WO2018036944A1 - Procédé et système de suivi de nourriture et de boissons et recommandations de consommation - Google Patents

Procédé et système de suivi de nourriture et de boissons et recommandations de consommation Download PDF

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Publication number
WO2018036944A1
WO2018036944A1 PCT/EP2017/070987 EP2017070987W WO2018036944A1 WO 2018036944 A1 WO2018036944 A1 WO 2018036944A1 EP 2017070987 W EP2017070987 W EP 2017070987W WO 2018036944 A1 WO2018036944 A1 WO 2018036944A1
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WIPO (PCT)
Prior art keywords
user
consumption
item
coffee
recommendation
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PCT/EP2017/070987
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English (en)
Inventor
Rick BEZEMER
Laurentia Johanna HUIJBREGTS
Original Assignee
Koninklijke Philips N.V.
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Application filed by Koninklijke Philips N.V. filed Critical Koninklijke Philips N.V.
Publication of WO2018036944A1 publication Critical patent/WO2018036944A1/fr

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/60—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders

Definitions

  • the present disclosure is directed generally to methods and systems for tracking food and beverage consumption and providing intake recommendations, and more specifically, to methods and systems for personalized tracking consumption of coffee and providing recommendations using physiological data.
  • Coffee beans are one of the most commonly traded commodities in the world, and coffee is one of the most popular drinks in the world. Across the globe millions of people consume at least one cup of coffee every day, and many millions of those people consume multiple cups of coffee a day.
  • Coffee is typically consumed for the stimulating effect resulting from the caffeine extracted from the coffee beans.
  • moderate coffee consumption is either benign or mildly beneficial to healthy adults
  • the substances/ingredient in coffee can affect different people in different ways.
  • coffee can have either beneficial or harmful effects. Indeed, one individual may react negatively to the same amount of coffee that another individual can consume without effect. Since coffee can increase the consumer's alertness and/or activity and can suppress appetite, it may help in losing weight. In contrast, too much coffee can lead to stress and increase sleep onset latency, and in some individuals can even result in serious health consequences such as arrhythmias or migraines.
  • the present disclosure is directed to methods and systems for providing individualized recommendations for consumption of an item.
  • Various embodiments aim at understanding the physiological response corresponding to a major ingredient/substantial ingredient of the consumed item.
  • ingredients include but are not limited to caffeine, alcohol, etc., and corresponding to items coffee and alcoholic beverage respectively.
  • Various embodiments and implementations herein are directed to a device or system that receives both physiological data and consumption data from a consumer to build a model that describes one or more characteristics of the user's physiological response to the consumption of the item. .
  • the model may use a variety of different approaches, including but not limited to a machine learning approach. Based on one or more goals set by the consumer, the generated model is utilized to generate recommendations that are delivered to the user.
  • Physiological data which can include physical data, behavioral data, and/or any other data about the user— and/or data about consumption may be collected, for example, via a wearable, personal machine, and/or intelligent device such as a cup, utensil, or other devices, among other sources.
  • a computer-implemented method for providing a recommendation to a user about consumption of an item includes the steps of: receiving information about the user's consumption of the item; receiving physiological sensor data about the user from one or more sensors; generating, based at least in part on the received information about the user's consumption of the item and the received physiological sensor data about the user, a model describing at least one characteristic of the user's physiological response to consumption of the item; generating, based at least in part on the generated model, a recommendation to the user about a future consumption of the item; and providing the generated recommendation to the user.
  • information about the user's physiological response to the consumption of the item is also provided to the user.
  • the information about the user's consumption of the item comprises the amount of the item consumed and/or the time of consumption.
  • the physiological sensor data about the user comprises one or more of blood pressure, heart rate, heart rhythm, heart rate variability, pulse, blood oxygen, body temperature, activity, posture, respiratory rate, muscle tension, brain activity, sleep stage, sleep onset latency, sleep duration, and activity level.
  • the method further includes the step of receiving a goal from the user, where the step of generating a recommendation is also based at least in part on the received goal.
  • the physiological sensor data is received from a sensor of a wearable device and/or from a sensor of a coffee machine.
  • the information about the user's consumption of the item is received from a coffee machine and/or from an intelligent coffee cup.
  • the recommendation comprises a period of time until the item should be consumed, or a time after which the item may be or should be consumed. According to an embodiment, the recommendation comprises a time until which the item may not or should not be consumed. According to an embodiment, the recommendation comprises a time after which the item may not or should not be consumed. According to an embodiment, the recommendation comprises a time before which the item should be consumed.
  • the step of generating a recommendation is also based at least in part on a physical or behavioral status of the user.
  • the item is coffee.
  • a system for providing a recommendation to a user about consumption of an item includes: a wearable device comprising a sensor, a processor, and a display, wherein the sensor is configured to provide physiological sensor data about the user; a consumption monitoring device configured to generate information about the user's consumption of the item, wherein the generated information is communicated to the processor; wherein the processor is configured to: (i) generate, based at least in part on the received information about the user's consumption of the item and the received physiological sensor data about the user, a model describing at least one characteristic of the user's physiological response to consumption of the item; (ii) generate, based at least in part on the generated model, a recommendation to the user about a future consumption of the item; and cause the display to provide the generated recommendation to the user.
  • the consumption monitoring device is configured to recognize the consumer.
  • a device for providing a recommendation to a user about consumption of an item includes: a sensor configured to collect physiological sensor data about the user and information about the user's consumption of the item; a processor configured to: (i) generate, based at least in part on the collected information about the user's consumption of the item and the collected physiological sensor data about the user, a model describing at least one characteristic of the user's physiological response to consumption of the item; (ii) generate, based at least in part on the generated model, a recommendation to the user about a future consumption of the item; and a display configured to provide the generated recommendation to the user.
  • processor is used generally to describe various apparatus components relating to the operation of the recommendation apparatus, system, or method.
  • a processor can be implemented in numerous ways (e.g., such as with dedicated hardware) to perform various functions discussed herein.
  • a "processor” can employ one or more microprocessors that may be programmed using software (e.g., microcode) to perform various functions discussed herein.
  • a processor may also be implemented as a combination of dedicated hardware to perform some functions. Examples of processor components that may be employed in various embodiments of the present disclosure include, but are not limited to, conventional microprocessors, application specific integrated circuits (ASICs), and field-programmable gate arrays (FPGAs).
  • ASICs application specific integrated circuits
  • FPGAs field-programmable gate arrays
  • a processor may be associated with one or more storage media (generically referred to herein as "memory,” e.g., volatile and non-volatile computer memory such as RAM, PROM, EPROM, and EEPROM, floppy disks, compact disks, optical disks, magnetic tape, etc.).
  • the storage media may be encoded with one or more programs that, when executed on one or more processors and/or controllers, perform at least some of the functions discussed herein.
  • Various storage media may be fixed within a processor or controller or may be transportable, such that the one or more programs stored thereon can be loaded into a processor or controller so as to implement various aspects discussed herein.
  • program or “computer program” are used herein in a generic sense to refer to any type of computer code (e.g., software or microcode) that can be employed to program one or more processors or controllers.
  • non-transitory machine-readable medium will be understood to encompass both volatile and non-volatile memories, but to exclude transitory signals.
  • user interface refers to an interface between a human user or operator and one or more devices that enables communication between the user and the device(s).
  • GUIs graphical user interfaces
  • Various embodiments of the present invention may further include non-transitory computer-readable storage media, having embodied thereon a firewall program executable by a processor to perform methods described herein.
  • FIG. 1 is a block diagram of a system for providing individualized recommendations, in accordance with an embodiment.
  • FIG. 2 is a block diagram of a system for providing individualized recommendations, in accordance with an embodiment.
  • FIG. 3 is a block diagram of a system for providing individualized recommendations, in accordance with an embodiment.
  • FIG. 4 is a block diagram of a system for providing individualized recommendations, in accordance with an embodiment.
  • FIG. 5 is a flowchart of a method for providing individualized recommendations, in accordance with an embodiment.
  • FIG. 6 is a flowchart of a method for providing individualized recommendations, in accordance with an embodiment.
  • FIG. 7 is a timeline of coffee consumption and recommendations, in accordance with an embodiment.
  • the present disclosure describes various embodiments of a method for providing individualized recommendations for coffee consumption. More generally, Applicant has recognized and appreciated that it would be beneficial to provide a device or system that receives both physiological data and coffee consumption data about a consumer, and analyzes the consumer's response to coffee over time using a machine learning approach. A particular goal of utilization of certain embodiments of the present disclosure is to provide consumption recommendations to a coffee consumer that accounts for one or more goals set by that consumer.
  • various embodiments and implementations are directed to a system that receives physiological data and coffee consumption data about a consumer.
  • the physiological data and/or data about coffee consumption may be collected, for example, via a wearable, coffee machine, and/or intelligent coffee cup, among other sources.
  • the received data is analyzed to determine the consumer's response to coffee consumption, and that analysis or model is utilized to generate recommendations that are delivered to the user.
  • a consumable can be an item that is taken by the user via a method other than eating or drinking.
  • the consumed item could be an inhalable such as a gas or vaporized item.
  • the consumed item could be an injectable such as a liquid that is injected into the individual.
  • the system or method could relate to a consumable that may affect the cardiovascular system of the consumer.
  • a system 100 comprising a consumer 110 and a wearable device 120.
  • the consumer 110 is any individual that has or is planning to consume a beverage that may affect the user physiologically.
  • the individual may be an average consumer of coffee, an above-average consumer of coffee, or an individual interesting in beginning to consume coffee.
  • the individual may also be a consumer of any other beverage that the consumer wishes to track, such as alcoholic beverages.
  • Wearable device 120 may be any device suitable for collecting the information utilized in the methods described or otherwise envisioned herein.
  • the wearable device 120 may be any type of mobile electronic device that can be worn on the body, any device that is attached to or embedded in clothes, and various other accessories of an individual. Examples of some wearable technology include the FitBit ® , Nike+ FuelBand ® , Apple Watch ® , the Philips ® Health Watch, Jabra ® Sport Pulse Wireless Earbuds, and chest- worn patches such as the Philips ® Wearable Biosensor, among others.
  • the wearable device 120 is a smartphone or similar mobile computing device.
  • the wearable device comprises a processor 12 which is configured or programmed to receive one or more signals from a sensor 14, a user interface 16, and/or a communications module 18, and configured or programmed to transmit one or more signals to a display 20.
  • the processor 12 may be programmed using software to perform various functions discussed herein, and can be utilized in combination with a memory 22 and/or database 24.
  • Memory 22 and/or database 24 can store data, including one or more commands or software programs for execution by processor 12, as well as various types of data.
  • the memory 22 may comprise a non-transitory computer readable storage medium that includes a set of instructions that are executable by processor 12, and which cause the system to execute one or more of the steps of the methods described herein.
  • the wearable device also comprises a battery or power supply 26, which provides power for operation of the wearable device 120.
  • the battery or power supply 26 may be implemented through the use of a lithium ion battery, for example.
  • the power supply 26 may also be implemented through the use of a capacitor. It may be possible to have the power supply 26 be capable of being charged or re-charged through the use of an external power source, such as a wired and/or wireless battery charger or charging field.
  • the communications module 18 facilitates wired or wireless communication between the wearable device 120 and other devices and/or networks, such as network 140.
  • the communication module 18 may be facilitated through the use of one or more antennas.
  • the communication module 18 can facilitate communication with one or more networks or with other devices, for example, by using wireless methods that are known, including but not limited to Wi-Fi, Bluetooth, 3G, 4G, LTE, and/or ZigBee, among others.
  • the wearable device also comprises one or more integrated sensors 14.
  • the one or more sensors 14 are used to monitor and obtain sensor data, and thus evaluate a condition of the user and/or a parameter of the environment in which the user is located.
  • the one or more sensors 14 may comprise, for example, a sensor configured to measure, determine, or derive blood pressure, heart rate, heart rhythm, heart rate variability, pulse, blood oxygen, body temperature, skin temperature, activity, body posture, vitamin levels, respiratory rate, heart sound, breathing sound, movement speed, movement acceleration, muscle tension, brain activity, sleep stages, sleep onset latency, sleep duration, steps walked or ran, skin moisture, sweat detection, sweat composition, nerve firings, or similar health measurements, and other sensors known in the art.
  • the one or more sensors 14 may be a sphygmomanometer, photoplethysmogram (PPG) sensor, ECG-based heart monitor, pulse oximeter, thermometer, electromyography sensor, electroencephalography sensor, accelerometer, microphone, pedometer, electromagnetic sensor, camera, pressure sensor, and/or any other type of sensor.
  • PPG photoplethysmogram
  • one or more combinations of sensors might be used to derive a physiological parameter.
  • a PPG sensor on the finger or wrist can be used in combination with an ECG-based patch on the chest in order to derive pulse arrival time, which can be utilized as a surrogate measure for blood pressure.
  • a single sensor can be used to derive various physiological parameters.
  • an Sp0 2 sensor might be used to measure oxygen saturation and measure heart rate.
  • An ECG-based patch on the chest could be used to measure heart rhythm and measure the width of the P-wave, height of the R peak, and/or other ECG characteristics.
  • a normal or thermal camera can be used to measure one or more of activity, pulse rate, blood oxygenation, respiration rate, and skin temperature.
  • the wearable device may comprise a user interface 16 to receive input from the consumer 110.
  • the user interface may be a button or multiple buttons, a microphone, a key stroke input, or any of a variety of other inputs.
  • consumer 110 may provide the input remotely via a computer which communicates the input to the wearable device via network 140.
  • the one or more physiological characteristics that are described by the model might be any physiological information derived from the sensor data, such as heart rhythm, blood pressure, or respiration rate, or any physiological information derived from user input, such as having had a bad sleep last night or the start of a migraine attack.
  • the invention aims at characteristics that can change in response to the intake of an ingredient (almost) instantly, i.e. during the time frame when the ingredient is being digested by the body and/or is still in a relatively large amount present in the body.
  • the model could reveal that for a certain user the heart rate drops in the first 30 minutes after consuming caffeine (ingredient) and increases back to normal in the following hour, or the model could reveal that the user has a large sleep onset latency when drinking two or more cups of coffee (item) on the same night.
  • the wearable device 120 also comprises a display 20 which is utilized by the wearable device to provide information or otherwise facilitate interaction between the user and the wearable device.
  • the display may be, for example, an LED-based, LCD-based, or e-paper type display.
  • the display may be a touch screen display that allows the user to directly interact with the wearable device through physical contact and/or gestures.
  • the display 20 is any interface configured to provide information to the user.
  • display 20 may be a speaker that provides audio information to the user.
  • the display 20 may also be a haptic component configured to give haptic feedback or information to the user.
  • a wide variety of mechanisms may be utilized for display 20 to provide information to the user.
  • the components of the wearable device 120 in FIG. 1 can be connected via a single bus, and/or through one or more data transport means. As such, some components may be connected via a local microprocessor bus, and others may be connected via one or more input/output (1/0) buses.
  • system 100 can optionally comprise a display device 130, which may also serve as a user input device.
  • the device 130 may be, for example, a smartphone or other portable device.
  • the device 130 may be a computer such as a desktop, laptop, tablet, or other permanent or semi-permanent computing device.
  • the device 130 may receive input from the consumer and may also display information to the consumer, as described or otherwise envisioned herein.
  • System 200 for providing individualized recommendations for coffee consumption.
  • System 200 comprises a consumer 110, a wearable device 120, and a coffee consumption monitoring device 150.
  • the system also optionally includes a network 140 and a computer or server 160.
  • the wearable device 120 in system 200 can be any of the devices described or otherwise envisioned herein.
  • wearable device 120 may comprise one or more of a processor 12, sensor 14, user interface 16, communications module 18, display 20, memory 22, database 24, and/or battery or power supply 26.
  • the wearable device 120 is configured or programmed to obtain physiological data about the consumer 110.
  • the coffee consumption monitoring device 150 can be, for example, any device that monitors or provides information about the consumer's consumption of coffee or the beverage of interest.
  • the wearable device 120 may monitor or provide information about the consumer's consumption of coffee or the beverage of interest.
  • the wearable device 120 may recognize repeated hand movements associated with prolonged or periodic sipping or drinking of the beverage of interest.
  • the coffee consumption monitoring device 150 provides the consumption tracking information.
  • coffee consumption monitoring device 150 may be a coffee maker.
  • the coffee maker can track coffee made and/or consumed by the consumer.
  • the coffee maker may comprise a volume sensor, a pour sensor, a motion sensor, or any of a variety of other monitors or sensors to obtain information about coffee creation and/or consumption.
  • the coffee consumption monitoring device 150 may therefore comprise a communications module to communicate the information via network 140 to wearable device 120 and/or another computer or server 160.
  • coffee consumption monitoring device 150 may be an intelligent handheld device associated with coffee consumption, such as a coffee cup, mug, or thermos.
  • the coffee cup could comprise, for example, temperature, motion, and/or other sensors to obtain information about coffee consumption by the consumer.
  • the coffee cup may also comprise a communications module to communicate the information via network 140 to wearable device 120 and/or another computer or server 160.
  • the consumption monitoring device 150 may be any intelligent device or system.
  • the device may be a utensil such as a smart spoon, smart knife, and/or smart fork.
  • the utensil for example, could be used to monitor the user's consumption, and may even directly analyze the item itself.
  • a smart spoon may provide information about the coffee or food to be monitored.
  • computer or server 160 is a data repository and/or analysis engine.
  • the computer or server 160 may receive and analyze coffee consumption and/or physiological information in order to create personalized consumption recommendations.
  • the functionality of computer or server 160 may be performed entirely by wearable device 120.
  • System 200 comprises a consumer 110 and a consumption monitoring device 150.
  • the system also optionally includes a network 140 and a display device 130.
  • the system also comprises software components.
  • at least a portion of the personalized consumption recommendation system may be an application installed and running on a smartphone, computer, tablet, or other computerized device.
  • at least a portion of the personalized consumption recommendation system may be software as a service, hosted in the cloud or locally on a server or computer, which receives information from the consumer. It analyzes the information and then provides recommendations to the consumer, such as part of a subscription service.
  • the consumption monitoring device 150 collects both physiological information about the consumer and information about the consumer's consumption of the beverage.
  • the device may comprise, for example, one or more of a processor 12, sensor 14, user interface 16, communications module 18, display 20, memory 22, database 24, and/or battery or power supply 26.
  • the consumption monitoring device 150 can be, for example, any device that monitors or provides information about the consumer's consumption of the beverage of interest.
  • consumption monitoring device 150 may be a coffee maker.
  • the coffee maker can track coffee made and/or consumed by the consumer.
  • the coffee maker may comprise a volume sensor, a pour sensor, a motion sensor, or any of a variety of other monitors or sensors to obtain information about coffee creation and/or consumption.
  • consumption monitoring device 150 may be an intelligent handheld device associated with beverage consumption, such as a bottle, cup, mug, or thermos.
  • the cup could comprise, for example, temperature, motion, and/or other sensors to obtain information about beverage consumption by the consumer.
  • the consumption monitoring device 150 will also comprise one or more sensors 14 configured to obtain physiological information about the consumer.
  • the device may comprise electrodes to obtain heart rate, a camera to obtain one or more vital signs such as blood pressure, pulse, and breathing rate, and/or any of a variety of other sensors.
  • the consumption monitoring device 150 may also comprise a wired and/or wireless communications module to communicate information, optionally via network 140, to another device, such as computing device 130.
  • the computing device will include, for example, a display that provides information to the consumer as described or otherwise envisioned herein. This enables the consumer to receive the information, which may include the personalized recommendations, while located remotely from the consumption monitoring device 150.
  • System 400 for providing individualized recommendations for coffee consumption.
  • System 400 comprises a consumer 110, a wearable device 120, a display device 130, a network 140, and a cloud-based database 24.
  • the wearable device 120 in system 400 can be any of the wearable devices described or otherwise envisioned herein.
  • wearable device 120 may comprise one or more of a processor 12, sensor 14, user interface 16, communications module 18, memory 22, database 24, and/or battery or power supply 26.
  • the wearable device 120 is configured or programmed to obtain physiological data about the consumer 110.
  • the display device 130 in system 400 can be any of the display devices described or otherwise envisioned herein.
  • display device 130 may be a smartphone or other portable device.
  • the device 130 may be a computer such as a desktop, laptop, tablet, or other permanent or semipermanent computing device.
  • the device 130 may receive input from the consumer and may also display information to the consumer, as described or otherwise envisioned herein.
  • the network 140 in system 400 provides at least a portion of the communication system between wearable device 120 and display device 130.
  • the cloud-based database 24 in FIG. 4 is any database, for example, remote from the wearable device 120, including one or more remote servers, or a remote data storage service.
  • database 24 can be any storage as a service offering.
  • a method 500 for providing individualized recommendations for consumption based on consumption information and physiological data about the consumer is provided.
  • the recommendation system may be any of the systems described or otherwise envisioned herein, including but not limited to system 100 in FIG. 1, system 200 in FIG. 2, system 300 in FIG. 3, and/or system 400 in FIG. 4, among many other embodiments.
  • the coffee consumption information is generated and received by the personalized consumption recommendation system or device.
  • the coffee consumption information can include information about when the coffee was consumed (including the start and stop times for the consumption), how much coffee was consumed, what was or was not added to the coffee, and/or the strength of the coffee.
  • the consumption information can be generated or shared by many different devices, and can be obtained by an analysis processor via a network such as a wired connection, WiFi, Bluetooth, NFC, or any other wired or wireless connection.
  • the consumption information can be sent to the cloud, where it can be stored and retrieved as needed.
  • the consumer can provide consumption information directly via a user interface.
  • the user can enter information about the amount, strength, and timing of coffee intake on a computing device such as the consumer's wearable device 120, a smartphone, tablet, PC, and/or any other computing device.
  • the data can be entered at home, at work, in a coffee shop, on the go, or anywhere.
  • the user could also take pictures with a phone or watch of the consumable(s) that the user is eating or drinking, and image recognition could be utilized to determine the amount of the substance of interest.
  • a home coffee maker is connected to the system and automatically collects information, while coffee consumption outside the home must be recorded, the consumer will only enter information for coffee consumed when outside the home.
  • the information may be entered by the consumer via selection of menus, automated selections, buttons, voice recognition, typing, or other user interface mechanisms.
  • the coffee machine itself may provide coffee consumption information. According to an embodiment, every time a coffee machine brews coffee, this can be used as time stamp for coffee intake after which a physiological response of the consumer can be measured.
  • the coffee machine at home is connected to the cloud or directly to a wearable device or other computing device, and collects the information needed for personalized recommendations.
  • the consumer's coffee machine at work is similarly connected to the cloud or directly to the consumer's wearable device or other computing device.
  • other mechanisms as described or otherwise envisioned herein will be utilized in settings or locations where a coffee machine is not collecting and sharing consumption information.
  • the machine may automatically record all information for that consumer.
  • the coffee machine may also be utilized by a spouse, family member, roommate, co-worker, or other individual within the home or office. This scenario requires extra measures to enable registration of all coffee intake and exclude intake by others. For example, in order to discriminate the desired consumer from other people using the same coffee machine, the consumer can enter his or her personal code on the coffee machine when starting the brewing of the coffee, or when pouring a cup or portion of coffee.
  • the coffee machine might automatically recognize the consumer, for example by finger print recognition on the buttons, by face recognition with a camera, or by proximity detection of the user's wearable, which might, for example, contain an RFID tag or other recognition means.
  • Recognition of the consumer by the coffee machine could, in addition to being used to obtain information on coffee intake, also be used to automatically brew the coffee blend, amount, and/or strength of the consumer's preference, or, as described herein, to automatically brew the blend, amount, and/or strength that is being recommend to the consumer by the personalized recommendation advisor.
  • the wearable device 120 may provide the consumption information.
  • a hand- or wrist-worn wearable such as a ring or watch with a motion sensor like an accelerometer or gyroscope can recognize the arm and/or hand movement of drinking, and the system can recognize not only the time of consumption, but possibly even the amount based on the number of movements, and/or on another quantitative or qualitative measurement of movement.
  • This information might also be combined with a known response to drinking coffee such as an increase in heart rate, especially when the system has already learned how the user would normally respond to drinking coffee. This might differentiate between the consumer drinking coffee versus drinking another beverage such as water.
  • a combination of physiological signs and/or motion recognition of drinking coffee could then lead to automatic recognition of when the user is drinking coffee.
  • the physiological response to drinking coffee is a decreasing heart rate and an increasing blood pressure, while for other people the opposite reaction is possible.
  • a personalized recommendation system can detect these differences in response and provide personalized recommendations .
  • the system may include an intelligent handheld device associated with coffee consumption, such as a coffee cup, mug, or thermos.
  • the coffee cup could comprise, for example, temperature, motion, and/or other sensors to obtain information about coffee consumption by the consumer.
  • the cup may comprise an RFID tag, which is recognized by the coffee machine and/or the wearable device. Accordingly, the cup may not be limited to use by one consumer, but may only be limited to the consumer at the time of drinking. Thus, the user's spouse, coworker, roommate, or other consumer could use the cup.
  • the coffee cup could also contain a sensor that detects coffee in the cup, for example by a thermometer, optionally combined with an optical sensor to discriminate between coffee and tea.
  • the intelligent cup might even measure how fast the beverage is being consumed.
  • a gyroscope or accelerometer in the cup might be used to detect the motion of bringing the cup to the mouth and drinking from it.
  • the cup might also contain sensors to measure physiological signals while the user holds his cup, such as a photoplethysmogram (PPG) sensor in the handgrip, among many other types of possible sensors.
  • PPG photoplethysmogram
  • the real ingredient that is of interest might be the caffeine contained in the coffee. Therefore it is desired to know or estimate the amount of caffeine contained in the coffee that is consumed by the user.
  • data obtained from the coffee machine could give the amount of caffeine in the coffee (/cappuchino/espresso), either directly or by giving the strength and amount of the brewed beverage.
  • an intelligent coffee cup it could contain sensors that can measure the strength and amount of coffee (e.g. optical sensors), from which the amount of caffeine can be derived.
  • the user input the user could be asked to select the strength and amount of coffee, e.g. on an app on his phone.
  • the system does not make use of a known or estimated amount of caffeine, but instead it could assume that the user is always consuming a similar amount of caffeine when he is drinking coffee.
  • physiological information about the consumer is generated and/or received by the personalized consumption recommendation system or device.
  • the physiological information can be generated, collected, and/or stored by the one or more sensors as described above. This physiological information will often be generated and collected for many different uses, one of which might be the consumption recommendation method.
  • a combination of wearables or remote devices can be used, such as a watch, ear plugs, a patch on the chest, and a camera, among many other possible devices and sensors.
  • the camera could for example be a camera on the coffee machine, the camera of a laptop or smartphone, or a camera integrated in a mirror, among others.
  • the physiological information can be generated and collected by a device other than a wearable device.
  • the physiological information can be generated and collected by a coffee maker, an intelligent beverage cup, or other device with a sensor.
  • the device will comprise one or more sensors 14 configured to obtain physiological information about the consumer.
  • the device may comprise an electrode to obtain heart rate, a camera to obtain one or more vital signs such as blood pressure, pulse, and breathing rate, and/or any of a variety of other sensors.
  • a smart coffee maker for example, there may be a sensor or camera on the handle or a button of the device.
  • a smart coffee cup for example, there may be a sensor on the handle of the cup.
  • Many other devices and configurations are possible.
  • the physiological data may be collected continuously, may be collected only during a time period when the beverage is being consumed and processed by the body, or may be collected continuously but only stored or analyzed during time periods when the beverage is being consumed and processed by the body.
  • monitoring when monitoring is only done during certain time periods, it is can be done from just before drinking coffee, in order to obtain a baseline, until a predetermined time period after finishing the coffee, to see the response. Monitoring could for example be triggered to start by a button press on the coffee machine, which also starts the brewing of coffee.
  • the measurement can be done at a fixed time each day, such as every morning before the first cup of coffee or only at night, in order to observe a general trend in health changes associated with altered coffee intake.
  • the system or device may also be configured to receive individualized feedback from the consumer about his or her physiology, psychology, well- being, or other feedback.
  • the smartphone or wearable device might be configured to obtain or receive custom input from or about the consumer regarding the quality of sleep, feeling of stress, feeling of well-being, having stomach pain, having a migraine, being pregnant, having an injury, weakness of stomach, a morbidity such as kidney or liver function, and/or any of a wide variety of other inputs.
  • the system may factor a physical and/or behavioral status into the recommendation.
  • the personalized consumption recommendation system or device analyzes the consumer's consumption information and physiological information to generate a model of the consumer's response to coffee consumption.
  • the parameters that might be included in the model are the consumer's responses to coffee based on the detection of altered heart rate or arrhythmias, altered blood pressure, altered activity levels, increased or decreased stress, altered sleep quality, and/or altered sleep onset, among many other possible parameters.
  • the model may be generated from a predetermined number of data points, such as a week or month of coffee consumption and physiological data.
  • the model may be generated from a predetermined number of beverage consumptions, such as 25 beverages consumed, for example.
  • the model may be continually adapted and refined with new data obtained or generated by the system.
  • the generated model may comprise, for example, an indication that the consumer's heart rate rises approximately 33% for approximately two (2) hours after coffee consumption. Or, the generated model may reveal that 93% of the time, the consumer's sleep onset is delayed by 30 minutes or more when consuming coffee within three (3) hours of sleep. The generated model may reveal that activity levels increase approximately one (1) hour after coffee consumption, but crash to below pre-coffee levels for the next three (3) hours. The generated model may contain information that the consumer's heart arrhythmia only appears on days when more than two (2) cups of coffee are consumed. Many, many other indications and much more information is possible in the consumer's personalized model.
  • the model also takes additional information into account. For example, in a scenario where the consumer was sitting before drinking coffee, but starts to walk after drinking coffee, the heart rate will logically go up due to activity regardless of the effect of the beverage. Accordingly, the heart rate incline should then be corrected for the change in activity, such as by comparing it to an increment in heart rate at other time periods where the consumer started walking after being seated, but without having consumed the beverage. Thus, activity recognition may be an important factor in the interpretation of the data. This information will be obtained, at least in part, from the one or more sensors 14 in the system.
  • the model may be generated in whole or in part from data extracted from averages or other data measured over one or more populations.
  • This information could be extracted, for example, from scientific literature.
  • scientific literature As an example, it is known from curated scientific literature when Cortisol levels normally rise during the day, or how long after waking up. This information might be important, for example, as research has shown that coffee addiction may more easily develop when coffee is consumed during periods of high Cortisol levels.
  • the model may be generated in whole or in part from data collected from other people.
  • the model may be based at least in part on data obtained from consumers of the same sex, age, profession, height, weight, activity level, and/or other characteristics of the target consumer. This data source may be utilized, for example, in order to enable faster understanding and tailoring the algorithms towards optimization of coffee intake.
  • the personalized consumption recommendation system or device receives one or more goals from or about the consumer.
  • the goals may be short-term and/or long-term goals.
  • the goal may be to lower blood pressure, avoid arrhythmias, increase activity levels, stay active, improve sleep, avoid, lessen, or cure coffee addiction, and/or lose weight, among many, many other possible goals.
  • the goal could be to reduce sleep onset latency, stay awake until a certain time, or live as healthy as possible in general.
  • the goal can be entered into or provided to the system via a variety of mechanisms.
  • the consumer can choose from among a variety of predetermined goals during registration and/or during use of the system or method.
  • a list of goals may be provided and selected from by means of a user input or interface.
  • the personalized consumption recommendation system or device generates, based at least in part on the physiological data and generated model, a consumption recommendation for the consumer.
  • the personalized consumption recommendation system or device also utilizes the consumer's predefined goal(s) when generating the recommendation.
  • the recommendation comprises a period of time until the item should be consumed, or a time after which the item may be or should be consumed.
  • the recommendation may be a time until which the item may not or should not be consumed.
  • the recommendation may be a time after which the item may not or should not be consumed.
  • the recommendation may be a time before which the item should be consumed.
  • information about beverage consumption is input into the trained algorithm or model in order to identify the likely outcomes, which are then compared to the consumer's goal(s).
  • the personalized consumption recommendation system or device generates a recommendation for the consumer's consumption of the item, such as "wait at least 3 hours before consuming additional coffee.”
  • the recommendation considers the relationship between the timing and/or dose of coffee intake in the evening and sleep quality at night, and will titrate coffee intake in such a way as to optimize sleep quality.
  • the system can create one recommendation or a series of recommendations that allow or suggest or recommend slowly decreasing amounts of coffee over time.
  • the system can also consider the consumer's goal to lessen coffee dependence in the evenings, and can create a series of recommendations that allow or suggest or recommend slowly decreasing amounts of coffee over a period of days, weeks, or months. This will lessen the side effects associated with rapid changes in coffee consumption.
  • the personalized consumption recommendation system or device also utilizes other information when generating the recommendation. For example, the system can consider the consumer's consumption preferences, such as the desired coffee times and strength, the user's health issues or circumstances, such as pregnancy and weakness of stomach, and/or a variety of other parameters or information.
  • the personalized consumption recommendation system or device pushes the recommendation to the consumer.
  • the recommendation can be provided to the consumer via a wearable device, via the coffee machine, on a local computer monitor or TV screen, or on another device such as a smartphone or tablet.
  • the wearable device, smartphone, or other display device provides a countdown until the consumer should consume additional coffee.
  • the wearable device, smartphone, or other display device can depict a countdown of two hours until additional coffee is recommended, after the consumer has consumed two cups of coffee within the past hour.
  • information about the user's physiological response to the consumption of the item is also provided to the user.
  • the physiological response information can be provided to the consumer via a wearable device, via a user machine, on a local computer monitor or TV screen, or on another device such as a smartphone or tablet.
  • the user may receive a graph or other display of the typical physiological response when the item is consumed at the recommended time.
  • the user may receive a graph or other display of the current physiological state, such as a heartbeat, temperature, behavior, or wide variety of other physiological information.
  • FIG. 6 in one embodiment, is a flowchart of a method 600 for providing individualized recommendations for consumption based on consumption information, physiological data about the consumer, and goal information from the consumer.
  • the method can be carried out by any of the consumption recommendation systems or devices described or otherwise envisioned herein, including but not limited to system 100 in FIG. 1 , system 200 in FIG. 2, system 300 in FIG. 3, and/or system 400 in FIG. 4, among many other embodiments.
  • the system receives several pieces of input, including physiological and/or behavioral data 610.
  • the data can include physical data, behavioral data, and/or any other data about the user.
  • the data can be obtained via any of the methods, systems, or devices described or envisioned herein, including but not limited to a wearable device.
  • the system also tracks consumption of the item, and provides consumption information 620.
  • the consumption information can be obtained via any of the methods, systems, or devices described or envisioned herein, including but not limited to a wearable device, a smart cup, a smart machine, or a variety of other mechanisms.
  • the system determines the user's response to consumption of the item. This can be based on one consumption event or numerous consumption events.
  • the system also receives goal information 640 from the user.
  • the goal may be one or more short-term and/or long-term goals.
  • the goal can be entered into or provided to the system via a variety of mechanisms. For example, the consumer can choose from among a variety of predetermined goals during registration and/or during use of the system or method.
  • a list of goals may be provided and selected from by means of a user input or interface.
  • the system uses information 630 about the user's response to consumption of the item, and modified by the goal information 640 from the user, the system generates one or more recommendations 650 to the user.
  • the recommendation comprises a period of time until the item should be consumed, or a time after which the item may be or should be consumed.
  • the recommendation may be a time until which the item may not or should not be consumed.
  • the recommendation may be a time after which the item may not or should not be consumed.
  • the recommendation may be a time before which the item should be consumed.
  • the recommendation which may or may not include the physiological, behavioral, or goal data, is provided to the user as output 660.
  • the output can be provided by a variety of mechanisms or devices, including but not limited to a visual display, an audio display, haptic feedback, and other methods.
  • FIG. 7 is a 24-hour timeline with coffee consumption and recommendations.
  • the consumer begins coffee consumption at approximately 7:30, followed by a second cup at 9:00.
  • the system detects an arrhythmia ("AF detected"), and the system recommends a three-hour pause in coffee consumption.
  • the display comprises a countdown until additional coffee may be consumed ("01 :45 until next cup of coffee”).
  • the consumer has a third cup of coffee. Since the consumer plans to sleep at approximately 22:00, which might be a predetermined goal or might be based on historical data or user input, the recommendation is that the consumer not consume coffee after 19:30 ("Do not consume coffee after 19:30").
  • the system may also inactivate or lock the coffee maker after 19:30.
  • the personalized consumption recommendation system or device optionally collects information about the amount, strength, and/or timing of the item consumed per day in order to generate and/or share information such as totals, averages, and trends to the consumer, a coach, a physician, a cardiologist, or another medical specialist to provide insight in habits and change in habits.
  • the shared information can reveal periods when the consumer is demanding too much from his or her body, resulting in tiredness, which the consumer compensates for by consuming more coffee, and/or to coach the user for further lifestyle improvement to reach the consumer's goals.
  • the personalized consumption recommendation system or device can collect physiological data and/or consumption data for a period of time about a particular user and can store it and/or send it to the user's physician, trainer, physical therapist, dietician, or other specialist for analysis.
  • a cardiologist may require that a patient utilize the personalized consumption recommendation system for a period of time in order to track the user's consumption of an item such as coffee and monitor the user's response with the physiological data or with other physiological data such as a heart monitor. The cardiologist could then analyze the data and provide information resulting in adjustment of the recommendations made by the system. If, for example, the cardiologist recommends less caffeine consumption, the personalized consumption recommendation system or device will consider that information when making recommendations to the user.
  • a personal trainer may receive physiological data and consumption data about an item in order to track the efficiency or outcome of a user's workout regimen.
  • the beverage could be tea, alcohol, soda, or any of a wide variety of other beverages.
  • the food could be anything consumed by the consumer, and will typically be something that the consumer has a desire to track or control, such as sugar consumption among many others.
  • the medications, treatments, or remedies could be aspirin, a pain reliever, antibiotic, salve, cream, lotion, or any of a variety of others.
  • inventive embodiments are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, inventive embodiments may be practiced otherwise than as specifically described and claimed.
  • inventive embodiments of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein.
  • a reference to "A and/or B", when used in conjunction with open-ended language such as “comprising” can refer, in one embodiment, to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc.
  • the phrase "at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements.
  • This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase "at least one" refers, whether related or unrelated to those elements specifically identified.
  • At least one of A and B can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.

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Abstract

L'invention concerne un procédé, un système et un dispositif pour fournir une recommandation à un utilisateur concernant la consommation d'un article tel que le café. Le procédé comprend les étapes consistant à : recevoir (520) des informations concernant la consommation de l'article par l'utilisateur; recevoir (530) des données de capteur physiologique concernant l'utilisateur à partir d'un ou plusieurs capteurs (14); générer (540), sur la base, d'au moins en partie, des informations reçues concernant la consommation de l'utilisateur de l'article et des données de capteur physiologique reçues concernant l'utilisateur, un modèle décrivant au moins une caractéristique de la réponse physiologique de l'utilisateur à la consommation de l'article; générer (560), sur la base, d'au moins en partie, du modèle généré, d'une recommandation à l'utilisateur concernant une consommation future de l'article; et la fourniture (570) de la recommandation générée à l'utilisateur.
PCT/EP2017/070987 2016-08-23 2017-08-21 Procédé et système de suivi de nourriture et de boissons et recommandations de consommation WO2018036944A1 (fr)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2574435A (en) * 2018-06-06 2019-12-11 Guud Ltd Personalised nutritional information system

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060064037A1 (en) * 2004-09-22 2006-03-23 Shalon Ventures Research, Llc Systems and methods for monitoring and modifying behavior
WO2015057713A1 (fr) * 2013-10-14 2015-04-23 Case Western Reserve University Analyseur métabolique pour optimiser une gestion de santé et de poids
WO2015084116A1 (fr) * 2013-12-06 2015-06-11 Samsung Electronics Co., Ltd. Procédé et système pour capturer des informations de consommation d'aliment d'un utilisateur

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060064037A1 (en) * 2004-09-22 2006-03-23 Shalon Ventures Research, Llc Systems and methods for monitoring and modifying behavior
WO2015057713A1 (fr) * 2013-10-14 2015-04-23 Case Western Reserve University Analyseur métabolique pour optimiser une gestion de santé et de poids
WO2015084116A1 (fr) * 2013-12-06 2015-06-11 Samsung Electronics Co., Ltd. Procédé et système pour capturer des informations de consommation d'aliment d'un utilisateur

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2574435A (en) * 2018-06-06 2019-12-11 Guud Ltd Personalised nutritional information system
WO2019233875A1 (fr) * 2018-06-06 2019-12-12 Guud Ltd Système d'informations nutritionnelles personnalisées

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