Method and device for estimating a user's state and controlling a device based on an estimated state

Abstract

A method relating in particular to estimating a current state of a user of one electronic device among several, the device being used to perform an activity including a cognitive component and the estimated state characterizing a risk to the user with regard to said activity, the method comprising: - obtaining (300) the results of a test, the test comprising, for each of a plurality of iterations: . presentation of an indication to aid in choosing an instruction to continue or stop a process according to which continuing implies a chance of additional gain and a risk of losing a cumulative gain; . receiving, from the user, an instruction to continue or stop the process; . executing the received instruction; - estimating (305) the user's current state based on the results and an estimator comprising a behavioral model of the user. Figure for the abstract: Figure 3

Description

Title of the invention: Method and device for estimating a user's state and controlling a device based on an estimated state technical field

[0001] The invention relates to a method and device for estimating the state of a user, in particular a user of an electronic device used to perform an activity including a cognitive component, based on their behavior, by evaluating risk-taking in situations of uncertainty, using a Bayesian model. It finds particular applications in fields where the type of behavior of a user in the face of a risky situation must be determined, especially for managing that risk. Previous techniques

[0002] There are many situations in which it is important to assess an individual's state, for example, to suggest, advise against, or even prohibit certain activities, such as granting access to a computer service, granting access to that service while drawing the individual's attention to a particular risk, or denying access to that service. Such a state can be assessed from among several predetermined point states, such as states of anxiety, fatigue, well-being, etc. Other states may relate to how an individual reacts to particular stimuli, for example, according to their ability to cope with a risky situation, that is, a situation over which they have only partial control. Such states are generally relatively constant over time.For example, services whose access is suggested, whose access is subject to an alert message, or whose access is blocked may include online betting or gambling.

[0003] Solutions exist for estimating an individual's state from among several predetermined states. Commonly used solutions are based on the analysis of results from questionnaires administered to the individual. Although such approaches make it possible to precisely target states to be estimated according to expected behaviors associated with these states, by adapting the questions to these behaviors, with the questions being asked directly or indirectly, the results obtained may be biased by the individual, who may answer spontaneously, but who may also be tempted to understand the purpose of the questions asked in order to try to provide the expected answers, according to their expectations, or may have a limited capacity for introspection.

[0004] Although these solutions often prove useful, they are difficult to implement because they are restrictive for users. Furthermore, they are not suitable for predicting a potential vulnerability of an individual to a particular activity, for example a risk of addiction to gambling, in particular to games of chance, making it possible to alert them, or even to limit their access to such activities.

[0005] There is therefore a need for a simple solution to implement in order to estimate an individual's state and predict a potential vulnerability to a particular activity. Description of the invention

[0006] The aim of the invention is thus to propose a solution enabling the estimation of the state of an individual confronted with a situation which he only partially controls in order to alert him to a risk linked to the pursuit of a particular activity, including a cognitive component.

[0007] According to one aspect, the invention relates to a computer method for estimating a current state of a user of at least one electronic device from among a plurality of predetermined states, the at least one device being used to perform an activity comprising at least one cognitive component and the estimated state characterizing a risk to said user with regard to said activity, the method comprising:

[0008] - obtaining results from a test, the test comprising, for each of a plurality of iterations,

[0009] . presentation of at least one indication to aid in the choice of an instruction of continuation or discontinuation of a process whereby a continuation involves a chance of additional gain and a risk of loss of cumulative gain;

[0010] . receipt, from the user, of an instruction to continue or stop the process;

[0011] . execution of said instruction received;

[0012] - estimation of the user's current state based on said results and a estimator including a behavioral model of the user.

[0013] The method according to the invention thus makes it possible to estimate the state of an individual and identify a potential vulnerability of a user in the face of an activity including a cognitive component such as gambling.

[0014] According to one feature, the estimator further comprises at least one classifier.

[0015] According to one aspect, the invention relates to a computer method for estimating a current state of a user of at least one electronic device from among a plurality of predetermined states, the at least one device being used to perform an activity comprising at least one cognitive component and the estimated state characterizing a risk to said user with regard to said activity, the method comprising:

[0016] - obtaining test results, the test comprising, for each of a plurality of iterations,

[0017] . receipt, from the user, of an instruction to continue or stop a process according to which a lawsuit involves a chance of additional gain and a risk of loss of cumulative gain;

[0018] . execution of said instruction received;

[0019] - estimation of the user's current state based on said results and a estimator comprising a user behavioral model and a classifier.

[0020] The method according to the invention thus makes it possible to estimate the state of an individual and identify a potential vulnerability of a user in the face of an activity including a cognitive component such as gambling.

[0021] According to one feature, the method further includes a presentation of at least one indication to aid in choosing an instruction to continue or stop the process.

[0022] According to one feature, at least one indication includes a probability of additional gain or loss of cumulative gain related to the instruction considered.

[0023] According to one feature, at least one indication includes a tendency for at least one other user to select a continue or stop instruction.

[0024] According to one characteristic, said results include at least one of the following elements: a total cumulative gain, a number of iterations, a number of iterations that led to a gain, a number of iterations that led to a loss, a number of instructions that led to obtaining a cumulative gain, a test execution time and an average time between two iterations.

[0025] According to one feature, the method further includes the presentation of a warning, access to a service, personalization of a service or prohibition of access to a service based on said estimated current state.

[0026] According to one characteristic, the method includes a preliminary parameterization of said estimator, the results obtained being initial results, the parameterization comprising:

[0027] - creation of at least one model of said test;

[0028] - obtaining second results of said test using said at least one model;

[0029] - obtaining at least one expected state of a corresponding current user state to the aforementioned second results; and

[0030] - determination of at least one parameter of the estimator according to the second results and the expected result.

[0031] According to one characteristic, the process comprises:

[0032] - creation of a plurality of models of said test;

[0033] - obtaining third results of said test; and

[0034] - the selection of a plurality model according to the third results and results obtained from each model of said plurality.

[0035] The device according to the invention thus makes it possible to estimate the state of an individual and identify a potential vulnerability to an activity including a cognitive component such as gambling.

[0036] A computer program, implementing all or part of the process described above, installed on pre-existing equipment, is in itself advantageous, since it makes it possible to estimate the state of an individual confronted with a situation that he only partially controls in order to alert him to a risk related to the pursuit of a particular activity, including a cognitive component.

[0037] Thus, the present invention also relates to a computer program comprising instructions for implementing the process described above, when this program is executed by a processor.

[0038] This program can use any programming language (for example, an object-oriented language or other) and be in the form of interpretable source code, partially compiled code or fully compiled code.

[0039] Another aspect relates to a non-transient storage medium for a computer-executable program, comprising a set of data representing one or more programs, said one or more programs comprising instructions for, when said one or more programs are executed by a computer comprising a processing unit operationally coupled to memory means and an input / output interface module, to execute all or part of the process described above. Brief description of the drawings

[0040] Other objects, features and advantages of the invention will become apparent from the following description, given solely by way of non-limiting example, and made with reference to the accompanying drawings in which:

[0041] [Fig.1] schematically represents a graphical interface used to perform a modified type B ART test to estimate the state of an individual, according to embodiments of the invention;

[0042] [Fig.2] illustrates an example of steps in a process according to embodiments to configure an estimator adapted to estimate a state of an individual and identify a potential vulnerability to an activity including a cognitive component;

[0043] [Fig.3] illustrates an example of steps in a process according to embodiments to estimate the state of an individual and identify a potential vulnerability to an activity including a cognitive component;

[0044] [Fig.4] illustrates an example of steps in a modified BART-type test according to embodiments; and

[0045] [Fig.5] illustrates an example of a device that can implement a process according to particular embodiments of the invention. Detailed description of at least one embodiment

[0046] A detailed description of particular embodiments of the invention will be given below, with reference to the drawings in which the same references identify the same structural elements in each of the figures.

[0047] The inventors determined that the early detection of potential vulnerability to activities involving one or more cognitive components, such as gambling, could be achieved by assessing an individual's state based on their behavior and evaluating risk-taking under uncertainty. They also determined that behavior could be analyzed based on the results of B ART (Balloon Analogue Risk Task) tests, which assess an individual's propensity to take risks (or risk aversion), modified to allow for the early detection of potential vulnerability to an activity involving a cognitive component, such as gambling.

[0048] It is recalled here that in a BART-type test, the participant must collect as many points as possible by inflating balloons and preventing them from bursting. The larger the balloon, the more points the participant earns. However, the balloon can burst at any time, causing the participant to lose all the points accumulated for that balloon. Thus, the participant can decide at any time to save their points by stopping inflating the balloon (before it bursts) and moving on to the next balloon (if there are any balloons left to inflate).

[0049] In summary, this test consists of presenting, for example via a computer graphical interface, a series of balloons to be inflated. Points can be earned with each pump or inflation action (representing the virtual addition of a unit quantity of air to the balloon, corresponding, for example, to pressing a particular key or clicking the mouse in a specific location on the interface). If the balloon bursts, the points associated with inflating the balloon are lost. Conversely, if the individual stops inflating the balloon before it bursts, the accumulated points are considered permanently acquired (they are, where applicable, added to previously acquired points). The number of balloons is predetermined.

[0050] Thus, a BART-type test makes it possible to measure risk-taking behavior in situations of uncertainty among participants for whom, as in real-world situations, risk is rewarded up to a certain point where taking an additional risk leads to poorer results (participants are not informed of the balloons' breaking points; they must learn and adapt their behavior). (as they gain experience with the contingencies of the task). The playful aspect of these tests allows for high acceptance, particularly among users wishing to test themselves. Furthermore, these tests are reliable and valid, and have good predictive power. They help to limit potential biases well-known in questionnaires (e.g., social desirability bias). This type of test is used, for example, to correlate behaviors with general risk-taking behaviors, including addictions such as the use of crack cocaine, cocaine, marijuana, alcohol, or tobacco.

[0051] According to particular embodiments of the invention, a modified type B ART test is carried out to obtain results representative of the behavior of a user confronted with a situation which he only partially controls, combining risk of loss and chance of gain, by evaluating risk-taking in a situation of uncertainty.

[0052] In particular embodiments, a Bayesian model, which may be associated with a classifier, is used to analyze behavior and relevant parameters in order to associate a participant's state with test results. The estimated state of the user can then be used to alert them to a particular risk or to control a device, for example, to suggest, authorize, or deny access to a service. By way of illustration, a user's state characterizing a potential vulnerability to games, particularly gambling, can, for example, be determined based on the tool known as the Canadian Problem Gambling Index. This tool was developed by the Canadian Centre on Substance Abuse and Alcoholism. Scores are assigned to each question in a multiple-choice questionnaire, and a state is determined based on the final score and predetermined thresholds.

[0053] The parameters of the modified BART type test may include, in particular, the number of balloons to be inflated, the resistance(s) of the balloons (there may be several types of balloons), the bursting point of the balloons, as well as contextual information (or feedback) such as the probability that the balloon being inflated will burst, the behavior of other participants in similar situations, etc.

[0054] Figure 1 schematically represents a graphical interface used to perform a modified BART test to assess an individual's state, according to embodiments of the invention. As illustrated, the graphical interface 100 includes a representation of a balloon 105 that can be inflated using a pump 110, here activated by means of a mouse and a clickable inflation area 115. According to the illustrated example, the number of points that can be earned per pump stroke is displayed on the pump. Here, by giving a pump For each pump, the user is likely to earn 3 points. The number of points earned with each pump can be constant or vary depending on parameters such as the balloon's inflation level. The number of points accumulated for inflating the balloon is displayed here in area 120, corresponding to a temporary bank balance. These points are earned if the user stops inflating the balloon and confirms the inflation, here by clicking on the confirmation area 125 with the mouse. In certain specific embodiments, several pumps can be performed in a group. In this case, the user can, for example, enter the number of pumps to be performed beforehand and then confirm this number.

[0055] In some embodiments, assistance may be provided to the user in the form of feedback. By way of illustration, and as shown with reference 130, this may include a risk level (or probability) of the balloon bursting, for example, expressed as a percentage, and / or a trend indicator representing the behavior of other users faced with similar situations, either continuing the inflation process (arrow pointing upwards here) or stopping it (arrow pointing downwards). Again, by way of illustration, a warning such as "Caution, risk of bursting" may also be displayed. This may, for example, be based on a risk level of balloon bursting and thresholds. Other indicators may be presented to the user.

[0056] If the balloon bursts, the points accumulated during its inflation are lost.

[0057] On the contrary, when the user stops inflating the balloon before it bursts, the points accumulated for its inflation are, where applicable, added to the points previously acquired and stored in a permanent bank, the amount of which is displayed here in zone 135.

[0058] As illustrated, the interface 100 can include a gauge 140 of test progress, the cursor of which evolves as the balloons are inflated, from the first balloon to the last balloon to be inflated (according to the example illustrated, the test includes 54 balloons).

[0059] The results are recorded as the test progresses. They may include, in addition to the number of points earned, the number of balloons inflated, the number of inflations (or pump strokes) per balloon before validation, the average number of inflations (or pump strokes) before validation, the number of inflations (or pump strokes) per balloon before bursting, the average number of inflations (or pump strokes) before bursting, the duration of the test, time deltas (i.e. for example the speed at which the participant clicks to inflate the balloon or the average time between two pump strokes) and / or the feedback information indicated.

[0060] Figure 2 illustrates an example of steps in a process according to embodiments for configuring an estimator suitable for estimating an individual's state and identifying potential vulnerability to an activity including a cognitive component.

[0061] According to the example illustrated in [Fig.2], the estimator configuration includes the creation of modified BART-type test models.

[0062] As illustrated, a first step (step 200) aims to identify potentially relevant parameters for modeling modified BART type tests and for estimating an individual's state and identifying potential vulnerability to an activity including a cognitive component.

[0063] Like the standard parameters of BART-type tests, the parameters of modified BART-type tests include, in particular, the number of balloons to be inflated by an individual. It should be noted that this number must be high enough to achieve a certain level of quality in the results and low enough not to discourage the individual and to encourage them to complete the test. Furthermore, the number of balloons must be sufficient so that the results are based on a genuine risk-taking and not on uncertainty (participants discovering the balloons' resistance based on indicators such as the average number of pumps before bursting, or the average number of pumps before bursting related to balloon characteristics such as color, etc.). The number of balloons can be between 20 and 100, for example, between 45 and 65, and can be as high as 54.

[0064] These standard parameters for BART-type tests may also include a balloon burst distribution law. This may be, for example, a normal or random distribution, using a uniform or geometric distribution.

[0065] In particular embodiments, standard parameter values ​​for BART-type tests are defined by default and can be modified by an administrator. Also in particular embodiments, the standard parameters for BART-type tests remain constant over time (i.e., they are not changed between different tests), so that the test results are comparable to one another.

[0066] To assess an individual's state and identify potential vulnerability to an activity with a cognitive component, additional parameters are necessary. For example, potentially relevant parameters for modeling modified BART-type tests might include providing a hint to help a user decide whether to continue inflating a balloon or stop, such as indicating the probability of the balloon bursting or the reaction of other participants faced with similar situations. This also removes an uncertainty component that complicates the analysis of the results. Again, for example, the parameters used to estimate assessing an individual's state and identifying potential vulnerability to an activity involving a cognitive component may include at least some of the results obtained by modified BART-type tests, including the number of points earned, the number of balloons inflated, the number of balloons burst, the average number of inflations (or pumps) before validation, the average number of inflations (or pumps) before bursting, the duration of the test and / or time deltas.

[0067] In a subsequent step, one or more modified BART-type test models are created and validated. These test models may be based on similar or different mathematical approaches and on identical or different parameters. Some of these models can directly estimate a user state (step 205). Other models can be combined with other mathematical tools, for example, classifiers, to estimate a user state (steps 205' and 215). By way of illustration, eight test models can be created, for example, with the following characteristics, based on the mathematical models presented in the appendix and called Model A, Model B, Model C, and Model D:

[0068] Test model 1: - Mathematical basis: Model A - Number of samples used to parameterize the model: 160 - Number of balloons: 54 - Balloon resistance: 3 possible resistances - Balloon bursting point: random probability of bursting with a uniform distribution - Additional parameters: none,

[0069] Test model 2: - Mathematical basis: Model B and Model D - Number of samples used to parameterize the model: 160 - Number of balloons: 54 - Balloon resistance: 3 possible resistances - Balloon bursting point: random probability of bursting with a uniform distribution - Additional parameters: none,

[0070] Test model 3: - Mathematical basis: Model C - Number of samples used to parameterize the model: 160 - Number of balloons: 54 - Balloon resistance: 3 possible resistances - Balloon bursting point: random probability of bursting with a uniform distribution - Additional parameters: display of the probability of balloon bursting for each inflation,

[0071] Test model 4: - Mathematical basis: Model A - Number of samples used to parameterize the model: 160 - Number of balloons: 54 - Balloon resistance: 3 possible resistances - Balloon bursting point: predetermined bursting point - Additional parameters: none,

[0072] Test model 5: - Mathematical basis: Model B and Model D - Number of samples used to parameterize the model: 160 - Number of balloons: 54 - Balloon resistance: 3 possible resistances - Balloon bursting point: predetermined bursting point - Additional parameters: none,

[0073] Test model 6: - Mathematical basis: Model A - Number of samples used to parameterize the model: 160 - Number of balloons: 54 - Balloon resistance: 3 possible resistances - Balloon bursting point: predetermined bursting point - Additional parameters: display of the number of points that could be earned for each ball before moving on to the next ball,

[0074] Test Model 7: - Mathematical basis: Model B and Model D - Number of samples used to parameterize the model: 160 - Number of balloons: 54 - Balloon resistance: 3 possible resistances - Balloon bursting point: predetermined bursting point - Additional parameters: display of the number of points that could be earned for each ball before moving on to the next ball, and

[0075] Test model 8: - Mathematical basis: Model C - Number of samples used to parameterize the model: 160 - Number of balloons: 54 - Balloon resistance: 3 possible resistances - Balloon bursting point: random bursting probability with a geometric distribution - Additional parameters: display of the probability of balloons bursting for each inflation.

[0076] These test models can be parameterized based on previously obtained results from modified BART-type tests, performed by participants to form a training database, using test conditions similar to those of the models to be parameterized. These results are, for example, stored in a database. The results used to parameterize the models are chosen according to the test conditions and the characteristics of the test models, for example, to take into account the presentation of a help indication to assist a user in choosing whether to continue inflating a balloon or to stop. The validation of a test model can also be performed using previously obtained results; for example, a test model is validated if the average error is less than a given threshold.

[0077] Mathematical models are, for example, based on models known as BSR models (Bayesian Sequential Risk-Taking in Anglo-Saxon terminology) or on the Lejuez model. BSR models are described in particular in the following documents: Wallsten et al, Wallsten T. S, Lejuez C. W, and Pleskac T. J (2005) Modeling Behavior in a Clinically Diagnostic Sequential Risk-Taking Task, Zhou et al, Zhou Ran, Myung JL, and Pitt MA (2021) Revisiting learning in the balloon analogue risk task and Coon and Lee, Coon J and Lee M D. (2021) A Bayesian Method for Measuring Risk Propensity in the Balloon Analogue Risk Task. The Lejuez model is notably described in the document Lejuez et al, Lejuez C. W, Richards J. B, Read J. P, Kahler C. W, Ramsey S. E, Stuart G. L, Strong D. R and Brown RA (2002) Evaluation of a Behavioral Measure of Risk Taking: The Balloon Analogue Risk Task.

[0078] Of course, these test models are provided only as an illustration; many other test models can be created and evaluated, for example on the basis of other mathematical models, by varying the number of balloons, etc.

[0079] As indicated by the dotted arrow, the creation and validation of a test model is an iterative process allowing the parameterization to be refined and converged towards an error level below a desired threshold.

[0080] When a model needs to be combined with another mathematical tool such as a classifier to estimate a user state, such a classifier is created and validated (step 215) for each relevant model previously created and validated. The classifier is designed to analyze the previously created and validated models in order to create classes (or user profiles) and establish links between test results. Modified BART test results and expected states for these results. For example, results from modified BART tests can be obtained for given participants who have also been analyzed, for instance, via a form such as the Canadian Problem Gambling Index. As illustrated, the results used from modified BART tests and the corresponding states can be stored in database 210. In other embodiments, they are stored in a separate database. Again, as indicated by the dashed arrow, the creation and validation of a classifier is an iterative process.

[0081] By way of illustration, the classifier can be of the Bayesian mixture model type and defined by Model D in the appendix.

[0082] In a subsequent step, a test model, possibly combined with a classifier, called an estimator, is selected (step 220). This is, for example, the test model or the test model combined with a classifier that gives the best results and whose error rate is below a given threshold. As indicated by the dashed arrows, the step of creating and validating a test model, the step of creating and validating a classifier, or the steps of creating and validating a model and a classifier can be repeated, for example, if the error rate of the classifier is greater than or equal to a given threshold.

[0083] Figure 3 illustrates an example of steps in a process according to embodiments for estimating an individual's state and identifying potential vulnerability to an activity including a cognitive component.

[0084] As illustrated, a first step involves executing a test (step 300), for example, a modified BART test. This is, for example, the test described with reference to [Fig. 1]. This test is offered, for example, to a user wishing to subscribe to or access a service, such as an online gambling offer. It can be offered on the device used by the user wishing to subscribe to or access the service, for example, in the form of a web interface, with the test steps being executed remotely. Alternatively, these steps can be executed on the user's device, for example, in the form of a plugin.

[0085] According to particular embodiments, the execution conditions of this test are similar to those of the test model selected as estimator, possibly combined with a classifier.

[0086] The results obtained are then analyzed to estimate a user state (step 305), for example using the test model selected in step 220 of [Fig. 2] as an estimator, possibly combined with a classifier. In particular embodiments, the state is a state characterizing a potential vulnerability to an activity including a cognitive component, for example gambling. line, or a state characterizing an absence of potential vulnerability to such activity.

[0087] A test can then be carried out (step 310) to determine whether the estimated state characterizes a potential vulnerability to an activity including a cognitive component. If so, specific measures can be taken (step 315), for example to alert the user to a particular risk, control a device, for example to authorize, suggest or deny access to a service, or personalize a service or a service environment.

[0088] If, on the other hand, the estimated state indicates an absence of potential vulnerability to an activity involving a cognitive component, information can be provided to the user (step 320), for example in the form of a displayed message. Such information can be used, in particular, for educational purposes, to provide explanations about the meaning of the test the user has performed.

[0089] Figure 4 illustrates an example of steps of a modified type B ART test according to embodiments.

[0090] As illustrated, a first step (step 400) has as its object the initialization of the indices i and j, the index i representing an index on the balloons to be inflated and the index j representing an index on the pump strokes during the inflation of the balloon i, and of the total account cpt_tot and temporary account cmp_tmp, the total account cpt_tot representing the total gain of a participant and the temporary account cpt_tmp representing a potential gain related to the balloon being inflated.

[0091] In a subsequent step, and according to the illustrated embodiment, a decision aid is provided (step 405) to assist a user in choosing whether to continue inflating a balloon or to confirm the balloon's inflation in order to acquire the accumulated points. For example, such an aid could represent a probability p^^ or a risk of the balloon bursting during inflation, for instance expressed as a percentage, related to the kth pump strokes of the lieth balloon being inflated. This risk could, for example, be estimated as follows:

[0092] * kl

[0093] where k is the number of pump strokes required to inflate the balloon in question (i.e., balloon 1) and n is the maximum possible number of pump strokes on the balloon (n can vary depending on the balloon's resistance; for example, a value of n can be associated with each level of balloon resistance). For example, the value of n can be 15 for the first balloon resistance, 20 for the second balloon resistance, and 25 for the third balloon resistance.

[0094] The selection aid is then provided to the user (step 410), for example in the form of an indication displayed on a graphical interface, and the number The number of points that can be earned with an additional pump is determined. In some embodiments, it is displayed. For example, it can be constant and predetermined or vary depending on the number of pumps already given to the ball in question.

[0095] The user is then prompted to enter their choice.

[0096] Upon receiving the user's command or instruction (step 415), for example via a keyboard keystroke or by clicking on a specific area, a test is performed (step 420) to determine whether the command is a continue command to give an additional pump stroke or a stop (validation) command to cease inflating the balloon and acquire the points accumulated for inflating the balloon in question. If the received command is a continue command to give an additional pump stroke, a virtual pump stroke is given to determine whether the balloon bursts or not (step 425). According to particular embodiments, the balloon burst points are pre-generated (i.e., a number of pump strokes corresponding to the burst point is associated with each balloon) according to a distribution law and the maximum possible number of pump strokes on the balloon (e.g., related to its resistance).Therefore, to determine whether the balloon bursts after this additional pump, simply compare the number of pumps performed on the balloon in question with its bursting point.

[0097] If, after the additional pump, the balloon has not burst, the temporary count is updated (step 430) by adding the gain from the pump (referenced as gain in [Fig. 4]) to the previous value of the temporary count (cpt_tmp). The index on the number of pumps is incremented by one. The algorithm then loops back to step 405 to propose a new pump.

[0098] If, after the additional pump stroke, the balloon bursts, the temporary count is reset to zero and the index on the number of pump strokes is reset to one (step 435). A test is then performed to determine if all the balloons have been inflated (step 440), that is, if the index i is less than the number of balloons to be inflated (denoted nb_tot). If all the balloons have been inflated, the test is terminated. Conversely, if there are still balloons to be inflated, the index i is incremented by one (step 445) and the algorithm loops back to step 405 to inflate the next balloon.

[0099] If the received command is a stop (or acknowledgment) command to cease inflating the balloon and to accrue points for inflating that balloon, the total count is updated (step 450) by adding the value of the temporary count to the previous value of the total count. The temporary count is then reset to zero, and the index on the number of pump strokes is reset to one. The algorithm then loops back to step 440 to determine if all the balloons have been inflated.

[0100] It is observed here that other implementations can be envisaged. In particular, other information can be presented to the user to guide them in their choices. Similarly, other calculation formulas can be used.

[0101] Figure 5 illustrates an example of a data processing device that can be used to implement, at least partially, embodiments of the invention, in particular the steps described with reference to Figures 2, 3 and 4.

[0102] The device 500 preferably comprises a communication bus 502 to which the following are connected:

[0103] - a central processing unit or microprocessor 504 (CPU, abbreviation for Central Processing Unit (in Anglo-Saxon terminology);

[0104] - a 506-bit read-only memory (ROM) Anglo-Saxon) which may include the operating system and programs such as "prog", "progl" and "prog2";

[0105] - a random access memory or cache memory 508 (RAM, acronym for Random Access Memory (in Anglo-Saxon terminology) comprising registers adapted to store variables and parameters created and modified during the execution of the aforementioned programs; and

[0106] - a 510 communication interface connected to a communication network distributed 512, for example a wireless communication network and / or a local communication network, the interface being capable of transmitting and receiving data, including to and from other devices.

[0107] Optionally, device 500 may also include the following elements:

[0108] - a 518 hard disk drive capable of containing the programs "prog", "progl" and "prog2" the aforementioned and the data processed or to be processed according to the invention;

[0109] - an input / output interface 520 to which a keyboard 522 can be connected, a mouse 524 and / or any other pointing device such as a light pen, a touch screen, a voice recognition device, a gesture recognition device, a camera, a microphone or a remote control allowing the user to interact with the programs according to the invention;

[0110] - a graphics card and a sound card or an audio / video card 514 connected to a screen and 516 speakers; and / or

[0111] - a 530 removable storage media reader 532 such as a memory card.

[0112] The communication bus enables communication and interoperability between the various elements included in or connected to the device 500. The bus representation is not limiting and, in particular, the central unit is capable of communicating instructions to any element of the device 500 directly or via another element of the device 500.

[0113] The executable code of each program enabling the programmable device to implement the processes according to the invention can be stored, for example, in the hard drive 518 or in read-only memory 506.

[0114] According to one variant, the executable code of the programs can be received via the communication network 512, via interface 510, to be stored in the same way as described above.

[0115] More generally, the program(s) may be loaded into one of the storage means of device 500 before being executed.

[0116] The central processing unit 504 will command and direct the execution of the instructions or portions of software code of the program(s) according to the invention, instructions which are stored in the hard drive 518 or in the read-only memory 506 or in the other aforementioned storage elements. Upon power-up, the program(s) stored in non-volatile memory, for example the hard drive 518 or the read-only memory 506, are transferred to the random access memory 508, which then contains the executable code of the program(s) according to the invention, as well as registers for storing the variables and parameters necessary for the implementation of the invention.

[0117] Of course, the present invention is not limited to the embodiments described above by way of example. It extends to other variants.

[0118] Depending on the embodiment chosen, certain acts, actions, events, or functions of each of the methods described in this document may be performed or occur in a different order than described, or may be added, merged, or not performed or occur, as appropriate. Furthermore, in some embodiments, certain acts, actions, or events are performed or occur concurrently rather than sequentially. In addition, while the examples provided are based on B ART type tests, other similar tests may be used.

[0119] Although described through a number of detailed embodiments, the proposed device, system, and method include various variants, modifications, and improvements that will be obvious to those skilled in the art, it being understood that these various variants, modifications, and improvements form part of the scope of the invention, as defined by the following claims. Furthermore, different aspects and features described above may be implemented together, separately, or substituted for one another, and all the different combinations and subcombinations of aspects and features form part of the scope of the invention. In addition, some systems and equipment described above may not incorporate all the modules and functions described for the preferred embodiments.

[0120] APPENDIX

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[0144] Mathematical model A (probability of a balloon bursting) According to this model, the probability, denoted n belief, that |e k1™6 balloon bursts at each rk The "coup de pompe" (energy drain) is expressed by the following relationship: V'A'-1 JM™™* OR (1 - 7?) rcPæscntc the a priori belief about the probability of the balloon bursting, EkX success represents the number of pump strokes on the k-1 balloons that do not have i=0n' burst and Ek l pumps represents the total number of inflations on the k-1 balloons. i=(ji The other components of the model are as follows: Wk npump__1____ &kl ~ ^tW^)] Or wk represents the optimal number of pumps at the kieme balloon. y+ represents the propensity for risk, [3 represents behavioral consistency, P^mp represents the probability of inflating the kieme balloon on the lieme time (1 pump stroke), dk is an indication of whether or not the kieme balloon has burst and nk represents the number of pump strokes performed for the kime balloon. Mathematical model A allows for direct estimation of a user's state without the need to present an indication to help with the choice of continuing or stopping the inflation of the balloon. Mathematical model B (probability of a balloon bursting) This model allows us to estimate the number of pumps that the ith participant intended to perform for the kth ball according to the following relationship: y'ik~Gaussian+ ( ) _\WJ Jik^uk 'ik~ \ bkîf y'ik^bk Or

[0145] yik represents the number of pump strokes actually performed by the ith participant to inflate the kiem balloon,

[0146] y represents the number of pump strokes that the ith participant intended to perform to inflate the kieme balloon and

[0147] bk is the bursting point of the kieme balloon.

[0148] Key parameters of the model here are risk propensity p and behavioral consistency [3.

[0149] Mathematical model C (displaying geometric and uniform probability)

[0150] This model is based on prospect theory, assuming that the probability of bursting the thieth balloon on the kiem pump stroke is defined by the following relation:

[0151] pfer.«=10()|

[0152] where n represents the number of possible pump strokes for the liith balloon.

[0153] The value function used here is as follows:

[0154] [ rC for gains relative to a status quo r > 0 v(r) = { y - [ - XI r I for losses related to a status quo r < 0

[0155] where

[0156] r represents the gain,

[0157] v(r) represents a value function,

[0158] g+ = g represents the propensity to take risks and

[0159] 1 represents risk aversion

[0160] It is observed that since the probability of a balloon bursting is known each time a user can choose to perform an additional pump, the evaluation of the options of inflating or not inflating the balloon before giving or not giving an additional pump can be used. Thus, it is possible to define the expected inflation and deflation values ​​on the kth balloon on the liem occasion, as follows:

[0161] ( ffpburstyr .phur^l.

[0162] 1^ = 0

[0163] where

[0164] U^HP represents the subjective value of inflating and

[0165] represents the subjective value of stopping swelling. Kl

[0166] A probabilistic choice rule, representing the probability of performing an additional pump to inflate the balloon and the probability of stopping, such as that given below, may be used:

[0167] pki ( Pump, stop ) =

[0168] Considering that b (which represents behavioral consistency) is greater than zero and that TJS^P is equal to zero, as stated above, the probability of performing kl an extra pump rather than stopping inflating the balloon may be defined as follows:

[0169] nPumP _ e^,v *kl - eB^+i

[0170] kl 1+e^« Mathematical model D (classification)

[0171] According to this example, the model assumptions are as follows:

[0172] n-DDir (1,1)

[0173] ( ~ categoriccd ( nv tt2 )

[0174] formativePriors

[0175] with, for example, as described above, with reference to model B:

[0176] y'^Gaussian+ ( p? ) [° 177 i

Claims

Demands

1. A computer method for estimating a current state of a user of at least one electronic device from among a plurality of predetermined states, the at least one device being used to perform an activity comprising at least one cognitive component and the estimated state characterizing a risk to said user with respect to said activity, the method comprising: - obtaining (300) results from a test, the test comprising, for each of a plurality of iterations, . presentation (410) of at least one indication to aid in choosing an instruction to continue or stop a process according to which continuing involves a chance of additional gain and a risk of losing a cumulative gain; . receiving (415), from the user, an instruction to continue or stop the process; .execution (420) of said instruction received; - estimation (305) of the current state of the user based on said results and an estimator comprising a behavioral model of the user.

2. A method according to claim 1, wherein the estimator further comprises at least one classifier.

3. A computer method for estimating a current state of a user of at least one electronic device from among a plurality of predetermined states, the at least one device being used to perform an activity comprising at least one cognitive component and the estimated state characterizing a risk to said user with respect to said activity, the method comprising: - obtaining (300) results from a test, the test comprising, for each of a plurality of iterations, . receiving (415) from the user an instruction to continue or stop a process whereby continuing involves a chance of additional gain and a risk of loss of cumulative gain; . executing said received instruction; - estimating (305) the current state of the user based on said results and an estimator comprising a behavioral model of the user and a classifier.

4. A method according to claim 3, further comprising a presentation (410) of at least one indication to aid in choosing an instruction to continue or stop the process.

5. A method according to claim 1, claim 2 or claim 4, wherein at least one indication includes a probability of additional gain or loss of cumulative gain related to the instruction considered.

6. A method according to claim 1, claim 2, claim 4 or claim 5, wherein at least one indication includes a tendency for at least one other user to select a continue or stop instruction.

7. A method according to any one of claims 1 to 6, wherein said results comprise at least one of the following: a total cumulative gain, a number of iterations, a number of iterations that resulted in a gain, a number of iterations that resulted in a loss, a number of instructions that resulted in a cumulative gain, a test execution time, and an average time between two iterations.

8. A method according to any one of claims 1 to 7, further comprising the presentation (315) of a warning, access to a service, personalization of a service or prohibition of access to a service based on said estimated current state.

9. A method according to any one of claims 1 to 8, comprising prior parameterization of said estimator, the results obtained being first results, the parameterization comprising: - creation (205, 205') of at least one model of said test; - obtaining second results of said test using said at least one model; - obtaining at least one expected state of a current state of the user corresponding to said second results; and - determining at least one parameter of the estimator according to the second results and the expected result.

10. A method according to claim 9, comprising: - creating a plurality of models of said test; - obtaining third results of said test; and - selecting (220) a model of the plurality according to the third results and the results obtained from each model of said plurality. 22

11. Device comprising at least one data processing unit and one memory unit, the device being configured to implement each of the steps of the method according to any one of claims 1 to 10.

12. Computer program comprising instructions for carrying out each of the steps of the process according to any one of claims 1 to 10 when the program is executed by a computer.

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