Information processing method and information processing system

Abstract

This information processing device executes: processing for acquiring biological data indicating a temporal change in biological information of a user; processing for deriving a curvature based on the biological data; and processing for determining a thought state related to thought of the user on the basis of at least one of an average value and a minimum value of the curvature.

Description

Information processing method and information processing system

[0001] The present disclosure relates to techniques for determining thought states.

[0002] Non-Patent Document 1 discloses that the state of the brain can be depicted as a dynamical picture of a brain that transitively emerges and moves through different global states one after another.

[0003] Non-Patent Document 2 discloses that spatiotemporal changes in global electrical activity (collective potential) within the brain or spatiotemporal changes in electrical activity (local collective potential) in brain regions where many cells are locally concentrated are significant, that these spatiotemporal changes are not simply random changes but appear as transient transition phenomena with certain characteristics, and that these transient transition phenomena can be interpreted in terms of chaotic wandering, which is a typical transition process in high-dimensional dynamical systems.

[0004] Hiroshi Fujii, "An introduction to brain perception and consciousness from a dynamics perspective," Abstracts of General Lectures and Special Lectures 2007, Vol. 2007, Autumn Meeting, No. 1, pp. 95-103 [Retrieved February 19, 2024], Internet <URL: https: / / doi.org / 10.11429 / emath1996.2007.Autumn-Meeting1_95> Ichiro Tsuda, "Mathematics of the brain: from the dynamical aspect of the brain," Journal of the Mathematical Society, Vol. 58, No. 2, pp. 133-150 (2006), [Retrieved February 19, 2024], Internet <URL: https: / / eprints.lib.hokudai.ac.jp / jp / dspace / handle / 2115 / 11329

[0005] However, the conventional techniques shown in Non-Patent Documents 1 and 2 merely state that changes in the brain's state can be visualized by focusing on chaotic wanderings, but do not disclose any specific algorithm for calculating the brain's thought state, which makes it impossible to accurately determine the user's thought state.

[0006] The present disclosure has been made to solve such problems, and aims to provide a technology for accurately determining a user's mental state.

[0007] An information processing method in one aspect of the present disclosure is an information processing method in a computer, which includes acquiring biometric data indicating changes in a user's biometric information over time, deriving a curvature based on the biometric data, and determining the user's thought state based on at least one of the average value and minimum value of the curvature.

[0008] According to the present disclosure, the user's mental state can be determined with high accuracy.

[0009] 16 is a diagram conceptually illustrating the chaotic itinerancy of the brain. FIG. 17 is a configuration diagram of an information processing system according to embodiment 1. FIG. 18 is a diagram illustrating an example of the arrangement of a plurality of electrodes constituting a sensor device unit. FIG. 19 is a diagram illustrating the relationship between chaotic itinerancy and changes in curvature over time. FIG. 20 is a flowchart illustrating an example of processing by the information processing system according to embodiment 1. FIG. 21 is a graph illustrating how curvature data is smoothed. FIG. 22 is a diagram illustrating how a Hankel matrix is ​​generated from an electroencephalogram. FIG. 23 is a diagram illustrating how curvature is calculated from a Hankel matrix. FIG. 24 is a diagram illustrating the results of an experiment on creativity. FIG. 25 is another diagram illustrating the results of an experiment on creativity. FIG. 26 is a diagram illustrating an example of a display screen for creativity assessment results. FIG. 27 is a diagram conceptually illustrating the chaotic itinerancy of the brain. FIG. 28 is a flowchart illustrating an example of processing by an information processing system according to embodiment 2. FIG. 29 is a diagram explaining an experiment conducted to verify the relationship between the presence or absence of a change in thought and curvature. FIG. 29 is a graph illustrating the experimental results of the experiment shown in FIG. 15. FIG. 29 is a diagram illustrating the data structure of a recording unit according to embodiment 2. FIG. 29 is a diagram illustrating a display screen for notifying a change in thought. FIG. 29 is a configuration diagram of an information processing system according to embodiment 3. FIG. 29 is a flowchart illustrating an example of processing by the information processing system according to embodiment 3.

[0010] (Findings underlying the present disclosure) Figure 1 is a diagram conceptually illustrating the chaotic itinerary of the brain. As shown in the above-mentioned prior art, the brain state changes according to the chaotic itinerary, and therefore the brain state transitions randomly among multiple attractors.

[0011] Curve C1 conceptually shows changes in the brain's state. When a user recalls a specific memory or thinks of an idea, an attractor appears on curve C1. An attractor is an area that has the property of attracting the surrounding trajectories. In this example, three attractors appear, corresponding to ideas A, B, and C.

[0012] In this example, three attractors corresponding to ideas A, B, and C transition in this order, and then the brain state returns to the attractor corresponding to idea A. When the attractor transitions, the curvature of curve C1 becomes smaller than when the brain state remains at a single attractor. As such, it can be seen that the curvature changes accordingly as the brain state changes. The present inventors have therefore discovered that the brain state can be accurately determined from changes in the curvature of biometric information from electroencephalograms, and have conceived the various aspects of the present disclosure.

[0013] (1) An information processing method in one aspect of the present disclosure is an information processing method in a computer, which includes acquiring biometric data indicating changes in a user's biometric information over time, deriving a curvature based on the biometric data, and determining the user's thought state based on at least one of an average value and a minimum value of the curvature.

[0014] According to this configuration, the curvature is derived from the user's biometric data, and the user's mental state is determined based on at least one of the average and minimum values ​​of the curvature. Therefore, this configuration can accurately determine the user's mental state.

[0015] (2) In the information processing method described in (1) above, the biological information may correspond to an electroencephalogram of the user.

[0016] According to this configuration, since brain waves are used as biological information, the user's mental state can be determined with higher accuracy.

[0017] (3) In the information processing method described in (1) or (2) above, determining the state of mind may include determining the creativity of the user.

[0018] According to this configuration, the creativity of the user can be determined with high accuracy.

[0019] (4) In the information processing method described in (3) above, determining the creativity may include determining that the user is more creative the greater the number of local minimum values, or determining that the user is more creative the lower the average value.

[0020] When creativity is being exercised, the transition between attractors becomes more intense. The transition between attractors can be captured by the minimum values ​​of the curvature of biometric data. Therefore, it can be said that the greater the number of minimum values, the more creative the user is. With this configuration, the greater the number of minimum values ​​of the curvature, the more creativity is determined to be exercised, so the exercise of creativity can be determined with high accuracy. Furthermore, as the number of minimum values ​​increases, the average value of the curvature decreases. With this configuration, the lower the average value of the curvature, the more creativity is determined to be exercised, so the exercise of creativity can be determined with high accuracy.

[0021] (5) In the information processing method described in (1) or (2) above, determining the thought state may include determining whether the thought in the user's cognitive process has changed based on the average value of the curvature.

[0022] There is a significant difference in curvature between when there is a thought change in the cognitive process and when there is no thought change. With this configuration, thought change is determined based on the average curvature, so thought change can be determined with high accuracy.

[0023] (6) In the information processing method described in (5) above, determining whether or not a change in thought has occurred in the cognitive process may include determining at least one of whether or not the information stored in the user's working memory has changed and whether or not the object of the user's attention has changed.

[0024] According to this configuration, it is possible to determine at least one of a change in information stored in the working memory and a change in the object of the user's attention.

[0025] (7) In the information processing method described in (5) or (6) above, when the average value of the curvature exceeds a preset threshold, it may be determined that a change in thought has occurred in the cognitive process.

[0026] The curvature when there is a thought change in the cognitive process is larger than the curvature when there is no thought change. With this configuration, when the average value of the curvature exceeds the threshold, it is determined that there has been a thought change. Therefore, it is possible to accurately determine whether there has been a thought change.

[0027] (8) In the information processing method described in (2) above, the method may further include detecting a first electrical signal from a first position on the user's head using a first electrode corresponding to the first position, detecting a second electrical signal from the second position using a second electrode corresponding to a second position on the head, deriving a potential difference between the first position and the second position based on the first electrical signal and the second electrical signal, and generating the biometric information corresponding to the brain waves based on the potential difference.

[0028] To measure brain waves, a reference potential is required, and therefore electrical signals must be acquired at at least two positions on the user's head. In this configuration, biometric information corresponding to brain waves is generated based on the potential difference between the first electrical signal and the second electrical signal obtained from the first electrode and the second electrode. Therefore, biometric information corresponding to brain waves can be obtained with high accuracy.

[0029] (9) In the information processing method described in (3) above, if it is determined that the user is not demonstrating creativity, a control signal that changes the control content may be output to at least one of lighting equipment, air conditioning equipment, audio equipment, video equipment, and fragrance release device.

[0030] According to this configuration, the environment of a user who is unable to express his or her creativity can be adjusted using at least one of lighting equipment, air conditioning equipment, audio equipment, video equipment, and a fragrance release device, thereby enabling the user to express his or her creativity.

[0031] (10) In the information processing method described in (9) above, if it is determined that the user is not demonstrating the creativity, a control signal may be output to the lighting device to lower the color temperature of the lighting device that illuminates the user and increase the illuminance.

[0032] According to this configuration, by adjusting the environment of a user who is not able to demonstrate his or her creativity, the user can be made to demonstrate his or her creativity.

[0033] (11) In the information processing method described in (8) above, the first electrode and the second electrode may be attached to earphones.

[0034] According to this configuration, it is possible to acquire the user's biometric data while reducing the burden on the user.

[0035] (12) In the information processing method described in (3) above, a creativity index for each time window may be calculated by detecting the number of local minima in each time window while shifting the time window in the curvature data showing the change in curvature over time, and a graph showing the change in the creativity index over time may be displayed on a display device based on the calculated creativity index for each time window.

[0036] According to this configuration, it is possible to have the user or the user's administrator confirm whether or not creativity is being exercised.

[0037] (13) In the information processing method described in (3) above, a moving average value of curvature data indicating the change in curvature over time may be further calculated, and a graph indicating the change in curvature over time may be displayed on a display device based on the moving average value, and the graph may be displayed in a manner that makes it possible to distinguish periods in which the moving average value exceeds a threshold value from other periods.

[0038] This configuration allows the user to check whether or not a change in thinking has occurred.

[0039] (14) In the information processing method described in (5) above, when it is determined that a change in thought has occurred in the cognitive process, a control signal that changes the control content may be output to at least one of lighting equipment, air conditioning equipment, audio equipment, video equipment, and a fragrance release device.

[0040] According to this configuration, the environment of a user whose thinking has changed in the cognitive process can be adjusted using at least one of lighting equipment, air conditioning equipment, audio equipment, video equipment, and a fragrance release device, allowing the user to express their creativity.

[0041] (15) An information processing method in another aspect of the present disclosure is an information processing method in a computer, comprising: acquiring biometric data indicating changes in a user's biometric information over time; deriving a curvature based on the biometric data; determining whether at least one of an average value and a minimum value of the curvature satisfies a predetermined first condition; and, if the first condition is satisfied, transmitting a control signal to equipment located in a space where the user is present, wherein the equipment includes at least one of air conditioning equipment, lighting equipment, and audio equipment, and the control signal includes information for changing the control content performed by the equipment.

[0042] (16) In the information processing method described in (15) above, the equipment may include the air conditioning equipment, and the transmitting may include outputting the control signal to the air conditioning equipment, the control signal including the information to raise the room temperature, when it is determined that the first condition is satisfied.

[0043] (17) In the information processing method described in (15) or (16) above, the device may include a lighting device, and the transmitting may include outputting, to the lighting device, the control signal including the information to reduce illuminance when it is determined that the first condition is satisfied.

[0044] (18) In yet another aspect of the present disclosure, an information processing method is an information processing method in a computer, comprising: acquiring biometric data indicating temporal changes in a user's biometric information; deriving a curvature based on the biometric data; determining whether at least one of an average value and a minimum value of the curvature satisfies a first condition; and, if it is determined that the second condition is satisfied, transmitting a control signal to a device located in a space where the user is present, wherein the device includes at least one of an audio device, a fragrance releasing device, and a vehicle control device, and the control signal includes information that changes the control content performed by the device.

[0045] (19) In the information processing method described in (18) above, the device may include the fragrance-releasing device, and the transmitting may include outputting the control signal including the information that causes the fragrance-releasing device to emit a mint scent when it is determined that the first condition is satisfied.

[0046] (20) In yet another aspect of the present disclosure, an information processing system includes a processor that acquires biometric data indicating changes in a user's biometric information over time, derives a curvature based on the biometric data, and determines the user's thought state based on at least one of an average value and a minimum value of the curvature.

[0047] According to this configuration, it is possible to provide an information processing system that can accurately determine the user's state of mind.

[0048] (21) In yet another aspect of the present disclosure, an information processing system includes a first electrode corresponding to a first position on a user's head, a second electrode corresponding to a second position on the head, and a processor, wherein the processor causes the first electrode to detect a first electrical signal from the first position, causes the second electrode to detect a second electrical signal from the second position, derives a potential difference between the first position and the second position based on the first electrical signal and the second electrical signal, generates biometric information corresponding to the user's brain waves based on the potential difference, derives a curvature based on the biometric information, and determines the user's mental state based on at least one of an average value and a minimum value of the curvature.

[0049] According to this configuration, it is possible to provide an information processing system that can accurately determine the user's thought state using electroencephalograms.

[0050] The present disclosure can also be realized as an information processing program that causes a computer to execute each of the characteristic configurations included in such an information processing method. Needless to say, such a computer program can be distributed on a computer-readable non-transitory recording medium such as a CD-ROM or via a communication network such as the Internet.

[0051] Note that each of the embodiments described below represents a specific example of the present disclosure. The numerical values, shapes, components, steps, and step orders shown in the following embodiments are merely examples and are not intended to limit the present disclosure. Furthermore, among the components in the following embodiments, components that are not described in the independent claims that represent the highest concept are described as optional components. Furthermore, in all of the embodiments, the respective contents can be combined.

[0052] First Embodiment First, terms used in this embodiment will be defined.

[0053] Chaotic itinerary refers to the phenomenon of irregular transitions between different chaotic states.

[0054] Chaos refers to a phenomenon that follows rules described by mathematical formulas, but whose behavior appears irregular at first glance.

[0055] Creativity refers to the uniqueness of a user's answer on a certain topic. A unique answer is one that gives the impression of being unusual, interesting, fun, innovative, different from the ordinary, or clever. For example, if the topic is "unique ways to use a scrubbing brush," answers that show uses that are far removed from the usual uses, such as "using it as a pin stand," "using it to stimulate acupressure points on the body," or "using it to resemble a hedgehog," would be unique answers.

[0056] Determining the user's creativity may include determining the user's divergent thinking state. That is, determining whether the user is creative may include determining whether the user is in a divergent thinking state, and determining the user's level of creativity may include determining the user's level of divergent thinking.

[0057] The curvature indicates the amount of change in a differential value or the like in a space (high-dimensional space) formed by decomposing biological data such as electroencephalograms into a plurality of waveforms.

[0058] Working memory is the brain function used to temporarily hold and manipulate information.

[0059] Working memory switching refers to the switching of the content stored in working memory to another content.

[0060] A change in the target of attention refers to a change in the target of attention of a user. For example, when a user switches from one task to another, the user's attention is changed.

[0061] 2 is a configuration diagram of an information processing system 1 according to the first embodiment. The information processing system 1 determines a user's creativity using a curvature based on the user's biometric data. The information processing system 1 includes an input device 10, a sensor device 20, a control device 30, and a display device 40.

[0062] The input device 10 is configured, for example, by a communication interface and includes an input unit 11. The input unit 11 receives biometric data of a user transmitted from a sensor device and inputs the received biometric data to the control device 30. An example of the input device 10 is a communication interface such as Bluetooth (registered trademark) or a wireless LAN.

[0063] The sensor device 20 is a device that measures biometric data of a user. In this embodiment, the sensor device 20 is assumed to be configured as an electroencephalogram (EEG) measuring apparatus. The sensor device 20 includes a sensor device unit 21 and a sensor communication unit 22. The sensor device unit 21 includes a plurality of electrodes that are worn on the user's head. Specifically, the sensor device unit 21 includes at least a first electrode and a second electrode. The first electrode is worn at a first position on the user's head and detects a first electrical signal from the first position. The second electrode is worn at a second position on the user's head and detects a second electrical signal from the second position. The first and second electrical signals are voltage signals.

[0064] Furthermore, the sensor device unit 21 includes a processing circuit. The processing circuit derives a potential difference between the first position and the second position based on the first electrical signal and the second electrical signal. The processing circuit generates biometric information corresponding to brain waves based on the derived potential difference. This potential difference indicates brain waves. The biometric information is information indicating brain wave values. The biometric data is brain wave values, i.e., data indicating temporal changes in biometric information. The sensor device unit 21 generates biometric data by measuring brain wave values ​​at a predetermined sampling period. This processing circuit may include an amplifier. In this case, the amplifier amplifies the derived potential difference. Therefore, the biometric information indicates the amplified brain wave value. Brain waves take values ​​within a range of, for example, plus or minus 50 μV.

[0065] In this embodiment, the biological information is described as being electroencephalograms, but the biological information may be composed of biological information other than electroencephalograms. For example, the biological information may be electrocardiogram, electrooculogram, or SPL (sensory perspiration loss). In this case, the sensor device is composed of an electrocardiogram measuring device, an electrooculogram measuring device, and an SPL measuring device.

[0066] A camera or a smart watch may be used as the sensor device unit 21. The sensor device unit 21 has a photodetector (not shown) that detects light (reflected light and / or diffused light) from the user.

[0067] This photodetector may be, for example, a photodetector that detects and converts light incident on a detection surface at a signal level corresponding to the intensity of the received light, or light incident at a light intensity equal to or greater than a threshold, into an electrical signal. The photodetector may be, for example, an image sensor that captures light incident on a detection surface (e.g., an imaging surface) and generates an image signal (electrical signal) at a signal level corresponding to the intensity of the received light. As the image sensor, a solid-state imaging element such as a CCD (Charge Coupled Device) or a CMOS (Complementary Metal-Oxide Semiconductor) can be appropriately used. Furthermore, the image sensor performs analog processing such as noise reduction, amplification, and A / D conversion on the generated electrical signal (analog signal) and outputs a digital electrical signal (digital signal). Note that some or all of this analog processing may be implemented in at least one other circuit (e.g., a processor) provided downstream of the image sensor as an analog front end (AFE).

[0068] As an example, the sensor device unit 21 further includes at least one processor that generates biometric information of the user based on an electrical signal (measurement signal) output from the photodetector. Note that the biometric information may be generated by the at least one processor based on the electrical signal output from the photodetector. In other words, the sensor device unit 21 may be configured as a device that outputs an electrical signal based on light from the user detected by the photodetector.

[0069] The photodetector may be sensitive not only to visible light but also to near-infrared light (NIR) or infrared light (IR). That is, the light from the user detected by the sensor device unit 21 may be visible light, near-infrared light, or infrared light. The photodetector may further include a light source (not shown) that irradiates the user with irradiating light including light of a wavelength corresponding to the light-receiving sensitivity of the image sensor, such as infrared light. In this case, the photodetector may detect return light (e.g., a field luminous flux) returning from the user due to the irradiating light.

[0070] The electrocardiogram measuring device generates information corresponding to the electrocardiogram. The information corresponding to the electrocardiogram may be the signal strength of an electrical signal transmitted to the myocardium, measured using electrodes placed on the skin surface of the chest, the amount of change in the signal strength, or a value based on these. For example, the bio-measuring device that acquires the information corresponding to the electrocardiogram may be an electrocardiogram monitor, a smartwatch, a smartphone, a PC, a tablet PC, a server device, or the like that cooperates with the electrocardiogram monitor.

[0071] The sensor communication unit 22 is configured with a communication interface for realizing predetermined communication such as Bluetooth (registered trademark), wireless LAN, etc. The sensor communication unit 22 transmits the biometric data generated by the sensor device unit 21 to the input device 10.

[0072] The control device 30 is configured, for example, by a computer. The control device 30 includes a communication unit 31, a control unit 32, a recording unit 33, a curvature calculation unit 34, and a determination unit 35. The control unit 32, the curvature calculation unit 34, and the determination unit 35 are configured by a processor such as a central processing unit (CPU). The control unit 32, the curvature calculation unit 34, and the determination unit 35 are realized by the CPU executing an information processing program. However, this is just one example, and the control unit 32, the curvature calculation unit 34, and the determination unit 35 may also be configured by dedicated hardware circuits.

[0073] The communication unit 31 is configured with a communication interface for realizing communication in accordance with communication standards such as Bluetooth (registered trademark) and wireless LAN. The communication unit 31 receives biometric data transmitted from the input device 10. The communication unit 31 transmits the determination result by the determination unit 35 to the display device 40.

[0074] The control unit 32 is responsible for controlling the overall operation of the control device 30 .

[0075] The recording unit 33 is configured with a non-volatile rewritable storage device such as a solid state drive, a hard disk drive, etc. The recording unit 33 stores the user's biometric data, the curvature calculated from the biometric data, a threshold value used to determine whether or not the user is creative, and the like.

[0076] The curvature calculation unit 34 derives a curvature based on biometric data. The curvature calculation unit 34 first generates a Hankel matrix by shifting a time window of a predetermined width by a predetermined step width for the biometric data. Next, the curvature calculation unit 34 compresses the dimensions of the Hankel matrix by performing singular value decomposition (SVD) on the Hankel matrix. Next, the curvature calculation unit 34 calculates differential values ​​from the first to the (i+1)th order for each column of the dimensionally compressed Hankel matrix. Next, the curvature calculation unit 34 applies Gram-Schmidt orthonormalization to the differential values ​​from the first to the (i+1)th order to calculate an (i+1)-dimensional orthonormalized vector. Next, the curvature calculation unit 34 calculates an i-dimensional curvature from the (i+1)-dimensional orthonormalized vector. As a result of the above, curvature data indicating the time change of the i-dimensional curvature based on the biometric data is calculated. This process will be described in detail later.

[0077] The determination unit 35 determines the mental state of the user based on at least one of the average value and the minimum value of the curvature calculated by the curvature calculation unit 34. In this embodiment, creativity is used as the mental state, and therefore the determination unit 35 determines the creativity of the user.

[0078] Specifically, the determination unit 35 determines that the user is exhibiting more creativity as the number of minimum values ​​in the curvature data increases. Alternatively, the determination unit 35 determines that the user is exhibiting more creativity as the average value of the curvature data decreases. More specifically, the determination unit 35 calculates a higher creativity index as the number of minimum values ​​in the curvature data increases. Alternatively, the determination unit 35 calculates a higher creativity index as the average value of the curvature data decreases. The creativity index may be a normalized value within a range of, for example, 0 to 1. Furthermore, the determination unit 35 may determine that the user is exhibiting creativity when the creativity index is equal to or greater than a threshold, and may determine that the user is not exhibiting creativity when the creativity index is less than the threshold.

[0079] The display device 40 is a display device with a communication function. The display device 40 may be a portable computer such as a smartphone or tablet terminal, or may be a display device of the control device 30. The display device 40 is held by at least one of the user whose creativity is to be judged and the user's administrator. The display device 40 includes a display unit 41, a communication unit 42, and an operation unit 43. The display unit 41 is a display device such as a liquid crystal display or an organic EL display. The display unit 41 displays the judgment results made by the judgment unit 35. The communication unit 42 is a communication interface for realizing communication in accordance with communication standards such as Bluetooth (registered trademark) and wireless LAN. The communication unit 42 receives the judgment results transmitted from the control device 30. The operation unit 43 accepts various instructions from the user. The operation unit 43 is composed of various input devices such as a keyboard, a mouse, and a touch panel.

[0080] FIG. 3 is a diagram showing an example of the arrangement of the multiple electrodes 204 constituting the sensor device unit 21. In FIG. 3, the electrode arrangement method according to the International 10-20 System is adopted. Each of the multiple electrodes 204 is given an electrode name. The electrode names are mainly composed of a combination of letters and numbers. In the electrode names, F stands for frontal, T for temporal, C for central, P for parietal, and O for occipital. The electrode names of the electrodes 204 provided on the left earlobe 202 and the right earlobe 203 are A1 and A2. When viewed from directly above the user's head 200, the electrodes 204 located on the left side are given odd numbers in their electrode names, and the electrodes 204 located on the right side are given even numbers in their electrode names. The more the electrodes 204 are located on the outer side of the head 200, the larger the numbers in their electrode names. The electrode 204 arranged on the center line of the head 200 is given the electrode name Z (zero).

[0081] The electrode that actually records EEG activity is called the probe electrode or active electrode. The electrode that serves as a reference is called the reference electrode. In the common reference electrode method, electrodes (A1, A2) are used as the reference electrodes. In this embodiment, the average potential of electrodes (A1) and (A2) is used as the reference potential. This is just an example, and the average value of the potentials derived from all electrodes may also be used as the reference potential.

[0082] The electrodes 204 may be shaped, for example, as dish electrodes or disk electrodes. The electrodes 204 may be made of, for example, silver / silver chloride electrodes or platinum subdural electrodes. The brain waves of each electrode 204 are obtained by appropriately amplifying the potential difference between two electrodes 204 attached to the scalp with an amplifier. In this embodiment, the brain waves of each electrode 204 are obtained by appropriately amplifying the potential difference between the potential of each electrode 204 and a reference potential with an amplifier.

[0083] Note that the above-mentioned example of electrode 204 placement and method of acquiring brain waves are merely examples, and other methods may be adopted as long as they are capable of acquiring brain waves at multiple positions on the scalp.

[0084] FIG. 4 illustrates the relationship between chaotic itineraries and changes in curvature over time. In FIG. 4, the upper diagram conceptually illustrates the chaotic itineraries of the brain shown in FIG. 1 , and the lower diagram illustrates a graph 401 showing the temporal progression of electroencephalographic curvature. Point P1 on graph 401 indicates the curvature at point 301 on curve C1, and point P2 on graph 401 indicates the curvature at point 302 on curve C1. When the attractor corresponding to idea C transitions to the attractor corresponding to idea A, a minimum value of curvature appears, as shown at point P1. Similarly, when the attractor corresponding to idea A transitions to the attractor corresponding to idea B, a minimum value of curvature appears, as shown at point P2. Therefore, detecting the minimum value of curvature allows detection of a change in the attractor, and ultimately, a change in thinking. Furthermore, when a user is demonstrating creativity, the transition of the attractor becomes more intense, and the number of minimum values ​​of curvature can be used to evaluate the user's creativity. Therefore, in this embodiment, the creativity of a user is evaluated based on the number of local minima in the curvature or the average value of the curvature. Note that in Fig. 4, the attractor transitions in the order of ideas A, B, C, and A, but this is just an example, and the transition of the attractor occurs randomly, so the order is not necessarily the same.

[0085] FIG. 5 is a flowchart showing an example of processing by the information processing system 1 according to the first embodiment.

[0086] In step S1, the communication unit 31 of the control device 30 acquires the biological data transmitted from the sensor device unit 21. Here, brain waves detected by the multiple electrodes 204 are acquired as the biological data. Therefore, the biological data is composed of brain waves from multiple channels. The measurement period of the biological data may be a predetermined fixed period (e.g., 20 seconds). This fixed period may be repeated.

[0087] Next, in step S2, the curvature calculation unit 34 generates a Hankel matrix from the biological data acquired in step S1 and calculates curvature data by singular value decomposition of the Hankel matrix. Here, curvature data expressed in multiple dimensions is calculated from electroencephalograms of multiple channels.

[0088] Next, in step S3, the determination unit 35 smooths the curvature data calculated in step S2. As a smoothing method, a method of calculating a moving average value of the curvature data or a method of applying a low-pass filter to the curvature data can be adopted. Here, the curvature data is smoothed for each of the multiple dimensions.

[0089] FIG. 6 is a graph showing how curvature data is smoothed. The first row from the top shows one-channel or five-channel electroencephalograms. The measurement period for these electroencephalograms is 20 seconds. In this embodiment, multiple-channel electroencephalograms, such as five channels, are used, but this is merely an example, and one-channel electroencephalograms may also be used. FIG. 6 shows how curvature data obtained from one-channel electroencephalograms is smoothed. The second row is a graph showing curvature data from one-channel electroencephalograms. The third row is a graph showing the curvature data shown in the second row after smoothing. Smoothing removes high-frequency components from the curvature data. Here, the curvature data is smoothed using a low-pass filter with a cutoff frequency of 8 Hz. Note that the cutoff frequency of 8 Hz is merely an example, and any suitable value other than 8 Hz can be used. This results in a smoother waveform for the curvature data. The fourth row is a graph showing the minimum and average values ​​of the curvature data calculated from the curvature data shown in the third row. Circles indicate the minimum values ​​of the curvature data. The horizontal lines indicate the average values ​​of the curvature data. By smoothing the curvature data in this way, unnecessary high-frequency noise is removed, and the accuracy of creativity assessment can be improved.

[0090] Next, in step S4, the control unit 32 obtains a selection instruction from the user as to whether to use the minimum value or the average value to evaluate the user's creativity. This selection instruction is given using, for example, the operation unit 43.

[0091] If the user selects the minimum value, the process proceeds to step S5, and if the user selects the average value, the process proceeds to step S7.

[0092] Next, in step S5, the determination unit 35 detects minimum values ​​from the curvature data smoothed in step S3, thereby calculating the number of minimum values. The minimum values ​​are calculated, for example, by differentiating the curvature data. Here, the number of minimum values ​​of the curvature data is calculated for each of the multiple dimensions.

[0093] Next, in step S6, the judgment unit 35 calculates a creativity index using the number of local minima calculated in step S5. When creativity is strongly demonstrated, the attractor transitions more vigorously, resulting in an increase in the number of local minima. Therefore, the judgment unit 35 calculates the creativity index so that the value increases as the number of local minima increases and decreases as the number of local minima decreases. Note that the judgment unit 35 may use the total number of local minima in all dimensions as the creativity index, or may use the average value of the number of local minima in all dimensions as the creativity index.

[0094] In step S7, the determination unit 35 calculates the average value of the curvature data smoothed in step S3. Note the fourth line in Fig. 6. As the number of minimum values ​​increases, the average value of the curvature data decreases due to the influence of the minimum values. Therefore, the average value of the curvature data can also be used as an index of creativity.

[0095] Next, in step S8, the determination unit 35 calculates the creativity index so that the lower the average value of the curvature data calculated in step S7, the higher the value. This average value may be the average value of the curvature data in all dimensions.

[0096] 5, the creativity index is calculated based on either the number of minimum values ​​or the average value of the curvature data, but the present disclosure is not limited to this. For example, a first creativity index calculated from the number of minimum values ​​of the curvature data and a second creativity index calculated from the average value of the curvature data may be combined to calculate a final creativity index. In this case, the determination unit 35 may calculate the average or weighted average of the first and second indices as the final creativity index.

[0097] The process of calculating the curvature will be described in detail below.

[0098] FIG. 7 is a diagram showing how a Hankel matrix is ​​generated from an electroencephalogram. For ease of explanation, the following describes processing for one channel of electroencephalogram. The curvature calculation unit 34 applies a time window 702 of a predetermined width (N-n) to an electroencephalogram 701. N is a value indicating the final sample point of the electroencephalogram and depends on the electroencephalogram measurement period. n is a value indicating how many steps the time window 702 is shifted by and is set in advance by the analyst. n<N.

[0099] The curvature calculation unit 34 fits the beginning of the time window 702 to the start time of the electroencephalogram 701, extracts N-n sample points of the electroencephalogram 701, and sets these as elements of the first row of the Hankel matrix. Next, the curvature calculation unit 34 shifts the time window 702 one step to the right, extracts N-n sample points of the electroencephalogram 701, and sets these as elements of the second row of the Hankel matrix. The curvature calculation unit 34 repeats this process n times to generate a Hankel matrix of n rows by N-n columns. One step is one sample width.

[0100] 8 is a diagram showing how the curvature is calculated from the Hankel matrix. The curvature calculation unit 34 performs singular value decomposition (SVD) on the Hankel matrix to reduce the dimension of the Hankel matrix.

[0101] Let H be the Hankel matrix. Singular value decomposition of matrix H gives H = U * Γ * VT. Matrices U and V are orthogonal matrices, and Γ is a diagonal matrix. The diagonal elements of Γ are called singular values, and are values ​​greater than or equal to 0. Matrix U is called a left singular vector, and V is called a right singular vector. Matrix U is an n x r matrix, matrix Γ is an r x r matrix, and matrix VT is an r x (N-n) matrix. r is the rank of matrix H.

[0102] First, the curvature calculation unit 34 performs singular value decomposition on the matrix H to obtain matrices U, Γ, and VT.

[0103] Next, the curvature calculation unit 34 dimensionally compresses the matrix U to n×m, the matrix Γ to m×m, and the matrix VT to m×(N−n), where n>r>m. Next, the curvature calculation unit 34 performs the calculation H′=U′*Γ′*VT′ using the dimensionally compressed matrices U′, Γ′, and VT′ to obtain the matrix H′. The matrix H′ is an m×(N−n) matrix. As a result, the matrix H′ is obtained by dimensionally compressing the matrix H.

[0104] Vector 801 indicates an element of a certain column in matrix H', and is a vector corresponding to one time (one sample point). Next, the curvature calculation unit 34 regards each column of matrix H' as time-series vector data and repeatedly differentiates each row. Here, higher-order derivatives up to the (i+1)th order are calculated for each row of matrix H'. As a result, a group of (i+1) vectors F, which have been differentiated up to the (i+1)th order for a certain time, are obtained. i is the order of the desired curve sequence and is a value set in advance by the analyst.

[0105] Next, the curvature calculation unit 34 applies Gram-Schmidt orthonormalization to the vector group F to obtain an (i+1)-dimensional orthonormalized vector e. The elements of e are (e1, e2, ..., ei+1).

[0106] Next, the curvature calculation unit 34 applies Equation 802 to the orthonormalized vector e to obtain the i-th curvature k. For example, the second-order curvature k2 is calculated by substituting 2 for i in Equation 802, and calculating k2 = ||e3|| / ||e1||·||e2||. This obtains the first- to i-th-order curvatures k1, k2, ..., k. The curvature calculation unit 34 performs this process on all vectors 801 that make up the matrix H'. This obtains the i-th-dimensional curvature k for each sample point.

[0107] If the electroencephalogram has multiple channels, the curvature calculation unit 34 calculates a Hankel matrix for each channel. The curvature calculation unit 34 generates a matrix in which the Hankel matrices for each channel are arranged vertically as a single matrix H. The curvature calculation unit 34 then applies the processing shown in FIG. 8 to this matrix H to calculate the i-dimensional curvature k for each sample point.

[0108] FIG. 9 shows the results of a creativity experiment. In this experiment, we examined whether there was a significant difference in the number of curvature minima between when 15 subjects provided creative responses and when they did not. In FIG. 9, the 15 diagrams labeled 1st, ..., and 15th are electrode maps showing electrodes 204 where statistically significant differences were observed in the number of curvature minima or average values ​​at each order. In the electrode map, electrodes 204 where significant differences were observed are indicated by large dots, and electrodes 204 where no significant differences were observed are indicated by small dots. In the experiment, the number of curvature minima from the 1st to the 15th orders was calculated for each EEG channel.

[0109] Each subject was given an Unusual Uses Task (UUT). The UUT task required subjects to come up with unique uses for a certain object. A slide containing a message requesting a unique answer about a certain object was presented to the subject for two minutes, during which time the subject was asked to come up with as many unique uses as possible. A unique use is one that has never been seen, heard, or thought of before. For example, if the object is a scrubbing brush, a unique use would be to display it on a tiered stand instead of a Hina doll. Each subject's answer was evaluated by multiple third parties to calculate a creativity score, and answers with a score exceeding a threshold were considered unique. As shown in Figure 9, it was confirmed that electrodes 204 exhibited significant differences in curvature at all orders.

[0110] FIG. 10 is another diagram showing the results of the creativity experiment. The 15 graphs in FIG. 10 are graphs showing the difference, for each order, between the number of curvature minima when subjects gave creative responses and the number of curvature minima when subjects gave uncreative responses. In each graph, the numbers written at the top outside the frame indicate the curvature order. Numbers with asterisks among these order numbers indicate orders where there was a statistically significant difference. In each graph, the vertical axis indicates the number of minima, while the horizontal axis indicates normal responses (left) and creative responses (right). In each graph, multiple points on each of two vertical lines indicate the number of minima for each channel. In each graph, the line connecting the left and right points indicates the same channel. If this line points diagonally upward to the right, it indicates that the number of minima was increased for the electrode 204 corresponding to this line when subjects gave creative responses compared to normal responses. As shown in FIG. 10, it was confirmed that in most degrees, the number of local minima increased when creative answers were given compared to when ordinary answers were given.

[0111] 11 is a diagram showing an example of a display screen 903 showing the creativity assessment results. The display screen 903 displays the creativity indexes for document creation and brainstorming in bar graphs 9031. The vertical axis of the bar graph 9031 indicates the creativity index, and the horizontal axis indicates the type of work. Graph 901 shows curvature data for document creation. Graph 902 shows curvature data for brainstorming. As shown in graphs 901 and 902, a greater number of local minima are detected for brainstorming than for document creation. Therefore, the user or their administrator can confirm from the bar graph 9031 that the creativity index for brainstorming is higher than that for document creation.

[0112] The determination unit 35 generates display data for the display screen 903 and transmits it to the display device 40 using the communication unit 31. The display device 40 outputs the display data received by the communication unit 42 to the display unit 41. As a result, the display screen 903 is displayed on the display unit 41.

[0113] The bar graph 9031 may be presented to at least one of the user who performed the work and the user's manager upon completion of the work.

[0114] FIG. 12 is a diagram showing another example of a creativity display screen 1002. The display screen 1002 includes a graph 1003 showing temporal changes in a creativity index. In this example, the graph 1003 shows temporal changes in a creativity index in brainstorming. The graph 1001 shows curvature data in brainstorming. The determination unit 35 detects the number of minimum values ​​within a time window 1004 having a predetermined time width while shifting the time window 1004 by a predetermined shift width. The determination unit 35 calculates a creativity index for each time window 1004 from the number of minimum values ​​detected for each time window 1004. The time width of the time window 1004 is, for example, 10 seconds, and the shift width is, for example, 2.5 seconds. However, these are merely examples, and the time width can be any suitable value other than 10 seconds, such as 20 seconds or 1 minute. The shift width can also be any suitable value other than 2.5 seconds, such as 5 seconds, 10 seconds, or 30 seconds. The determination unit 35 updates the display data of the graph 1003 each time the time window 1004 is shifted, and transmits the updated display data to the display device 40 using the communication unit 31. The display device 40 displays the display data received by the communication unit 42 on the display unit 41. This updates the graph 1003 in real time, allowing the user or administrator to understand during which time period the user was creative. Note that in the example of FIG. 12, the display screen 1002 shows an index of creativity in brainstorming, but it may also display an index of creativity in other tasks, such as document creation. The index of creativity for each time window 1004 can be, for example, the number of minimum values.

[0115] As described above, according to this embodiment, the greater the number of minimum values ​​of curvature, the more creativity is determined to be exhibited, making it possible to accurately determine whether creativity is being exhibited. Furthermore, according to this configuration, the greater the average value of curvature, the more creativity is determined to be exhibited, making it possible to more accurately determine whether creativity is being exhibited.

[0116] (Embodiment 2) In embodiment 2, a change in thought in the user's cognitive process is determined based on curvature data. In this embodiment, the same components as those in embodiment 1 are assigned the same reference numerals and their description will be omitted. In this embodiment, the overall configuration diagram shown in FIG. 2 is used.

[0117] FIG. 13 is a conceptual diagram illustrating the chaotic itinerary of the brain. Curve C3 shows the attractor when a user repeatedly performs the same task and the brain's thoughts do not change. Curve C4 shows the attractor when the brain switches thoughts from one task to another. As shown in FIG. 13, curve C4 has a smaller radius and a larger curvature than curve C3. This is thought to be because when thoughts change, more functional connections occur in the brain than when thoughts do not change, resulting in a more stable brain state. Therefore, in embodiment 2, the user's thoughts are determined from the curvature of the electroencephalogram. Functional connections in the brain indicate the interconnected functioning of different parts of the brain, and are observed from the interconnection with each frequency band of the electroencephalogram.

[0118] 2. In this embodiment, the determination unit 35 determines whether or not a change in thought has occurred in the user's cognitive process based on the average value of the curvature data. A change in thought in the cognitive process refers to at least one of a change in the information stored in the user's working memory and a change in the target of the user's attention.

[0119] The determination unit 35 determines that a thought change has occurred in the cognitive process when the average value of the curvature data exceeds a preset threshold. Specifically, the determination unit 35 determines that a thought change has occurred when the average value of the curvature data is equal to or greater than the threshold, and determines that a thought change has not occurred when the average value of the curvature data is less than the threshold. The determination unit 35 employs such processing because the greater the average value of the curvature data, the greater the possibility that a thought change has occurred.

[0120] 14 is a flowchart showing an example of the processing of the information processing system 1 in embodiment 2. The processing of steps S11 to S13 is the same as the processing of steps S1 to S3 in Fig. 5, and therefore description thereof will be omitted. In step S14, the determination unit 35 calculates the average value of the curvature data calculated in step S3.

[0121] Next, in step S15, the determination unit 35 determines whether the average value calculated in step S14 is equal to or greater than a threshold value. If the average value is less than the threshold value (NO in step S15), the determination unit 35 determines that there has been no switching of the working memory or no switching of the object of attention (step S16).

[0122] On the other hand, if the average value is equal to or greater than the threshold value (YES in step S15), the determination unit 35 determines that a switch in working memory has occurred or that a switch in the target of attention has occurred (step S17). Here, the determination is made as to whether or not a switch in working memory has occurred or whether or not a switch in the target of attention has occurred, but the determination unit 35 may determine whether or not both have occurred. For example, if the average value is less than the threshold value, the determination unit 35 may determine that a switch in working memory has not occurred and that a switch in the target of attention has not occurred, whereas if the average value is equal to or greater than the threshold value, the determination unit 35 may determine that a switch in working memory has occurred and that a switch in the target of attention has occurred.

[0123] Next, an experiment in the second embodiment will be described. Fig. 15 is a diagram illustrating an experiment conducted to verify the relationship between the presence or absence of thought switching and curvature. In this experiment, subjects were assigned a task called the "Task Switching Paradigm."

[0124] First, a cue image 1100 was displayed on the display, and then target images 1200 were randomly displayed 3 to 6 times. The display time of the cue image 1100 and the display time of each target image 1200 were both 100 ms.

[0125] Cue image 1000 includes cue image 1101 that requests the subject to continue the task and cue image 1102 that requests the subject to switch tasks. Cue image 1101 is an image with horizontal stripes, and cue image 1102 is an image with vertical stripes.

[0126] The target image 1200 includes four patterns of target images 1201 to 1204. The target image 1201 includes a blue circular figure, the target image 1202 includes a red circular figure, the target image 1203 includes a red square figure, and the target image 1204 includes a blue square figure.

[0127] The tasks include a first task T1 and a second task T2. The first task T1 is a task in which the test subject answers the shape of a figure included in a target image 1200. In the first task T1, the test subject presses the left button when target image 1201 or target image 1202 including a circular figure is displayed, and presses the right button when target image 1203 or target image 1204 including a square figure is displayed.

[0128] The second task T2 is a task to answer the color of a figure included in the target image 1200. In the second task T2, the test subject presses the left button when the target image 1202 or the target image 1204 including a blue figure is displayed, and presses the right button when the target image 1201 or the target image 1203 including a red figure is displayed.

[0129] When cue image 1101 was displayed, the subject continued the same task as that performed before the display of cue image 1101 for each of the four to six target images 1200 that were then randomly displayed. On the other hand, when cue image 1102 was displayed, the subject performed a task for each of the target images 1200 that were subsequently displayed that was different from the task that was performed before the display of cue image 1102.

[0130] In the experiment, the cue images 1101 and 1102 were each displayed 80 times, with the goal of obtaining 80 pieces of curvature data for each of the cue images 1101 and 1102. Displaying one target image 1200 constituted one trial. After displaying the cue image 1100, three to six target images 1200 (an average of 4.5) were displayed. Therefore, the total number of trials was 160 x 4.5 = 720.

[0131] Figure 16 is a graph showing the experimental results of the experiment shown in Figure 15. In Figure 16, 15 graphs labeled Order 1, ..., Order 15 show the temporal changes in curvature in each dimension from the 1st to the 15th. In each graph, the vertical axis represents curvature and the horizontal axis represents time. Note that the average value of the curvature on the vertical axis is set to 0 for the period from -200 ms to 0 ms from the display time of the cue image 1100.

[0132] Vertical line 1501 indicates the display time of cue image 1100, which is set to 0. Vertical line 1502 indicates the display time of target image 1200, and vertical line 1503 indicates the time when the subject pressed the button. In each graph, the solid line indicates curvature data when cue image 1102 is displayed, i.e., when the subject switches tasks. The dotted line indicates curvature data when cue image 1101 is displayed, i.e., when the subject continues the task. Note that the solid line in each graph represents the average curvature data of all trials when a task is switched, and the dotted line in each graph represents the average curvature data of all trials when the task is continued.

[0133] As can be seen by comparing the solid and dotted lines, the curvature data for task switching was statistically significantly higher than for task continuation at all orders, demonstrating that task switching and task continuation can be distinguished by curvature.

[0134] 17 is a diagram showing the data configuration of the recording unit 33 in embodiment 2. The recording unit 33 stores biometric data 331, curvature data 332, a threshold value 333, and a determination result table 334. The biometric data 331 indicates the user's electroencephalogram measured by the sensor device 20. Although the waveform of the electroencephalogram is shown here, the recording unit 33 actually stores time-series data of the electroencephalogram values ​​as the biometric data 331.

[0135] The curvature data 332 indicates a change over time in the curvature calculated by the curvature calculation unit 34 from the biometric data 331. Although a waveform of the curvature is shown here, the recording unit 33 actually stores time-series data of the curvature values ​​as the curvature data 332. The threshold value 333 is a threshold value used by the determination unit 35 when determining a change in thought. In this example, 13.9 is adopted as the threshold value 333. However, this is just an example, and a value other than 13.9 may be adopted as the threshold value 333 as long as it is a value that can determine a change in thought. The threshold value 333 is set in advance by the service provider.

[0136] The judgment result table 334 records the judgment results by the judgment unit 35. In the judgment result table 334, the horizontal axis indicates the number of minimum values, the moving average value, and the judgment result, and the vertical axis indicates the period. The number of minimum values ​​is the number of minimum values ​​in the curvature data 332. The moving average value is the moving average value of the curvature data 332. The moving average value is the average value of the curvature over a period determined by a time window fitted to the curvature data 332. The judgment result indicates whether the moving average value is greater than or equal to a threshold value. If the moving average value is greater than or equal to the threshold value, "up" is entered, and if the moving average value is less than the threshold value, "down" is entered. The period indicates the period over which the moving average value is calculated. In this example, each period is determined by shifting a 10-second time window by one second for the curvature data 332.

[0137] 18 is a diagram showing a display screen 1602 for notifying a change in thought. The display screen 1602 includes a graph 1603 showing the average value per unit time of the curvature data 332. In the graph 1603, the vertical axis represents the moving average value of the curvature data 332, and the horizontal axis represents time. The determination unit 35 calculates the moving average value for the curvature data 332 while shifting a time window 1604 by one second from the beginning of the time axis. The determination unit 35 calculates a graph showing the change in curvature over time based on the calculated moving average value.

[0138] Graph 1603 displays a period 1605 in which the moving average value of curvature data 332 is equal to or greater than threshold value 333 in a manner that allows it to be distinguished from other periods. In this example, period 1605 is displayed in gray. That is, determination unit 35 determines that a change of thought occurred during the period in which the moving average value is equal to or greater than threshold value 333, and generates display data for display screen 1602 that indicates the determination result. This display data is transmitted by communication unit 31 to display device 40, and display unit 41 displays display screen 1602 using this display data.

[0139] Furthermore, the display screen 1602 includes a notification field 1606 that notifies the user of whether or not a change in thinking has occurred. The notification field 1606 includes a vertical line 1607. The vertical line 1607 indicates the time at which a change in thinking occurred in the graph 1603. In this example, the vertical line 1607 is positioned at the midpoint of the period 1605. To the left of the vertical line 1607, the name of the task the user was performing before the change in thinking is displayed. In this example, a stamping task is displayed as the task before the change. To the right of the vertical line 1607, the name of the task the user will perform after the change in thinking is displayed. In this example, a slip processing task is displayed as the task after the change. The determination unit 35 may, for example, determine the user's task by performing image recognition on an image of the user captured by a camera. This allows the user to confirm whether the change in thinking has been properly performed when performing the next task (slip processing task) from the previous task (sealing task). For example, if a user starts the next task without switching their mindset, they are likely to make a mistake, so they can approach the next task carefully or take a short break before starting the next task.On the other hand, if a user can switch their mindset, they can tackle the next task with confidence.

[0140] In FIG. 18, a change in thought is notified, but a change in working memory or a change in the object of attention may also be notified.

[0141] As described above, according to this embodiment, a change in thought is determined based on the average value of the curvature data, and therefore a change in thought can be determined with high accuracy.

[0142] (Embodiment 3) In embodiment 3, a control signal is output to a device arranged in a space where a user is present. Fig. 19 is a configuration diagram of an information processing system 1A in embodiment 3. Note that in embodiment 3, the same components as those in embodiments 1 and 2 are assigned the same reference numerals and descriptions thereof will be omitted.

[0143] The information processing system 1A further includes devices 50 in addition to the components of the information processing system 1 shown in Fig. 2. The devices 50 include a lighting device 51, an air conditioning device 52, an audio device 53, and a fragrance release device 54.

[0144] The device 50 is installed in a space where the user is present, such as a room or booth where the user is present.

[0145] The lighting device 51 irradiates light into the space where the user is present. The air conditioning device 52 is, for example, an air conditioner or an air purifier. The audio device 53 includes a playback device, an amplifier, a speaker, etc., and outputs audio content. The fragrance release device 54 is a device that releases fragrance.

[0146] In the third embodiment, the determining unit 35 determines whether at least one of the average value and the minimum value of the curvature data calculated by the curvature calculating unit 34 satisfies a predetermined first condition.

[0147] In the third embodiment, when the determination unit 35 determines that the curvature data satisfies the first condition, the communication unit 31 transmits a control signal to the device 50. The control signal includes information for changing the content of control executed by the device 50. For example, when the communication unit 31 determines that the curvature data satisfies the first condition, the communication unit 31 outputs a control signal to the air conditioning device 52 that includes information for increasing the room temperature. For example, when the communication unit 31 determines that the curvature data satisfies the first condition, the communication unit 31 outputs a control signal to the lighting device 51 that includes information for decreasing the illuminance.

[0148] FIG. 20 is a flowchart showing an example of processing by the information processing system 1A according to the third embodiment.

[0149] Steps S21 to S23 are the same as steps S1 to S3 shown in FIG.

[0150] In step S24, the determination unit 35 determines whether the curvature data smoothed in step S23 satisfies a first condition. The first condition corresponds to low creativity (not demonstrating creativity).

[0151] Specifically, the first condition may be that the number of local minima in the curvature data is lower than a predetermined threshold, or that the average value of the curvature data is higher than a predetermined threshold.

[0152] In step S24, the judgment unit 35 may perform a judgment process of "Is the creativity score derived from the curvature data lower than a predetermined threshold value?" instead of the process of "Does the curvature data satisfy the first condition?"

[0153] In step S24, if it is determined that the curvature data satisfies the first condition (YES in step S24), the processing proceeds to step S25, and if it is determined that the curvature data does not satisfy the first condition (NO in step S24), the processing ends.

[0154] In step S25, the determination unit 35 determines whether or not the biological data satisfies a second condition. The second condition corresponds to a low level of relaxation.

[0155] If the biological information indicates a heart rate, the second condition may be that the heart rate is lower than a predetermined threshold. Alternatively, the determination unit 35 may determine the degree of relaxation using the method described in Japanese Patent No. 7200856, and determine that the second condition is satisfied if the degree of relaxation is lower than the predetermined threshold.

[0156] Alternatively, when the biological information indicates an electroencephalogram, the determination unit 35 may determine that the second condition is satisfied if the frequency of alpha waves in the electroencephalogram is equal to or lower than a predetermined threshold.

[0157] The method is not limited to these, and a known method may be used to determine whether the second condition corresponding to a low level of relaxation is satisfied.

[0158] In step S25, if it is determined that the biometric data satisfies the second condition (YES in step S25), the processing proceeds to step S26, and if it is determined that the biometric data does not satisfy the second condition (NO in step S25), the processing proceeds to step S28.

[0159] If the judgment results for both the first condition and the second condition are YES, that is, if it is judged that creativity is not being exercised and the level of relaxation is low, the communication unit 31 outputs a control signal to the lighting equipment 51 to reduce the illuminance of the lighting (step S26), and also outputs a control signal to the air conditioning equipment 52 to perform air conditioning control to raise the room temperature and make it warmer (step S27).

[0160] This allows a user who is not relaxed and is in a tense state and is therefore thought to be unable to demonstrate creativity to be guided into a relaxed state, thereby guiding the user into a state where creativity is being demonstrated.

[0161] The communication unit 31 may output either a control signal to lower the illuminance of the lighting or a control signal to raise the room temperature, or may output both control signals.

[0162] If the judgment regarding the first condition is YES and the judgment regarding the second condition is NO, that is, if it is judged that creativity is not being exercised and the level of relaxation is high, the communication unit 31 outputs a control signal to the lighting equipment 51 to increase the illuminance (step S28), and outputs a control signal to the air conditioning equipment 52 to perform air conditioning control to lower the room temperature and make it cooler (step S29).

[0163] This allows a user who is thought to be too relaxed and therefore unable to demonstrate creativity to be guided to a lower level of relaxation, thereby guiding the user into a state where creativity is being demonstrated.

[0164] The communication unit 31 may output either a control signal to increase the illuminance of the lighting or a control signal to decrease the room temperature, or may output both control signals.

[0165] In addition, if the communication unit 31 determines that the curvature data satisfies the first condition (YES in step S24), it may output a control signal to the audio equipment 53, causing the audio equipment 53 to output music that has a relaxing effect or music that suppresses drowsiness and wakes the user up.

[0166] When the sensor for acquiring the biological information is an earphone, the audio device may be the earphone. For example, when the curvature data satisfies the first condition (YES in step S24), the communication unit 31 may output a control signal for switching the music played from the earphone of the audio device 53 to another music.

[0167] 20, the process of determining the second condition (step S25) may be omitted. When the determination of the second condition is omitted, it may be set in advance whether to perform the first flow for reducing the illuminance of the lighting or the second flow for increasing the illuminance of the lighting, and when it is determined as YES in step S24, the control device 30 may execute the preset flow from the first flow to the second flow.

[0168] (Embodiment 4) In embodiment 4, a control signal for releasing a fragrance is output. In embodiment 4, the same components as those in embodiments 1 to 3 are assigned the same reference numerals and their description will be omitted. In addition, the block diagram in embodiment 4 is the same as the block diagram in embodiment 3 (FIG. 19).

[0169] FIG. 21 is a flowchart showing an example of processing by the information processing system 1B according to the fourth embodiment.

[0170] Steps S31 to S33 are the same as steps S1 to S3 shown in FIG.

[0171] In step S34, the determination unit 35 determines whether the curvature data smoothed in step S33 satisfies a first condition. In the fourth embodiment, the first condition corresponds to a change in thought in the cognitive process.

[0172] Specifically, the first condition may be that the number of local minima in the curvature data exceeds a predetermined threshold.

[0173] If it is determined in step S34 that the curvature data satisfies the first condition (YES in step S34), the communication unit 31 outputs a control signal to the fragrance release device 54 to release a fragrance (step S35). The fragrance may be a fragrance that suppresses drowsiness, such as a mint fragrance. The fragrance may also be a fragrance that provides a relaxing effect, such as at least one of lavender, bergamot, rose, and jasmine.

[0174] If a user working on a task is determined to have switched thoughts even though they are continuing to perform the same task, it is suspected that they are unable to concentrate on the task and are thinking about something other than the task due to drowsiness or fatigue, etc. Therefore, if the first condition is met, the user's ability to concentrate on the task can be improved by emitting a scent that suppresses drowsiness or a scent that has a relaxing effect.

[0175] If it is determined that the curvature data does not satisfy the first condition (NO in step S34), the processing ends.

[0176] The control device 30 may control the audio device 53 instead of the fragrance releaser 54. For example, when the communication unit 31 determines that the curvature data satisfies the first condition, the communication unit 31 may cause the audio device 53 to output music that provides a relaxing effect or music that suppresses drowsiness and wakes the user up. Furthermore, the control device 30 may control the lighting device 51 instead of the fragrance releaser 54.

[0177] In a case where the user is a driver of a car and the task is driving the car, if the determination unit 35 determines that the curvature data satisfies the first condition, the communication unit 31 may output a control signal to switch from manual driving to automatic driving, a control signal to decelerate the car, or the like to a vehicle control device. The vehicle control device is a device that controls the car, and may be, for example, an ECU of the car.

[0178] The device 50 may include a video device. In this case, the communication unit 31 may output a control signal that changes the control content of the video device. The video device may be, for example, a liquid crystal display. The video device may also be a video projection system including a projector and a screen.

[0179] The air conditioning device 52 may be a blower such as a fan.

[0180] The present disclosure can employ the following modifications.

[0181] (1) The sensor device 20 may be configured as an earphone capable of measuring biometric data. The earphone includes an earpiece and a tip portion attached to the earpiece. A reference electrode is disposed in the earpiece. The tip portion is a dome-shaped member made of silicone and inserted into the ear canal. Multiple active electrodes are disposed in the tip portion. A processor in the earphone acquires the potential difference between each of the multiple active electrodes and the reference electrode, and transmits the acquired potential difference to the control device 30 as biometric data indicating brain waves. In this case, the user's brain waves can be acquired while reducing the burden on the user.

[0182] (2) The sensor device 20 may be configured as headphones. These headphones include a pair of speaker units worn on both ears and a band unit that connects the pair of speaker units and is worn on the user's head. A reference electrode is disposed in the speaker unit. A plurality of active electrodes are disposed in the band unit. A processor in the headphones acquires the potential difference between each of the plurality of active electrodes and the reference electrode, and transmits the acquired potential difference to the control device 30 as biometric data indicating brain waves. In this case, the user's brain waves can be acquired while reducing the burden on the user.

[0183] (3) In the first embodiment, if the determination unit 35 determines that the user lacks creativity, it may control lighting equipment to enhance the user's creativity. A non-patent document (Kouichirou Tomimoto et al., "Office Lighting System Aimed at Improving Worker Creativity - Research on Lighting Conditions Suitable for Creative Office Scenes," Proceedings of the Kansai Branch Conference of the Japan Ergonomics Society, Volume: 2009, Pages: 171-174, Published: December 5, 2009) describes the use of high-illuminance, low-color-temperature lighting as a suitable condition for tasks requiring creativity, such as brainstorming. Therefore, when the user is performing a task requiring divergent thinking, the determination unit 35 generates a control signal that lowers the color temperature by a predetermined decrease and increases the illuminance by a predetermined increase, and transmits the generated control signal to a lighting device that illuminates the user via the communication unit 31. The determination unit 35 may repeat the control of lowering the color temperature by a predetermined decrease and increasing the illuminance by a predetermined increase until it determines that the user has creativity. This can enhance the creativity of users who are not yet using their creativity.

[0184] The present disclosure is useful in the technical field of providing support for improving work efficiency.

[0185] 1: Information processing system 10: Input device 11: Input section 20: Sensor device 21: Sensor device section 22: Sensor communication section 30: Control device 31: Communication section 32: Control section 33: Recording section 34: Curvature calculation section 35: Determination section 40: Display device 41: Display section 42: Communication section 43: Operation section

Claims

1. An information processing method in a computer, comprising: acquiring biometric data indicating changes in a user's biometric information over time; deriving a curvature based on the biometric data; and determining the user's mental state based on at least one of the average value and minimum value of the curvature.

2. The information processing method according to claim 1, wherein the biological information corresponds to the user's brain waves.

3. The information processing method according to claim 1 or 2, wherein determining the state of mind includes determining the creativity of the user.

4. The information processing method of claim 3, wherein determining the creativity includes determining that the user is more creative the greater the number of local minimum values, or determining that the user is more creative the lower the average value.

5. The information processing method according to claim 1 or 2, wherein determining the thought state includes determining whether or not the thought in the user's cognitive process has changed based on the average value of the curvature.

6. The information processing method of claim 5, wherein determining whether or not a change in thought has occurred in the cognitive process includes determining at least one of whether or not the information stored in the user's working memory has changed and whether or not the object of the user's attention has changed.

7. The information processing method according to claim 5, wherein it is determined that a change in thought has occurred in the cognitive process when the average value of the curvature exceeds a preset threshold value.

8. The information processing method of claim 2, further comprising: detecting a first electrical signal from a first position on the user's head using a first electrode corresponding to the first position; detecting a second electrical signal from a second position on the head using a second electrode corresponding to the second position; deriving a potential difference between the first position and the second position based on the first electrical signal and the second electrical signal; and generating the bioinformation corresponding to the brain waves based on the potential difference.

9. The information processing method according to claim 3, further comprising outputting a control signal to change the control content to at least one of lighting equipment, air conditioning equipment, audio equipment, video equipment, and fragrance release equipment when it is determined that the user is not demonstrating creativity.

10. The information processing method according to claim 9, wherein, if it is determined that the user is not demonstrating the creativity, a control signal is output to the lighting device that illuminates the user, lowering the color temperature of the lighting device and increasing the illuminance.

11. The information processing method according to claim 8, wherein the first electrode and the second electrode are attached to earphones.

12. An information processing method as described in claim 3, further comprising: detecting the number of minimum values ​​in each time window while shifting the time window in the curvature data indicating the change in curvature over time; calculating an index of creativity in each time window; and displaying a graph showing the change in the index of creativity over time on a display device based on the calculated index of creativity in each time window.

13. An information processing method according to claim 1 or 2, further comprising: calculating a moving average value of curvature data showing the change in curvature over time; and displaying a graph showing the change in curvature over time on a display device based on the moving average value; and displaying the graph in a manner that allows periods in which the moving average value exceeds a threshold value to be distinguished from other periods.

14. An information processing method according to claim 5, wherein, when it is determined that a change in thought has occurred in the cognitive process, a control signal is output to at least one of lighting equipment, air conditioning equipment, audio equipment, video equipment, and a fragrance release device to change the control content.

15. An information processing system including a processor that performs the following operations: acquiring biometric data indicating changes in a user's biometric information over time; deriving a curvature based on the biometric data; and determining the user's mental state based on at least one of an average value and a minimum value of the curvature.

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