Information processing system, information processing method and program

Abstract

To provide a technology for improving the convenience of users who provide content. [Solution] According to one aspect of the present invention, there is provided an information processing system comprising at least one processor, the processor being configured to execute the following steps by reading a program: in the acquisition step, analytical data relating to predetermined content is acquired, the content including a plurality of provision units, and the plurality of provision units are configured to be provided to viewing users at different times; the analytical data includes viewing data relating to the behavior of the viewing users viewing the predetermined provision units and billing data relating to the behavior of the viewing users charged for viewing the provision units; in the identification step, a first indicator relating to the performance of the analysis target is identified based on the analytical data relating to the provision units to be analyzed, the analysis target including the first provision unit which is the latest provision unit in a first period and a second provision unit which is the latest provision unit in a second period prior to the first period; and in the presentation step, the first indicator relating to the first provision unit and the second provision unit is presented so that the providing user who provides the content can compare them.

Description

[Technical Field]

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

[0002] Patent Document 1 discloses a technology that can easily and accurately extract frames from a manga image. This technology includes a binarization step of binarizing the manga image into black and white, a first extraction step of extracting outlines from the manga image and extracting regions surrounded by frame lines in the manga image as frames, a clustering step of clustering black regions from the remaining regions in the manga image that were not extracted as frames by the first extraction step, and a second extraction step of treating each of the clustered regions as a rectangular region and extracting the rectangular regions as frames. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-60875 Summary of the Invention [Problem to be solved by the invention]

[0004] However, there is room for improvement in providing content in terms of improving the convenience for users who receive the content.

[0005] In view of the above circumstances, the present invention provides a technology for improving the convenience of users who receive content. [Means for solving the problem]

[0006] According to one aspect of the present invention, there is provided an information processing system comprising at least one processor, the processor being configured to execute each of the following steps by reading a program: in the acquisition step, analytical data relating to predetermined content is acquired, the content including a plurality of provision units, and the plurality of provision units are configured to be provided to a viewing user at different times; the analytical data includes viewing data relating to the behavior of the viewing user viewing the predetermined provision units and billing data relating to the behavior of the viewing user charged for viewing the provision units; in the identification step, a first indicator relating to the performance of the analysis target is identified based on the analytical data relating to the provision units to be analyzed, the analysis target including the first provision unit which is the latest provision unit in a first period and a second provision unit which is the latest provision unit in a second period prior to the first period; and in the presentation step, the first indicator relating to the first provision unit and the second provision unit is presented so that the providing user who provides the content can compare them.

[0007] According to this aspect, a technique for improving the convenience of users who receive content is provided. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a configuration diagram illustrating an information processing system 1 according to an embodiment of the present invention. [Figure 2] 2 is a block diagram showing functions realized by a control unit 23 and the like in the information processing device 2. FIG. [Figure 3] 3 is a flowchart showing an outline of information processing according to the present embodiment. [Figure 4] FIG. 2 is an activity diagram showing the flow of information processing according to the present embodiment. [Figure 5] FIG. 2 is a diagram for explaining various types of information used in analysis in this embodiment. [Figure 6] FIG. 10 is a diagram showing an example of a first index and a second index presented to a providing user. [Figure 7] FIG. 1 illustrates a portfolio of multiple pieces of content. [Figure 8] FIG. 10 is a diagram showing the evaluation results of content based on diffusion data presented to a providing user. [Figure 9] FIG. 10 is a diagram showing another example of the first index and the second index presented to the providing user. DETAILED DESCRIPTION OF THE INVENTION

[0009] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described below with reference to the accompanying drawings. Various features shown in the following embodiments can be combined with each other.

[0010] Incidentally, the program for realizing the software appearing in one embodiment may be provided as a non-transitory computer-readable medium, or may be provided so that it can be downloaded from an external server, or may be provided so that the program is started on an external computer and its functions are realized on a client terminal (so-called cloud computing).

[0011] Furthermore, various information processing according to an embodiment may realize input and output corresponding to the input. Here, the form of information referenced in such information processing (hereinafter referred to as reference information) is not limited as long as an output is obtained as a result of the input. The reference information may be, for example, rule-based information such as a database, a lookup table, or a predetermined function (including a decision formula such as a regression formula constructed using a statistical method), a trained model that has previously learned the correlation between input and output, or a generative AI such as a large-scale language model (these models include parameters that establish the correlation between input and output) or a visual language model that can output a desired result in response to a prompt.

[0012] In one embodiment, a "unit" may include, for example, a combination of hardware resources implemented by a circuit in the broad sense and software information processing that can be specifically realized by these hardware resources. In one embodiment, various information is handled, and this information is represented, for example, by physical values ​​of signal values ​​representing voltage and current, high and low signal values ​​as a binary bit set consisting of 0 or 1, or quantum superposition (so-called quantum bits), and communication and calculations can be performed on a circuit in the broad sense.

[0013] Furthermore, a circuit in the broad sense is a circuit realized by at least an appropriate combination of a circuit, circuitry, processor, memory, etc. The processor may be a general-purpose processor or a dedicated circuit. That is, it includes an application specific integrated circuit (ASIC), a programmable logic device (e.g., a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA)), etc.

[0014] 1. Hardware Configuration In this section, the hardware configuration of this embodiment will be described.

[0015] 1 is a configuration diagram showing an information processing system 1 according to this embodiment. The information processing system 1 includes a user terminal 3 and an information processing device 2, which are connected via a network 4. The user terminals 3 include a first user terminal 3A associated with a providing user who provides content, and a second user terminal 3B associated with a viewing user who views the content. The first user terminal 3A and the second user terminal 3B have substantially the same configuration, and will be collectively referred to as the user terminal 3 when there is no need to distinguish between them.

[0016] In this embodiment, a providing user is, for example, a person who provides content. A viewing user is a person who views the provided content. The providing users include individuals and organizations who are directly or indirectly involved in the creation, editing, distribution, and delivery of content in the process of delivering the content to viewing users, and may include, for example, content creators (original authors, illustrators, assistants, etc.), editors, publishers, bookstores, distributors, promotion staff, media mix development staff (animation, game adaptation, etc.), merchandise planners, rights business staff, derivative creators, review posters, UGC platform administrators, etc.

[0017] In one embodiment, the information processing system 1 is comprised of one or more devices or components. For example, if the information processing system 1 is comprised only of an information processing device 2, the information processing system 1 may be the information processing device 2. If the information processing system 1 is comprised only of a user terminal 3, the information processing system 1 may be the user terminal 3. If the information processing system 1 is comprised of an information processing device 2 and a user terminal 3, the information processing system 1 may be a combination of the information processing device 2 and the user terminal 3. More specifically, the information processing system 1 may include an element selected from the group consisting of the information processing device 2 and the user terminal 3. The unselected element may not be included in the information processing system 1, but may be electrically connected to the selected element as an external element. These components will be described below.

[0018] <Information processing device 2> 1, the information processing device 2 includes a communication unit 21, a storage unit 22, and a control unit 23, and these components are electrically connected via a communication bus 20 inside the information processing device 2. Each component will be further described.

[0019] <Communications Department 21> The communication unit 21 is preferably a wired communication means such as USB, IEEE1394, Thunderbolt (registered trademark), wired LAN network communication, etc., or a wireless communication means such as wireless LAN network communication, mobile communication such as 3G / LTE / 5G, Bluetooth (registered trademark), etc. The information processing device 2 transmits and receives various information to and from external devices via the communication unit 21 and the network.

[0020] <Storage section 22> The storage unit 22 stores various pieces of information defined above. The storage unit 22 is a storage device such as a solid state drive (SSD) and stores various pieces of information such as data and programs related to the information processing device 2 executed by the control unit 23. Alternatively, the storage unit 22 is a memory such as a random access memory (RAM) and stores temporarily required information (arguments, arrays, etc.) related to program calculations. Note that the various pieces of information may be configured to be stored in a distributed manner across multiple external devices using blockchain technology or the like.

[0021] <Control unit 23> The control unit 23 is a processor that processes and controls the overall operations related to the information processing device 2. The control unit 23 includes, for example, a central processing unit (CPU) and a graphics processing unit (GPU). The control unit 23 realizes various functions related to the information processing device 2 by reading predetermined programs stored in the memory unit 22. For example, the control unit 23 reads various information stored in a storage area that is at least a part of the memory unit 22 and writes the read information to a memory that is at least a part of the memory unit 22, thereby executing various information processing based on the acquired information. In other words, information processing by software stored in the memory unit 22 is specifically realized by the control unit 23, which is an example of hardware, and can be executed as each functional unit included in the control unit 23. The control unit 23 is not limited to being single, and the configuration may include multiple control units 23 for each function.

[0022] The information processing device 2 is a computer such as a server device or a workstation, and may be in an on-premise form or a cloud form. As a cloud-based information processing device 2, the above-mentioned functions and processes may be provided in the form of, for example, SaaS (Software as a Service) or cloud computing.

[0023] <User terminal 3> 1, the user terminal 3 includes a communication unit 31, a memory unit 32, a control unit 33, an output interface (hereinafter referred to as I / F) 34, and an input I / F 35, and these components are electrically connected via a communication bus 30 inside the user terminal 3. The explanations of the communication unit 31, the memory unit 32, and the control unit 33 are the same as the explanations of each unit in the information processing device 2, so redundant explanations will not be repeated.

[0024] <Output I / F34> The output I / F 34 is configured to output various information so that the user can recognize it through his or her five senses. The output I / F 34 is configured to output various stimuli generated by the control unit 33 so that the user can perceive them, and for example, the various stimuli are output as images, sounds, tactile stimuli, smells, tastes, etc. The output I / F 34 is configured to output the various stimuli individually or in combination. The output I / F 34 may be included in the housing of the user terminal 3 or may be externally attached.

[0025] The output I / F 34 is, for example, any display device capable of displaying images, such as a CRT display, a liquid crystal display, an organic EL display, a plasma display, or a projector. The image displayed on the output I / F 34 may be generated based on image information stored in the storage unit 32 or image information generated outside the information processing device 2 or the like and received via the communication unit 31. The image displayed on the output I / F 34 includes a graphical user interface (GUI) that can be operated by the user.

[0026] The output I / F 34 is, for example, an audio output device such as a speaker, headphones, bone conduction earphones, hearing aids, or cochlear implants that can output audio. The output I / F 34 is, for example, a tactile output device that provides contact stimuli such as vibrations or temperature changes, or non-contact stimuli such as air spray. The output I / F 34 is, for example, an olfactory output device that provides a change in smell by spraying aroma or the like into the air. The output I / F 34 is, for example, a taste output device that induces a change in taste in the user by electrical stimuli or the like.

[0027] The input I / F 35 is configured to accept operation inputs from the user. The operation inputs are transferred as command signals to the communication unit 31 via the communication bus 30, and the communication unit 31 executes predetermined control and calculations. The input I / F 35 may be included in the housing of the user terminal 3 or may be attached externally. For example, the input I / F 35 may be configured as a touch panel integrated with the output I / F 34, and accept operation inputs such as tapping and swiping based on coordinate information of a touch operation performed by the user. The input I / F 35 may be a mouse, a QWERTY keyboard, a touchpad, a switch button, a game controller, or the like.

[0028] The input I / F 35 may also accept user operation inputs by employing a voice recognition device that acquires voice, a gesture detection device that detects the posture or movement of an object, a gaze detection device that detects the user's gaze direction, a biosignal detection device that detects various biosignals of the user, a position information detection device that acquires position information such as longitude and latitude information, etc. The input I / F 35 may also include a camera that can acquire images (still images, videos, etc.). The camera may be provided in the user terminal 3, or may be configured to acquire images captured by a separately provided camera.

[0029] 2. Functional configuration In this section, the functional configuration of this embodiment will be described. As described above, the information processing system 1 includes at least one processor, and the processor is configured to include the following units by reading a program. In other words, the program causes a computer to function as each unit of the information processing system 1.

[0030] 2 is a block diagram showing functions realized by the control unit 23 and the like in the information processing device 2. The information processing device 2, which is an example of the information processing system 1, includes an acquisition unit 231, an identification unit 232, a presentation unit 233, and an artificial intelligence unit 234.

[0031] The acquisition unit 231 is configured to be able to execute an acquisition step. In the acquisition step, the acquisition unit 231 executes processing for acquiring various pieces of information from the storage unit 22 or an external device.

[0032] The specifying unit 232 is configured to be able to execute a specifying step. In the specifying step, the specifying unit 232 executes processing for specifying various pieces of information used in information processing, based on the information acquired by the acquiring unit 231.

[0033] The presentation unit 233 is configured to be able to execute a presentation step. In the presentation step, the presentation unit 233 controls processing for causing the output I / F 34 of the user terminal 3 to present various types of information.

[0034] For example, the presentation unit 233 may draw various screens to be displayed on the display device of the user terminal 3 and transmit them to the user terminal 3, or may transmit display information including visual information such as images including still images or videos, icons, and messages to the user terminal 3 so that various screens are drawn on the user terminal 3. The control unit 33 of the user terminal 3 draws the screen to be displayed on the display device based on the received display information and information such as parameters defining the various visual information stored in the storage unit 22. That is, at least some of the functions of the presentation unit 233 may be executed by the control unit 33 of the user terminal 3.

[0035] The artificial intelligence unit 234 is configured to receive input from each functional unit and return the instructed output. The artificial intelligence unit 234 receives a predetermined input and instructs the artificial intelligence module to return the instructed output. The artificial intelligence module used by the artificial intelligence unit 234 may be stored in the memory unit 22 and the memory unit 32, or may be stored in an external artificial intelligence server different from the information processing device 2 and the user terminal 3. The artificial intelligence unit 234 inputs predetermined input information to the artificial intelligence module and causes the artificial intelligence module to output predetermined output information. The artificial intelligence module used by the information processing device 2 may be a common one, or may be prepared individually for each functional unit.

[0036] The AI ​​module used by the AI ​​unit 234 includes reference information configured to return a predetermined output in response to an input. The reference information is configured to identify the correspondence between input information and output information, and may include, for example, at least information regarding the correlation between the two. The reference information may include a table, a function, a simple algorithm, etc.

[0037] The artificial intelligence module is an AI (Artificial Intelligence) equipped with a learning model such as a language model, such as a Transformer including a Generative Pretrained Transformer (GPT) and a Bidirectional Encoder Representations from Transformers (BERT), or a Recurrent Neural Network (RNN), and may be a generative AI or an AI agent.

[0038] In the reference information, a correlation between input information and output information can be established by statistically analyzing data recording input information and corresponding output information. The reference information may be a trained model, a language model, or the like that has been trained to be able to output output information in response to input information. In these, parameters calculated, tuned, or the like through training constitute the correlation of the reference information. Note that in the reference information, the correspondence between input information and output information is not limited to a correlation, as long as a predetermined correspondence is established. In the following description, redundant explanations regarding the method of constructing reference information will not be repeated.

[0039] A trained model can be configured to perform a specific task by a learning method such as supervised learning, unsupervised learning, or self-supervised learning. In supervised learning, machine learning is performed using training data (training data). The training data includes pairs of input data and output data (correct answer data) for learning.

[0040] A language model is an example of a learning model based on a machine learning algorithm. Specific examples of machine learning algorithms include nearest neighbor algorithms, naive Bayes algorithms, decision trees, support vector machines, and deep learning using neural networks. The artificial intelligence module can apply the above algorithms as appropriate. Furthermore, a language model may not only be one trained for a specific task, but also a general-purpose model that can be used for a wide range of tasks (for example, large language models (LLMs) trained on massive amounts of data).

[0041] Generative AI uses language models to perform various tasks. Specifically, generative AI is configured to process a wide range of tasks, such as understanding sentence patterns and context, answering questions, and generating text, images, and audio, in response to given input (prompts), and return the specified output. Prompts may be provided in any format, including text, images, and audio. Tasks may also be processed interactively, alternating between receiving input and generating and outputting information.

[0042] An AI agent uses a language model to perform various tasks. Specifically, when a specific instruction is input, the AI ​​agent is configured to break down the processing required to accomplish the specified task into subtasks, actions, etc., and to collect and analyze necessary data, generate and execute programs, etc. The AI ​​agent may autonomously plan and execute the plans, evaluate the execution results, and autonomously learn to achieve its goals.

[0043] The AI ​​modules may be stored in multiple AI servers. The AI ​​servers may provide services using a language model as a learning model, or may execute language processing tasks using a language model. The AI ​​service servers may be constructed using the LLM, accept prompt inputs such as text, images, and voice, and generate and respond to the prompts.

[0044] 3. Information Processing This section describes each step of the information processing method executed by the information processing system 1. The information processing method is executed by a processor included in at least one of the user terminal 3 and the information processing device 2 reading out a program.

[0045] 3.1 Overview of Information Processing FIG. 3 is a flowchart showing an overview of information processing of this embodiment. As shown in FIG. 3, the acquisition unit 231 acquires analytical data related to predetermined content (step S1). The content includes multiple provision units, and the multiple provision units are configured to be provided to viewing users at different times. The analytical data includes viewing data related to the behavior of the viewing user viewing the predetermined provision units and billing data related to the behavior of the viewing user charged for viewing the provision units. The identification unit 232 identifies a first indicator related to the performance of the analysis target based on the analytical data related to the provision units to be analyzed (step S2). The analysis target includes a first provision unit that is the latest provision unit in a first period and a second provision unit that is the latest provision unit in a second period prior to the first period. The presentation unit 233 presents the first indicators related to the first provision unit and the second provision unit so that the providing user who provides the content can compare them (step S3). According to this aspect, information related to the performance of the content can be easily grasped. 3.2 Details of information processing

[0046] Fig. 4 is an activity diagram showing the flow of information processing in this embodiment. Fig. 5 is a diagram for explaining various types of information used in analysis in this embodiment.

[0047] In this embodiment, the content provided by the providing user includes multiple provision units, and the multiple provision units are configured to be provided to the viewing user at different times. The content is, for example, a manga, and as shown in FIG. 5, stories corresponding to the next chapter are provided every predetermined period, such as chapter n-2, chapter n-1, chapter n, and chapter n+1. The predetermined period is determined in advance depending on the content. For example, a manga published in a monthly magazine is provided with a new story every month, and a manga published in a weekly magazine is provided with a new story every week. In other words, the provision units are provided repeatedly every predetermined provision period. Note that the predetermined period may include an irregular period, multiple stories may be provided after a hiatus, or a long hiatus may be included.

[0048] In this embodiment, the provision unit for which analysis is performed may be referred to as the analysis target. The analysis target includes a first provision unit, which is the latest provision unit in a first period, and a second provision unit, which is the latest provision unit in a second period prior to the first period. For example, when episode n is provided as the latest episode, episode n is the first provision unit to be analyzed, and episode n-1 does not correspond to the first provision unit at that time. On the other hand, when episode n-1 is provided as the latest episode, episode n-1 corresponds to the second provision unit to be analyzed. In other words, whether each provision unit corresponds to the analysis target may differ depending on whether the provision unit is the latest at that time.

[0049] Furthermore, in this embodiment, content may be images, including still images and videos, music, and other user-recognizable information, or a combination thereof. In content, a single set of content is made up of multiple provision units. The content may be a fictional or non-fiction story, or training content in which the content of each provision unit is independent but the collection of these content forms a single piece of content. Furthermore, mutually related content provided through different channels may be collectively referred to as a "work." For example, mutually related content such as manga, anime, movies, and merchandise for the same work may be provided through different channels.

[0050] 4, first, the acquisition unit 231 acquires analytical data related to a predetermined content. Specifically, the acquisition unit 231 acquires analytical data for a second period prior to the first period (activity A101). The acquisition unit 231 also acquires analytical data for the first period (activity A102). For example, the second period is a period in which episode n-1 is provided as the latest provision unit, and the first period is a period in which episode n is provided as the latest provision unit.

[0051] The analysis data includes viewing data related to the behavior of the viewing user viewing a predetermined provision unit, and billing data related to the behavior of the viewing user charged to view the provision unit. The viewing data includes, for example, PV (page views: the number of times each provision unit was viewed, the number of views per page), free PV (the number of views of the provision unit provided free of charge), UU (number of unique users: the number of unique viewing users for each provision unit), reading completion rate (the rate at which each provision unit was viewed to the last page: PV of the last page / PV of the first page), continuation rate (the rate at which the viewing user proceeded to the next provision unit), reread rate (the rate at which the same provision unit was viewed again), chapter-by-chapter Cliff-hanger Score (the rate at which users proceeded to the next provision unit without leaving at the chapter boundary), reading completion speed (the speed at which they can read: pages per minute), etc. The charging data also includes the charging amount (total charging amount per provision unit, total charging amount for multiple provision units (such as per book), weekly, monthly, etc.), charging rate (proportion of total viewing users who have been charged), unit price (amount required to view each provision unit), latest episode charging rate (proportion of users who have been charged for viewing the latest episode), continuous charging rate (proportion charged to proceed to the next provision unit), paid conversion charging rate (proportion of users who have viewed the next provision unit after viewing a free provision unit), ARPPU (Average Revenue Per Paying User: average charging amount of viewing users who have been charged), etc. The acquisition unit 231 may also acquire various primary data for identifying this information.

[0052] Next, the identification unit 232 identifies a first indicator related to the performance of the analysis target based on the analysis data related to the provision unit to be analyzed. Specifically, the identification unit 232 identifies a first indicator related to the second provision unit based on the analysis data for the second period (activity A103). Furthermore, the identification unit 232 identifies a first indicator related to the first provision unit based on the analysis data for the first period (activity A103). For example, a first indicator related to episode n-1 is identified based on the analysis data for the period when episode n-1 is provided as the latest episode. Furthermore, a first indicator related to episode n is identified based on the analysis data for the period when episode n is provided as the latest episode.

[0053] The first index may be determined based on analytical data related to only the most recent provision unit, or based on analytical data related to the most recent and a predetermined number of consecutive provision units. For example, the first index related to the first provision unit (episode n) may be determined based on analytical data for three episodes from episodes n to n-2, and the first index related to the second provision unit (episode n-1) may be determined based on analytical data for three episodes from episodes n-1 to n-3. In this case, the degree of anticipation for each episode may be determined using analytical data from the period when each episode was provided as the most recent provision unit, and then the first index of the target provision unit may be determined as an average value. For example, the degree of anticipation for the first provision unit (episode n) may be the average value of the anticipation for the three episodes from episodes n to n-2, or may be a weighted average value such as 0.6 * (anticipation for episode n) + 0.3 * (anticipation for episode n-1) + 0.1 * (anticipation for episode n-2).

[0054] The first index is an index showing the performance of the latest provision unit of the content. Performance may include financial numerical data such as sales and profits, as well as any information directly or indirectly related to the financial numerical data, such as the number of times the content has been viewed by users and the number of users who have viewed it. In this embodiment, the first index may be an index that statistically expresses the level of attention and expectations of viewing users for the content, thereby making it possible to visualize the enthusiasm of existing and new viewing users for the work. By visualizing fluctuations in the first index, the performance trend of the work can be identified from the perspective of the level of attention and expectations of viewing users. In this embodiment, a first index having such characteristics may be referred to as anticipation.

[0055] The anticipation level can be determined based on at least one of the viewing data and billing data related to the provision unit to be analyzed. The anticipation level may be, for example, the "latest chapter payment rate" or the "latest chapter UU rate." The latest chapter payment rate indicates the rate of viewing users who have paid for the latest chapter of a manga work among the viewing users of that latest chapter. The latest chapter UU rate indicates the rate of viewing users (unique users) who have viewed the latest chapter among all viewing users (unique users) who have viewed at least one of the provision units of the manga work.

[0056] Next, the identification unit 232 identifies a second indicator related to the performance of the content based on the analysis data related to the provision units to be analyzed. Specifically, the identification unit 232 identifies a second indicator related to the performance of the content in the second period based on the billing data related to multiple provision units in the second period (activity A105). Furthermore, the identification unit 232 identifies a second indicator related to the performance of the content in the first period based on the billing data related to multiple provision units in the first period (activity A106).

[0057] Like the first index, the second index may be determined based on analytical data for the period during which each episode is provided as the latest episode. The second index may also be determined based on analytical data related only to the latest provision unit, or based on analytical data related to the latest and a predetermined number of consecutive provision units. The second index is preferably an index related to the profitability of the content for each period. That is, the second index may be determined based on billing data, such as the total amount of charges paid by the viewing user for each provision unit, including the latest and previous provision units, during a predetermined period, the average value of the total amount per provision unit, or the average charge rate for each provision unit. In other words, the determination unit 232 determines the second index related to the performance of the content for the first period based on billing data related to multiple provision units during the first period. The determination unit 232 also determines the second index for the second period based on billing data related to multiple provision units during the second period.

[0058] The second index may also take into account viewing data for each period (for example, the average completion rate for each story). In this case, the second index may be, for example, the sum of the charge rate and the completion rate (hereinafter, sometimes referred to as OPE: Open & Earn Rate), or the product of the completion rate, the charge rate, and the unit price of the charge (hereinafter, sometimes referred to as MOPS). This makes it possible to identify an index that indicates the profitability of the content, taking into account viewing data.

[0059] Next, the presentation unit 233 presents the first indicators for the first provision unit and the second provision unit so that the providing user who provides the content can compare them (activity A107). Furthermore, the identification unit 232 may identify an evaluation result of the performance of the content based on the first indicator and the second indicator. Then, the presentation unit 233 may present the evaluation result so that the providing user can recognize it (activity A108).

[0060] Fig. 6 is a diagram showing an example of the first index and the second index presented to the providing user. Fig. 6(A) and Fig. 6(B) are diagrams showing the fluctuation of the first index and the second index in a predetermined content according to the time series (progress of each episode). Fig. 6(C) is a diagram showing the results of comparing the fluctuation of the first index for each content.

[0061] In the graph 600A shown in FIG. 6(A), the anticipation level 610 as a first indicator and the charge amount 620 as a second indicator are shown for each period during which each distribution unit was provided as the latest episode. That is, the first indicator and the second indicator are shown for comparison over time (progression of each episode). The horizontal axis indicates the most recently provided distribution unit (progressing episode number) from left to right, thereby showing the fluctuations in the anticipation level 610 and the charge amount 620 over time. The anticipation level 610 indicates the latest episode UU ratio as of 14 days after the release of each episode. The charge amount 620 indicates the average charge amount for all episodes during the period during which each episode was provided as the latest episode. The charge amount 14 days after release 621 indicates the charge amount for 14 days after release, and the charge amount 3 days after release 622 indicates the charge amount for 3 days after release. Note that for this content, a new distribution unit that becomes the latest episode is provided every 30 days.

[0062] Note that the 14-day release charge 621 may be determined based on analysis data for a number of days corresponding to the release period of the release unit. Meanwhile, the 3-day release charge 622 may be determined based on analysis data for a set number of days regardless of the release period. Therefore, the number of analysis days for the 3-day release charge 622 is preferably shorter than the release interval of a weekly magazine, for example, between 1 and 5 days, and more preferably between 1 and 3 days. Meanwhile, the number of analysis days for the 14-day release charge 621 is preferably shorter than the release period of a monthly magazine and longer than the release interval of a weekly magazine, for example, between 10 and 25 days, and more preferably between 10 and 20 days.

[0063] Furthermore, the anticipation level 610 may be determined based on analysis data for a number of days corresponding to the provision period of the provision unit, or may be determined based on analysis data for a number of days set regardless of the provision period. For example, the latest episode UU ratio as of 3 days and 14 days after the release of each episode may be displayed. In other words, the determination unit 232 determines a variable index as at least one of the first index and the second index (at least one of the first variable index as the first index and the second variable index as the second index). The variable index is determined based on analysis data for an analysis period set depending on the provision period. The determination unit 232 also determines a fixed index as at least one of the first index and the second index (at least one of the first fixed index as the first index and the second fixed index as the second index). The fixed index is determined based on analysis data for an analysis period set regardless of the provision period.

[0064] Graph 600B shown in FIG. 6(B) shows fluctuations over time (progression of each episode) of the anticipation index 630 as a first indicator and the charge amount 620 as a second indicator. Graph 600B is the same as graph 600A, except that the anticipation index 630 is shown instead of the anticipation level 610. The anticipation index 630 is a value obtained by dividing the anticipation level in the current provision unit by the anticipation level in the previous provision unit, and is an index that shows fluctuations in the anticipation level value as an index. This makes it possible to clearly present short-term fluctuations in anticipation level.

[0065] The presentation unit 233 presents the first index for the first provision unit and the second provision unit and the second index for the first period and the second period in the graphs 600A and 600B so that the providing user can compare them. In particular, since the trends in the first index and the second index for each provision unit are visualized, the providing user can easily understand the trends in the performance of the content. For example, the content shown in the graphs 600A and 600B can be understood to be a highly profitable and stable main content because the fluctuations in the first index have slowed while the second index has continuously improved.

[0066] Table 600C shown in Figure 6(C) shows the results of comparing the fluctuations in the first index for each content. Table 600C shows the anticipation index as the first index for the latest provision unit and OPE as the second index. The anticipation index indicates the ratio of the anticipation value of the latest episode (first provision unit) to the anticipation value of the previous episode (second provision unit), and the anticipation index indicates the amount of change in the first index for each content from the previous episode. OPE indicates the sum of the average reading completion rate and the average billing rate for each provision unit during the specified period in which the latest provision unit was provided. By showing table 600C to the providing user, the providing user can understand the evaluation of each content as follows. ·Works A and B have high OPEs, making them major profitable content. Among them, work A has a high anticipation index and is expected to grow further. On the other hand, work B has a low anticipation index and growth is slowing, so some kind of measure should be considered. Although work C has a low OPE and is currently not very profitable, it has a high anticipation index and is generating a lot of buzz among viewers. It is a dark horse candidate with high potential for future growth.

[0067] FIG. 7 is a diagram showing a portfolio of multiple contents. Graph 700 shown in FIG. 7 is a plot of each work shown in table 600C. The vertical axis indicates the eagerness index, and the horizontal axis indicates OPE. In other words, identification unit 232 identifies the evaluation results for multiple contents. Then, presentation unit 233 presents the evaluation results for multiple contents together so that the providing user can recognize them.

[0068] The first quadrant, with a high anticipation index and OPE, represents "stars," and represents main content that is highly sought after and profitable by viewers. The second quadrant, with a high OPE but a low anticipation index, represents "high potential." It represents content that is highly profitable but tends to mature, and is expected to grow into main content by improving its popularity among viewers. The third quadrant, with a high anticipation index but low profitability, represents "cash cows." It represents up-and-coming content that is highly sought after by viewers and is expected to grow into main content by improving its sales methods. The fourth quadrant, with a low anticipation index and OPE, represents "promising stocks," and is often new content with low profitability and popularity. By managing works in each quadrant in a balanced manner based on Graph 700, content providers can develop long-term content management strategies.

[0069] Returning to FIG. 4, the acquisition unit 231 further acquires diffusion data (activity A109). The diffusion data is information indicating a history of how browsing users have spread information about content on the Internet. The diffusion data includes, for example, a history of posts by users on a social networking service (SNS), and can be acquired via a web scraping technique or an application programming interface (API) provided by each service.

[0070] Next, the identification unit 232 identifies an evaluation result of the performance of the content based on at least one of the first index and the second index and the diffusion data. Then, the presentation unit 233 presents the evaluation result so that it can be recognized by the providing user (activity A110). The diffusion data may be configured to be acquired when at least one of the first index and the second index satisfies a predetermined condition (e.g., the content belongs to the first quadrant shown in graph 700). Alternatively, the diffusion data may be acquired regardless of whether the predetermined condition is satisfied, while the evaluation result may be identified when the predetermined condition is satisfied.

[0071] Furthermore, when at least one of the first index and the second index satisfies a predetermined condition, the presentation unit 233 presents recommendation information indicating that a predetermined channel for providing the content is recommended so as to be recognizable by the providing user (activity A111). The diffusion data is a proactive action taken by the viewing user toward the content and indicates the degree of attention the viewing user has paid to the content beyond the boundaries of the medium. Therefore, for content that has particularly favorable evaluation results based on the diffusion data, it is preferable to recommend providing the content on a new channel. For example, for manga content, the presentation unit 233 presents recommendation information recommending expansion to new channels such as anime, movies, games, and merchandise.

[0072] Figure 8 shows the results of content evaluations based on diffusion data presented to users. Figure 8(A) shows graph 800A displaying a list of the desired content and diffusion data for multiple pieces of content. Figure 8(B) shows table 800B, in which indicators indicating the strength of content identified based on diffusion data are organized for each piece of content.

[0073] Graph 800A is a so-called fandom chart that statistically indexes and visualizes the external reactions of users viewing content based on multiple observational data. Graph 800A shows the number of posts for each content on SNS1 to SNS3, the number of merchandise purchasers, sales for each content, and the degree of anticipation. The number of posts on SNS1 to SNS3 may be the total number of posts, the number of times a specific medium such as a video was posted, or the total number of posts tagged with a specific account or keyword (including hashtags). The number of posts on SNS1 to SNS3 corresponds to diffusion data. The number of merchandise purchasers is determined based on merchandise sales performance; for example, items purchased at the same time may be determined to have been purchased by the same user. The degree of anticipation corresponds to the first index, and sales correspond to the second index. Note that the anticipation value is not shown in the figure.

[0074] In graph 800A, work A810 is shown to be a flagship work with high content sales, a large number of posts on SNS, and a large number of merchandise purchasers. Work B820 has a large number of posts on SNS2, but small content sales, indicating room for future growth in content charges. Work C830 has a small number of SNS posts and content sales, but a large number of merchandise purchasers and a devoted fan base, indicating room for future growth as a work as a whole. Work D840 has a large content sales, but few SNS posts, indicating room for growth as a work by generating more buzz in the future.

[0075] Table 800B shows the anticipation index (first index), OPE (second index), and M-WAR index (third index) for each work. It also shows the evaluation results of the work based on each index and recommended channels for new work releases. OPE is the sum of the reading completion rate and the pay rate, with the breakdown shown in parentheses. The M-WAR index is an index that indicates the strength of content identified based on diffusion data, and is calculated, for example, as follows:

[0076]

number

[0077] In Equation 1, a through e are appropriate coefficients. The charge amount is the sales amount (total or average amount) of the content during the period for which the second indicator is calculated, and indicates the performance of content sales based on the charge data during that period. The number of followers is the total number of followers of the content or the author's official account on a specified SNS during that period, and indicates the degree to which new users who newly came into contact with the work through the content and related content (such as content that has been secondary developed from the same work) have been acquired. The repeat rate is the re-read rate for each unit of content provided during that period. This reflects the strength of the fan base that leads to secondary development of the content on other channels, rather than a single hit. The number of SNS posts is the total number of posts on a specified SNS. Merchandise sales is the amount charged by viewing users for merchandise related to the content, and indicates secondary development performance, which indicates performance based on the sale of merchandise related to the content on channels other than the content itself. Merchandise sales may include, for example, stationery, tableware, clothing, and books of derivative content. Preferably, the coefficients have a relationship of d, e>a>b>c.

[0078] M-WAR can evaluate the overall strength of each work, taking into account user interest in content other than specific content (manga). Therefore, it is preferable to present recommendation information indicating specific channels for providing content based on the values ​​of each indicator. In Table 800B, since all indicators for Work 1 are high, development into "anime" and "movies," which are higher-risk, higher-return channels, is recommended. While Work B's anticipation and OPE values ​​are high, its M-WAR value is low, so development into "merchandise" is recommended as a channel to further increase its popularity. Since all indicators for Work C are low, development into any channel is not recommended, and it is recommended to increase the popularity and profitability of the current content.

[0079] In other words, the identification unit 232 identifies a third indicator related to the performance of the content in the first period based on the diffusion data and billing data for the first period, viewing data indicating that a predetermined viewing user has re-viewed a predetermined provision unit that he or she previously viewed in the first period, and secondary development performance indicating performance based on the sale of merchandise related to the content through a channel different from the content itself. The diffusion data may include the number of followers and the number of SNS posts. Furthermore, the identification unit 232 identifies an evaluation result based on the first indicator and the third indicator. The evaluation result may be identified based only on the third indicator, or may be identified by taking the first indicator into consideration. Furthermore, the evaluation result may be identified by taking the second indicator into consideration.

[0080] Returning to FIG. 4 , the identification unit 232 identifies predictive information regarding the performance of the content or the provision units to be provided after the first period based on the first, second, and third indicators and predetermined reference information (activity A112). The reference information includes at least a correlation between the first indicator and performance. The presentation unit 233 then presents the identified predictive information in a manner that is recognizable to the providing user (activity A113). The identification unit 232 may identify predictive information based on any reference information accessible to the artificial intelligence unit 234. Preferably, the reference information includes a neural network, a regression model, or a statistical model based on time-series changes as a predictive model configured to input current or past performance values ​​of predetermined parameters and output predicted values ​​of the predetermined parameters.

[0081] For example, as shown in Fig. 5, the artificial intelligence unit 234 inputs at least one of the anticipation level, OPE, diffusion data, or M-WAR index (values ​​of the first to third indices) for a predetermined number of provision units, and outputs predicted values ​​of the anticipation level, OPE, and diffusion data or M-WAR for the next provision unit (predicted values ​​of the first to third indices). This predicts the performance trend of the content, so the providing user can use the predicted values ​​to consider the direction of the next provision unit or to decide whether to continue the content. For example, if a stagnation in anticipation is predicted, the providing user can consider measures such as the content of the next provision unit or strengthening information dissemination on SNS, etc.

[0082] In addition, the artificial intelligence unit 234 inputs the initial analysis data (for example, the number of views, read-listening rate, and charge rate on the first day of publication) and diffusion data (for example, the number of SNS posts on the first day of publication) of the latest provision unit (first provision unit), and outputs predicted values ​​of the degree of anticipation 3 days and 14 days after publication. As a result, the medium-term progress of the performance of the provision unit is predicted, and the providing user can consider implementing further measures regarding the provision unit based on the initial analysis data.

[0083] The artificial intelligence unit 234 also inputs the cumulative values ​​of each work's anticipation, OPE, and M-WAR (values ​​of the first to third indices) and outputs a predicted performance value for the work on a predetermined channel (a channel presented as recommended information). The predicted performance value may include, for example, the probability of success if the work is made into an anime (the probability that the viewer rating, number of SNS posts, amount charged for the content, etc., will exceed a predetermined value) and the scale of sales for each merchandise category. This allows the providing user to determine the success of the work if it is expanded on the recommended channel, allowing the providing user to consider whether or not to expand the work into other media.

[0084] Furthermore, the artificial intelligence unit 234 inputs information about other content related to the new content for which no provision units have been provided in the past (for example, analytical data such as the genre, publication medium, and performance trends of works previously published by the same author or other authors with similarities), and outputs a predicted value for the first week of at least one of the anticipation and OPE (first to second indexes) when the first provision unit of the new content is provided. This allows the performance of the new content, which has limited past data, to be predicted, allowing the providing user to consider measures regarding the new content.

[0085] Furthermore, the artificial intelligence unit 234 receives as input diffusion data for a predetermined period (for example, the number of tags on a predetermined SNS, the number of images and videos posted, and the content of posts (derivative works, cosplay, etc. included in the images)), as well as sales results for goods (the number of newly released derivative works, sales, etc.), and outputs predicted values ​​for the timing of peaks in diffusion data and sales results. This allows for the timing to determine an appropriate time for releasing the work on a new channel or holding an event. Since this is predicted, the providing user can consider implementing measures regarding the work based on the initial analysis data.

[0086] 4. Variations The information processing system 1 according to this embodiment may adopt the following aspects.

[0087] In the above-described embodiment, an index based on billing data is exemplified as the second index, but this is not limiting. The second index may be an index determined based on the viewing data of the viewing user, and may be, for example, a continuation index indicating that the viewing user continuously views the content. Examples of the continuation index may include a completion rate indicating that a predetermined provision unit has been continuously viewed to the end, a reread UU (the number of unique users who viewed the provision unit again) or a reread rate (the proportion of unique users who viewed the provision unit again among all unique users who viewed the provision unit), a follow-up reach rate, etc.

[0088] FIG. 9 is a diagram showing another example of the first index and the second index presented to the user. Graph 900 shows the fluctuations over time (progression of each episode) of the anticipation index 910 as the first index and the completion rate 920 as the second index. Anticipation 911 14 days after release indicates the UU rate for the latest episode as of 14 days after the release of each episode. Anticipation 912 3 days after release indicates the UU rate for the latest episode as of 3 days after the release of each episode. Completion rate 920 indicates the rate at which each episode is viewed to the end. Note that for this content, a new unit of provision, which becomes the latest episode, is provided every 30 days.

[0089] Based on graph 900, the providing user can consider whether to continue the content. If the anticipation level is low and the completion rate also tends to be low, it can be determined that it is difficult to continue the content. Furthermore, if the anticipation level is high but the completion rate tends to be declining, there is a concern that the anticipation level will decrease in the future, so changes to the content or the introduction of new measures can be considered. On the other hand, if the anticipation level is low but the completion rate is high, it suggests that there are enthusiastic fans, so it can be recommended that the content be continued.

[0090] Furthermore, in the above-described embodiment, an example was given in which analytical data was acquired for each period corresponding to each episode shown in FIG. 5 and stored in the storage unit 22 of the information processing device 2, and various indices and evaluation results were presented to the providing user, but this is not limiting. For example, analytical data acquired for each period may be stored in the storage unit 32 of the user terminal 3, and acquired by the acquisition unit 231 of the information processing device 2 when various indices and evaluation results are presented to the providing user. For example, analytical data used for analysis may be provided to the information processing device 2 in response to an operation by the providing user. This allows the user terminal 3 to access various functions provided by the control unit 23 of the information processing device 2, for example, via an API (Application Programming Interface).

[0091] In this case, the information processing device 2 may provide at least a portion of the analytical data depending on the identification result of the providing user associated with the user terminal 3. For example, the content to which analytical data is provided may be limited depending on the providing user. For example, the scope of analytical data that can be viewed may be set according to the providing user's authority, such as limiting it to works for which the providing user holds the copyright. In this case, for analytical data not provided to the providing user, the providing user may obtain data owned by the providing user, as described above, or the information processing device 2 may provide alternative analytical data. For example, if the billing data for a given content itself is not provided to the providing user, the estimated sales amount predicted from the number of users viewing the content during a given period (using related analytical data such as the billing rate) may be provided as the billing data. This improves the convenience of the providing user while maintaining the confidentiality of the analytical data.

[0092] Furthermore, the various functions provided by the control unit 23 of the information processing device 2 may be configured to be realized by the control unit 33 of the user terminal 3. This allows the user terminal 3 to execute the various functions in a local environment.

[0093] Although the example in which the presentation unit 233 displays an image including the suggested information on a display device has been described, this is not limiting. The presentation unit 233 may present the suggested information so that it can be perceived by the user through the five senses. For example, the user terminal 3 may output various information via the output I / F 34 so that the user can recognize it through the five senses. The output I / F 34 outputs various stimuli so that the user can perceive them based on instructions from the presentation unit 233. For example, the various stimuli may be output as sounds, tactile stimuli, smells, tastes, etc. The output I / F 34 may be configured to output the various stimuli individually or in combination, and these may be output together with an image displayed on the display device. The output I / F 34 may be included in the housing of the user terminal 3 or may be externally attached.

[0094] The output I / F 34 may be, for example, an audio output device such as a speaker or headphone capable of outputting audio, a bone conduction earphone, a hearing aid, or a cochlear implant. The output I / F 34 may also be a tactile output device that provides contact stimuli such as vibration or temperature change, or non-contact stimuli such as air injection. The output I / F 34 may also be an olfactory output device that provides a change in smell by injecting an aroma or the like into the air. The output I / F 34 may also be a taste output device that induces a change in taste in the user by electrical stimulation or the like.

[0095] 5.Other In the above embodiment, the acquisition unit 231, the identification unit 232, the presentation unit 233, and the artificial intelligence unit 234 are described as functional units realized by the control unit 23 of the information processing device 2. However, at least some of these may be implemented as functional units realized by components other than the information processing device 2. Furthermore, while the user terminal 3 and the information processing device 2 store various pieces of information and control information processing, multiple external devices may be used in place of any of these. That is, various pieces of information and programs may be distributed and stored in multiple external devices using blockchain technology or the like. Furthermore, at least one of the devices included in the information processing system 1 may be installed outside the country in which the functions of the information processing system 1 are performed.

[0096] Furthermore, it may be provided in the following aspects.

[0097] (1) An information processing system comprising at least one processor, the processor being configured to execute each of the following steps by reading a program: in the acquisition step, analytical data related to predetermined content is acquired, wherein the content includes a plurality of provision units, and the plurality of provision units are configured to be provided to a viewing user at different times; the analytical data includes viewing data related to the behavior of the viewing user viewing the predetermined provision units and billing data related to the behavior of the viewing user paying to view the provision units; in the identification step, a first indicator related to the performance of the analysis target is identified based on the analytical data related to the provision units to be analyzed, wherein the analysis target includes a first provision unit that is the latest provision unit in a first period and a second provision unit that is the latest provision unit in a second period prior to the first period; and in the presentation step, the first indicator related to the first provision unit and the second provision unit is presented so that the providing user who provides the content can compare them.

[0098] According to this embodiment, convenience for the providing user is improved, and in particular, information regarding the performance of the content can be easily grasped.

[0099] (2) In the information processing system described in (1) above, in the identification step, a second indicator regarding the performance of the content during the first period is identified based on the billing data regarding multiple units of provision during the first period, and an evaluation result of the performance of the content is identified based on the first indicator and the second indicator, and in the presentation step, the evaluation result is presented so that it can be recognized by the providing user.

[0100] According to this embodiment, information regarding the performance of the content can be easily grasped.

[0101] (3) In the information processing system described in (2) above, in the identification step, the evaluation results for multiple pieces of content are identified, and in the presentation step, the evaluation results for multiple pieces of content are presented together so that the providing user can recognize them.

[0102] According to this embodiment, information regarding the performance of a plurality of contents can be easily grasped.

[0103] (4) In the information processing system described in any one of (1) to (3) above, in the identification step, a second indicator regarding the performance of the content in the first period is identified based on the billing data regarding the plurality of provision units in the first period, and the second indicator in the second period is identified based on the billing data regarding the plurality of provision units in the second period, and in the presentation step, the first indicator regarding the first provision unit and the second provision unit and the second indicator in the first period and the second period are presented so that the providing user can compare them.

[0104] According to this embodiment, fluctuations in the performance of a given content can be easily grasped.

[0105] (5) In the information processing system described in any one of (1) to (4) above, the provision unit is repeatedly provided for each predetermined provision period, and in the identification step, a variable index is identified as the first index, wherein the variable index is identified based on the analysis data for an analysis period set according to the provision period.

[0106] According to this aspect, appropriate information can be provided according to the period during which the content is provided.

[0107] (6) In the information processing system described in (5) above, in the identification step, a fixed index is further identified as the first index, and the fixed index is identified based on the analysis data for an analysis period set regardless of the provision period.

[0108] According to this aspect, it is possible to provide information that evaluates content from various viewpoints.

[0109] (7) In the information processing system described in any one of (1) to (6) above, the acquisition step further acquires diffusion data, where the diffusion data is information indicating the history of the viewing user spreading information about the content on the Internet, the identification step identifies an evaluation result of the performance of the content based on the first indicator and the diffusion data, and the presentation step presents the evaluation result in a manner that can be recognized by the providing user.

[0110] According to this aspect, it is possible to provide information that evaluates content from various viewpoints.

[0111] (8) In the information processing system described in (7) above, in the identification step, a third indicator regarding the performance of the content in the first period is identified based on the diffusion data and the billing data in the first period, the viewing data indicating that a specific viewing user has viewed a specific provision unit that he or she had previously viewed in the first period, and secondary development performance indicating performance based on the sale of merchandise related to the content through a channel different from the content, and the evaluation result is identified based on the first indicator and the third indicator.

[0112] According to this aspect, it is possible to provide information that evaluates content from various viewpoints.

[0113] (9) In the information processing system described in (7) or (8) above, in the presentation step, when the first indicator satisfies a predetermined condition, recommendation information indicating that a predetermined channel for providing the content is recommended is presented in a manner that is recognizable to the providing user.

[0114] According to this aspect, it is possible to provide information that evaluates content from various viewpoints.

[0115] (10) In the information processing system described in any one of (1) to (9) above, in the identification step, predictive information regarding the performance of the provision unit or the content to be provided after the first period is identified based on the first indicator and predetermined reference information, wherein the reference information includes at least a correlation between the first indicator and the performance, and in the presentation step, the identified predictive information is presented so as to be recognizable to the providing user.

[0116] According to this embodiment, information regarding the performance of the content can be easily grasped.

[0117] (11) An information processing method, comprising the steps of the information processing system according to any one of (1) to (10) above.

[0118] (12) A program that causes a computer to execute each step of the information processing system according to any one of (1) to (10) above. Of course, this is not the case.

[0119] Finally, while various embodiments of the present invention have been described, these are presented by way of example only and are not intended to limit the scope of the invention. The novel embodiments may be embodied in various other forms, and various omissions, substitutions, and modifications may be made without departing from the spirit of the invention. Such embodiments and modifications are intended to be included within the scope and spirit of the invention, as well as within the scope of the inventions and their equivalents as defined in the accompanying claims. [Explanation of symbols]

[0120] 1: Information processing system 2: Information processing equipment 23: Control section 231: Acquisition Department 232: Specific part 233:Presentation part 234: Artificial Intelligence Department 3: User terminal 4: Network 600A:Graph 600B:Graph 600C: Table 610: Anticipation 620: Charge amount 700:Graph 800A:Graph 800B: Table 900:Graph 910: Anticipation Index 920: Completion rate

Claims

1. An information processing system, At least one processor is provided, the processor being configured to execute the following steps by reading a program: The acquisition step acquires analytical data relating to the predetermined content, wherein: The content includes a plurality of provision units, and the plurality of provision units are configured to be provided to the viewing user at different times; The analysis data includes viewing data relating to the behavior of the viewing user viewing a predetermined provision unit, and billing data relating to the behavior of the viewing user charged for viewing the provision unit, In the identification step, the analysis data regarding the provision unit to be analyzed is input to a first artificial intelligence module, and a first indicator regarding the performance of the analysis target is output, thereby identifying the first indicator, wherein: the first artificial intelligence module is provided with first reference information including a parameter that establishes a correlation between an input and an output so as to receive the analysis data as an input and output the first indicator; The analysis target includes a first providing unit that is the latest providing unit in a first period, and a second providing unit that is the latest providing unit in a second period prior to the first period, At least a portion of the analysis data for the first period related to the first provision unit is input into the first artificial intelligence module, thereby identifying the first indicator related to the first provision unit, An information processing system in which at least a portion of the analysis data for the second period relating to the second provision unit is input into the first artificial intelligence module, thereby identifying the first indicator relating to the second provision unit, and in a presentation step, the first indicator relating to the first provision unit and the second provision unit is presented so that a providing user who provides the content can compare them.

2. An information processing system, At least one processor is provided, the processor being configured to execute the following steps by reading a program: The acquisition step acquires analytical data relating to the predetermined content, wherein: The content includes a plurality of provision units, and the plurality of provision units are configured to be provided to the viewing user at different times; The analysis data includes viewing data relating to the behavior of the viewing user viewing a predetermined provision unit, and billing data relating to the behavior of the viewing user charged for viewing the provision unit, In the identification step, the analysis data regarding the provision unit to be analyzed is input to a first artificial intelligence module, and a first indicator regarding the performance of the analysis target is output, thereby identifying the first indicator, wherein: the first artificial intelligence module is provided with first reference information including a parameter that establishes a correlation between an input and an output so as to receive the analysis data as an input and output the first indicator; The analysis target includes a first providing unit that is the latest providing unit in a first period, and a second providing unit that is the latest providing unit in a second period prior to the first period, In the presenting step, the first indicators relating to the first provision unit and the second provision unit are presented so that a providing user who provides the content can compare them; In the identifying step, inputting the billing data relating to the plurality of provision units in the first period into a second artificial intelligence module, and outputting a second indicator relating to the performance of the content in the first period, thereby identifying the second indicator; the second artificial intelligence module is provided with second reference information including a parameter for establishing a correlation between an input and an output so as to receive the billing data as an input and output the second index; Identifying an evaluation result of the performance of the content based on the first index and the second index; In the presenting step, the information processing system presents the evaluation result in a manner that can be recognized by the providing user.

3. 3. The information processing system according to claim 2, In the identifying step, the evaluation results regarding the plurality of pieces of content are identified; In the presenting step, the evaluation results for the plurality of pieces of content are presented collectively so as to be recognizable by the providing user.

4. An information processing system, At least one processor is provided, the processor being configured to execute the following steps by reading a program: The acquisition step acquires analytical data relating to the predetermined content, wherein: The content includes a plurality of provision units, and the plurality of provision units are configured to be provided to the viewing user at different times; The analysis data includes viewing data relating to the behavior of the viewing user viewing a predetermined provision unit, and billing data relating to the behavior of the viewing user charged for viewing the provision unit, In the identification step, the analysis data regarding the provision unit to be analyzed is input to a first artificial intelligence module, and a first indicator regarding the performance of the analysis target is output, thereby identifying the first indicator, wherein: the first artificial intelligence module is provided with first reference information including a parameter that establishes a correlation between an input and an output so as to receive the analysis data as an input and output the first indicator; The analysis target includes a first providing unit that is the latest providing unit in a first period, and a second providing unit that is the latest providing unit in a second period prior to the first period, In the presenting step, the first indicators relating to the first provision unit and the second provision unit are presented so that a providing user who provides the content can compare them; In the identifying step, inputting the billing data relating to the plurality of provision units in the first period into a second artificial intelligence module, and outputting a second indicator relating to the performance of the content in the first period, thereby identifying the second indicator in the first period; The charging data relating to the plurality of provision units in the second period is input to the second artificial intelligence module, and the second indicator in the second period is output, thereby identifying the second indicator in the second period, wherein: the second artificial intelligence module is provided with second reference information including a parameter for establishing a correlation between an input and an output so as to receive the billing data as an input and output the second index; In the presentation step, the information processing system presents the first indicator for the first provision unit and the second provision unit and the second indicator for the first period and the second period so that the providing user can compare them.

5. An information processing system, At least one processor is provided, the processor being configured to execute the following steps by reading a program: The acquisition step acquires analytical data relating to the predetermined content, wherein: The content includes a plurality of provision units, and the plurality of provision units are configured to be provided to the viewing user at different times; The analysis data includes viewing data relating to the behavior of the viewing user viewing a predetermined provision unit, and billing data relating to the behavior of the viewing user charged for viewing the provision unit, In the identification step, the analysis data regarding the provision unit to be analyzed is input to a first artificial intelligence module, and a first indicator regarding the performance of the analysis target is output, thereby identifying the first indicator, wherein: the first artificial intelligence module is provided with first reference information including a parameter that establishes a correlation between an input and an output so as to receive the analysis data as an input and output the first indicator; The analysis target includes a first providing unit that is the latest providing unit in a first period, and a second providing unit that is the latest providing unit in a second period prior to the first period, In the presenting step, the first indicators relating to the first provision unit and the second provision unit are presented so that a providing user who provides the content can compare them; The provision unit is repeatedly provided every predetermined provision period, In the identifying step, a variable index is identified as the first index, wherein: An information processing system in which the variable index is identified based on the analysis data during an analysis period set according to the provision period.

6. 6. The information processing system according to claim 5, In the specifying step, a fixed index is further specified as the first index, wherein: An information processing system in which the fixed index is identified based on the analysis data for a set analysis period regardless of the provision period.

7. An information processing system, At least one processor is provided, the processor being configured to execute the following steps by reading a program: The acquisition step acquires analytical data relating to the predetermined content, wherein: The content includes a plurality of provision units, and the plurality of provision units are configured to be provided to the viewing user at different times; The analysis data includes viewing data relating to the behavior of the viewing user viewing a predetermined provision unit, and billing data relating to the behavior of the viewing user charged for viewing the provision unit, In the identification step, the analysis data regarding the provision unit to be analyzed is input to a first artificial intelligence module, and a first indicator regarding the performance of the analysis target is output, thereby identifying the first indicator, wherein: the first artificial intelligence module is provided with first reference information including a parameter that establishes a correlation between an input and an output so as to receive the analysis data as an input and output the first indicator; The analysis target includes a first providing unit that is the latest providing unit in a first period, and a second providing unit that is the latest providing unit in a second period prior to the first period, In the presenting step, the first indicators relating to the first provision unit and the second provision unit are presented so that a providing user who provides the content can compare them; The acquiring step further acquires diffusion data, wherein: the diffusion data is information indicating a history of the browsing user spreading information about the content on the Internet, In the identifying step, an evaluation result of the performance of the content is identified based on the first index and the diffusion data; In the presenting step, the information processing system presents the evaluation result in a manner that can be recognized by the providing user.

8. 8. The information processing system according to claim 7, In the identifying step, Identifying a third indicator related to the performance of the content in the first period based on the diffusion data and the billing data in the first period, the viewing data indicating that a predetermined viewing user has viewed a predetermined provision unit that he or she has previously viewed in the first period again, and secondary development performance indicating performance based on sales of merchandise related to the content through a channel different from that of the content; An information processing system that identifies the evaluation result based on the first index and the third index.

9. 8. The information processing system according to claim 7, In the presentation step, an information processing system presents recommendation information indicating that a specified channel for providing the content is recommended when the first indicator satisfies a specified condition in a manner that is recognizable to the providing user.

10. An information processing system, At least one processor is provided, the processor being configured to execute the following steps by reading a program: The acquisition step acquires analytical data relating to the predetermined content, wherein: The content includes a plurality of provision units, and the plurality of provision units are configured to be provided to the viewing user at different times; The analysis data includes viewing data relating to the behavior of the viewing user viewing a predetermined provision unit, and billing data relating to the behavior of the viewing user charged for viewing the provision unit, In the identification step, the analysis data regarding the provision unit to be analyzed is input to a first artificial intelligence module, and a first indicator regarding the performance of the analysis target is output, thereby identifying the first indicator, wherein: the first artificial intelligence module is provided with first reference information including a parameter that establishes a correlation between an input and an output so as to receive the analysis data as an input and output the first indicator; The analysis target includes a first providing unit that is the latest providing unit in a first period, and a second providing unit that is the latest providing unit in a second period prior to the first period, In the presenting step, the first indicators relating to the first provision unit and the second provision unit are presented so that a providing user who provides the content can compare them; In the identifying step, the first indicator is input to a third artificial intelligence module, and the third artificial intelligence module outputs predicted information regarding the provision unit to be provided after the first period or the performance of the content, thereby identifying the predicted information, wherein: The third artificial intelligence module further includes third reference information including a parameter that establishes a correlation between an input and an output so as to input the value of the first index and output the prediction information; In the presenting step, the information processing system presents the identified prediction information in a manner that is recognizable by the providing user.

11. An information processing method, comprising: A method comprising the steps of the information processing system according to any one of claims 1 to 10.

12. A program, A program that causes a computer to execute each step of the information processing system according to any one of claims 1 to 10.

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