battery life of the mobile terminal 2.

[0024] In summary, this service can be expected to have the following effects, for example: In other words, this service allows the entire industry in the used goods market to recommend battery degradation diagnosis, thereby enabling it to recommend battery repair or guide device user C towards replacement. In this case, the buyback price can also be adjusted according to the battery's condition at the time of replacement. This helps to ensure fair valuations for mobile devices 2 in the used goods market. As a result, the normal circulation of mobile devices 2 in the used goods market is ensured, allowing those who wish to purchase mobile devices 2 in the used goods market to do so with full understanding of their condition (e.g., estimated remaining lifespan). Furthermore, since many device users (C) change their devices when they notice battery degradation, applying this service can encourage device changes not only in the used market but also in the new market. Furthermore, machine learning using AI (artificial intelligence) can optimize the battery state, which varies from person to person. This makes it possible to create buying and selling opportunities at the best timing for device user C. Furthermore, store staff member S can guide device user C towards repairs or replacements based on legitimate grounds. As a result, this can also generate new advertising demand within the dedicated app. Furthermore, since this service is applicable to major consumer goods such as smartphones, its value and the benefits it offers are expected to be enormous.

[0025] Next, referring to Figure 4, the configuration of the information processing system, which includes a server 1 according to one embodiment of the information processing device of the present invention, will be described to realize the provision of the service described above. Figure 4 shows an example of the configuration of an information processing system, including a server according to one embodiment of the information processing device of the present invention.

[0026] The information processing system shown in Figure 4 is configured to include Server 1, mobile terminals 2-1 to 2-m (where m is any integer greater than or equal to n), and store terminal 3. In this specification, when it is not necessary to distinguish each of the mobile terminals 2-1 to 2-m individually, they are collectively referred to as "mobile terminal 2." In this case, the person operating mobile terminal 2 is referred to as "terminal user C." Furthermore, if mobile terminal 2 is simply being held for sale, it does not constitute an information processing system. However, the following explanation assumes that mobile terminal 2 is not simply being held for sale, but is being operated by terminal user C. Server 1, mobile terminal 2, and store terminal 3 are each interconnected via a predetermined network N, such as the Internet.

[0027] Server 1 is an information processing device managed by service provider G. Server 1 performs various processes necessary to realize this service while communicating with mobile terminal 2 and store terminal 3 as needed.

[0028] Mobile terminal 2 is an information processing device operated by terminal user C. Mobile terminal 2 can be composed of, for example, a smartphone or a tablet.

[0029] Store terminal 3 is an information processing device operated by store staff S. Store terminal 3 consists of, for example, a personal computer, smartphone, tablet, etc.

[0030] Figure 5 is a block diagram showing an example of the server hardware configuration in the information processing system shown in Figure 4.

[0031] Server 1 comprises a CPU (Central Processing Unit) 11, ROM (Read Only Memory) 12, RAM (Random Access Memory) 13, a bus 14, an input / output interface 15, an input unit 16, an output unit 17, a storage unit 18, a communication unit 19, and a drive 20.

[0032] The CPU 11 executes various processes according to the program recorded in the ROM 12 or the program loaded from the storage unit 18 into the RAM 13. RAM13 also stores data and other information necessary for the CPU11 to perform various processes.

[0033] The CPU 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output interface 15 is also connected to this bus 14. An input / output interface 15 is connected to an input unit 16, an output unit 17, a storage unit 18, a communication unit 19, and a drive 20.

[0034] The input unit 16 is composed of, for example, a keyboard, and outputs various types of information. The output unit 17 consists of a display such as an LCD and a speaker, and outputs various information as images and sounds. The memory unit 18 is composed of DRAM (Dynamic Random Access Memory) and stores various types of data. The communication unit 19 communicates with other devices (for example, the mobile terminal 2 and the store terminal 3 in Figure 2) via a network N including the Internet.

[0035] A removable media 40, such as a magnetic disk, optical disk, magneto-optical disk, or semiconductor memory, is appropriately mounted in the drive 20. Programs read from the removable media 40 by the drive 20 are installed in the storage unit 18 as needed. Furthermore, the removable media 40 can store various types of data stored in the storage unit 18, just as the storage unit 18 does.

[0036] Although not shown in the diagram, the mobile terminal 2 and store terminal 3 in Figure 4 can have essentially the same hardware configuration as those shown in Figure 5. Therefore, a description of the hardware configurations of mobile terminal 2 and store terminal 3 will be omitted.

[0037] Through the collaboration of various hardware and software components of Server 1 as shown in Figure 5, Server 1 can perform various processes, including battery degradation prediction and purchase price calculation. As a result, service provider G can provide the aforementioned service to terminal user C and store staff S. "Battery degradation prediction processing" refers to the process of predicting the battery degradation of user C's mobile device 2 using machine learning based on the results of the regression analysis described above. "Purchase price calculation process" refers to the process of calculating the purchase price of user C's mobile device 2 based on the results of the battery degradation prediction process. The following describes the functional configuration for executing the battery degradation prediction process and the purchase price calculation process, which are performed on Server 1.

[0038] Figure 6 is a functional block diagram showing the functional configuration of the server in Figure 5, specifically for executing the battery degradation prediction process and the purchase price calculation process.

[0039] As shown in Figure 6, in the CPU 11 of server 1, when the execution of the battery degradation prediction process is controlled, the notification control unit 101, the investigation result acquisition unit 102, the SOH analysis unit 103, and the degradation diagnosis unit 104 function. Furthermore, when the execution of the purchase price calculation process is controlled, the purchase price calculation unit 105 also functions. Furthermore, one area of ​​the storage unit 18 of server 1 is provided with the investigation results DB 181 and the analysis results DB 182.

[0040] The notification control unit 101 executes control to notify each of the n terminal users C of a fact-finding survey questionnaire consisting of one or more input items. Specifically, the notification control unit 101 executes control to display the input items of the fact-finding survey questionnaire on each of the n mobile terminals 2. However, the method of notifying the fact-finding survey questionnaire is not limited to this. For example, the questionnaire may be attached to an email addressed to terminal users C.

[0041] Furthermore, the notification control unit 101 executes control to notify at least one of the mobile terminal 2 and the store terminal 3 of the battery degradation diagnosis results generated by the degradation diagnosis unit 104.

[0042] Furthermore, the notification control unit 101 executes control to notify at least one of the mobile terminal 2 and the store terminal 3 of the purchase price calculated by the purchase price calculation unit 105.

[0043] The survey result acquisition unit 102 acquires information regarding the use of each of the n mobile terminals 2, including at least information regarding the battery status of each of the n mobile terminals 2, which is reported by each of the n terminal users C who use each of the n mobile terminals 2, as survey result information. The survey result information acquired by the survey result acquisition unit 102 is stored and managed in the survey result DB 181.

[0044] The SOH analysis unit 103 performs regression analysis based on the survey results to generate a multiple regression analysis curve graph showing the correlation between the usage period t of each of the n mobile terminals 2 and SOH. The multiple regression analysis curve graph generated by the SOH analysis unit 103 is stored and managed in the analysis results DB 182.

[0045] The degradation diagnosis unit 104 predicts the degradation of the battery of the mobile terminal 2k (where k is any integer value between 1 and m) based on the multiple regression analysis curve graph generated by the SOH analysis unit 103, and generates a battery degradation diagnosis result as a result. Specifically, the degradation diagnosis unit 104 diagnoses the state of battery degradation by fitting the mobile terminal 2k to the multiple regression analysis curve graph, and generates a battery degradation diagnosis result as a result.

[0046] The purchase price calculation unit 105 calculates the purchase price of the mobile terminal 2k based on the battery degradation diagnosis results of the mobile terminal 2k generated by the degradation diagnosis unit 104.

[0047] Although one embodiment of the present invention has been described above, the present invention is not limited to the embodiments described above, and any modifications, improvements, etc. that can achieve the objectives of the present invention are considered to be included in the present invention.

[0048] For example, in the embodiment described above, the AI-based machine learning output information showing the relationship between the multiple regression analysis curve graph in Figure 3 and the responses to the survey questionnaire in Figure 2. However, the information used to determine the relationship with the multiple regression analysis curve graph in Figure 3 is not particularly limited to the questionnaire responses, but can be any information. For example, battery degradation is caused by a combination of factors, including charging while using other devices, partial charging, charging time, number of times overheating occurs, and communication frequency. Therefore, information showing the correspondence between one or more factors that contribute to the degradation of mobile device 2 and the multiple regression analysis curve graph in Figure 3 may be output. Here, this item can be obtained as a response to a survey questionnaire, but it can also be obtained from the state of mobile device 2 (including how device user C uses it and their goals, etc.). In other words, in such cases, machine learning by AI may output the multiple regression analysis curve graph shown in Figure 3, and information showing the relationship between each of the one or more items that are factors in the deterioration (hereinafter referred to as "item-related information"). As a result of diagnosing the battery degradation of a predetermined mobile terminal 2, an evaluation of the predetermined mobile terminal 2 for each of one or more items (for example, an evaluation such as "Excellent" or "Good") is obtained based on the actual condition of the predetermined mobile terminal 2 and item-related information.

[0049] Figure 7 shows an example of a screen from a dedicated application displayed on a mobile device, which displays an evaluation of each item that is a factor in battery degradation as a result of a battery degradation diagnosis of the mobile device. The "○○○○-kun" at the top of the screen in Figure 7 is the name of the character corresponding to mobile device 2, and was set by device user C when the dedicated app was installed. In Figure 7, the text "Everything's going smoothly" below "○○○○-kun" represents the overall result of the battery degradation diagnosis for mobile device 2. The text "2 points earned" to the right indicates that a predetermined number of points have been acquired. These points can be earned, for example, depending on the overall result of the battery degradation diagnosis, and can be used by user C to replace the device. At the bottom of the screen in Figure 7, under "Report," the evaluation of a designated mobile terminal 2 (e.g., "Excellent," "Good," etc.) for each of the one or more items that contribute to degradation is displayed. In the example in Figure 7, such items include "charging while using," "topping up," "charging time," "charging time zone," "number of times overheating," and "communication frequency."

[0050] Furthermore, the display format of the battery degradation diagnosis results (evaluation) on the screen is not limited to the example in Figure 7, and various other formats can be adopted. For example, if an image representing a character named "○○○○-kun" is evaluated as having a poor quality due to factors that cause deterioration, then an image showing the character in a poorly rated state of deterioration, such as swelling, cracking, melting red, or burning, may be displayed on the mobile device 2. Conversely, if the factors that cause deterioration are rated positively, an image indicating that the deterioration is progressing smoothly and favorably may be displayed on mobile device 2, such as making the character look shiny, sharp, giving off an elite vibe, or creating a resort atmosphere.

[0051] Figure 8 shows an example of a screen from a dedicated application displayed on a mobile device, which suggests how to use the mobile device based on the results (Report) of a battery degradation diagnosis. In the example in Figure 8, as a comprehensive control measure to prevent further deterioration, a suggestion is displayed that an alert (push notification) will be sent if one or more of the items that cause deterioration are used beyond the set limits. This alert initially displays recommended values, but it is designed so that user C can freely configure them. This is because the recommended values ​​may not necessarily be appropriate for user C. Furthermore, below these alert suggestions, content (such as articles or reading material) is displayed that informs the device user C of tips and techniques for reducing battery degradation.

[0052] Once the above alert settings are complete, the screen shown in Figure 9 will be displayed on mobile device 2. Figure 9 shows the screen of the dedicated app displayed on a mobile device, indicating that the alert settings have been completed as shown in Figure 8. As shown in the screen in Figure 9, terminal user C can choose whether or not to perform another battery degradation diagnosis (1-week check) after using mobile terminal 2 for one week based on the alerts set in this way. In order to maintain the user's intention to suppress battery degradation, points may be awarded if good results are obtained in this 1-week check.

[0053] Figure 10 shows an example of a screen from a dedicated application displayed on a mobile device, which suggests how to handle the mobile device in the future based on the results of a battery degradation diagnosis. If the battery degradation diagnosis result for mobile device 2 is significantly poor, for example, if the degradation is so severe that the result does not improve even if the suggestions presented on the screen shown in Figure 8 above are followed, then the screen shown in Figure 10 will be displayed on mobile device 2. Specifically, software buttons indicating how to handle Mobile Device 2 in the future, such as a "Get a trade-in appraisal now" button, a "Keep using it" button, and a "Donate to the Smartphone Frog Project" button, are displayed on the screen in Figure 10. When the software button labeled "Get a buyback appraisal now" is pressed, the user is taken to a screen where the service provider appraises the mobile device 2 for buyback. When the software button labeled "Continue using" is pressed, it is determined that mobile device 2 will continue to be used, and the system transitions to a predetermined screen (not shown in the diagram). When the software button labeled "Donate to the Smartphone Frog Project" is pressed, the user is redirected to a screen where they can proceed with the donation process on mobile device 2.

[0054] In addition to the battery degradation diagnosis function mentioned above, the dedicated app also includes a function to check the condition of each part of the mobile device 1 (whether or not there are any malfunctions) (hereinafter referred to as the "malfunction check function"). The malfunction check function is activated by a predetermined trigger. These predetermined triggers may include the expiration of a set period of time or an explicit instruction or operation from terminal user C. Examples of items checked for such defects include screen cracks, battery issues, charging problems, speaker issues, microphone issues, home button issues, and camera malfunctions. Here, a predetermined method using the multiple regression analysis curve graph shown in Figure 3 is employed to check for battery malfunctions. If no problems are found as a result of these operational checks, the screen shown in Figure 11 will be displayed on the mobile device 2. Figure 11 shows an example of a screen displayed in a dedicated application on a mobile device. This screen is displayed when no problems are found during the device's malfunction check and suggests how to handle the device going forward.

[0055] The predetermined trigger for initiating the malfunction check function is not limited to the examples described above. For example, if various sensors (not shown) built into the mobile terminal 2 detect a problematic behavior in the mobile terminal 2, the screen shown in Figure 12 may be displayed on the mobile terminal 2, and the malfunction check may be triggered when the software button labeled "Perform operation check now" on the screen is pressed. Specifically, Figure 12 shows an example of a screen for a dedicated application that appears when a malfunction occurs on a mobile device, and includes a software button that activates a malfunction check function. Furthermore, the problematic behavior is not particularly limited as long as it can be detected by the various sensors built into the mobile device 2, and can include, for example, dropping, collision, water / submersion, pressure, and others.

[0056] If a malfunction check is performed due to such problematic behavior in mobile device 2, and no abnormality is found in mobile device 2, the screen shown in Figure 11 will be displayed. However, if a malfunction or other abnormality is found, the screen shown in Figure 13 will be displayed. Figure 13 shows an example of a screen displayed in a dedicated application on a mobile device. This screen is displayed when an abnormality is detected during a malfunction check of the mobile device, and it suggests how to handle the mobile device going forward. Specifically, software buttons indicating how to handle Mobile Device 2 going forward—for example, a software button labeled "Repair and send it in now," a software button labeled "Keep using it," and a software button labeled "Donate to the Smartphone Frog Project"—are displayed on the screen in Figure 13. When the software button labeled "Repair and Send Now" is pressed, the user is taken to a screen where the service provider initiates the repair process for mobile device 2. When the software button labeled "Continue using" is pressed, it is determined that mobile device 2 will continue to be used, and the system transitions to a predetermined screen (not shown in the diagram). When the software button labeled "Donate to the Smartphone Frog Project" is pressed, the user is redirected to a screen where they can proceed with the donation process on mobile device 2. Figure 13 shows an example of a screen displayed in a dedicated application on a mobile device. This screen is displayed when an abnormality is detected during a malfunction check of the mobile device, and it suggests how to handle the mobile device going forward.

[0057] Figure 14 shows an example of a screen from a dedicated application displayed on a mobile device, which is displayed when a service provider assesses the value of the mobile device and proposes how to handle the device in the future. Once photos from mobile device 2 are uploaded, the service provider performs a buyback assessment, and the buyback price (23,800 yen in the example in Figure 14) is displayed as a result of that assessment. Specifically, software buttons indicating how to handle mobile device 2 in the future, such as a software button that says "Sell it on the mobile market and get double QUOH points," and a software button that says "", are displayed on the screen in Figure 14. When the software button that says "Sell on the mobile market and get double QUOH points" is pressed, the user is taken to a screen where procedures for the service provider to purchase mobile device 2 are carried out. If the purchase of mobile device 2 is completed on the next screen, the predetermined points (double the normal points) as described in Figure 7 will be awarded. When the software button labeled "Rough Prediction: Price in 1 Year" is pressed, it is determined that mobile device 2 will continue to be used, and the system transitions to a screen showing the buyback price and other details if the device is used for 12 months without being bought back.

[0058] Beyond the embodiments described above, there are other possible embodiments, such as the following: In other words, for example, in the above-described embodiment, battery degradation of a mobile terminal 2, such as a smartphone or tablet, is predicted. However, the "target device" in the present invention is not limited to the above-described embodiment. That is, any device that is powered by electricity from a battery is included in the "target device" in the present invention. For this reason, any device such as a battery-powered automobile, drone, or various types of hardware can be included in the "target device".

[0059] Furthermore, the questions in the survey questionnaire shown in Figure 3 are merely examples. In addition to the questions shown in Figure 3, other questions may be included to obtain various information necessary to understand the usage status of mobile device 2.

[0060] Furthermore, the system configuration shown in Figure 4 and the hardware configuration of Server 1 shown in Figure 5 are merely illustrative examples for achieving the objectives of the present invention and are not particularly limited.

[0061] Furthermore, the functional block diagram shown in Figure 6 is merely illustrative and not particularly limiting. In other words, it is sufficient that the information processing system in Figure 4 has the functionality to execute the series of processes described above as a whole, and the functional blocks used to realize this functionality are not particularly limited to the example in Figure 6.

[0062] Furthermore, the location of the functional block is not limited to Figure 6, but can be any location. For example, in the example in Figure 6, the battery degradation prediction process and the purchase price calculation process are configured to be performed on the server 1 side, but the system is not limited to this configuration, and at least part of these processes may be performed on the mobile terminal 2 side or the store terminal 3 side. In other words, the functional blocks necessary for executing the battery degradation prediction process and the purchase price calculation process are configured to be provided on the server 1 side, but this is merely an example. At least some of the functional blocks located on the server 1 side may be provided on the mobile terminal 2 side or the store terminal 3 side.

[0063] Furthermore, the series of processes described above can be executed by hardware or by software. Furthermore, a single functional block may consist of hardware alone, software alone, or a combination of both.

[0064] When a series of processes are executed by software, the programs that make up that software are installed on a computer or other device from a network or storage medium. The computer may be a computer that is built into dedicated hardware. Furthermore, a computer can be any computer capable of performing various functions by installing various programs, such as a server, a general-purpose smartphone, or a personal computer.

[0065] Such recording media containing programs may consist not only of removable media (not shown) distributed separately from the main unit of the device to provide the program to the user, but also of recording media provided to the user in a state where they are pre-installed in the main unit of the device.

[0066] In this specification, the step of describing a program to be recorded on a recording medium includes not only processes that are performed chronologically in that order, but also processes that are not necessarily performed chronologically, but are executed in parallel or individually. Furthermore, in this specification, the term "system" refers to an overall system composed of multiple devices, means, etc.

[0067] In summary, the information processing device to which the present invention applies only needs to have the following configuration, and various embodiments can be adopted. That is, the information processing device to which the present invention is applied (for example, Server 1 in Figure 4) is: An acquisition means (for example, the investigation result acquisition unit 102 in Figure 6) that acquires first information regarding the use of each of the one or more target devices (for example, the investigation result information described above), which includes at least information regarding the battery of each of the one or more target devices (for example, the battery capacity displayed on the mobile terminal 2) that is reported by each of the one or more users (for example, the n terminal users C described above) that use each of the one or more target devices powered by battery power, Based on the first information acquired by the acquisition means, a generation means (e.g., SOH analysis unit 103 in Figure 6) generates second information (e.g., multiple regression analysis curve graph in Figure 2) that shows the correlation between the usage period of each of the one or more target devices (e.g., usage period t in Figure 2) and the battery state (e.g., SOH in Figure 2), which is used to estimate the battery state of the one or more target devices or other target devices. It is equipped with.

[0068] This generates second information showing the correlation between the usage period of each of the one or more target devices and the battery status, based on first information regarding battery usage reported from each of the one or more target devices powered by the battery. As a result, it becomes possible to predict battery degradation using the second set of information. In other words, it becomes possible to individually predict battery degradation based on the user's actual usage, without relying on rules of thumb.

[0069] Furthermore, the system includes a notification control means (for example, the notification control unit 101 in Figure 6) that performs control to notify each of the one or more users of a questionnaire consisting of one or more input items (for example, the actual situation survey questionnaire in Figure 1). The acquisition means can acquire the results of the survey conducted by the questionnaire, which are notified by the control of the notification control means, as the first information. Alternatively, for example, the acquisition means acquires information indicating the state of the target device as the first information.

[0070] As a result, a questionnaire consisting of one or more input items is sent to each of one or more users. The results of the survey conducted through the sent questionnaire are then obtained as primary information regarding battery usage. Alternatively, information indicating the status of the target device is also obtained as primary information. Based on the primary information regarding the battery reported from each of the one or more target devices, secondary information is generated showing the correlation between the usage period of each of the one or more target devices and the battery status. As a result, it becomes possible to predict battery degradation using the results of surveys from one or more users, or second information that takes into account the condition of the target device. In other words, it becomes possible to individually predict battery degradation in accordance with the actual usage of the user, without relying on rules of thumb.

[0071] Furthermore, the system may include a second generation means (for example, a degradation diagnosis unit 104 in Figure 6) that generates a third piece of information (battery degradation diagnosis result) indicating the degradation state of the battery of the target device based on the first piece of information and the second piece of information.

[0072] This generates third information indicating the degradation status of the battery in the target device, based on first information regarding battery usage and second information showing the correlation between the usage period of the target device and the battery status. As a result, it becomes possible to individually predict battery degradation based on the user's actual usage patterns.

[0073] Furthermore, the system may further include a second notification control means (for example, the notification control unit 101 in Figure 6) that performs control to notify the user of the third information generated by the second generation means.

[0074] This allows users to become aware of the battery degradation of the device in question, and at the same time, provides them with an indicator to help them decide when to replace the device. As a result, users can replace their devices at the optimal time. In this case, the third information can also be communicated to the user by displaying an image showing the third information on at least a portion of the screens displayed when the application installed on the target device is running.

[0075] Furthermore, the system may include a calculation means (for example, the purchase price calculation unit 105 in Figure 6) that calculates the purchase price of the target device based on the third information generated by the second generation means.

[0076] This allows the purchase price of the target device to be calculated based on a third piece of information indicating the battery degradation status of the target device. As a result, the devices, traded in at a fair price that takes into account the battery's degradation level, will be circulated in the used market. [Explanation of symbols]

[0077] 1...Server, 2...Mobile terminal, 3...Store terminal, 11...CPU, 12...ROM, 13...RAM, 14...Bus, 15...Input / Output interface, 16...Input unit, 17...Output unit, 18...Storage unit, 19...Communication unit, 20...Drive, 40...Removable media, 101...Notification control unit, 102...Results acquisition unit, 103...SOH analysis unit, 104...Degradation diagnosis unit, 105...Purchase price calculation unit, 181...Results DB, 182...Analysis results DB, G...Service provider, C...Terminal user, S...Person in charge, N...Network, L...Regression curve

Claims

1. An acquisition means for acquiring first information regarding the use of each of the one or more target devices, which is powered by a battery, and which includes at least information regarding the battery of each of the one or more target devices, as notified by each of the one or more users who use each of the one or more target devices. Based on the first information acquired by the acquisition means, a first generation means generates second information that shows the correlation between the usage period of each of the one or more target devices and the battery state, which is used to estimate the battery state of one or more target devices or other target devices. An information processing device equipped with the following features.

2. The system further comprises a first notification control means that performs control to notify each of the one or more users of a questionnaire consisting of one or more input items, The acquisition means acquires the results of the survey conducted by the questionnaire, which are notified by the control of the first notification control means, as first information. The information processing apparatus according to claim 1.

3. The acquisition means acquires information indicating the state of the target device as the first information. The information processing apparatus according to claim 1 or 2.

4. The system further comprises a second generation means for generating third information indicating the degradation state of the battery of the target device based on the first information and the second information. The information processing apparatus according to any one of claims 1 to 3.

5. The system further comprises a second notification control means that performs control to notify the user of the third information generated by the second generation means. The information processing apparatus according to claim 4.

6. The third information is notified to the user by displaying an image representing the third information on at least a portion of the screens displayed when the application installed on the target device is running. The information processing apparatus according to claim 5.

7. The system further comprises a calculation means for calculating the purchase price of the target device based on the third information generated by the second generation means. The information processing apparatus according to claim 4.

8. In an information processing method executed by an information processing device, An acquisition step to acquire first information regarding the use of each of the one or more target devices, which includes at least information regarding the battery of each of the one or more target devices, as reported by each of the one or more users who use each of the one or more target devices powered by a battery; Based on the first information obtained through the processing of the acquisition step, a first generation step generates second information that shows the correlation between the usage period of each of the one or more target devices and the battery state, which is used to estimate the battery state of one or more target devices or other target devices. Information processing methods including

9. On the computer, An acquisition step to acquire first information regarding the use of each of the one or more target devices, which includes at least information regarding the battery of each of the one or more target devices, as reported by each of the one or more users who use each of the one or more target devices powered by a battery; Based on the first information obtained through the processing of the acquisition step, a first generation step generates second information that shows the correlation between the usage period of each of the one or more target devices and the battery state, which is used to estimate the battery state of one or more target devices or other target devices. A program that executes control processes, including those mentioned above.

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