Generating insights based on signals from measurement devices

Abstract

A system is described that generates insights regarding product consumption or usage behavior by analyzing real-time usage of the product by one or more users. The product is placed on a measurement device that continuously monitors the product usage by one or more users in real time. The measurement device includes a sensor unit that generates and transmits product metric data, movement data, location data, and consumption time data to a computing device. The communication device records the user's consumption or usage of the product along with feedback from the user. The computing device generates insights based on the sensory data generated by the measurement device and the recording and feedback at the communication device. The computing device generates reports including the respective insights, i.e., a report for the user and a report for the product and / or a merchant of similar products.

Description

[Technical Field]

[0001] This application claims the benefit of and priority to U.S. Patent Application No. 17 / 175,576, filed February 12, 2021, which claims priority to U.S. Provisional Patent Application No. 63 / 089,956, filed October 9, 2020, the entire contents of each of which are incorporated herein by reference.

[0002] The subject matter described herein relates to various implementations of devices configured to detect consumption or usage of products and generating insights based on the detected consumption or usage. [Background technology]

[0003] Merchants (e.g., manufacturers, distributors, retailers, etc.) can benefit from identifying consumption behaviors that indicate how users (e.g., consumers) interact with products (e.g., how users purchase, perceive, and / or consume or use products). For example, merchants can interpret consumption behaviors to understand users' needs, desires, habits, patterns, preferences, and problems. Based on understanding users' needs, desires, habits, patterns, preferences, and problems, merchants may make several changes and identifications to maximize sales and customer satisfaction. Such changes include changes to the location where the product is placed within various distribution channels, the product's structural details or other features, the product's packaging, the content of messages in digital components related to the product, the mode of sending the digital components to each user (e.g., email or text message), the schedule for new product launches, etc. Such identifications may include identifying new opportunities, new segments of target audiences, the geography of various business processes (e.g., marketing, sales, manufacturing), etc.

[0004] However, traditional approaches to collecting and understanding consumption or usage behavior may not be optimal. For example, while merchants have a large amount of data on consumer purchasing behavior based on retailer data and / or their own sales data, merchants typically have little or no knowledge of the actual consumption or usage data of those products and / or the actual consumption or usage behavior of consumers (i.e., users) who use those products after they are purchased by the consumers. In some cases, merchants may conduct ad hoc research studies, either independently or through other companies, or utilize syndicated research data to attempt to identify consumption behavior. However, such research and survey activities are ineffective for a number of reasons. First, such studies typically provide some indication of consumption behavior at a specific point in time, rather than over a long period of time that can better represent consumer behavior. Second, because such research and survey activities generate results based on usage data reported or claimed by consumers, rather than on actual consumption, which may not match what users report or claim, such results may be inaccurate. Third, while some such studies may involve company representatives visiting users' homes or other locations to verify usage, such practices are overly burdensome, prohibitively expensive, may discourage users, and provide only infrequent data on which products are completed (fully used / consumed), but not when, how much, how often, or where they are consumed.

[0005] Due to these ineffectiveness, such traditional research and survey activities do not allow merchants to gain deep, reliable, and accurate insights into product consumption or usage data and / or consumer behavior regarding product consumption or usage by consumers or users of those products. For example, merchants have traditionally been unable to obtain insights that (a) are based on actual consumption or usage of products, (b) in an automated and passive manner, (c) in a rapid or timely manner, such as in real time, (d) are scalable to large numbers of users, regions, categories, or markets, (e) persist over the life of a product or over extended periods of time, such as weeks, months, or years, and (f) are presented in a clear and understandable manner. This ineffectiveness of traditional approaches to collecting and understanding consumer behavior prevents these merchants from providing effectively targeted recommendations, advertising, promotions, or messaging to consumers or users and also hinders the merchants' ability to develop new products that are effective for those and / or similar consumers or users. Summary of the Invention [Means for solving the problem]

[0006] A system is described that addresses at least the above-mentioned deficiencies of conventional approaches for collecting and understanding consumption or usage behavior while achieving many advantages. In some implementations, the system includes a measurement device capable of measuring (i.e., detecting) a user's consumption of a product. The product can be any product consumed by a user, such as food, medicine (e.g., pharmaceuticals), beverages, cleaning supplies, hygiene products, or any other product that a user can use or consume. The amount of product consumed over a period of time measured by the measurement device can indicate the user's consumption behavior with respect to that product. The measurement device can be configured to be physically coupled to the product or communicatively coupled via a communication network. In some examples, the measurement device can be in the form of a coaster, a tray, a sleeve (which can be configured to surround the side of a container such as a glass or bottle), a container (e.g., a can), an electronic device such as a remote control or electronic button, or any other accessory. A consumer or user can record a video on a communication device showing how they consume or use the product, along with feedback and / or explanations provided by the consumer while they consume or use the product.

[0007] The measurement device and communication device can be connected to a backend server that can generate insights based on the measured consumption. The backend server can generate recommendations based on data received from the measurement device and communication device. Some insights and recommendations can be generated specifically for the user, and some insights and recommendations can be generated specifically for the merchant. User-specific insights and / or recommendations can be provided to the user's communication device, and entity-specific insights and / or recommendations can be provided to the merchant's client device. In some examples, the device may output the recommendations (e.g., display the recommendations in a graphical user interface, generate audio of the recommendations that are output through a speaker on the device, etc.). In additional examples, the backend server and / or device may send the recommendations to the user via any other channel, such as email, short messaging service (SMS), social media messages, etc.

[0008] In one aspect, a method is described that includes one or more of the following: A computing device is configured to be coupled to a product and is capable of receiving measurements indicative of consumption or use of the product by a user from a hub measurement device of a plurality of measurement devices arranged according to a hub-and-spoke architecture that includes a hub measurement device and a plurality of spoke measurement devices coupled to the hub measurement device. The computing device is capable of generating an output that includes a plurality of insights based on the measurements.

[0009] In some implementations, one or more of the following may be further implemented individually or in any workable combination: The computing device may generate a report including at least some of the plurality of insights, and the report may be specific to one or more merchants of the product. The computing device may transmit the report to a client device. In particular implementations, the computing device may generate a report including at least some of the plurality of insights, and the report may be specific to a user. The computing device may transmit the report to a communication device of the user.

[0010] Receiving measurements from the hub measurement device can include receiving measurements from a communication module of the hub measurement device. The communication module can receive sensed data from a sensor module of each active measurement device in the plurality of measurement devices. The sensor module can include a motion sensor, a weight sensor, a position sensor, and a time clock. Measurements can be generated using the sensed data for each active measurement device. Generating measurements can include removing one or more of duplicate data, inconsistent data, null values, and data collected when an error is reported during sensing of the sensed data by the sensor module to obtain the measurements. The sensor module can have the form of a capsule configured to be inserted into various types of measurement devices.

[0011] The computing device can receive data from the smart device indicating activity indicative of consumption or use of the product. The computing device can activate a measuring device from a plurality of measuring devices that is available and geographically closest to the smart device to receive additional measurements indicative of further consumption or use of the product by the user. The smart device can include an appliance, and the activity includes opening and closing at least a portion of the appliance. The appliance can be one of a smart refrigerator, a smart trash can, a smart cabinet, a smart sink, or a smart washer.

[0012] The computing device can receive measurements from a hub measuring device of the plurality of measuring devices after the hub measuring device is powered up by activating an electronic switch. The computing device can receive measurements from the hub measuring device in real time. In some implementations, the computing device can receive measurements from the hub measuring device at programmed time intervals.

[0013] At least one of the plurality of measuring devices can have the form of a coaster, a tray, or a container. Each of the plurality of measuring devices can include a hardware module in which the electrical circuitry of the measuring device resides. The hardware module can be configured to be placed on either the coaster, the tray, or the container.

[0014] Each measurement device of the plurality of measurement devices can be configured to be physically or communicatively coupled to the product via a communication network.

[0015] One of the plurality of measuring devices may identify a user, and receipt of the measurement may occur in response to identifying the user.

[0016] The computing device can identify content to output to the user based on the insight. The output can include digital content output in an application on the user's communication device. The application can be a web browser or a native application installed on the communication device.

[0017] The computing device can be coupled to a hub measurement device via a first communications network, the hub measurement device can be coupled to a plurality of spoke measurement devices via a second communications network different from the first communications network, and at least some of the spoke measurement devices are coupled to other spoke measurement devices via the second communications network.

[0018] At least one measuring device of the plurality of measuring devices has the form of a coaster, the coaster configured to be coupled to an apparatus having a movable sleeve for holding a product.

[0019] In another aspect, one or more non-transitory computer program products are described that can store instructions that, when executed by at least one programmable processor, cause the at least one programmable processor to perform operations including receiving, by a computing device, measurements indicative of consumption or use of the product by a user from a hub measurement device of a plurality of measurement devices configured to be coupled to a product and arranged according to a hub-and-spoke architecture including a hub measurement device and a plurality of spoke measurement devices coupled to the hub measurement device; and generating, by the computing device, an output including a plurality of insights based on the measurements.

[0020] In yet another aspect, a computing device is described that includes at least one programmable processor and a machine-readable medium storing instructions that, when executed by the at least one processor, cause the at least one programmable processor to perform operations including receiving measurements indicative of consumption or use of the product by a user from a hub measurement device of a plurality of measurement devices configured to be coupled to a product and arranged according to a hub-and-spoke architecture including a hub measurement device and a plurality of spoke measurement devices coupled to the hub measurement device; and generating an output including a plurality of insights based on the measurements.

[0021] Related systems, devices, methods, non-transitory computer program products, processors, machine-readable media, and articles of manufacture are included within the scope of this disclosure.

[0022] The subject matter described herein provides many advantages. For example, the systems and techniques described herein enable merchants to obtain deep, reliable, and accurate insights into product consumption or usage data and / or consumer behavior regarding the consumption or use of one or more products by consumers or users of one or more products. For example, such systems and techniques enable merchants to obtain insights (a) based on actual consumption or usage of products; (b) in an automatic and passive manner; (c) in a rapid or timely manner, such as in real time; (d) scalable to large numbers of users, regions, categories, or markets; (e) continuing over the life of a product or over an extended period of time, such as weeks, months, or years; and (f) presented in a clear and understandable manner. The effectiveness of the systems and techniques described herein for collecting and understanding consumer behavior enables these merchants to provide precisely targeted recommendations, advertising, promotions, or messaging to consumers or users and also enables merchants to develop new products that are effective for those and / or similar consumers or users.

[0023] The details of one or more variations of the subject matter described herein are set forth in the accompanying drawings and the description below. Other features and advantages of the subject matter described herein will become apparent from the description and drawings, and from the claims. [Brief explanation of the drawings]

[0024] [Figure 1] FIG. 1 is a system diagram of a system for generating insights into user product consumption or usage behavior. [Figure 2] FIG. 1 is an exploded view of the measuring device. [Figure 3] FIG. 1 is an exploded view of a communication module of a measurement device communicatively connected to a computing device. [Figure 4] FIG. 1 is an exploded view of the measuring device. [Figure 5] FIG. 1 is an exemplary diagram of a measurement device communicatively connected to a computing device for triggering notifications on the communication device. [Figure 6] 1 is an exemplary diagram of a product connected to a measurement device. [Figure 7] 1 is an exemplary diagram of one or more products communicatively connected with one or more measurement devices to determine usage behavior of the one or more products by a user. [Figure 8] FIG. 1 is an exemplary diagram of a graphical user interface of a computing device. [Figure 9] FIG. 1 is an exemplary diagram of a graphical user interface of a computing device. [Figure 10] 1 is a graphical representation of a report showing weekly consumption of a product. [Figure 11] 1 is a graphical representation of a report showing average weekly, weekend, and weekday consumption of a product. [Figure 12] FIG. 2 is an exemplary diagram of a graphical user interface of a client device. [Figure 13]1 is a graphical representation of a report detailing weekly consumption of one or more products by users. [Figure 14] 1 is a graphical representation of a report showing weekly consumption of one or more products by users. [Figure 15] FIG. 1 illustrates an exemplary button device for analyzing a product. [Figure 16] FIG. 1 illustrates an exemplary weight measurement device for determining metrics data for one or more products. [Figure 17] 1 is a flow diagram illustrating a method performed by the system for generating insights into user product consumption or usage behavior. [Figure 18] 1A-1C show different views of an example coaster-shaped measuring device. [Figure 19] 1A-1C show different views of an example coaster-shaped measuring device. [Figure 20] 1A-1C show different views of an example coaster-shaped measuring device. [Figure 21] 1A-1C show different views of an example coaster-shaped measuring device. [Figure 22] 1A-1C show different views of an example coaster-shaped measuring device. [Figure 23] 1A-1C show different views of an example coaster-shaped measuring device. [Figure 24] 10A-10C show different views of an apparatus configured to form a sleeve for a measuring device and to be attached to a coaster to form the measuring device. [Figure 25] 10A-10C show different views of an apparatus configured to form a sleeve for a measuring device and to be attached to a coaster to form the measuring device. [Figure 26] 10A-10C show different views of an apparatus configured to form a sleeve for a measuring device and to be attached to a coaster to form the measuring device. [Figure 27]10A-10C show different views of an apparatus configured to form a sleeve for a measuring device and to be attached to a coaster to form the measuring device. [Figure 28] 10A-10C show different views of an apparatus configured to form a sleeve for a measuring device and to be attached to a coaster to form the measuring device. [Figure 29] 10A-10C show different views of an apparatus configured to form a sleeve for a measuring device and to be attached to a coaster to form the measuring device. [Figure 30] 29A-29C show different views of the measuring device with the apparatus of FIGS. 24-29 attached to a coaster (eg, the coaster of FIGS. 18-23). [Figure 31] 29A-29C show different views of the measuring device with the apparatus of FIGS. 24-29 attached to a coaster (eg, the coaster of FIGS. 18-23). [Figure 32] 1A-1C illustrate different views of the tray of a particular measurement device that is enabled by coupling (e.g., attaching) the tray to one or more coasters. [Figure 33] 1A-1C illustrate different views of the tray of a particular measurement device that is enabled by coupling (e.g., attaching) the tray to one or more coasters. [Figure 34] 1A-1C illustrate different views of the tray of a particular measurement device that is enabled by coupling (e.g., attaching) the tray to one or more coasters. [Figure 35] 35A-35C show different views of a measuring device formed by combining the tray of FIGS. 32-34 with a coaster (an example of which is shown in FIGS. 18-23). [Figure 36] 35A-35C show different views of a measuring device formed by combining the tray of FIGS. 32-34 with a coaster (an example of which is shown in FIGS. 18-23). [Figure 37]35A-35C show different views of a measuring device formed by combining the tray of FIGS. 32-34 with a coaster (an example of which is shown in FIGS. 18-23). [Figure 38] 2A-2C show different views of the container of a particular measurement device that is enabled by coupling (e.g., attaching) the container to a coaster (an example of which is shown in FIGS. 18-23). [Figure 39] 2A-2C show different views of the container of a particular measurement device that is enabled by coupling (e.g., attaching) the container to a coaster (an example of which is shown in FIGS. 18-23). [Figure 40] 39A-39C show different views of a measuring device formed or to be formed by combining the container of FIGS. 38 and 39 with a coaster (an example of which is shown in FIGS. 18-23). [Figure 41] 39A-39C show different views of a measuring device formed or to be formed by combining the container of FIGS. 38 and 39 with a coaster (an example of which is shown in FIGS. 18-23). [Figure 42] 39A-39C show different views of a measuring device formed or to be formed by combining the container of FIGS. 38 and 39 with a coaster (an example of which is shown in FIGS. 18-23). [Figure 43] 10A-10C illustrate different views of an adapter configured to modify the form factor of a measurement device. [Figure 44] 10A-10C illustrate different views of an adapter configured to modify the form factor of a measurement device. [Figure 45] 10A-10C illustrate different views of an adapter configured to modify the form factor of a measurement device. [Figure 46] FIG. 1 illustrates a hub-and-spoke architecture of multiple measurement devices. [Figure 47]FIG. 47 illustrates an example architecture of a measurement device configured to function as a spoke within the hub-and-spoke architecture of FIG. 46. [Figure 48] FIG. 47 illustrates an example architecture of a measurement device configured to function as a hub in the hub-and-spoke architecture of FIG. 46. [Figure 49] FIG. 1 is a block diagram of an exemplary computer system that can be used to perform the operations described herein. DETAILED DESCRIPTION OF THE INVENTION

[0025] Like reference symbols in the various drawings indicate like elements.

[0026] Summary A system is described that can generate insights and recommendations based on the consumption or use of one or more products by one or more users. The system includes a measurement device for measuring product characteristics (e.g., weight, movement, location, time of consumption or use, etc.), a communication device for providing instructions to the user and receiving feedback while the user is consuming or using the product, a computing device that performs machine learning on data received from the measurement device and communication device to generate insights and recommendations specific to the user and / or entity (e.g., a merchant of the product), respectively, and a client device configured to be operated by the entity. The computing device transmits the insights and / or recommendations for the user to the communication device and transmits the insights and / or recommendations for the entity to the client device. Various architectural variations are possible, as described in more detail below.

[0027] The insights or recommendations for a consumer or user can help the consumer or user modify their consumption or usage habits to improve their quality of life. The insights or recommendations for an entity can help the entity evaluate its product portfolio and campaigns, create new products, modify existing products, product content, product sizes, manufacturing processes, advertising campaigns, distribution channels, and / or serve any other purpose.

[0028] Example architecture for an insight and recommendation generation system 1 illustrates a system 100 for generating insights into a user's usage or consumption behavior of a product 102 according to some implementations described herein. The product 102 may include any tangible good that can be consumed or used. For example, the product 102 may include household cleaning supplies, consumer goods, food and beverages, etc. The system 100 may include a measurement device 104, a communication device 106, a computing device 108, a client device 110, and a communication network 114. The measurement device 104 may measure (e.g., sense or detect) the user's consumption or use of the product 102.

[0029] The communication device 106 may be a computing system (e.g., a phone, tablet computer, phablet computer, laptop, etc.) configured to be operated by a consumer or user of the product 102 and capable of recording consumer or user data (e.g., video, photos, or audio) while consuming or using the product 102 or during other related activities, such as opening or storing the product 102. The computing device 108 may receive data regarding consumption or usage measurements from the measurement device 104 and / or consumption or usage data from the communication device 106 and may use these data to generate (a) insights based on the measured consumption or usage of the product 102 and / or (b) recommendations based on the insights. Respective insights and recommendations may be generated (i) for an entity dealing in the product 102, such as a merchant (e.g., manufacturer, distributor, retailer, etc.) of the product 102, or (ii) for a user consuming or using the product. The client device 110 may be a computing system (e.g., a computer) configured to be operated by a merchant (e.g., a manufacturer, distributor, retailer, etc.) of the product 102. The computing device 108 may transmit user-specific insights and / or recommendations to the communication device 106 over the communication network 114, and may transmit entity-specific insights and / or recommendations to the client device 110 over the communication network 114.

[0030] Measurement devices, communication devices, and data collection used for insights A measurement device 104 may be coupled to the product 102 to measure (eg, detect or sense) consumption or use of the product 102 .

[0031] Details of the coupling of the measuring device with the product Coupling can occur physically or remotely via a communications network. Physical coupling can include the measurement device 104 simply being placed under the product 102 or being attached (e.g., by gluing, knitting or threading, welding, twisting and turning multiple components, pressing components against or toward each other, and / or affixing by any other one or more attachment mechanisms) to any suitable area on the packaging (e.g., housing or any external boundary) of the product 102. In some cases, the measurement device 104 can have separate components, such that a first set of one or more components is placed under the product 102 and a second set (e.g., another, different set) of one or more components is attached (e.g., affixed) to any suitable area on the packaging of the product 102.

[0032] The region, portion, or location on the product 102 to which the measurement device 104 is coupled can vary depending on the characteristics (e.g., properties or characteristics) of the product, such as the form of the product 102 (e.g., solid, liquid, or gas), the shape of the product 102, the volume of the product, the cohesive properties of the product 102, the adhesive properties of the product 102, the surface tension of the product 102, the capillary action of the product 102, the pressure of the product 102, the temperature of the product 102 at which the product is used and / or stored, the viscosity of the product 102, etc. For example, in some implementations, the characteristics may require the product 102 to be as close as possible to the measurement device 104 so that the measurement device 104 can make an accurate measurement, in such cases, the location of coupling on the measurement device 104 may be selected accordingly. In other implementations, the characteristics may require the product 102 to be as far as possible from the measurement device 104 so that the characteristics do not interfere with measurement functions or related components within the measurement device 104, in such cases, the location of coupling on the measurement device 104 may be selected accordingly.

[0033] To physically couple the product 102 and the measuring device 104, the measuring device 104 can be integrated into or attached to a suitable location within the product 102 by adhesive bonding, knitting or threading, welding, magnetic attachment, fastening, taping, other mechanical methods (e.g., moving, twisting, turning, or pushing two components together to couple them), and / or any other attachment mechanism or mechanisms. If the attachment requires the combination of two or more separate components, both the product 102 and the measuring device 104 may include some or all of those components in their respective implementations. For example, a magnetic attachment may include a metal strip affixed to the product 102 and one or more magnets affixed to the measuring device 104. Alternatively, the metal strip may be affixed to the measuring device 104, or one or more magnets may be affixed to the measuring device 104. In another example, the fastening attachment mechanism may include a fastener including a first strip (e.g., a first fabric strip) having small hooks that can interlock with a second strip (e.g., a second fabric strip) having smaller loops so that the hooks temporarily interlock with the loops until the hooks are pulled. In such a case, the first strip can be affixed to the product 102 and the second strip can be affixed to the measuring device 104, or the second strip can be affixed to the product 102 while the first strip is affixed to the measuring device 104. For a taping attachment mechanism, different types of tape can be used to bond the product 102 and the measuring device 104 depending on the desired strength or length of attachment. For example, the material and / or thickness forming the tape can be varied based on the attachment strength and / or length; different examples of such materials include polypropylene, polyurethane, thermoplastic olefin, low surface energy clear coat systems, rubber (e.g., EPDM rubber), etc.

[0034] In some implementations, the product 102 can be communicatively coupled to the measurement device 104 via one or more communication networks, as described above. For example, the measurement device 104 can remotely detect the movement of the product 102, or any component or substance within the product (i.e., detection may occur remotely without attachment or a wired connection between the product and the motion sensor). The motion sensor can be an infrared-based motion sensor, an optical-based motion sensor, a radio frequency-based motion sensor, a sound-based motion sensor, a vibration-based motion sensor, and / or a magnetic-based motion sensor. The infrared-based motion sensor can include passive and / or active sensors. The optical-based motion sensor can include video and / or camera systems. The radio frequency-based motion sensor can include sensors based on radar, microwave, and / or tomographic signals. The sound-based motion sensor can include a microphone and / or acoustic sensor. The vibration-based motion sensor can include triboelectric, seismic, and / or inertial switch sensors. The magnetic-based motion sensor can include a magnetic sensor and / or magnetometer.

[0035] Examples of measuring device electronics The measurement device 104 includes one or more sensors for measuring various functions or characteristics of the product. For example, the one or more sensors may include a weight sensor for determining the weight of the product 102, a motion sensor for determining the motion of the product 102, and a time sensor for determining the duration of consumption or use of the product 102. Some additional or alternative examples of sensors can be configured to detect other characteristics or properties including the form of the product 102 (e.g., solid, liquid, or gas), the shape of the product 102, the volume of the product 102, the cohesive properties of the product 102, the adhesive properties of the product 102, the surface tension of the product 102, the capillary action of the product 102, the pressure of the product 102, the temperature of the product 102 as the product is used and / or stored, the viscosity of the product 102, etc.

[0036] The measurement device 104 further includes a communication module, which may include communication circuitry, enabling communication between the measurement device 104 and other components of the system 100, such as the communication device 106 and the computing device 110. The term module, as referred to herein, may include software instructions and code for performing a specified task or function. A module, as used herein, may be a software module or a hardware module. A software module may be part of a computer program that may include multiple independently developed modules that may be combined or linked via link modules. A software module may include one or more software routines. A software routine is computer-readable code that performs a corresponding procedure or function. A hardware module may be a self-contained component with independent circuitry capable of performing various operations described herein.

[0037] The measuring device 104 may include an electronic switch that can activate and / or deactivate the measuring device 104's measurement of a characteristic of the product 102 (e.g., weight, movement, position, etc.). In some implementations, the activation or deactivation of the electronic switch can be remotely controlled by the communication device 106 and / or the computing device 108. In some implementations, the electronic switch may be in an activated position, and a manufacturer of the measuring device 104 may place the electronic switch inside the measuring device 104 so that it cannot be deactivated without the expertise to open and repair such a measuring device. In other implementations, the electronic switch may be located outside the measuring device 104 (e.g., on the housing), such that any user may manipulate the electronic switch to activate or deactivate the electronic switch. When activated, the electronic switch can initiate measurements by one or more sensors of the measuring device 104. When deactivated, the electronic switch can pause or stop measurements by one or more sensors of the measuring device 104.

[0038] In some implementations, the measuring device 104 may have one or more sensors and minimal electronic circuitry, such as a transmitter that transmits all of the detected data to the computing device 108 in real time or at programmed (or programmable) time intervals (e.g., every 5 seconds, 15 seconds, 1 minute, 5 minutes, 15 minutes, or any other time interval). Such implementations may facilitate use, installation, and / or repair of the measuring device 104 and may be less bulky due to less electronic circuitry in the measuring device. In such implementations, collected data may be transmitted directly from the measuring device 104 to the communication device 106 and / or computing device 108, and the devices 106 and 108 may process the collected data.

[0039] In some other implementations, the measurement device may have at least one programmable processor and a machine-readable medium capable of storing instructions that, when executed by the at least one programmable processor, cause the at least one programmable processor to process measurements from one or more sensors before the measurements are transmitted to the computing device 108. Such implementations may be slightly larger due to more electronic circuitry, but such differences may be minimized. The processing by the measurement device 104 may include removing redundancy from the collected data and then consolidating the de-redundant data so that the consolidated data reduces the time it takes for the communication device 106 and / or computing device 108 to retrieve data from the measurement device 104.

[0040] In some examples, the process of removing redundancy may include removing one or more of duplicate data, inconsistent data, null values, data values ​​collected when errors are reported during the measurement process, and / or other erroneous values ​​from the collected data. In some examples, the process of removing redundancy may include normalizing the collected data to organize the data according to attributes of the collected data and relationships between different collected data. The process may also include verifying the completeness of the data to be transmitted prior to transmission of the collected data. Such processing of the data may advantageously optimize transmission bandwidth, thereby streamlining the transmission of data over the communication network 114, because irrelevant or redundant data is prevented from being transmitted to the communication device 106 or computing device 108 over the communication network 114.

[0041] In some implementations, the measurement device 104 may have electronic components such as one or more controllers, one or more memories, one or more storage devices, one or more input or output devices, etc. Additionally, although the measurement device 104 and the communication device 106 are shown as separate devices, in some implementations the measurement device 104 and the communication device 106 may be attached to one another. In some implementations, the measurement device 104 and the communication device 106 may be integrated with one another to form a single structure having a common housing and a shared set of electronic components.

[0042] Various sensors are described as being embedded within the measurement device 104. In some other implementations, at least some of these sensors may be embedded within the packaging of the product 102, and the remaining sensors may be embedded within the measurement device 104. In yet other implementations, the sensors used may be distributed across the packaging of the product 102, the measurement device 104, and the communication device 106. The communication device 106 and / or the computing device 108 may synchronize the sensed data between the different devices in which the sensors are implemented.

[0043] Ensuring accuracy of collected data The one or more sensors on the measuring device 104 perform various detections or measurements. To ensure the accuracy of the measurements, it is necessary to ensure that the measuring device 104 is stable enough to perform the measurements. For example, when detecting the weight of the product 102, the measuring device 104 must be placed on a stable surface to ensure an accurate measurement of the weight.

[0044] To ensure stability, the measuring device 104 can compare measurements taken at different times. For example, a first reading of a particular piece of data at 12:00 PM, a second reading at 1:00 PM on that particular day, and a third reading at 2:00 PM on that particular day. If the comparison results in inconsistent data, e.g., while the product was not replenished, the reading at 12:00 PM indicates that the product 102 weighed 4 kilograms, the reading at 1:00 PM indicates that the product weighed 3.8 kilograms (indicating that some of the product was consumed), and the reading at 2:00 PM indicates that the product weighed 4.5 kilograms (indicating that the weight of the product 102 has increased), the measuring device 104 can conclude (i.e., determine) that the measuring device 104 is not stable. If such instability is detected, the measuring device 104 can (a) discard (or initiate a process to discard) the collected data and / or (b) flag such inconsistency to the communication device 106 and / or computing device 108 and notify the user to place the measuring device 104 on a stable surface. In implementations in which collected data is discarded, discarding such data prevents improperly collected data from being used to generate insights, thereby making the insights more accurate and reliable.

[0045] Similarly, the communication device 106 and / or computing device 108 may compare data received at different times or from different devices to determine whether there are any inconsistencies. If the communication device 106 and / or computing device 108 determines that there are any inconsistencies, the communication device 106 and / or computing device 108 may discard (or initiate a process to discard) the conflicting data and / or provide a notification to the user about the inconsistency (e.g., a notification indicating that the user mistakenly placed the wrong product on the measuring device 104). This process may remove any inconsistencies, including those that may have occurred during transmission from the measuring device 104 to the communication device 106 and / or computing device 108.

[0046] Measurement timing example using a measurement device The measuring device 104 can use an array of sensors to monitor consumption or use of the product 102. In some implementations, the measuring device 104 can continuously monitor use of the product 102 in real time. In particular implementations, the measuring device 104 can monitor use of the product 102 at programmed (or programmable) time intervals (e.g., every second, every 5 seconds, every 15 seconds, every 30 seconds, every minute, every 5 minutes, every 30 minutes, every hour, every day, every 15 days, every month, or any other programmable time value). In particular implementations, the measuring device 104 can monitor use of the product 102 on an event-by-event basis by identifying actions that indicate a change. Some examples of such actions include removing a used product 108 or a portion of the product 102, refilling or replacing the product 102 for storage and / or future use, moving the product 102, etc.

[0047] The frequency of monitoring may be varied based on (a) the storage capacity of the measurement device 104, communication device 106, and / or computing device 108, and / or (b) the processing power of the measurement device 104, communication device 106, and / or computing device 108, and / or (c) the bandwidth of one or more channels (e.g., one or more communication channels within the communication network 108) through which the measurement device 104, communication device 106, and / or computing device 108 communicate with each other.

[0048] Measuring instrument structural design example The measuring device 104 can have a structure that matches the design of the product 102 to which it is being coupled. For example, if the measuring device is configured to be placed under the product 102, the measuring device 104 can have a cylindrical or box shape (each resembling a coaster) that can fit under the product 102. If the product 102 (or its packaging or housing) has a cross-section of a particular shape (e.g., circular, square, rectangular, polygonal, etc.) at the bottom, the measuring device 104 may have the same or a similar cross-section at the top so that the combination of the measuring device 104 and product 102 occupies less space for design efficiency.

[0049] In some implementations, the shape of the measuring device 104 can be customized for each product 102. In other implementations, the shape of the measuring device 104 may be based on a set of products. For example, a box-shaped measuring device 104 may be designed to be used for all products 102 packaged in box-shaped packages, and a cylindrical measuring device 104 may be designed to be used for all products 102 that are often packaged in cylindrical packages (e.g., bottles or jars) so that the mating cross-sections are similar. When employing a custom design for the measuring device 104, customization may be performed to ensure (a) a compact design of the measuring device 104 or the combination of the measuring device 104 with the product 102, (b) low or optimal use of physical space, (c) improved or optimal performance of the electronic components within the measuring device 104, and / or the like.

[0050] Another example of a measuring device design - modular design of a sensor capsule In some implementations, the measurement device 104 can be in the form of a physical module (which in some implementations can also be referred to as a sensor capsule because it contains a sensor) so that the same sensor capsule can be inserted into a variety of items, such as a coaster, a tray, a sleeve (which can be configured to surround the side of a container such as a glass or bottle), a container, or any other accessory. The sleeve can be made of cloth, foam, plastic, or other material. In some implementations, such items can have some means by which the sensor capsule can be inserted into or attached to the item.

[0051] The sensor module may have a housing that may be made of plastic, rubber, and / or glass. The modules described herein may include software instructions and code for performing specified tasks or functions. As used herein, a module may refer to a software module or a hardware module. A software module may be part of a computer program that may include multiple independently developed modules that may be combined or linked via link modules. A software module may include one or more software routines. A software routine is computer-readable code that performs a corresponding procedure or function. A hardware module may be a self-contained component with independent circuitry capable of performing the various operations described herein.

[0052] To insert the sensor capsule into such an item (e.g., a coaster, tray, sleeve, container, etc.), the item may have an opening that may be designed to hold the sensor capsule. For example, a coaster may have a space above or below its top surface to hold the sensor capsule. Similarly, a tray may have a space below its top surface to hold the sensor capsule. A sleeve may have a space at the bottom of the cylindrical sleeve so that the sensor capsule can be inserted therein. A container (such as a cup, glass, or jug) may have a physical space at the bottom into which the sensor capsule can be inserted.

[0053] To attach the sensor capsule to an item (such as a coaster, tray, sleeve, container, etc.), attachment can be performed by adhesive bonding, knitting or threading, welding, taping, joining using one or more screws or nails, twisting two or more components together until securely attached (e.g., locked), pushing one component onto another until securely attached (e.g., locked relative to one another), and / or any other attachment mechanism or mechanisms. Attachment can be to any suitable area on the item. The area, portion, or location on the item to which the sensor capsule is coupled can vary depending on (a) the function (e.g., property or characteristic) of the product 102 for which the item is designed and (b) the type of sensor in the sensor capsule to be used. Such features of the product 102 may include the form of the product 102 (e.g., solid, liquid, or gas), the shape of the product 102, the volume of the product 102, the cohesive properties of the product 102, the adhesive properties of the product 102, the surface tension of the product 102, the capillary action of the product 102, the pressure of the product 102, the temperature of the product 102 when the product is used and / or stored, the viscosity of the product 102, etc.

[0054] For example, in some implementations, a feature may require the product 102 to be as close as possible to the sensor so that the sensor capsule can take an accurate measurement, in such cases the binding location on the item may be selected accordingly. In other implementations, a feature may require the product 102 to be as far as possible from the sensor so that the feature does not interfere with the measurement functions or associated components within the sensor capsule, in such cases the binding location on the item may be selected accordingly.

[0055] If the attachment requires the combination of two or more separate components, either the sensor capsule or the item may include some or all of those components in their respective implementations. For example, a magnetic attachment may include a metal strip affixed to the sensor capsule and one or more magnets affixed to the item, or alternatively, a metal strip may be affixed to the item and one or more magnets affixed to the sensor capsule. In another example, a fastening attachment mechanism may include a fastener including a first strip (e.g., a first cloth strip) having a small hook that can interlock with a second strip (e.g., a second cloth strip) having a smaller loop so that the hook temporarily interlocks with the loop until the hook is pulled. In such a case, the first strip can be affixed to the item and the second strip can be affixed to the sensor capsule, or the second strip can be affixed to the item and the first strip can be affixed to the sensor capsule. For a taping attachment mechanism, various types of tape can be used to connect the sensor capsule and the item depending on the required attachment strength or length. For example, the material and / or thickness forming the tape can be varied based on attachment strength and / or length, and different examples of such materials include polypropylene, polyurethane, thermoplastic olefin, low surface energy clear coat systems, rubber (e.g., EPDM rubber), etc.

[0056] Materials forming the measuring device The measuring device 104, or a housing (e.g., enclosure) of the measuring device 104, can be made of a material that is compatible with the material of the product 102 to protect the product 102 and / or the measuring device 104 from damage. For example, the measuring device 104 (or the housing of the measuring device 104) can be made of a material that can maintain stable and fully operational conditions at the storage or operating temperatures of the product 102. In some examples, the measuring device 104 (or the housing of the measuring device 104) can be configured to maintain stable and fully operational conditions over a wide temperature range, which in some examples can range from -40°C to 150°C, in certain examples can range from 0°C to 100°C, and in some examples can range from 0°C to 50°C. Some examples of such materials include fiberglass (which advantageously does not absorb liquids such as water that may be present in the product 102), mineral wool (which advantageously does not melt or support combustion, making it safe for use in many types of products 102), cellulose (which is environmentally friendly due to its high recycled content), polyurethane foam (which has the advantage of also being a sound insulator that may be advantageous for use in loud products), etc.

[0057] The measuring device 104 can have protection mechanisms to avoid damage from spills or leaks of the product. For example, in some implementations, the measuring device 104 can have a liquid-resistant or fluid-resistant (e.g., waterproof or water-resistant) housing, which can be advantageous when the product is a liquid because spills or leaks of the product 102 onto the measuring device 104 cannot damage the measuring device 104. The liquid-resistant or fluid-resistant housing can be made of a liquid-resistant or fluid-resistant material, such as polyurethane laminate (PUL), thermoplastic polyurethane (TPU), waxed cotton, nylon, polyester, PVC-coated polyester, laminated fabric, patent leather, polyester fleece, microfiber, wool, vinyl, synthetic leather, plastic, etc.

[0058] The measuring device 104 may additionally or alternatively be made of a material with a low coefficient of friction and wear resistance, such as polytetrafluoroethylene (PTFE). Such materials may prevent or reduce friction and wear, and therefore may advantageously extend the life of the measuring device 104.

[0059] In some implementations, the material forming the measuring device 104 may have high strength; examples of such materials include dense materials such as wood or polymers, and sturdy materials such as steel. In some examples, the material forming the measuring device 104 may have high electrical resistivity; examples of such materials include insulating materials such as polymers and ceramics. In particular examples, the material forming the measuring device 104 may be flexible or elastic; examples of such materials include rubber, thermosets, or rubber. In other examples, the material may be rigid; examples of such materials include steel, aluminum alloys, or carbon fiber. In some examples, the material forming the measuring device 104 may have low recycling costs; examples of such materials include metals that can be easily sorted, remelted, and shaped. In some examples, the material forming the measuring device 104 may have low energy costs, which can be based on (a) the energy required to collect / mine the material and / or (b) the energy required to refine, extract, or synthesize the material; an example of such a material is aluminum.

[0060] Different implementations may use a respective set of one or more materials that may be arranged in a manner that allows for compactness and efficiency of use of the measurement device 104 .

[0061] Starting the measuring device The measuring device 104 may include an electronic switch that can activate and / or deactivate the measuring device 104's measurement of a characteristic of the product 102 (e.g., weight, movement, position, etc.), as described above. The location of the electronic switch within the measuring device 104 can be varied to allow for easy activation or deactivation of the measurement. For example, in some implementations, the electronic switch may be external to the measuring device 104 (e.g., on a housing or enclosure) to allow a user of the product 102 to activate or deactivate the measurement. In other implementations, the electronic switch may be present only internal to the measuring device 104 (e.g., in internal electrical circuitry) to discourage (or, in some implementations, prevent) a user of the product 102 from activating or deactivating the measuring device 104. In implementations in which the electronic switch is internal to the measuring device 104, the measuring device 104 may be configured to be remotely activated or deactivated by the communication device 106 and / or the computing device 108.

[0062] In some implementations, one or more controllers or processors in the measurement device may activate the measurement process (i.e., activate sensors in the measurement device) in response to an event. Such events may vary from example to example.

[0063] For example, in some instances, the event may be receipt of a user scanning a machine-readable data representation (e.g., a barcode, a matrix code, a QR code, etc.).

[0064] In a particular example, the event may be the formation or initiation of a communication channel connecting the measurement devices 104 .

[0065] In other examples, the event may be receipt of a signal from another electronic component (which may or may not be shown in FIG. 1 ) in system 100 indicating that product 102 has been consumed or used, has been consumed or used, or is about to be consumed or used. For example, the controller of measuring device 104 may activate a sensor in measuring device 104 in response to a sensor (such as a motion sensor) in one or more other devices (e.g., one or more of a refrigerator, a trash can, a cabinet, a sink, a washing machine, a dishwasher, any other one or more locations or devices in which product 102 may be placed, and / or any combination thereof) communicatively coupled to measuring device 104 via a communications network, such as communications network 114, indicating that product 102 has been consumed or used, has been consumed or used, or is about to be consumed or used.

[0066] For example, a motion sensor in a refrigerator can indicate the opening of the refrigerator door, which can indicate that the product 102 material has been removed and is about to be poured into the product 102 container. Similarly, a motion sensor in a trash can can indicate the opening of the trash can lid, which can indicate that the product 102 or the product 102 material is being tossed and not reused. Similarly, a motion sensor in a washing machine or washer can indicate the opening of the lid, which can indicate that the product 102 (e.g., dish detergent or cleaning liquid) is being used. Similarly, a motion sensor in a cabinet can indicate the opening of the cabinet door, which can indicate that the product 102 stored in the cabinet is being used.

[0067] When the communications network 114 is the Internet, the components of the system 100, including such other components, e.g., smart refrigerators, smart trash cans, smart cabinets, smart sinks, etc. (not shown), can form an Internet of Things, which can also be referred to as the Internet of Products.

[0068] In some examples, the event can be confirmation of the identity of a user attempting to consume a product. The product's identity can be created by a user entering authentication data, radio frequency identification, biometric-based identification (which may implement facial recognition, fingerprint scanning, voice identification, eye scanning, etc.), identification via a magnetic strip used by the user, optical character recognition of user-entered data, the user's use of one or more smart cards, voice recognition, etc. The user may be required to provide input to facilitate identification on the measurement device 104. In some implementations, the user may need to provide input to facilitate identification on the communication device 106 (e.g., on an application, which may be a browser or a native application on the communication device 106). The identification process for mapping a user to received input data may be performed on the measurement device 104, the communication device 106, and / or the computing device 108 in various implementations.

[0069] Associating product consumption or usage with the corresponding user and collecting additional user data to enable us to generate more useful insights Associating product consumption or usage with a particular user can be beneficial when multiple users (such as different members of a family) use the same product, and such implementations allow tracking of the consumption or usage of the product 102 by various consumers or users. This allows insights to be generated that are specific to each user (rather than all family members using the same product). In other examples, the technology may be modified to allow for a set of insights to be generated that is common across all users (e.g., all family members) who use a particular product.

[0070] The product's identity can be created by a user entering authentication data, radio frequency identification, biometric-based identification (which may implement iris and / or facial recognition systems), identification by a magnetic strip used by the user, optical character recognition of user-entered data, the user's use of one or more smart cards, voice recognition, etc. The user may be required to provide input to facilitate identification on the measurement device 104. In some implementations, the user may need to provide input to facilitate identification on the communication device 106 (e.g., on an application, which may be a browser or a native application on the communication device 106). The identification process for mapping the user to the received input data may be performed on the measurement device 104, the communication device 106, and / or the computing device 108 in various implementations. In some implementations, user identification may be performed by detecting proximity between the measuring device 104 and a wearable device worn by the user (e.g., a bracelet, necklace, anklet, ring, watch, etc., each configured to communicate with the measuring device 104 via a communications network) when a usage event associated with the product 102 occurs (e.g., a weight change and / or movement detected by the measuring device 104).

[0071] In some implementations, authentication of a user to confirm the user's identity may include a combination of one or more technologies, such as multi-factor authentication. In particular examples, authentication may include certificate-based authentication, biometric authentication (e.g., facial recognition, fingerprint scan, voice identification, eye scan), token-based authentication, etc.

[0072] Measurement device startup permission The communication device 106 and / or computing device 108 may be required to allow initialization of the measurement device 104. For example, a user may be required to use the communication device 106 to upload data indicating the coupling between the product 102 and the measurement device 104 (e.g., the physical proximity between the product 102 and the measurement device 104, or the placement, installation, or mounting of the measurement device 104 on the product 102) before the measurement device 104 is powered up or initialized. The data indicating the coupling may be in the form of a photograph, a video, or text data. The video or text data may include feedback provided by the user, which may indicate or confirm the coupling.

[0073] The computing device 108 may use natural language processing techniques to analyze the text feedback to determine that a bond has occurred, and / or may use image processing techniques to analyze the photograph or video to determine that a bond has occurred. In some implementations, the computing device 108 may implement machine learning models to perform such natural language processing and / or image processing. In some implementations, the bond information may be extracted using various algorithms, such as latent semantic analysis, probabilistic latent semantic analysis, latent Dirichlet allocation, correlation topic models, any other one or more identification algorithms, and / or any combination thereof.

[0074] The machine learning model used to perform such identification may be trained on historical binding data, i.e., data indicative of or associated with binding between (a) measurement device 104 or similar devices and (b) communication device 106 or similar devices and / or computing device 108 or similar devices. The machine learning model may have been previously trained by and / or on computing device 108 and / or any other device or devices coupled to computing device 108 via a communication network and capable of providing the machine learning model to computing device 108. In some implementations, computing device 108 may implement Software as a Service (SaaS) that performs training and deployment of machine learning models, and the training, deployment, and / or storage of such machine learning models may be performed on a cloud computing system coupled to computing device 108.

[0075] The machine learning models trained and deployed to identify bindings can be supervised models (e.g., models that involve learning a function that maps inputs to outputs based on examples of input-output pairs) or unsupervised models (e.g., models used to draw inferences and find patterns from input data without reference to labeled outcomes). Supervised models can be regression models (e.g., models with continuous outputs) or classification models (e.g., models with discrete outputs).

[0076] The image processing techniques can be performed by a machine learning model or by the computing device 108 without the use of a machine learning model. Image processing techniques for determining binding can be used to determine that the user has physically placed the measuring device 104 adjacent to the product 102 in the appropriate location (e.g., placed the measuring device in the form of a coaster under the product 102). Image processing techniques for determining that the user has physically placed the measuring device 104 adjacent to the product 102 in the appropriate location can include image classification, object recognition, object tracking, semantic segmentation, instance segmentation, pattern recognition, etc.

[0077] In response to determining the coupling, the computing device 108 may transmit a signal to the measuring device 104 that may activate or initialize the measuring device 104. Ensuring the coupling before activating or initializing the measuring device 104 may ensure that the measuring device 104 operates only when necessary, conserving computing resources (and optimizing or improving its functionality), including processing, storage, and transmission capabilities.

[0078] Generate notifications and trigger sensors upon user confirmation The controller of the measuring device 104 may activate a sensor in the measuring device 104 in response to activity (e.g., opening a refrigerator door, lid, cabinet door, etc.) of a sensor (e.g., a motion sensor) in one or more other devices (e.g., one or more of a refrigerator, a trash can, a cabinet, a sink, a washing machine, a dishwasher, any other one or more locations or devices in which the product 102 may be placed, and / or any combination thereof) communicatively coupled to the measuring device 104 via a communications network, such as communications network 114, indicating that the product 102 has been consumed or used, has been consumed or used, or is about to be consumed or used.

[0079] In some implementations, the controller of the measuring device 104 may not activate sensors in the measuring device 104 in response to detecting such activity in other appliances (e.g., refrigerators, trash cans, cabinets, sinks, washing machines, dishwashers, etc.). Instead, the controller may (a) generate a notification of the activity to the user, (b) transmit a request for confirmation that the user performed such activity, and (c) activate sensors in the measuring device 104 in response to confirmation by the user. Activating sensors in response to confirmation by the user can ensure that data specific to a particular user is recorded by the measuring device and prevent recording of data that should not be associated with or mapped to a user. This reduces storage requirements in the measuring device 104 or any other component in the system 100 that stores measurement data and reduces the bandwidth required for transmission of the measurement data.

[0080] Collecting user and other attributes To generate more useful insights, in some implementations, the communication device 106 may collect (for transmission to the computing device 108) various other details (e.g., user attributes and / or attributes of the product 102, the measurement device 104, and / or the communication device 106) that can be collected by an application on the communication device 106 before the user begins using the measurement device 104 to capture the user's consumption or use of the product 102.

[0081] The user attributes collected by the communication device 106 may include the user's name, the user's date of birth, the user's ethnicity, the user's product preferences, the user's brand preferences, the user's shopping habits, the user's consumption or usage habits, and / or the user's address. In some implementations, the user attributes may further include the user's household details, such as the number of people living in the household, the approximate size of the home in square feet, household income, the number of pets in the household, the type of pets in the household, and / or a description of the current living situation.

[0082] Other attributes collected by the communication device 106 may include details of the product 102, details of the measuring device 104, details of the communication device 106, etc. The one or more details of the product 102 may include a machine-readable data representation on the product 102 (e.g., a barcode on the product 102, a QR code on the product, etc.), the name of the product 102, the merchant of the product 102 (e.g., a manufacturer, distributor, retailer, etc.), a photo or video of the product 102, a weight reading of the product 102, etc. In some implementations, the measuring device 104 includes a built-in barcode scanner that scans the barcode on the product 102.

[0083] In some implementations, the communication device 106 may automatically obtain such attributes from an application (e.g., a browser or native application) of the communication device 106. In some implementations, the communication device 106 may automatically obtain such attributes from the operating system of the communication device 106. The communication device 106 may automatically retrieve these details after receiving consent from a user of the communication device, advantageously respecting and protecting the user's privacy. In some implementations, the communication device 106 may obtain such attributes in the form of input provided or initiated by the user.

[0084] The communication device 106 may transmit these attributes to the computing device 108, which may also use these attributes to perform machine learning to generate insights.

[0085] Providing data files to communication devices and receiving feedback from users while consuming or using the product The computing device 108 may transmit a message requesting the user to activate the camera device before consuming or using the product 102. Such a message requesting activation of the camera device may be transmitted to an application (e.g., a browser or native application) of the communication device 106 and / or the measurement device 104. The application may output a message to the user (e.g., display or generate an audio signal indicating a message to the user).

[0086] The camera device may be incorporated within the communication device 106 or may be external to and communicatively coupled to the communication device 106. In implementations where the camera device is external to the communication device 106, the camera device may be physically coupled to the communication device 106 or remotely coupled via a communication network. In these cases where the camera device is external, the camera device may be in the form of a wearable device that can be worn by a user.

[0087] For example, the camera device may be in the form of a neckband or necklace that a consumer or user can wear around the user's neck. The neckband or necklace may be worn partially around the neck or entirely around the neck. For example, in some cases, the neckband or necklace may be in the shape of an arc (which may be semicircular, part of a circle, part of an ellipse, etc.) that can be placed around a portion of the neck. In other examples, the neckband or necklace may have a closed shape (e.g., a circle, an ellipse, etc.) that can be closed while the user is wearing the neckband or necklace around their neck and can be opened (e.g., by undoing two ends) when the user wishes to remove the neckband or necklace from around their neck.

[0088] Although a necklace neckband is described, in other implementations, the camera device can take the form of a brace configured to be placed around the user's wrist. In another implementation, the camera device can take the form of a belt configured to be placed around the user's waist. In some implementations, the camera device can be in the form of an anklet configured to be placed around the user's ankle. Other variations of the camera device are possible for other body parts of the user.

[0089] Camera devices may include a flash that is used automatically based on the lighting in the location where the photo is taken. In some implementations, the flash can be manually activated or deactivated.

[0090] The camera device may record the consumption or use of the product 102 through audio and / or video recording. The audio or video recording may occur in real time (i.e., as the consumer or user consumes or uses the product 102 in response to instructions). In other words, the audio and / or video recording may be a recording that shows or represents the consumer or user consuming or using the product.

[0091] While recording the audio and / or video recording, the computing device 108 can transmit a data file (which can include audio, video, and / or text components) that can include one or more instructions for the user. The one or more instructions can include one or more instructions for placing the measuring device 104 in a specific location on or adjacent to the product 102, one or more instructions for holding the product 102 in a specific manner, one or more instructions for consuming or using the product 102 in a specified manner, etc. The audio and / or video recording can include the instructions and the user's responses to the instructions. The user's response to the instructions can be a specific action, such as placing the measuring device 104 in a specific location on or adjacent to the product 102, holding the product 102 in a specific manner, consuming or using the product 102 in a specified manner, etc. In some implementations, the data file can include a question (which can be in audio, video, and / or text format), and the camera device can allow the user to provide a response within the data file.

[0092] The communication device 106, including the camera device, can transmit the audio or video recording along with a data file that can include responses provided by the user in response to the questions to the computing device 108. The computing device 108 can use the audio or video recording along with the data file for machine learning to generate insights.

[0093] The stability and quality of data files (including audio, video, and / or text) transmitted from the computing device 108 to the measurement device 104 and / or communication device 106 can be improved. Similarly, the stability and quality of data files (including responses provided by the consumer or user), as well as audio and video recordings, transmitted from the measurement device 104 and / or communication device 106 to the computing device 108 can be improved. These improvements can be performed by at least one controller of the computing device 108, communication device 106, and / or measurement device 104 in various implementations. Such controllers can be improved as follows:

[0094] The controller can separate data packets within the transmitted data. The transmitted data can be data files transmitted from the computing device 108 to the measurement device 104 and / or the communication device 106, or data files (including responses provided by consumers or users) and audio and video recordings transmitted from the measurement device 104 and / or the communication device 106 to the computing device 108. The controller can allocate the data packets to multiple channels associated with the communication network 114. The separated data packets can be allocated to the multiple channels based on a continuous evaluation of the latency of data transmission within one or more of the multiple channels. The controller can transmit data packets over the multiple channels, including metadata indicating information about the sequence of each packet within the video. The controller can combine data packets transmitted from different channels of the multiple channels based on the metadata.

[0095] Generate reminders for users to use the product In some implementations, the computing device 108 may allow a user to program an application in the communication device 106 to get a reminder to the consumer or use a product at programmed (or programmable) intervals, such as 1 hour, 2 hours, 6 hours, 12 hours, 1 day, 5 days, 10 days, 15 days, 1 month, or any other time interval. This feature may be beneficial for the consumption of certain products, the consumption of which is important, such as medicines, any other products (including food) that may have been recommended by a clinician, perishable products such as fruits and vegetables, etc. In such cases, the computing device 108 and / or communication device 110 may generate a reminder or alarm via an application installed on the communication device 110.

[0096] Product classification for communication device applications An application on the communication device 106 can receive data that can be used by a machine learning model to generate insights. Such data can include various actions taken by a user in the application, timestamps for taking such actions, and the like. In some examples, the actions can include designating categories of products used by the user over a past programmed (or programmable) period (e.g., products used over the past month, past six months, past year, past two years, and / or any other period), receiving or purchasing products used by the user over a past programmed (or programmable) period as determined by a user-entered survey or physical and / or digital receipt scan, designating categories of products purchased by the user but not yet used, purchasing such products, barcode scanning of products at the user's location (e.g., home or office), and designating a temporal preference for the consumption or use of each product (e.g., breakfast, lunch, dinner, etc.). In some implementations, categories may be pre-programmed for all users, while in other implementations, one or more users may be authorized to change, add, or delete at least some of the categories. Such product categorization is sometimes referred to as product tagging. In certain implementations, such product categorization or tagging may be used to determine which particular products the user is requested or commanded to place on the measurement device 104 .

[0097] Internet of Things - Interacting with other smart devices to gather additional event data to be used in machine learning The measurement device 104, the communication device 106, and / or the computing device 108 may be connected to and obtain data from one or more other devices (e.g., one or more of a refrigerator, a trash can, a cabinet, a sink, a washer, any other one or more locations or devices in which the product 102 may be placed, and / or any combination thereof) that may have sensing capabilities (e.g., by one or more sensors in the other devices). Such data may indicate activity (e.g., opening a refrigerator door, lid, cabinet door, etc.) that indicates that the product 102 has been consumed or used, has been consumed or used, or is about to be consumed or used. Such connections may be made over a communications network. When the communications network 114 is the Internet, the components of the system 100, including such other devices (e.g., smart refrigerators, smart trash cans, smart cabinets, smart sinks, etc. (not shown)), can form an Internet of Things, which may also be referred to as an Internet of Products.

[0098] The controller of the measuring device 104 may activate a sensor in the measuring device 104 in response to an activity (e.g., opening a refrigerator door, lid, cabinet door, etc.) of a sensor (e.g., a motion sensor) in one or more other devices (e.g., one or more of a refrigerator, a trash can, a cabinet, a sink, a washing machine, a dishwasher, any other one or more locations or devices in which the product 102 may be placed, and / or any combination thereof) that is communicatively coupled to the measuring device 104 via a communications network, such as communications network 114, that indicates that the product 102 has been consumed or used, has been consumed or used, or is about to be consumed or used.

[0099] The measurement device 104 and / or communication device 106 may transmit this data collected from these other devices (e.g., one or more of a refrigerator, a trash can, a cabinet, a sink, any other location or apparatus where the product 102 may be placed, and / or any combination thereof) to the computing device 108, which may also process such data to perform machine learning to generate insights.

[0100] In some implementations, the computing device 108 may detect an activity (e.g., opening or closing of a refrigerator door, lid, cabinet door, etc.) without using one or more sensors that may or may not be present in those other devices (e.g., refrigerator, trash can, cabinet, sink, washer, etc.). In such a case, the computing device 108 may transmit a message to the communication device 106 requesting a user to activate a camera device to record such activity. The camera device may record an activity such as the opening or closing of a refrigerator door, lid, cabinet door, etc. The computing device 108 may obtain the recordings and implement one or more machine learning models to determine the occurrence of an activity. Then, upon detecting the occurrence of an activity, the computing device 108 may transmit an instruction to the measurement device 104 or the communication device 106 to activate or deactivate a sensor in the measurement device. These implementations may be advantageous when it is difficult to place a sensor in one or more of those other devices (e.g., refrigerator, trash can, cabinet, sink, washer, etc.) to detect an activity (e.g., opening a refrigerator door, lid, cabinet door, etc.).

[0101] Additional features for smart devices Some of the smart devices that may be part of the system 100 include refrigerators, trash cans, cabinets, sinks, washers, dishwashers, etc. Such devices are sometimes referred to as smart devices because, as previously mentioned, electronic circuitry that may include sensors is embedded within the device.

[0102] In some implementations, a smart device may also include a camera that can be used to read a machine-readable data representation of a product (e.g., a barcode, a matrix code, a QR code, etc.) placed within the smart device to identify the product and determine (based on the type of smart device) whether the product is being stored, used, or discarded. For example, a product 102 placed in a trash can indicates that the product is being discarded, a product 102 placed in a washer indicates that the product is being used, and a product 102 placed in a cabinet indicates that the product is being stored.

[0103] The smart device may further include weight sensors, time sensors, location sensors, energy or power meters, and any other sensors (separate but similar to the sensors in the measurement device 104). The weight sensor may indicate the weight of the product 102 at different times, which may further indicate consumption or usage of the product 102. The time sensor may determine the time at which consumption or usage was determined. Location sensors may include a global positioning system (GPS) device that indicates the address location of the smart device, an internal location sensor that may indicate an internal location within the smart device (e.g., the second tray from the top in a smart refrigerator), and the like.

[0104] In some implementations, the movement of an already located object may be used to infer changes in the object's position. The object's movement may be measured by a motion sensor, which may be one or more of an infrared-based motion sensor, an optical-based motion sensor, a radio frequency-based motion sensor, an audio-based motion sensor, a vibration-based motion sensor, and / or a magnetic-based motion sensor. The infrared-based motion sensor may include a passive sensor and / or an active sensor. The optical-based motion sensor may include a video and / or camera system. The radio frequency-based motion sensor may include a sensor based on radar, microwave, and / or tomographic signals. The audio-based motion sensor may include a microphone and / or an acoustic sensor. The vibration-based motion sensor may include a triboelectric, seismic, and / or inertial switch sensor. The magnetic-based motion sensor may include a magnetic sensor and / or a magnetometer.

[0105] The computing device 108 can receive time-stamped data including the output of each of these sensors directly from the smart device or via the communication device 106. Such data can be input into machine learning models to generate more accurate and / or detailed insights.

[0106] Smart Device Installation While the smart device is being installed, an application (e.g., a browser or native application) on the communication device 106 may provide instructions to the user for installing the smart device. The application may require the user to enter some information to complete the installation process. The computing device 108 may provide such information as input to a machine learning model used to generate insights. Such data may include user details (which may include details that can uniquely identify the user), specific features of the smart device programmed by the user, user preferences for using the smart device, desired behavior of the smart device, etc.

[0107] Synchronizing product consumption or usage To ensure that each component in the system 100 has the most up-to-date data regarding the consumption or usage of the product 102, the components in the system 100, including the measurement device 104, the communication device 106, the computing device 108, and the smart device, can synchronize with one another. The term synchronization can also be referred to as syncing, docking, cradle, etc. In some implementations, synchronization may occur at programmed (or programmable) time intervals, such as 30 seconds, 1 minute, 5 minutes, 30 minutes, 1 hour, 6 hours, 12 hours, 1 day, 2 days, 5 days, etc. The shorter the time interval, the more accurate the insights. Also, this implementation in which synchronization occurs at specific intervals saves computing resources compared to implementations in which synchronization occurs in real time. In certain implementations, synchronization may occur in real time, which results in the advantage of generating more accurate insights.

[0108] To synchronize all of these devices in system 100, computing device 108 can update its software with data received from any of these devices and render the updated data to each device in software so that each device has the most current data at the time of synchronization. The computing devices 108 can communicate with each other via communication network 114 and / or any other communication network or networks. Some such communication networks can include Bluetooth communication, Bluetooth Low Energy, Wi-Fi, a cellular network, or any other communication network. In some implementations, a communication network connecting two or more smart devices described herein can be a low-power, low-data-rate, proximity (e.g., personal area) wireless ad-hoc network, such as a Zigbee network. In certain implementations, the communication network can be any low-rate personal area network, the operation of which may be defined by the IEEE 802.15.4 technology standard. In some implementations, the communication network can be any network implementing Bluetooth Mesh, a computer mesh networking standard based on Bluetooth Low Energy that enables many-to-many communication over Bluetooth radios.

[0109] Computing devices, and generating insights and rewards The computing device 108 may generate insights using data generated within the system 100. The generation of such insights will now be described.

[0110] The sensors in the measuring device 104 can detect their respective characteristics. For example, one or more weight sensors can weigh the product 102, one or more motion sensors can detect the movement of the product 102, and one or more time sensors can observe the time spent by the weight sensor and / or the motion sensor. The motion sensor can detect the movement of the product 102 and generate motion data. In some implementations, the sensor unit includes an accelerometer that detects movement of the product 102 to verify use of the product 102, for example. The weight sensor may weigh the product 102 when the product 102 is in a stable state after movement and generate the weight data. The time sensor may observe the time spent with the weight sensor and the motion sensor and generate the time spent data. The time sensor may time stamp the usage of the product 102. The time sensor can be a clock or a timer. Optionally, the time stamp includes morning, afternoon, evening, and night. The time stamp may include real-time usage of the product 102.

[0111] In some implementations, the measuring device 104 may be used for a single product 102. For example, the measuring device 104 may be discarded or disposed of after consumption or use of the product 102. In other implementations, the same measuring device 104 may be reused for several different products. For example, the measuring device 104 may be used to perform a measurement of the product 102, after which the consumer or user may couple the measuring device 104 to another product and perform a measurement of the other product. In some implementations, the computing device 108 may allow the consumer or user to couple a new product to the measuring device 104 only after the measurement of the product 102 is complete or after the product 102 is completely consumed (e.g., the computing device 108 may allow the consumer or user to scan the barcode of the new product and capture a photo (or in some implementations, a video) of the new product with the camera of the communication device 106 only after the measurement of the product 102 is complete). This advantageously avoids errors in the measurements of different products by keeping the measurements of different products discrete. As described above, measurements for different products are taken separately, but in other implementations, the computing device 108 may couple the measurement device 104 to multiple products, whereby the measurement device 104 may include some processing functionality for organizing the measurements for different products separately. In such cases, processing by the measurement device 104 may prevent significant amounts of data from constantly traveling back and forth over the communications network 114, reducing bandwidth usage and latency.

[0112] In some implementations, the measurement device 104 includes space for a label that can be manually affixed to the measurement device 104 or a digital label that can be programmed from either an application, the communication device 106, the computing device 108, the client device 110, any other computer or computers, and / or a cloud platform.

[0113] The measuring device 104 transmits the metric data, movement data, consumption time data, location data, and any other relevant data of the product 102 to the computing device 108 via the communications network 114. The computing device 108 also receives data (e.g., video or audio) indicative of the consumption or use of the product 102, along with feedback from the consumer or user (which may be in the form of video, audio, or text, such as answers to pre-set questions, and may be overlaid on the data (e.g., video or audio) indicative of the consumer's or user's consumption or use of the product 102).

[0114] Machine learning to generate insights, recommendations, and / or rewards The computing device 108 can input all of this data received from the measurement device 104 and the communication device 106 into a trained machine learning model to generate insights. The machine learning model may have been trained with past metric data, past movement data, past time-spent data, and / or other similar data, all of which may be of the consumer or user of the same or similar products, and / or other consumers or users. The machine learning model may have been previously trained by and / or on the computing device 110 and / or any other device or devices.

[0115] Machine learning models that are trained and deployed to perform machine learning can be supervised models (e.g., models that involve learning a function that maps inputs to outputs based on example input-output pairs) or unsupervised models (e.g., models used to draw inferences and find patterns in input data without reference to labeled outcomes). Supervised models can be regression models (e.g., models with continuous outputs) or classification models (e.g., models with discrete outputs).

[0116] The regression model can be one or more of: (a) a linear regression model (e.g., a model that finds a line or curve that best fits the data); (b) a decision tree model (e.g., a model with nodes, where the last node in the tree, which can also be called the leaves of the tree, makes the decision; increasing the number of nodes can increase the accuracy of the decision-making, and decreasing the number of nodes can increase the speed and reduce latency); (c) a random forest model (e.g., a model that involves creating multiple decision trees using a bootstrapped dataset of the original data and randomly selecting a subset of variables at each step in the decision tree; this model advantageously reduces the risk of error from individual trees); or (d) a neural network (e.g., a model that takes a vector of inputs, runs equations at various stages, and generates a vector of outputs).

[0117] The classification model can be one or more of: (a) a logistic regression model (e.g., a model similar to linear regression but used to model the probability of a finite number (e.g., two) of outcomes; e.g., a logistic curve or equation may be created such that output values ​​can only be between 0 and 1); (b) a support vector machine (e.g., a model that finds a hyperplane or boundary between two classes of data that maximizes the margin or distance between the two classes); (c) a naive Bayes model (e.g., a model that implements Bayes' theorem to determine classes).

[0118] The unsupervised learning model can be one or more of: (a) a clustering model (e.g., a model that involves grouping or clustering of data points. Such models can include various clustering techniques such as k-means clustering, hierarchical clustering, mean-shift clustering, and density-based clustering), and (b) a dimensionality reduction model (e.g., a model that removes or extracts features to reduce the number of random variables under consideration by obtaining a set of main variables).

[0119] The computing device 108 can implement any of these machine learning models to generate insights based on the data received from the measurement device 104 and the communication device 106. Some insights are specific and beneficial to the consumer or user, and certain insights are specific and beneficial to the product merchant (e.g., manufacturer, distributor, retailer, etc.). The insights can include qualitative and quantitative insights.

[0120] The computing device 108 may (a) implement a reward system that rewards consumers or users for tracking consumption or usage of the product 102, (b) generate reports that are useful to entities (e.g., merchants, such as manufacturers, distributors, retailers, etc., of the product 102), and (c) generate reports that are useful to the consumer or user. The reports may include respective insights that may be qualitative and / or quantitative in various implementations.

[0121] The reward system can be a points-based system for determining reward points to consumers or users for tracking consumption or usage of products 102. In various implementations, the computing device can assign reward points to consumers or users based on (a) use of one or more devices to track consumption or usage of one or more products, (b) performing various activities using the communication device 106 (e.g., responding to various prompts in the form of questions, such as survey questions, or requests to perform tasks, such as video, photo, and / or audio tasks), etc. The computing device 108 can also map reward points to one or more payment instruments, such as cash, gift cards, cryptocurrency, etc.

[0122] The computing device 108 may generate one or more reports including qualitative and quantitative insights. For example, the computing device 108 may generate a report for a consumer or user that includes insights specific to the consumer or user, and another report for an entity that includes insights specific to the entity (e.g., a merchant, such as a manufacturer, distributor, or retailer of goods).

[0123] The qualitative and quantitative insights may include the use of the product 102 over various time periods. The report may include a graphical representation of the usage of the product 102 over a period of time and / or any of the columns in a table detailing the usage of the product 102 over a period of time. In some implementations, the period of time can vary from an initial period (e.g., week 1) to a subsequent or final period (e.g., week 52). In some implementations, the report includes a graphical representation of the consumption or usage of one or more products used by a consumer or user. In particular implementations, the report includes a graphical representation of the usage of one particular product. The computing device 108 generates a report including qualitative and quantitative insights that reflect the accurate use and / or consumption of the product 102 with little or no human intervention or error. In some implementations, the computing device 108 generates a report including qualitative and quantitative insights including the use of one or more products by one or more consumers or users and timestamps to determine the usage behavior of the one or more products by the one or more consumers or users.

[0124] Transmission of reports containing insights and rewards to communication devices and client devices The computing device 108 can transmit data characterizing rewards earned by a user of the product 102 to the communication device 106, and the communication device 106 can output (e.g., display) the data for the user.

[0125] The computing device 108 can transmit reports specific to an entity (e.g., a merchant, such as a product manufacturer, distributor, or retailer) to the client device 110. The entity-specific reports may not identify the user whose consumption or usage is being reported to ensure and protect the user's privacy. However, the entity-specific reports can anonymously reference various users without providing information that could compromise the confidentiality or privacy of individual users. In some implementations, the merchant-specific reports can include (a) different products specific to and relevant to that merchant, including the product 102; (b) characteristics of each product; (c) various analytical metrics associated with each characteristic; (d) consumption usage behavior for each product as a whole or for all users with common characteristics (e.g., the same geographic region, the same income bracket, the same gender, the same education level, etc.). Such reports can advantageously enable an entity (e.g., a merchant) to evaluate its product campaigns, create new products, modify existing products, product content, manufacturing processes, advertising campaigns, distribution channels, and / or any other purpose.

[0126] The computing device 108 can transmit a user-specific report of the product 102 to the communication device 106. The user-specific report can indicate the user's consumption or usage behavior of various products, including the product 102. For example, the report can specify (a) the categories of products consumed or used by the user (e.g., convenience products, shopping items, specialty products, and / or non-search products), (b) the consumption or usage of products in each category, (c) the amount of each product consumed or used, etc. Such reports can advantageously enable the user to make informed decisions to modify their consumption and / or usage behavior (e.g., habits).

[0127] Use of data platforms (e.g., software as a service) In some implementations, the computing device 108 may be integrated with the communication device 106 and / or the client device 110 as a data platform for providing services to clients. The data platform may be integrated with the computing device 108 to provide services organized by product category, country, potentially by type / size of patronage, etc. An example of such a data platform is a Software as a Service (SaaS) platform. In such a case, the computing device 108 may be a cloud computing server that can provide services on demand, such that SaaS platforms are also called on-demand software.

[0128] In some implementations, a SaaS platform can have a multi-tenant architecture in which a single version of an application facilitated by a computing device 108 and with a single configuration of hardware, network, and operating system is used for all consumers or users (also referred to as tenants within the multi-tenant architecture). This architecture can be advantageous over traditional software, in which multiple physical copies of the software (each potentially different versions, potentially different configurations, and often customized) may be installed at various customer sites, because SaaS allows the underlying devices on which it is used (e.g., communication device 106 and / or client device 110) to be thin clients, which may be low-performance computers optimized for establishing remote connections with a server-based computing environment.

[0129] Although a multi-tenant architecture is described, in other implementations, the SaaS platform may implement other mechanisms, such as virtualization. Virtualization may be the creation of a virtual machine that behaves like a real computer with an operating system. Software running on the virtual machine may be isolated from the underlying hardware resources.

[0130] Although the data platform is described above as a SaaS platform, in some implementations the data platform can be Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Desktop as a Service (DaaS), Managed Software as a Service (MSaaS), Mobile Backend as a Service (MBaaS), Data Center as a Service (DCaaS), Information Technology Management as a Service (ITMaaS), etc.

[0131] Some product examples Some examples of products 102 described herein include cleaning liquids, wipes, sprays, floor cleaning pads, body wash, shampoo, hand soap, sauce cans, beverages, alcohol, etc. Products 102 can be categorized as consumer packaged goods (CPG), fast-moving consumer goods (FMCG), and / or food and beverage (F&B). CPG and F&B include dish care (e.g., dishwashing liquid, dishwasher pods, dishwasher sprays, and / or dishwasher detergent), household cleaning (e.g., surface wipes and sprays, all-purpose sprays, air freshener sprays, liquid floor cleaners, and / or pad-based systems), laundry & fabric care (e.g., laundry liquids, detergents, pods, fabric refresher sprays, fabric softeners, and / or dryer sheets), personal cleaning (e.g., skin care, body wash, moisturizers, hair care, shampoo / conditioner, etc.), and other products. The categories may include: beauty products (such as soaps, hand soaps, and / or shaving gels), family care (such as paper towels and / or tissues), fragrances (such as perfumes and / or deodorants), feminine care, oral care (such as mouthwash and / or toothpaste), personal health, alcohol and / or spirits, baking products, beverages, biscuits and / or cookies, cereals, chocolates, dairy products, fruits, gum and / or candy, ice cream, meals, pasta, pet food, snacks, spices, vegetables, yogurt, etc. An application on the communication device 106 may categorize the products 102 into these categories or allow the user to perform such categorization.

[0132] Learn more about adding a measurement device 2 illustrates an example of a measuring device 104 according to some implementations described herein. The measuring device 104 may include a weight sensor 202, a motion sensor 204, a time sensor 206, and a communication module 208. The weight sensor 202 weighs the product 102 when it is placed on the measuring device 104 and generates weight data for the product 102. The weight sensor 202 may include at least one load cell (e.g., any number of load cells, such as four load cells), which is a transducer that converts force into a measurable electrical output indicative of weight. Any of the load cells described herein may be hydraulic load cells, pneumatic load cells, strain gauge load cells, piezoelectric load cells, inductive and magnetoresistive load cells, magnetostrictive load cells, capacitive load cells, etc. Any of the load cells may have any shape or size. In particular implementations, the weight sensor 202 can automatically weigh the product 102 (a) when the product 102 is in a stable state, (b) when the product 102 is used within a preset period (e.g., 1 hour, 2 hours, 6 hours, 12 hours, 24 hours, or any other period), (c) when the product is used periodically (e.g., at least two times, at least three times, etc.) over the preset period, and / or (d) whenever the product is removed from the measuring device 104 (which may indicate product use) and / or returned to the measuring device 104. The motion sensor 204 can detect movement of the product 102 to generate motion data. For example, the motion sensor 204 detects the movement and / or direction of movement of the product 102 to generate the motion data. The motion sensor 204 can be an accelerometer. The time sensor 206 detects time spent by the weight sensor 202 and time spent by the motion sensor 204 to generate time-spent data. The time-spent data can include a time and a date. In some implementations, the time sensor 206 can timestamp one or more of the movement of the product 102, the length of time the product 102 moves, the time the weight sensor 202 weighs the product 102, etc.

[0133] While the measuring device 104 is shown as including a weight sensor 202, a motion sensor 204, and a time sensor 206, in some implementations, the measuring device 104 can additionally or alternatively include other sensors, such as a position sensor, a temperature sensor, a proximity sensor, an infrared sensor, an ultrasonic sensor, a humidity sensor, a tilt sensor, a level sensor, a touch sensor, etc. The position sensors can include (a) a GPS sensor capable of detecting the geographic coordinates of the measuring device (e.g., detecting a specific address such as a house or building on a map) and / or (b) an internal position sensor capable of detecting a specific location within a specific address (e.g., a specific room in a house, a specific unit or apartment in a building, a store in a shopping mall, an exhibit location in a conference, a room in a museum, etc.). To track internal locations using internal position sensors, beacons can be installed at address locations (e.g., a house, building, mall, or museum), and such beacons can sense the proximity of the internal position sensor in the measuring device 104 from each of the beacons at preset time intervals or in real time. In some implementations, there can be a one-time mapping of the beacons using the communication device 106. Using the proximity between the measuring device 104 and each beacon, the internal location sensor can estimate the approximate location of the measuring device 104, which can be useful for knowing the specific internal location of the product 102 when the measuring device 104 is physically coupled to (e.g., affixed to or placed adjacent to) the product 102. The beacons can be Bluetooth Low Energy (BLE) beacons and / or IEEE 802.15.4 or low-rate wireless personal area network (LR-WPAN) beacons. In some implementations, an external beacon may not be necessary because the internal location sensor of the measuring device may function as the beacon.Although beacons are described as being used to detect internal location, in other implementations, the internal location sensors can implement other technologies for detecting internal location, such as WiFi, magnetic field detection, near-field communication, gyroscopes and accelerometers, ultra-wideband, and / or microelectromechanical systems (MEMS) sensors, as described below.

[0134] Wi-Fi can be used in a similar manner to BLE beacons, but this technology may require an external power source and additional equipment. In some implementations, the equipment may be expensive. In some such implementations, BLE beacon technology may be preferred over Wi-Fi technology to identify the internal location of the measuring device 104. Wi-Fi technology may be advantageous in some implementations compared to BLE beacon technology because Wi-Fi technology signals are stronger and can cover greater distances. In magnetic field detection, an internal location sensor in the measuring device 104 may include a compass for indoor positioning, and fingerprinting technology may be used to map the venue's magnetic field, which the measuring device 104 can use to find its indoor location. Magnetic field detection technology may not be implemented in certain situations where indoor magnetic fields are stable. Near-field communication (NFC) technology includes a small chip that does not require a power source, and an internal location sensor in the measuring device 104 detects each chip to read its serial number when the measuring device 104 is within a certain distance (e.g., 30 centimeters) from the chip. NFC technology is advantageous in locations where the product 102 can be stored in a small space, allowing detection within a specific distance, such as 30 centimeters. A gyroscope is a device for measuring or maintaining orientation based on the principle of conservation of angular momentum. This orientation information can be used to more precisely position the measurement device 104. Ultra-wideband (UWB) is the most accurate method for detecting indoor location. Under UWB, UWB anchors are placed at corners of a venue up to a predetermined distance (e.g., 25 meters) away, and UWB locator tags are attached to the asset to be tracked. The UWB locator tag can transmit radio signal pulses within a specific frequency range (e.g., 3-7 GHz), which the UWB anchor can use to position the tag in three-dimensional space with some precision (e.g., up to 30 centimeters) and update its location at predetermined intervals (e.g., every 50 milliseconds).

[0135] The measuring device 104 can use the communications module 208 to transmit the metric data, motion data, time-spent data, and any other sensor data to the computing device 108, either directly or via the communications device 106. In particular implementations, the communications module 208 transmits the metric data, motion data, and time-spent data to the computing device 108 using the communications network 114. The measuring device 104 may automatically determine the metric data, motion data, and time-spent data when the product 102 is in a steady state, when the product 102 is not being used, and / or when the product 102 is being used.

[0136] The term module as referred to herein may include software instructions and code for performing a specified task or function. A module as used herein may be a software module or a hardware module. A software module may be part of a computer program that may include multiple independently developed modules that may be combined or linked via link modules. A software module may include one or more software routines. A software routine is computer-readable code that performs a corresponding procedure or function. A hardware module may be a self-contained component with independent circuitry capable of performing various operations described herein.

[0137] Measurement device internal communication module 3 illustrates a communication module 208 of a measurement device 104 communicatively connected to a computing device 108 according to some implementations described herein. The communication module 208 of the measurement device 104 is communicatively connected to the computing device 108 via a communication network 114. The communication module 208 includes at least one of a Wi-Fi module 302, a Bluetooth module 304, and / or a SIM module 306 for transmitting metric data, movement data, and time-spent data from the measurement device 104 to the computing device 108 via the communication network 114.

[0138] The SIM module 306 may include an IoT SIM card to provide the necessary connectivity to automatically transmit the weight data, movement data, and time spent data to the computing device 108. In some implementations, the measuring device 104 automatically transmits the weight data, movement data, and time spent data to the computing device 108. In some implementations, the measuring device 104 transmits the weight data, movement data, and time spent data to the communication device 106 via the communication network 114, and the data may then be further transferred to the computing device 108 via the communication network 114.

[0139] Although the communications module 208 is described as including a Wi-Fi module 302, a Bluetooth module 304, and a SIM module 306, in other implementations, the communications module 208 may include additional or alternative modules that may enable communications over the respective communications networks.

[0140] Another example of a measuring device 4 illustrates another example of a measuring device 104 according to some implementations described herein. The measuring device 104 includes a weight sensor 202, a motion sensor 204, a time sensor 206, a communications module 208, an indicator (e.g., an LED indicator) 402, and a local storage module 404. The LED indicator 402 indicates when the weight sensor 202, the motion sensor 204, and the time sensor 206 determine metric data, motion data, and time-spent data, respectively. The LED indicator 402 may display the metric data to indicate the measurement value of the product 102. Using an LED for the indicator 402 can be beneficial for reasons such as: (a) LEDs are energy efficient; (b) LEDs require low maintenance; (c) LEDs have a long lifespan (e.g., greater than 10 years) because they have no filaments to burn out; (d) the initial cost of LED lighting is low; (e) LEDs emit little heat or ultraviolet (UV) light; and (f) LEDs are typically cool to the touch and safe to handle.

[0141] Although indicator 402 is described as an LED indicator, in other implementations, any other light-based indicator can be used, such as an incandescent lamp, a halogen lamp, a fluorescent lamp, a compact fluorescent lamp, a high-pressure sodium lamp, and a low-pressure sodium lamp. Although indicator 402 is described as a light-based indicator (i.e., an indicator that emits light), in other implementations, the indicator can be an audio indicator (e.g., a sound alarm) or an audio-visual indicator (e.g., an alarm that can include an LED that generates light and audio circuitry that generates sound).

[0142] In some implementations, the local storage module 404 can store the metric data, motion data, and time-spent data. The local storage module 404 can be one or more of a hard disk drive (HDD), a solid-state drive (SSD), or an external storage device such as a thumb drive or disk. In particular examples, the local storage module 404 can be a non-volatile memory card (e.g., an SD card). Having a local storage module 404 in the architecture can be beneficial because such local storage allows for faster access to data and can be beneficial for implementations where the metric data, motion data, and time-spent data need to be processed by the measurement device 104 (e.g., to consolidate the data or process the data to remove redundancies) before transmitting all of that data to the computing device 108, preventing uploading and downloading of data containing redundant data if the storage were remote from the measurement device 104. Having a local storage module 404 in the architecture may be further beneficial in some implementations where data is synchronized with the computing device 108 (e.g., a cloud computing server) only once or a few times a day, as such local storage allows the measurement device 104 to store data and transmit it only at specific intervals, thereby conserving and optimizing bandwidth. Such local storage is also beneficial when the communication network or communication device (e.g., a subscriber identity module (SIM) card in the communication device 106) is temporarily inoperable or malfunctioning, because in such a situation, the local storage can store the data until the communication device can synchronize with the communication device 106.

[0143] The measuring device 104 may include a control key (which may also be referred to as an on / off control key) for activating (i.e., switching on) or deactivating (i.e., switching off) the measuring device 104. When the measuring device 104 is activated (i.e., switched on), the measuring device 104 may automatically determine weight data, movement data, and time-spent data. The measuring device 104 may be battery-powered or configured to consume low power. The battery may be a primary cell (i.e., designed to be used until it runs out of energy) or a rechargeable battery (i.e., capable of being recharged by applying an electric current to the cell to reverse a chemical reaction). In some implementations, the battery may include one or more of an electrochemical cell, such as a galvanic cell, an electrolytic cell, a fuel cell, a flow battery, and / or a voltaic cell. In some implementations, the measuring device 104 may be powered by power extracted (e.g., received) from a power outlet.

[0144] In some implementations, the measuring device 104 is automatically deactivated (i.e., switched off) when not in use and activated (i.e., switched on) after the product 102 has been in use for a while, for a period that includes a schedule for weighing the product 102 at a set frequency and / or in response to the movement of the product 102. For a scheduled frequency, the period may be any preset time, such as 1 hour, 2 hours, 4 hours, 6 hours, 12 hours, 18 hours, 24 hours, or any other value. In some implementations, to conserve power, only components that may not be needed (e.g., components required for WiFi or other communications) may be deactivated (e.g., powered off or switched off), while other components, such as sensors (e.g., weight sensor 202, motion sensor 204, time sensor 206, etc.), may go into sleep mode but can be immediately activated upon detecting a specific (e.g., respective threshold-based) change in weight, motion, or time.

[0145] Generate notifications that appear on communication devices FIG. 5 shows an example diagram of a measuring device 104 communicatively connected to a computing device 108 to trigger a notification on a communication device 106, according to some implementations described herein. The measuring device 104 can continuously and / or automatically transmit metric data, movement data, and time-spent data to the computing device 108. When the product 102 is completed, the weight sensor 202 may transmit a zero reading to the computing device 108. The computing device 108 receives the zero reading and triggers a notification in a mobile application on the communication device 106 to check whether the product 102 is out of stock. The notification may enable a consumer or user to attach a new product to the measuring device 104. In some implementations, the notification is triggered continuously until the weight sensor 202 determines the metric data. For example, if the consumer or user forgets to replace the product 102, the computing device 108 triggers a notification to attach a new product to the measuring device 104.

[0146] If a new product is attached to the measuring device 104 without scanning the barcode of the new product, a significant change in weight may enable the computing device 108 to trigger a notification to confirm that the measuring device 104 has been installed on the new product. If the measuring device 104 is not attached to any product, the computing device 108 may trigger a notification to remind the consumer or user to attach the measuring device 104 to the new product.

[0147] The consumer or user may set preferences in the mobile application of the communication device 106 to receive notifications indicating that the product 102 is low and / or to automatically reorder / purchase the product or add the product to a shopping list. The mobile application of the communication device 106 may be used as a home management system. The computing device 108 (and / or, in some implementations, the communication device 106) may provide additional value in the form of discounts / deals on new and / or available products, and may also provide recommendations for new and / or alternative products. In some implementations, recommendations may include paid advertising or promotions, recommendations tailored based on the consumer's or user's overall product preferences, all products tracked or measured by the user, the consumer's or user's consumption or usage behavior of the product 102, all other consumers or users of the product 102, and / or other consumers or users similar to the consumer or user. Because recommendations may be based on actual consumption or usage behavior, they are less biased than reviews, which may be subjective. In some implementations, such content (e.g., advertisements, promotions, or deals) can be delivered when only a certain threshold (e.g., percentage) of the product 102 remains (i.e., the remainder of the product 102 has been consumed or used). In addition to advertisements or promotions, there may be merchant-sponsored product placements to see how user consumption or usage behavior changes when a new product is introduced into the environment (e.g., home or business) where the measurement device 104 is located.

[0148] Attachment mechanism for connecting the measurement device to the product 6 shows a product 102 connected to a measuring device 104 according to some implementations described herein. The measuring device 104 includes a connector 602 that can connect (e.g., attach) the measuring device 104 to the product 102. The connector 602 may include attachment means that include gluing, knitting or threading, welding, magnetic attachment, fastening, taping, joining using one or more screws or nails, twisting two or more components together until securely attached (e.g., locked), pressing one component onto another until securely attached (e.g., locked relative to each other), and / or any other attachment mechanism or mechanisms.

[0149] Measurement systems for the consumption or use of multiple products 7 shows an example diagram of one or more products 102A-N communicatively connected to one or more measuring devices 104A-N that determine a consumer's or user's consumption or usage behavior of the one or more products 102A-N, according to some implementations described herein. The one or more products 102A-N connect to the one or more measuring devices 104A-N via one or more connectors or without a connector (e.g., the one or more products 102A-N can be connected simply by being located on the respective one or more measuring devices 104A-N). The one or more measuring devices 104A-N transmit metric data, movement data, and time-spent data of the one or more products 102A-N to a computing device 108 via a communications network 114. The computing device 108 determines a consumer's or user's consumption or usage behavior of the one or more products 102A-N based on the metric data, movement data, and time-spent data associated with the one or more products 102A-N. The computing device 108 transmits / provides to the client device 110 consumption or usage behavior of one or more products 102A-N by the consumer or user.

[0150] Example of a user interface for a computing device FIG. 8 shows an example diagram of a graphical user interface 800 of a computing device 108 according to some implementations described herein. The graphical user interface 800 of the computing device 108 illustrates one or more consumer or user sessions, the consumption or use of one or more products 102A-N by the one or more consumers or users, and surveys or other activities conducted by the one or more consumers or users. A session may include the participant names of one or more consumers or users and / or the location of one or more consumers or users. In some implementations, a session includes a video survey to learn about one or more consumers or users. The video survey may include questions such as, "What is your first name? Where do you live?" and "How often do you purchase this product?" as well as tasks such as, "Show me how to use this product" and "What is your opinion on this new concept?" The video may be recorded by the communication device 106, which can transmit the recorded video to the computing device 108. In some implementations, the computing device 108 transcribes the recorded video into text. The recorded videos may be filtered by either tasks / questions, audio keywords, and / or visual objects / scenes. The recorded videos may include self-moderated interviews of one or more consumers or users using one or more products 102A-N.

[0151] While various specific activities are shown in Figure 8, in some implementations there may be data for a session of activities performed by a consumer or user, such as video activities, survey activities, photo activities, metering activities, sensor activities, audio activities, barcode activities, receipt capture activities, screen recording activities, etc. In some examples, each consumer or user may need to perform multiple activities over a period of time.

[0152] Example of a user interface for a computing device 9 shows an example diagram of a graphical user interface 900 of a computing device 108 according to some implementations described herein. The graphical user interface 900 of the computing device 108 may include a task / question for initializing the product 102. The product 102 may be dishwashing liquid. The task / question may include scanning the product 102 using the communication device 106, weighing the product 102 using the measurement device 104, and / or entering a reading of the measurement device 104. In certain implementations, the measurement device 104 may automatically provide a reading to the computing device 108 (directly or via the communication device 106), and there may be no task / question corresponding to entering such a reading of the measurement device 104; therefore, a user may not need to manually enter a reading of the measurement device 104. In some implementations, the communication device 106 scans a barcode of the product 102, and the computing device 108 determines details of the product 102 based on the barcode of the product 102. The product 102 details may include a barcode number, barcode type, manufacturer, category, brand, UPC details, base size, sub-brand, main category, collection, form, and / or other available barcode data. Although the graphical user interface 900 is specific to a particular product (i.e., dishwashing liquid), in other implementations, the graphical user interface 900 may be generated for any product (e.g., any type of product).

[0153] An example of a user report displayed in the user interface of a client device FIG. 10 is a graphical representation 1000 of a report showing weekly (and in some implementations, daily, hourly, specific days of any week, weekdays, weekends, or any time range) consumption of a product 102 according to some implementations described herein. The graphical representation 1000 of the report includes weekly consumption or use of the product 102 over a period of time. The period may include weeks 1 through 9. While the period is shown as 9 weeks, in some implementations, the length of time can be any amount of time depending on the needs of a particular project and / or the needs and / or desires of the entity for which the insights are being generated. For example, in some variations, the length of time can be 2 weeks, 6 weeks, 12 weeks, 20 weeks, 6 months, 1 year, 2 years, 3 years, or any other length of time. The graphical representation 1000 of the report includes a y-axis showing use of the product 102 by the consumer or user and an x-axis showing the time period in weeks. The graphical representation 1000 includes one or more points representing the consumer's or user's use of the product 102 from week 2 through week 9. While the graphical representation 1000 is described as being presented on the client device 110, in some implementations the graphical representation 1000 may additionally or alternatively be represented on the computing device 108. Optionally, the graphical representation 1000 may be displayed on the communication device 106. While the graphical representation 1000 is specific to a particular product category (i.e., dishwashing liquid), in some implementations the graphical representation 1000 may alternatively or additionally be generated for any product (e.g., dishwashing liquid by a particular brand).

[0154] Example of a user report displayed on a client device 11 is a graphical representation 1100 of a report showing the average weekly consumption, average weekend consumption, average weekday consumption of a product 102, or the product category to which the product 102 belongs, according to some implementations described herein. While depictions of average weekly consumption, average weekend consumption, and average weekday consumption are described, in some implementations, the techniques can be extended to monitor average consumption by day, hour, specific day of any week, or month. The graphical representation 1100 of the report includes the average weekly consumption, the average weekly consumption index for the previous week, the average weekend consumption, the average weekday consumption, the number of weighings of the same product, the number of weighings of a new product, the number of out-of-stock items, and the total number of weighings for X weeks. While the graphical representation 1100 is described as being presented on the client device 110, in some implementations, the graphical representation 1100 can additionally or alternatively be rendered on the computing device 108. Optionally, the graphical representation 1100 can be displayed on the communication device 106.

[0155] Although the period is shown as 9 weeks, in some implementations, the length of time can be any amount of time depending on the needs of a particular project and / or the needs and / or desires of the entity for which the insights are being generated. For example, in some variations, the length of time can be 2 weeks, 6 weeks, 12 weeks, 20 weeks, 6 months, 1 year, 2 years, 3 years, or any other length of time.

[0156] An example of the user interface displayed on a client device 12 shows an example diagram of a graphical user interface 1200 of a client device 110 according to some implementations described herein. The graphical user interface 1200 includes tasks / questions for initializing either a second product, a third product, etc. Although the graphical representation 1200 is described as being presented on the client device 110, in some implementations, the graphical representation 1200 may additionally or alternatively be represented on the computing device 108 or, optionally, on the communication device 106.

[0157] An example of a merchant report displayed in the user interface of a client device. 13 is a graphical representation 1300 of a report showing weekly (and in some implementations, daily, time of day, day of week, etc.) consumption details of one or more products 102A-N or categories of products by consumer or user or group of consumers or users, according to some implementations described herein. The graphical representation 1300 of the report includes a y-axis showing consumption or usage of one or more products 102A-N and an x-axis showing time period in weeks. The one or more products 102A-N include a fabric refresher spray 1302, a constantly unpowered air freshening product 1304, an air freshening electric plug-in 1306, an air freshening spray 1308, a cleaning system for dusting surfaces 1310, disposable dry floor cleaning wipes 1312, disposable moist floor cleaning wipes 1314, a kitchen cleaning spray 1316, a disinfecting surface wipe 1318, an all-purpose cleaning spray 1320, a floor cleaner 1322, and a dishwashing liquid 1324. Optionally, the graphical representation 1300 can be displayed on a communication device 106.

[0158] While the time period is shown as 9 weeks, in some implementations, the length of time can be any amount of time depending on the needs of a particular project and / or the needs and / or desires of the entity for which the insights are being generated. For example, in some variations, the length of time can be 2 weeks, 6 weeks, 12 weeks, 20 weeks, 6 months, 1 year, 2 years, 3 years, or any other length of time. Additionally, while the graphical representation 1300 is specific to a particular product as described above, in other implementations, the graphical representation 1300 may be generated for any type or number of products.

[0159] An example of a merchant report displayed in the user interface of a client device. 14 is a graphical representation 1400 of a report showing weekly (and in some implementations daily, by hour, by day of the week, etc.) consumption of one or more products 102A-N by a consumer or user, according to some implementations described herein. The graphical representation 1400 of the report includes the weekly consumption amount in grams of one or more products 102A-N. While the graphical representation 1400 is specific to the particular products shown in the drawing, in other implementations the graphical representation 1400 may be generated for any type or number of products.

[0160] Although various graphical representations are shown in Figures 8-14, these graphical representations are merely exemplary, and the representations may be modified or alternative representations may be generated based on the data collected and tracked, the needs and / or desires of the entity for which the insights are being generated, and / or any other reason or reasons.

[0161] Button devices for quantifying product consumption or usage 15 is an example button device 1502 for analyzing a product 102 according to some implementations described herein. The button device 1502 includes a button 1504 that a consumer or user can press when consuming or using the product 102. In some implementations, the button device 1502 detects consumption or usage of the product 102 when the consumer or user manually presses the button 1504 while using the product 102. The button device may generate button press data when the consumer or user manually presses the button while using the product 102 and transmit the button press data to the computing device 108 to determine the consumer's or user's consumption or usage behavior of the product 102. The button device 1502 may be secured to the product 102 or a door / opening of a cabinet, refrigerator, or other appliance for automatic operation of the button device 1502. In an example implementation, the button device 1502 is located within the refrigerator. When the refrigerator is opened, the button 1504 is activated (e.g., released), and when the refrigerator is closed, the button 1504 is deactivated (e.g., pressed). In some implementations, the button device 1502 may include control configurations including a long press, a short press, a double press, and / or a triple press, etc.

[0162] While the button device 1502 is described for quantifying (e.g., counting) consumption or use of the product 102, in some implementations the measuring device 104 can additionally or instead perform such quantification. An example of a use of the button device 1502 could be a button device 1502 that determines the number of times a user has shaved in response to the user manually pressing the button each time they shave. An example of a use of the measuring device 104 could be a measuring device 104 (e.g., a coaster) into which shaving cream is placed and that uses a sensor (e.g., a weight sensor) to determine the amount of shaving cream used and then also determines whether the associated activity (i.e., the entire process of shaving) is complete.

[0163] Example of a weighing device that weighs a product 16 is an example weighing device 1602 for determining weighing data for one or more products 102A-N, according to some implementations described herein. One or more products 102A-N may be placed on the weighing device 1602 to determine the weighing data for the one or more products 102A-N. While the weighing device 1602 is shown as a rectangle, in another implementation, the weighing device 1602 can be circular (e.g., coaster-shaped). In still other implementations, the weighing device 1602 can have any other shape, such as an oval, a polygon with any number of sides, a triangle, any irregular shape, etc. The weighing device 1602 can display the weight on an optional display 1604, which can be one or more of a liquid crystal display (LCD), an electroluminescent display (ELD), a light-emitting diode (LED) display, a plasma display panel (PDP), a quantum dot (QD) display, etc. In some implementations, the metering device 1602 may not have a display, such as the display 1604.

[0164] Examples of how to generate insights 17 is a flow diagram illustrating a method performed by a system for generating insights regarding a consumer's or user's consumption or usage behavior of a product 102, according to some implementations described herein. At step 1702, the computing device 108 may be initialized by scanning a barcode of the product 102 and pairing the measurement device 104 with the product 102 using the communication device 106. At step 1704, the computing device 108 may be woken up in response to pairing the product 102 with the measurement device 104. At step 1706, weight data of the product 102 may be determined by the weight sensor 202, motion data of the product 102 may be determined by the motion sensor 204, and time-spent data related to the weight sensor 202 and the motion sensor 204 may be determined by the time sensor 206. At step 1708, a communication network 114 may be enabled between the measurement device 104 and the computing device 108 to transmit the weight data, motion data, and time-spent data. At step 1710, a report may be generated on the computing device 108 to determine consumption or usage behavior of the product 102 by the consumer or user based on the metric data, movement data, and time spent data.

[0165] Measurement device example - Coaster 18-23 show different views of an example of a measurement device 104 in the form of a coaster 1800. FIG.

[0166] FIG. 18 shows a top view of a coaster 1800. The coaster 1800 can have a female coupling portion (e.g., a groove) 1802 that can be used to couple the coaster with a male coupling portion of another device, such as a sleeve shown in FIGS. 24-29, to grip (e.g., by firmly holding) the product 102 when placed on the coaster 1800. In other implementations, the coaster 1800 can include a coupling or fastening device (e.g., a clamp; not shown) to grip (e.g., by firmly holding) the product 102 when placed on the coaster 1800. While a female coupling portion 1802 is described, in some implementations, the coaster 1800 may not need to be coupled with another device, such as a sleeve, and thus may or may not have such a female coupling portion 1802. If multiple coasters 1800 are packaged together (e.g., 10 coasters packaged together), the coasters 1800 may only include some sleeves (e.g., three sleeves) because the circuitry in the sleeves connects with the coasters 1800 only when necessary (e.g., when the location of the product 102 needs to be detected), and therefore, only some of the coasters 1800 (e.g., three or four of the ten coasters 1800 in the package) may use the female coupling portion 1802. In such a package, one of the coasters 1800 may be the hub and the other coasters 1800 may be the spokes, with the hub and spokes configured to be arranged in a hub-and-spoke architecture (e.g., as shown in FIG. 46 and described below).

[0167] FIG. 19 shows a side view of a coaster 1800. The coaster 1800 includes anti-skid devices 1804 that prevent the coaster 1800 from sliding on a surface. The anti-skid devices 1804 can be made of anti-skid materials such as certain types of rubber, foam, polyester, fabric, etc. In some examples, the anti-skid devices 1804 can be rubber stoppers or rubber pads. Although four anti-skid devices 1804 are shown in the drawing, there can be any other number of anti-skid devices 1804 in other implementations.

[0168] FIG. 20 shows a bottom view of the coaster 1800. The coaster 1800 can include fastening devices 1806 (e.g., screws) that can combine multiple layers of the coaster 1800 or combine the coaster 1800 with one or more other devices. While the combination is described as being screwed, in some implementations, other means of combination can be used, such as gluing, knitting or threading, welding, taping, joining using one or more screws or nails, twisting two or more components together until securely attached (e.g., locked), pressing one component onto another until securely attached (e.g., locked relative to one another), and / or any other attachment mechanism or mechanisms for joining materials or devices together. The coaster 1800 can include a power button 2002 that can be used to start or stop the coaster 1800.

[0169] FIG. 21 shows a front view of the coaster 1800. The coaster 1800 can include a port 1808 for charging the device. The coaster 1800 can include an indicator (e.g., a light-emitting diode indicator) 1810 that can indicate the status of charging. For example, the indicator can display a red light when the coaster 1800 has a low battery (e.g., less than 20%), a yellow light when the battery is medium (e.g., 20% to 70%), and a green light when the battery is high (e.g., 70% or more). In some implementations, the indicator 1810 can further generate a lighted notification when the coaster 1800 is activated (i.e., on), inactivated (i.e., off), or in use. Although three levels of indicators are described, in a particular implementation, there can be any number of other levels, each represented by a respective color.

[0170] Although the coaster 1800's charging port is shown, in some implementations, the coaster 1800 may have other ports or communication networks that allow the coaster 1800 to communicatively couple to several devices (e.g., computers or peripheral devices such as a mouse, keyboard, monitor or display unit, printer, speakers, flash drive, etc.). Ports include (a) a serial port that can be used to couple the coaster 1800 to an external modem or some computer mouse devices, (b) a parallel port that can be used to couple the coaster 1800 to a scanner or printer, (c) a PS / 2 port that can be used to couple the coaster 1800 to some computer keyboards or mice, (d) a Universal Serial Bus (USB) port that can be used to couple the coaster 1800 to external USB devices such as an external hard disk, printer, scanner, mouse, keyboard, etc., (e) a Video Graphics Array (VGA) port that can couple the coaster 1800's display to a video card, and (f) a power port—which can vary for different connectors, including USB-A. , a Universal Serial Bus (USB) connector such as USB-B, USB-C, micro-USB, Lightning cable, etc., where such USB can have any speed standard such as USB 1.x, 2.0, or 3.x, and can be used to accept a power cable or any other charging device for charging a battery in the coaster 1800, and can include one or more of: (a) a Firewire port that can couple the coaster 1800 to data equipment such as a camcorder or video equipment; (b) a modem port that can be used to couple the coaster 1800 to a communications network; (c) an Ethernet port that can connect the coaster 1800 to a communications network (e.g., the Internet), or any other port.

[0171] FIG. 22 shows a top perspective view of coaster 1800.

[0172] FIG. 23 shows a bottom perspective view of coaster 1800.

[0173] Measuring device example - Sleeve with coaster 24-29 show different views of an apparatus 2400 configured to form a sleeve for the measurement device 104 and to be attached to the coaster 1800 to form the measurement device 104. FIG.

[0174] Figure 24 shows a top view of apparatus 2400. Apparatus 2400 includes a gripping device 2402 configured to move along region 2404 to firmly grip product 102 (as further clarified in Figure 28).

[0175] Figure 25 shows a side view of device 2400. Device 2400 has a male mating portion 2502 that can mate with female mating portion 1802 shown in Figures 18, 19, and 21-23.

[0176] FIG. 26 shows a front view of the device 2400.

[0177] FIG. 27 shows a bottom view of device 2400. Device 2400 can optionally include a quick-release button 2702 that can be used to mechanically decouple (e.g., unclamp) product 102 from device 2400. Device 2400 can further include fastening devices (e.g., screws) that can hold multiple layers of device 2400 together and / or anti-slip devices that prevent the device from slipping. Such fastening devices (e.g., screws) and additional or alternative anti-slip devices are collectively referenced as 2704. While the assembly is described as screwed, in some implementations, other assembly means can be used, such as gluing, knitting or threading, welding, taping, joining using one or more screws or nails, twisting two or more components together until securely attached (e.g., locked), pressing one component onto another until securely attached (e.g., locked relative to one another), and / or any other method of combining materials or devices.

[0178] FIG. 28 shows a top perspective view of the device 2400.

[0179] FIG. 29 shows a bottom perspective view of the device 2400.

[0180] Figures 30 and 31 show different views of the measurement device 104 formed by attaching the apparatus 2400 of Figures 24-29 to the coaster 1800 of Figures 18-23. The coupling is made by mating the male coupling portion 2502 of the apparatus 2400 with the female coupling portion 1802 of the coaster 1800.

[0181] Example of a measuring device - a tray with coasters 32-34 show different views of the tray 3200 of a particular measuring device 104 that is enabled by coupling (e.g., attaching) the tray 3200 to one or more coasters 1800. The tray 3200 may be useful when larger or heavier items need to be weighed or measured, such as paper towels, toilet paper, diapers, pet food, etc. Although the drawings show each tray 3200 coupled to two coasters 1800, in some implementations, each tray 3200 may be configured to be coupled to any other number of coasters 1800 (e.g., 1, 3, 4, 5, 6, etc.). In certain implementations, the number of coasters 1800 coupled to a single tray 3200 may depend on the size (e.g., diameter or area) of the tray 3200 and / or the size (e.g., diameter or area) of each coaster 1800.

[0182] In some variations, the hardware component (which may also be referred to as a hardware module) that contains all of the electrical circuitry of the coaster 1800 may be inserted into various other form factors, such as a tray. In such implementations, one or more load cells (e.g., weight sensors) of the hardware module may be extended and placed at an edge or corner of the tray (e.g., as opposed to being constrained within the circumference of the coaster 1800 for which the electrical circuitry may be designed).

[0183] In some implementations, the tray 3200 can have bevels or edges in some places. In certain implementations, the tray 3200 can be flat so that the products 102 can be placed anywhere on the tray 3200.

[0184] 35-37 show different views of the measuring device 104 formed by combining the tray of FIGS. 32-34 with a coaster 1800. FIG.

[0185] Measurement device example - Container with coaster 38 and 39 show different views of a container 3800 of a particular measurement device 104 that is made operable by coupling (eg, attaching) the container to a coaster 1800. FIG.

[0186] 40-42 show different views of the measuring device 104 that is or has been formed by combining a container 3800 with a coaster 1800. FIG.

[0187] Coaster adapter example 43-45 show different views of an adapter 4300 configured to change the form factor of the measuring device 104. The adapter 4300 can be a connector that allows various types of items, such as containers, trays, etc., to be attached to the coaster 1800. Such items can form various types, shapes, and / or sizes of measuring devices 104. The connection can be made by magnetic forces (e.g., the adapter 4300 can have built-in magnets that allow for magnetic coupling), taping (e.g., using double-sided tape), gluing, braiding or threading, welding, screwing, twisting two or more components together until they are securely attached (e.g., locked), pressing one component onto another until they are securely attached (e.g., locked relative to each other), and / or by one or more other attachment mechanisms.

[0188] Example of a hub-and-spoke architecture for measurement devices FIG. 46 illustrates a hub-and-spoke architecture for multiple measurement devices 104. The hub-and-spoke architecture includes a hub measurement device 4602 (also referred to as a hub) and multiple spoke measurement devices 4604 (also referred to as spokes). The hub 4602 may be connected to each spoke 4604 via a communication network 4606, which may be different from the communication network 114. For example, the communication network 4606 may be a local area network serving a single location (e.g., a single building), and the communication network 114 may be the Internet. In some implementations, the network 4606 may be any low-rate personal area network, the operation of which may be defined by the IEEE 802.15.4 technology standard. In some implementations, the communication network may be a Bluetooth network. The hub 4602 communicates with external systems (e.g., computing devices 108) via the communication network 114, such as the Internet.

[0189] The spokes 4604 communicate directly with the hub 4602 but may not communicate directly with the computing devices 108 and may not be connected to the communications network 114. In some implementations, at least some (e.g., some or all) of the spokes 4604 can communicate with one or more other spokes 4604 via the communications network 4606.

[0190] Because only the hub communicates with external systems (e.g., computing devices 108), the hub-and-spoke architecture is simple and easy to implement, reducing complexity, cost, and risk. The hub-and-spoke architecture may allow new spokes to be added at any time, including after the hub 4602 and previous spokes 4604 are operational, making the architecture scalable.

[0191] The spokes 4604 may include active spokes and passive spokes. An active spoke is a spoke 4604 that is currently or recently (e.g., for a preset amount of time) in communication with the hub 4602. An inactive spoke is a spoke 4604 that is not currently or recently (e.g., for a preset amount of time) in communication with the hub 4602. In some implementations, the hub 4602 may always maintain a connection with only active spokes 4604 over the communications network 4606, but may establish a connection with inactive spokes 4604 over the communications network 4606 only at preset time intervals. Connecting with inactive spokes 4604 over the communications network 4606 only at preset time intervals eliminates the need to maintain a constant connection with the inactive spokes 4604, thereby conserving bandwidth and / or power.

[0192] Although each measuring device 104 is shown in the form of a coaster, in some implementations, any measuring device 104 in the hub-and-spoke architecture may have any form (e.g., a coaster, a tray and coaster combination, a container and coaster combination, etc.) In certain implementations, different measuring devices 104 may collectively be part of a hub-and-spoke architecture.

[0193] 47 shows an example architecture of a measurement device 104 configured to function like a spoke 4604 in the hub-and-spoke architecture of FIG. 46. The spoke 4604 may include a controller 4702, an interaction interface 4704, a position tracking radio 4706, a power management system 4708, a battery 4710, a weight sensor 202, a motion sensor 204, and a near-field communication (NFC) radio 4712. The controller 4702 may include a radio for communicating with other measurement devices 104 that may be connected with the spoke 4604 in the hub-and-spoke architecture. The controller 4702 may perform various operations. In some implementations, the functionality of the controller 4702 may be established and / or modified remotely by a computing device 108, which may be a cloud computing system.

[0194] Figure 48 shows the architecture of a measurement device 104 configured to function like a hub 4602 in the hub-and-spoke architecture of Figure 46. In addition to the components described above with respect to Figure 47, the hub 4602 may include an internet communications radio 4802 that can communicate with computing devices 108 over the internet 114. Although the internet is described, in some implementations, any other communications network 114 may be present and the communications radio 4802 may be configured for that communications network 114.

[0195] Example of a computer system 49 is a block diagram of an example computer system 4900 (which in some examples may be one or more computing components in system 100, such as measurement device 104, communication device 106, computing device 108, client device 110, and / or any other one or more computing components) that may be used to perform the operations described above, according to some implementations described herein. System 4900 includes a processor 4910, a memory 4920, a storage device 4930, and an input / output device 4940. Each of the components 4910, 4920, 4930, and 4940 may be interconnected using, for example, a system bus 4950. Processor 4910 may process instructions for execution within system 4900. In some implementations, processor 4910 is a single-threaded processor. In another implementation, processor 4910 is a multi-threaded processor. Processor 4910 may process instructions stored in memory 4920 or storage device 4930.

[0196] The memory 4920 stores information within the system 4900. In one implementation, the memory 4920 is a computer-readable medium. In some implementations, the memory 4920 is a volatile memory unit. In other implementations, the memory 4920 is a non-volatile memory unit.

[0197] The storage device 4930 can provide mass storage for the system 4900. In some implementations, the storage device 4930 is a computer-readable medium. In various different embodiments, the storage device 4930 can include, for example, a hard disk device, an optical disk device, a storage device shared over a network by multiple computing devices (e.g., a cloud storage device), or some other mass storage device.

[0198] The input / output device(s) 4940 provide input / output operations to the system 4900. In some implementations, the input / output device(s) 4940 may include one or more of a network interface device, such as an Ethernet card, a serial communication device, such as an RS-232 port, and / or a wireless interface device, such as an 802.11 card. In another implementation, the input / output device(s) may include a driver device configured to receive input data and send output data to an external device 4960, such as a keyboard, a printer, and a display device. However, other implementations may be used, such as a mobile computing device, a mobile communication device, a set-top box, a television client device, etc.

[0199] Although an exemplary processing system is illustrated in FIG. 49, implementations of the subject matter and functional operations described herein can be implemented in other types of digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed herein and their structural equivalents, or in one or more combinations thereof.

[0200] Implementations of the subject matter and operations described herein can be implemented in digital electronic circuitry, or computer software, firmware, or hardware, including the structures disclosed herein and their structural equivalents, or one or more combinations thereof. Implementations of the subject matter described herein can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on one or more computer storage media for execution by or control the operation of a data processing device. Alternatively, or additionally, the program instructions can be encoded in an artificially generated propagated signal (e.g., a machine-generated electrical, optical, or electromagnetic signal) generated to encode information for transmission to a suitable receiving device for execution by the data processing device. A computer storage medium can be, or be included in, a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device, or one or more combinations thereof. Furthermore, while a computer storage medium is not a propagating signal, a computer storage medium can be a source or destination of computer program instructions encoded in an artificially generated propagated signal. A computer storage medium may be, or may be contained in, one or more separate physical components or media (e.g., multiple CDs, disks, or other storage devices).

[0201] The operations described herein may be implemented as operations performed by a data processing apparatus on data stored in one or more computer-readable storage devices or on data received from other sources.

[0202] The term "data processing apparatus" encompasses all types of apparatus, devices, and machines for processing data, including, by way of example, a programmable processor, a computer, a system-on-chip, or a plurality or combination of the foregoing. An apparatus may include special-purpose logic circuitry, such as a field-programmable gate array (FPGA) or an application-specific integrated circuit (ASIC). In addition to hardware, an apparatus may also include code that creates an execution environment for the computer program in question, such as code constituting processor firmware, a protocol stack, a database management system, an operating system, a cross-platform runtime environment, a virtual machine, or one or more combinations thereof. The apparatus and execution environment may implement a variety of different computing model infrastructures, such as web services, distributed computing, or grid computing infrastructures.

[0203] A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted, declarative or procedural, and can be deployed in any form, such as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program can be stored in a single file dedicated to the program in question, or in multiple coordinated files (e.g., a file storing one or more modules, subprograms, or portions of code), or as part of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document). A computer program can be deployed to be executed on a single computer or on multiple computers located at a single site or distributed across multiple sites and interconnected by a communications network.

[0204] The term module as referred to herein may include software instructions and code for performing a specified task or function. A module as used herein may be a software module or a hardware module. A software module may be part of a computer program that may include multiple independently developed modules that may be combined or linked via link modules. A software module may include one or more software routines. A software routine is computer-readable code that performs a corresponding procedure or function. A hardware module may be a self-contained component with independent circuitry capable of performing various operations described herein.

[0205] The processes and logic flows described herein may be performed by one or more programmable processors executing one or more computer programs to perform actions by operating on input data and generating output. The processes and logic flows may also be performed by, or implemented as, special purpose logic circuitry, such as an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit).

[0206] Processors suitable for executing computer programs include, by way of example, both general-purpose and special-purpose microprocessors. Typically, a processor receives instructions and data from a read-only memory or a random-access memory, or both. The essential elements of a computer are a processor that performs actions in accordance with the instructions and one or more memory devices that store the instructions and data. Typically, a computer includes one or more mass storage devices, such as magnetic, magneto-optical, or optical disks, for storing data, or is operatively coupled to receive or transfer data, or both. However, a computer need not be equipped with such devices. Furthermore, a computer can be incorporated into another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device (e.g., a universal serial bus (USB) flash drive), to name just a few. Devices suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, including semiconductor memory devices such as EPROM, EEPROM, and flash memory devices; magnetic disks (such as internal hard disks and removable disks); and magneto-optical disks; CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, dedicated logic circuitry.

[0207] To provide for user interaction, implementations of the subject matter described herein can be implemented on a computer having a display device, such as a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user, and a keyboard and pointing device (such as a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide for user interaction; for example, feedback provided to the user can be any form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback, and input from the user can be received in any form, including acoustic, speech, or tactile input. Additionally, a computer can interact with a user by sending documents to and receiving documents from devices used by the user, for example, by sending a web page to a web browser on a user's client device in response to a request received from the web browser.

[0208] Implementations of the subject matter described herein may include back-end components, such as data servers, or middleware components, such as application servers, or front-end components, such as client computers with graphical user interfaces or web browsers through which users can interact with an implementation of the subject matter described herein, or may be implemented with any combination of one or more of such back-end, middleware, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication, such as a communications network. Examples of communications networks include local area networks (“LANs”) and wide area networks (“WANs”), internetworks (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks).

[0209] A computing system may include clients and servers. Clients and servers are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In some implementations, a server transmits data (e.g., HTML pages) to client devices (e.g., for the purpose of displaying the data and receiving user input from a user interacting with the client device). Data generated at the client device (e.g., a result of user interaction) can be received from the client device by the server.

[0210] While this specification contains many specific implementation details, these should not be construed as a limitation on the scope of the innovation or what may be claimed, but rather as a description of features specific to particular implementations of a particular innovation. Certain features described in this specification in the context of separate implementations may also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation may also be implemented separately in multiple implementations or in any suitable subcombination. Furthermore, while features may be described above as working in a particular combination and initially claimed as such, one or more features from a claimed combination may, in some cases, be deleted from the combination, and the claimed combination may be directed to subcombinations or variations of subcombinations.

[0211] Similarly, while operations are shown in the figures in a particular order, this should not be understood as requiring such operations to be performed in the particular order or sequential order shown, or that all of the operations shown be performed, to achieve desirable results. In certain situations, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system components in the above implementations should not be understood as requiring such separation in all implementations, and it should be understood that the described program components and systems may generally be integrated into a single software product or packaged into multiple software products.

[0212] Thus, specific implementations of the subject matter have been described. Other implementations are within the scope of the claims. In some cases, the actions recited in the claims may be performed in a different order and still achieve desirable results. Moreover, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In certain implementations, multitasking and parallel processing may be advantageous. [Explanation of symbols]

[0213] 100 systems 102 products 104 Measuring Devices 104A Measuring Device 104B Measuring Devices 104N Measuring Device 106 Communication Devices 108 Computing Devices 110 client devices 114 Communication Network 202 Weight Sensor 204 Motion Sensor 206 Time Sensor 208 Communication Module 302 Wi-Fi module 304 Bluetooth module 306 SIM module 402 LED indicator 404 Local Storage Module 4602 Hub Measuring Device, Hub 4604 Spoke measuring device, spoke 4606 Communication Networks 4702 Controller 4704 Interactive Interface 4706 Location Tracking Radio 4708 Power Management System 4710 Battery 4712 NFC radio 4802 Internet communication wireless 4900 Computer Systems 4910 processor 4920 memory 4930 storage device 4940 I / O device 4950 system bus 4960 External Device

Claims

1. 1. A device for housing a container for a consumer product, comprising: a housing in the form of a coaster for containing the container; a sensor module housed in the housing in the form of the coaster and including a weight sensor, the sensor module configured to detect a change in weight of the consumer product contained in the container corresponding to use of the consumer product; Detecting that the container has been removed from the housing; a sensor configured to activate the weight sensor in response to detecting that the container has been removed from the housing; a sensor module, the weight sensor configured to measure the weight of the container in response to the sensor module detecting that the container is contained by the housing; a communication module housed in the housing, the communication module in communication with the sensor module, the communication module configured to wirelessly transmit data about the usage of the consumer product from the sensor module based on the detected change in the consumer product; A device comprising:

2. 10. The device of claim 1, further comprising: i) a device separate from the container; and ii) an attachment device coupled to an upper surface of the container and adapted to couple the housing to the container.

3. The device of claim 2 , wherein the attachment device comprises a movable sleeve coupled to the coaster, the movable sleeve configured to hold the container.

4. 3. The device of claim 2, wherein the attachment device comprises a pair of arms extending from a top surface of the coaster, the arms configured to grip opposite sides of the container.

5. The device of claim 4 , wherein the pair of arms are on opposite sides of the top surface of the coaster.

6. The device of claim 2 , further comprising a gripping device, the gripping device comprising one or more coupling elements for attaching the gripping device to the housing.

7. 10. The device of claim 1, wherein the consumer product is selected from the group consisting of customer packaged goods (CPG), fast moving consumer goods (FMCG), food and beverages (F&B), and pharmaceuticals.

8. The device of claim 1 , wherein the sensor module further comprises a time sensor configured to observe a time during which the consumer product is used and to generate time-spent data based on the observed time.

9. 9. The device of claim 8, wherein the consumption time data comprises data selected from the group consisting of a time of movement of the consumer product, a length of time of movement of the consumer product, or a time at which the device weighs the consumer product.

10. The device of claim 1 , wherein the sensor module further comprises at least one of a time sensor or a motion sensor.

11. The device of claim 1 , wherein the sensor module further comprises a time sensor and a motion sensor.

12. The device of claim 1 , wherein the housing comprises a lower surface opposite an upper surface of the housing, the upper and lower surfaces being parallel.

13. The device of claim 12 , further comprising an anti-skid device attached to the underside of the housing.

14. The device of claim 1 , further comprising a tray configured to receive one or more housings, the one or more housings coupled to the tray.

15. 10. The device of claim 1, wherein the sensor module further comprises one or more sensors selected from the group consisting of a temperature sensor, a proximity sensor, an infrared sensor, an ultrasonic sensor, a position sensor, a humidity sensor, a tilt sensor, a level sensor, an optical-based motion sensor, or a touch sensor.

16. The device of claim 1 , wherein the communication module is selected from the group consisting of a Wi-Fi module, a Bluetooth module, a Bluetooth Low Energy module, or a SIM module.

17. The device of claim 1 , further comprising a local storage module configured to store the data.

18. The device of claim 1 , further comprising a camera coupled to the communication module and configured to record use of the consumer product.

19. 1. A system comprising: a computing device; 10. The device of claim 1, configured to communicate with the computing device over a communications network and transmit the data about the use of the consumer product to the computing device; Equipped with the computing device is configured to generate a report based on the data about the usage of the consumer product. system.

20. 20. The system of claim 19, further comprising one or more additional devices each associated with a respective different consumer product, each of the one or more additional devices in communication with the computing device.

21. 1. A method for monitoring a user's use of a consumer product, comprising: detecting, via a sensor module housed in a housing in the form of a coaster housing the container containing the consumer product, that the container has been removed from the housing; activating a weight sensor included in the sensor module housed in the coaster-shaped housing in response to detecting via the sensor module that the container has been removed from the housing; detecting that the housing has received the container; detecting, with the weight sensor, a change in weight of the consumer product contained in the container in response to detecting that the housing has received the container; wirelessly transmitting data about the usage of the consumer product based on the detected change in the consumer product via a communication module in communication with the sensor module and to a computing device; monitoring, by the computing device, consumption of the consumer product by the user based on the data about the use of the consumer product; A method comprising:

22. 22. The method of claim 21, wherein the consumer product is selected from the group consisting of customer packaged goods (CPG), fast moving consumer goods (FMCG), food and beverages (F&B), and pharmaceuticals.

23. 22. The method of claim 21, further comprising observing a time that the consumer product is used and generating time-spent data based on the observed time.

24. 24. The method of claim 23, wherein the consumption time data comprises data selected from the group consisting of a time of movement of the consumer product, a length of time of movement of the consumer product, or a time at which the weight sensor weighs the consumer product.

25. 22. The method of claim 21, further comprising generating a report about the usage of the consumer product by the user based on the monitored consumption of the consumer product by the user.

26. The device of claim 1 , wherein the sensor comprises at least one of a camera, a smart sensor, or a motion sensor.

27. detecting, via the sensor module, that the container has been removed from the housing before detecting that the housing has received the container; activating, by the sensor module, the weight sensor in response to detecting that the container has been removed from the housing; 22. The method of claim 21, comprising:

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