WO2023067895A1 - 情報処理装置 - Google Patents

情報処理装置 Download PDF

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Publication number
WO2023067895A1
WO2023067895A1 PCT/JP2022/032091 JP2022032091W WO2023067895A1 WO 2023067895 A1 WO2023067895 A1 WO 2023067895A1 JP 2022032091 W JP2022032091 W JP 2022032091W WO 2023067895 A1 WO2023067895 A1 WO 2023067895A1
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WIPO (PCT)
Prior art keywords
nudge
information
wear
user
intervention
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PCT/JP2022/032091
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English (en)
French (fr)
Japanese (ja)
Inventor
亮勢 酒井
哲哉 山口
拓弥 泉澤
佑輔 中村
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NTT Docomo Inc
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NTT Docomo Inc
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Priority to US18/688,844 priority Critical patent/US20240366910A1/en
Priority to JP2023554963A priority patent/JP7653535B2/ja
Publication of WO2023067895A1 publication Critical patent/WO2023067895A1/ja
Anticipated expiration legal-status Critical
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/01—Customer relationship services
    • G06Q30/015—Providing customer assistance, e.g. assisting a customer within a business location or via helpdesk
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G06Q30/0269—Targeted advertisements based on user profile or attribute
    • G06Q30/0271—Personalized advertisement
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Recommending goods or services
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/22—Social work or social welfare, e.g. community support activities or counselling services
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    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry
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    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6887—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient mounted on external non-worn devices, e.g. non-medical devices
    • A61B5/6898—Portable consumer electronic devices, e.g. music players, telephones, tablet computers
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    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
    • A61M2021/0005—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus
    • A61M2021/0044—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus by the sight sense
    • A61M2021/005—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus by the sight sense images, e.g. video
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M2205/00—General characteristics of the apparatus
    • A61M2205/50—General characteristics of the apparatus with microprocessors or computers

Definitions

  • One aspect of the present invention relates to an information processing device.
  • Patent Literature 1 describes a system that encourages users to change their behavior by utilizing nudges that encourage them to voluntarily take desirable behaviors for individuals and society based on cognitive biases.
  • the nudges mentioned above are considered to wear out.
  • the wear of the nudge here means that the behavior modification effect of the nudge decreases due to repeated intervention (recommendation to the user). Conventionally, wear of such nudges has not been adequately considered.
  • the present invention has been made in view of the above circumstances, and an object of the present invention is to appropriately quantify nudge wear.
  • An information processing apparatus acquires a storage unit that stores nudge information that is a mechanism for prompting a user to voluntarily take a desired action, and nudge intervention information that is information related to nudge intervention. and a wear derivation unit for deriving and outputting nudge wear information indicating the degree of reduction in effect of the nudge related to the nudge intervention information based on the nudge intervention information.
  • An information processing apparatus acquires nudge intervention information that is information related to nudge intervention (information related to intervention of a user regarding nudge), and reduces the effect of nudge based on the nudge intervention information.
  • Nudge wear information indicating the degree is derived and output.
  • nudge wear information is estimated (derived) from information (for example, nudge intervention frequency) related to nudge intervention to the user, so that the nudge is worn out by, for example, repeated nudge intervention (nudge is reduced), the wear of the nudge can be quantified appropriately (with high accuracy).
  • information processing device according to the aspect of the present invention, it is possible to appropriately quantify the wear of the nudge.
  • nudge wear can be quantified appropriately.
  • FIG. 1 is a diagram illustrating a series of flows for prompting a user's behavioral change by nudging.
  • FIG. 2 is a diagram illustrating nudge wear.
  • FIG. 3 is a functional block diagram of the nudge management device according to the first embodiment.
  • FIG. 4 is a diagram for explaining nudge wear calculation.
  • FIG. 5 is a diagram for explaining nudge wear calculation.
  • FIG. 6 is a diagram for explaining nudge recovery calculation.
  • FIG. 7 is a sequence diagram showing nudge wear update processing.
  • FIG. 8 is a functional block diagram of a nudge management device according to the second embodiment.
  • FIG. 9 is a diagram for explaining wear feature quantity generation.
  • FIG. 10 is a diagram for explaining behavioral change estimation.
  • FIG. 11 is a diagram illustrating nudge selection.
  • FIG. 12 is a sequence diagram illustrating nudge recommendation processing.
  • 13 is a diagram illustrating a hardware configuration of a nudge management device according to the embodiment; FIG
  • FIG. 1 is a diagram illustrating a series of flows for prompting a user's behavioral change by nudging.
  • FIG. 2 is a diagram illustrating nudge wear.
  • a nudge is a mechanism or mechanism based on cognitive biases that encourages users to voluntarily take desirable actions for individuals and society.
  • a nudge is a display such as a message displayed on a communication terminal such as a smartphone
  • FIG. 1 assumes a scene in which user Y is heading to a bus stop 300 to board a bus.
  • the bus (next bus) on which user Y is supposed to board is highly congested.
  • a push notification is sent to the communication terminal 100 such as a smart phone of the user Y through a predetermined application (for example, a bus application that assists getting on the bus).
  • the push notification is a notification related to a nudge that urges the user to change behavior, specifically, a notification related to a nudge that urges the user to get on a bus (recommended bus) other than the next busy bus.
  • Timing of nudge intervention may be determined, for example, based on information obtained through an application of communication terminal 100 (“bus application” in the above example), user Y's location information, and the like. In this embodiment, a detailed description of the timing of nudge intervention is omitted. For effective nudges, for example, based on information acquired through the application of the communication terminal 100 (“bus application” in the above example), select the nudge that has the highest behavioral change effect in the current scene. can be considered.
  • the probability that the user will generally select is the highest. It is conceivable to select a nudge that prompts the driver to see off the boarding.
  • the wear of the nudge when the wear of the nudge has not progressed, the user is highly likely to show an interest in the nudge (information), and it is possible to effectively encourage the user to change his/her behavior.
  • the nudge wear information By deriving the nudge wear information in this way, it is possible to select nudges that are truly highly effective in modifying behavior, so it is important to derive the nudge wear information.
  • the content related to the derivation of the nudge wear information will be described in detail.
  • FIG. 3 is a functional block diagram of the nudge management device 10 (information processing device) included in the nudge recommendation system 1 according to the first embodiment.
  • the nudge recommendation system 1 includes an application 101 of a communication terminal 100 and a nudge management device 10 .
  • the nudge recommendation system 1 actually includes applications 101 of a plurality of communication terminals 100 of different users, but for convenience of explanation, only the application 101 of one communication terminal 100 will be explained.
  • the communication terminal 100 may be any terminal capable of wireless communication, such as a smartphone, a tablet terminal, or a PC terminal.
  • the application 101 may be any application that can be activated in the communication terminal 100, such as a bus application for assisting boarding of the bus.
  • the nudge management device 10 is a device that manages multiple types of nudge information and updates the nudge information based on information from the application 101 .
  • the nudge management device 10 includes an acquisition unit 11 , a storage unit 12 , a wear derivation unit 13 , a recovery derivation unit 14 and an update unit 15 .
  • the acquisition unit 11 acquires various information from the application 101 .
  • the acquisition unit 11 acquires nudge intervention information, which is information related to nudge intervention (information related to user intervention regarding nudge), for each of a plurality of types of nudge.
  • the nudge intervention information may include, for example, the presence/absence of push notification on the previous day, the presence/absence of nudge intervention on the previous day, and the result of behavior modification on the previous day.
  • the previous day is given as an example of a predetermined period of time, and may be a predetermined period other than the previous day (for example, the last three days, etc.).
  • the presence/absence of push notification here is information indicating whether or not a push notification has been received in the communication terminal 100 of the user via the application 101 .
  • the presence or absence of nudge intervention is information indicating whether or not the nudge has been viewed by the user on the nudge screen corresponding to the push notification.
  • the behavior modification result is information indicating whether or not the user has taken the behavior urged by the nudge.
  • the acquisition unit 11 further acquires user information that is information related to the attributes of the user and that affects the reduction of the nudge effect.
  • Information related to a user's attribute is information unique to the user according to the unique characteristics of the user.
  • the user information may include, for example, the usage time of the communication terminal 100 (smartphone) on the previous day, the presence or absence of the corona vaccine, and the corona infection status on the previous day. Since it is possible to estimate how the user uses the communication terminal 100 from the usage time of the communication terminal 100, for example, it is possible to see through whether the user is a user who does not check every corner of the screen, and how the nudge affects the human mind. Whether or not it is a user can be estimated.
  • the user's attitude toward risk can be inferred from the presence or absence of the corona vaccine. From the corona infection status, the current mental and physical health status of the user can be estimated.
  • the acquisition unit 11 may acquire, as other user information, whether or not the user has visited a racecourse, whether or not each tourist spot has been visited, the number of points acquired for the application 101, and the like.
  • the degree of user's risk appetite can be estimated from the presence or absence of visits to racetracks. From the presence or absence of visits to each tourist spot, the strength of conformity bias and desire for approval can be inferred. From the number of points earned, the degree of participation in the campaign can be estimated, and the bias regarding loss avoidance and gain can be estimated.
  • Each of the user information described above is information that can affect the reduction of the nudge effect.
  • the acquisition unit 11 further acquires environment information indicating the state of the user's external environment during a predetermined period.
  • a user's external environment is an element that surrounds the user and can affect the user's behavior, and is an element that is not related to the user's attributes.
  • the acquisition unit 11 may acquire the previous day's weather, the previous day's temperature, the previous day's day of the week (whether it is a weekday or a weekend), the previous day's number of corona infected persons, and the like, as environmental information. These pieces of information are information that can be used to estimate whether or not to go out or take an action that is close to each other.
  • the acquisition unit 11 may acquire factors that are likely to affect the behavior of the user, in addition to the above-described information.
  • the acquisition unit 11 stores each acquired information in the storage unit 12 .
  • the storage unit 12 is a database that stores information on multiple types of nudges and stores information stored from the acquisition unit 11 .
  • the storage unit 12 stores, as information on a plurality of types of nudges, for example, information (nudge ID) that uniquely identifies a nudge, intervention conditions, contents of actions to be encouraged, behavior modification effects, incentives, and behavior modification means. , and nudge contents are linked and stored.
  • the intervention condition is information indicating in what cases the nudge is a candidate for intervention, and is defined as, for example, "when the bus to be boarded is congested.”
  • the content of the action to be urged is the specific content of the action that the nudge urges the user to take, and is defined, for example, as "get on the next bus that is not crowded”.
  • the behavior modification effect is information indicating the degree (probability) of the user taking the action prompted by the nudge when nudge intervention is performed, and is defined as "10%", for example.
  • An incentive is an incentive that a user receives when the user performs an action prompted by a nudge, and is defined as "point 10p", for example.
  • the behavior modification means is information indicating what kind of means, such as a message, image, or voice, is used to modify the behavior.
  • the nudge content is information indicating the specific content of the nudge (for example, in the case of a message, the content of the message (words of the nudge)). For example, a plurality of phrases such as "There is a rare vacant bus after one! and "Avoiding congestion leads to social contribution.”
  • the storage unit 12 stores the information stored from the acquisition unit 11.
  • the storage unit 12 stores a feature amount f_a1 indicating whether or not a push notification was made on the previous day by 0 or 1, and a feature amount f_a2 indicating whether or not nudge intervention was performed on the previous day by 0 or 1.
  • a feature value f_a3 indicating the result of behavioral change on the previous day by 0 or 1
  • a feature value f_a4 indicating whether the weather on the previous day was rainy or not by 0 or 1
  • a feature value f_a6 indicating whether the weather on the previous day was cloudy with 0 or 1
  • a feature value f_a7 indicating whether the weather on the previous day was snowy with 0 or 1
  • a feature value f_a8 indicating the temperature on the previous day.
  • a feature value f_a9 that indicates whether the previous day is a weekday or not
  • a feature value f_a10 that indicates whether or not the previous day is a Saturday or Sunday
  • a feature value f_a11 that indicates the smartphone usage time on the previous day, and a corona vaccine.
  • FIG. 4 shows feature amounts of examples 1 to 3 acquired by the acquisition unit 11 at different timings.
  • the wear derivation unit 13 is configured to derive and output nudge wear information that indicates the degree of reduction in the nudge effect related to the nudge intervention information based on the nudge intervention information.
  • the nudge intervention information is, as described above, information including, for example, the presence or absence of push notification on the previous day, the presence or absence of nudge intervention on the previous day, and the behavior modification effect on the previous day.
  • the wear derivation unit 13 may derive nudge wear information for each of a plurality of types of nudges.
  • the wear derivation unit 13 refers to the information stored in the storage unit 12 to specify each feature amount related to the nudge intervention information, and derives the nudge wear information based on the value of each specified feature amount. In the example shown in FIG.
  • the feature quantity f_a1 indicating the presence or absence of the push notification on the previous day is 1 when there is a notification, and 0 when there is no notification.
  • the feature quantity f_a2 indicating the presence or absence of nudge intervention on the previous day is set to 1 when there is intervention and 0 when there is no intervention.
  • the feature value f_a3 indicating the behavior change result of the previous day is 1 when there is behavior change (when the user takes the action urged by the nudge), and 1 when there is no behavior change (when the user does not take the action urged by the nudge) case) is set to 0.
  • the wear derivation unit 13 further considers the user information and the environment information, Derive nudge wear information.
  • User information is, as described above, information that includes, for example, how long the smartphone was used on the previous day, whether or not the user received the corona vaccine, and the corona infection status on the previous day.
  • the feature amount f_a11 indicating the smartphone usage time on the previous day is a value between 0 and 24 hours.
  • the feature value f_a12 indicating the presence or absence of the corona vaccine is set to 1 when inoculated and 0 when not inoculated.
  • the feature value f_a13 indicating the state of corona infection on the previous day is 1 when infected and 0 when not infected.
  • environmental information is information that includes, for example, the previous day's weather, the previous day's temperature, the previous day's day of the week (whether it's a weekday or a weekend), and the previous day's number of corona-infected people.
  • the feature value f_a4 indicating whether or not the weather on the previous day was rainy is 1 if it is raining, and 0 if it is not raining.
  • a feature value f_a5 indicating whether or not the weather on the previous day was fine is 1 when it is fine, and 0 when it is not fine.
  • a feature value f_a6 indicating whether or not the weather on the previous day was cloudy is 1 when it is cloudy, and 0 when it is not cloudy.
  • a feature value f_a7 indicating whether or not the weather on the previous day is snow is 1 if it is snow, and 0 if it is not snow.
  • the feature value f_a8 indicating the temperature of the previous day is a value indicating the temperature.
  • a feature value f_a9 indicating whether or not the previous day is a weekday is 1 if it is a weekday, and 0 if it is not a weekday.
  • a feature value f_a10 indicating whether or not the previous day is Saturday and Sunday is 1 if it is Saturday and Sunday, and is 0 if it is not Saturday and Sunday.
  • the feature value f_a14 indicating the number of corona-infected persons on the previous day is a value indicating the number of infected persons.
  • w ⁇ is a weight and takes different values depending on the model case.
  • Abrasion(date), which indicates wear, is adjusted to have a value in the range of 0-1.
  • the weight w ⁇ may be derived, for example, by obtaining a regression equation using the method of least squares or the like. Also, w ⁇ indicating the weight may be derived from, for example, a correlation coefficient between each feature amount (column) and the behavioral change result, which is the objective variable. Also, the weight w ⁇ may be derived, for example, by creating a machine learning model for predicting the result of behavioral change, which is the objective variable, and using the contribution rate of each feature quantity (column).
  • a behavior modification result considering the nudge wear information may be derived.
  • the behavior modification result is 1 when it is predicted that there is behavior modification (when it is predicted that the user will take the action prompted by the nudge), and 1 when it is predicted that there is no behavior modification (when the nudge is predicted to take action) is set to 0 when it is predicted that the user will not take the action to be urged.
  • the wear derivation unit 13 calculates abrasion (date) indicating wear by the above formula (1).
  • the wear information may be derived as follows. Now, the previous day's push notification presence or absence (f1), the previous day's nudge intervention presence or absence (f2), and the previous day's behavioral change effect (f3), which are nudge intervention information, and a visit to a certain sightseeing spot, which is user information An example of deriving wear information from the number of times (f4) will be described.
  • the weights of f1 to f4 in deriving wear information are 0.1, 0.2, 0.6, and 0.1, respectively.
  • the recovery deriving unit 14 is configured to derive and output nudge recovery information that indicates the degree of recovery of the effect of the nudge related to the nudge intervention information based on the nudge intervention information.
  • the nudge recovery information is information indicating the degree of recovery of a nudge once worn.
  • the nudge intervention information is, as described above, information including, for example, the presence or absence of push notification on the previous day, the presence or absence of nudge intervention on the previous day, and the behavior modification effect on the previous day.
  • the recovery deriving unit 14 may derive nudge recovery information for each of multiple types of nudges.
  • the recovery derivation unit 14 refers to the information stored in the storage unit 12 to specify each feature amount related to the nudge intervention information, and derives the nudge recovery information based on the value of each specified feature amount.
  • the feature quantity f_r1 indicating the presence or absence of push notification on the previous day is 1 when there is notification, and 0 when there is no notification.
  • the feature quantity f_r2 indicating the presence or absence of nudge intervention on the previous day is set to 1 when there is intervention and 0 when there is no intervention.
  • the feature value f_r3 indicating the behavior change result of the previous day is 1 when there is a behavior change (when the user takes the action urged by the nudge), and 1 when there is no behavior change (when the user does not take the action urged by the nudge) case) is set to 0.
  • the recovery derivation unit 14 further considers other information to derive nudge recovery information.
  • other information includes smartphone usage time on the previous day, days since the last push notification, days since the last nudge intervention, days since the last behavioral change, and other information likely to affect behavior. factors are exemplified.
  • the recovery derivation unit 14 derives recovery(date) indicating recovery by the following equation (2), taking into account the other information described above.
  • wr is a weight and takes different values depending on the model case. wr can be calculated, for example, by a method similar to w ⁇ , which indicates a weight related to wear derivation.
  • recovery(date), which indicates recovery, is adjusted to have a value in the range of 0-1.
  • the update unit 15 is configured to update the nudge information stored in the storage unit 12 based on the nudge wear information and the nudge recovery information.
  • the update unit 15 acquires nudge wear information of each nudge from the wear derivation unit 13 .
  • the update unit 15 acquires the nudge recovery information of each nudge from the recovery derivation unit 14 .
  • the update unit 15 acquires current wear information of each nudge from the nudge information stored in the storage unit 12 .
  • the update unit 15 derives the latest wear information based on the nudge wear information, the nudge recovery information, and the current wear information, and based on the derived wear information, each item stored in the storage unit 12 is updated. Update nudge information.
  • the update unit 15 considers both nudge wear information of one nudge included in a plurality of types of nudges and nudge wear information of a nudge similar to the one nudge to update the information of the one nudge. good too.
  • nudge wear is represented by the following recurrence formulas (3) and (4).
  • formula (4) when deriving the wear F(N)I of one nudge, for each nudge, the recovery (date) indicating recovery is subtracted from the abrasion (date) indicating wear, and the current F(N ⁇ 1)I, which is information on the wear of the above one nudge, is added to derive a value multiplied by the weight WI_J. Which indicates that. Weights WI_J are set to zero for nudges that are not similar to the one nudge above. Thereby, nudge wear is derived by considering only the information of nudges that are similar to each other.
  • the wear F(N)A of the nudge A is derived from the following equation (5).
  • WA_A is the weight for determining the wear of nudge A itself
  • WA_B is the weight for deriving the wear of nudge B when determining the wear of nudge A
  • WA_C is the weight for deriving the wear of nudge A. This is the weight related to the derivation of the wear of the nudge C when the wear is produced.
  • WA_B (or WA_C) is set based on the degree of similarity between nudge A and nudge B (or nudge C).
  • the weight WI_J will be explained in detail.
  • the wear of one nudge is derived from the wear of a plurality of types of nudges, the weight of the wear of the one nudge (target nudge) is maximized. Then, the higher the degree of similarity to the target nudge, the greater the wear weight.
  • a high degree of similarity means a high degree of duplication of cognitive biases (psychological biases) associated with nudges.
  • nudge A is associated with biases a, b, and c
  • nudge B is associated with biases b, c, and d
  • nudge C is associated with bias d. do.
  • a similarity according to the degree of overlap of biases may be derived.
  • nudge A is the target nudge.
  • a common bias of 0 indicates that the two nudges are dissimilar.
  • nudge B is the target nudge.
  • FIG. 7 is a sequence diagram showing nudge wear update processing.
  • various information including nudge intervention information is acquired from the application 101 by the acquisition unit 11 and stored in the storage unit 12 (step S11).
  • the wear derivation unit 13 acquires information (information including nudge intervention information) stored in the storage unit 12 (step S12). Then, the wear deriving unit 13 derives the nudge wear information of each nudge based on the nudge intervention information (step S13).
  • the information (including the nudge intervention information) stored in the storage unit 12 is acquired by the recovery derivation unit 14 (step S14). Then, the recovery deriving unit 14 derives the nudge recovery information for each nudge based on the nudge intervention information (step S15).
  • the derivation result of the nudge wear information by the wear derivation unit 13 is acquired by the update unit 15 (step S16), and the derivation result of the nudge recovery information by the recovery derivation unit 14 is acquired by the update unit 15 (step S17). ). Also, the current wear information of each nudge is acquired by the updating unit 15 from the storage unit 12 (step S18).
  • the update unit 15 performs wear update processing based on the acquired information (step S19), and the wear update result is reflected in the information of each nudge stored in the storage unit 12 (step S20). ).
  • the nudge management apparatus 10 includes a storage unit 12 that stores nudge information, which is a mechanism for prompting the user to voluntarily take a desired action, and information related to nudge intervention (user intervention related to nudge). and a wear derivation unit 13 for deriving and outputting nudge wear information indicating the degree of reduction in effect of the nudge related to the nudge intervention information based on the nudge intervention information.
  • nudge intervention information which is information related to nudge intervention (information related to user intervention related to nudge)
  • nudge intervention information which is information related to nudge intervention (information related to user intervention related to nudge)
  • the degree of reduction in nudge effect is calculated.
  • nudge wear information is estimated (derived) from information (for example, nudge intervention frequency) related to nudge intervention to the user, so that the nudge is worn out by, for example, repeated nudge intervention (nudge is reduced), the wear of the nudge can be quantified appropriately (with high accuracy).
  • the nudge management device 10 according to the present embodiment can appropriately quantify nudge wear.
  • nudge wear has the following advantages, for example.
  • First of all when recommending a nudge to a user, it is possible to make a recommendation that considers the wear of the nudge. It is possible to make a recommendation based on the magnitude relationship of the numerical values, or use digitized wear as a feature amount of machine learning related to the recommendation.
  • the recommendation considering wear will be described in detail in the second embodiment.
  • an upper wear limit can be specified.
  • the upper limit of wear means a value at which no more wear occurs or the effect does not change even if the number of apparent wear increases. By specifying the upper limit of wear in the usage scene, it becomes possible to determine that there is no point in continuously applying nudges. Fourth, the accuracy of nudge recovery verification and data collection is improved.
  • the nudge intervention information includes information indicating whether or not the nudge was viewed by the user within a predetermined period of time, information indicating whether or not the user took the action prompted by the nudge due to the nudge, and may include at least According to such a configuration, it is specified whether or not the nudge was intervened (viewed by the user) with respect to the user, and whether or not the user actually changed his or her behavior due to the nudge. Based on information closely related to nudge wear, nudge wear can be derived more favorably.
  • the acquisition unit 11 further acquires user information that is information related to the attributes of the user and that influences the decrease in the nudge effect, and the wear derivation unit 13 further considers the user information to derive nudge wear information. good.
  • the nudge wear information is calculated by further considering the information related to the attributes of the user, such as the usage time of the smartphone and the presence or absence of visits to a predetermined place, which may affect the reduction of the effect of the nudge. Since it is derived, the wear of the nudge can be derived more appropriately.
  • the acquisition unit 11 may further acquire environmental information indicating the state of the user's external environment during a predetermined period, and the wear derivation unit 13 may derive nudge wear information by further considering the environment information. According to such a configuration, since the nudge wear information is derived by further considering the environmental information indicating the state of the user's external environment, such as the weather and the number of people infected with corona, the wear of the nudge can be derived more appropriately. be able to.
  • the nudge management device 10 may further include an update unit 15 that updates the nudge information related to the nudge intervention information stored in the storage unit 12 based on the nudge wear information.
  • an update unit 15 that updates the nudge information related to the nudge intervention information stored in the storage unit 12 based on the nudge wear information.
  • the nudge wear information that is quantified and derived is used to update the nudge information in the storage unit 12, so that the nudge wear information can be reflected in the original nudge information.
  • the storage unit 12 stores information on multiple types of nudges
  • the wear derivation unit 13 derives nudge wear information for each of the multiple types of nudges
  • the updating unit 15 is included in the multiple types of nudges.
  • the information for a nudge may be updated by considering both the nudge wear information for the nudge and the nudge wear information for nudges similar to the nudge. If, for example, a nudge similar to one nudge is repeatedly intervened by the user, it is considered that the nudge will also wear out due to its influence.
  • the one nudge information can be updated based on the wear condition along the .
  • the nudge management device 10 further includes a recovery derivation unit 14 for deriving nudge recovery information indicating the degree of effect recovery of the nudge related to the nudge intervention information based on the nudge intervention information, and the update unit 15 further considers the nudge recovery information. Then, the nudge information related to the nudge intervention information may be updated. For example, even if a nudge has been worn once due to repeated intervention by the user, it is considered that the effect of the nudge will recover if the intervention is not performed for a predetermined period of time. Therefore, the nudge recovery information is derived based on the nudge intervention information, and the nudge information is updated in consideration of the nudge recovery information, so that the nudge information can be updated with information more in line with the actual situation.
  • a recovery derivation unit 14 for deriving nudge recovery information indicating the degree of effect recovery of the nudge related to the nudge intervention information based on the nudge intervention information
  • nudge management device 10A information processing device included in the nudge recommendation system 1A according to the second embodiment will be described with reference to FIGS. 8 to 12.
  • FIG. 8 explanations common to the first embodiment will be omitted, and differences from the first embodiment will be mainly explained.
  • FIG. 8 is a functional block diagram of a nudge management device 10A included in a nudge recommendation system 1A according to the second embodiment.
  • the nudge management device 10A has a function of recommending to the user a nudge with a high probability of behavior change, which is the probability that the user will take the action urged by the nudge, in consideration of wear of the nudge.
  • the nudge management device 10A includes an acquisition unit 11, a storage unit 12, a wear feature quantity generation unit 16 (wear derivation unit), a behavior change estimation unit 17, a nudge selection unit 18, It has
  • the acquisition unit 11 acquires various pieces of information in association with each other from the application 101 when a specific condition is satisfied.
  • a specific condition is a specific condition under which a nudge can be recommended to the user. Specifically, it is, for example, checking in at a specific spot (shop, etc.).
  • the acquisition unit 11 acquires, for example, nudge intervention information.
  • the nudge intervention information in this case may include, for example, the presence or absence of a push notification on the previous day, the scheduled intervention nudge, the presence or absence of the nudge intervention on the previous day, and the behavior change result on the previous day.
  • a scheduled intervention nudge is a nudge that was intended (or actually) to be intervened by the user when the user opened the push notification.
  • the acquisition unit 11 may acquire user information, which is information related to user attributes, as information included in the above-mentioned various information.
  • User information includes, for example, a user ID that uniquely identifies a user, gender, age, family structure, and the like.
  • the acquisition unit 11 obtains the weather, temperature, day of the week, smartphone usage time of the user, whether or not the user has been vaccinated against COVID-19, the user's Information such as the corona infection status, the number of corona infected people on the previous day, the user's close contact flag, etc. may be acquired.
  • Acquisition unit 11 stores the acquired information in storage unit 12 .
  • the wear feature quantity generation unit 16 generates a wear feature quantity representing nudge wear information for each of a plurality of types of nudges based on the nudge intervention information.
  • FIG. 9 is a diagram for explaining wear feature quantity generation.
  • FIG. 9A shows information (log) including nudge intervention information acquired by the acquisition unit 11 and stored in the storage unit 12 . Such a log is created each time a nudge is pushed.
  • the wear feature amount generator 16 converts the log into the wear feature amount shown in FIG. 9(b).
  • a feature amount f_1 indicating the user ID
  • a feature amount f_2 indicating the number of days since the previous push notification
  • a feature amount indicating the number of days since the last push notification indicating the number of days since the last push notification.
  • the same feature amount as nudge A is defined for each nudge.
  • the cumulative number of times of each nudge may be defined as a feature amount.
  • the behavior change estimation unit 17 estimates the behavior change probability, which is the probability that the user will take the action prompted by the nudge.
  • the behavior change estimating unit 17 estimates a behavior change probability for each of the multiple types of nudges based on the wear feature amounts for the multiple types of nudges.
  • the behavior change estimation unit 17 generates an estimation model (machine learning model) for estimating the probability of behavior change for each of multiple types of nudges based on the wear feature amount and user information.
  • FIG. 10 is a diagram for explaining behavior change estimation, and more specifically, a diagram for explaining generation of an estimation model for estimating a behavior change probability.
  • the behavior change estimating unit 17 includes a wear feature amount, a user ID, which is user information, gender, age, and family structure, and other information (a feature amount likely to contribute to behavior).
  • the behavior change estimation unit 17 estimates a behavior change probability for each of multiple types of nudges based on the above estimation model.
  • the behavior change estimation unit 17 estimates the behavior change probability for each nudge of the target user by inputting data similar to the learning data to each estimation model for each nudge.
  • the nudge selection unit 18 selects a nudge to recommend to the target user based on the behavior change probability. Specifically, the nudge selection unit 18 selects the nudge with the highest probability of behavior modification as the nudge to be recommended to the user.
  • FIG. 11 is a diagram illustrating nudge selection.
  • the behavior change probability estimated from the nudge A model is 0.6
  • the behavior change probability estimated from the nudge B model is 0.5
  • the behavior change probability estimated from the nudge C model is 0.4.
  • the nudge selection unit 18 selects the nudge A with the highest probability of behavior modification as the recommended nudge.
  • the behavior change probability estimated from the nudge B model is 0.4
  • the behavior change probability estimated from the nudge B model is 0.5
  • the behavior change probability estimated from the nudge C model is 0.4.
  • the nudge selection unit 18 selects the nudge B with the highest probability of behavior modification as the recommended nudge. In this way, the behavior change probabilities are updated based on the nudge intervention information and the optimal nudge is selected.
  • FIG. 12 is a sequence diagram illustrating nudge recommendation processing.
  • step S101 various information including nudge intervention information is acquired from the application 101 by the acquisition unit 11 and stored in the storage unit 12 (step S101).
  • the wear feature amount generation unit 16 acquires the log data including the nudge intervention information stored in the storage unit 12 (step S102), and the wear feature amount of each nudge is generated based on the log data ( step S103).
  • the behavior change estimation unit 17 acquires various feature amounts such as user information stored in the storage unit 12 (step S104), and the wear feature amount of each nudge is acquired from the wear feature amount generation unit 16. (step S105), and the behavior change probability (ease of behavior change) of each nudge is estimated based on the wear feature amount, user information, etc. (step S106).
  • the nudge selection unit 18 acquires the behavior modification probability (estimated effect value) of each nudge, and selects a nudge to be recommended to the target user from among a plurality of nudges based on the behavior modification probability (step S108). ). Then, the selected nudge is recommended to the target user via the application 101 (step S109). The nudge intervention information about the recommended nudge is stored in the storage unit 12 via the acquisition unit 11 (step S110), and is used as log data for generation of the subsequent wear feature amount.
  • the nudge management device 10A includes a behavior change estimation unit 17 that estimates a behavior change probability that is the probability that the user will take the action urged by the nudge, and a nudge that selects a nudge to recommend to the user based on the behavior change probability. and a selection unit 18, wherein the storage unit 12 stores information on a plurality of types of nudges, and the wear derivation unit expresses nudge wear information for each of the plurality of types of nudges based on the nudge intervention information.
  • the behavior change estimation unit 17 estimates a behavior change probability for each of the plurality of types of nudges based on the wear feature amounts for the plurality of types of nudges.
  • the nudge selection unit 18 selects the nudge with the highest probability of behavior modification as the nudge to be recommended to the user.
  • the behavior change probability is estimated for each of a plurality of types of nudges based on the wear feature quantity representing the nudge wear information, and the nudge with the highest behavior change probability is selected as the nudge for recommendation. It is possible to estimate the probability of behavior change with higher accuracy by adding information, and to suitably select a nudge that can prompt behavior change of the user.
  • the behavior change estimation unit 17 generates an estimation model for estimating a behavior change probability for each of a plurality of types of nudges based on the wear feature amount and user information that is information related to user attributes, and based on the estimation model , a behavior change probability may be estimated for each of a plurality of types of nudges. In this way, an estimation model is generated for each nudge in consideration of the wear feature amount and the user information, and the behavior change probability of each nudge is estimated based on the estimation model. can estimate the behavioral change probability of
  • the nudge management devices 10 and 10A may physically be configured as computer devices including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, and the like.
  • the term "apparatus” can be read as a circuit, device, unit, or the like.
  • the hardware configuration of the nudge management devices 10 and 10A may be configured to include one or more of each device shown in the figure, or may be configured without some of the devices.
  • Each function of the nudge management devices 10 and 10A is performed by loading predetermined software (programs) on hardware such as the processor 1001 and the memory 1002. and by controlling reading and/or writing of data in the storage 1003 .
  • the processor 1001 for example, operates an operating system and controls the entire computer.
  • the processor 1001 may be configured with a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic device, registers, and the like.
  • CPU central processing unit
  • the control functions of the acquisition unit 11 and the like may be implemented by the processor 1001 .
  • the processor 1001 also reads programs (program codes), software modules and data from the storage 1003 and/or the communication device 1004 to the memory 1002, and executes various processes according to them.
  • programs program codes
  • software modules software modules
  • data data from the storage 1003 and/or the communication device 1004
  • the program a program that causes a computer to execute at least part of the operations described in the above embodiments is used.
  • control functions of the acquisition unit 11 and the like may be implemented by a control program stored in the memory 1002 and operated by the processor 1001, and other functional blocks may be similarly implemented.
  • processor 1001 may be executed by two or more processors 1001 simultaneously or sequentially.
  • Processor 1001 may be implemented with one or more chips.
  • the program may be transmitted from a network via an electric communication line.
  • the memory 1002 is a computer-readable recording medium, and is composed of at least one of, for example, ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), RAM (Random Access Memory), etc. may be
  • ROM Read Only Memory
  • EPROM Erasable Programmable ROM
  • EEPROM Electrical Erasable Programmable ROM
  • RAM Random Access Memory
  • the memory 1002 may also be called a register, cache, main memory (main storage device), or the like.
  • the memory 1002 can store executable programs (program codes), software modules, etc. for implementing a wireless communication method according to an embodiment of the present invention.
  • the storage 1003 is a computer-readable recording medium, for example, an optical disc such as a CDROM (Compact Disc ROM), a hard disk drive, a flexible disc, a magneto-optical disc (for example, a compact disc, a digital versatile disc, a Blu-ray (registered disk), smart card, flash memory (eg, card, stick, key drive), floppy disk, magnetic strip, and/or the like.
  • Storage 1003 may also be called an auxiliary storage device.
  • the storage medium described above may be, for example, a database, server, or other suitable medium including memory 1002 and/or storage 1003 .
  • the communication device 1004 is hardware (transmitting/receiving device) for communicating between computers via a wired and/or wireless network, and is also called a network device, network controller, network card, communication module, etc., for example.
  • the input device 1005 is an input device (for example, keyboard, mouse, microphone, switch, button, sensor, etc.) that receives input from the outside.
  • the output device 1006 is an output device (eg, display, speaker, LED lamp, etc.) that outputs to the outside. Note that the input device 1005 and the output device 1006 may be integrated (for example, a touch panel).
  • Each device such as the processor 1001 and the memory 1002 is connected by a bus 1007 for communicating information.
  • the bus 1007 may be composed of a single bus, or may be composed of different buses between devices.
  • nudge management devices 10 and 10A include hardware such as microprocessors, digital signal processors (DSPs), ASICs (Application Specific Integrated Circuits), PLDs (Programmable Logic Devices), FPGAs (Field Programmable Gate Arrays), etc. , and part or all of each functional block may be implemented by the hardware.
  • processor 1001 may be implemented with at least one of these hardware.
  • LTE Long Term Evolution
  • LTE-A Long Term Evolution-Advanced
  • SUPER 3G IMT-Advanced
  • 4G 5G
  • FRA Full Radio Access
  • W-CDMA registered trademark
  • GSM registered trademark
  • CDMA2000 Code Division Multiple Access 2000
  • UMB Universal Mobile Broad-band
  • IEEE 802.11 Wi-Fi
  • IEEE 802.16 WiMAX
  • IEEE 802.20 UWB (Ultra-Wide Band)
  • Bluetooth® other suitable systems and/or extended next generation systems based on these.
  • Input and output information may be saved in a specific location (for example, memory) or managed in a management table. Input/output information and the like can be overwritten, updated, or appended. The output information and the like may be deleted. The entered information and the like may be transmitted to another device.
  • the determination may be made by a value represented by one bit (0 or 1), by a true/false value (Boolean: true or false), or by numerical comparison (for example, a predetermined value).
  • notification of predetermined information is not limited to being performed explicitly, but may be performed implicitly (for example, not notifying the predetermined information). good too.
  • Software whether referred to as software, firmware, middleware, microcode, hardware description language or otherwise, includes instructions, instruction sets, code, code segments, program code, programs, subprograms, and software modules. , applications, software applications, software packages, routines, subroutines, objects, executables, threads of execution, procedures, functions, and the like.
  • software, instructions, etc. may be transmitted and received via a transmission medium.
  • the software can be used to access websites, servers, or other When transmitted from a remote source, these wired and/or wireless technologies are included within the definition of transmission media.
  • data, instructions, commands, information, signals, bits, symbols, chips, etc. may refer to voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, light fields or photons, or any of these. may be represented by a combination of
  • information, parameters, etc. described in this specification may be represented by absolute values, may be represented by relative values from a predetermined value, or may be represented by corresponding other information. .
  • Communication terminals are defined by those skilled in the art as mobile communication terminals, subscriber stations, mobile units, subscriber units, wireless units, remote units, mobile devices, wireless devices, wireless communication devices, remote devices, mobile subscriber stations, access terminals, It may also be called a mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client or some other suitable term.
  • any reference to the elements does not generally limit the quantity or order of those elements. These designations may be used herein as a convenient method of distinguishing between two or more elements. Thus, references to first and second elements do not imply that only two elements may be employed therein, or that the first element must precede the second element in any way.

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JP2021086280A (ja) * 2019-11-26 2021-06-03 日本電信電話株式会社 状況判定装置、方法およびプログラム
WO2021106099A1 (ja) * 2019-11-27 2021-06-03 日本電信電話株式会社 行動支援情報生成装置、方法およびプログラム
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JP2021086280A (ja) * 2019-11-26 2021-06-03 日本電信電話株式会社 状況判定装置、方法およびプログラム
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