Portable monitoring device, associated system and method

Abstract

The present disclosure relates to a portable monitoring device, associated system and method The system (100) includes a portable monitoring device (102) configured to share data and resources with a network (104). The portable monitoring device (102) is configured to monitor movements, activities and related data. The network (104) facilitates exchange of information and resources among a plurality of interconnected nodes (106). The nodes (106) are configured to communicate with each other via one or more communication pathways (108). Implementations of the system, including the portable monitoring device, and methods disclosed are configured to generate evaluated data, including an energy requirement metric, based on data which includes data from one or more sensors of the portable monitoring device (102).

Description

PORTABLE MONITORING DEVICE, ASSOCIATED SYSTEM AND METHODFIELD OF THE INVENTION

[0001] The present disclosure relates to a portable monitoring device, associated system and method. The invention has been developed primarily for use as a system and method for generating evaluated data, including an energy requirement metric. Implementations of the system and method disclosed herein include a portable monitoring device. The generation of evaluated data, including an energy requirement metric, may assist with monitoring and managing health issues. While some embodiments and implementations will be described herein with particular reference to this application, it will be appreciated that the disclosure is not limited to such a field of use, and is applicable in broader contexts.BACKGROUND TO THE INVENTION

[0002] Reference in this specification to any prior publication (or information derived from it), or to any matter which is known, is not, and should not be taken as, an acknowledgement or admission or any form of suggestion that that prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of endeavour to which this specification relates.

[0003] Portable monitoring devices, such as fitness trackers, wearable trackers, activity trackers and the like, are used to monitor various activity data. Activity data from such trackers may assist with monitoring health issues such as weight, including underweight and overweight. Health issues associated with weight, such as obesity, increase the risk of diseases, such as cancer and heart disease, that reduce quality of life and in turn decreases lifespan. Activity data may be used to determine calorific needs to assist with managing changes in weight. Existing trackers communicate to an app on limited data, often requiring a user to log into the app to recover data. To determine calorific needs, a user manually measures and weighs a food of choice after making manual calculations of the calorie value of the food, usually obtained from the food packet.

[0004] Such trackers, being accelerometer and / or GPS driven devices worn on a human body, are increasingly migrating their way into pet wearable devices, such as wearable devices for dogs, cats and other pets and animals.

[0005] Traditionally, to control food intake for pets, owners were required to manually ascertain a correct volume of food and then measure and / or weigh the food into a separate bowl.

[0006] US10091972B1 discloses a smart bowl system that comprises food and water smart bowls equipped with processors, memory, and bowl weight sensors. These bowls connect to a cloud server and caretaker devices. Caretakers manually input the pet's physical data, and the system determines a healthy weight range and feeding schedule. The system reminds caretakers to feed the pet, monitors food and water levels, and sends reports. The system maintains the feeding schedule to keep the pet's weight healthy or adjusts it to achieve that. Additionally, it can interact with a pet activity sensor, which detects the pet's activity level through a global positioning system (GPS) sensor and / or accelerometers and adjusts the feeding amount based on activity levels detected. For instance, increased exercise could lead to a proportional increase in the next feeding's food amount.

[0007] US20200305387A1 discloses an animal food and water bowl system designed to dispense food and water to pets. The system can operate with activity trackers and wearable devices for pets, that include accelerometers or GPS. These devices provide data, such as energy expenditure and temperature, which the system uses to adjust food portions. Data input to the system can be wired, wireless, or manual.

[0008] US11361368B2 discloses recommending pet food based on received pet information. A temperature classification and recipe score are determined for the pet from this information. A pet food recommendation is made from a database, and this recommendation is communicated to a user through a network. The platform can also collect data from a wearable device worn by the pet owner and may use this data to infer the pet’s activity levels.

[0009] US20200236901 A1 presents devices, systems, and methods for animal care. It outlines methods for measuring a dog's energy expenditure and movements, encouraging activities or games to earn food. The document describes a hub including a food dispenser, connected to a weight measurement and / or a dog-borne device. The dog borne device can track energy expenditure and movement, such as via an accelerometer or GPS. The hub provides signals to engage the dog in games for food, while considering the dog's activity level, age, weight, and health information to regulate food intake based on calorie needs.

[0010] It is desired to address or ameliorate one or more disadvantages or limitations associated with the prior art, provide a portable monitoring device, associated system and method, or to at least provide the public with a useful alternative.SUMMARY OF THE INVENTION

[0011] The present disclosure provides a portable monitoring device, associated system and method.

[0012] According to one aspect, the present disclosure may broadly provide a system including: a portable monitoring device connected to a network by at least one communication pathway; the portable monitoring device includes: one or more sensors for detecting raw activity data; at least one processing unit configured to receive the raw activity data from the one or more sensors, the at least one processing unit further configured to: create activity data based on the raw activity data; and create one or more active data packets including the activity data; and at least one transceiver configured to transmit the active data packets to the network by the at least one communication pathway; the network includes: an evaluation system including: at least one processing unit configured to retrieve baseline data and generate evaluated data based on the active data packets received from the portable monitoring device and the baseline data; and at least one transceiver configured to transmit the evaluated data, wherein the evaluated data includes a baseline energy requirement metric.

[0013] The baseline data may include one or more of baseline health metrics, age, life stage, breed, size, leg length, gender, and metabolism factor. The baseline health metrics may include one or more of a current weight and a Body Condition Scoring (BCS). The baseline energy requirement metric may include a target number of calories.

[0014] The at least one processing unit of the evaluation system may be further configured to receive one or more updated active data packets from the portable monitoring device. The updated active data packets may be received at regular intervals. The at least one processing unit of the evaluation system may be further configured to receive updated health metrics. The at least one processing unit of the evaluation system may be further configured to generate updated evaluated data based on the updated active data packets. The at least one processing unit of the evaluation system may be further configured to generate updated evaluated data based on the updated health metrics. The at least one processing unit of the evaluation system may be further configured to generate updated evaluated data based on the updated active data packets and the updated health metrics.

[0015] The at least one transceiver of the evaluation system may be further configured to transmit the updated evaluated data, the updated evaluated data including an updated energy requirement metric. The updated health metrics may include one or more of an updated current weight and an updated Body Condition Scoring (BCS). The updated energy requirement metric may include an updated target number of calories.

[0016] The evaluation system may include a hub. The evaluation system may include a cloud server. The network may further include a smart device.

[0017] The baseline data may be received from the smart device. The updated health metrics may be received from the smart device.

[0018] The system may further include a dynamic substance measuring device. The dynamic substance measuring device may include one or more sensors for detecting substance data associated with a substance. The substance data may include one or more of a volume, weight and quantity of the substance. The dynamic substance measuring device may be configured to communicate with the portable monitoring device by at least one communication pathway.

[0019] The dynamic substance measuring device may be configured to communicate with the network by at least one communication pathway. The dynamic substance measuring device may be configured to communicate with the evaluation system by at least one communication pathway. The dynamic substance measuring device may include at least one processing unit configured to receive the substance data from the one or more sensors. The dynamic substance measuring device may include at least one processing unit configured to receive the baseline energy requirement metric. The dynamic substance measuring device may include at least one processing unit configured to receive the updated energy requirement metric.

[0020] Each active data packet may be created when a subset of activity data in a payload of a data packet including activity data detected over a predetermined interval is above a threshold.

[0021] The one or more processors of the evaluation system may use a predictive algorithm to generate the evaluated data including the baseline energy requirement metric. The predictive algorithm may generate the evaluated data based on the baseline data. The predictive algorithm may generate the evaluated data based on the active data packets.

[0022] The one or more processors of the evaluation system may use a predictive algorithm to generate the updated evaluated data including the updated energy requirement metric. The predictive algorithm may generate the updated evaluated data based on the substance data received from at least one transceiver of the dynamic substance measuring device. The predictive algorithm may generate the updated evaluated data based on the updated active packets received from the at least one transceiver of the portable monitoring device. The predictive algorithm may generate the updated evaluated data based on theupdated health metrics. The predictive algorithm may generate the updated evaluated data based on historical consumption data.

[0023] A measurement of the substance detected by the one or more sensors of the dynamic substance measuring device may be determined based on the baseline energy requirement metric. A measurement of the substance detected by the one or more sensors of the dynamic substance measuring device may be determined based on the updated energy requirement metric.

[0024] According to another aspect, the present disclosure may broadly provide a portable monitoring device including: one or more sensors for detecting raw activity data; at least one processing unit configured to receive the raw activity data from the one or more sensors, the at least one processing unit further configured to: create activity data based on the raw activity data; and create one or more active data packets including the activity data.

[0025] Each active data packet may include activity data detected over a predetermined interval that meets a predefined criteria. The predefined criteria may be that the activity data is above a predetermined threshold.

[0026] The portable monitoring device may include at least one transceiver configured to transmit the active data packets to a network by at least one communication pathway.

[0027] The network may include an evaluation system including: at least one processing unit configured to retrieve baseline data and generate evaluated data based on the active data packets received from the portable monitoring device and the baseline data; and at least one transceiver configured to transmit the evaluated data, wherein the evaluated data includes a baseline energy requirement metric.

[0028] The at least one processing unit of the evaluation system may be further configured to receive one or more updated active data packets from the portable monitoring device. The updated active data packets may be received at regular intervals.

[0029] The at least one processing unit of the evaluation system may be further configured to receive updated health metrics. The at least one processing unit of the evaluation system may be further configured to generate updated evaluated data based on the updated active data packets. The at least one processing unit of the evaluation system may be further configured to generate updated evaluated data based on the updated health metrics. The at least one processing unit of the evaluation system may be further configured to generate updated evaluated data based on the updated active data packets and the updated health metrics.

[0030] The at least one transceiver of the evaluation system may be further configured to transmit the updated evaluated data, the updated evaluated data including an updated energy requirement metric.

[0031] The raw activity data may include one or more of force, acceleration, and angular motion.

[0032] The activity data may include one or more of movement data, steps, active minutes, movement signature data, activity classification data, intensity data, calories, sleep and sleep patterns, heart rate, and skin or body temperature.

[0033] The one or more sensors of the portable monitoring device may include one or more of an accelerometer, GPS, gyroscope, optical sensor, biometric pressure sensor, heart rate monitor, oximetry, bio-impedance, magnetometer, skin temperature, environmental temperature, UV sensor, light sensor, altimeter and proximity sensor.

[0034] According to another aspect, the present disclosure may broadly provide a dynamic substance measuring device including: one or more sensors for detecting substance data associated with a substance; and at least one processing unit configured to receive: the substance data from the one or more sensors; and an energy requirement metric; wherein the at least processing unit evaluates the substance data and the energy requirement metric.

[0035] The at least processing unit may evaluate the substance data and the energy requirement metric to determine whether a measurement of the substance corresponds with the energy requirement metric.

[0036] The energy requirement metric may include a baseline energy requirement metric and an updated energy requirement metric.

[0037] The dynamic substance measuring device may include a dispenser.

[0038] The dynamic substance measuring device may include a base and a measuring portion separable from the base. The measuring portion may be a receptacle. The receptacle may be a bowl.

[0039] The dynamic substance measuring device may further include at least one indicator. The at least one processing unit of the dynamic substance measuring device may be configured to communicate with the at least one indicator. The at least one indicator may be configured to provide feedback in response to the substance data being detected by theone or more sensors. The feedback provided by the at least one indicator may be visual, audible, tactile, haptic or a combination thereof.

[0040] The at least one indicator may include a speaker. The at least one indicator may include a light. The light may include a ring device, a radial light, a radial display, a radial light display, a circular light, or a logarithmic light. The light may be configured to progressively illuminate. The light may be configured to progressively dim.

[0041] The dynamic substance measuring device may further include at least one transceiver configured to transmit the substance data to a network by at least one communication pathway.

[0042] At least one of the one or more sensors of the dynamic substance measuring device may be configured to detect a change in pressure. The one or more sensors of the dynamic substance measuring device may include one or more of multi-load cells, high frequency electronical scales, and logarithmic scales. The multi-load cells may be configured to detect at high frequency intervals.

[0043] According to another aspect, the present disclosure may broadly provide a method including: receiving, by at least one processing unit, one or more active data packets; retrieving, by the at least one processing unit, baseline data; and generating, by the at least one processing unit, evaluated data based on the active data packets and the baseline data, wherein the evaluated data includes a baseline energy requirement metric.

[0044] The method may further include transmitting, by at least one transceiver, the evaluated data. The method may further include receiving, by the at least one processing unit, one or more updated active data packets. The method may further include receiving, by the at least one processing unit, updated health metrics. The method may further include generating, by the at least one processing unit, updated evaluated data based on the updated active data packets. The method may further include generating, by the at least one processing unit, updated evaluated data based on the updated health metrics. The method may further include generating, by the at least one processing unit, updated evaluated data based on the updated active data packets and the updated health metrics. The method may further include transmitting, by the at least one transceiver, the updated evaluated data, wherein the updated evaluated data includes an updated energy requirement metric.

[0045] The one or more active data packets may be received from a portable monitoring device connected to a network by at least one communication pathway. The one or more updated active data packets may be received from the portable monitoring device.

[0046] According to another aspect, the present disclosure may broadly provide a method implemented by a portable monitoring device including: detecting, by one or more sensors, raw activity data; receiving the raw activity data, by at least one processing unit, from the one or more sensors; creating, by the at least one processing unit, activity data based on the raw activity data; and creating, by at least one processing unit, one or more active data packets including the activity data.

[0047] The method may further include transmitting, by at least one transceiver, the active data packets to a network by at least one communication pathway.

[0048] The method may further include detecting, by one or more sensors, substance data associated with a substance; receiving the substance data, by at least one processing unit, from the one or more sensors; receiving, by the at least one processing unit, an energy requirement metric; and evaluating, by the at least one processing unit, the substance data and the energy requirement metric.

[0049] The method may further include evaluating, by the at least one processing unit, the substance data and the energy requirement metric to determine whether a measurement of the substance corresponds with the energy requirement metric. The method may further include communicating, by the at least one processing unit, with at least one indicator. The method may further include providing feedback, by the at least one indicator, in response to the substance data being detected by the one or more sensors. The method may further include transmitting, by at least one transceiver, the substance data to a network by at least one communication pathway. The method may further include detecting, by at least one of the one or more sensors, a change in pressure. The method may further include detecting, by the one or more sensors, at high frequency intervals.

[0050] The method may further include communicating, by the at least one processing unit, with a portable monitoring device by at least one communication pathway. The method may further include communicating, by the at least one processing unit, with a network by at least one communication pathway. The method may further include communicating, by the at least one processing unit, with an evaluation system by at least one communication pathway.

[0051] The method may further include receiving, by the at least one processing unit, an updated energy requirement metric. The method may further include detecting, by the one or more sensors, a measurement of the substance based on the energy requirement metric. The method may further include detecting, by the one or more sensors, a measurement of the substance based on the updated energy requirement metric.

[0052] According to another aspect, the present disclosure may broadly provide a computer program including instructions which, when the program is executed by a computer, cause the computer to carry out the method as herein disclosed.

[0053] According to another aspect, the present disclosure may broadly provide a computer-readable medium including instructions which, when executed by a computer, cause the computer to carry out the method as herein disclosed.

[0054] The following embodiments may relate to any of the above aspects.

[0055] The term “comprising” as used in this specification means “consisting at least in part of”. When interpreting statements in this specification which include that term, the features, prefaced by that term in each statement, all need to be present but other features can also be present. Related terms such as “comprise” and “comprised” are to be interpreted in the same manner.

[0056] As used herein “(s)” following a noun means the plural and / or singular forms of the noun.

[0057] As used herein the term “and / or” means “and” or “or” or both.

[0058] It is intended that reference to a range of numbers disclosed herein (for example, 1 to 10) also incorporates reference to all rational numbers within that range (for example, 1 , 1.1 , 2, 3, 3.9, 4, 5, 6, 6.5, 7, 8, 9 and 10) and also any range of rational numbers within that range (for example, 2 to 8, 1 .5 to 5.5 and 3.1 to 4.7) and, therefore, all sub-ranges of all ranges expressly disclosed herein are hereby expressly disclosed. These are only examples of what is specifically intended and all possible combinations of numerical values between the lowest value and the highest value enumerated are to be considered to be expressly stated in this application in a similar manner.

[0059] This invention may also be said broadly to consist in the parts, elements and features referred to or indicated in the specification of the application, individually or collectively, and any or all combinations of any two or more said parts, elements or features, and where specific integers are mentioned herein which have known equivalents in the art to which this invention relates, such known equivalents are deemed to be incorporated herein as if individually set forth.

[0060] Although the present invention is broadly as defined above, those persons skilled in the art will appreciate that the invention is not limited thereto and that the invention also includes embodiments of which the following description gives examples.BRIEF DESCRIPTION OF THE FIGURES

[0061] Implementations of the present disclosure will now be described by way of example only and with reference to the Figures in which,

[0062] FIG. 1 is a system diagram according to an implementation of the present disclosure.

[0063] FIG. 2 is a block diagram of a portable monitoring device according to the present disclosure.

[0064] FIG. 3 is a system diagram according to another implementation of the present disclosure.

[0065] FIG. 4 is a block diagram of a dynamic substance measuring device according to the present disclosure.

[0066] FIG. 5 is a stateflow diagram of a smart device connection to a portable monitoring device via an app according to one implementation.

[0067] FIG. 6 is a stateflow diagram of a smart device disconnection from a portable monitoring device according to one implementation.

[0068] FIG. 7 is a stateflow diagram of a hub Wi-Fi setup according to one implementation.

[0069] FIG. 8 is a stateflow diagram of a dynamic substance measuring device connection to a hub according to one implementation.

[0070] FIG. 9 is a stateflow diagram of a portable monitoring device connection to a hub according to one implementation.

[0071] FIG. 10 is a stateflow diagram of an operation of a system according to one implementation.

[0072] FIG. 11 is a stateflow diagram of a bowl system according to one implementation.

[0073] FIG. 12 is a data flow diagram of a bowl system according to one implementation.

[0074] FIG. 13 is a flowchart of a Personalised Nutrition Program (PNP) according to one implementation.

[0075] FIG. a to FIG. 14d is a PNP according to one implementation.

[0076] FIG. 15 is a flowchart of a PNP according to one implementation.

[0077] FIG. 16 is example implementation of the portable monitoring device.

[0078] FIG. 17 is example implementation of the dynamic substance measuring device.

[0079] FIG. 18 is example implementation of the hub.DETAILED DESCRIPTION

[0080] The following description and Figures make use of reference numerals to assist the addressee understand the structure and function of the illustrated embodiments. Like reference numerals are used in different embodiments to designate features having the same or similar function and / or structure.

[0081] The Figures need to be viewed as a whole and together with the associated text in this specification. In particular, some of the Figures selectively omit features to provide greater clarity about the specific features being described. While this is done to assist the reader, it should not be taken that those features are not disclosed or are not required for the operation of the relevant embodiment.

[0082] Described herein is technology relating to a portable monitoring device, associated system and method. The term "portable monitoring device" as used throughout the specification is also referred to herein as a "portable tracking device", "portable tracker", “activity tracker” or simply as a "tracker".

[0083] FIG. 1 is a system diagram according to an implementation of the present disclosure. The system 100 includes a portable monitoring device 102 configured to share data and resources with a network 104. The portable monitoring device 102 is configured to monitor movements, activities and related data, and share such data with the network 104. The terms “monitor” and “track” may be used interchangeably throughout the specification. Network 104 includes one or more local-area networks (LANs), private networks, social networks, or wide-area networks (WANs) including the Internet. The network 104 facilitates exchange of information and resources among a plurality of interconnected nodes 106. The nodes 106 may include devices (for example, personal computers, smartphones, tablets, multimedia devices, and other smart devices and mobile devices), servers, routers, switches and other devices capable of sending and / or receiving data. The plurality of nodes 106 are configured to communicate with each other via one or more communication pathways 108. The portable monitoring device 102 is connected to the network 104 by at least one communication pathway 108. Throughout the specification reference to communicationpathways 108 or links between nodes may be wired or wireless, such as Ethernet cables, fiber optics, Wi-Fi and / or Bluetooth.

[0084] FIG. 2 is a block diagram of a portable monitoring device 102 according to the present disclosure. The portable monitoring device 102 includes one or more sensors 202 for detecting raw activity data. The portable monitoring device also includes at least one processing unit 204, at least one memory 206, at least one input / output (I / O) interface 208 and at least one power source 210. The portable monitoring device 102 may be configured to accurately track movement patterns.

[0085] Sensors 202 detect acceleration. Acceleration may be in the form of measured G forces. Sensors 202 may include one or more of an accelerometer, GPS, gyroscope, optical sensor, biometric pressure sensor, heart rate monitor, oximetry, bio-impedance, magnetometer, skin temperature, environmental temperature, UV sensor, light sensor, altimeter and proximity sensor. In particular, sensors 202 include one or more motion sensors, such as accelerometers, gyroscopes and the like, for detecting and outputting raw activity data indicative of the motion of the portable monitoring device 102. Data detected from the one or more sensors 202 of the portable monitoring device may also be referred to as raw activity data. The detected raw activity data may include one or more of force, acceleration, and angular motion. The one or more accelerometers detect acceleration or other movement data in each of, for example, three directions, which may be orthogonal. The sensors 202 may also include one or more gyroscopes, including for example a 6-axis gyroscope for detecting rotation data. The 6-axis gyroscope allows for acceleration of any motion and / or activity to be detected. The rotation data being rotation about each of, for example, three axes, which may be orthogonal. In some implementations, the accelerometer is configured to measure acceleration along three axes in space — the forward and back X- axis, the left and right Y-axis, and the up and down Z-axis. Portable monitoring device 102, including one or more sensors 202, measures acceleration thereby detecting raw activity data. In some implementations, the accelerometer is configured to measure acceleration along three axes in space — the forward and back X-axis, the left and right Y-axis, and the up and down Z-axis, as well as angular motion or tilt. The combined measure of acceleration and angular motion advantageously provides a more accurate representation of movement associated with the portable monitoring device 102. Measuring acceleration and force advantageously allows the system to provide data relating to intensity and / or stress of the activity being measured. Intensity, for example, is determined based on acceleration detected by the one or more sensors 202 over time, where the acceleration may be in the form of measured G forces. In some implementations, the accelerometer is a 3-axis accelerometer. In other implementations the accelerometer is a 6-axis accelerometer. The sensors 102 may also include GPS.

[0086] The portable monitoring device 102 may further include one or more of external temperature sensors, GPS, magnetometer, charging coil, battery, RF antenna, LED light and Wi-Fi.

[0087] The processing unit 204 is configured to interpret, process and execute instructions or code to perform various operations associated with the portable monitoring device 102. The processing unit 204 is configured to receive raw activity data from the one or more sensors 202. The processing unit may execute instructions stored in memory 206 to perform various operations including, but not limited to, obtaining or receiving raw activity data from the one or more sensors 202, storing raw activity data obtained from the one or more sensors 202 in the memory, processing raw activity data from the one or more sensors 202, storing data generated, derived, processed or otherwise created from the raw activity data in memory, retrieving activity data (or data generated, derived, processed or created from raw activity data) from the memory, loading activity data (or data generated, derived, processed or created from raw activity data) from the memory, and transmitting and receiving activity data via I / O interface 208. The processing unit 204 is configured to create activity data based on the raw activity data. The processing unit 204 is configured to create one or more active data packets including the activity data.

[0088] The activity data includes, but is not limited to, one or more of movement data, steps, active minutes, movement signature data, activity classification data, intensity data, calories, sleep and sleep patterns, heart rate, and skin or body temperature.

[0089] Processing unit 204 may include one or more processors, controllers, microcontrollers, control units, computing units, computing circuits or processing circuits. The processing unit may be a microprocessor, field programmable gate array (FPGA), application specific integrated circuit (ASIC), programmable logic, or combinations thereof.

[0090] In addition to storing activity data obtained from one or more sensors 202 and / or data derived from activity data, memory 206 may additionally store configuration data. Configuration data may be used to configure the portable monitoring device and / or used during execution of instructions or code to perform various operations associated with the portable monitoring device. Memory 206 may also store data derived or processed by the processing unit or data received from one or more devices and / or servers within network 104 for subsequent use. Subsequent use may include, but is not limited to, analysis, viewing, and communication and / or transmission via I / O interface 208.

[0091] The memory 206 may include any suitable memory architecture, non-volatile and / or volatile storage media, and one or more different classes of storage devices or unitsconfigured to store different classes of data. Processing unit 204 may include its own memory additionally or alternatively to memory 206.

[0092] The processing unit 204 may communicate activity data received from sensors 202 or retrieved from memory 206 to one or more devices and / or servers within network 104, via I / O interface 208.

[0093] The I / O interface 208 may include at least one transceiver configured to transmit data, including activity data, to one or more devices and / or servers within network 104. The at least one transceiver is configured to transmit one or more active data packets to the network 104 by at least one communication pathway. The transmission of data to the network 104 may be through wired and / or wireless communication. Wired communication may include transmission of data via a Universal Serial Bus (USB) interface. Wireless communication may include transmission of data via one or more communications technologies such as, for example, Wi-Fi, Bluetooth, RFID, Near-Field Communications (NFC), optical data transmission, among others. Wireless communication advantageously enables data transmission from the portable monitoring device 102 to one or more devices and / or servers within network 104 when the portable monitoring device comes within range. In one implementation, I / O interface 208 includes Bluetooth so that when the portable monitoring device comes within range of one or more devices and / or servers within network 104, activity data is automatically synced or uploaded to the one or more devices and / or servers within the network.

[0094] The power source 210 may be one or more batteries. The one or more batteries may be rechargeable batteries or removable batteries.

[0095] The one or more sensors 202, processing unit 204, memory 206, input / output (I / O) interface 208, and power source 210 are communicatively coupled with one or more of one another via a communication bus 212.

[0096] FIG. 3 is a system diagram according to another implementation of the present disclosure. Similar to system 100 of FIG. 1 , the system 300 includes a portable monitoring device 102 configured to share data and resources with a network 104. In the implementation of FIG. 3, network 104 includes an evaluation system 302, a smart device 304 and a dynamic substance measuring device 306. The evaluation system 302 includes a cloud server 310 and / or a hub 312.

[0097] The evaluation system 302 includes at least one memory for storing data. The memory may be communicatively coupled to the cloud server and / or hub. The data stored in the memory of the evaluation system 302 may include data retrieved from the portablemonitoring device 102, the dynamic substance monitoring device 306, the smart device 304, and / or the one or more data repositories and / or servers in communication with the cloud server 310. The stored data may be accessed or otherwise retrieved by one or more application programs being executed on at least one processing unit associated with the memory of the evaluation system 302. The stored data may be in the form of a data structure stored in the memory of the evaluation system 302.

[0098] An application program is designed to carry out specific tasks. For example, the application program may be a computer program designed to carry out a specific task other than one relating to the operation of the computer itself. The computer program may be used by one or more end-users of a system. The term “application program” as used throughout the specification is also referred to as a “software application”, “application”, or simply “app” for short.

[0099] The evaluation system 302 includes at least one processing unit configured to retrieve baseline data. The at least one processing unit of the evaluation system is configured to generate evaluated data based on the active data packets received from the portable monitoring device and the baseline data. The baseline data may be retrieved from smart device 304.

[0100] Evaluation system 302 includes at least one transceiver configured to transmit the evaluated data, wherein the evaluated data includes a baseline energy requirement metric. The baseline energy requirement metric may be a baseline daily energy requirement metric.

[0101] The baseline data includes one or more of baseline health metrics, age, life stage, breed, size, leg length, gender and metabolism factor. The baseline health metrics include one or more of a current weight and a Body Condition Scoring (BCS). The baseline energy requirement metric includes a target number of calories. Where the baseline energy requirement metric is a baseline daily energy requirement metric, the target number of calories is a target daily number of calories.

[0102] In some implementations, life stage of a pet may be determined according to one or more categories. For example, the life stages for a dog may include puppy, adult, and senior. As another example, the life stages for a dog may include puppy, adolescent, adult, senior, very senior.

[0103] The at least one processing unit of the evaluation system 302 is further configured to receive one or more updated active data packets from the portable monitoring device. The updated active data packets are received at regular intervals. The at least one processing unit of the evaluation system is further configured to receive updated health metrics. The atleast one processing unit of the evaluation system is further configured to generate updated evaluated data based on the updated active data packets. The at least one processing unit of the evaluation system is further configured to generate updated evaluated data based on the updated health metrics. The at least one processing unit of the evaluation system is further configured to generate updated evaluated data based on the updated active data packets and the updated health metrics.

[0104] The at least one transceiver of the evaluation system is further configured to transmit the updated evaluated data, the updated evaluated data including an updated energy requirement metric. The updated energy requirement metric may be an updated daily energy requirement metric.

[0105] Activity data, detected by the one or more sensors, is collected in a plurality of data packets. These data packets may be detected at predetermined intervals. In one implementation, the predetermined interval may be 15 seconds. A data packet is an active data packet if the data packet meets a predefined criteria. That is, if a data packet meets a predefined criteria, then that data packet is defined as an active data packet thereby creating an active data packet including activity data. The predefined criteria may be that the activity data is above a predetermined threshold. The predefined criteria may be that the activity data is detected for at least a threshold percentage of the predetermined interval. For example, the predefined criteria may be that the activity data is detected for at least 46.67% of the predetermined interval. For example, the predefined criteria may be that the activity data is detected for 7 seconds of a predetermined interval of 15 seconds. In another example, the predefined criteria may be that the activity data is detected for 3 seconds of a predetermined interval of 5 seconds. Whilst a percentage of 46.67% has been described above, it will be appreciated that other threshold percentages may be used. The creation of active data packets as described herein similarly applies to the creation of updated active data packets, wherein the activity data including in the data packets of updated active data packets include data detected by the one or more sensors of the portable monitoring device at some subsequent or future period. Activity data which is not accounted for activity in the “active”, that is such activity data not forming an active data packet, may include movement corresponding to getting up and / or down. Active data packets are distinguished from data packets including activity data not considered to be in the “active”.

[0106] The at least one processing unit of the evaluation system is further configured to receive updated health metrics. The updated evaluated data may be generated based on the updated active data packets and the updated health metrics. The updated health metrics include one or more of an updated current weight and an updated Body Condition Scoring (BCS). The updated energy requirement metric includes an updated target number ofcalories. Where the updated energy requirement metric is an updated daily energy requirement metric, the updated target number of calories is an updated target daily number of calories.

[0107] Each active data packet may be created when a subset of activity data in a payload of a data packet including activity data detected over a predetermined interval is above a threshold.

[0108] Each active data packet may include activity data detected over a predetermined interval that meets a predefined criteria. A data packet includes activity data detected over a predetermined interval. A data packet is an active data packet if the data packet meets a predefined criteria. The predefined criteria is that the activity data is above a predetermined threshold and / or activity is detected for at least 46.67% of the predetermined interval.

[0109] The one or more processors of the evaluation system is configured to execute predictive analytics, wherein the predictive analytics includes one or more of predictive algorithms, adaptive algorithms, predictive modeling, machine learning, Al and the like. The evaluation system, using the predictive analytics, may interpret data from one or more of the portable monitoring device, the dynamic substance monitoring device, and devices and / or servers within network 104. In some implementations, the predictive analytics is associated with the cloud server. In some implementations, the predictive analytics is associated with the hub.

[0110] The one or more processors of the evaluation system uses a predictive algorithm to generate the evaluated data including the baseline energy requirement metric. The predictive algorithm may generate the evaluated data based on the baseline data. The predictive algorithm may generate the evaluated data based on the active data packets.

[0111] The one or more processors of the evaluation system uses a predictive algorithm to generate the updated evaluated data including the updated energy requirement metric. The predictive algorithm may generate the updated evaluated data based on the substance data received from at least one transceiver of the dynamic substance measuring device. The predictive algorithm may generate the updated evaluated data based on the updated active packets received from the at least one transceiver of the portable monitoring device. The predictive algorithm may generate the updated evaluated data based on the updated health metrics. The predictive algorithm may generate the updated evaluated data based on historical consumption data.

[0112] The cloud server 310 may be one or more distributed servers, data centres, virtualised servers in distributed data centres and the like. The cloud server 310 isconfigured to communicate with one or more data repositories and / or one or more servers. The data repositories and / or servers provide access to data relating to one or more of activity data, substance data, baseline data, updated health metrics, weather data, calendar data, date data, time data, heart rate data, oxygen intensity (respiration) data, acceleration data, gas expulsion data, temperature data, ambient temperature data, intensity data, target weight data, ideal weight data, activity based calorific expenditure data across all breeds of pets, including all breeds of dogs, and recommended weight data of all breeds of pets, including all breeds of dogs.

[0113] The cloud server 310 is configured to communicate with each of the smart device 304, dynamic substance measuring device 306, and portable monitoring device 102 via one or more communication pathways. In some implementations, where the evaluation system 302 includes hub 312, the cloud server 310 is configured to communicate with the hub 312 via one or more communication pathways.

[0114] In some implementations, the cloud server, using one or more processors, is configured to process activity data and identify the activity data as one or more of active and sedentary. The cloud server, using one or more processors, may be configured to calculate intensity data based on activity data. The cloud server, using one or more processors, may be configured to generate evaluated data and / or updated evaluated data based on the plurality of data accessible via the one or more data repositories and / or one or more servers described herein, and / or data from the portable monitoring device, dynamic substance measuring device, and smart device. In some implementations, the cloud server is configured to transmit the evaluated data and / or updated evaluated data to the dynamic substance measuring device. In some implementations, the cloud server is configured to transmit the evaluated data and / or updated evaluated data to the dynamic substance measuring device via the hub. The evaluated data and / or updated evaluated data is identified / calculated and dynamically adjusted based on the pet’s activity for the prior period (vs expectations) and actual consumption in prior meals, including under and over consumption.

[0115] The term “cloud server” as used throughout the specification is also referred to simply as “cloud”.

[0116] The hub 312 is configured to communicate with each of the smart device 304, dynamic substance measuring device 306, and portable monitoring device 102 via one or more communication pathways. In some implementations, where the evaluation system 302 includes cloud server 310, the hub 312 is configured to communicate with the cloud server 310 via one or more communication pathways.

[0117] The hub 312 is configured with one or more charging systems. The one or more charging systems provide charging capabilities for one or more of the hub itself, the portable monitoring device and the dynamic substance measuring device. The hub 312 may provide connectivity to the cloud. The hub 312 may act as a gateway configured to provide communication between the dynamic substance measuring device, such as a bowl system, and the cloud. The hub 312 may act as a gateway configured to provide communication between the portable monitoring device, such as a tracker, and the cloud. In some implementations, the dynamic substance measuring device and / or portable monitoring device are configured such that they may communicate directly with the cloud.

[0118] The hub includes at least one user input device. The user input device may be a switch, a button or similar.

[0119] The smart device 304 is configured to communicate with the evaluation system 302, including the cloud server 310 and / or the hub 312, via at least one communication pathway 308. The smart device 304 is further configured to communicate with each of the portable monitoring device 102 and the dynamic substance measuring device 306 either directly via at least one communication pathway or via the evaluation system 302. The baseline data is received from the smart device. For example, the smart device 304 may be configured to receive baseline data input by a user.

[0120] The transmission of data from the smart device 304 to the evaluation system 302, portable monitoring device 102 and / or dynamic substance measuring device 306, may be through wired and / or wireless communication. Using wireless communication, the smart device 304 may connect to other devices or networks via different wireless protocols (such as Wi-Fi, Bluetooth, RFID, Near-Field Communications (NFC)).

[0121] The smart device 304 includes at least one processing unit configured to execute an activity tracking application. The term “activity tracking application” as used throughout the specification is also referred to simply as “app”.

[0122] The smart device 304 may be any electronic device (for example, a personal computer, smartphone, tablet, multimedia device, and other mobile devices), as long as it can process information, and load and execute an application.

[0123] By using algorithms that can be continually tuned, data from the tracker can be processed into active data packets and sent through to the evaluation system. By selectively sending these active data packets through to the evaluation system, and avoiding the need to stream all data from the tracker to the evaluation system, the tracker transmits a relativelysmaller amount of data over a period of time (compared with streaming all data) which results in a longer battery life.

[0124] The processing unit 204 of the portable monitoring device 102 may be configured to create intensity data based on raw activity data, including acceleration data, received from the one or more sensors 202 of the portable monitoring device. The intensity data relates to the intensity of an activity associated with the relevant raw activity data. Acceleration data may be in the form of measured G forces.

[0125] In some implementations, using an accelerometer and gyroscope in the portable monitoring device provides a measure of acceleration in a consistent direction over time. In some implementations, using a magnometer in the portable monitoring device provides a measure of direction. Data may be calculated by combining data from a 3 axis gyrometer and 3 axis accelerometer. In one example implementation, a G force change may be determined by assessing movement 3 seconds before and 3 seconds after. If a G force change is detected, the data may be categorised as low, medium or high intensity data.

[0126] The processing unit 204 of the portable monitoring device 102 may be configured to create activity classification data. In one example implementation, activity classification data includes five main types of classifications as follows:1 . Rest - When the dog is mostly stationary with a few steps.2. Lazing Around - When the dog is not constantly moving but is not stationary.3. Walking - When the dog is moving at a low constant speed.4. Running - When the dog is moving at a medium / high constant speed.5. Zoomie - When the dog is moving at a high speed.

[0127] Threshold values may be used to determine activity classification data, such as the five types above, and these threshold values may be dynamic based on baseline data such as breed, size and leg length.

[0128] Whilst the above activity classification data has been described as being created by the processing unit 204 of the portable monitoring device 102, it will be appreciated that the creation of the activity classification data may occur in one or more other devices in the network 104. For example, the processing unit of the evaluation system 302 may be configured to create activity classification data. In one implementation, the activity classification data created by the processing unit of the evaluation system 302 may be fed back to the portable monitoring device 102 for dynamically updating future activity classification data.

[0129] In some implementations, active minutes may be defined as all period of windows where the dog is either walking, running or zoomie. If a dog is resting or lazing around then this activity is detected, but not used for generating evaluated data and / or updated evaluated data. In one implementation, thresholds values associated with the active minutes of low, medium and high may be mapped as follows:"walking" low"running" medium"zoomie" high

[0130] The processing unit 204 of the portable monitoring device 102 may be configured to create movement signature data. In one example implementation, movement signature data may be identified, detected, captured and preloaded to the portable monitoring device. In some implementations, movement signature data may be used to create activity classification data.

[0131] The processing unit 204 of the portable monitoring device 102 may be configured to retrieve baseline data.

[0132] Evaluated data and / or updated evaluated data, generated by the at least one processing unit of the evaluation system, may be based one or more of movement data, steps, active minutes, movement signature data, activity classification data, intensity data, calories, sleep and sleep patterns, heart rate, and skin or body temperature.

[0133] The dynamic substance measuring device 306 will be described in detail below with reference to FIG. 4. FIG. 4 is a block diagram of a dynamic substance measuring device 306 according to the present disclosure.

[0134] The dynamic substance measuring device 306 includes one or more sensors 402 for detecting substance data associated with a substance. The substance may be any food type including food, liquid and / or any other food material and liquid material for consumption, wherein the food may be dry food and / or wet food. The substance data includes one or more of a volume, weight, quantity and / or other measurement of the substance. The dynamic substance measuring device 306 also includes at least one processing unit 404, at least one memory 406, at least one I / O interface 408, at least one indicator 410, at least one power source 412, and at least one user input device 414. The dynamic substance measuring device is configured to communicate with the portable monitoring device 102 by at least one communication pathway. The dynamic substance measuring device is configured to communicate with the network 104 by at least one communication pathway.

[0135] Processing unit 404 may be configured to receive an energy requirement metric. The processing unit 404 may evaluate the substance data and the energy requirement metric.

[0136] A volume, weight, quantity and / or other measurement of the substance, such as food type, detected by the one or more sensors 402 of the dynamic substance measuring device 306 can be determined based on the baseline energy requirement metric.

[0137] A volume, weight, quantity and / or other measurement of the substance, such as food type, detected by the one or more sensors 402 of the dynamic substance measuring device 306 can be determined based on the updated energy requirement metric.

[0138] The dynamic substance measuring device 306 may include a dispenser. In one implementation, the dynamic substance measuring device 306 includes a base and a measuring portion separable from the base. The measuring portion may be a receptacle. The receptacle may be a bowl. In one implementation, the dynamic substance measuring device 306 is a bowl system including a base and a bowl separable from the base.

[0139] In one example implementation, the base includes high duro point contact feet. High duro material removes any settling, including variations in volume, weight, quantity and / or other measurement detected, caused by flex and introduces a point contact while being nonslip. Use of high duro material secures contact with load cell and surface.

[0140] Sensors 402 include one or more of multi-load cells, high frequency electronic scales, and logarithmic scales. The multi-load cells may include a plurality of scales. For example, the multi-load cells may include between 4 to 10 scales. The multi-load cells may be configured to detect at high frequency intervals. In some implementations, detecting at high frequency intervals provides substantially continuous measurements being detected by the sensors 402. The substantially continuous measurements provides an accurate measure of the amount of food a pet is consuming as the sensors are detecting the volume, weight, quantity and / or other measurement of the substance at different detection intervals. The detection intervals may vary and / or be adjusted based on the type of pet and / or the pet’s consumption behaviours. Consumption behaviours or habits include, for example, consumption at varying rates, such as grazing over a period of time, fast eating, eating everything quickly in one sitting, how much food is being consumed and / or the rate of food consumption. For every detection / time intervals, substance data from the dynamic substance measuring device may be transmitted to one or more of the portable monitoring device and devices and / or servers within network 104. In one example implementation, the detection intervals may be every 15 seconds for the first 3 mins, then every 30 seconds until 15 mins, then every 1 min until 180 mins or the bowl is substantially empty / registering zero.It will be appreciated that this example detection intervals are indicative of one example only, and that other detection intervals may be used.

[0141] The sensors 402 are configured to accurately detect the substance data associated with the substance in real-time. With such sensors 402, the dynamic substance measuring device 306 is configured to detect a plurality of different substances. Sensors 402 may be configured to detect a change in pressure. For example, the sensors 402 detect a change (such as an increase and / or decrease) in pressure from the substances in the dynamic substance measuring device 306, and the sensors communicate substance data, including one or more of a volume, weight, quantity and / or other measurement of the substance, to at least one processing unit 404. The processing unit 404 may be configured to determine a total volume, weight, quantity and / or other measurement of all the substance. The processing unit 404 is configured to compare the substance data with a desired measurement and / or amount of the substance. The comparison of the substance data with the desired measurement and / or amount of the substance is in real-time.

[0142] The desired measurement may be the energy requirement metric. The energy requirement metric includes a target number of calories. The target number of calories, such as total calorie intake, food amount, and the like may be measured or otherwise detected by the dynamic substance measuring device 306. The energy requirement metric may be a daily energy requirement metric which includes a target daily number of calories.

[0143] Evaluating or otherwise comparing the substance data, detected by the dynamic substance measuring device, and the energy requirement metric may include determining whether one or more of a volume, weight, quantity and / or other measurement of the substance detected corresponds with the energy requirement metric. The energy requirement metric may be the baseline energy requirement metric, the updated energy requirement metric, the baseline daily energy requirement metric and / or the updated daily energy requirement metric.

[0144] The dynamic substance measuring device, using the sensors 402 in communication with the processing unit 404, is configured to determine if the substance detected is: less than the energy requirement metric, greater than the energy requirement metric, and equal to the energy requirement metric.

[0145] The sensors of the dynamic substance measuring device may be configured such that one or more scales re-tare automatically and accept new values without any need for resetting, for example without any interaction from the user input device 414.

[0146] In one example implementation, the dynamic substance measuring device 306 is configured to detect up to seven types of substances. This is achieved with the use of the app as will be described in more detail below. It will be appreciated that the number of substances detected may be another number of substances. The I / O interface 408, including a transceiver, transmits substance data relating to each of the plurality of different substances, including up to seven substances. The substance data may include a total volume, weight, quantity and / or other measurement of all the substance.

[0147] In one example implementation, the sensors, including the multi-load cells, includes four sets of scales (one for each of the four sides of the dynamic substance measuring device).

[0148] Processing unit 404 is configured to receive the substance data from the one or more sensors. The processing unit 404 is configured to interpret, process and execute instructions or code to perform various operations associated with the dynamic substance measuring device 306. The processing unit executes instructions stored in memory 406 to perform various operations including, but not limited to, obtaining or receiving substance data from the one or more sensors 402, storing substance data obtained from the one or more sensors 402 in the memory, processing substance data from the one or more sensors 402, storing data generated, derived, processed or otherwise created from the substance data in memory, retrieving substance data (or data generated, derived, processed or created from substance data) from the memory, loading substance data (or data generated, derived, processed or created from substance data) from the memory, and transmitting and receiving data via I / O interface 408.

[0149] The processing unit may evaluate the substance data and the energy requirement metric to determine whether a volume, weight, quantity and / or other measurement of the substance corresponds with the energy requirement metric.

[0150] The energy requirement metric may include a baseline energy requirement metric and an updated energy requirement metric.

[0151] The processing unit may be configured to receive the baseline energy requirement metric. The baseline energy requirement metric may be a baseline daily energy requirement metric. The processing unit may be configured to receive the updated energy requirement metric. The updated energy requirement metric may be an updated daily energy requirement metric.

[0152] The processing unit 404 of the dynamic substance measuring device 306 is configured to communicate with the at least one indicator 410.

[0153] Processing unit 404 may include one or more processors, controllers, microcontrollers, control units, computing units, computing circuits or processing circuits. The processing unit may be a microprocessor, field programmable gate array (FPGA), application specific integrated circuit (ASIC), programmable logic, or combinations thereof.

[0154] In addition to storing substance data obtained from one or more sensors 402 and / or data derived from substance data, memory 406 may additionally store configuration data. Configuration data may be used to configure the dynamic substance measuring device 306 and / or used during execution of instructions or code to perform various operations associated with the dynamic substance measuring device. Memory 406 may also store data derived or processed by the processing unit 404 or data received from one or more devices and / or servers within network 104 for subsequent use. Subsequent use may include, but is not limited to, analysis, viewing, and communication and / or transmission via I / O interface 408.

[0155] The memory 406 may include any suitable memory architecture, non-volatile and / or volatile storage media, and one or more different classes of storage devices or units configured to store different classes of data. The memory 406 is configured to store the substance data, including one or more of a volume, weight, quantity and / or other measurement of the substance. Processing unit 404 may include its own memory additionally or alternatively to memory 406.

[0156] The processing unit 404 may communicate substance data received from sensors 402 or retrieved from memory 406 to one or more devices and / or servers within network 104, via I / O interface 408.

[0157] The I / O interface 408 may include a transceiver configured to transmit data, including substance data, to one or more devices and / or servers within network 104. The transceiver may be configured to transmit the substance data to a network by at least one communication pathway. The transmission of data to the network 104 may be through wired and / or wireless communication. Wired communication may include transmission of data via a Universal Serial Bus (USB) interface. Wireless communication may include transmission of data via one or more communications technologies such as, for example, Wi-Fi, Bluetooth, RFID, Near-Field Communications (NFC), optical data transmission, among others. Wireless communication advantageously enables data transmission from the dynamic substance measuring device 306 to one or more devices and / or servers within network 104 when the dynamic substance measuring device comes within range. In one implementation, I / O interface 408 includes Bluetooth so that when the dynamic substance measuring device comes within range of one or more devices and / or servers within network 104, substancedata is automatically synced or uploaded to the one or more devices and / or servers within the network.

[0158] The indicator 410 may be any type of indicator configured to provide feedback in response to substance data being detected by the sensors 402. The feedback provided by indicator 410 may be visual, audible, tactile, haptic and the like, or a combination thereof. The indicator 410 may be configured to provide feedback incrementally or otherwise progressively. The indicator 410 may be one or more of a light, ring device, radial light, radial display, radial light display, circular light, logarithmic lighting, and / or any other light configured to incrementally or otherwise progressively illuminate and / or dim. The indicator 410 may be one or more of a display and / or LCD. The indicator 410 may be one or more of a speaker and / or any other device configured to provide an audible alert. The indicator 410 may be any device configured to provide tactile and / or haptic feedback.

[0159] In one example implementation, a logarithmic light allows a user to adjust an input rate, for example slow down, as the light incrementally or otherwise progressively illuminates. In one example, the logarithmic light may include a plurality of segments, such as eight segments, that progressively illuminate. For example, a logarithmic lighting fill line may be across eight segments at values of 40%, 65%, 75%, 90%, 95%, 97%, 98%. That is, indicator 410, is graduated at 40%, 65%, 75%, 90%, 95%, 97%, 98% increments to enable a user to better adjust and control an input rate into the dynamic substance measuring device to the desired level. It will be appreciated that segment values and / or graduated values of the indicator may be values other than those described above such that the light otherwise incrementally or otherwise progressively illuminates.

[0160] The indicator 410 adapts with the desired measurement and / or amount of the substance as detected by the sensors. In other words, as the desired measurement and / or amount of the substance changes for each meal, the indicator will adapt accordingly thereby providing accurate feeding. The indicator may adapt based on data received by the progressing unit 404, of the dynamic substance measuring device 306, from evaluation system 302. For example, the data received may include an energy requirement metric. The energy requirement metric includes a baseline energy requirement metric and an updated energy requirement metric.

[0161] The desired measurement may be the energy requirement metric. The energy requirement metric includes a target number of calories. The target number of calories, such as total calorie intake, food amount, and the like may be measured or otherwise detected by the dynamic substance measuring device 306. The energy requirement metric may be a daily energy requirement metric which includes a target daily number of calories.

[0162] In one implementation, the indicator progressively illuminates as a substance, such as food, is detected by the dynamic substance measuring device. The indicator fully illuminates once the substance detected corresponds with the energy requirement metric. The energy requirement metric may be the baseline energy requirement metric, the updated energy requirement metric, the baseline daily energy requirement metric and / or the updated daily energy requirement metric.

[0163] In one implementation, the indicator is fully illuminated and progressively dims as a substance, such as food, is detected by the dynamic substance measuring device. The indicator fully dims once the substance detected corresponds with the energy requirement metric. The energy requirement metric may be the baseline energy requirement metric, the updated energy requirement metric, the baseline daily energy requirement metric and / or the updated daily energy requirement metric.

[0164] In one implementation, the indicator, in the form of a speaker, progressively sounds as a substance, such as food, is detected by the dynamic substance measuring device. The indicator sounds to a predetermined level once the substance detected corresponds with the energy requirement metric. The energy requirement metric may be the baseline energy requirement metric, the updated energy requirement metric, the baseline daily energy requirement metric and / or the updated daily energy requirement metric.

[0165] In one implementation, the indicator 410 includes both a light and a speaker (a combined visual and audible feedback).

[0166] Whilst implementations of the indicate may be described as incrementally or otherwise progressively illuminating. It will be appreciated that, in some implementations, the indicator may start fully illuminated and progressively dim as the amount of substance is detected.

[0167] The app, executable on the smart device 304. may include a feeding routine developed based on the evaluated data. For example, the feeding routine may provide information on the target number of calories. The target number of calories may be a volume, weight, quantity and / or other measurement of each of one or more substances.

[0168] In one example implementation, a user enters, into the app, one or more substances to be consumed. The user may also enter the order in which the one or more substances will be added to the dynamic substance measuring device, such as a bowl system.

[0169] If one food type is being added to the bowl system, the indicator will progressively illuminate / dim as the food type is added to the bowl system, and the indicator will completelyilluminate / dim once the measurement of the food type is detected and determined to be equal to the target number of calories.

[0170] If a plurality of food types are being added to the bowl system, the user will add the plurality of food types to the bowl system in the same order as entered into the app. The indicator will progressively illuminate / dim as each food type is added to the bowl system, and the indicator will completely illuminate / dim once the measurement of each food type is detected and determined to be equal to the target number of calories for that food type.

[0171] The power source 412 may be one or more batteries. The one or more batteries may be rechargeable batteries or removable batteries.

[0172] The user input device 414 is configured to receive input data and transmit the input data to the processing unit 404. The input data may include data relating to meal requests. The user input device may be a switch, a button or similar.

[0173] The one or more sensors 402, processing unit 404, memory 406, I / O interface 408, indicator 410, user input device 414, and power source 412 are communicatively coupled with one or more of one another via a communication bus 416.

[0174] Throughout the specification reference to transmitting and / or receiving data includes transmitting and / or receiving data in the form of data packets.

[0175] Example implementations

[0176] The system as herein disclosed provides an integrated system, having both hardware and software. One example implementation of the system herein disclosed and described below.

[0177] The evaluation system as herein disclosed makes use of predictive analytics, including for example a predictive algorithm, to generate or otherwise calculate evaluated data. The evaluated data includes a baseline daily energy requirement metric and updated daily energy requirement metric. The daily energy requirement metric may include calorie requirements of a human, pet or animal.

[0178] In an example implementation, the system herein disclosed draws from a library of calorie data by brand fed to a pet (which may be entered into an app at setup or otherwise retrievable by the app). This calorie data is then transmitted to the hub to assist with determining the volume of food required by the pet with consideration of activity data detected by the portable monitoring device, as indicated by the energy requirement metric. The hub then communicates to the bowl system the weight of food required. A user maydispense the volume of food required to the bowl system. An indicator coupled to the bowl system progressively illuminates as the food is dispensed, and on the bowl system detecting the required amount of food (for example by weight of food) the indicator completely illuminates to signaling the user to stop dispensing the food. In one example implementation, the indicator light turns green on complete illumination.

[0179] Activity data, such as steps, is detected by the portable monitoring device. A predefined algorithm is used and continually tuned. The tuning of the algorithm is unique to each pet and derived from:1 . Baseline data, for example one or more of leg length of the pet, BCS, weight, gender and breed.2. Machine learning data across pet breeds on historical data from dogs, wherein the data is labelled based on size and breed of the pet and categorised by activities (for example, walking, fast walk, run, sprint / zoomie).

[0180] The predefined algorithm may be used by the evaluation system and data generated from the predefined algorithm may be transmitted to one or more of the portable monitoring device, the dynamic substance measuring device, and one or more devices and / or servers within network 104.

[0181] The activity data is detected by the portable monitoring device and bundled into data packets over a predetermined interval, for example in 15 seconds windows. If a data packet has activity for less than 46.67% (for example activity for less than 7 seconds in the 15 second window) the steps are counted, but activity is not accounted for activity in the “active”. The data packet is otherwise considered an active data packet. The timeframes provided above are examples only and it will be appreciated that other timeframes may be used. In one implementation, over 14,000 data packets are collected daily.

[0182] Active data packets may be combined to create active minutes. The data of the combined active data packets is used by the evaluation system disclosed herein.

[0183] With the sensors 202 of the portable monitoring device 102, acceleration on all three axis is detected as activity data. This detected data provides X at low stress, Y at medium stress and Z at high stress. The system herein disclosed determines caloric requirements for pets based on the effects of low, medium and high stress activity on individual pet sizes. This data is advantageously used by the disclosed predictive analytics.

[0184] The system herein discussed uses individual pet data to inform the predictive analytics to predict the amount of activity a pet will do on any given day. This allows the system to provide predictive activity data in the morning and validate in the evening, giving greater accuracy and individual meals based on pet activity, life stage, weight, and BCS.

[0185] A combinations of formulas is used by the predictive analytics of the evaluation system.

[0186] In one example implementation, a pet’s Resting Energy Requirement (RER) is a function of metabolic body size and represents the energy requirement of the pet while at rest at a controlled temperature. Examples for calculating RER based on body weight in kilograms (BW kg) include:

[0187] RER = 30 x (BW kg) + 70

[0188] RER = 70 x (BW kg)0.75

[0189] The evaluation system of the system herein disclosed generates evaluated data which includes a Daily Energy Requirement (DER) metric.

[0190] In addition, the system herein disclosed interprets pet consumption and / or feeding behaviour, using the dynamic substance measuring device, to provide return feedback to the app on any “abnormality or deviation from the norm” feeding behaviour of the pet that may indicate illness or injury providing early notification to the use (through the app) assisting with early medical intervention resulting in potentially less serious illness, dental issues, gut issues and the like.

[0191] The system as herein disclosed assists with the management of a healthy weight by providing accurate feeding and health alerts for pets based on activity (movement signatures) and calorific output. The system herein disclosed assesses calorific needs for each pet, using a combination of baseline data, activity data synced from sensors of the portable monitoring device, which enables measure of velocity and force as well as location (for example, sitting, lying, standing, acceleration and the like) and predictive analytics which flows as illustrated in the Personalised Nutrition Program (PNP) herein disclosed. The system as herein disclosed provides accurate calorific estimates for each meal.

[0192] The predictive analytics is used to accurately predict the activity for the pet to assist with determining amounts for morning meals, that data is then validated through activity later in the day. In one example implementation, the predictive analytics “learns” the behaviour and activity of the pet during a period, including a historical period, for example through the first 4 weeks of use. Once “educated”, the predictive analytics is able to predict the pet’s behaviour and activity by reference to a number of metrics including activity levels anticipated by day of the week (for example, weekends are more active than weekdays for many pets), weather (for example, less active on rainy days), and / or public holidays. This predictive analytics then informs the anticipated calorie requirements, this may be supplemented by any deficit in calories from a previous day (for example, if the pet did notconsume all food or skipped a meal as detected by the dynamic substance measuring device).

[0193] The system as herein disclosed assist a user to easily feed an appropriate amount of food to a pet via the dynamic substance measuring device including a radial display and logarithmic scale.

[0194] The system as herein disclosed, in some implementations, combines the dynamic substance measuring device and portable monitoring device, as well as uses predictive analytics to assist with determining accurate calorie intake requirements, including food and liquid portions. Accurate calorie intake data assists with managing weight (including weight loss and weight gain). For example, with the system as herein disclosed, the approach to weight loss includes gradually reducing calorie intake by the pet until they reach a desired calorie intake and weight.

[0195] In one implementation, the first few weeks of data is used to optimise breakfast and dinners and to validate patterns of a pet’s day, including minutes of play, physical exercise, rest and sleep. This data also assists with developing a predictive model of the pet’s day today. In subsequent periods, for example, months and years, the activity tracker plays a monitoring role to keep calorific intake appropriate for the pet’s longer-term physiological needs, promoting healthy ageing. Together, this data assists users to increase their interactions with and optimise the diet, frequency, portions and activities enjoyed by their pet to promote an overall improvement in quality of and length of life.

[0196] Portable monitoring device

[0197] In an implementation of the present disclosure, a portable monitoring device 102, in the form of a tracker has specifications as outlined in Table 1 .Table 1

[0198] It will be appreciated that the tracker specifications as outlined in Table 1 relate to one specific example implementation and is not intended to be the only implementation. It will be appreciated that portable monitoring devices with other specifications fall within the scope of this disclosure. For example, a portable monitoring device may be of any different shapes and sizes, and weight, and may be formed of a variety of materials.

[0199] The tracker tracks a pet’s activity and rest. Each pet’s unique data will inform a daily energy requirement metric, which may include a caloric recommendation, determined by the evaluation system 302. The tracker may be small in footprint and lightweight. The tracker may be fitted to any pet collar. The tracker may include a snap attach mechanism for attachment to a housing to avoid the tracker being lost or consumed. The tracker may include a battery. In some implementations, the battery has at least a 14 day battery life. In some implementations, the battery has a 21 day battery life. The tracker may be formed of a waterproof material. The waterproof material may permit immersion in water. The tracker may include wireless charging capabilities.

[0200] The tracker may be configured for setting goals including distance, calories burned and the like. The processing unit of the tracker may be configured to track and determine when the activity data received from the sensors or retrieved from the memory, or the metrics generated from such activity data, indicate that a goal has been achieved or a progress point has been reached. For example, such a goal can be specific metrics including, but not limited to, a distance, a number of steps, an elevation change, or number of calories burned and the like.

[0201] In one example implementation, the tracker includes a 6-axis sensor. A 6-axis sensor provides more accurate and complete activity data. In one example implementation, the tracker is waterproof and is configured for wireless charging.

[0202] The smart device 304 includes at least one processing unit configured to execute an activity tracking application also known as an app. The evaluation system interprets tracker data and feeds data through to the bowl. The app may collect data relating to a pet’s including, but not limited to, breed, age, and size information. The app interprets activity data detected by the one or more sensors of the tracker, to deliver a Personalised Nutrition Program (PNP) for the pet. The app may be powered by predictive analytics via evaluation system 302. In some implementations, the predictive analytics is via the hub 312. In other implementations, the predictive analytics is via the cloud 310. In other implementations, the predictive analytics is via the hub 312 and cloud 310. The predictive analytics includes predictive algorithms, predictive modeling, machine learning, Al and the like. The app may learn new insights about your pet. The app may be updated regularly with new and improved features. The smart device 304 includes wireless connectivity capabilities, such as Bluetooth and Wi-Fi, to support use of the app

[0203] In some implementations, the portable monitoring device provides data to the app via the evaluation system, and may operate without the dynamic substance measuring device.

[0204] A number of advantageous features of the portable monitoring device are listed below.• Waterproof seal and induction charging: This provides no contact points for salt water to corrode anywhere on the tracker or water to damage the internal components including electronics and the like. The tracker is fully sealed, for example by welding. In one implementation, an ultrasonically welded enclosure for the tracker gives it an IP rating of IP67. A completely sealed unit means that the tracker has induction charging capabilities.• Use of algorithms (instead of streaming data): By using algorithms that can be continually tuned, data from the tracker can be processed into active data packets and sent through to the evaluation system. By selectively sending these active data packets through to the evaluation system, and avoiding the need to stream all data from the tracker to the evaluation system, the tracker transmits a relatively smaller amount of data over a period of time (compared with streaming all data) which results in a longer battery life. Activity data, such as steps, is detected by the portable monitoring device. A predefined algorithm is used and continually tuned. The activity data is detected by the portable monitoring device and bundled into data packets over a predetermined interval, for example in 15 seconds windows. If a data packet has activity for less than 46.67% (for example activity for less than 7 seconds in the 15 second window) the steps are counted, but activity is not accounted for activity in the “active”. The data packet is otherwise considered an active data packet. Activity which is not accounted for activity in the “active” may include movement corresponding to getting up and / or down. In one implementation, over 14,000 data packets are collected daily.• 6-axis sensor: By using 6 axis, acceleration and force may be detected from the tracker (in addition to movement in active minutes), this may be converted into indications of the stress of the exercise of the pet. A 6 axis accelerometer detects acceleration on all 3 axis in the data packets, giving minutes X at low stress, Y at medium stress and Z at high stress. The evaluation system (including the cloud) has software to read from the 6 axis and label low, medium and high activity data and produces evaluated data. The intensity of movement allows accurate calculation of energy expenditure of the pet. Daily movement patterns are tracked so that future calorific needs from food can be predicted.

[0205] The above listed advantages is not intended to be an exhaustive list, but rather examples of advantages in some implementations. Other advantageous features and aspects are described throughout the specification.

[0206] Dynamic substance measuring device

[0207] In an implementation of the present disclosure, the dynamic substance measuring device 306 includes a base and a measuring portion separable from the base. The measuring portion may be a receptacle. In one implementation, the receptacle is a bowl. In one implementation, the dynamic substance measuring device 306 is a bowl system including the base and the bowl separable from the base. In one implementation, that bowl system has specifications as outlined in Table 2.Table 2

[0208] It will be appreciated that the bowl system specifications as outlined in Table 2 relate to one specific example implementation and is not intended to be the only implementation. It will be appreciated that dynamic substance measuring devices with other specifications fall within the scope of this disclosure. For example, a dynamic substance measuring device and components therefor may be of any different shapes and sizes, and weight, and may be formed of a variety of materials.

[0209] In one example implementation, the bowl system includes four sets of scales (one for each of the four sides) and averages the weight of food measured. With existing technology, unless the food is poured into a bowl system evenly, the weight is not accurate. The four sets of scales advantageously allows the weight of food to be measured more accurately than existing technology. It will be appreciated that, in other implementations, the number of scales may be more or less than four. For example, in one implementation, there are up to 10 scales.

[0210] In one example implementation, the indicator 410 of the bowl system includes a ring device that illuminates as food is added to the bowl. As food is added to the bowl, the ring progressively illuminates until the ring is complete. If too much food is added into the bowl then the ring changes colour, for example to red, which indicates that food should be removed.

[0211] In one example implementation, with the sensors 402 described herein, the dynamic substance measuring device 306 is configured to detect a plurality of different substances, for example up to seven types of substances. The sensors of the dynamic substance measuring device are configured such that one or more scales re-tare automatically and accept new values without any need for resetting, for example without anyinteraction from the user input device 414. The app, executable on the smart device 304, includes code to send data relating to a plurality of weights corresponding to the plurality of substances, for example up to seven individual weights and a total weight, to the dynamic substance measuring device. The sensors 402 of the dynamic substance measuring device 306 automatically tares once each weight has been reached and the sensors then detect a new amount.

[0212] The bowl system dynamically updates daily food portions. In particular, the bowl system allows portions of food to be measured for a pet with dynamically updated daily meal sizes. The bowl system may include a portion indicator. The bowl system may include a battery. In some implementations, the battery has at least a 40 day battery life. In some implementations, the battery has a 100 day battery life. The bowl may be removable from the base, wherein the bowl is formed of a dishwasher safe material. The bowl may also be formed of waterproof material suitable for indoor and outdoor feeding. The base of the dynamic substance measuring device may also be formed of a waterproof material suitable for indoor and outdoor feeding. The waterproof material may permit immersion in water. The dynamic substance measuring device may include Bluetooth for wireless communication with one or more other devices in system 300.

[0213] The bowl system when connected with the portable monitoring device, via the evaluation system, monitors your pet’s activity and seamlessly updates a Personalised Nutrition Program (PNP). The bowl of the bowl system may be formed of easy wipe-clean materials. The bowl may be a removable stainless steel bowl for easy-of-washing. The bowl system may include a radial backlit indicator to indicate an ideal food portion without the need to show numerical weights, once the indicator is fully illuminated, for example, glowing white, the ideal portion has been served; if it glows a different colour, for example, if it glows red, this indicates that removal of food is necessary to achieve the ideal food portion.

[0214] Smart scales in the bowl system that communicate with the evaluation system, including the cloud, for a variable weight means that for every meal, a different desired food amount, such as the desired weight of the food, may be sent to the bowl system allowing dynamic feeding. The bowl system receives live calculations of dietary requirements of the pet from the cloud (in some implementations this is via the hub) allowing the desired volume of food to be easily and cleanly measured. Using incremental lights, the bowl system incrementally alerts the pet owner of the impending full bowl. This incremental alert may be a progressively illumination and / or dimming of the incremental lights. As described herein, an incremental alert may be one or more of visual, audible, tactile, haptic and the like, or a combination thereof.

[0215] In one example implementation, the bowl system having a radial light display means that, without having to hit a target numeric weight, the bowl system weighs backwards using a logarithmic scale on a radial light which simplifies the feeding process. The radial light is graduated at 40%, 65%, 75% 90% 95% 97% 98% increments to enable a user to better control the flow of food into the bowl to the correct level.

[0216] The bowl system, being in two-parts including the base and the bowl separable from the base, allows the bowl system to be easily cleaned. For example, the base and the bowl may both be formed of washable and / or waterproof material to allow for easy cleaning without compromising the bowl system electronics (for example, scales and electronic components) through water ingress.

[0217] The dynamic substance measuring device may function without the tracker. For example, if a pet loses the portable monitoring device, the evaluation system takes an average DER over a period of time, for example an average of the last 14 days DER, to inform the bowl system the volume of food required.

[0218] A number of advantageous features of the dynamic substance measuring device are listed below.• Multi feeding: With the sensors 402 described herein, the dynamic substance measuring device 306 is configured to detect a plurality of different substances, for example up to seven types of substances. The sensors of the dynamic substance measuring device are configured such that one or more scales re-tare automatically and accept new values without any need for resetting, for example without any interaction from the user input device 414. The app, executable on the smart device 304, includes code to send data relating to a plurality of weights corresponding to the plurality of substances, for example up to seven individual weights and a total weight, to the dynamic substance measuring device. The sensors 402 of the dynamic substance measuring device 306 automatically tares once each weight has been reached and the sensors then detect a new amount.• Accurate feeding: By using an indicator which is configured to provide feedback incrementally or otherwise progressively and adapts with a desired amount of substance detected in the bowl system, accurate feeding is achieved.• Logarithmic indicator: By using a logarithmic light, a user may adjust an input rate of the substance, such as to slow down input of the substance, as the user reaches a fill line as eight segments light up, introducing logarithmic lighting fill line across eight segments at values of 40%, 65%, 75% 90% 95% 97% 98%.• High duro point contact feet: High duro material removes any settling, including variations in volume, weight, quantity and / or other measurement detected, caused by flex and introduces a point contact while being nonslip. Use of high duro material secures contact with load cell and surface.• Waterproof seal: A fully sealed bowl system may be used indoors and outdoors in all weather. In one example implementation, by fully sealing the bowl system with a IP65 rating provides protection from low-pressure water jets from any direction.• Bluetooth and battery: The bowl system may detect the amount of substance in the bowl, the rate in which the substance is being consumed., changes in consumption, and accept new meal amounts daily, including every morning and every evening. This may be achieved with Bluetooth connectively with the bowl system being communicatively coupled to the evaluation system. By having the bowl system communicatively coupled to the hub, via Bluetooth, and the hub powered, the bowl system may be configured to operate with a relatively longer battery life, such as at least 90 days, than with other communication pathways. It will be appreciated that the number of days may vary in other implementations.

[0219] The above listed advantages is not intended to be an exhaustive list, but rather examples of advantages in some implementations. Other advantageous features and aspects are described throughout the specification.

[0220] Hub

[0221] In an implementation of the present disclosure, the hub 312 has specifications as outlined in Table 3.Table 3

[0222] It will be appreciated that the hub specifications as outlined in Table 3 relate to one specific example implementation and is not intended to be the only implementation. It will be appreciated that hubs with other specifications fall within the scope of this disclosure. For example, a hub may be of any different shapes and sizes, and weight, and may be formed of a variety of materials. The hub may be configured for various charging capabilities including a USB C charging cable that has an external AC / DC converter (power adapter).

[0223] The hub 312 may be a cloud connected hub and / or charging station. The hub may act as a Wi-Fi bridge for the dynamic substance measuring device 306 (including bowl and base) and portable monitoring device 102 (tracker). The hub 312 receives data at regular intervals (for example, every 30 seconds) when the tracker is in proximity. The hub 312 may include charging capabilities, wherein the charging capabilities may be wired and / or wireless and configured to connect with the portable monitoring device and / or dynamic substance measuring device. With wireless charging capabilities, the hub may remove risks associated with water ingress. The hub 312 may include both Bluetooth and Wi-Fi for wireless communication with one or more other devices in system 300. The hub 312 may be connected to the portable monitoring device and / or the dynamic substance measuring device via Bluetooth. The hub may be connected to the cloud via Wi-Fi. The hub receives data and provides that data to the app via the cloud. For example, the portable monitoring device, in the first instance, may provide data to the hub, the hub then provides that data to the cloud, and the cloud in turn provides that data to the app. In one implementation, when the portable monitoring device is not in range of the hub and the app is open on the smart device, if there is a gap in data on the cloud, the app will transmit data from the portable monitoring device to the cloud to populate the missing data. For example, the app will connect with the portable monitoring device, via Bluetooth connection (if in range), to transfer data to the cloud via the app to populate the missing data identified as the gap in data on the cloud.

[0224] Technical implementation aspects of the system

[0225] 1. Firmware implementation of activity data Bluetooth Low Energy (BLE) service for some implementations of the present disclosure is described below.

[0226] 1 .1 Service OverviewTable 4

[0227] Activity service

[0228] With reference to Table 4, the Activity Data Service has a single Characteristic of length 10 bytes. The first 8 bytes are the UTC timestamp in LCB:MSB order, the 9th byte is the interval of the bucket in seconds, and the final byte is the step count for that interval.

[0229] 1 .2 Operating Modes

[0230] 1 .2.1 Connected Operation - No Cached Data

[0231] When the portable monitoring device 102, also referred to as a tracker, is connected to a network 104, or other central device, provided there is no cached data to be uploaded, the Timestamp and Data Characteristics will update at a rate approximately equal to the bucket sampling interval.

[0232] 1 .2.2 Connected Operation - Cached Data

[0233] When the portable monitoring device 102 is connected to a network 104, or other central device and there is cached data to be uploaded, the Timestamp and Data Characteristics will update continuously with data retrieved from the cache until it is emptied. Once the cache is uploaded, the portable monitoring device 102 operates in the ‘No Cached Data’ mode.

[0234] 1 .2.3 Disconnected Operation

[0235] When the portable monitoring device 102 is disconnected from the network 104, the buckets are cached at the bucket sampling interval. This cache behaves like a FIFO, although its internal operation is more elaborate than a simple array of memory. This is described in a later section. During ‘offline’ operation, the portable monitoring device pushes buckets onto the FIFO, which holds them until the next time there is a connection to the network 104 or central device.

[0236] 1 .3 Data Cache

[0237] 1.3.1 Overview

[0238] As mentioned previously, the Data Cache is behaviourally a FIFO. It is accessed by two methods; a push, or a pop. A push operation adds data to the cache, while a pop operation retrieves data from the cache. Both operations transact a single sample / bucket. This is for a few reasons, for example: each bucket uploaded to the network 104 or central device is paired with a timestamp, relieving the central device from having to be aware of the sample rate, as would be required if it were transmitted as an array of data.

[0239] 2. Bowl Hub / Gateway Interface Control Document (ICD) for some implementations of the present disclosure is described below with reference to FIG. 5 to FIG. 10.

[0240] 2.1 Stateflow Diagram: Initial Setup Steps are described as follows:

[0241] Referring to FIG. 5, step 1 , which relates to smart device 304, such as a mobile device, connection to portable monitoring device 102 via an app, includes:1 . Mobile device app BLE scan for the portable monitoring device.2. User selects the portable monitoring device from a list and the mobile device app establishes a connection.3. Mobile device app saves MAC / GATTUUIDID and name of the portable monitoring device (for example, tracker name) to memory to persistent storage.4. tracker name to be used in step 5.

[0242] Referring to FIG. 6, step 2, which relates to smart device 304, such as a mobile device, disconnection from portable monitoring device 102, includes:1 . Mobile device disconnects from portable monitoring device 102 to allow it to connect to hub 312.

[0243] Referring to FIG. 7, step 3, which relates to hub 312 Wi-Fi setup, includes:1 . Mobile device BLE scan for hub. User selects the hub and app establishes BLE connection.2. Mobile device sends request for Wi-Fi list to hub via BLE Characteristic.3. Mobile device receives Wi-Fi network list via BLE Characteristic.4. Mobile device sends selected Wi-Fi SSID.5. Mobile device send user entered Wi-Fi password.6. Mobile device receives network status (<ip_address> or “failed”).

[0244] Referring to FIG. 8, step 4, which relates to dynamic substance measuring device 306, such as a bowl system, connection to hub 312, includes:1 . Mobile device requests list of available bowl systems via previous acquired ip / search?device=bowls.2. Hub then does a BLE scan for “bowl”.3. Hub sends list of bowl systems as the API response.4. User selects a bowl system from list on phone to ip_address / bowl?address=<address>.5. Hub attempts to connect to a bowl system.6. Hub sends back connection result, success or failure as API response.

[0245] Referring to FIG. 9, step 5, which relates to portable monitoring device 102 connection to hub 312, includes:1 . Mobile device sends request for hub to connect to portable monitoring device with the name from step 1 (tracker name).2. Hub then does a BLE scan to confirm tracker with tracker name is still available.3. Hub attempts to connect to the portable monitoring device.4. Hub sends back connection result, success or failure as API response.

[0246] Referring to FIG. 10, step 6, which relates to an operation of system 300, includes:1 . Hub will check if the portable monitoring device 102 and bowl system 306 are still connected at predefined time intervals (for example 60 seconds). If not connected, the hub will scan for them and if found try and reconnect.2. User is able to change the connected portable monitoring device and bowl by following steps 4 and 5. To change the Wi-Fi, hold reset button located on the hub 312 for a period of time (for example, 10 seconds) and start from step 3 again.3. User is able to initiate over the air updates via the / ota endpoint on the hub.

[0247] 2.2 Application flow

[0248] In one example implementation, the hub 312 follows the process outlined below.1. Power on.2. Check Wi-Fi. a. Check if Wi-Fi has been setup already. If so, try and connect to Wi-Fi network. Keep doing this until successfully connected. b. If no Wi-Fi credentials are stored on the hub, put the hub into Wi-Fi setup mode: i. This mode makes the hub available over BLE and can be connected to via the mobile device. ii. User then selects the Wi-Fi network and password, which then the hub tries to connect to.3. Now Wi-Fi is connected, update the cloud with the IP address if it has changed from the cached IP address.4. Next check if the tracker or the bowl have been setup before and have a stored address for them. Also get the stored user tokens from memory.5. Start the webserver that allows the app to query the status, connect the tracker and bowl or start over the air updates.6. At regular intervals, for example every 60 seconds, check if the bowl or the tracker as still connected. If not and an address is stored for them, scan for them and connect if found.7. The phone can use the API to search for available tracker or bowls and initiate a connection via <ip>:81 / search ?device=trackers or <ip>:81 / search?device=bowls.a. On connection of either a tracker or bowl, the hub will scan the BLE service, characteristics and descriptors to make sure they are the correct services. It will then record the handles for the required characteristics and subscribe to the notifys. The tracker notifys will fire straight away after being subscribed if data has been backlogged on the device. a. To reduce the amount of small traffic to the cloud servers, new step data is stored in one of two buffers until they reach 20 records and the data is converted to json and sent to the server. b. The buffer is then switched to the second to ensure no data loss while the data is being sent from one buffer. c. Battery updates are also sent to the cloud when the device battery drops. The bowl has two notify events, one for rx data and one for the battery. a. The hub will receive data via the rx notify. The first byte of the data from the bowl indicates its purpose: i. "77" or "M" for meal request. Button has been pushed to indicate a meal request. Hub then sends a request to the cloud server requesting the meal information the hub receives this data as a json response, the hub then converts this data to bytes and it’s forwarded to the bowl. Also a session ID is generated on the cloud and stored in the hub for the meal weights in the next steps. If there is no internet connection, cached data from the last successful meal will be used. ii. "70" or "F" for finished meal. The bowl has sent the confirmation the meal process has been completed and the corresponding weights for each meal type. This is sent to the cloud by the hub. A timer is set to request the bowl to return the current weight of the bowl at regular intervals, for example every 30 seconds. The will continue a plurality of times, for example 30 times, or until the weight in the bowl is less than 10 grams. iii. "87" or "W" for current weight in the bowl. This is the current weight read from the bowl. This is sent to the cloud using the session ID, unless the weight is under 10 grams. If so end the timer.The hub needs to calculate the UTC time here unlike the tracker as the bowl does not have time data. OTA is available at the / ota endpoint and requires the esp32micropython firmware to be broken up into byte size packets, for example into 900 byte size packets, with a corresponding correct sha checked. A short button press will restart the esp32 but the Wi-Fi, tracker / bowl addresses and tokens will remain and will be reconnected automatically if still available. A long buttonpress of over a period of time, for example 10 seconds, will erase the Wi-Fi, tracker and bowl details allowing the device to be setup again.

[0249] The above example implementation may be described in the context of a substance being consumed until a weight of the substance is detected to be less than 10 grams. It will be appreciated that implementations of the system detects instances when the substance is not fully consumed. For example, the system herein disclosed interprets pet consumption and / or feeding behaviour, using the dynamic substance measuring device, to provide return feedback, including where the substance contained in the dynamic substance measuring device is not fully consumed.

[0250] 3. Bowl Bluetooth Interface Control Document (ICD) for some implementations of the present disclosure is described below with reference to FIG. 11 and FIG 12.

[0251] 3.1 Purpose

[0252] This following provides an overview of the Bowl Bluetooth ICD. This following is used to implement both sides of the Bluetooth interface with the bowl system 306.

[0253] 3.2 A stateflow diagram relating to one example implementation of the bowl system 306 is illustrated in FIG. 11 .

[0254] The stateflow diagram details various states including device status (OFF / ON mode), send data and received data. In this example implementation, the bowl system receives data from the hub (for example, the tracker provides data to the cloud, via the hub, the cloud transmits data to the bowl, via the hub), and sends data to the cloud via the hub.

[0255] When the bowl system receives a current weight request command starting with “W”, the bowl system sends data of the current weight with sequence: “W”+current weight[2byte].

[0256] When the bowl system receives a dinner command starting with “D” and includes the following sequence: “D”+“Maximum weight of meal1 [2byte]”+“Minimum weight of meall [2byte]”+ “Maximum weight of meal2[2byte]”+“Minimum weight of meal 2[2byte]”+ “Maximum weight of meal3[2byte]”+“Minimum weight of meal 3[2byte]”, the bowl system sends data, for example “D”, as confirmation of the dinner meal data being received.

[0257] When the bowl system receives a breakfast command starting with “B” and includes the following sequence: “B”+“Maximum weight of meal[2byte]”+“Minimum weight ofmeal [2byte]”, the bowl system sends data, for example “B”, as confirmation of the breakfast meal data being received.

[0258] 3.3 A data flow diagram relating to one example implementation of the bowl system 306 is illustrated in FIG. 12.

[0259] The data flow diagram details various states including device status (ON mode and OFF mode) and send data.

[0260] The bowl system is configured to send data to the cloud via the relating to battery percentage when there is a change in battery level.

[0261] When the bowl system is turned ON, the bowl system sends data, for example “O”, to the cloud via the hub. If one or more sensors 402 of the bowl system 306 does not detect any substance data, the bowl system sends data, for example “M”, to indicate that no meal has been set and the bowl system changes to OFF mode.

[0262] If the one or more sensors of the bowl system detects substance data associated with a substance, such as food, and the meal corresponds to breakfast, as the one or more sensors detect the weight of the food decreasing to below a threshold amount, the bowl system sends data to the cloud via the hub to confirm that the breakfast meal process has finished using the following sequence: “F”+“meal1 weight[2bytes]” +“meal2 weight[2bytes]” +“meal3 weight[2bytes]”. The bowl system then changes to OFF mode. In one example implementation, the threshold amount is 10 grams. However, it will be appreciated that other threshold amounts can be set.

[0263] If the one or more sensors of the bowl system detects substance data associated with a substance, such as food, and the meal corresponds to dinner, as the one or more sensors detect the weight of the food decreasing to below a threshold amount, the bowl system sends data to the cloud via the hub to confirm that the dinner meal process has finished using the following sequence: “F”+“meal weight[2bytes]”. The bowl system then changes to OFF mode. In one example implementation, the threshold amount is 10 grams. However, it will be appreciated that other threshold amounts can be set.

[0264] In one implementation, the bowl system transmits data to the hub and that data includes a data byte, for example the first byte of data, for identifying the type of data being transmitted to the hub. A "77" or "M" identifies the data as being for a meal request. A "70" or "F" identifies the data as being for a finished meal. A "87" or "W" identifies the data as being for a current weight in the bowl system.

[0265] Breakfast and dinner meals are determined based on data relating to time of day, for example data indicating UTC time. In one implementation, the evaluation system directly provides the data relating to time of day. In another implementation, the evaluation system retrieves or otherwise accesses information to provide data relating to time of day.

[0266] 3.4 Bluetooth Implementation

[0267] 3.4.1 1. Battery Service

[0268] The Battery Service is a standardised BLE service. The characteristic “Battery Level” offers the current battery level in percentage encoded as an unsigned integer (for example the value 0x64 is 100%).

[0269] While example implementations have been described with the dynamic substance measuring device 306 being in connection with the cloud 310 via the hub 312, it will be appreciated that, in some implementations, the dynamic substance measuring device 306 is configured to communicate directly with the cloud server 310.

[0270] Personalised Nutrition Program (PNP)

[0271] As described above, the evaluation system 302 makes use of predictive analytics, including, but not limited to, for example a predictive algorithms, predictive modeling, machine learning, Al and the like, to generate or otherwise calculate evaluated data and / or updated evaluated data, which may be described in the context of a Personalised Nutrition Program (PNP).

[0272] A PNP according to one implementation is described in Table 5 with reference to FIG. 13. In this example implementation, the pet is a dog.Table 5

[0273] The specific timeframes outlined in Table 5 are example timeframes according to the example implementation described, and it will be appreciated that other timeframes may be used.

[0274] A PNP according to one implementation is described in Table 6 with reference to FIG. 14a to FIG. 14d. In this example implementation, the pet is a dog.Table 6

[0275] The specific timeframes outlined in Table 6 are example timeframes according to the example implementation described, and it will be appreciated that other timeframes may be used.

[0276] A PNP according to one implementation is described in Table 7 with reference to FIG. 15. In this example implementation, the pet is a dog.Logistics behind planning the order of STAGE 2 (BASELINE SETTING) according to this implementation includes:1) User provides baseline data, including measured weight and BCS, within the first 5 days of the cycle. The user may use the smart device to provide the baseline data via the app.2) Tracker provides activity data on the 5thday of the cycle. The goal is previous 28 days, and as many days as possible towards that goal will be considered (e.g. 5 days, 19 days, 33 days, etc.).3) Calculation of DER based on measured current weight, BCS factor, metabolism factor and activity factor.STAGE 2 (BASELINE SETTING) FLOWCHARTTable 7

[0277] The PNP as described in Table 7 with reference to FIG. 15 may be used to set a baseline in a first 28 day period.

[0278] The specific timeframes outlined in Table 7 and discussed above are example timeframes according to the example implementation described, and it will be appreciated that other timeframes may be used.

[0279] In one example implementation, vet recommended weight loss plans may include:• Rate of weight loss: 1 -2% body weight per week or 3-5% body weight per month• Monthly weight checks until ideal weight is reached• Most dogs reach their ideal body weight in 6 - 8 months• Rather than set difficult long-term target weight, vets usually set an achievable shortterm target weight, which when reached, triggers the onset of a new achievable shortterm target weight.• Vets caution against rapid changes in weight or feeding amounts, as it is associated with negative health outcomes.

[0280] As disclosed herein, “target weight” is a weight that a pet is targeted to reach at the end of a predetermined period, for example, at the end of each month. In some implementations, the target weight may be set by a vet.

[0281] As disclosed herein, “ideal weight” is a weight that the pet needs to reach for it to have a healthy life.

[0282] In one example implementation, the ideal weight of a dog is determined using the following equation:

[0283] Ideal weight = Current weight x (1 -body fat factor) / 0.80

[0284] It will be appreciated that ideal weight may be determined using other equations. The ideal weight may be different for two different pets of the same breed and size. In one example implementation, pet size can be categorised into different types. For example dog sizes may include: toy, small, medium, large, and giant. In some implementations, the pet size may be determined based on one or more of breed, sex, weight, BCS, and leg length. The system as herein disclosed may determine a correct amount to feed a pet, such as a dog, based on size, as categorised, and weight.

[0285] Smart device 304 may be used to provide user input, including, but not limited to, baseline data such as health metrics and updated health metrics, via the app associated with the smart device.

[0286] Daily Energy Requirement (DER) includes the total calculated calories for a specific day. The total calculated calories can be calculated across a number of meals throughout a day, for example two meals, for example one breakfast meal and one dinner meal. DER may be determined by based on baseline data, activity data and substance data. Baseline data includes one or more of baseline health metrics, age, breed, metabolism factor. The baseline health metrics also referred to as initial health metrics, include one or more of a current weight and a Body Condition Scoring (BCS). Activity data, including updated activity data, is detected or otherwise obtained from a portable monitoring device, such as a tracker. Activity data may be continuously detected. Substance data is detected or otherwise obtained from a dynamic substance measuring device, such as a bowl system.DER may also be determined based on updated health metrics, including an updated current weight and updated BCS. DER may also be determined based on measured current weight, BCS factor, metabolism factor and activity factor. DER may also be determined based on a target weight, recommended weight, ideal weight and the like. DER may also be determined based on data relating to activity based calorific expenditure across all breeds of pets including all breeds of dogs.

[0287] FIG. 16 is example implementation of the portable monitoring device 102. In this example implementation, the portable monitoring device is attached to a housing 600. The housing includes openings 602 for receiving a pet collar.

[0288] FIG. 17 is example implementation of the dynamic substance measuring device 306. In this example implementation, the dynamic substance measuring device 306 is a bowl system including a base 700 and a bowl 702 separable from the base 700. In this implementation, indicator 410 is located on the base 700.

[0289] FIG. 18 is example implementation of the hub 312. In this example implementation, the hub 312 includes a receptacle 800. The receptacle 800 is configured to receive the portable monitoring device 102 for charging.

[0290] As described herein, system as herein disclosed is a fully integrated system, having both hardware and software. Some implementations of the integrated system of the present disclosure provide a pet health system for providing personalised health and feeding insights unique to specific pets. Using predictive analytics as described herein, some implementations of the integrated system learn, interpret and transform a pet’s activities, behaviours, sleep patterns and health signals, into actionable insights to assist with improving their health and wellbeing by connecting their lifestyle to nutritional needs. These actionable insights may be personalised, actionable daily changes to help pets live longer, healthier lives. Some implementations of the integrated system accurately assesses caloric needs of a pet using data on their activity levels, age, breed, and weight.

[0291] Pet health relates to the nutrients they absorb, the energy they burn, their rest and their sleep. The integrated system as herein disclosed assists with determining food portions which are calorie-controlled thereby assisting with the management of a healthy weight.

[0292] By using data relating to a pet’s activity levels, age, breed and weight, the system as herein disclosed may distinguish between the needs of different breeds across all stages of life. Tracking pet behaviour, such as fitness and activity, using the system as herein disclosed provides the benefit of ensuring a specific pet is getting enough exercise based ontheir breed, weight, and age. Tracking sleep patterns provides benefits, for example, disrupted sleep may indicate an underlying health issue. Furthermore, tracking eating habits provides benefits, for example, changes may be linked to oral health issues, metabolic disorders and the like.

[0293] Although embodiments have been described with reference to a number of illustrative embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

[0294] Many modifications will be apparent to those skilled in the art without departing from the scope of the present invention as herein described with reference to the accompanying drawings.

[0295] Unless specifically stated otherwise, as apparent from the following discussions, it is appreciated that throughout the specification discussions utilizing terms such as "processing," "computing," "calculating," “determining”, analyzing” or the like, refer to the action and / or processes of a computer or computing system, or similar electronic computing device, that manipulate and / or transform data represented as physical, such as electronic, quantities into other data similarly represented as physical quantities.

[0296] In a similar manner, the term "processor" may refer to any device or portion of a device that processes electronic data, e.g., from registers and / or memory to transform that electronic data into other electronic data that, e.g., may be stored in registers and / or memory. A “computer” or a “computing machine” or a "computing platform" may include one or more processors. The terms “processor” and “processing unit” may be used interchangeably throughout the specification.

[0297] It should be appreciated that the present disclosure can be implemented in numerous ways, including as a process, an apparatus, a system, a device, a method, or a computer-readable medium such as a computer-readable storage medium or embedded system containing computer-readable instructions or computer program code, or as a computer program product, comprising a computer-usable medium having a computer- readable program code embodied therein. The methodologies described herein are, in one embodiment, performable by one or more processors that accept computer-readable (also called machine-readable) code containing a set of instructions that when executed by one or more of the processors carry out at least one of the methods described herein. Any processor capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken are included. Thus, one example is a typical processing system that includes one or more processors. Each processor may include one or more of a CPU, agraphics processing unit, and a programmable DSP unit. The processing system further may include a memory subsystem including main RAM and / or a static RAM, and / or ROM. A bus subsystem may be included for communicating between the components. The processing system further may be a distributed processing system with processors coupled by a network. If the processing system requires a display, such a display may be included, e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT) display. If manual data entry is required, the processing system also includes an input device such as one or more of an alphanumeric input unit such as a keyboard, a pointing control device such as a mouse, and so forth. The term memory unit as used herein, if clear from the context and unless explicitly stated otherwise, also encompasses a storage system such as a disk drive unit. The processing system in some configurations may include a sound output device, and a network interface device. The memory subsystem thus includes a computer-readable carrier medium that carries computer-readable code (e.g., software) including a set of instructions to cause performing, when executed by one or more processors, one or more of the methods described herein. Note that when the method includes several elements, e.g., several steps, no ordering of such elements is implied, unless specifically stated. The software may reside in the hard disk, or may also reside, completely or at least partially, within the RAM and / or within the processor during execution thereof by the computer system. Thus, the memory and the processor also constitute computer-readable carrier medium carrying computer-readable code.

[0298] Furthermore, a computer-readable carrier medium may form, or be included in a computer program product.

[0299] In alternative embodiments, the one or more processors operate as a standalone device or may be connected, e.g., networked to other processor(s), in a networked deployment, the one or more processors may operate in the capacity of a server or a user machine in server-user network environment, or as a peer machine in a peer-to-peer or distributed network environment. The one or more processors may form a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine.

[0300] Note that while diagrams only show a single processor and a single memory that carries the computer-readable code, those in the art will understand that many of the components described above are included, but not explicitly shown or described in order not to obscure the inventive aspect. For example, while only a single machine is illustrated, the term "machine" shall also be taken to include any collection of machines that individually orjointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

[0301] Thus, one embodiment of each of the methods described herein is in the form of a computer-readable carrier medium carrying a set of instructions, e.g., a computer program that is for execution on one or more processors, e.g., one or more processors that are part of web server arrangement. Thus, as will be appreciated by those skilled in the art, embodiments of the present invention may be embodied as a method, an apparatus such as a special purpose apparatus, an apparatus such as a data processing system, or a computer-readable carrier medium, e.g., a computer program product. The computer- readable carrier medium carries computer readable code including a set of instructions that when executed on one or more processors cause the processor or processors to implement a method. Accordingly, aspects of the present invention may take the form of a method, an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of carrier medium (e.g., a computer program product on a computer-readable storage medium) carrying computer-readable program code embodied in the medium.

[0302] The software may further be transmitted or received over a network via a network interface device. While the carrier medium is shown in an exemplary embodiment to be a single medium, the term "carrier medium" should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) that store the one or more sets of instructions. The term "carrier medium" shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by one or more of the processors and that cause the one or more processors to perform any one or more of the methodologies of the present invention. A carrier medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media includes, for example, optical, magnetic disks, and magneto-optical disks. Volatile media includes dynamic memory, such as main memory. Transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise a bus subsystem. Transmission media also may also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications. For example, the term "carrier medium" shall accordingly be taken to included, but not be limited to, solid-state memories, a computer product embodied in optical and magnetic media; a medium bearing a propagated signal detectable by at least one processor of one or more processors and representing a set of instructions that, when executed, implement a method; and a transmission medium in a network bearing a propagated signal detectable by at least one processor of the one or more processors and representing the set of instructions.

[0303] It will be understood that the steps of methods discussed are performed in one embodiment by an appropriate processor (or processors) of a processing (i.e., computer) system executing instructions (computer-readable code) stored in storage. It will also be understood that the invention is not limited to any particular implementation or programming technique and that the invention may be implemented using any appropriate techniques for implementing the functionality described herein. The invention is not limited to any particular programming language or operating system.

[0304] It should be appreciated that in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, FIG., or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the Detailed Description are hereby expressly incorporated into this Detailed Description, with each claim standing on its own as a separate embodiment of this invention.

[0305] Furthermore, while some embodiments described herein include some but not other features included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention, and form different embodiments, as would be understood by those skilled in the art. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0306] Furthermore, some of the embodiments are described herein as a method or combination of elements of a method that can be implemented by a processor of a computer system or by other means of carrying out the function. Thus, a processor with the necessary instructions for carrying out such a method or element of a method forms a means for carrying out the method or element of a method. Furthermore, an element described herein of an apparatus embodiment is an example of a means for carrying out the function performed by the element for the purpose of carrying out the invention.

[0307] In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In other instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

[0308] Similarly, it is to be noticed that the term coupled, when used in the claims, should not be interpreted as being limited to direct connections only. The terms "coupled" and"connected," along with their derivatives, may be used. It should be understood that these terms are not intended as synonyms for each other. Thus, the scope of the expression a device A coupled to a device B should not be limited to devices or systems wherein an output of device A is directly connected to an input of device B. It means that there exists a path between an output of A and an input of B which may be a path including other devices or means. "Coupled" may mean that two or more elements are either in direct physical or electrical contact, or that two or more elements are not in direct contact with each other but yet still co-operate or interact with each other.

[0309] Thus, while there has been described what are believed to be the preferred embodiments of the invention, those skilled in the art will recognize that other and further modifications may be made thereto without departing from the spirit of the invention, and it is intended to claim all such changes and modifications as falling within the scope of the invention. For example, any formulas given above are merely representative of procedures that may be used. Functionality may be added or deleted from the block diagrams and operations may be interchanged among functional blocks. Steps may be added or deleted to methods described within the scope of the present invention.

Claims

CLAIMS:

1. A system including: a portable monitoring device connected to a network by at least one communication pathway; the portable monitoring device includes: one or more sensors for detecting raw activity data; at least one processing unit configured to receive the raw activity data from the one or more sensors, the at least one processing unit further configured to: create activity data based on the raw activity data; and create one or more active data packets including the activity data; and at least one transceiver configured to transmit the active data packets to the network by the at least one communication pathway; the network includes: an evaluation system including: at least one processing unit configured to retrieve baseline data and generate evaluated data based on the active data packets received from the portable monitoring device and the baseline data; and at least one transceiver configured to transmit the evaluated data, wherein the evaluated data includes a baseline energy requirement metric.

2. A system according to claim 1 wherein the baseline data includes one or more of baseline health metrics, age, life stage, breed, size, leg length, gender, and metabolism factor.

3. A system according to claim 2 wherein the baseline health metrics include one or more of a current weight and a Body Condition Scoring (BCS).

4. A system according to any one of the preceding claims wherein the baseline energy requirement metric includes a target number of calories.

5. A system according to any one of the preceding claims wherein the at least one processing unit of the evaluation system is further configured to receive one or more updated active data packets from the portable monitoring device.

6. A system according to claim 5 wherein the updated active data packets are received at regular intervals.

7. A system according to claim 5 or claim 6 wherein the at least one processing unit of the evaluation system is further configured to receive updated health metrics.

8. A system according to claim 5 or claim 6 wherein the at least one processing unit of the evaluation system is further configured to generate updated evaluated data based on the updated active data packets.

9. A system according to claim 7 wherein the at least one processing unit of the evaluation system is further configured to generate updated evaluated data based on the updated health metrics.

10. A system according to claim 7 wherein the at least one processing unit of the evaluation system is further configured to generate updated evaluated data based on the updated active data packets and the updated health metrics.11 . A system according to any one of claims 8 to 10 wherein the at least one transceiver of the evaluation system is further configured to transmit the updated evaluated data, the updated evaluated data including an updated energy requirement metric.

12. A system according to claim 7 wherein the updated health metrics include one or more of an updated current weight and an updated Body Condition Scoring (BCS).

13. A system according to claim 11 wherein the updated energy requirement metric includes an updated target number of calories.

14. A system according to any one of the preceding claims wherein the evaluation system includes a hub.

15. A system according to any one of the preceding claims wherein the evaluation system includes a cloud server.

16. A system according to any one of the preceding claims wherein the network further includes a smart device.

17. A system according to any one of the preceding claims wherein the baseline data is received from the smart device.

18. A system according to claim 7 wherein the updated health metrics is received from the smart device.

19. A system according to any one of the preceding claims wherein the system further includes a dynamic substance measuring device.

20. A system according to claim 19 wherein the dynamic substance measuring device includes one or more sensors for detecting substance data associated with a substance.21 . A system according to claim 20 wherein the substance data includes one or more of a volume, weight and quantity of the substance.

22. A system according to any one of claims 19 to 21 wherein the dynamic substance measuring device is configured to communicate with the portable monitoring device by at least one communication pathway.

23. A system according to any one of claims 19 to 22 wherein the dynamic substance measuring device is configured to communicate with the network by at least one communication pathway.

24. A system according to any one of claims 19 to 23 wherein the dynamic substance measuring device is configured to communicate with the evaluation system by at least one communication pathway.

25. A system according to any one of claims 19 to 24 wherein the dynamic substance measuring device includes at least one processing unit configured to receive the substance data from the one or more sensors.

26. A system according to any one of claims 19 to 25 wherein the dynamic substance measuring device includes at least one processing unit configured to receive the baseline energy requirement metric.

27. A system according to any one of claims 19 to 21 wherein the dynamic substance measuring device includes at least one processing unit configured to receive the updated energy requirement metric.

28. A system according to any one of the preceding claims wherein each active data packet is created when a subset of activity data in a payload of a data packet including activity data detected over a predetermined interval is above a threshold.

29. A system according to any one of the preceding claims wherein the one or more processors of the evaluation system uses a predictive algorithm to generate the evaluated data including the baseline energy requirement metric.

30. A system according to claim 29 wherein the predictive algorithm generates the evaluated data based on the baseline data.31 . A system according to claim 29 or claim 30 wherein the predictive algorithm generates the evaluated data based on the active data packets.

32. A system according to claim 11 or claim 13 wherein the one or more processors of the evaluation system uses a predictive algorithm to generate the updated evaluated data including the updated energy requirement metric.

33. A system according to claim 32 wherein the predictive algorithm generates the updated evaluated data based on the substance data received from at least one transceiver of the dynamic substance measuring device.

34. A system according to claim 32 or claim 33 wherein the predictive algorithm generates the updated evaluated data based on the updated active packets received from the at least one transceiver of the portable monitoring device.

35. A system according to any one of claims 32 to 34 wherein the predictive algorithm generates the updated evaluated data based on the updated health metrics.

36. A system according to any one of claims 32 to 35 wherein the predictive algorithm generates the updated evaluated data based on historical consumption data.

37. A system according to claim 19 wherein a measurement of the substance detected by the one or more sensors of the dynamic substance measuring device can be determined based on the baseline energy requirement metric.

38. A system according to claim 19 wherein a measurement of the substance detected by the one or more sensors of the dynamic substance measuring device can be determined based on the updated energy requirement metric.

39. A portable monitoring device including: one or more sensors for detecting raw activity data; at least one processing unit configured to receive the raw activity data from the one or more sensors, the at least one processing unit further configured to: create activity data based on the raw activity data; andcreate one or more active data packets including the activity data.

40. A portable monitoring device according to claim 39 wherein each active data packet includes activity data detected over a predetermined interval that meets a predefined criteria.41 . A portable monitoring device according to claim 40 wherein the predefined criteria is that the activity data is above a predetermined threshold.

42. A portable monitoring device according to any one of claims 39 to 41 wherein the portable monitoring device includes at least one transceiver configured to transmit the active data packets to a network by at least one communication pathway.

43. A portable monitoring device according to any one of claims 39 to 42 wherein the network includes: an evaluation system including: at least one processing unit configured to retrieve baseline data and generate evaluated data based on the active data packets received from the portable monitoring device and the baseline data; and at least one transceiver configured to transmit the evaluated data, wherein the evaluated data includes a baseline energy requirement metric.

44. A portable monitoring device according to claim 43 wherein the at least one processing unit of the evaluation system is further configured to receive one or more updated active data packets from the portable monitoring device.

45. A portable monitoring device according to claim 44 wherein the updated active data packets are received at regular intervals.

46. A portable monitoring device according to claim 44 or claim 45 wherein the at least one processing unit of the evaluation system is further configured to receive updated health metrics.

47. A portable monitoring device according to claim 44 or claim 45 wherein the at least one processing unit of the evaluation system is further configured to generate updated evaluated data based on the updated active data packets.

48. A portable monitoring device according to claim 46 wherein the at least one processing unit of the evaluation system is further configured to generate updated evaluated data based on the updated health metrics.

49. A portable monitoring device according to claim 46 wherein the at least one processing unit of the evaluation system is further configured to generate updated evaluated data based on the updated active data packets and the updated health metrics.

50. A portable monitoring device according to any one of claims 47 to 49 wherein the at least one transceiver of the evaluation system is further configured to transmit the updated evaluated data, the updated evaluated data including an updated energy requirement metric.51 . A portable monitoring device according to any one of claims 39 to 50 wherein the raw activity data includes one or more of force, acceleration, and angular motion.

52. A portable monitoring device according to any one of claims 39 to 51 wherein the activity data includes one or more of movement data, steps, active minutes, movement signature data, activity classification data, intensity data, calories, sleep and sleep patterns, heart rate, and skin or body temperature.

53. A portable monitoring device according to any one of claims 39 to 52 wherein the one or more sensors of the portable monitoring device include one or more of an accelerometer, GPS, gyroscope, optical sensor, biometric pressure sensor, heart rate monitor, oximetry, bio-impedance, magnetometer, skin temperature, environmental temperature, UV sensor, light sensor, altimeter and proximity sensor.

54. A dynamic substance measuring device including: one or more sensors for detecting substance data associated with a substance; and at least one processing unit configured to receive: the substance data from the one or more sensors; and an energy requirement metric; wherein the at least processing unit evaluates the substance data and the energy requirement metric.

55. A dynamic substance measuring device according to claim 54 wherein the at least processing unit evaluates the substance data and the energy requirement metric to determine whether a measurement of the substance corresponds with the energy requirement metric.

56. A dynamic substance measuring device according to claim 54 or claim 55 wherein the energy requirement metric includes a baseline energy requirement metric and an updated energy requirement metric.

57. A dynamic substance measuring device according to any one of claims 54 to 56 wherein the dynamic substance measuring device includes a dispenser.

58. A dynamic substance measuring device according to any one of claims 54 to 56 wherein the dynamic substance measuring device includes a base and a measuring portion separable from the base.

59. A dynamic substance measuring device according to claim 58 wherein the measuring portion is a receptacle.

60. A dynamic substance measuring device according to claim 59 wherein the receptacle is a bowl.61 . A dynamic substance measuring device according to any one of claim 54 to 60 further including at least one indicator.

62. A dynamic substance measuring device according to claim 61 wherein the at least one processing unit of the dynamic substance measuring device is configured to communicate with the at least one indicator.

63. A dynamic substance measuring device according to claim 61 or claim 62 wherein the at least one indicator is configured to provide feedback in response to the substance data being detected by the one or more sensors.

64. A dynamic substance measuring device according to claim 63 wherein the feedback provided by the at least one indicator is visual, audible, tactile, haptic or a combination thereof.

65. A dynamic substance measuring device according to any one of claim 61 to 64 wherein the at least one indicator includes a speaker.

66. A dynamic substance measuring device according to any one of claim 61 to 65 wherein the at least one indicator includes a light.

67. A dynamic substance measuring device according to claim 66 wherein the light includes a ring device, a radial light, a radial display, a radial light display, a circular light, or a logarithmic light.

68. A dynamic substance measuring device according to claim 66 or claim 67 wherein the light is configured to progressively illuminate.

69. A dynamic substance measuring device according to claim 66 or claim 67 wherein the light is configured to progressively dim.

70. A dynamic substance measuring device according any one of claims 54 to 69 wherein the dynamic substance measuring device further includes at least one transceiver configured to transmit the substance data to a network by at least one communication pathway.71 . A dynamic substance measuring device according any one of claims 54 to 70 wherein at least one of the one or more sensors of the dynamic substance measuring device is configured to detect a change in pressure.

72. A dynamic substance measuring device according any one of claims 54 to 70 wherein the one or more sensors of the dynamic substance measuring device includes one or more of multi-load cells, high frequency electronical scales, and logarithmic scales.

73. A dynamic substance measuring device according to claim 72 wherein the multi-load cells are configured to detect at high frequency intervals.

74. A method including: receiving, by at least one processing unit, one or more active data packets; retrieving, by the at least one processing unit, baseline data; and generating, by the at least one processing unit, evaluated data based on the active data packets and the baseline data, wherein the evaluated data includes a baseline energy requirement metric.

75. A method according to claim 74 further including transmitting, by at least one transceiver, the evaluated data.

76. A method according to claim 74 or claim 75 further including receiving, by the at least one processing unit, one or more updated active data packets.

77. A method according to any one of claims 74 to 76 further including receiving, by the at least one processing unit, updated health metrics.

78. A method according to claim 76 further including generating, by the at least one processing unit, updated evaluated data based on the updated active data packets.

79. A method according to claim 77 further including generating, by the at least one processing unit, updated evaluated data based on the updated health metrics.

80. A method according to claim 77 further including generating, by the at least one processing unit, updated evaluated data based on the updated active data packets and the updated health metrics.81 . A method according to any one of claims 74 to 80 further including transmitting, by the at least one transceiver, the updated evaluated data, wherein the updated evaluated data includes an updated energy requirement metric.

82. A method according to any one of claims 74 to 81 wherein the one or more active data packets are received from a portable monitoring device connected to a network by at least one communication pathway.

83. A method according to claim 82 wherein the one or more updated active data packets are received from the portable monitoring device.

84. A method implemented by a portable monitoring device including: detecting, by one or more sensors, raw activity data; receiving the raw activity data, by at least one processing unit, from the one or more sensors; creating, by the at least one processing unit, activity data based on the raw activity data; and creating, by at least one processing unit, one or more active data packets including the activity data.

85. A method implemented by a portable monitoring device according to claim 84 further including: transmitting, by at least one transceiver, the active data packets to a network by at least one communication pathway.

86. A method implemented by a dynamic substance measuring device including: detecting, by one or more sensors, substance data associated with a substance; receiving the substance data, by at least one processing unit, from the one or more sensors; receiving, by the at least one processing unit, an energy requirement metric; and evaluating, by the at least one processing unit, the substance data and the energy requirement metric.

87. A method implemented by a dynamic substance measuring device according to claim 86 further including: evaluating, by the at least one processing unit, the substance data and the energy requirement metric to determine whether a measurement of the substance corresponds with the energy requirement metric.

88. A method implemented by a dynamic substance measuring device according to claim 86 or claim 87 further including: communicating, by the at least one processing unit, with at least one indicator.

89. A method implemented by a dynamic substance measuring device according to claim 88 further including: providing feedback, by the at least one indicator, in response to the substance data being detected by the one or more sensors.

90. A method implemented by a dynamic substance measuring device according to any one or claims 86 to 89 further including: transmitting, by at least one transceiver, the substance data to a network by at least one communication pathway.91 . A method implemented by a dynamic substance measuring device according to any one or claims 86 to 90 further including: detecting, by at least one of the one or more sensors, a change in pressure.

92. A method implemented by a dynamic substance measuring device according to any one or claims 86 to 91 further including: detecting, by the one or more sensors, at high frequency intervals.

93. A method implemented by a dynamic substance measuring device according to any one or claims 86 to 92 further including: communicating, by the at least one processing unit, with a portable monitoring device by at least one communication pathway.

94. A method implemented by a dynamic substance measuring device according to any one or claims 86 to 93 further including: communicating, by the at least one processing unit, with a network by at least one communication pathway.

95. A method implemented by a dynamic substance measuring device according to any one or claims 86 to 94 further including: communicating, by the at least one processing unit, with an evaluation system by at least one communication pathway.

96. A method implemented by a dynamic substance measuring device according to any one or claims 86 to 95 further including: receiving, by the at least one processing unit, an updated energy requirement metric.

97. A method implemented by a dynamic substance measuring device according to any one or claims 86 to 96 further including: detecting, by the one or more sensors, a measurement of the substance based on the energy requirement metric.

98. A method implemented by a dynamic substance measuring device according to claim 96 further including:detecting, by the one or more sensors, a measurement of the substance based on the updated energy requirement metric.

99. A computer program including instructions which, when the program is executed by a computer, cause the computer to carry out the method according to any one of claims 74 to 98.

100. A computer-readable medium including instructions which, when executed by a computer, cause the computer to carry out the method according to any one of claims 74 to 98.

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