System and method of machine learning and autonomous execution on user preferences for use in garments
11561561 · 2023-01-24
Assignee
Inventors
- Antoine Vandenheste (Hong Kong, HK)
- Sebastien Vandenheste (Hong Kong, HK)
- Francois Vandenheste (Hong Kong, HK)
Cpc classification
G06N5/01
PHYSICS
International classification
Abstract
The present invention relates to a system with active learning and execution of user's preference functionalities for use in a garment. The present system includes a sensor module, an optional user input panel and/or interface, a printed circuit board, a power source and an output. In an event that a user of the present system voluntarily changes the output setting during the operation of the system, the system performs an active learning action to execute the output setting initiated by the user over a passive learning action triggered by a change in sensor data with respect to the changing environment. In other event, the present system performs passive learning action with respect to the changing environment and also any comparative data from similar user of a particular instance. The present invention also relates to a power management unit and how to use the same in a garment.
Claims
1. A system with machine learning and autonomous execution on user preferences for use in a garment comprising: one or more sensors; an electronic printed circuit board (PCB); an internal or external power source, wherein said power source comprises a power bank and/or a rechargeable battery electrically connected to said PCB and other parts of the system or garment, or a power network connecting different parts of the garment, or an external power supply which is external to the system and connected to the system wirelessly; and an output device embedded into or attached onto the garment, or an output signal transmitter; wherein said sensors are embedded into the garment and electrically connected to the PCB, which are configured to generate sensor data; wherein said PCB comprises one or more computer processors and electronic circuities comprising a sensor data processor, a power control processor, a data storage processor, a data exchange processor, and optionally a wireless communication processor for wirelessly communicate with one or more third-party devices comprising a third-party sensor, and/or a data center, and/or a mobile device, and/or a server; wherein said output device comprises heating and/or cooling components being embedded into or attached onto a surface of the garment, or said output signal transmitter transmits an output signal from the system to control an external output device; wherein said PCB preforms learning actions comprising active learning and passive learning actions for changing output setting of said output device or modulating output signal to said output device; and wherein said active learning and passive learning actions are triggered by a change in output setting initiated by the user and a change in sensor data received from a corresponding sensor of the plurality of sensors which is over a reference value, respectively; wherein said computer processors and electronic circuities in the PCB comprise a first timer for counting down the time within a time period since the output setting is initiated by the user during the operation of the system, and a second timer for counting down the time within a time period since the change in sensor data is over a reference value during the operation of the system; and wherein when said first or second timer completes a round of count-down without any reset, the user's initiated output setting and/or the change in sensor data is/are stored in the data storage processor as a learned database.
2. The system of claim 1, further comprising an internal and/or external user input panel and/or interface; wherein said user input panel and/or interface is/are embedded into or attached to the garment, and/or provided by and executed in the mobile device and wirelessly communicates with said PCB, and/or as a built-in electronics including one or more sensors and/or mechanical device that can turn into instructions, signals and/or behavioral information according to the user's motion or preference; wherein said user input panel comprises one or more knobs, buttons and/or multi-touch screen for controlling power supply and/or power level from the power source to different parts of the system or the garment, for inputting personal data, and/or for a user to control the setting of said output device; wherein said built-in electronics comprises one or more built-in sensors sensing a change in position of said mechanical device in the garment within a time interval in order to trigger the generation of an instruction or signal for controlling the output while the behavior of the user at that particular instance is stored in the system; and wherein said mechanical device comprises one or more of a zipper, Velcro, buttons, button clips, clips, magnetic clips, and buckles, which activates said one or more built-in sensors when there is a change in position thereof.
3. The system of claim 1, wherein said change of the output setting initiated by the user is compared with the previous output setting by the user at the time of starting the system or during the operation of the system, whichever is the latest, such that the active learning process is triggered.
4. The system of claim 1, wherein said change in the sensor data is a difference between a sensor data received at a current time point and a sensor data received at a previous time point which is over the reference value such that the passive learning process is triggered.
5. The system of claim 1, wherein said computer processors and electronic circuities in the PCB are configured to take priority of the change in output setting initiated by the user over the change in sensor data for performing said learning actions when the change in output setting initiated by the user and the change in sensor data concurrently happen.
6. The system of claim 1, wherein said first timer is reset when there is a subsequent output setting initiated by the user during the previous round of count-down; and wherein said second timer is reset when there is a subsequent change in sensor data which is over the reference value during the previous round of count-down or when there is an output setting initiated by the user during the previous round of count-down.
7. The system as claimed in claim 1, further performing an inference action simultaneously when there is a subsequent change in sensor data, wherein the system searches through the learning database to determine the output settings.
8. The system as claimed in claim 7, wherein said computer processors and electronic circuities in the PCB retrieve the learning database of the user for output setting of said output device from the one or more third-party devices comprising a third-party sensor, and/or a data center, and/or a mobile device, and/or a server, which was previously recorded.
9. The system as claimed in claim 8, wherein said computer processors and electronic circuities in the PCB retrieve the learning database of a similar user for output setting of said output device from the one or more third-party devices comprising a third-party sensor, and/or a data center, and/or a mobile device, and/or a server.
10. The system as claimed in claim 1, wherein said computer processors and electronic circuities in the PCB are configured for performing a procedure of fetch similar users for the first time of using the garment.
Description
BRIEF DESCRIPTION OF THE DRAWINGS
(1) Embodiments of the present invention are described in more detail hereinafter with reference to the drawings, in which:
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(15)
DETAILED DESCRIPTION OF THE INVENTION
(16) The present invention is not to be limited in scope by any of the following descriptions. The following examples or embodiments are presented for exemplification only.
(17) References in the specification to “one embodiment”, “an embodiment”, “an example embodiment”, etc., indicate that the embodiment described can include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described.
(18) Values expressed in a range format should be interpreted in a flexible manner to include not only the numerical values explicitly recited as the limits of the range, but also to include all the individual numerical values or sub-ranges encompassed within that range as if each numerical value and sub-range is explicitly recited. For example, a concentration range of “about 0.1% to about 5%” should be interpreted to include not only the explicitly recited concentration of about 0.1 wt. % to about 5 wt. %, but also the individual concentrations (e.g., 1%, 2%, 3%, and 4%) and the sub-ranges (e.g., 0.1% to 0.5%, 1.1% to 2.2%, and 3.3% to 4.4%) within the indicated range.
(19) As described herein, the terms “a” or “an” are used to include one or more than one and the term “or” is used to refer to a nonexclusive “or” unless otherwise indicated. In addition, it is to be understood that the phraseology or terminology employed herein, and not otherwise defined, is for the purpose of description only and not of limitation. Furthermore, all publications, patents, and patent documents referred to in this document are incorporated by reference herein in their entirety, as though individually incorporated by reference. In the event of inconsistent usages between this document and those documents so incorporated by reference, the usage in the incorporated reference should be considered supplementary to that of this document; for irreconcilable inconsistencies, the usage in this document controls.
(20) In the methods described herein, the steps can be carried out in any order without departing from the principles of the invention, except when a temporal or operational sequence is explicitly recited. Recitation in a claim to the effect that first a step is performed, and then several other steps are subsequently performed, shall be taken to mean that the first step is performed before any of the other steps, but the other steps can be performed in any suitable sequence, unless a sequence is further recited within the other steps. For example, claim elements that recite “Step A, Step B, Step C, Step D, and Step E” shall be construed to mean step A is carried out first, step E is carried out last, and steps B, C, and D can be carried out in any sequence between steps A and E, and that the sequence still falls within the literal scope of the claimed process. A given step or sub-set of steps can also be repeated.
(21) Furthermore, specified steps can be carried out concurrently unless explicit claim language recites that they be carried out separately. For example, a claimed step of doing X and a claimed step of doing Y can be conducted simultaneously within a single operation, and the resulting process will fall within the literal scope of the claimed process.
Definitions
(22) The singular forms “a,” “an” and “the” can include plural referents unless the context clearly dictates otherwise.
(23) The term “about” can allow for a degree of variability in a value or range, for example, within 10%, or within 5% of a stated value or of a stated limit of a range.
(24) The term “Sensor Data” or “SD” refers to data from sensors embedded into the garment, fetched from external hardware connected via wireless or wired means, and/or data from the Internet.
(25) The term “User Body” or “UB” refers to an aggregate of data about the user's body including but not limited to mass, height, Body Mass Index (BMI), body dimensions, date of birth, gender, health conditions, location, hobbies, tastes and preferences, and ethnicity.
(26) The term “Similar Users” or “SU” refers to a list of users with similar UB to the current user.
(27) The term “User Settings” or “US” refers to an aggregate of all the modes set on all heating and/or cooling outputs (includes zones activated and heating/cooling modes), including but not limited to their state (On/Off) and/or their output level at a specific instance.
(28) The term “Data Point” or “DP” refers to an aggregate of all the SD at a specific instance.
(29) The term “Temporary Data Point” or “TDP” refers to DP temporarily stored when PLD is triggered.
(30) The term “Learned Database” or “LD” refers to a data structure containing a list of corresponding DP and US.
(31) The term “Fetch Similar Users” or “FSU” refers to an addition of the DP and corresponding US of SU to the LD.
(32) The term “Learning Action” or “LA” refers to an addition of a new DP and corresponding US to the LD.
(33) The term “Current Data” or “CD” refers to an aggregate of all the current SD.
(34) The term “Inference Action” or “IA” refers to an automated change of current modes on heating and/or cooling outputs by finding the closest DP to the CD and changing the modes to the US corresponding to that DP.
(35) The term “Reference Tolerance” or “RT” refers to a fraction or decimal value subject to calibration indicating the maximum difference allowed between a DP and the CD for an IA to take place.
(36) The term “Precedence Tolerance” or “PT” refers to a fraction or decimal value subject to calibration indicating how to prioritize data in the LD during an IA according to how new it is. Generally, newer data will be prioritized over older data.
(37) The term “Similar User Tolerance” or “SUT” refers to a fraction or decimal value subject to calibration indicating how to weigh data in the LD during an IA that is added via FSU compared to data added via LA. Generally, data added via LA is prioritized over data added via FSU.
(38) The term “Maximum Discardable” or “MD” refers to a data structure indicating how many and which SD can be disregarded in the computation of how close a DP and a CD are.
(39) The term “Active Learning Action” or “ALA” refers to an LA performed after the user has changed his settings.
(40) The term “Active Learn Delay” or “ALD” refers to a timer subject to calibration that waits until the user has finished changing his settings before performing an ALA.
(41) The term “Passive Learning Action” or “PLA” refers to an LA performed after the CD has been changed and the user has not altered his settings, allowing the system to learn user preferences with near fluid granularity.
(42) The term “Passive Learn Delay” or “PLD” refers to a timer subject to calibration that wait until the CD is stable before performing an PLA.
(43) The term “Temporary Data Point” or “TDP” refers to a CD stored in the LD when PLD is triggered for comparing with the SD at the next DP to determine whether a PLD needs to be restarted with reference to RT.
(44) The term “garment” used herein can refer to typical garment, any types of clothing including but not limited to zip-top, jacket, footwear, headgear, any form of gear worn by or attached to a part of the user's body, and any types of accessories.
EXAMPLES
(45) The embodiments of the present invention can be better understood by reference to the following examples which are offered by way of illustration. The present invention is not limited to the examples given herein.
Example 1
(46) First Use
(47) As illustrated in
(48) Normal Operation
(49) As illustrated in
(50) Conversely, when the CD changes the PLD is triggered and the CD is stored as a temporary DP (TDP). Since sensor data may fluctuate every fraction of a second, each fluctuation will be compared to the TDP and if it exceeds the RT, the PLD will be restarted, reducing inaccurate learnings. Once the PLD elapses, a PLA is performed. If an ALA is triggered at any time during this process, the PLD and PLA will be cancelled, so that the user's preferences are accurately reflected in the LD.
(51) At the same time when the CD changes, the system will try to perform an IA. In order to do this, it searches through the LD for the closest DP to the CD and factor in weights assigned by PT and SUT, as well as conditions specified in MD. If this match is higher than the RT, the settings will be set to the US corresponding to the matched DP.
Example 2
(52) Below is a working example of the present system in a smart garment:
(53)
(54) The present garment can be connected wirelessly to a smartphone (320), or other smart device such as a smartwatch, tablet or other device, which provides an application layer. This layer includes data processing, as well as a user interface, for the user's profile management, consultation of data, as well as data entry. In particular, the user may enter his commands directly from the application, such as On/Off, power level controls (change power to heating and cooling levels) and area controls (heating and cooling area activation and controls). The mobile application can also fetch data from the phone (or other smart device such as a smartwatch, tablet or other device), in-built sensors, such as accelerometers, gyroscope, magnetometer, and GPS, as well as data from the user's past history, as well as data from third parties on the cloud, such as local wind speed or climactic and weather conditions, as well as data from the system's servers, for instance, data correlated from other users.
(55) An example of the garment integrated with the present system is shown in
(56) In an event that a user uses the present garment for skiing, if the user is a first-time user, he/she is asked by the system to input his/her own personal data such as weight, height, age, health conditions, etc., into the system. He/she can manually input location or the GPS which is optionally embedded into the system is activated automatically once the system is started to detect his/her location before the system enters into normal operation stage. Without the user's input, the present garment is able to learn by itself from similar data from a third-party device stored in a database. For security concern, he/she may be asked to provide a user login details for future access to or retrieval of data from local memory or data storage or from a remote server. If the user is a return user, the user is asked to provide his/her latest login details for registering his/her own data into the garment from the server (340). Once the server recognizes the login details input by a return user, the garment will download the previously stored user data from the server via a wireless communication network, or will retrieve the user data from the local memory or data storage of the garment. In any case, the garment is fetched by the server (340) or a third-party device such as a third sensor (310) or third-party data center (330) with data from similar user of a specific instance, for example, under this instance, is a ski user with comparable personal data. The user may also be asked to input his/her preference of heating and/or cooling settings in the garment, e.g., keep the garment at certain temperature range when skiing; which part of the pads are activated; what power level is at certain part of the garment, etc. Alternatively, the system may be pre-set with the conditions for the heating and/or cooling output for the garment according to the data from similar user if the user does not provide such information at the data verification stage. The user, however, is allowed to change his/her user setting anytime during the operation stage of the garment.
(57) After the data verification stage, the user can use the garment at its operation mode. During the normal operation stage, the garment is basically responsive to the sensor data from time to time. If at one time point the corresponding sensor data, e.g., temperature, pressure, humidity, wind speed, etc., detected by the sensors is over a reference difference between the instant record and the previous record at the previous time point, the system will automatically change the user setting and execute the corresponding output, e.g., increase or decrease the temperature of the pad in the garment, after certain period of time. In case where there is another record of sensor data which exceeds the reference value during that period of time, the count-down of a new cycle will start until there is no interruption or change of sensor data detected during a cycle. The time interval of each cycle can be adjusted according to user's preference, but the system is capable to detect fluctuation of sensor data in the degree of every millisecond, and the count-down of each cycle is achieved by a timer in the system.
(58) Apart from the change in sensor data from time to time, the user may interrupt the cycle by inputting user setting with respect to his/her preferred temperature of the pad in the garment. The garment is configured to take user's preference as first priority over the change in sensor data in the output setting. That is, when the user feels like he/she need more warmth or cold in certain part of the body from the garment during skiing, he/she may adjust the pad temperature via a mobile application wirelessly connected to the garment or other means such as a button or switch or some physical means attached to or on the garment, such user's instruction to increase the temperature of the pad in the garment will take priority over the change of the setting due to the sensor data difference exceeding a reference threshold.
(59) The data whichever received from the sensors or input by the users are stored in the local data storage of the system. Comparison and matching of the data so received and stored in the system of the garment with other comparative data from similar user or from a server are also performed almost concurrently or subsequently to each data collection cycle by the system to ensure the data accuracy and the consistency between the output and the user's preference at a specific instance.
(60) The data stored in the local memory or data storage of the garment can be used for the next time skiing activity of similar kind and conditions by the same user. Such data can also be shared with similar users of the same model of garment and/or stored in the server as a database. A matrix of data sets including both sensor data and body characteristics of a particular user is preferably stored in the database of the server for analysis and optimization of the system for different applications.
Example 3
(61)
(62)
(63)
(64)
(65)
(66)
Example 4
(67)
(68) The present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments and examples are therefore to be considered in all respects as illustrative and not restrictive. The scope of the present invention is indicated by the appended claims rather than by the foregoing description, and all changes that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
INDUSTRIAL APPLICABILITY
(69) The present system is useful in garment with specific applications such as heating and cooling in specific instance such as skiing or professional diving which require regulation of the body conditions in an extreme environment. The active and passive learning mechanisms of the present system are also useful in intelligent automation of other articles apart from garment which also do not favor human manual micromanagement during operation but quick response to the environmental changes is required, e.g., space suit.
(70) The embodiments disclosed herein may be implemented using general purpose or specialized computing devices, computer processors, or electronic circuitries including but not limited to digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA), and other programmable logic devices configured or programmed according to the teachings of the present disclosure. Computer instructions or software codes running in the general purpose or specialized computing devices, computer processors, or programmable logic devices can readily be prepared by practitioners skilled in the software or electronic art based on the teachings of the present disclosure.
(71) The present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiment is therefore to be considered in all respects as illustrative and not restrictive. The scope of the invention is indicated by the appended claims rather than by the foregoing description, and all changes that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.