FR3113972A1 - System for generating product recommendations using biometric data - Google Patents

Abstract

System for Generating Product Recommendations Using Biometric Data Systems, devices, and methods are disclosed for generating user experiences and product recommendations using biometric data. For example, one system includes a tuning unit including a processing circuit configured to detect an evoked potential based on a response to an olfactory stimulus; and a fragrance selection unit including a processing circuit configured to generate one or more virtual examples of a subset of fragrances based on at least one input associated with the evoked potential. Figure for abstract: Figure 1

Description

System for generating product recommendations using biometric data

The subject of the invention is a method for recommending a product to a user, comprising:

• obtaining questionnaire data from the subject;
• obtaining biometric data of a subject based on exposure to a stimulus;
• analysis of biometric data; And
• recommending a product to the subject on the basis of the analyzed biometric data and one or more of the said questionnaire data indicative of a preference for a characteristic parameter of a product.

DESCRIPTION OF DRAWINGS

The foregoing aspects and many of the advantages of the present invention flowing therefrom will be better understood by reference to the following detailed description, made in conjunction with the accompanying drawings, in which:

There is a schematic diagram that illustrates a non-limiting example of a system for generating and providing recommendations of products, such as fragrances, to a subject according to one aspect of the present disclosure;

There shows an example of a helmet suitable for use in the system of the ;

There is a block diagram that illustrates a non-limiting example of a mobile computing device suitable for use in the system of the ;

There is a block diagram which illustrates a non-limiting example of a server computing device suitable for use in the system of the ;

There is a block diagram that illustrates a non-limiting example of a computing device suitable for use as a computing device with embodiments of the present disclosure;

There is a flowchart that illustrates a non-limiting example of a method of generating and providing product recommendations to a subject according to one aspect of the present disclosure;

There is an example of a fragrance wheel;

There is an example of an electroencephalograph generated as a result of processing EEG signals collected in accordance with the present disclosure;

There is an example of a brain activity map generated as a result of processing EEG signals collected in accordance with the present disclosure;

There is an example of a system-generated note chart presented to the subject in accordance with an aspect of the present disclosure; And

There is an example of an EEG electrode placement diagram in accordance with aspects of the present disclosure.

DETAILED DESCRIPTION

In order to provide recommendations for cosmetics, most existing techniques only attempt to directly discern a subject's product preferences. Some techniques may attempt to determine a subject's product preferences based on the subject's stated preferences for a product characteristic, such as scent, color, finish, feel, etc. However, this technique produces suboptimal recommendations, at least because only explicitly stated subject preferences are taken into account. Even in the presence of explicitly stated subject preferences, other factors (e.g., physical reactions (subconscious or conscious), personality traits, etc.) may also influence which products a given subject will prefer.

In this regard, in certain embodiments of the present disclosure, the subject's biometric data is considered in determining product preferences and/or product recommendations. In other embodiments, other subject factors, although optional, may also be considered in determining product preferences and/or product recommendations for a subject. These product preferences or recommendations are then represented to the subject, either automatically via a display device or with the assistance of a product consultant.

The examples described throughout the disclosure relate to recommendations for a fragrance, such as a perfume or cologne. It will be appreciated that the techniques and methodologies of the present disclosure transcend product types, and thus, can be used to provide recommendations to the subject for products other than fragrances.

In the examples described below, a subject will be exposed to a number of fragrance/scent stimuli. Biometric data will then be collected from the subject based on their response to these fragrance/scent stimuli. In some embodiments, the collected biometric data relates to a subject's evoked potential (EP), that is, the measured brain response that is the direct result of a specific sensory, cognitive, or motor event. With this biometric data, a computer system, for example, will recommend a specific fragrance or develop a fragrance profile from which a recommendation can be made with the assistance of, for example, a technician or fragrance consultant. In other embodiments, the computer system will use the biometric data in conjunction with optional data, such as data obtained in a questionnaire, historical purchase data of the subject, etc., in order to present a product recommendation to the subject.

In certain embodiments described herein, the fragrances/scents presented to the subject may include two or more notes of a chord. Generally, an accord is a scent made up of several perfume notes, ingredients, etc., which blend together to form a distinct fragrance. For example, a chord usually includes a number of notes. Notes are descriptors of odors that can be smelled, and include base notes, middle or middle notes, and top notes. These odor descriptors or notes are well known and widely used to describe the character of a scent (e.g. a characteristic parameter) or a fragrance.

Notes are normally chosen from fragrance families. Generally, fragrance families can be visually presented by a fragrance wheel. An example of a fragrance wheel is shown in FIGURE 7. A fragrance wheel is a round diagram showing the inferred relationships between olfactory groups based on similarities and differences in their scent. The juxtaposed groups are assumed to share common olfactory characteristics. Fragrance wheels are frequently used as a classification tool in oenology and perfumery.

Turning now to FIGURE 1, there is shown a schematic diagram which illustrates a non-limiting example of a system 100 according to one aspect of the present disclosure. In the illustrated system, the electrical activity of a subject's brain 102 in the form of EEG signals is measured using physical collection devices 106, such as EEG sensors, in response to a subject's exposure to a stimulus, such as a fragrance. As shown, a plurality of physical collection devices 106 are placed on different regions of the subject's brain, for example via an appropriate headset 114, in order to measure the electrical activity of the subject's brain. In some embodiments, as will be explained in more detail below, brain regions associated with, for example, stimulation/relaxation and/or approach/avoidance are measured. Other regions of the brain can be additionally or alternatively measured to collect data from EEG sensors.

Still with reference to the , a mobile computing device 104 is coupled to physical collection devices 106 in a wired or wireless manner to collect the EEG signals generated by the physical collection devices 106. In some embodiments, the mobile computing device 104 is used to process the collected signals, and based on the processed signals, determines a product recommendation to present to the subject 102. In other embodiments, the mobile computing device 104 develops a fragrance profile based on the processed signals, from which a recommendation can be made with the assistance of, for example, a technician or fragrance consultant.

In one embodiment, processing the collected EEG signals includes generating an electroencephalograph, such as the example shown in . In other embodiments, processing the collected EEG signals includes generating a map of brain activity, such as the example shown in . The electroencephalograph and/or brain map can then be used by the mobile computing device 104 to recommend a product for the subject or present a fragrance profile that can aid the subject in selecting products. Of course, mobile computing device 104 may use the collected EEG signals in other ways, including via non-graphical processing techniques, to provide a product recommendation to the subject.

In other embodiments, mobile computing device 104 may transmit the EEG signals (processed or unprocessed) as EEG data to an optional server computing device 108 via network 110. In other embodiments, network 110 may include any appropriate wireless communication technology (including, but not limited to, Wi-Fi, WiMAX, Bluetooth, 2G, 3G, 4G, 5G, and LTE), wired communication technology (including, but not limited, Ethernet, USB, and FireWire), or combination thereof.

With EEG data received from mobile computing device 104, server computing device 108 may respond to mobile computing device 104 with a product recommendation to present to subject 102. In other embodiments, server computing device 108 may develop a fragrance profile from EEG data. The fragrance profile may then be transmitted to mobile computing device 104. Once received by mobile computing device 104, a recommendation may be made with the assistance of, for example, a fragrance technician or consultant. Of course, in some embodiments, the server computing device 108 can access via the network 110 a cloud -based computing processing system (not shown) to increase its processing, analysis, generation, etc. capabilities.

In some embodiments, server computing device 108 processes EEG data and generates an electroencephalograph and/or brain map. The electroencephalograph and/or brain map can then be used by server computing device 108 to provide a product recommendation to mobile computing device 104 for presentation to subject 102. Alternatively, the data generated electroencephalograph and/or brain map are transmitted to mobile computing device 104 for use by mobile computing device 104 to provide a product recommendation to subject 102.

In some embodiments, mobile computing device 104 may also be used to present an optional questionnaire to subject 102. The questionnaire may include questions that allow subject 102's preferences to be determined. In some embodiments, the questionnaire may also allow at least one personality trait to be determined. For example, in some embodiments, a personality trait may be correlated to one or more fragrance preferences, etc.

In some embodiments, the questionnaire may be transmitted to mobile computing device 104 by optional server computing system 108 for presentation to subject 102. In other embodiments, mobile computing device 104 may generate and present the questionnaire to the subject.

In some embodiments, questionnaire responses are received and processed locally by mobile computing device 104. In other embodiments, responses received by mobile computing device 104 are sent to optional server computing system 108 for processing. . Of course, in some embodiments, server computing device 108 may access via network 110 a cloud -based computing processing system (not shown) to augment its processing capabilities.

In either case, the processed questionnaire responses may be used by either the mobile computing device 104 or the server computing device 108 in conjunction with the EEG signals collected from the subject to provide, for example, a product recommendation.

In some embodiments, the product recommendation may be provided to subject 102 in a simple and convenient format. For example, the product recommendation can be a specific fragrance, for example, identified by a trade name such as Trade Winds. Other information about subject 102, such as scent preferences, personality trait, past fragrance purchases, etc., may also be presented to the subject. Additionally or alternatively, the product recommendation may take the form of a fragrance profile. The fragrance profile can be presented as a description in words or visually represented as a note chart. An example of a note chart by system 100 and presented to subject 102 is shown in . With the word description or rating chart, a fragrance with a high likelihood of liking by the subject can be selected, either independently or with the assistance of a fragrance consultant.

There is a block diagram that illustrates various components of a non-limiting example of a headset 114 according to one aspect of the present disclosure. Headset 114 supports a number of physical collection devices 106 in the form of EEG sensors ("EEG sensors 106") and processing and transmission circuitry 118. Typically, voltage changes occur from ion current to within and between brain neurons. EEG sensors 106, sometimes referred to as EEG electrodes, are configured to measure these voltage changes in the subject's brain as EEG signals. EEG signals measured by EEG sensors 106 may be suitably processed for transmission to mobile computing device 104 for data storage, processing and/or analysis, etc. For example, in some embodiments, the EEG signals are amplified by an amplifier 120 and digitized by an A/D converter 122 before they arrive at the transmitter 130. In some embodiments, the EEG signals may be filtered in the analog domain before conversion by A/D converter 122 or in the digital domain after conversion by A/D converter 122 via one or more filters 124. In some embodiments, the filtered signals (optional) are sent to a multiplexer (MUX) 126 before transmission via transmitter 130 to mobile computing device 104.

EEG 106 electrodes are usually formed of electrically conductive material, such as stainless steel, silver/silver chloride (Ag/AgCl) material, etc. EEG electrodes 106 can be of the wet type (eg, used with an electrolyte gel material serving as a conductor between the skin and the electrode) or of the dry type (eg, a single metal electrode that acts as a conductor between the skin and the electrode). In some embodiments, a material that is not a gel, such as saline, can be used as a conductive layer between the skin layer and the electrode.

In some embodiments, the EEG electrodes 106 may be active, in which the electrodes include a pre-amplification circuit immediately after the conductive material between the skin and the electrode. This allows the EEG signal to be amplified before additional noise can be added by the system in charge of capturing, processing or amplifying the EEG signal. Alternatively, the EEG electrodes may be passive in other embodiments. Passive electrodes do not include a pre-amplification circuit. Instead, passive electrodes simply extend the connection of the electrode's conductive material to system components that process, amplify, and/or transmit the signal.

When placed on the subject's head, the EEG electrodes 106 of the helmet 114 are generally aligned with different regions of the brain. Referring to FIGURE 11, there is shown an example of an EEG electrode placement diagram in accordance with aspects of the present disclosure. It will be understood that several abbreviations on the diagram relate to the international 10-20 system, including "N" for nasion, "F" for frontal (e.g., in relation to the frontal lobe of a brain, which is the area located at the front of each cerebral hemisphere), "A" for each lobe, "C" for center (for example, in relation to a central area of the brain), "T" for temporal (for example, in relation to the temporal lobe of the brain, which is located below and behind the frontal lobe in each cerebral hemisphere), "P" for parietal (for example, in relation to the parietal lobe of the brain, which is located behind the frontal lobe), "O" for occipital (for example, in relation to the occipital lobe of the brain, which is located at the back of the head), "I" for inion, and the subscript "z" for readings taken on the line middle of the brain. The diagram in FIGURE 11 also includes abbreviations for AF, which is located between Fp and F, and FC, which is located between F and C.

As shown in the example of FIGURE 11, the EEG electrodes are positionally associated with regions AF3, AF4, F3, F4, F7, F8, FC5, FC6, T7, T8, P7, P8, O1, and O2 of the subject's brain. In the embodiment shown, the reference electrodes are located at P3 and P4, although other locations could be used. In some embodiments, only two electrodes are used, which are left front (F7) and right front (F8) associated with approach (left brain activity) and avoidance (right brain activity), respectively. . Of course, left front (F7) and right front (F8), along with any other combination of brain regions, can be practiced with embodiments of the present disclosure. In some embodiments, the location of the EEG electrodes can be fixed by the headset. In other embodiments, the location of the EEG electrodes may be adjustable.

A non-limiting example of a headset 114 that produces suitable signals for use by system 100 is Emotive's EPOC + EEG Headset. Other headsets that can be used are available from companies such as Brain Products, EGI, Cognionics, among others.

FIGURE 3 is a block diagram that illustrates various components of a non-limiting example of a mobile computing device 104 according to one aspect of the present disclosure. Mobile computing device 104 is configured to collect information from subject 102 that reflects brain activity based on exposure to one or more fragrances in, for example, sequential order. In particular, mobile computing device 104 is configured to receive EEG signals from one or more EEG sensors 106 for processing, recording, transmission (optional), and/or analysis (optional). In some embodiments, mobile computing device 104 is configured to receive EEG signals from transmitter 130 of headset 114 (see ).

In some embodiments, mobile computing device 104 processes the EEG signals for use in determining a product recommendation for subject 102. In other embodiments, as will be further described In detail below, the EEG signals (processed or not) are transmitted as EEG data to the optional server computer system 108 via the network 110 for processing and/or generating a product recommendation, etc. In either case, mobile computing device 104 may then present the product recommendation to subject 102, or to a cosmetic consultant assisting subject 102. In some embodiments, generating a product recommendation product can be the identification of a specific product (e.g. a specific perfume/cologne). In other embodiments, the generation of a product recommendation may be information, such as a fragrance profile, that aids in the selection of a specific product or product family.

In some embodiments, mobile computing device 104 may be a smartphone . In some embodiments, mobile computing device 104 may be any other type of computing device having the illustrated components, including, but not limited to, a tablet computing device or a laptop computing device. In some embodiments, mobile computing device 104 may not be mobile, but may instead be a stationary computing device such as a desktop computing device or a computing kiosk. In some embodiments, the illustrated components of mobile computing device 104 may be inside a single enclosure. In some embodiments, the illustrated components of mobile computing device 104 may be in separate enclosures that are commutatively coupled by wired or wireless connections. Mobile computing device 104 also includes components that are not shown in the figure. , including, but not limited to, one or more processors, non-transitory computer-readable media, a power source, and one or more network communication interfaces.

In order to implement some or all of the technology and methodology disclosed herein, the mobile computing device 104 includes in some embodiments, for example, a display device 302, an EEG engine 306, a user interface 308, an optional questionnaire analysis engine 310, a recommendation engine 312, a user data store 314 and a product data store 316. Each of these components will be described in turn.

In some embodiments, display device 302 is any suitable type of display device, including but not limited to an LED display, OLED display, or LCD display, that is capable of presenting interfaces to subject 102. As will be described in more detail below, these interfaces include a questionnaire, product recommendation, etc., to be presented to subject 102. In some embodiments, display device 302 may include an integrated tactile part which accepts inputs from the subject 102.

In some embodiments, EEG engine 306 is configured to collect EEG signals from EEG sensors 106, process the EEG signals, and store the EEG signals in a temporal manner as EEG data in user data store 314. In some embodiments, EEG signal processing may include, but is not limited to, conversion, filtering, transformation, and/or the like. For example, in some embodiments, the EEG signals can be band-pass filtered to appropriately pass signals in, for example, the alpha and/or beta frequency range. In some embodiments, the EEG engine 306 is also configured to process the signals to generate an electroencephalograph, such as the example shown in , and/or a brain activity map, such as the example shown in the .

In some embodiments, UI engine 308 is configured to present a user interface on display device 302. In some embodiments, UI engine 308 is configured to present a product recommendation, such as a product name or fragrance profile to user 102. In some embodiments, user interface engine 308 may be configured to present visualizations of EEG data as either an electroencephalograph or map of brain activity on the display device 102.

In some embodiments, user interface engine 308 may optionally be configured to present on display device 302 at least one questionnaire to subject 102 to collect information from subject 102. In some embodiments, the questionnaire aims to collect information that may be relevant to the characteristic parameters of the fragrances to which the subject has been or will be exposed. For example, the quiz might ask a series of true/false or multiple-choice questions that elicit references for certain top scores, middle scores, or bottom scores. For example, a quiz question may present the subject with a number of images representing scenes, such as beach, forest, etc., to elicit an answer in which the depicted scene is associated with a subject's favorite fragrance. . In another question of the questionnaire, the subject can also be asked if he prefers feminine, masculine or unisex scents. In yet another question of the questionnaire, the subject can also be asked if he prefers the scent to be perceptible, subtle, complementary or powerful. In yet another question of the questionnaire, the subject may also be asked to enter their favorite fragrances, including the most recently purchased fragrance. Some or all of the collected data may be stored, for example, in user data store 314.

In some embodiments, questionnaire analysis engine 310 may be configured to receive questionnaire responses from subject 102 via user interface engine 308, and may determine at least one preference, e.g., scent preference, scent characteristic, etc., of subject 102 based on one or more of the responses. For example, if subject 102 has selected the forest scene as a favorite, questionnaire analysis engine 310 may be configured to determine that subject 102 prefers woodsy notes, as shown for example in the fragrance wheel of the . In some embodiments, quiz analysis engine 310 may be configured to compare responses to data stored, for example, in product data store 316. In one embodiment, quiz analysis engine 310 may be configured to determine at least one personality trait of subject 102 based on one or more of the responses.

In some embodiments, recommendation engine 312 may be configured to generate at least one product recommendation for subject 102 based on at least the EEG data. In other embodiments, recommendation engine 312 may be configured to generate at least one product recommendation for subject 102 based on at least EEG data and data from an optional questionnaire. In some embodiments, the product recommendation is in the form of a specific product, such as Trade Winds branded perfume. In other embodiments, the product recommendation is in the form of a fragrance profile. In these embodiments, the fragrance profile may be presented as a description in words, visually represented as a score chart, and so on. In some embodiments, recommendation engine 312 provides the product recommendation to present to subject 102 via display device 302.

For example, the is an example of a fragrance rating chart that may be generated by recommendation engine 312 and presented to subject 102. The rating chart represents characteristic parameters of the fragrances preferred by subject 102. As shown in Figure , the note diagram visually represents the top notes, middle or middle notes, and optionally, the bottom notes, which are preferable for the subject. These notes are represented in a pattern that forms a wheel, with bar segments indicating the intensity of preference. For example, regarding the top notes, the aromatic is represented by five (5) bars, which the subject prefers more than the floral, which is represented by one (1) bar. Similarly, regarding the middle notes, fruity is represented by four (4) bars, which the subject prefers more than spicy, which is represented by three (3) bars. In addition to or instead of the number of bars, the color of the bars can also indicate the intensity of the appreciation. In some embodiments of the score chart, the intensity values can be linear (i.e., two bars is twice as good as one bar, etc.) or non-linear, such as logarithmic , exponential, etc.

On the diagram shown on the , this subject appreciates the aromatic, and to a lesser extent the citrus, in the top notes, the green and the fruity, and to a lesser extent the spicy, in the middle notes, and the balsamic, and in a to a lesser extent musky or woody, in the base notes. In some embodiments, this score chart may be presented on display device 302 via user interface engine 308. In some embodiments, the score chart may be used by a fragrance consultant to recommend a product type or product line that matches the grade profile of the grade chart. In other embodiments, the rating chart may be used by subject 102 for comparison with a chart of fragrances from one or more fragrance vendors. In yet other embodiments, the recommendation engine 312 may analyze, for example, the rating chart image or the underlying data used to generate the rating chart, and based on the analysis, automatically present a product recommendation to subject 102.

In some embodiments, the recommendation engine 312 employs one or more algorithms to analyze the images (e.g., electroencephalograph, brain activity map, etc.) generated from the biometric data. Based on this analysis, one or more characteristic parameters of the fragrances preferred by the subject 102 can be determined. For example, in some embodiments, areas of the electroencephalograph or brain activity map with the F7 and F8 regions of the brain are analyzed for the presence of an increased stimulus. Such an increased stimulus may represent whether subject 102 likes or dislikes the fragrance to which he is exposed. Of course, other combinations of brain regions can be analyzed in different embodiments.

In some embodiments, the preferred fragrance characteristic parameters are determined based on the image(s) of the EEG data. For example, in some embodiments, image processing techniques are applied to images to determine preferred fragrance characteristic parameters. In some embodiments, recommendation engine 312 may include or access an artificial neural network that is trained to determine characteristic parameters based on the images. Of course, any other type of suitable machine learning technique and/or conventional image processing technique can be performed to determine the preferred characteristic parameters of the fragrances exposed to subject 102.

For example, in some embodiments, recommendation engine 312 includes a machine learning model to help determine the product recommendation. The machine learning model can be trained using, for example, electroencephalograph images, brain activity maps, etc., of subjects exposed to known fragrances (with known characteristic parameters, such as note) which proved to be appreciated by the subject. In some embodiments, the known fragrance images are used to create a supervised training dataset, and a machine learning model such as an artificial neural network can be trained with the training data using any proper technique, including but not limited to gradient descent. The resulting machine learning model will accept an image from EEG engine 336 as input, and produce either preferred feature parameters or a product recommendation that has a high probability of likeability by subject 102. In some embodiments, the parameters Preferred characteristics can be used to generate a fragrance profile, such as the fragrance note chart. , of subject 102.

Accordingly, with knowledge of the preferred fragrance characteristic parameters determined from the EEG data, and/or data from an optional questionnaire, the recommendation engine 312 is configured to determine an appropriate product stored in a product data store. 316 that matches or is highly correlated to preferred feature parameters determined by system 100. For example, recommendation engine 312 may compare the results to a product map, lookup table, etc., stored in the data store of products 316. The comparison may be based, for example, on a confidence level of potential match.

In some embodiments, mobile computing device 104 may also include user data store 314, which is configured to store records for each subject 102 who uses system 100. Records may include, for example, at least a fragrance product, at least one fragrance profile, answers to a questionnaire, at least one personality trait, at least one product recommendation, and/or other information collected or determined by the system 100. In one mode As an embodiment, feedback received from subject 102 after using the recommended product(s) may also be stored in user data store 322 or transferred to product data store 316 in order to improve future product recommendations by the 100 system.

Further details on the actions performed by each of these components are provided below.

"Engine" means the logic embodied in hardware or software instructions, which may be written in a programming language, such as C, C++, COBOL, JAVA™, PHP, Perl, HTML, CSS, JavaScript, VBScript, ASPX, Microsoft .NET™, Go, and/or the like. An engine can be compiled into executable programs or written in interpreted programming languages. Software engines can be callable by other engines or by themselves. Generally, the engines described here refer to logic modules which can be merged with other engines, or can be divided into sub-engines. Engines may be stored in any type of computer readable medium or computer storage device and stored on and run by one or more general purpose computers, creating a special purpose computer configured to provide the engine or functionality of that -this. In some embodiments, the motors may be implemented by one or more circuits, programmable processors, application-specific integrated circuits (ASICs), programmable logic devices (PLDs), and/or field programmable logic devices (FPLDs). ), etc.

"Data Store" means any suitable device configured to store data for access by a Computing Device. An example of a data store is a fast and highly reliable relational database management system (DBMS) running on one or more computing devices and accessible over a high-speed network. Another example of a data store is a key value store. However, any other suitable technique and/or storage device capable of quickly and reliably providing the stored data in response to queries may be used, and the computing device may be accessed locally instead of be on a network, or may be provided as a cloud -based service. A data store may also include data stored in an organized manner on a computer-readable storage medium, such as a hard disk, flash memory, RAM, ROM, or any other type of computer-readable storage medium . Those skilled in the art will recognize that separate data stores described herein may be combined into a single data store, and/or a single data store described herein may be separated into multiple data stores, without departing from the scope of this disclosure.

FIGURE 4 is a block diagram that illustrates various components of a non-limiting example of an optional server computer system 108 according to one aspect of the present disclosure. In these embodiments, one or more functions of the mobile computing device discussed above may additionally or alternatively be performed by the server computing device 108. For example, the preference information fragrances (eg, scent preferences from, eg, biometric data (eg, brain activity) from exposure to scents and/or subject's optional questionnaire 102) collected by the mobile computing device 104 may be transmitted, with or without processing (e.g., filtering, transformation, etc.) and/or additional storage, to server computing device 108 via network 110. In this regard, server computing system 108 may include, for example , the EEG engine 306 ( ) to process and store EEG signals and optionally generate an electroencephalograph, such as the example shown in FIGURE 8 and/or a brain activity map, such as the example shown in .

In some embodiments, server computing system 108 uses information received from mobile computing device 104 to determine a product recommendation for use by subject 102, and relays the recommendation to mobile computing device 104 for presentation to subject 102. In this regard, server computer system 108 may additionally or alternatively include questionnaire analysis engine 310, recommendation engine 312, and/or product data store 316, the functionality of which has been described in detail above. In some embodiments, server computer system 108 may also include user data store 314.

There is a block diagram that illustrates aspects of a representative computing device 400 suitable for use as a computing device of the present disclosure. Although multiple different types of computing devices have been discussed above, representative computing device 400 depicts various elements that are common to the many different types of computing devices, such as mobile computing device 104 and/or server computing device 108 Although the is described with reference to a computing device that is implemented as a device on a network, the description below is applicable to servers, personal computers, mobile phones, smartphones, tablet computers, computer terminals, embedded computing devices, and other devices that may be used to implement portions of embodiments of this disclosure. Additionally, those skilled in the art and others will recognize that computing device 400 can be any of any number of devices currently available or yet to be developed.

In its most basic configuration, computing device 400 includes at least one processor 402 and system memory 404 linked by communication bus 406. Depending on the exact configuration and device type, system memory 404 may be volatile or nonvolatile, such as read-only memory (“ROM”), random-access memory (“RAM”), EEPROM, flash memory, or similar memory technology. Those of skill in the art and others will recognize that system memory 404 generally stores data and/or program modules that are immediately accessible to and/or currently operated by processor 402. In this regard, processor 402 may serve as a computing center of the computing device 400 by supporting the execution of instructions.

As further illustrated in the , computing device 400 may include a network interface 410 comprising one or more components for communicating with other devices on a network. Embodiments of the present disclosure can access basic services that use network interface 410 to perform communications using common network protocols. Network interface 410 may also include a wireless network interface configured to communicate via one or more wireless communication protocols, such as Wi-Fi, 2G, 3G, LTE, WiMAX, Bluetooth, Bluetooth low energy, and/or the like. . As will be appreciated by those skilled in the art, the network interface 410 illustrated in the may represent one or more wireless interfaces or physical communication interfaces described and illustrated above with respect to particular components of computing device 400.

In some embodiments in which the headset includes a multiplexer for combining multiple signal channels (one channel for each electrode), the network interface of the computing device, such as mobile computing device 104, includes, for example, a demultiplexer implemented in hardware or software to separate received EEG signals into their respective channels. Alternatively, EEG engine 306 may include such a demultiplexer. In these embodiments, the collected (eg, received) EEG signals are routed through a demultiplexer so that the mobile computing device 104 treats each channel of the EEG signals as EEG data.

In the exemplary embodiment shown in , the computing device 400 also includes a storage medium 408. However, services can be accessed using a computing device that does not include means for persisting data on a local storage medium. Therefore, the storage medium 408 shown in the is shown with a dotted line to indicate that storage medium 408 is optional. In either event, storage medium 408 may be volatile or non-volatile, removable or non-removable, implemented using any technology capable of storing information, such as, but not limited to, a hard drive, solid state disc, CD-ROM, DVD, or other disc storage device, magnetic cassettes, magnetic tape, magnetic disc storage device, and/or the like.

As used herein, the term "computer-readable medium" includes volatile and non-volatile and removable and non-removable media embodied in any process or technology capable of storing information, such as computer-readable instructions. computer, data structures, program modules, or other data. In this regard, the system memory 404 and the storage medium 408 shown in the are examples of computer-readable media only.

Suitable implementations of computing devices that include processor 402, system memory 404, communication bus 406, storage medium 408, and network interface 410 are known and commercially available. For ease of illustration and because it is not material to an understanding of the subject matter claimed, the does not show some of the typical components of many computing devices. In this regard, computing device 400 may include input devices, such as a keyboard, numeric keypad, mouse, microphone, touch input device, touchscreen, tablet, and/or the like. . These input devices may be coupled to computing device 400 by wired or wireless connections, including RF, infrared, serial, parallel, Bluetooth, Bluetooth low energy, USB, or other suitable connection protocols using wireless connections. wire or physical. Similarly, computing device 400 may also include output devices such as a display, speakers, printer, and the like. Since these devices are well known in the art, they are not further illustrated or described herein.

There is a flowchart that illustrates a non-limiting example of a method for generating and/or providing product recommendations to a subject according to one or more aspects of the present disclosure. The representative method, generally designated 600, will be described with reference to one or more components of system 100 shown in FIGURES 1-5.

Prior to beginning method 600, one or more EEG sensors 106 are coupled to subject's head 102. In some embodiments, only two EEG sensors 106 are placed at the F7 and F8 regions of the subject's brain (with or without reference). In other embodiments, a helmet 114 composed of a number of EEG sensors 106 is placed on the head of a subject 102. In some of these embodiments, the sensors 106 are located at regions F7 and F8 in addition to other regions of the subject's brain. In some embodiments, reference electrodes may be positioned on the subject's head.

Once the sensors have been appropriately associated with subject's head 102, the process can begin. From a starting block, process 600 proceeds to block 602, where subject 102 is exposed to one or more fragrances. For example, the subject is exposed to a sequence of fragrances. Subject 102 is exposed to each fragrance for a period of time. In one embodiment, the time period is about 45 seconds. Of course, shorter or longer time periods can be employed. In other embodiments, the time period is about five (5) minutes or more. In some embodiments, the fragrances exposed to the subject include two or more fragrance notes, such as top notes and middle notes.

The subject's reaction to one or more fragrances is captured by the EEG sensors 106 and transmitted as EEG signals to the mobile computing device 104. In this regard, the mobile device 104 is communicatively coupled to the EEG sensors 106 and receives the EEG signals ( e.g., biometrics) from subject 102 to block 604.

At block 606, the EEG signals can then be processed by the EEG engine 306 to generate, for example, EEG data. In some embodiments, the EEG signals are processed by the EEG engine 306 residing on the mobile computing device 104. In other embodiments, the EEG signals are processed by the EEG engine 306 residing on the server computing device 108. In these or other embodiments, EEG data is stored in user data store 314, either locally on mobile computing device 104 or remotely on server computing system 108.

In some embodiments, the actions performed at blocks 602, 604, 606 are repeated for each fragrance to be exposed to subject 102. In other embodiments, the actions performed at blocks 602 and 604 may be performed for each fragrance before the actions performed at block 606. In one embodiment, the subject is exposed to four fragrances. Of course, a number of fragrances, more or less than four, can be used in embodiments of the present disclosure.

In one embodiment, a preselected set of fragrances are exposed to the subject 102. For example, the preselected set of fragrances may be the first four sales of a company's fragrance line. In other embodiments, as will be described in greater detail with respect to the , subject 102's reaction to the previous fragrance can be used by system 100 to influence the choice of subsequent fragrance(s) to be exposed to subject 102. For example, EEG data generated by the motor EEG 306 from fragrance exposure can be presented on display device 302, for example, as an electroencephalograph or brain activity map. With the help of a fragrance consultant, the next fragrance can be chosen to be presented to the subject 102. Alternatively, the system can be configured to automatically choose the next fragrance to be presented to the subject based on the reaction from the subject to the previous fragrance(s).

In some embodiments, each fragrance exposed to subject 102 includes at least two notes (eg, a top note and a middle note, two middle notes, a top note and a base note, etc.). In some embodiments, each fragrance exposed to subject 102 includes at least three notes (e.g., a top note, a middle note, and a base note (vertical chord), three middle notes (a horizontal chord), etc. .). In any event, the characteristic parameters (e.g., top notes, middle notes, and/or base notes) of the fragrances exposed to subject 102 are known and stored in product data store 316. In some embodiments , the characteristic parameters are stored, for example, as a note diagram.

In some embodiments, a user interface engine 308 of mobile computing device 104 optionally presents a questionnaire to subject 102 at block 608. In some embodiments, the questionnaire may include questions that directly represent values for subject 102 For example, the questionnaire may specifically ask subject 102 to enter a preference for fragrances, including specific product names, favorite notes, or other fragrance characteristics, such as whether the subject likes masculine fragrances. , feminine or unisex, etc. In other embodiments, UI engine 308 presents one or more questions to subject 102, the answers to which can be used to infer the subject's preferences mentioned above or others.

At block 610, user interface engine 308 receives questionnaire responses and passes the responses to questionnaire parsing engine 310 for processing. At block 612, the questionnaire analysis engine 310 determines one or more fragrance preferences based on the questionnaire responses. In some embodiments, responses are processed by questionnaire parsing engine 310 residing on mobile computing device 104. In other embodiments, responses are processed by questionnaire parsing engine 310 residing on mobile computing device 104. located on the server computing device 108. The user interface engine 308 may receive the responses via inputs into the user interface presented on the display device 302. The questionnaire responses and the results of processing the responses may be stored in user data store 314. It will be appreciated that the actions performed at blocks 610 and 612 are also optional.

At block 614, a recommendation engine 312 determines a product recommendation based on at least the EEG data, and optionally, subject preferences determined by the questionnaire engine 310. In doing so, the recommendation engine 312 can access to data in product data store 316. In some embodiments, the product recommendation is a particular product. In other embodiments, the product recommendation is a fragrance profile. In some embodiments, the product recommendation is determined by recommendation engine 312 residing on mobile computing device 104. In other embodiments, the product recommendation is determined by recommendation engine 312 residing on the server computing device 108.

At block 616, the product recommendation is presented to the subject. For example, in one embodiment where a specific product is featured, the product recommendation may be displayed on display device 302 along with, for example, a description (e.g., rating profile) of the product, price of the product, where the product can be purchased, etc. In other embodiments in which the product recommendation is in the form of a fragrance profile, the fragrance profile may be displayed to a fragrance consultant by display device 302. With assistance from the consultant in fragrances, one or more products may be presented to the subject based on the fragrance profile.

Method 600 then proceeds to a final block and ends.

In an alternate embodiment, the actions of blocks 602, 604, and 606 are performed sequentially for each fragrance to be exposed to the subject. In this embodiment, instead of fragrances being pre-selected, recommendation engine 312, or another system engine, determines the next fragrance to present to subject 102 based on biometric data generated from exposure to the previous fragrance. In some embodiments, recommendation engine 312 determines the next fragrance to present to subject 102 based on biometric data generated from previous fragrance exposure and one or more questionnaire responses from block 608 Once all fragrances have been presented to subject 102, the process then proceeds to block 616, where recommendation engine 312 determines a product recommendation.

In some embodiments, the first fragrance to be selected is determined based on one or more questionnaire responses. Consequently, the questionnaire can be presented to the subject before any exposure to a fragrance.

The detailed description set out above, in conjunction with the accompanying drawings, in which identical reference numerals designate identical elements, are intended as a description of different embodiments of the present disclosure and are not intended to represent the only modes of achievement. Each embodiment described in this disclosure is provided for exemplary or illustrative purposes only and should not be construed as preferred or advantageous over other embodiments. The illustrative examples provided herein are not intended to be exhaustive or to limit the disclosure to the precise forms disclosed. Similarly, any steps described herein may be interchangeable with other steps, or combinations of steps, to achieve the same or substantially similar result. Additionally, some of the process steps may be performed in series or in parallel, or in any order unless expressly expressed or understood in the context of other process steps.

In the foregoing description, specific details are set forth to provide a thorough understanding of exemplary embodiments of the present disclosure. It will be apparent to those skilled in the art, however, that the embodiments disclosed herein can be practiced without incorporating all of the specific details. In some examples, well-known process steps have not been described in detail so as not to unnecessarily obscure various aspects of the present disclosure. Further, it will be appreciated that embodiments of the present disclosure may employ any combination of features described herein.

This application may also refer to quantities and numbers. Unless expressly stated, these quantities and numbers are not to be construed as limiting, but as examples of the possible quantities or numbers associated with this application. Also in this regard, this application may use the term "plurality" to refer to quantity or number. In this regard, the term "plurality" means any number greater than one, for example, two, three, four, five, etc. The term “approximately”, “approximately”, etc., means plus or minus 5% of the value indicated.

Throughout this specification, specialized terms may be used. These terms shall take their ordinary meaning in the art from which they derive, unless otherwise defined herein or the context of their use clearly suggests otherwise.

The principles, exemplary embodiments, and modes of operation of the present disclosure have been described in the foregoing description. However, aspects of this disclosure, which are intended to be protected, should not be construed as being limited to the particular embodiments disclosed. Further, the embodiments described herein should be considered illustrative rather than restrictive. It will be appreciated that variations and modifications may be made by others, and equivalents employed, without departing from the spirit of this disclosure. Accordingly, it is expressly understood that all such variations, modifications, and equivalents are within the spirit and scope of this disclosure as claimed.

Claims (1)

A method for recommending a product to a user, comprising:

• obtaining questionnaire data from the subject;
• obtaining biometric data of a subject based on exposure to a stimulus;
• analysis of biometric data; And
• recommending a product to the subject based on the analyzed biometric data and one or more of said questionnaire data indicative of a preference for a characteristic parameter of a product.