Information processing system and information processing method

Abstract

To promote purchase by providing information to consumers.SOLUTION: An information processing system comprising: a prediction information acquisition unit configured to predict a behavior and / or a purchase of a user; a result information acquisition unit configured to acquire information related to a behavior result and / or a purchase result of the user; an analysis unit configured to analyze a difference between a prediction and a result based on the predicted behavior and / or the predicted purchase of the user predicted by the prediction information acquisition unit and the behavior result and / or the purchase result of the user acquired by the result information acquisition unit; and an intention determination unit configured to determine an intention of the user based on the difference between the prediction and the result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Japanese Patent Laid-Open Publication No. 2021-131640 (Patent Document 1) describes a technology for analyzing consumer behavior. This publication states that "a comprehensive supply chain analysis is carried out without any human understanding of business characteristics, taking into account consumer consumption behavior." and "The feature extraction device 1 is a computer equipped with, for example, a CPU, memory, etc., and is equipped with a data linkage platform 2 that accumulates data on purchasing, sales, and consumption activities between multiple connected companies, as well as consumer purchasing, sales, and consumption, and an analysis engine device 3 that builds a matrix structure combining two pieces of data accumulated in this data linkage platform 2, and builds and analyzes a neural network that shows the relationship between this matrix structure and key performance indicator data 42." [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-131640 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have not considered approaches to encourage consumer purchases. For example, if a consumer's actual behavior differs from predictions, it is thought that providing appropriate information to the consumer can encourage them to make a purchase.

[0005] Therefore, the present invention aims to promote purchases by providing information to consumers. [Means for solving the problem]

[0006] In order to achieve the above-mentioned object, one representative information processing system of the present invention is characterized by comprising a prediction information acquisition unit that predicts a user's behavior and / or purchases, a result information acquisition unit that acquires information related to the user's behavioral results and / or purchase results, an analysis unit that analyzes the difference between the prediction and the result based on the user's predicted behavior and / or predicted purchases predicted by the prediction information acquisition unit and the user's behavioral results and / or purchase results acquired by the result information acquisition unit, and an intention determination unit that determines the user's intention based on the difference between the prediction and the result analyzed by the analysis unit. Furthermore, one representative information processing method of the present invention is characterized in that it includes a prediction information acquisition step in which a computer predicts a user's behavior and / or purchases, a result information acquisition step in which information related to the user's behavioral results and / or purchase results is acquired, an analysis step in which the difference between the prediction and the result is analyzed based on the user's predicted behavior and / or predicted purchases predicted by the prediction information acquisition step and the user's behavioral results and / or purchase results acquired by the result information acquisition unit, and an intention determination step in which the computer determines the user's intention based on the difference between the prediction analyzed by the analysis step and the result. [Effects of the Invention]

[0007] According to the present invention, by providing information to consumers, it is possible to encourage purchases. Problems, configurations and effects other than those described above will become apparent from the following description of the embodiment. [Brief explanation of the drawings]

[0008] [Figure 1] System configuration diagram of Example 1 [Figure 2] 1 is a flowchart showing the processing operation of an information processing system. [Figure 3] Diagram of purchasing predictions using personas [Figure 4] User behavior information diagram [Figure 5] Flowchart showing the analysis process [Figure 6] Flowchart showing the procedure for determining a prediction error [Figure 7] Flowchart showing the processing procedure for determining user intention [Figure 8] Flowchart showing the procedure for determining recommendations [Figure 9] Recommendation information diagram DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, an embodiment will be described with reference to the drawings. [Example]

[0010] FIG. 1 is a system configuration diagram of an information processing system according to a first embodiment. The information processing system 10 is connected to a sensor 30 and an information display unit 40. The sensor 30 detects the behavior of a user who is a consumer. For example, the sensor 30 is a camera installed in a shopping mall or a store. Alternatively, a terminal such as a smartphone carried by a user may be used as the sensor 30 to acquire location information and payment information. Alternatively, a POS (Point of Sale) terminal may be used as the sensor 30 to acquire payment information. The information display unit 40 is a display device such as a liquid crystal display.

[0011] The information processing system 10 is a computer having, as its hardware configuration, a CPU (Central Processing Unit), memory, storage, and a communication interface 124 (not shown). The information processing system 10 also has, as its functional configuration, a prediction information acquisition unit 21, a result information acquisition unit 22, an analysis unit 23, an intention determination unit 24, and a presentation information generation unit 25.

[0012] For example, the prediction information acquisition unit 21, the result information acquisition unit 22, the analysis unit 23, the intention determination unit 24, and the presentation information generation unit 25 are realized by a program executed by a CPU. Alternatively, the prediction information acquisition unit 21, the result information acquisition unit 22, the analysis unit 23, the intention determination unit 24, and the presentation information generation unit 25 may be realized by hardware. Alternatively, one or more of the prediction information acquisition unit 21, the result information acquisition unit 22, the analysis unit 23, the intention determination unit 24, and the presentation information generation unit 25 may be implemented by separate computers.

[0013] The prediction information acquisition unit 21 predicts user behavior and purchases. When predicting user behavior and purchases, the prediction information acquisition unit 21 uses a purchasing behavior model corresponding to the user's attributes. Specifically, the prediction information acquisition unit 21 creates a persona based on the user's attributes such as gender and age, accompanying persons, and past behavioral history and purchase history. A persona is a typical customer model that uses products and services, and has a specific and detailed profile. Based on the persona and product information, the prediction information acquisition unit 21 predicts how the user will behave and what products they will purchase. When making this prediction, it is desirable to take into account various information that affects user behavior, such as the weather on the day, the time, and sales information.

[0014] The result information acquisition unit 22 acquires information related to the user's behavioral results and purchasing results. For example, it acquires information such as the store entered, the route traveled, the user's gaze point, whether the user picked up a product (pick up the product and put it back, looked closely at the back of the package, etc.) using cameras, motion sensors, and apps on the user's smartphone installed on the premises of the shopping mall or in the store.

[0015] The analysis unit 23 compares the predicted behavior and predicted purchases of the user predicted by the prediction information acquisition unit 21 with the user's behavioral results and purchase results acquired by the result information acquisition unit 22, and analyzes the difference between the prediction and the result. Through this analysis, the analysis unit 23 identifies products that were predicted to be purchased but were not purchased.

[0016] The intention determination unit 24 determines the user's intention based on the difference between the prediction analyzed by the analysis unit 23 and the result. The intention determination unit 24 determines whether the user recognized the product but showed no interest and did not purchase it, whether the user recognized the product and showed interest but did not purchase it, or whether the user did not recognize the product. Whether or not the user showed interest can be determined by, for example, determining that the user showed interest when the user picked up the recognized product.

[0017] The presentation information generation unit 25 generates information to be presented to the user. In this embodiment, the information presented to the user is called a recommendation, and the information presented to the user is called recommendation information. The presentation information generation unit 25 changes the recommendation information presented for the same product based on the user intention determined by the intention determination unit 24.

[0018] Specifically, if the user does not recognize a product, the presentation information generation unit 25 generates recommendation information that prompts the user to recognize the product. For example, a message such as "Did you forget to buy ____?" may be displayed on the user's smartphone.

[0019] When a user recognizes a product and shows interest but does not purchase it, the presentation information generation unit 25 generates recommendation information that increases interest in the product. Recommendation information that increases interest in a product includes information about price reductions for the product, information showing variations of the product (different sizes or colors), and information about other products similar to the product. If a user shows interest but does not purchase a product, possible reasons include the price being too high, the size not fitting, or the color not liking. Therefore, providing information that resolves these reasons can encourage product purchases.

[0020] The presentation information generation unit 25 suppresses the provision of information about a product when the user recognizes the product but shows no interest and does not purchase it. This is because if excessive information about the product is provided even though the user did not purchase it because they judged it unnecessary for them, it not only does not lead to promotion of purchase but also has the possibility of damaging trust in the information provided.

[0021] The presentation information generation unit 25 can further use the user's current location to vary the information to be presented. Based on the user's location information, the unit can guide the user to the nearest sales area that sells the target product. The timing of providing information can also be determined based on the user's location information. For example, by providing information when the user has been staying in a rest area or restaurant for a predetermined period of time or more, the information can be provided at a timing that is easy for the user to check.

[0022] 2 is a flowchart showing the processing operation of the information processing system 10. The information processing system 10 sequentially executes the following steps S101 to S108. In step S101, the prediction information acquisition unit 21 executes purchase prediction using a persona, and then the process proceeds to step S102. In step S102, the result information acquisition unit 22 acquires the user behavior information, and then the process proceeds to step S103.

[0023] In step S103, the analysis unit 23 analyzes the user's behavior and purchases, and then the process proceeds to step S104. In step S104, the analysis unit 23 determines the discrepancy between the predicted behavior and the actual behavior, and then the process proceeds to step S105. In step S105, the intention determination unit 24 determines the user's intention from the difference between the predicted and actual behavior, and then the process proceeds to step S106.

[0024] In step S106, the presentation information generating unit 25 determines whether a recommendation is required. If a recommendation is required, the process proceeds to step S107. If a recommendation is not required, the process ends. In step S107, the presentation information generating unit 25 generates recommendation information, and then the process proceeds to step S108. In step S108, the presentation information generating unit 25 presents the recommendation information, and the process ends.

[0025] FIG. 3 is an explanatory diagram of purchase prediction using a persona. The prediction information acquisition unit 21 executes persona creation 54 using in-store sensors 51, a smartphone app 52, POS information 53, etc. The in-store sensors 51 can acquire information on the user's attributes and accompanying persons. For example, image recognition processing of images captured by a camera can identify the user's gender, age group, and relationship with accompanying persons. Furthermore, the smartphone app 52 can acquire user attributes, past behavior history, favorite information, and information on possessions. Purchase information can be acquired as POS information 53.

[0026] The prediction information acquisition unit 21 sets the created persona in an LLM (Large Language Model) (55). It also acquires information 56 about the current day, such as weather, time, and sales information, and performs purchase prediction 57 using the persona.

[0027] 4 is an explanatory diagram of user behavior information. The result information acquisition unit 22 acquires user behavior information from cameras on the premises or in the store, motion sensors, smartphone apps, etc. The user behavior information includes information such as the store the user entered, the user's movement route, the user's gaze point, and whether the user picked up a product (pick up the product and put it back, looked closely at the back of the package, etc.).

[0028] In the example of Figure 4, a user entering through the shopping mall entrance first enters store A. After leaving store A, the user passes store B and enters store C. After leaving store C, the user enters a restaurant. After leaving the restaurant, the user moves to a rest area. This type of movement route information can be obtained by tracking users from the images captured by multiple cameras in the shopping mall.

[0029] Similarly, it is possible to identify how a user moved within a store from the images captured by multiple cameras within the store. Furthermore, it is possible to identify the user's line of sight from images of the user's face and recognize which part of the shelves in the store the user is looking at. Furthermore, it is possible to recognize the actions the user took with respect to a product (e.g., picking up a product, checking the back, etc.) through image processing. In the example of Figure 4, a user passes in front of shelves a and d in a certain store, but does not pass by shelves b and c. Furthermore, shelf a has five tiers, from top to bottom, shelf a-1 to shelf a-5, with three products displayed on each tier. The user checked the product on the left of shelf a-1, the product on the left of shelf a-2, the products on the left and middle of shelf a-3, and all of the products on shelf a-4, but did not check any other products.

[0030] 5 is a flowchart showing the procedure of the analysis process. The analysis unit 23 sequentially executes the following steps S201 to S205. In step S201, the analysis unit 23 acquires information about the movement route of the subject, and then the process proceeds to step S202. In step S202, the analysis unit 23 acquires information about the line of sight of the subject, and then the process proceeds to step S203.

[0031] In step S203, the analysis unit 23 acquires the action performed by the subject toward the product, and then the process proceeds to step S204. In step S204, the analysis unit 23 updates the store entry information and the product shelf visual information for each store. Then, the process proceeds to step S205. It may be possible to determine whether the store or product shelf that was passed by was the intended one based on the presence or absence of the store entry and the product shelf visual information, as well as the line of sight and hesitation while walking. In step S205, the analysis unit 23 updates the information on the actions taken with respect to the products in the store. Furthermore, based on the gaze and action information, it may be determined whether or not each product was overlooked, whether or not the customer was interested, or whether or not the customer was unsure about the product and therefore did not purchase it. Then, the process ends.

[0032] 6 is a flowchart showing the procedure for determining the prediction error. The analysis unit 23 sequentially executes the following steps S301 to S304. In step S301, the analysis unit 23 acquires information about the product predicted to be purchased, and then the process proceeds to step S302. In step S302, the analysis unit 23 acquires information about the store and the shelf where the product is installed, and then the process proceeds to step S303. In step S303, the analysis unit 23 acquires behavior analysis result information and purchase result information. For example, the information acquired includes whether or not the customer entered each store, whether or not they passed by the product shelves, whether or not they looked at the products on the product shelves, whether or not they took any action toward the products (whether or not they were interested), and whether or not they made a purchase (from the POS, etc.). Then, the process proceeds to step S304. In step S304, the analysis unit 23 determines the deviation from the predicted purchased product, related stores, and whether or not there is visual recognition of the product shelf, behavior, or purchase, and ends the process.

[0033] 7 is a flowchart showing the processing procedure for determining a user's intention. The intention determination unit 24 sequentially executes the following steps S401 to S412. In step S401, the intention determination unit 24 acquires the purchase prediction information of the target person, and then the process proceeds to step S402. In step S402, the intention determination unit 24 acquires information about the store and the shelf where the product is located, and then the process proceeds to step S403.

[0034] In step S403, the intention determination unit 24 acquires information about the movement route of the target person, and then the process proceeds to step S404. In step S404, the intention determination unit 24 acquires information about the line of sight of the target person, and then the process proceeds to step S405. In step S405, the intention determination unit 24 acquires information about the target person's action toward the product, and then the process proceeds to step S406.

[0035] In step S406, the intention determination unit 24 determines whether the target person has passed in front of the product. If the target person has passed in front of the product, the process proceeds to step S407. If the target person has not passed in front of the product, the process proceeds to step S410. In step S407, the intention determination unit 24 determines whether the subject has been viewing the target store or the target product shelf. If the subject has been viewing the target store or the target product shelf, the process proceeds to step S408. If the subject has not been viewing the target store or the target product shelf, the process proceeds to step S409.

[0036] In step S408, the intention determination unit 24 determines that the user intention of the target person is "not interested," and then ends the process. In step S409, the intention determination unit 24 determines that the user's intention is "the product was overlooked." Then, the process ends.

[0037] In step S410, the intention determination unit 24 determines whether the subject has performed a predetermined action on the product. The predetermined action, such as picking up the product, is defined in advance. If the subject has performed the predetermined action, the process proceeds to step S411. If the subject has not performed the predetermined action, the process proceeds to step S412.

[0038] In step S411, the intention determination unit 24 determines that the user intention of the target person is "interested in the product", and then ends the process. In step S412, the intention determination unit 24 determines that the user intention of the target person is “not interested.” Then, the process ends.

[0039] 8 is a flowchart showing the procedure for determining a recommendation. The presentation information generating unit 25 sequentially executes the following steps S501 to S504. In step S501, the presentation information generating unit 25 acquires the result of the user intention determination, and then the process proceeds to step S502.

[0040] In step S502, the presentation information generation unit 25 determines the user's intention. If the user's intention is "not interested," the process ends without presenting any recommendation information. If the user's intention is "has overlooked the product," the process proceeds to step S503. If the user's intention is "is interested in the product," the process proceeds to step S504.

[0041] In step S503, the presentation information generating unit 25 generates recommendation information for the user to help with forgetting to buy something, and the process ends. In step S504, the presentation information generating unit 25 generates recommendation information that will attract the user's attention, and the process ends.

[0042] FIG. 9 is an explanatory diagram of recommendation information. In FIG. 9, recommendation information is generated according to the recommendation determination result and the user's situation. In FIG. 9, the recommendation determination result is one of "not interested," "follow up on forgotten purchase," and "increase interest." In FIG. 9, the user's situation is one of "taking a break or eating," "just before going home," and "after returning home."

[0043] If the recommendation judgment result is "not interested" and the user's situation is "taking a break, eating," the presentation information generation unit 25 provides information about products other than the product judged to be "not interested," such as introducing alternative products or products related to the product already purchased.

[0044] If the recommendation determination result is "not interested" and the user's situation is "just about to go home", the presentation information generation unit 25 does not provide information about the product determined to be "not interested" or other products.

[0045] If the recommendation determination result is "not interested" and the user's situation is "after returning home," the presentation information generation unit 25 provides information about products other than the product determined to be "not interesting," such as introduction of alternative products or introduction of products related to the product already purchased. In this recommendation, purchase by mail order may be presented as an option.

[0046] If the recommendation determination result is "follow up on forgotten purchase" and the user situation is "taking a break or eating," the presentation information generation unit 25 recommends shopping at the store. In addition to product information, a shopping route may be presented as recommendation information.

[0047] If the recommendation determination result is "follow up on forgotten purchase" and the user's situation is "just before returning home," the presentation information generation unit 25 recommends shopping at a store or online and encourages the purchase of the relevant product. With this recommendation, the user can choose whether to purchase the product before returning home or to purchase it online after returning home.

[0048] If the recommendation determination result is "follow up on forgotten purchase" and the user situation is "after returning home", the presentation information generation unit 25 presents recommendation information so that the forgotten purchase item can be purchased online.

[0049] If the recommendation determination result is "increase interest" and the user's situation is "taking a break or eating," the presentation information generation unit 25 recommends shopping at the store along with additional information. The additional information may be information about price reductions for the product, information about variations of the product (different sizes or colors), information about other products similar to the product, etc.

[0050] If the recommendation determination result is "increase interest" and the user's situation is "just before returning home," the presentation information generation unit 25 recommends shopping at a store along with additional information. The additional information may be information about a discount on the product, information indicating variations of the product (different sizes or colors), information about other products similar to the product, etc. In this case, it would be acceptable to recommend mail order, but since the user has already checked the product at the store and decided not to purchase it, a recommendation that assumes purchase at the store is more effective.

[0051] If the recommendation determination result is "increase interest" and the user situation is "after returning home," the presentation information generation unit 25 recommends shopping at a store along with additional information. The additional information may be information about price reductions for the product, information indicating variations of the product (different sizes or colors), information about other products similar to the product, etc. In this case, mail order may be recommended, but since the user has already checked the product at the store and decided not to purchase it, a recommendation assuming purchase at the store is effective.

[0052] As described above, the information processing system 10 is characterized by comprising a prediction information acquisition unit 21 that predicts a user's behavior and / or purchases; a result information acquisition unit 22 that acquires information related to the user's behavioral results and / or purchase results; an analysis unit 23 that analyzes the difference between the prediction and the result based on the user's predicted behavior and / or predicted purchase predicted by the prediction information acquisition unit 21 and the user's behavioral results and / or purchase results acquired by the result information acquisition unit 22; and an intention determination unit 24 that determines the user's intention based on the difference between the prediction and the result analyzed by the analysis unit 23. This configuration and operation can encourage purchases by providing information to consumers. This is because behavior that deviates from predicted behavior can be analyzed and information can be provided as a measure to address the deviated behavior. For example, when behavior deviates from prediction, such as not buying something that was predicted to be purchased or passing by a store that was predicted to be entered, the system determines whether or not recommendations should be presented and selects the information to be presented, thereby efficiently stimulating the user's desire to purchase. In addition, it can infer and recommend items that the user is more likely to purchase based on other purchases and past purchase history.

[0053] The system further includes a presentation information generation unit 25 that generates information to be presented to the user, and the presentation information generation unit 25 varies the information to be presented for the same product based on the user's intention. According to this configuration and operation, appropriate information that matches the user's intentions can be provided, thereby efficiently stimulating purchasing motivation.

[0054] In addition, the analysis unit 23 identifies products that were predicted to be purchased but were not purchased, the intention determination unit 24 determines whether the user recognized the product but showed no interest and did not purchase it, whether the user recognized the product and showed interest but did not purchase it, or whether the user does not recognize the product, and the presentation information generation unit 25 generates information to increase interest in the product if the user recognized the product and showed interest but did not purchase it, or generates information to encourage recognition of the product if the user does not recognize the product. This configuration and operation makes it possible to suggest products that better suit the user's needs and to prevent the user from forgetting to buy something.

[0055] Furthermore, the information that increases interest in the product is information about price reductions for the product, information showing variations of the product, and information about other products similar to the product. Therefore, if a product does not reach the purchase due to a slight difference from the user's desire, a product that meets the user's desire can be suggested.

[0056] Furthermore, when the user recognizes the product but shows no interest and does not purchase it, the presentation information generating unit 25 suppresses the provision of information about the product. This makes it possible to avoid providing excessive information about products that the customer did not purchase.

[0057] The intention determination unit 24 is characterized in that it determines that the user is interested in the recognized product when the user picks up the recognized product. Therefore, it is possible to determine with high accuracy whether the user is interested or not from their actions.

[0058] Furthermore, the prediction information acquisition unit 21 predicts the behavior and / or purchases of the user using a purchase behavior model corresponding to the attributes of the user. This makes it possible to predict with high accuracy the products that a user may purchase, thereby providing highly accurate information.

[0059] Moreover, the presentation information generating unit 25 further uses the current location of the user to vary the information to be presented. According to this configuration and operation, recommendation information can be presented taking into consideration the user's location and situation.

[0060] The present invention is not limited to the above-described embodiments, but includes various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, not only can the configurations be deleted, but also replacements and additions of configurations are possible. For example, the behavior of pre-registered users may be tracked. Also, even if a user is not registered, the behavior of the same person can be traced using multiple cameras.

[0061] Also, if a customer looks closely at the back of the package but doesn't end up buying it, they may compare similar products at multiple stores, so stores that sell products in the same genre may be recommended.Also, if a customer purchases a product that they weren't expecting to buy, the persona may be revised or related products may be recommended. [Explanation of symbols]

[0062] 10: Information processing system, 21: Prediction information acquisition unit, 22: Result information acquisition unit, 23: Analysis unit, 24: Intention determination unit, 25: Presentation information generation unit, 30: Sensor, 40: Information presentation unit

Claims

1. a prediction information acquisition unit that predicts user behavior and / or purchases; a result information acquisition unit that acquires information related to the user's behavioral results and / or purchase results; an analysis unit that analyzes a difference between a prediction and a result based on the predicted behavior and / or predicted purchase of the user predicted by the prediction information acquisition unit and the behavioral result and / or purchase result of the user acquired by the result information acquisition unit; an intention determination unit that determines a user's intention based on a difference between the prediction analyzed by the analysis unit and the result; An information processing system comprising:

2. 2. The information processing system according to claim 1, a presentation information generation unit that generates information to be presented to the user; The information processing system is characterized in that the presentation information generation unit changes the information presented about the same product based on the user's intention.

3. 3. The information processing system according to claim 2, The analysis unit identifies products that were predicted to be purchased but were not purchased, the intention determination unit determines whether the user recognized the product but showed no interest and did not purchase it, whether the user recognized the product and showed interest but did not purchase it, or whether the user did not recognize the product; The presentation information generation unit generates information to increase interest in the product when the user recognizes the product and shows interest but does not purchase it, and generates information to encourage recognition of the product when the user does not recognize the product. An information processing system comprising:

4. 4. The information processing system according to claim 3, An information processing system characterized in that the information that increases interest in the product is information about price reductions for the product, information showing variations of the product, and information about other products similar to the product.

5. 4. The information processing system according to claim 3, The information processing system is characterized in that the presentation information generation unit suppresses provision of information about the product when the user recognizes the product but shows no interest and does not purchase it.

6. 4. The information processing system according to claim 3, The information processing system is characterized in that the intention determination unit determines that the user has shown interest when the user picks up the recognized product.

7. 2. The information processing system according to claim 1, The information processing system is characterized in that the prediction information acquisition unit predicts the behavior and / or purchases of the user using a purchasing behavior model corresponding to the attributes of the user.

8. 3. The information processing system according to claim 2, The information processing system is characterized in that the presentation information generation unit varies the information to be presented by further using a current location of the user.

9. The computer a prediction information acquisition step for predicting user behavior and / or purchases; a result information acquisition step of acquiring information relating to the user's behavioral results and / or purchase results; an analysis step of analyzing a difference between the prediction and the result based on the predicted behavior and / or predicted purchase of the user predicted by the prediction information acquisition step and the behavioral result and / or purchase result of the user acquired by the result information acquisition unit; an intention determination step of determining a user's intention based on a difference between the prediction analyzed by the analysis step and the result; An information processing method comprising:

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