Device and method
Patent Information
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Filing Date
- 2024-01-19
- Publication Date
- 2025-07-24
Abstract
Description
Apparatus and method
[0001] The present invention relates to an apparatus and a method.
[0002] Patent Literature 1 describes a device that estimates changes in a user's interests and preferences for each category. The device described in Patent Literature 1 calculates operation scores according to user operations on content belonging to a category, and recommends content to the user based on category scores corresponding to the operation scores. The device described in Patent Literature 1 performs a process of attenuating the entire category score at a fixed rate, thereby forgetting the user's old preferences from the category score and reflecting the user's new preferences in the category score. As a result, changes in the user's interests and preferences for each category are reflected in each category score.
[0003] JP 2015-79309 A
[0004] In conventional devices, the more the process for estimating changes in a user's hobbies and preferences is specialized for each user, the greater the processing load on the device. For example, in the device disclosed in Patent Document 1, in order to specialize the process for estimating changes in a user's hobbies and preferences for each user, a process for attenuating category scores must be performed for each category and user. This results in a huge processing load on the device. For these reasons, it is desirable to be able to determine whether a user's hobbies and preferences have changed while reducing the processing load on the device.
[0005] The present invention has been made in consideration of the above, and aims to provide an apparatus and method that can determine whether there have been changes in a user's hobbies and tastes while reducing the processing load on the apparatus.
[0006] In order to achieve the above object, the device according to the present invention includes an estimation model that is trained to receive information relating to a user's past operating tendencies as input and output information similar to the information relating to the user's past operating tendencies, a comparison unit that compares output information obtained by inputting information relating to the user's recent operating tendencies into the estimation model with the information relating to the user's recent operating tendencies, and a determination unit that determines whether or not there has been a change in the user's hobbies and tastes based on the comparison result by the comparison unit.
[0007] It is possible to determine whether or not there has been a change in the user's hobbies and preferences while suppressing the processing load on the device.
[0008] 1(a) and 1(b) are diagrams for explaining an overview of processing in this embodiment. FIG. 2 is a block diagram showing a functional configuration of a device according to this embodiment. FIG. 3 is a diagram showing an example of information indicating a user's operation tendency. FIG. 4 is a schematic diagram showing an example of an estimation model. FIG. 5 is a diagram showing an example of a display screen of a terminal when an application is recommended to a user. FIG. 6 is a flowchart showing an example of processing for training an estimation model. FIG. 7 is a flowchart showing an example of processing for recommending an application to a user. FIG. 8 is a diagram showing an example of a hardware configuration of a device according to an embodiment of the present disclosure.
[0009] Hereinafter, an embodiment of the device and method according to the present invention will be described in detail with reference to the drawings. In the description of the drawings, the same elements are designated by the same reference numerals, and duplicated explanations will be omitted.
[0010] 1(a) and 1(b) are diagrams for explaining an overview of the processing in this embodiment. In the processing in this embodiment, a terminal stores information regarding a user's operation tendencies. In the example shown in FIG. 1(a), the device in this embodiment is a terminal 10A held by a user A. The terminal 10A stores a history of application use by the user A. The terminal 10A stores the time when an application was used for each category to which each application belongs.
[0011] The terminal estimates changes in the user's interests and tastes based on information about the user's operation trends. In the example shown in Fig. 1(b), the terminal 10A estimates changes in the user's interests and tastes for each category using an estimation model based on information about the user A's past operation trends and information about the user A's recent operation trends.
[0012] The terminal recommends applications to the user based on changes in the user's interests and tastes. For example, the terminal recommends applications to the user that correspond to categories in which the user's interests and tastes have changed.
[0013] Next, the process of estimating changes in a user's interests and tastes will be described in detail. Fig. 2 is a block diagram showing the functional configuration of the device according to this embodiment. As shown in Fig. 2, the device 10 includes an acquisition unit 11, a storage unit 12, a construction unit 13, a comparison unit 14, a determination unit 15, and a recommendation unit 16. The comparison unit 14 includes an estimation model M acquired from the storage unit 12.
[0014] The acquisition unit 11 acquires information about the user's operation tendency. The acquisition unit 11 stores the acquired information in the storage unit 12. For example, the information about the user's operation tendency is information indicating a feature amount related to the user for each hobby / interest category. The feature amount for each category is a numerical value indicating the application usage tendency for each category. As an example, the feature amount is the total value of the time or number of times the user has used at least one or more applications associated with a category. Note that, when information indicating the time or number of times a specific website is displayed in a browser for each category is acquired, the time or number of times may be added to the total value of the time or number of times the user has used at least one or more applications associated with each category. Note that the information about the user's operation tendency may be information other than the above.
[0015] Specifically, the acquisition unit 11 calculates a feature quantity related to the user for each hobby / interest category based on information indicating, for each category, the time when an application was displayed to the user on the device 10 (i.e., the application was executed in the foreground on the device 10). As an example, the acquisition unit 11 calculates, for each date, the time when the user launched at least one application associated with each category, and outputs the calculated information to the storage unit 12. FIG. 3 is a diagram illustrating an example of information indicating a user's operation tendency. In the example illustrated in FIG. 3, three categories, "SNS," "Entertainment," and "News," and the time when the user launched applications belonging to each category on April 1, 2023, are shown. Examples of categories are "Business," "Comics," "Entertainment," "Finance," "Food," "Games," "Health," "Lifestyle," "News," "Shopping," "SNS," "Sports," "Study," and "Travel."
[0016] The information on the user's operation tendency includes information on the user's past operation tendency and information on the user's recent operation tendency. The information on the user's recent operation tendency is information indicating the user's operation tendency in the period from a first time point, which is a past time point, to the current time point. For example, the first time point refers to a time point one week before the current time point. In this case, the information on the user's recent operation tendency is information indicating the user's operation tendency in the most recent week. The information on the user's past operation tendency is information indicating the user's operation tendency in the period from a second time point, which is a time point earlier than the first time point, to the first time point. For example, the second time point is a time point four weeks before the first time point. In this case, the information on the user's past operation tendency is information indicating the user's operation tendency in the period from five weeks ago to one week ago. Note that the length of the period from the second time point to the first time point may be at least four times the length of the period from the first time point to the current time point. For example, if the period from the first time point to the current time point is one week, the period from the second time point to the first time point may be at least four weeks.
[0017] The construction unit 13 constructs the estimation model M based on the information on the user's operation tendencies stored in the storage unit 12. Specifically, the construction unit 13 inputs information on the user's past operation tendencies and trains the estimation model M so as to output information similar to the information on the user's past operation tendencies. It is sufficient that all of the differences for each category between the feature amounts included in the information similar to the information on the user's past operation tendencies and the feature amounts included in the information on the user's past operation tendencies are equal to or less than a predetermined numerical value. The information similar to the information on the user's past operation tendencies may be the same information as the information on the user's past operation tendencies.
[0018] FIG. 4 is a schematic diagram showing an estimation model M. In the example shown in FIG. 4, the estimation model M is a neural network having an input layer IL, an intermediate layer ML, and an output layer OL. The estimation model M is, for example, an Auto Encoder. The construction unit 13 inputs information indicating a user's operation tendency from five weeks ago to four weeks ago as input information to the input layer IL, and trains the estimation model M so that the output information output from the output layer OL is information similar to the input information. At this time, the input information and the output information include features related to the user for each date in each category.
[0019] In the example shown in FIG. 4 , the input layer IL and the output layer OL have three nodes, and the intermediate layer ML has two nodes, but the number of nodes is not limited to this. The input layer IL and the output layer OL have the same number of nodes as the number of data contained in the information input to the estimation model M. As an example, the construction unit 13 inputs the usage time for 15 categories for each day over seven days into the estimation model M. In this case, the input layer IL and the output layer OL have 115 nodes. In this case, the intermediate layer ML has fewer than 1,115 nodes.
[0020] The construction unit 13 performs the same process as described above based on each of the periods from four weeks ago to three weeks ago, the period from three weeks ago to two weeks ago, and the period from two weeks ago to one week ago, to train the estimation model M. In other words, if one learning session is defined as the process of training the estimation model M using information indicating the user's operation trends for one week, the construction unit 13 performs learning four times. The construction unit 13 stores the constructed estimation model M in the storage unit 12. As an example, the construction unit 13 saves the weights and biases of the parameters of the intermediate layer ML of the constructed estimation model M in the storage unit 12.
[0021] After constructing the estimation model M, the construction unit 13 may re-learn the estimation model M based on the usage time of applications for each category on one day one week ago. The construction unit 13 may perform the above-described re-learning once a week or daily.
[0022] The comparison unit 14 compares output information obtained by inputting information about the user's recent operation tendency into the estimation model M with the information about the user's recent operation tendency. The output information is information indicating the user's operation tendency over a period from a first time point to the present time. Specifically, the comparison unit 14 compares, for each category, the feature amounts included in the output information with the feature amounts included in the information about the user's recent operation tendency. As an example, the comparison unit 14 calculates, for each category, the difference between the feature amounts included in the output information and the feature amounts included in the information about the user's recent operation tendency.
[0023] For example, the comparison unit 14 inputs information about the user's operation tendency over the past week as input information to the estimation model M, and obtains output information. The comparison unit 14 calculates the difference between the feature amounts included in the input information and the feature amounts included in the output information for each category and day. The comparison unit 14 aggregates the differences for each category and calculates the difference for each category.
[0024] The determination unit 15 determines whether or not there has been a change in the user's hobbies and tastes based on the comparison result by the comparison unit 14. Specifically, the determination unit 15 determines whether or not there has been a change in the user's hobbies and tastes for each category based on the comparison result by the comparison unit 14. As an example, when the difference for each category is equal to or greater than a threshold, the determination unit 15 determines that there has been a change in the user's hobbies and tastes for that category.
[0025] The threshold is a preset numerical value. The threshold may be set by the determination unit 15. For example, the determination unit 15 may input information on the user's past operation tendencies to the estimation model M to obtain output information. The determination unit 15 may output, for each category, the difference between the feature amount included in the information on the user's past operation tendencies and the feature amount included in the output information. The determination unit 15 may set a threshold for each category based on the difference for each category. As an example, the determination unit 15 may set a numerical value that is twice the difference for each category as the threshold for each category.
[0026] When the determination unit 15 determines that the user's interests and tastes have changed in a category, the recommendation unit 16 executes a process for recommending at least one or more applications associated with the category to the user. Specifically, when the determination unit 15 determines that the user's interests and tastes have changed in multiple categories, the recommendation unit 16 selects at least one or more categories from the multiple categories based on the degree of change in the user's interests and tastes. The recommendation unit 16 executes a process for recommending at least one or more applications associated with the selected category to the user.
[0027] For example, when it is determined that the user's interests and preferences have changed across multiple categories, the recommendation unit 16 selects at least one of the multiple categories based on the magnitude of the difference in feature values between the categories. The recommendation unit 16 recommends applications corresponding to the selected categories to the user. FIG. 5 is a diagram illustrating an example of a display screen of the device 10 when applications are recommended to the user. In the example illustrated in FIG. 5, when it is determined that the user's interests and preferences have changed across multiple categories, the recommendation unit 16 selects three categories in descending order of the difference in feature values. The recommendation unit 16 controls a display unit (not shown) to recommend applications corresponding to the selected categories to the user. The display unit displays the names of the three applications recommended to the user in PinP (Picture in Picture) format in the lower right corner of the screen G. The display unit displays the applications recommended to the user on the screen G in order from third place to first place. When five seconds have elapsed since the application was displayed on screen G, the display unit fades out the display of the application from screen G while moving the display of the application horizontally, and fades in the display of the next application onto screen G while moving the display horizontally. When the user taps on the display of an application, the display unit displays an installation page for the application on screen G. In this way, it becomes possible for the user to select one application from the three applications. Note that the recommendation unit 16 may select the category with the largest difference in feature amount.
[0028] In this way, the device 10 aggregates the time the user spent using applications for each category based on the logs stored in the device 10. The device 10 inputs the user's recent operational trends into the estimation model M every week and performs inference. At this time, the device 10 compares the output (information about the user's normal operational trends) when there is no change in the user's hobbies and preferences with the output from the estimation model M to check whether there has been a change in the user's operational trends. If there is a change in the user's operational trends, the device 10 determines that there has been a change in the user's hobbies and preferences. The device 10 takes an approach to the user, such as proposing a service or an application, in response to the change. For example, the device 10 recommends to the user a service or application corresponding to a category in which the user's hobbies and preferences have changed. Note that the device 10 performs this series of processes internally.
[0029] Next, the processing executed by the device 10 according to this embodiment will be described with reference to the flowcharts of Fig. 6 and Fig. 7. Fig. 6 is a flowchart showing an example of the processing for training the estimation model M. The processing for training the estimation model M is repeatedly executed at a predetermined cycle. The predetermined cycle is, for example, one week.
[0030] First, the acquisition unit 11 acquires application usage data for the device 10 (step S101). The application usage data is collected once a day. For example, information indicating the time and date when the application is launched is collected. The collected information is collected by date and category, and the total launch time of the application for each date and category is calculated.
[0031] The acquisition unit 11 determines whether sufficient application usage data has been accumulated (step S102). Specifically, the acquisition unit 11 determines whether information indicating the user's operation tendencies has been accumulated for N weeks. For example, the acquisition unit 11 determines whether information indicating the user's operation tendencies has been accumulated for four weeks, with one week being one data set.
[0032] If the acquisition unit 11 determines that sufficient application usage data has not been accumulated (step S102: NO), the process for training the estimation model M ends. Then, in the next cycle, the process of step S01 is executed again.
[0033] If the acquisition unit 11 determines that sufficient application usage data has been accumulated (step S102: YES), the acquisition unit 11 acquires information about the user's past operation trends (step S103). The construction unit 13 constructs an estimation model M based on the information about the user's past operation trends (step S104). Finally, the construction unit 13 stores the constructed estimation model M in the storage unit 12 (step S105).
[0034] 7 is a flowchart showing an example of a process for recommending an application to a user. The process for recommending an application to a user is repeatedly executed at a predetermined interval. First, the acquisition unit 11 acquires information about the user's recent operation tendency (step S201).
[0035] The comparison unit 14 compares output information obtained by inputting information about the user's recent operation tendency into the estimation model M with the information about the user's recent operation tendency (step S202). Specifically, the comparison unit 14 compares the feature amounts included in the output information with the feature amounts included in the information about the user's recent operation tendency for each category. For example, the comparison unit 14 calculates the difference between the feature amounts included in the output information and the feature amounts included in the information about the user's recent operation tendency for each category.
[0036] The determination unit 15 determines whether or not there has been a change in the user's hobbies and interests based on the comparison result (step S203). Specifically, the determination unit 15 determines whether or not there has been a change in the user's hobbies and interests for each category based on the comparison result by the comparison unit 14. For example, if the difference for each category is equal to or greater than a threshold, the determination unit 15 determines that there has been a change in the user's hobbies and interests for that category, and if the difference for each category is below the threshold, the determination unit 15 determines that there has been no change in the user's hobbies and interests.
[0037] If the determination unit 15 determines based on the comparison result that there has been no change in the user's interests (step S204: NO), the process of recommending applications to the user ends. Then, in the next cycle, the process of step S01 is executed again.
[0038] If the determination unit 15 determines based on the comparison result that there has been a change in the user's interests and tastes (step S204: YES), the recommendation unit 16 recommends applications to the user (step S205). Specifically, when the determination unit 15 determines that there has been a change in the user's interests and tastes in a category, the recommendation unit 16 executes a process for recommending at least one or more applications associated with the category to the user.
[0039] For example, when the determination unit 15 determines that the user's interests and tastes have changed in multiple categories, the recommendation unit 16 selects at least one of the multiple categories based on the degree of change in the user's interests and tastes. The recommendation unit 16 executes a process for recommending to the user at least one application associated with the selected category. As an example, the recommendation unit 16 selects one category with the largest difference and controls the display unit to display a user interface (UI) that encourages the installation of an application and use of a service corresponding to the selected category.
[0040] Next, the effects of the device 10 and method according to this embodiment will be described. In the device 10 and method according to this embodiment, the estimation model M receives information about the user's past operating tendencies as input and learns to output information similar to the information about the user's past operating tendencies. This allows the estimation model M to be trained for each user while reducing the processing load on the device 10. Furthermore, if information about the user's recent operating tendencies differs from the output information obtained by inputting the information about the user's recent operating tendencies into the estimation model M, it can be determined that there has been a change in the user's interests and tastes. From the above, it is possible to determine whether there has been a change in the user's interests and tastes while reducing the processing load on the device 10.
[0041] Furthermore, in this embodiment, the information regarding the user's recent operation tendency and the output information may be information indicating the user's operation tendency during a period from a first time point in the past to the present time, and the information regarding the user's past operation tendency may be information indicating the user's operation tendency during a period from a second time point in the past to the first time point. In this case, it is possible to compare the user's operation tendency during the period from the second time point to the first time point with the user's operation tendency during the period from the first time point to the present time. This makes it possible to more accurately determine whether there has been a recent change in the user's hobbies and interests.
[0042] Furthermore, in the present embodiment, the information indicating the user's operation tendency may be information indicating a feature amount related to the user for each category of hobby or preference, and the comparison unit 14 may compare, for each category, the feature amount included in the output information with the feature amount included in the information related to the user's recent operation tendency, and the determination unit 15 may determine, for each category, whether or not there has been a change in the user's hobby or preference, based on the comparison result by the comparison unit 14. In this case, it is possible to determine, for each category, whether or not there has been a change in the user's hobby or preference.
[0043] In this embodiment, the comparison unit 14 may calculate the difference between the feature amount included in the output information and the feature amount included in the information related to the user's recent operation tendency for each category, and the determination unit may determine that there has been a change in the user's hobbies and interests for that category if the difference for each category is equal to or greater than a threshold value. In this case, it is possible to more accurately determine whether there has been a change in the user's hobbies and interests for each category.
[0044] In this embodiment, the feature may be the total amount of time or the total number of times the user has used at least one application associated with a category, which allows for more accurate determination of whether or not there has been a change in the user's interests for each category.
[0045] In the present embodiment, the recommendation unit 16 may execute a process for recommending at least one application associated with a category to the user when the determination unit 15 determines that there has been a change in the user's interests in a category. In this case, it is possible to recommend an application that matches the user's recent interests and tastes based on the result of determining, for each category, whether there has been a change in the user's interests and tastes.
[0046] Furthermore, in the present embodiment, when the determination unit 15 determines that the user's interests and tastes have changed in a plurality of categories, the recommendation unit 16 may select at least one of the plurality of categories based on the degree of change in the user's interests and tastes, and may execute processing to recommend to the user at least one application associated with the selected category. In this case, when the user's interests and tastes have changed significantly in a predetermined category, it is possible to recommend to the user an application corresponding to the predetermined category.
[0047] In this way, when a change in the user's interests is detected, the user can be suggested (urged) to use a new service or install a new application. At this time, the usage data for each user of the application is utilized to approach the user, so the approach to the user can be optimized for each user (the approach to the user can be personalized).
[0048] In the present embodiment, the construction unit 13 may construct the estimation model M based on information about the user's past operation trends. In this case, the device 10 can execute the process of constructing the estimation model M and determining whether or not there has been a change in the user's interests and tastes. This makes it possible to determine whether or not there has been a change in the user's interests and tastes without placing a load on a server or the like external to the device 10.
[0049] Furthermore, since the estimation model M is constructed within the device 10 using data from only one user, the process of determining whether the user's hobbies and preferences have changed using the estimation model M can be specialized for each user. This enables a more user-specific approach. In other words, since a completely unique estimation model M can be constructed within the device 10, a more user-specific approach than before is possible. In addition, since a server or the like for constructing the estimation model M is not required, the cost of preparing a server or the like is reduced to zero, and the estimation model M can be automatically constructed and updated within the device 10.
[0050] [About the Present Disclosure] The device 10 of the present disclosure has the following configuration.
[0051] [1] A device comprising: an estimation model that receives information about a user's past operating tendencies as input and is trained to output information similar to the information about the user's past operating tendencies; a comparison unit that compares output information obtained by inputting information about the user's recent operating tendencies into the estimation model with the information about the user's recent operating tendencies; and a determination unit that determines whether or not there has been a change in the user's interests and tastes based on the comparison result by the comparison unit.
[0052] [2] The device described in [1], wherein the information relating to the user's recent operation tendency and the output information are information indicating the user's operation tendency in a period from a first time point, which is a past time point, to the present time point, and the information relating to the user's past operation tendency is information indicating the user's operation tendency in a period from a second time point, which is a time point earlier than the first time point, to the first time point.
[0053] [3] The device described in [2], wherein the information indicating the user's operation tendency is information indicating features related to the user for each category of hobbies and preferences, the comparison unit compares the features included in the output information with the features included in information related to the user's recent operation tendency for each category, and the determination unit determines whether there has been a change in the user's hobbies and preferences for each category based on the comparison result by the comparison unit.
[0054] [4] The device described in [3], wherein the comparison unit calculates a difference between the feature included in the output information and the feature included in information relating to the user's recent operating tendencies for each category, and the determination unit determines that there has been a change in the user's hobbies and preferences for that category if the difference for each category is equal to or greater than a threshold, and determines that there has been no change in the user's hobbies and preferences if the difference for each category is below the threshold.
[0055] [5] The device according to [3] or [4], wherein the feature amount is a total value of the time or the number of times the user has used at least one or more applications associated with the category.
[0056] [6] The device according to any one of [3] to [5], further comprising a recommendation unit that, when the determination unit determines that there has been a change in the user's hobbies and tastes in the category, executes a process for recommending at least one application associated with the category to the user.
[0057] [7] The device described in [6], wherein the recommendation unit, when the determination unit determines that there has been a change in the user's interests and tastes in the plurality of categories, selects at least one or more of the plurality of categories based on the degree of change in the user's interests and tastes, and performs a process to recommend to the user at least one or more applications associated with the selected categories.
[0058] [8] The device according to any one of [1] to [7], further comprising a construction unit that constructs the estimation model based on information about the user's past operation tendencies.
[0059] [9] A method comprising: a comparison step of inputting information about a user's past operation tendencies and inputting information about the user's recent operation tendencies into an estimation model trained to output information similar to the information about the user's past operation tendencies, and comparing the output information obtained by inputting information about the user's recent operation tendencies with the information about the user's recent operation tendencies; and a determination step of determining whether or not there has been a change in the user's tastes and preferences based on the comparison result in the comparison step.
[0060] [Definition of Terms, etc.] The block diagrams used to explain the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of hardware and / or software. Furthermore, the method for realizing each functional block is not particularly limited. That is, each functional block may be realized using a single device that is physically or logically coupled, or may be realized using two or more physically or logically separated devices that are connected directly or indirectly (e.g., via wire, wirelessly, etc.) and these multiple devices. The functional block may also be realized by combining the single device or multiple devices with software.
[0061] Functions include, but are not limited to, judgment, determination, assessment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, consideration, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs transmission is called a transmitting unit or transmitter. As mentioned above, there are no particular limitations on how these functions are implemented.
[0062] For example, the device 10 according to an embodiment of the present disclosure may function as a computer that performs processing to determine whether or not there has been a change in hobbies and preferences according to the present disclosure. Fig. 8 is a diagram illustrating an example of the hardware configuration of the device 10 according to an embodiment of the present disclosure. The device 10 described above may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, and the like.
[0063] In the following description, the term "apparatus" can be interpreted as a circuit, a device, a unit, etc. The hardware configuration of apparatus 10 may be configured to include one or more of the apparatuses shown in the drawings, or may be configured to exclude some of the apparatuses.
[0064] Each function of the device 10 is realized by loading specified software (programs) onto hardware such as the processor 1001 and memory 1002, causing the processor 1001 to perform calculations, control communication via the communication device 1004, and control at least one of reading and writing data in the memory 1002 and storage 1003.
[0065] The processor 1001 controls the entire computer by running, for example, an operating system. The processor 1001 may be configured by a central processing unit (CPU) including an interface with peripheral devices, a control unit, an arithmetic unit, a register, etc. For example, the above-mentioned comparison unit 14 and the like may be realized by the processor 1001.
[0066] The processor 1001 also reads programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002 and executes various processes in accordance with these programs. The programs used are those that cause a computer to execute at least some of the operations described in the above-described embodiments. For example, the comparison unit 14 and the like may be implemented by a control program stored in the memory 1002 and running on the processor 1001, and similar implementations may be used for other functional blocks. While the above-described various processes have been described as being executed by one processor 1001, they may also be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The programs may also be transmitted from a network via a telecommunications line.
[0067] The memory 1002 is a computer-readable recording medium and may be configured by, for example, at least one of a read-only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a random access memory (RAM), etc. The memory 1002 may also be called a register, a cache, a main memory (primary storage device), etc. The memory 1002 can store executable programs (program codes), software modules, etc. for implementing a method according to an embodiment of the present disclosure.
[0068] Storage 1003 is a computer-readable recording medium, and may be composed of at least one of, for example, an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray (registered trademark) disk), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) disk, a magnetic strip, etc. Storage 1003 may also be referred to as an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, a server, or other appropriate medium including at least one of memory 1002 and storage 1003.
[0069] The communication device 1004 is hardware (transmission / reception device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as, for example, a network device, a network controller, a network card, or a communication module. The communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc. to realize at least one of frequency division duplex (FDD) and time division duplex (TDD). For example, the above-mentioned acquisition unit 11 may be realized by the communication device 1004. The communication device 1004 may be implemented with a transmitter and a receiver that are physically or logically separated from each other.
[0070] The input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that receives input from the outside. The output device 1006 is an output device (e.g., a display, a speaker, an LED lamp, etc.) that outputs to the outside. Note that the input device 1005 and the output device 1006 may be integrated into one device (e.g., a touch panel).
[0071] Furthermore, each device, such as the processor 1001 and the memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses between each device.
[0072] The device 10 may also be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, the processor 1001 may be implemented using at least one of these pieces of hardware.
[0073] The notification of information is not limited to the aspects / embodiments described in the present disclosure and may be performed using other methods. For example, the notification of information may be performed by physical layer signaling (e.g., Downlink Control Information (DCI) and Uplink Control Information (UCI)), higher layer signaling (e.g., Radio Resource Control (RRC) signaling, Medium Access Control (MAC) signaling, broadcast information (Master Information Block (MIB) and System Information Block (SIB))), other signals, or a combination thereof. Furthermore, the RRC signaling may be referred to as an RRC message, and may be, for example, an RRC Connection Setup message, an RRC Connection Reconfiguration message, or the like.
[0074] The order of the procedures, sequences, flowcharts, etc. of each aspect / embodiment described in this disclosure may be changed unless it is consistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.
[0075] Input and output information may be stored in a specific location (for example, memory) or may be managed using a management table. Input and output information may be overwritten, updated, or added to. Output information may be deleted. Input information may be sent to another device.
[0076] The determination may be made based on a value represented by one bit (0 or 1), a Boolean value (true or false), or a numerical comparison (e.g., comparison with a predetermined value).
[0077] The aspects / embodiments described in this disclosure may be used alone, in combination, or switched depending on the implementation. Notification of predetermined information (e.g., notification that "X is true") is not limited to explicit notification, but may be implicit (e.g., not notifying the predetermined information).
[0078] Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended to be illustrative and does not have any limiting meaning on the present disclosure.
[0079] Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.
[0080] Software, instructions, information, etc. may also be transmitted or received over a transmission medium. For example, if software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave), then these wired and / or wireless technologies are included within the definition of transmission media.
[0081] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.
[0082] Note that terms described in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings. For example, at least one of a channel and a symbol may be a signal (signaling). Furthermore, a signal may be a message. Furthermore, a component carrier (CC) may be called a carrier frequency, a cell, a frequency carrier, etc.
[0083] Furthermore, the information, parameters, etc. described in the present disclosure may be expressed using absolute values, may be expressed using relative values from a predetermined value, or may be expressed using other corresponding information. For example, a radio resource may be indicated by an index.
[0084] The names used for the above-described parameters are not intended to be limiting in any way. Furthermore, the mathematical expressions using these parameters may differ from those explicitly disclosed in this disclosure. The various channels (e.g., PUCCH, PDCCH, etc.) and information elements may be identified by any suitable names, and therefore the various names assigned to these various channels and information elements are not intended to be limiting in any way.
[0085] In this disclosure, the terms "Mobile Station (MS)," "user terminal," "User Equipment (UE)," "terminal," and the like may be used interchangeably.
[0086] A mobile station may also be referred to by those skilled in the art as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other suitable terminology.
[0087] As used in this disclosure, the terms "determining" and "determining" may encompass a wide variety of actions. "Determining" and "determining" may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiring (e.g., searching in a table, database, or other data structure), ascertaining, and the like. "Determining" and "determining" may also include receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in memory), and the like. Furthermore, "judgment" and "decision" can include regarding resolving, selecting, choosing, establishing, comparing, etc. as having been "judged" or "decided." In other words, "judgment" and "decision" can include regarding some action as having been "judged" or "decided." Furthermore, "judgment (decision)" can be interpreted as "assuming," "expecting," "considering," etc.
[0088] The terms "connected," "coupled," or any variation thereof, refer to any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are "connected" or "coupled" to each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connected" may be read as "access." As used in this disclosure, two elements may be considered to be "connected" or "coupled" to each other using one or more wires, cables, and / or printed electrical connections, as well as electromagnetic energy having wavelengths in the radio frequency range, microwave range, and optical (both visible and invisible) range, as some non-limiting and non-exhaustive examples.
[0089] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."
[0090] As used in this disclosure, any reference to an element using a designation such as "first," "second," etc. does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient method of distinguishing between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed or that the first element must in some way precede the second element.
[0091] When the terms "include," "including," and variations thereof are used in this disclosure, these terms are intended to be inclusive, similar to the term "comprising." Furthermore, when the term "or" is used in this disclosure, it is not intended to be an exclusive or.
[0092] In this disclosure, where articles are added by translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.
[0093] In the present disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "coupled" may also be interpreted in the same way as "different."
[0094] 10...device, 14...comparison unit, 15...determination unit, M...estimation model, 1001...processor, 1002...memory, 1003...storage, 1004...communication device, 1005...input device, 1006...output device.
Claims
1. An apparatus comprising: an estimation model learned to take information on the user's past operation tendency as input and output information similar to the information on the user's past operation tendency; a comparison unit that compares the output information obtained by inputting information on the user's recent operation tendency into the estimation model with the information on the user's recent operation tendency; and a determination unit that determines whether there has been a change in the user's hobby based on the comparison result by the comparison unit.
2. The apparatus according to claim 1, wherein the information on the user's recent operation tendency and the output information are information indicating the user's operation tendency during a period from a first time point in the past to the current time point, and the information on the user's past operation tendency is information indicating the user's operation tendency during a period from a second time point in the past before the first time point to the first time point.
3. The apparatus according to claim 2, wherein the information indicating the user's operation tendency is information indicating feature amounts related to the user for each category of hobbies, the comparison unit compares the feature amounts included in the output information with the feature amounts included in the information on the user's recent operation tendency for each category, and the determination unit determines whether there has been a change in the user's hobby for each category based on the comparison result by the comparison unit.
4. The apparatus according to claim 3, wherein the comparison unit calculates, for each category, a difference between the feature amounts included in the output information and the feature amounts included in the information on the user's recent operation tendency, and the determination unit determines that there has been a change in the user's hobby in the category when the difference for each category is equal to or greater than a threshold value, and determines that there has been no change in the user's hobby when the difference for each category is less than the threshold value.
5. The apparatus according to claim 3, wherein the feature amount is a total value of the time or the number of times the user has used at least one or more applications associated with the category.
6. The apparatus according to claim 3, further comprising a recommendation unit that, when the determination unit determines that there has been a change in the user's hobby in the category, executes a process for recommending at least one or more applications associated with the category to the user.
7. The recommendation unit, when it is determined by the determination unit that there is a change in the user's hobby orientation in a plurality of the categories, selects at least one or more categories out of the plurality of the categories based on the degree of change in the user's hobby orientation, and executes a process for recommending to the user at least one or more applications associated with the selected category. The apparatus according to claim 6.
8. The apparatus according to claim 1, further comprising a construction unit that constructs the estimation model based on information regarding the user's past operation tendency.
9. A method comprising: a comparison step of inputting information regarding the user's most recent operation tendency into an estimation model that has been learned to input information regarding the user's past operation tendency and output information similar to the information regarding the user's past operation tendency, and comparing the output information thus obtained with the information regarding the user's most recent operation tendency; and a determination step of determining whether or not there is a change in the user's hobby orientation based on the comparison result in the comparison step.