Information processing device

Abstract

An information processing device (10) is provided with: a waveform data generation unit (12) that, from acquired action data including action types and execution time information regarding a user's actions, determines the points in time of chronological events relating to the user's action characteristics, and connects chronologically adjacent points in time using a prescribed waveform to generate waveform data for each action; and an image generation unit (13) that generates a two-dimensional image representing the user's action characteristics by converting the generated waveform data into a power spectrum image.

Description

Technical Field

[0001] The present disclosure relates to an information processing apparatus that generates a two-dimensional image representing user behavior characteristics, and the generated two-dimensional image is used as an explanatory variable in machine learning, for example.

Background Art

[0002] When estimating information (objective variable) regarding user behavior characteristics from user behavior data (explanatory variable) using machine learning, it is important to generate and use explanatory variables with a sufficient amount of information in order to accurately estimate the objective variable. Usually, user behavior data is often input into a learning model as the total number (statistical value) of targets (for example, start of behavior) within a certain period. Further, Patent Document 1 below discloses a technique for generating a pie chart and a bar graph that represent information regarding behavior in time series in order to easily grasp the type, behavior time, and quality of behavior of a user on a certain day.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, there has been a need to predict, by machine learning, indicators that are not determined immediately or in the short term, such as the satisfaction of services for users. In order to meet such needs, it is necessary to appropriately capture characteristics formed in the medium to long term. However, with the technique of Patent Document 1, it is difficult to appropriately add information that changes over time, such as the execution frequency and execution cycle of various user behaviors, as explanatory variables in machine learning.

[0005] This disclosure aims to appropriately acquire time-series information on various user behaviors, taking into account the challenges mentioned above. [Means for solving the problem]

[0006] The information processing device relating to this disclosure includes a waveform data generation unit that generates waveform data for each action by determining the time-series timing of events related to the user's behavioral characteristics from action data including the type of action and execution time information of acquired user actions, and connecting adjacent timings in the time series with a predetermined waveform; and an image generation unit that generates a two-dimensional image representing the user's behavioral characteristics by converting the generated waveform data into a power spectrum image.

[0007] In the above-described information processing device, the waveform data generation unit determines the time-series timing of events related to the user's behavioral characteristics from the acquired behavioral data, which includes information on the type of action and execution time related to the user's actions. By connecting adjacent timings in the time series with predetermined waveforms, waveform data for each action is generated. Such waveform data for each action can represent information that changes over time, such as the frequency and cycle of user actions. The image generation unit then generates a two-dimensional image representing the user's behavioral characteristics by converting the generated waveform data into a power spectrum image. This makes it possible to appropriately acquire time-series information related to various user actions. Such information can be used as explanatory variables in machine learning. For example, it is very effective when predicting long-term indicators (indicators that are not determined immediately or in the short term), such as user satisfaction with a service, using machine learning, and is extremely useful in accurately estimating such indicators. [Effects of the Invention]

[0008] According to this disclosure, it is possible to appropriately acquire time-series information on various user behaviors. [Brief explanation of the drawing]

[0009] [Figure 1] This is a functional block diagram of the information processing device in the first and second embodiments. [Figure 2] This is a flowchart illustrating the process in the first embodiment. [Figure 3] This is a diagram to explain how to acquire behavioral data. [Figure 4] (a) is a diagram showing the generation of waveform data related to the timing of app launches within the service, and (b) is a diagram showing the generation of waveform data related to the timing of purchase actions within the service. [Figure 5] This figure shows an image of the actual data of the generated waveform data. [Figure 6] This figure shows the generation of a power spectral image of a waveform. [Figure 7] This figure shows an example of using the generated power spectral image as an explanatory variable. [Figure 8] This is a flowchart illustrating the process in the second embodiment. [Figure 9] This is a diagram to explain waveform synthesis. [Figure 10] This figure shows the generation of a power spectral image from a composite waveform. [Figure 11] This figure shows an example of using a power spectral image generated from a composite waveform as an explanatory variable. [Figure 12] This figure shows an example of the hardware configuration of an information processing device. [Modes for carrying out the invention]

[0010] Hereinafter, various embodiments of this disclosure will be described with reference to the drawings. In the following, as the first embodiment, a basic embodiment will be described in which waveform data for each action is generated by connecting the time-series timings of events related to the behavioral characteristics from the user's behavioral data, and a two-dimensional image representing the user's behavioral characteristics is generated by power spectral imaging of the individual waveform data. In the second embodiment, an embodiment will be described in which processing is further carried out to group related waveform data from the multiple waveform data generated. Note that "user behavior" includes various actions, but in the following first and second embodiments, in order to obtain indicators that are formed over the medium to long term, such as the satisfaction level of the target service (hereinafter abbreviated as "service") for the user, "user behavior" will be described using user actions related to the use of the service as an example.

[0011] (First Embodiment) As shown in Figure 1, the information processing device 10 in the first embodiment includes a behavior data acquisition unit 11, a waveform data generation unit 12, an image generation unit 13, and an estimation unit 14 as functional blocks for realizing the functions related to this disclosure. The functions of each unit will be outlined below. Details of the functions will be described in the processing explanation in accordance with Figure 2.

[0012] The behavior data acquisition unit 11 is a functional unit that acquires behavior data, including the type of behavior and execution time information, related to the behavior of various users from external devices (for example, mobile terminals 20 (Figure 3) of various users), and stores the acquired user-specific behavior data in the built-in behavior database 11A.

[0013] The waveform data generation unit 12 is a functional unit that obtains the timing in the time series of events related to the user's behavior characteristics from the above-mentioned behavior data, and generates waveform data for each behavior by connecting adjacent timings in the time series with a predetermined waveform. Examples of the events related to the user's behavior characteristics include the startup of an application within the service (hereinafter abbreviated as "app") and purchases within the service. Examples of the "predetermined waveform" for connecting adjacent timings in the time series include a sine wave, a cosine wave, and a triangular wave. Note that the waveform data generation unit 12 may have a function of summarizing related waveform data among the generated plurality of waveform data, but the function of summarizing waveform data in this way will be described in the second embodiment.

[0014] The image generation unit 13 is a functional unit that generates a two-dimensional image representing the user's behavior characteristics by performing power spectrum imaging on the generated waveform data. Examples of the above-mentioned power spectrum imaging include processes such as continuous Fourier transform and wavelet transform.

[0015] The estimation unit 14 inputs the two-dimensional image (the two-dimensional image representing the behavior characteristics related to the user's use of the service) generated by the image generation unit 13 as an explanatory variable into a learning model 14A that uses the user's behavior characteristic information as an explanatory variable and a predetermined index (an index formed in the medium to long term (not an index determined immediately or in the short term), here, as an example, an index related to service satisfaction) as an objective variable, and is a functional unit that estimates the above index. It is assumed that the learning model 14A has already been generated by supervised learning that uses the past user's behavior characteristic information as an explanatory variable and an index related to service satisfaction as an objective variable, and is included in the estimation unit 14. Further, the estimation unit 14 appropriately outputs the obtained estimation result (index). For example, the estimation result (index) may be displayed and output or printed and output by a predetermined operation by the operator of the information processing apparatus 10.

[0016] Next, referring to FIGS. 3 to 7 along with the flowchart of FIG. 2, the processing executed in the information processing apparatus 10 will be described.

[0017] First, the action data acquisition unit 11 acquires action data including the type of action and execution time information regarding the actions of various users from an external device (e.g., the user's mobile terminal 20 shown in FIG. 3), and stores the acquired action data for each user in the action database 11A (step S1 in FIG. 2). The action data acquired and stored here includes information such as a user ID for identifying a user, the type of action, and the execution time, for example, as shown in FIG. 3. Among these, examples of the type of action include app startup within a service and purchase within a service.

[0018] Next, the waveform data generation unit 12 obtains the timing in the time series of events related to the action characteristics of the target user (target user) from the action data of the target user, and connects the adjacent timings in the time series with a predetermined waveform (here, a sine wave) to generate waveform data for each action (step S2 in FIG. 2). For example, FIG. 4(a) shows an example of obtaining the timing in the time series of app startup within a service and connecting the adjacent timings in the time series with a sine wave to generate waveform data related to app startup within the service, and FIG. 4(b) shows an example of obtaining the timing in the time series of purchase within a service and connecting the adjacent timings in the time series with a sine wave to generate waveform data related to purchase within the service. Also, FIG. 5 shows an actual data image of the generated waveform data, and it can be seen that the frequency changes at a certain timing along the time series. Note that the generated waveform data is generated to grasp information that can change over time such as the execution frequency and execution period of an action, so the amplitude on the vertical axis is not limited to a predetermined value and can be arbitrarily determined. For example, FIGS. 4(a), 4(b), and 5 show examples of generating sine wave waveform data with the maximum amplitude aligned to a constant value.

[0019] Next, the image generation unit 13 generates a two-dimensional image representing the user's behavioral characteristics by performing power spectral imaging on the generated waveform data (step S3 in Figure 2). For example, Figure 6 shows an example in which a two-dimensional image representing the user's behavioral characteristics is generated by performing power spectral imaging on waveform data (Figure 5) at the time of an event related to the target user's behavioral characteristics (launching an application within a certain service).

[0020] Furthermore, the estimation unit 14 inputs the two-dimensional images generated in step S3 as explanatory variables into the learning model 14A to estimate a predetermined indicator as the target variable (in this case, an indicator related to service satisfaction) (step S4 in Figure 2). Figure 7 shows an example in which two two-dimensional images (i.e., a power spectrum image of the waveform at the time the service application was launched during one year, and a power spectrum image of the waveform at the time a purchase was made within the service during one year) are input into the learning model 14A as explanatory variables, and an estimation result such as an indicator related to service satisfaction (for example, NPS (Net Promoter Score) = 3) is obtained as the target variable. The estimation result (indicator related to service satisfaction) can be displayed or printed out by, for example, a predetermined operation by the operator of the information processing device 10.

[0021] According to the first embodiment described above, a two-dimensional image representing the user's behavioral characteristics is generated by power spectral imaging of waveform data that can represent time-series information such as the frequency and cycle of user behavior. This makes it possible to appropriately obtain time-series information (information on execution frequency, execution cycle, etc.) related to various behaviors that could not be obtained with conventional tabular data. Such information can be used as explanatory variables in machine learning, and is very effective when predicting indicators that are formed over the medium to long term (indicators that are not determined immediately or in the short term), such as service satisfaction for users, using machine learning. Accordingly, it is possible to accurately estimate indicators that are formed over the medium to long term, such as service satisfaction, and to formulate appropriate service improvement policies early on.

[0022] Furthermore, compared to using conventional tabular data, for example, time-series information such as (a) the number of uses in a given month, (b) the number of uses in the following month, and (c) the number of uses in the month after that, which had to be created individually with tabular data, can be represented by a single feature (explanatory variable in machine learning). This enables machine learning that takes overall trends into account from the similarity of the generated two-dimensional images. In addition, unlike using conventional tabular data, there is the advantage that features (explanatory variables in machine learning) can be obtained without pre-determining the data acquisition period.

[0023] (Second Embodiment) Below, as a second embodiment, we will describe an embodiment in which a process is further carried out to group related waveform data from among the multiple generated waveform data.

[0024] The configuration of the information processing device 10 in the second embodiment is the same as the configuration of the information processing device 10 in the first embodiment (Figure 1), so redundant explanations will be omitted. However, the waveform data generation unit 12 has a function to combine related waveform data (for example, by multiplication, summation, etc.) if related waveform data for related actions (related waveform data) exists after generating waveform data for each action of the target user. In addition, the image generation unit 13 has a function to generate a two-dimensional image representing the user's behavioral characteristics by performing power spectrum imaging on the waveform data combined by the waveform data generation unit 12, in addition to performing power spectrum imaging on individual waveform data as in the first embodiment.

[0025] Next, following the flowchart in Figure 8, and referring to Figures 9 to 11, the processes executed in the information processing device 10 will be explained.

[0026] First, the behavior data acquisition unit 11 acquires behavior data, including the type of behavior and execution time information related to various user behaviors, from an external device (for example, the user's mobile terminal 20 shown in Figure 3), similar to the first embodiment, and stores the acquired user-specific behavior data in the behavior database 11A (step S11 in Figure 8).

[0027] Next, the waveform data generation unit 12 obtains the time-series timing of events related to the target user's behavioral characteristics (in this case, launching an app within the service, making a purchase within the service, etc.) from the target user's behavioral data, and generates waveform data for each action by connecting adjacent timings in the time series with a predetermined waveform (in this case, a sine wave) (step S12 in Figure 8). For example, Figure 9 shows an example in which waveform data for the timing of purchasing a product in category A within the service and waveform data for the timing of purchasing a product in category B within the same service are generated.

[0028] Furthermore, in step S12, the waveform data generation unit 12 determines whether there is related waveform data for mutually related actions (related waveform data), and if there is related waveform data, it combines the related waveform data (for example, by multiplication, addition, etc.: step S13 in Figure 8). Figure 9 shows an example in which the waveform data at the time of purchasing a product of category A within the service and the waveform data at the time of purchasing a product of category B within the same service are determined to be related waveform data, and these waveform data are combined to generate the composite waveform shown in the lower part of Figure 9.

[0029] The waveform data generation unit 12 then generates a two-dimensional image representing the user's behavioral characteristics by performing power spectral imaging on the combined waveform data or individual waveform data (step S14 in Figure 8). Figure 10 shows an example in which a two-dimensional image representing the user's behavioral characteristics is generated by performing power spectral imaging on the combined waveform data (synthetic waveform data) in step S13.

[0030] Furthermore, the estimation unit 14, similar to the first embodiment, inputs the two-dimensional image generated in step S14 as an explanatory variable to the learning model 14A, thereby estimating a predetermined index (in this case, an index related to service satisfaction) (step S15 in Figure 8). Figure 11 shows an example in which two two-dimensional images (i.e., a power spectrum image of the composite waveform of purchases by genre over one year, and a power spectrum image of the waveform at the timing of service application launches during one year) are input to the learning model 14A as explanatory variables, and an estimation result such as an index related to service satisfaction (for example, NPS (Net Promoter Score) = 3) is obtained as the target variable. The estimation result (index related to service satisfaction) can be displayed or printed out by, for example, a predetermined operation by the operator of the information processing device 10.

[0031] According to the second embodiment described above, in addition to the effects described in the first embodiment, a composite waveform is generated by combining waveform data of interrelated actions (for example, by multiplication, summation, etc.), a two-dimensional image is generated by power spectral imaging of the composite waveform, and the obtained two-dimensional image (i.e., information-rich information including the relationships and interactions between actions) can be used as an explanatory variable to input into a learning model for estimating service satisfaction. As a result, service satisfaction can be estimated with greater accuracy using explanatory variables that have sufficient information.

[0032] In the first and second embodiments, "user behavior" refers to user actions related to service use, and "indicators" that can be determined over the medium to long term refer to user satisfaction with the service, respectively. However, "user behavior" and "indicators" are not limited to these. In addition to indicators related to user satisfaction with the service, the "indicators" to be estimated can be broadly applied to predictive indicators related to service use, such as indicators related to the user's intention to continue using the service. Furthermore, waveform data may be cosine waves or triangular waves in addition to the sine wave exemplified. Also, for power spectrum imaging, processing such as continuous Fourier transform and wavelet transform can be employed.

[0033] The gist of this disclosure is found in the following [1] to [4]. [1] A waveform data generation unit generates waveform data for each action by determining the time-series timing of events related to the user's behavioral characteristics from the acquired behavioral data, which includes information on the type of action and execution time of the user's actions, and by connecting adjacent timings in the time series with a predetermined waveform. An image generation unit generates a two-dimensional image representing the user's behavioral characteristics by converting the generated waveform data into a power spectrum image, An information processing device equipped with the following features. [2] The waveform data generation unit generates waveform data for each of a plurality of related types of actions, and combines the obtained waveform data in a predetermined way to generate a single waveform data relating to the plurality of actions. [1] The information processing device described above. [3] An estimation unit estimates the indicator by inputting two-dimensional images representing the user's behavioral characteristics, generated by the image generation unit, as explanatory variables into a learning model that uses user behavioral characteristic information as explanatory variables and a predetermined indicator as the target variable. The information processing apparatus described in [1] or [2] further comprises the following: [4] The aforementioned actions relate to the use of the service in question, The estimation unit estimates indicators related to the user's use of the service by using two-dimensional images representing the user's behavioral characteristics related to the use of the service, generated by the image generation unit, as explanatory variables. [3] The information processing device described above. [5] The predetermined waveform includes at least one of a sine wave, a cosine wave, and a triangular wave. An information processing device as described in any one of items [1] to [4]. [6] The power spectral imaging includes at least one of a continuous Fourier transform and a wavelet transform. An information processing device as described in any one of items [1] to [5].

[0034] (Explanation of terms, explanation of hardware configuration (Figure 12), etc.) The block diagrams used in the description of the above embodiments show functional units. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method of realizing each functional block is not particularly limited. That is, each functional block may be realized using one device that is physically or logically coupled, or it may be realized using two or more physically or logically separated devices that are directly or indirectly connected (for example, using wired or wireless connections). A functional block may also be realized by combining the above one device or the above multiple devices with software.

[0035] Functions include, but are not limited to, judgment, decision, judgment, calculation, calculation, processing, derivation, investigation, exploration, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, assumption, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating (mapping), and assigning. For example, a functional block (configuration part) that enables transmission is called a transmitting unit or transmitter. As mentioned above, the method of implementation is not particularly limited.

[0036] For example, the information processing device 10 in this embodiment may function as a computer that performs the processing of the disclosure. Figure 12 is a diagram showing an example of the hardware configuration of the information processing device 10. The information processing device 10 described above may be physically configured as a computer device including a processor 1001, memory 1002, storage 1003, communication device 1004, input device 1005, output device 1006, bus 1007, etc.

[0037] In the following explanation, the term "device" can be replaced with "circuit," "device," "unit," etc. The hardware configuration of the information processing device 10 may include one or more of the devices shown in the figure, or it may be configured to omit some of the devices.

[0038] Each function in the information processing device 10 is realized by loading predetermined software (programs) onto hardware such as the processor 1001 and memory 1002, which allows the processor 1001 to perform calculations, control communication by the communication device 1004, and control at least one of data reading and writing in the memory 1002 and storage 1003.

[0039] The processor 1001 controls the entire computer, for example, by running an operating system. The processor 1001 may consist of a central processing unit (CPU) that includes interfaces with peripheral devices, control units, arithmetic units, registers, and so on.

[0040] Furthermore, the processor 1001 reads programs (program code), 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 accordingly. The program used is one that causes the computer to execute at least a part of the operations described in the above embodiment. Although the above processes have been described as being executed by one processor 1001, they may be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The program may also be transmitted from a network via a telecommunications line.

[0041] Memory 1002 is a computer-readable recording medium and may consist of at least one of the following: ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), RAM (Random Access Memory), etc. Memory 1002 may also be called a register, cache, main memory, etc. Memory 1002 can store executable programs (program code), software modules, etc., for carrying out a wireless communication method according to one embodiment of the present disclosure.

[0042] Storage 1003 is a computer-readable recording medium and may consist of at least one of the following: an optical disc such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disc, a digital multipurpose disc, a Blu-ray® disc), a smart card, flash memory (e.g., a card, a stick, a key drive), a floppy® disk, a magnetic strip, etc. Storage 1003 may also be called an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, server, or other suitable medium including at least one of memory 1002 and storage 1003.

[0043] The communication device 1004 is hardware (transceiver / receiver device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as a network device, network controller, network card, communication module, etc. The communication device 1004 may be configured to include, for example, a high-frequency switch, duplexer, filter, frequency synthesizer, etc., in order to implement at least one of frequency division duplex (FDD) and time division duplex (TDD).

[0044] The input device 1005 is an input device that accepts input from an external source (e.g., a keyboard, mouse, microphone, switch, button, sensor, etc.). The output device 1006 is an output device that outputs to an external source (e.g., a display, speaker, LED lamp, etc.). The input device 1005 and the output device 1006 may be configured as an integrated unit (e.g., a touch panel).

[0045] Furthermore, each device, such as the processor 1001 and memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or different buses may be configured for each device.

[0046] Furthermore, the information processing device 10 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), and an FPGA (Field Programmable Gate Array), and some or all of each functional block may be realized by such hardware. For example, the processor 1001 may be implemented using at least one of these hardware components.

[0047] Information notification is not limited to the embodiments described herein and may be carried out by other means. For example, information notification may be carried out by physical layer signaling (e.g., DCI (Downlink Control Information), UCI (Uplink Control Information)), upper layer signaling (e.g., RRC (Radio Resource Control) signaling, MAC (Medium Access Control) signaling, broadcast information (MIB (Master Information Block), SIB (System Information Block))), other signals, or combinations thereof. RRC signaling may also be called RRC messages, and may be, for example, RRC Connection Setup messages, RRC Connection Reconfiguration messages, etc.

[0048] Each aspect / embodiment described in this disclosure includes LTE (Long Term Evolution), LTE-A (LTE-Advanced), SUPER 3G, IMT-Advanced, 4G (4th generation mobile communication system), 5G (5th generation mobile communication system), 6th generation mobile communication system (6G), xth generation mobile communication system (xG) (xG (where x is, for example, an integer or decimal)), FRA (Future Radio Access), NR (new Radio), New radio access (NX), Future generation radio access (FX), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), and IEEE This may apply to at least one system utilizing 802.20, UWB (Ultra-WideBand), Bluetooth®, or other appropriate systems, and to next-generation systems extended, modified, created, or defined based thereon. It may also apply to a combination of multiple systems (for example, a combination of at least one of LTE and LTE-A with 5G).

[0049] The processing procedures, sequences, flowcharts, etc., of each aspect / embodiment described herein may be reordered, provided they are consistent with each other. For example, the methods described herein present various step elements in an exemplary order and are not limited to that specific order.

[0050] Input and output information may be stored in a specific location (e.g., memory) or managed using a management table. Input and output information may be overwritten, updated, or appended to. Output information may be deleted. Input information may be transmitted to other devices.

[0051] The determination may be made by a value represented by 1 bit (0 or 1), by a boolean value (true or false), or by a numerical comparison (for example, a comparison with a predetermined value).

[0052] Each aspect / embodiment described herein may be used individually, in combination, or switched between as needed during implementation. Furthermore, notification of specific information (e.g., notification that "X is") is not limited to explicit notification, but may also be implicit (e.g., by not providing such notification).

[0053] Although the present disclosure has been described in detail above, it will be 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 intent and scope of the present disclosure as defined by the claims. Therefore, the descriptions in the present disclosure are illustrative and not intended to be restrictive in any way.

[0054] Software should be broadly interpreted to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, procedures, functions, and so on, whether they are called software, firmware, middleware, microcode, hardware description languages, or by any other name.

[0055] Furthermore, software, instructions, information, etc., may be transmitted and received via a transmission medium. For example, if software is transmitted from a website, server, or other remote source using at least one of wired technology (such as coaxial cable, fiber optic cable, twisted pair, or digital subscriber line (DSL)) and wireless technology (such as infrared or microwave), then at least one of these wired and wireless technologies is included in the definition of a transmission medium.

[0056] The information, signals, etc. described in this disclosure may be represented using any of the various different techniques. For example, the data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0057] In addition, terms used 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 the communication channel and the symbol may be a signal (signaling). Also, the signal may be a message. Furthermore, the component carrier (CC) may be called a carrier frequency, cell, frequency carrier, etc.

[0058] The terms “system” and “network” as used in this disclosure are interchangeable.

[0059] Furthermore, the information, parameters, etc., described in this disclosure may be expressed using absolute values, relative values ​​from a given value, or other corresponding information. For example, wireless resources may be indicated by an index.

[0060] The names used for the parameters described above are not restrictive in any way. Furthermore, the formulas and other expressions using these parameters may differ from those expressly disclosed in this disclosure. Various communication channels (e.g., PUCCH, PDCCH, etc.) and information elements can be identified by any suitable name, and therefore, the various names assigned to these various communication channels and information elements are not restrictive in any way.

[0061] As used in this disclosure, the terms “determining” and “determining” may encompass a wide variety of actions. “Determining” may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiry (e.g., searching in a table, database, or other data structure), and ascertaining. “Determining” may also include, for example, receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, and accessing (e.g., accessing data in memory). Furthermore, "judgment" and "decision" can include considering something as having been "judged" or "decided" after resolving, selecting, choosing, establishing, comparing, etc. In other words, "judgment" and "decision" can include considering something as having been "judged" or "decided" after some action. Also, "judgment (decision)" can be reinterpreted as "assuming," "expecting," or "considering."

[0062] In this disclosure, the phrase "based on" does not mean "based solely on" unless otherwise specified. In other words, the phrase "based on" means both "based solely on" and "based at least on."

[0063] Any reference to elements using the designations “first,” “second,” etc., as used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Accordingly, references to the first and second elements do not imply that only two elements may be employed, or that the first element must precede the second element in any way.

[0064] Where the terms “include,” “including,” and variations thereof are used in this disclosure, these terms are intended to be inclusive, as is the term “comprising.” Furthermore, the term “or” as used in this disclosure is not intended to mean exclusive OR.

[0065] In this disclosure, if articles are added through translation, such as a, an, and the in English, this disclosure may include the fact that the noun following these articles is plural.

[0066] In this disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "combine" may be interpreted similarly to "different." [Explanation of Symbols]

[0067] 10... Information processing device, 11... Behavioral data acquisition unit, 11A... Behavioral database, 12... Waveform data generation unit, 13... Image generation unit, 14... Estimation unit, 14A... Learning model, 20... Mobile terminal, 1001... Processor, 1002... Memory, 1003... Storage, 1004... Communication device, 1005... Input device, 1006... Output device, 1007... Bus.

Claims

1. A waveform data generation unit generates waveform data for each action by determining the time-series timing of events related to the user's behavioral characteristics from behavioral data including the type of action and execution time information related to the acquired user's actions, and connecting adjacent timings in the time series with a predetermined waveform. An image generation unit generates a two-dimensional image representing the user's behavioral characteristics by converting the generated waveform data into a power spectrum image, Equipped with, The waveform data generation unit generates waveform data for each of a plurality of interrelated types of actions, and combines the obtained waveform data in a predetermined way to generate a single waveform data relating to the plurality of actions. Information processing device.

2. An estimation unit estimates the indicator by inputting a two-dimensional image representing the user's behavioral characteristics, generated by the image generation unit, as an explanatory variable into a learning model that uses user behavioral characteristic information as an explanatory variable and a predetermined indicator as an objective variable. The information processing apparatus according to claim 1, further comprising:

3. The aforementioned actions relate to the use of the service in question. The estimation unit estimates indicators related to the user's use of the service by using two-dimensional images representing the user's behavioral characteristics related to the use of the service, generated by the image generation unit, as explanatory variables. The information processing apparatus according to claim 2.

4. The predetermined waveform includes at least one of a sine wave, a cosine wave, and a triangular wave. The information processing apparatus according to claim 1.

5. The power spectral imaging includes at least one of a continuous Fourier transform and a wavelet transform. The information processing apparatus according to claim 1.

Images